diff --git a/spaces/0xqtpie/doodle2vid/README.md b/spaces/0xqtpie/doodle2vid/README.md
deleted file mode 100644
index 8ee44ba06dccfec088c6c5f5e8389a5dd56808ab..0000000000000000000000000000000000000000
--- a/spaces/0xqtpie/doodle2vid/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Doodle2vid
-emoji: 🐢
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.1
-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/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Cs6 Master Collection Keygen Xforce Rar Zip Learn How to Generate and Apply Xforce Keygen for Adobe Cs6.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Cs6 Master Collection Keygen Xforce Rar Zip Learn How to Generate and Apply Xforce Keygen for Adobe Cs6.md
deleted file mode 100644
index 4642871978ceda76403902ec0f1e85ee3eb405af..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Cs6 Master Collection Keygen Xforce Rar Zip Learn How to Generate and Apply Xforce Keygen for Adobe Cs6.md
+++ /dev/null
@@ -1,148 +0,0 @@
-
-
Adobe Cs6 Master Collection Keygen Xforce Rar Zip
-
If you are looking for a way to get the most out of Adobe Creative Suite 6 Master Collection, you might be interested in using a keygen tool that can generate valid serial numbers and activation codes for you. In this article, we will explain what Adobe Cs6 Master Collection is, what Xforce Keygen is, and how to download and install Adobe Cs6 Master Collection with Xforce Keygen. We will also cover some of the benefits and risks of using this method, and answer some frequently asked questions.
-
What is Adobe Cs6 Master Collection?
-
Adobe Cs6 Master Collection is a software bundle that includes all the Adobe creative tools you need to create stunning digital content for any platform. Whether you are a graphic designer, web developer, video editor, photographer, or animator, you can find the right tool for your project in Adobe Cs6 Master Collection. Some of the applications included in this bundle are:
Photoshop CS6: The industry-standard software for editing and enhancing images.
-
Illustrator CS6: The vector graphics software for creating logos, icons, illustrations, and more.
-
InDesign CS6: The page layout software for designing print and digital publications.
-
Dreamweaver CS6: The web design software for creating responsive websites and apps.
-
Flash Professional CS6: The animation software for creating interactive content for web, mobile, and games.
-
Premiere Pro CS6: The video editing software for producing professional-quality videos.
-
After Effects CS6: The motion graphics and visual effects software for adding cinematic flair to your videos.
-
Audition CS6: The audio editing software for mixing, restoring, and enhancing sound.
-
And many more...
-
-
Adobe Cs6 Master Collection also comes with Adobe Bridge CS6, a file management tool that lets you organize and preview your media files; Adobe Media Encoder CS6, a tool that lets you encode your videos to various formats; and Adobe Acrobat X Pro, a tool that lets you create, edit, and sign PDF documents.
-
Features of Adobe Cs6 Master Collection
-
Some of the features that make Adobe Cs6 Master Collection stand out are:
-
-
Blazing-fast performance: Thanks to the 64-bit native support and GPU acceleration, you can work faster and smoother on complex projects.
-
Groundbreaking creative tools: You can explore new ways to design for the latest devices with innovative tools like Content-Aware Patch, Puppet Warp, Mercury Graphics Engine, Adaptive Wide Angle, and more.
-
Exceptional power and precision: You can create inspiring experiences that go anywhere with precise control over every aspect of your work.
-
Cross-platform compatibility: You can work seamlessly across Mac OS and Windows platforms with consistent results.
-
Integration with other Adobe products: You can easily exchange files with other Adobe applications like Photoshop Extended, Illustrator, InDesign, Dreamweaver, Flash Professional, After Effects, Premiere Pro, Audition, and more.
-
-
System requirements for Adobe Cs6 Master Collection
-
To run Adobe Cs6 Master Collection smoothly on your computer, you need to meet the following system requirements:
-
-
Operating system
Windows
Mac OS
-
Processor
Intel® Pentium® 4 or AMD Athlon® 64 processor (Intel Core™2 Duo or AMD Phenom® II recommended); Intel Core i7 required for Adobe SpeedGrade™
Multicore Intel processor with 64-bit support
-
RAM
4 GB of RAM (8 GB recommended)
4 GB of RAM (8 GB recommended)
-
Hard disk space
15.5 GB of available hard-disk space for installation; additional free space required during installation (cannot install on removable flash storage devices)
15.5 GB of available hard-disk space for installation; additional free space required during installation (cannot install on a volume that uses a case-sensitive file system or on removable flash storage devices)
-
Display
1280 x 900 display (1280 x 1024 recommended) with 16-bit color and 512 MB of VRAM; OpenGL 2.0–capable system
1280 x 900 display (1680 x 1050 recommended) with 16-bit color and 512 MB of VRAM; OpenGL 2.0–capable system
-
DVD-ROM drive
DVD-ROM drive compatible with dual-layer DVDs (DVD+-R burner for burning DVDs; Blu-ray burner for creating Blu-ray Disc media)
DVD-ROM drive compatible with dual-layer DVDs (SuperDrive for burning DVDs; external Blu-ray burner for creating Blu-ray Disc media)
-
Other requirements
- Java™ Runtime Environment 1.6 (included) - Eclipse™ 3.7 (for plug-in installation of Adobe Flash® Builder®); the following distributions are supported: Eclipse IDE for Java EE and Java Developers, Eclipse Classic, Eclipse for PHP Developers - QuickTime 7.6.6 software required for QuickTime features - Optional: GPU card for GPU-accelerated performance in Adobe Premiere Pro - Optional: Tangent CP200 family or Tangent Wave control surface for SpeedGrade - Optional: For SDI output, NVIDIA Quadro SDI Output card required for SpeedGrade - Optional: 7200 RPM hard drive (multiple fast disk drives preferred) for video products - This software will not operate without activation. Broadband Internet connection and registration are required for software activation,
- Java Runtime Environment 1.6 - Eclipse 3.7 Cocoa version (for plug-in installation of Adobe Flash Builder); the following distributions are supported: Eclipse IDE for Java EE and Java Developers, Eclipse Classic, Eclipse for PHP Developers - QuickTime 7.6.6 software required for QuickTime features - Optional: GPU card for GPU-accelerated performance in Adobe Premiere Pro - Optional: Tangent CP200 family or Tangent Wave control surface for SpeedGrade - Optional: For SDI output, NVIDIA Quadro SDI Output card required for SpeedGrade - Optional: 7200 RPM hard drive (multiple fast disk drives preferred) for video products - This software will not operate without activation. Broadband Internet connection and registration are required for software activation,
-
-
What is Xforce Keygen?
-
Xforce Keygen is a tool that can generate valid serial numbers and activation codes for various software products. It is also known as a crack or a patch because it bypasses the original authentication process of the software. Xforce Keygen is created by a group of hackers called X-Force who are known for cracking many popular software products such as Autodesk AutoCAD, CorelDRAW Graphics Suite, Microsoft Office, etc.
-
How Xforce Keygen works
- ```html prevent the software from detecting the crack and asking for online activation. Xforce Keygen usually comes in a zip or rar file that contains the keygen executable file and a text file with instructions on how to use it.
-
Benefits of using Xforce Keygen
-
Some of the benefits of using Xforce Keygen are:
-
-
You can get access to the full features and functions of the software without paying for it.
-
You can use the software offline without needing an internet connection or an Adobe account.
-
You can update the software to the latest version without losing the crack.
You may violate the software's terms of service and end up facing legal consequences.
-
You may expose your computer to malware or viruses that may be hidden in the keygen file or the modified files.
-
You may compromise the quality and security of your work as the software may not function properly or may contain bugs or errors.
-
You may miss out on some features or updates that are only available for genuine users.
-
-
How to download and install Adobe Cs6 Master Collection with Xforce Keygen
-
If you want to download and install Adobe Cs6 Master Collection with Xforce Keygen, you need to follow these steps carefully:
-
Step 1: Disable your network card or pull the network cable out
-
This is to prevent the software from connecting to the internet and verifying your serial number and activation code. You also need to make sure you don't have any of these entries in your hosts file:
The hosts file is located in C:\windows\system32\drivers\etc\hosts for Windows and /etc/hosts for Mac OS.
-
Step 2: Install the Master Collection CS6 with a serial generated from Xforce Keygen
-
You need to download Xforce Keygen from a reliable source and run it as administrator. Then, you need to select Adobe Cs6 Master Collection from the drop-down menu and click on Generate Serial. You will get a serial number that you need to copy and paste in the installation window of Adobe Cs6 Master Collection. Do not close the keygen yet. When the error "Please connect to the internet and retry" shows, click on Connect Later.
-
Step 3: Launch an Adobe application and confirm you have a connection problem
-
You need to launch any Adobe application from the Master Collection, such as Photoshop, Illustrator, or InDesign. You will see a message that says "We are unable to start your subscription for Adobe Cs6 Master Collection". Click on Having Trouble Connecting To The Internet. Then, click on Offline Activation and then on Generate Request Code. You will get a request code that you need to copy and paste in the keygen window.
-
Step 4: Generate and validate an activation code with Xforce Keygen
-
In the keygen window, click on Activate and then on Generate Activation Code. You will get an activation code that you need to copy and paste in the Adobe application window. Then, click on Activate and then on Close Application.
-
Step 5: Run disable_activation.cmd or disable_activation_osx as root
-
This is to block Adobe from accessing its servers and checking your activation status. You need to run disable_activation.cmd for Windows or disable_activation_osx for Mac OS as administrator or root. These files are usually included in the zip or rar file of Xforce Keygen. Alternatively, you can manually add these lines to your hosts file:
Step 6: Re-enable your network card and update your software to the latest version
-
This is to restore your internet connection and enjoy the latest features and updates of Adobe Cs6 Master Collection. You can use Adobe Updater to check for updates and install them without losing the crack.
-
Conclusion
-
In this article, we have explained what Adobe Cs6 Master Collection is, what Xforce Keygen is, and how to download and install Adobe Cs6 Master Collection with Xforce Keygen. We have also covered some of the benefits and risks of using this method, and answered some frequently asked questions. We hope you have found this article helpful and informative.
-
FAQs
-
-
Q: Is Xforce Keygen legal?
-
A: No, Xforce Keygen is not legal as it violates the software's terms of service and infringes its intellectual property rights.
-
Q: Is Xforce Keygen safe?
-
A: No, Xforce Keygen is not safe as it may contain malware or viruses that can harm your computer or compromise your work.
-
Q: Can I use Xforce Keygen for other software products?
-
A: Yes, Xforce Keygen can generate serial numbers and activation codes for other software products such as Autodesk AutoCAD, CorelDRAW Graphics Suite, Microsoft Office, etc.
-
Q: Can I use Adobe Cs6 Master Collection online after using Xforce Keygen?
-
A: Yes, you can use Adobe Cs6 Master Collection online after using Xforce Keygen, but you may not be able to access some features or services that require online verification or registration.
-
Q: Can I uninstall Adobe Cs6 Master Collection after using Xforce Keygen?
-
A: Yes, you can uninstall Adobe Cs6 Master Collection after using Xforce Keygen, but you need to delete these folders as well:
-
-
C:\Program Files (x86)\Common Files\Adobe\SLCache for Windows
-
C:\ProgramData\Adobe\SLStore for Windows
-
/Library/Application Support/Adobe/SLStore for Mac OS
-
/Library/Application Support/Adobe/SLCache for Mac OS
-
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Como Ativar O Malwarebytes Premium.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Como Ativar O Malwarebytes Premium.md
deleted file mode 100644
index fa9eb122a113cc3f28b203da4f479a7051b66fb6..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Como Ativar O Malwarebytes Premium.md
+++ /dev/null
@@ -1,33 +0,0 @@
-
-
No canto superior direito do Painel, clique em Ativar licença.
-
Clique em Inserir chave de licença.
-
Se a sua chave de licença tem este formato XXXXX-XXXXX-XXXXX-XXXXX, digite-a no campo Chave de licença e clique em Ativar.
-
Se a sua chave de licença tem este formato XXXX-XXXX-XXXX-XXXX e tem uma ID de licença com o formato XXXXX ou XXXXX-XXXXX, selecione Minha licença veio com uma ID de licença abaixo da entrada Chave de licença. Digite a sua ID de licença e a sua chave de licença e clique em Ativar.
-
-
Para verificar se a ativação foi bem-sucedida, Premium será exibido no canto superior esquerdo do Painel do programa.
- cec2833e83
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Adobe Photoshop Cs6 Amtlib Dll Files Everything You Need to Know.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Adobe Photoshop Cs6 Amtlib Dll Files Everything You Need to Know.md
deleted file mode 100644
index 5e322f5b2a99b7184829f66237f8fb5f6e7fd8dc..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Adobe Photoshop Cs6 Amtlib Dll Files Everything You Need to Know.md
+++ /dev/null
@@ -1,150 +0,0 @@
-
-
How to Crack Adobe Photoshop CS6 with Amtlib.dll Files
-
Adobe Photoshop CS6 is one of the most popular and powerful image editing software in the world. However, it is also one of the most expensive ones, costing hundreds of dollars for a single license. If you want to use Adobe Photoshop CS6 without paying for it, you might be tempted to crack it using Amtlib.dll files. But what are these files and how do they work? In this article, we will explain everything you need to know about cracking Adobe Photoshop CS6 with Amtlib.dll files, including the benefits, risks, and alternatives.
Adobe Photoshop CS6 is the 13th major release of the Adobe Photoshop software, which was launched in May 2012. It is a creative image editing suite that offers a range of features and tools for professional and amateur photographers, graphic designers, web developers, and video editors. Some of the new features and enhancements in Adobe Photoshop CS6 include:
-
-
A new user interface with dark and light themes
-
A new Content-Aware tool that can automatically fill in gaps or remove unwanted objects from images
-
A new Crop tool that can straighten images and adjust perspective
-
A new Blur Gallery that can create realistic blur effects such as tilt-shift, iris, and field
-
A new Adaptive Wide Angle filter that can correct distortion in wide-angle or fisheye lenses
-
A new Video Editing tool that can edit video clips directly within Photoshop
-
A new 3D engine that can render 3D graphics faster and more realistically
-
A new Mercury Graphics Engine that can boost performance and speed up processing
-
A new Background Save feature that can save images in the background while you work on other tasks
-
A new Auto Save feature that can automatically save your work every few minutes
-
-
What is Amtlib.dll?
-
Amtlib.dll is a dynamic link library file that is part of the Adobe Application Manager. It is responsible for activating and validating the licenses of various Adobe products, such as Photoshop, Illustrator, Dreamweaver, Premiere Pro, After Effects, etc. It is located in the installation folder of each Adobe product.
-
When you crack Adobe Photoshop CS6 with Amtlib.dll files, you are essentially replacing the original Amtlib.dll file with a modified one that bypasses the license verification process. This way, you can use Adobe Photoshop CS6 without entering a serial number or signing in with an Adobe ID.
-
How to crack Adobe Photoshop Cs6 with Amtlib Dll file
-Amtlib Dll file download for Adobe Photoshop Cs6 crack
-Adobe Photoshop Cs6 crack Amtlib Dll file missing error
-Fix Adobe Photoshop Cs6 crack Amtlib Dll file corrupted issue
-Adobe Photoshop Cs6 crack Amtlib Dll file location on Windows
-Adobe Photoshop Cs6 crack Amtlib Dll file location on Mac
-Adobe Photoshop Cs6 crack Amtlib Dll file not working solution
-Adobe Photoshop Cs6 crack Amtlib Dll file virus scan
-Adobe Photoshop Cs6 crack Amtlib Dll file backup and restore
-Adobe Photoshop Cs6 crack Amtlib Dll file alternative methods
-Adobe Photoshop Cs6 crack Amtlib Dll file free download link
-Adobe Photoshop Cs6 crack Amtlib Dll file installation guide
-Adobe Photoshop Cs6 crack Amtlib Dll file compatibility check
-Adobe Photoshop Cs6 crack Amtlib Dll file update and patch
-Adobe Photoshop Cs6 crack Amtlib Dll file license key generator
-Adobe Photoshop Cs6 crack Amtlib Dll file activation code
-Adobe Photoshop Cs6 crack Amtlib Dll file serial number
-Adobe Photoshop Cs6 crack Amtlib Dll file registration code
-Adobe Photoshop Cs6 crack Amtlib Dll file product key
-Adobe Photoshop Cs6 crack Amtlib Dll file full version download
-Adobe Photoshop Cs6 crack Amtlib Dll file trial reset tool
-Adobe Photoshop Cs6 crack Amtlib Dll file offline installer
-Adobe Photoshop Cs6 crack Amtlib Dll file online activation
-Adobe Photoshop Cs6 crack Amtlib Dll file safe and secure download
-Adobe Photoshop Cs6 crack Amtlib Dll file latest version download
-Adobe Photoshop Cs6 crack Amtlib Dll file review and feedback
-Adobe Photoshop Cs6 crack Amtlib Dll file tutorial and tips
-Adobe Photoshop Cs6 crack Amtlib Dll file features and benefits
-Adobe Photoshop Cs6 crack Amtlib Dll file pros and cons
-Adobe Photoshop Cs6 crack Amtlib Dll file comparison and contrast
-Adobe Photoshop Cs6 crack Amtlib Dll file best practices and recommendations
-Adobe Photoshop Cs6 crack Amtlib Dll file troubleshooting and support
-Adobe Photoshop Cs6 crack Amtlib Dll file FAQs and answers
-Adobe Photoshop Cs6 crack Amtlib Dll file forum and community
-Adobe Photoshop Cs6 crack Amtlib Dll file blog and articles
-Adobe Photoshop Cs6 crack Amtlib Dll file video and audio tutorials
-Adobe Photoshop Cs6 crack Amtlib Dll file case studies and testimonials
-Adobe Photoshop Cs6 crack Amtlib Dll file coupons and discounts
-Adobe Photoshop Cs6 crack Amtlib Dll file affiliate program and commission
-Adobe Photoshop Cs6 crack Amtlib Dll file refund policy and guarantee
-How to uninstall Adobe Photoshop Cs6 crack Amtlib Dll file
-How to upgrade from Adobe Photoshop Cs5 to Cs6 with Amtlib Dll file
-How to use Adobe Photoshop Cs6 with other cracked software using Amtlib Dll files
-How to fix common errors and bugs in Adobe Photoshop Cs6 with cracked Amtlib Dll files
-How to optimize the performance of Adobe Photoshop Cs6 with cracked Amtlib Dll files
-How to customize the settings of Adobe Photoshop Cs6 with cracked Amtlib Dll files
-How to create stunning graphics and designs with Adobe Photoshop Cs6 with cracked Amtlib Dll files
-How to edit photos and images with Adobe Photoshop Cs6 with cracked Amtlib Dll files
-How to add filters and effects with Adobe Photoshop Cs6 with cracked Amtlib Dll files
-How to share your work with others using Adobe Photoshop Cs6 with cracked Amtlib Dll files
-
How to Download and Install Adobe Photoshop CS6
-
Before you can crack Adobe Photoshop CS6 with Amtlib.dll files, you need to download and install it on your computer. Here are the steps to do so:
Follow the instructions on the screen to download the installer file.
-
Run the installer file and follow the instructions on the screen to install Adobe Photoshop CS6.
-
When prompted, choose "Try" instead of "Buy" or "Enter serial number".
-
Wait for the installation to complete.
-
You have now installed Adobe Photoshop CS6 as a trial version. You can use it for 30 days before it expires.
-
-
How to Crack Adobe Photoshop CS6 with Amtlib.dll Files
-
Now that you have installed Adobe Photoshop CS6 as a trial version, you can crack it using Amtlib.dll files. Here are the steps to do so:
-
Step 1: Download Amtlib.dll Files
-
The first thing you need to do is download the cracked Amtlib.dll files for both 32-bit and 64-bit versions of Adobe Photoshop CS6. You can find them from various sources online, but make sure they are safe and reliable. One possible source is https://davi24.com/download-file-amtlib-dll/, where you can download them for free.
-
Step 2: Locate the Installation Folder of Adobe Photoshop CS6
-
The next thing you need to do is locate the installation folder of Adobe Photoshop CS6 on your computer. The default location depends on your operating system and whether you have installed the 32-bit or 64-bit version of Adobe Photoshop CS6. Here are some possible locations:
-
-
If you have Windows 10/8/7/Vista (64-bit) and have installed the 64-bit version of Adobe Photoshop CS6, go to C:\Program Files\Adobe\Adobe Photoshop CS6 (64 Bit)
-
If you have Windows 10/8/7/Vista (64-bit) and have installed the 32-bit version of Adobe Photoshop CS6, go to C:\Program Files (x86)\Adobe\Adobe Photoshop CS6
-
If you have Windows XP (32-bit) and have installed either version of Adobe Photoshop CS6, go to C:\Program Files\Adobe\Adobe Photoshop CS6
-
-
Step 3: Replace the Original Amtlib.dll File with the Cracked One
-
The final thing you need to do is replace the original Amtlib.dll file with the cracked one. To do this:
-
-
Open the installation folder of Adobe Photoshop CS6.
-
Find and rename the original Amtlib.dll file as something else, such as "Amtlib.bak". This way, you can restore it later if needed.
-
Copy and paste the cracked Amtlib.dll file into the same folder.
-
You have now replaced the original Amtlib.dll file with the cracked one.
-
-
Step 4: Run Adobe Photoshop CS6 and Enjoy
-
The last thing you need to do is run Adobe Photoshop CS6 and enjoy using it without any restrictions. To do this:
-
-
Launch Adobe Photoshop CS6 from your desktop or start menu.
-
You should not see any prompts asking for a serial number or an Adobe ID.
-```html any limitations.
-
You have now successfully cracked Adobe Photoshop CS6 with Amtlib.dll files.
-
-
Benefits of Cracking Adobe Photoshop CS6 with Amtlib.dll Files
-
Cracking Adobe Photoshop CS6 with Amtlib.dll files has some benefits, such as:
-
-
You can use Adobe Photoshop CS6 for free without paying for a license.
-
You can use Adobe Photoshop CS6 offline without signing in with an Adobe ID.
-
You can use Adobe Photoshop CS6 on multiple computers without any activation issues.
-
You can use Adobe Photoshop CS6 for as long as you want without worrying about expiration dates.
-
-
Risks of Cracking Adobe Photoshop CS6 with Amtlib.dll Files
-
However, cracking Adobe Photoshop CS6 with Amtlib.dll files also has some risks, such as:
-
-
You may violate the terms and conditions of Adobe and face legal consequences.
-
You may expose your computer to viruses, malware, or spyware that may harm your system or steal your data.
-
You may encounter errors, bugs, or crashes that may affect your work or damage your files.
-
You may miss out on updates, patches, or new features that may improve your experience or fix issues.
-
You may lose access to customer support or online services that may help you with your problems or questions.
-
-
Alternatives to Cracking Adobe Photoshop CS6 with Amtlib.dll Files
-
If you are not comfortable with cracking Adobe Photoshop CS6 with Amtlib.dll files, you may want to consider some alternatives, such as:
-
-
Buying a legitimate license of Adobe Photoshop CS6 from the official website or an authorized reseller.
-
Subscribing to Adobe Creative Cloud and getting access to the latest version of Adobe Photoshop and other Adobe products.
-
Using a free trial of Adobe Photoshop CS6 for 30 days and deciding whether to buy it or not.
-
Using a free or cheaper alternative to Adobe Photoshop CS6, such as GIMP, Paint.NET, Pixlr, etc.
-
-
Conclusion
-
In conclusion, cracking Adobe Photoshop CS6 with Amtlib.dll files is a way to use the software for free without any restrictions. However, it also comes with some drawbacks and dangers that you should be aware of. Therefore, you should weigh the pros and cons carefully before deciding to crack Adobe Photoshop CS6 with Amtlib.dll files. Alternatively, you can opt for some other options that may suit your needs and budget better. We hope this article has been helpful and informative for you. Thank you for reading!
-
FAQs
-
Here are some frequently asked questions and answers about cracking Adobe Photoshop CS6 with Amtlib.dll files:
-
Q: Is cracking Adobe Photoshop CS6 with Amtlib.dll files illegal?
-
A: Yes, cracking Adobe Photoshop CS6 with Amtlib.dll files is illegal. It violates the copyright and license agreement of Adobe and may result in legal action against you. You should respect the intellectual property rights of the software developers and pay for their products if you want to use them.
-
Q: Is cracking Adobe Photoshop CS6 with Amtlib.dll files safe?
-
A: No, cracking Adobe Photoshop CS6 with Amtlib.dll files is not safe. It may expose your computer to malicious software that may harm your system or steal your data. It may also cause errors, bugs, or crashes that may affect your work or damage your files. It may also prevent you from getting updates, patches, or new features that may improve your experience or fix issues. You should protect your computer and data by using only trusted and secure sources of software.
-
Q: Is cracking Adobe Photoshop CS6 with Amtlib.dll files worth it?
-
A: That depends on your personal preference and situation. Cracking Adobe Photoshop CS6 with Amtlib.dll files may save you some money and give you some freedom in using the software. However, it also comes with some risks and disadvantages that you should consider carefully. You may also miss out on some benefits and opportunities that come with using a legitimate version of the software. You should weigh the pros and cons carefully before deciding to crack Adobe Photoshop CS6 with Amtlib.dll files.
-
Q: How can I crack other Adobe products with Amtlib.dll files?
-
A: The process of cracking other Adobe products with Amtlib.dll files is similar to cracking Adobe Photoshop CS6. You need to download and install the trial version of the product you want to crack, then download and replace the original Amtlib.dll file with the cracked one in the installation folder of the product. However, you should be careful about the compatibility and reliability of the cracked Amtlib.dll files for different products and versions. You should also be aware of the risks and consequences of cracking other Adobe products with Amtlib.dll files.
-
Q: Where can I find more information about cracking Adobe Photoshop CS6 with Amtlib.dll files?
-
A: You can find more information about cracking Adobe Photoshop CS6 with Amtlib.dll files from various sources online, such as blogs, forums, videos, etc. However, you should be careful about the accuracy and credibility of these sources. You should also be careful about the safety and security of these sources. You should not download or click on any links or files that may contain viruses, malware, or spyware. You should also not share any personal or sensitive information that may compromise your privacy or identity.
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Minecraft on Windows 10 Everything You Need to Know.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Minecraft on Windows 10 Everything You Need to Know.md
deleted file mode 100644
index b51a878b2f746e676413d8e2dc07e1a64d01d98f..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Minecraft on Windows 10 Everything You Need to Know.md
+++ /dev/null
@@ -1,37 +0,0 @@
-
-
How long does it take to download Minecraft on Windows 10?
-
Minecraft is one of the most popular games in the world, with millions of players exploring, building and fighting in its blocky world. If you want to join them, you might wonder how long it takes to download Minecraft on Windows 10. The answer depends on a few factors, such as your internet speed, the version of Minecraft you want to install, and the size of the game files.
-
how long does it take to download minecraft on windows 10
In this article, we will explain how to download Minecraft for Windows 10, and how long you can expect it to take. We will also compare the two versions of Minecraft available for PC: Java Edition and Bedrock Edition (also known as Windows 10 Edition).
-
How to download Minecraft for Windows 10
-
Before you can download Minecraft for Windows 10, you need to purchase the game from either the Microsoft Store or the Minecraft website. The game costs $29.99 / £24.99 / AUS$39.95, but you can get it for free or at a discounted price if you have an Xbox Game Pass subscription.
-
Once you have bought the game, you will need to create a Microsoft account if you don't have one already. This is an Outlook email address that you can use to sign in to the Minecraft Launcher and access online features. You will also need to verify your email address and enter your birthdate and country/region.
-
After creating your Microsoft account, you can download and open the Minecraft Launcher from either the Microsoft Store or the Minecraft website. This is where you can choose which version of Minecraft you want to install: Java Edition or Bedrock Edition.
-
-
Which version of Minecraft should you install?
-
Minecraft Java Edition and Bedrock Edition are both compatible with Windows 10, but they have some differences in features, performance and cross-play options. Here are some of the main differences between them:
-
-
Java Edition is the original version of Minecraft, and it has more advanced features such as modding support, custom servers and snapshots (beta versions of upcoming updates). It also has exclusive mini-games and servers such as Hypixel and Mineplex.
-
Bedrock Edition is the newer version of Minecraft, and it has better performance, graphics and stability. It also supports cross-play with other devices that run Bedrock Edition, such as Xbox One, PlayStation 4, Nintendo Switch, iOS and Android. It also has access to the Minecraft Marketplace, where you can buy skins, maps and other content created by the community.
-
-
The good news is that you don't have to choose between them. If you buy Minecraft for Windows 10 from the Minecraft website, you will get both Java Edition and Bedrock Edition for free. You can install both versions on your PC and switch between them using the Minecraft Launcher.
-
How long does it take to download Minecraft on Windows 10?
-
The download time for Minecraft on Windows 10 depends on your internet speed and the size of the game files. According to our tests, these are the approximate download times for each version of Minecraft:
-
-
Version
File size
Download time
-
Java Edition
500 MB
5 minutes
-
Bedrock Edition
300 MB
3 minutes
-
-
Note that these are only estimates based on average internet speeds of 25 Mbps. Your actual download time may vary depending on your internet connection and other factors.
-
If you are having trouble downloading Minecraft on Windows 10, you can try some of these troubleshooting steps:
-
-
Check your internet connection and make sure it is stable and fast enough.
-
Restart your PC and router/modem.
-
Clear your browser cache and cookies.
-
Disable any antivirus or firewall software that may interfere with the download.
-
Contact Microsoft or Mojang support if none of the above works.
-
-
Conclusion
-
Minecraft is a fun and creative game that you can enjoy on your Windows 10 PC. To download it, you need to buy it from either the Microsoft Store or
ddb901b051
-
-
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Cccam 2.3.0 Ipk Vix [BEST].md b/spaces/1gistliPinn/ChatGPT4/Examples/Cccam 2.3.0 Ipk Vix [BEST].md
deleted file mode 100644
index d21fc508b0191e3f043d0f18e32c5fa49e365b37..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Cccam 2.3.0 Ipk Vix [BEST].md
+++ /dev/null
@@ -1,10 +0,0 @@
-
-
a cccam is a device that is connected to a dreambox, and it controls the picture output of the dreambox. the cccam allows you to control the dreambox picture quality and settings. you can control the contrast, brightness, and color of the picture. the cccam will allow you to preview the picture quality on the dreambox.
this can be a great option if you have a dreambox that is out of warranty and you want to upgrade the firmware. the cccam firmware upgrade can be installed on your dreambox if you have the dreambox installed and you have the right cables. (the dreambox will not work without the right cables, and the cccam firmware upgrade can only be installed after the dreambox is installed.)
-
there are a few different dreambox models that support cccam firmware upgrades. the dreambox is listed on this website if it supports cccam firmware upgrades. make sure that you are installing the correct cccam firmware for your dreambox model.
-
cccam is a great feature for those that want to upgrade their dreambox firmware. the cccam firmware upgrade will allow you to upgrade the firmware of the dreambox. the dreambox firmware upgrade can be installed on your dreambox if you have the dreambox installed and you have the right cables. the cccam firmware upgrade can only be installed after the dreambox is installed.
-
-
the cccam is a device that is connected to a dreambox, and it controls the picture output of the dreambox. the cccam allows you to control the dreambox picture quality and settings. you can control the contrast, brightness, and color of the picture.
899543212b
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Download Clash Royale 3.3024.2 APK and Join the Arena with Your Favorite Clash Characters.md b/spaces/1phancelerku/anime-remove-background/Download Clash Royale 3.3024.2 APK and Join the Arena with Your Favorite Clash Characters.md
deleted file mode 100644
index 11675180ef4f0d5a6fec7d6c5ce52233f8c6b216..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Clash Royale 3.3024.2 APK and Join the Arena with Your Favorite Clash Characters.md
+++ /dev/null
@@ -1,123 +0,0 @@
-
-
Clash Royale 3.3024.2 APK: Everything You Need to Know
-
If you are a fan of strategy games, you have probably heard of Clash Royale, one of the most popular and addictive mobile games in the world. Clash Royale is a real-time multiplayer game where you can collect and upgrade dozens of cards featuring your favorite characters from Clash of Clans, as well as spells and defenses. You can also build your own battle deck and challenge other players online in fast-paced duels.
-
But did you know that there is a new version of Clash Royale available for download? Yes, you heard that right. Clash Royale 3.3024.2 APK is the latest update of the game, and it comes with a lot of new features, improvements, and bug fixes that will make your gaming experience even better. In this article, we will tell you everything you need to know about Clash Royale 3.3024.2 APK, including what's new, how to download and install it, and why you should play it. Let's get started!
Before we dive into the details of the new version, let's have a quick recap of what Clash Royale is all about. Clash Royale is a strategy game developed by Supercell, the same company behind the hit game Clash of Clans. It was released in 2016 and has since become one of the most downloaded and played games on both Android and iOS devices.
-
Clash Royale is a game where you can create your own army of troops, spells, and buildings, and use them to attack your opponent's towers and destroy their king tower. You can also defend your own towers from enemy attacks using various strategies and tactics. The game features different arenas, each with its own theme and difficulty level, where you can compete with other players from around the world.
-
Clash Royale is not only a game of skill, but also a game of luck. You never know what cards you will get in your hand or what cards your opponent will play next. You have to think fast and act smart to win the battles. You can also join or create clans, where you can chat with other players, share cards, request donations, and participate in clan wars.
-
Clash Royale is a game that is easy to learn but hard to master. It requires strategic thinking, quick reflexes, and constant adaptation to changing situations. It is also a game that is constantly updated with new content, such as cards, modes, events, rewards, and more.
-
What's new in Clash Royale 3.3024.2 APK?
-
Now that you have a general idea of what Clash Royale is, let's see what's new in the latest version of the game: Clash Royale 3.3024.2 APK. This version was released on June 19th, 2023, and it brings some exciting changes and additions to the game.
-
New cards and balance updates
-
The most noticeable change in Clash Royale 3.3024.2 APK is the introduction of two new cards: the Firecracker and the Royal Delivery. The Firecracker is a common card that costs 3 elixir and shoots fireworks that deal splash damage to enemies. The Royal Delivery is a rare card that costs 4 elixir and drops a Royal Recruit on the battlefield after a short delay. Both cards are available in Arena 7 and above.
-
Another change in Clash Royale 3.3024.2 APK is the balance update that affects several cards in the game. Some of the cards that have been buffed are the Electro Dragon, the Goblin Cage, the Zappies, and the Heal Spirit. Some of the cards that have been nerfed are the Magic Archer, the Battle Healer, the Elixir Golem, and the Skeleton Barrel. You can check the full list of balance changes on the official website of Clash Royale.
-
New game modes and events
-
Clash Royale 3.3024.2 APK also introduces some new game modes and events that will spice up your gameplay. One of them is the Firecracker Rush, where both players start with a Firecracker on each lane, and more Firecrackers spawn throughout the match. Another one is the Royal Delivery Challenge, where you can win the new card by reaching 12 wins. There are also some seasonal events, such as the Summer of 2v2, where you can play different 2v2 modes with your friends or random partners.
-
clash royale 3.3024.2 apk download for android
-clash royale 3.3024.2 apk mod unlimited gold/gems
-clash royale 3.3024.2 apk latest version uptodown
-clash royale 3.3024.2 apk free download softpedia
-clash royale 3.3024.2 apk update new features
-clash royale 3.3024.2 apk hack no root
-clash royale 3.3024.2 apk offline installer
-clash royale 3.3024.2 apk mirror link
-clash royale 3.3024.2 apk file size
-clash royale 3.3024.2 apk gameplay review
-clash royale 3.3024.2 apk old version download
-clash royale 3.3024.2 apk obb data
-clash royale 3.3024.2 apk direct download
-clash royale 3.3024.2 apk for pc windows 10
-clash royale 3.3024.2 apk cheats codes
-clash royale 3.3024.2 apk original from supercell
-clash royale 3.3024.2 apk android requirements
-clash royale 3.3024.2 apk how to install guide
-clash royale 3.3024.2 apk best decks tips
-clash royale 3.3024.2 apk changelog not available
-clash royale 3.3024.2 apk online multiplayer mode
-clash royale 3.3024.2 apk unlimited elixir hack
-clash royale 3.3024.2 apk private server download
-clash royale 3.3024.2 apk full unlocked all cards
-clash royale 3.3024.2 apk bug fixes and improvements
-clash royale 3.3024.2 apk strategy game genre
-clash royale 3.3024.2 apk compatible devices list
-clash royale 3.3024.2 apk safe and secure download
-clash royale 3.3024.2 apk ratings and reviews
-clash royale 3.3024.2 apk screenshots and videos
-clash royale 3.3024.2 apk clan wars update
-clash royale 3.3024.2 apk legendary cards unlock
-clash royale 3.3024.2 apk arena challenges rewards
-clash royale 3.3024.2 apk new characters and skins
-clash royale 3.3024.2 apk fun and addictive gameplay
-clash royale 3.3024.2 apk support and feedback
-clash royale 3.3024.2 apk alternative download links
-clash royale 3.3024.2 apk frequently asked questions
-clash royale 3 .30242.apk no ads version premium
-
Additionally, Clash Royale 3.3024.2 APK brings back some of the classic game modes that have been missing for a while, such as Triple Elixir, Ramp Up, Sudden Death, and Draft. You can play these modes in friendly battles, tournaments, or special challenges.
-
New rewards and improvements
-
Finally, Clash Royale 3.3024.2 APK offers some new rewards and improvements that will make your gaming experience more enjoyable and rewarding. One of them is the Pass Royale Season 11, which gives you access to exclusive perks, such as unlimited entries to special challenges, a golden name, a unique tower skin, and more. You can also unlock new emotes, chests, gold, gems, and cards by completing quests and tiers.
-
Another improvement in Clash Royale 3.3024.2 APK is the Clan Wars 2.0 update, which is coming soon to the game. This update will revamp the clan wars system and make it more fun and competitive for all clans. You can expect new features such as boat battles, river tasks, clan leagues, and more.
-
How to download and install Clash Royale 3.3024.2 APK?
-
Now that you know what's new in Clash Royale 3.3024.2 APK, you might be wondering how to download and install it on your Android device. Don't worry, we have got you covered. Here is a step-by-step guide for you:
-
Requirements and permissions
-
Before you download and install Clash Royale 3.3024.2 APK, you need to make sure that your device meets the following requirements:
-
-
Your device must have Android 4.1 or higher operating system.
-
Your device must have at least 100 MB of free storage space.
-
Your device must have a stable internet connection.
-
-
You also need to enable the installation of apps from unknown sources on your device settings. To do this, follow these steps:
-
-
Go to your device settings and tap on Security or Privacy.
-
Find the option that says Unknown Sources or Install Unknown Apps and toggle it on.
-
Confirm your choice by tapping OK or Allow.
-
-
Download link and installation process
-
Once you have met the requirements and enabled the permissions, you can proceed to download and install Clash Royale 3.3024.2 APK by following these steps:
-
-
Click on this link to download Clash Royale 3.3024.2 APK file on your device.
-
Wait for the download to finish and then locate the file on your device storage or download folder.
-
Tap on the file and then tap on Install to start the installation process.
-
Wait for the installation to finish and then tap on Open to launch the game.
-
-
Troubleshooting tips and FAQs
-
If you encounter any problems or errors while downloading or installing Clash Royale 3.3024.2 APK, here are some troubleshooting tips and FAQs that might help you:
-
-
If you get a message that says "App not installed" or "Installation failed", try clearing your cache and data from your device settings or use a different file manager app to install the file.If you get a message that says "There was a problem parsing the package" or "The package appears to be corrupt", try downloading the file again from a different source or check if your device is compatible with the game.
-
If you get a message that says "This app is not available in your country" or "This app is incompatible with your device", try using a VPN app or a different Google account to access the game.
-
If you have any other questions or issues, you can contact the Clash Royale support team or visit their official website or social media pages for more information.
-
-
Why should you play Clash Royale 3.3024.2 APK?
-
Now that you know how to download and install Clash Royale 3.3024.2 APK, you might be wondering why you should play it. Well, there are many reasons why you should play the latest version of Clash Royale, and here are some of them:
-
Enjoy the new features and content
-
One of the main reasons why you should play Clash Royale 3.3024.2 APK is to enjoy the new features and content that it offers. You can try out the new cards, such as the Firecracker and the Royal Delivery, and see how they fit in your deck and strategy. You can also play the new game modes and events, such as the Firecracker Rush and the Royal Delivery Challenge, and have fun with different rules and objectives. You can also explore the new seasonal events, such as the Summer of 2v2, and team up with your friends or random partners for some epic battles.
-
Compete with other players online
-
Another reason why you should play Clash Royale 3.3024.2 APK is to compete with other players online and test your skills and knowledge. You can join or create clans, where you can chat with other players, share cards, request donations, and participate in clan wars. You can also enter tournaments, where you can play against players from around the world and win prizes. You can also climb the ladder, where you can rank up and earn trophies and rewards.
-
Have fun and challenge yourself
-
The last reason why you should play Clash Royale 3.3024.2 APK is to have fun and challenge yourself. Clash Royale is a game that is easy to learn but hard to master. It requires strategic thinking, quick reflexes, and constant adaptation to changing situations. It is also a game that is constantly updated with new content, such as cards, modes, events, rewards, and more. You will never get bored or run out of things to do in Clash Royale.
-
Conclusion
-
In conclusion, Clash Royale 3.3024.2 APK is the latest version of the game that brings a lot of new features, improvements, and bug fixes that will make your gaming experience even better. You can download and install it on your Android device by following our guide above. You can also enjoy the new cards, game modes, events, rewards, and more that it offers. You can also compete with other players online, join or create clans, enter tournaments, climb the ladder, and have fun and challenge yourself.
-
So what are you waiting for? Download Clash Royale 3.3024.2 APK now and join the millions of players who are already playing this amazing game!
-
FAQs
-
Here are some frequently asked questions about Clash Royale 3.3024.2 APK:
-
-
Q: Is Clash Royale 3.3024.2 APK safe to download and install?
-
A: Yes, Clash Royale 3.3024.2 APK is safe to download and install as long as you get it from a trusted source and follow our guide above.
-
Q: Is Clash Royale 3.3024.2 APK free to play?
-
A: Yes, Clash Royale 3.3024.2 APK is free to play, but it also offers in-app purchases that can enhance your gameplay.
-
Q: Can I play Clash Royale 3.3024.2 APK offline?
-
A: No, Clash Royale 3.3024.2 APK requires an internet connection to play.
-
Q: Can I play Clash Royale 3.3024.2 APK on PC or iOS devices?
-
A: No, Clash Royale 3.3024.2 APK is only compatible with Android devices.
-
Q: How can I update my existing version of Clash Royale to Clash Royale 3.3024.2 APK?
-
A: You can update your existing version of Clash Royale to Clash Royale 3.302 2 APK by following these steps:
-
-
Open the Google Play Store app on your device and tap on the menu icon.
-
Tap on My Apps & Games and find Clash Royale on the list of installed apps.
-
Tap on Update and wait for the download and installation to finish.
-
Tap on Open to launch the game.
-
-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/1yukikaze/img-to-music/style.css b/spaces/1yukikaze/img-to-music/style.css
deleted file mode 100644
index 8f7397fe7f0971636015170df075cd2d070344ec..0000000000000000000000000000000000000000
--- a/spaces/1yukikaze/img-to-music/style.css
+++ /dev/null
@@ -1,51 +0,0 @@
-#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-div#music-output .h-full {
- min-height: 5rem;
-}
-.footer {
- margin-bottom: 45px;
- margin-top: 10px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
- }
- .footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- }
- .dark .footer {
- border-color: #303030;
- }
- .dark .footer>p {
- background: #0b0f19;
- }
-.animate-spin {
- animation: spin 1s linear infinite;
-}
-@keyframes spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
-}
-#share-btn-container {
- display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-}
-#share-btn {
- all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-}
-#share-btn * {
- all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
- width: auto !important;
- min-height: 0px !important;
-}
-#share-btn-container .wrap {
- display: none !important;
-}
\ No newline at end of file
diff --git a/spaces/801artistry/RVC801/Makefile b/spaces/801artistry/RVC801/Makefile
deleted file mode 100644
index 44de020e6feb7fcd58016d7c3c736681f533b597..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/Makefile
+++ /dev/null
@@ -1,63 +0,0 @@
-.PHONY:
-.ONESHELL:
-
-help: ## Show this help and exit
- @grep -hE '^[A-Za-z0-9_ \-]*?:.*##.*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
-
-install: ## Install dependencies (Do everytime you start up a paperspace machine)
- apt-get -y install build-essential python3-dev ffmpeg
- pip install --upgrade setuptools wheel
- pip install --upgrade pip
- pip install faiss-gpu fairseq gradio ffmpeg ffmpeg-python praat-parselmouth pyworld numpy==1.23.5 numba==0.56.4 librosa==0.9.1
- pip install -r requirements.txt
- pip install --upgrade lxml
- apt-get update
- apt -y install -qq aria2
-
-basev1: ## Download version 1 pre-trained models (Do only once after cloning the fork)
- mkdir -p pretrained uvr5_weights
- git pull
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d pretrained -o D32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d pretrained -o D40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d pretrained -o D48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d pretrained -o G32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d pretrained -o G40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d pretrained -o G48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth -d pretrained -o f0D32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth -d pretrained -o f0D40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth -d pretrained -o f0D48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth -d pretrained -o f0G32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth -d pretrained -o f0G40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth -d pretrained -o f0G48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
-
-basev2: ## Download version 2 pre-trained models (Do only once after cloning the fork)
- mkdir -p pretrained_v2 uvr5_weights
- git pull
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D32k.pth -d pretrained_v2 -o D32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d pretrained_v2 -o D40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D48k.pth -d pretrained_v2 -o D48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G32k.pth -d pretrained_v2 -o G32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d pretrained_v2 -o G40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G48k.pth -d pretrained_v2 -o G48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D32k.pth -d pretrained_v2 -o f0D32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d pretrained_v2 -o f0D40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D48k.pth -d pretrained_v2 -o f0D48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G32k.pth -d pretrained_v2 -o f0G32k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d pretrained_v2 -o f0G40k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G48k.pth -d pretrained_v2 -o f0G48k.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth
- aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt
-
-run-ui: ## Run the python GUI
- python infer-web.py --paperspace --pycmd python
-
-run-cli: ## Run the python CLI
- python infer-web.py --pycmd python --is_cli
-
-tensorboard: ## Start the tensorboard (Run on separate terminal)
- echo https://tensorboard-$$(hostname).clg07azjl.paperspacegradient.com
- tensorboard --logdir logs --bind_all
\ No newline at end of file
diff --git a/spaces/A666sxr/Genshin_TTS/stft.py b/spaces/A666sxr/Genshin_TTS/stft.py
deleted file mode 100644
index ef754544a88a1a4ff2e39760000d707c6b160b4b..0000000000000000000000000000000000000000
--- a/spaces/A666sxr/Genshin_TTS/stft.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""
-BSD 3-Clause License
-Copyright (c) 2017, Prem Seetharaman
-All rights reserved.
-* Redistribution and use in source and binary forms, with or without
- modification, are permitted provided that the following conditions are met:
-* Redistributions of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
-* Redistributions in binary form must reproduce the above copyright notice, this
- list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-* Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from this
- software without specific prior written permission.
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-"""
-
-import torch
-import numpy as np
-import torch.nn.functional as F
-from torch.autograd import Variable
-from scipy.signal import get_window
-from librosa.util import pad_center, tiny
-import librosa.util as librosa_util
-
-def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
- n_fft=800, dtype=np.float32, norm=None):
- """
- # from librosa 0.6
- Compute the sum-square envelope of a window function at a given hop length.
- This is used to estimate modulation effects induced by windowing
- observations in short-time fourier transforms.
- Parameters
- ----------
- window : string, tuple, number, callable, or list-like
- Window specification, as in `get_window`
- n_frames : int > 0
- The number of analysis frames
- hop_length : int > 0
- The number of samples to advance between frames
- win_length : [optional]
- The length of the window function. By default, this matches `n_fft`.
- n_fft : int > 0
- The length of each analysis frame.
- dtype : np.dtype
- The data type of the output
- Returns
- -------
- wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
- The sum-squared envelope of the window function
- """
- if win_length is None:
- win_length = n_fft
-
- n = n_fft + hop_length * (n_frames - 1)
- x = np.zeros(n, dtype=dtype)
-
- # Compute the squared window at the desired length
- win_sq = get_window(window, win_length, fftbins=True)
- win_sq = librosa_util.normalize(win_sq, norm=norm)**2
- win_sq = librosa_util.pad_center(win_sq, n_fft)
-
- # Fill the envelope
- for i in range(n_frames):
- sample = i * hop_length
- x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
- return x
-
-
-class STFT(torch.nn.Module):
- """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
- def __init__(self, filter_length=800, hop_length=200, win_length=800,
- window='hann'):
- super(STFT, self).__init__()
- self.filter_length = filter_length
- self.hop_length = hop_length
- self.win_length = win_length
- self.window = window
- self.forward_transform = None
- scale = self.filter_length / self.hop_length
- fourier_basis = np.fft.fft(np.eye(self.filter_length))
-
- cutoff = int((self.filter_length / 2 + 1))
- fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
- np.imag(fourier_basis[:cutoff, :])])
-
- forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
- inverse_basis = torch.FloatTensor(
- np.linalg.pinv(scale * fourier_basis).T[:, None, :])
-
- if window is not None:
- assert(filter_length >= win_length)
- # get window and zero center pad it to filter_length
- fft_window = get_window(window, win_length, fftbins=True)
- fft_window = pad_center(fft_window, filter_length)
- fft_window = torch.from_numpy(fft_window).float()
-
- # window the bases
- forward_basis *= fft_window
- inverse_basis *= fft_window
-
- self.register_buffer('forward_basis', forward_basis.float())
- self.register_buffer('inverse_basis', inverse_basis.float())
-
- def transform(self, input_data):
- num_batches = input_data.size(0)
- num_samples = input_data.size(1)
-
- self.num_samples = num_samples
-
- # similar to librosa, reflect-pad the input
- input_data = input_data.view(num_batches, 1, num_samples)
- input_data = F.pad(
- input_data.unsqueeze(1),
- (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
- mode='reflect')
- input_data = input_data.squeeze(1)
-
- forward_transform = F.conv1d(
- input_data,
- Variable(self.forward_basis, requires_grad=False),
- stride=self.hop_length,
- padding=0)
-
- cutoff = int((self.filter_length / 2) + 1)
- real_part = forward_transform[:, :cutoff, :]
- imag_part = forward_transform[:, cutoff:, :]
-
- magnitude = torch.sqrt(real_part**2 + imag_part**2)
- phase = torch.autograd.Variable(
- torch.atan2(imag_part.data, real_part.data))
-
- return magnitude, phase
-
- def inverse(self, magnitude, phase):
- recombine_magnitude_phase = torch.cat(
- [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
-
- inverse_transform = F.conv_transpose1d(
- recombine_magnitude_phase,
- Variable(self.inverse_basis, requires_grad=False),
- stride=self.hop_length,
- padding=0)
-
- if self.window is not None:
- window_sum = window_sumsquare(
- self.window, magnitude.size(-1), hop_length=self.hop_length,
- win_length=self.win_length, n_fft=self.filter_length,
- dtype=np.float32)
- # remove modulation effects
- approx_nonzero_indices = torch.from_numpy(
- np.where(window_sum > tiny(window_sum))[0])
- window_sum = torch.autograd.Variable(
- torch.from_numpy(window_sum), requires_grad=False)
- window_sum = window_sum.to(inverse_transform.device()) if magnitude.is_cuda else window_sum
- inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
-
- # scale by hop ratio
- inverse_transform *= float(self.filter_length) / self.hop_length
-
- inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
- inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
-
- return inverse_transform
-
- def forward(self, input_data):
- self.magnitude, self.phase = self.transform(input_data)
- reconstruction = self.inverse(self.magnitude, self.phase)
- return reconstruction
-
-
-class TorchSTFT(torch.nn.Module):
- def __init__(self, filter_length=800, hop_length=200, win_length=800, window='hann'):
- super().__init__()
- self.filter_length = filter_length
- self.hop_length = hop_length
- self.win_length = win_length
- self.window = torch.from_numpy(get_window(window, win_length, fftbins=True).astype(np.float32))
-
- def transform(self, input_data):
- forward_transform = torch.stft(
- input_data,
- self.filter_length, self.hop_length, self.win_length, window=self.window,
- return_complex=True)
-
- return torch.abs(forward_transform), torch.angle(forward_transform)
-
- def inverse(self, magnitude, phase):
- inverse_transform = torch.istft(
- magnitude * torch.exp(phase * 1j),
- self.filter_length, self.hop_length, self.win_length, window=self.window.to(magnitude.device))
-
- return inverse_transform.unsqueeze(-2) # unsqueeze to stay consistent with conv_transpose1d implementation
-
- def forward(self, input_data):
- self.magnitude, self.phase = self.transform(input_data)
- reconstruction = self.inverse(self.magnitude, self.phase)
- return reconstruction
-
-
diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/loss.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/loss.py
deleted file mode 100644
index cc66298a14997da4aa2efc71e37c0a6bcda53fd1..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/loss.py
+++ /dev/null
@@ -1,398 +0,0 @@
-from multiprocessing.sharedctypes import Value
-import torch
-import torch.distributed.nn
-from torch import distributed as dist, nn as nn
-from torch.nn import functional as F
-import numpy as np
-from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
-
-try:
- import horovod.torch as hvd
-except ImportError:
- hvd = None
-
-
-def gather_features(
- audio_features,
- text_features,
- audio_features_mlp=None,
- text_features_mlp=None,
- local_loss=False,
- gather_with_grad=False,
- rank=0,
- world_size=1,
- use_horovod=False,
- mlp_loss=False,
-):
- if use_horovod:
- assert hvd is not None, "Please install horovod"
- if gather_with_grad:
- all_audio_features = hvd.allgather(audio_features)
- all_text_features = hvd.allgather(text_features)
- if mlp_loss:
- all_audio_features_mlp = hvd.allgather(audio_features_mlp)
- all_text_features_mlp = hvd.allgather(text_features_mlp)
- else:
- with torch.no_grad():
- all_audio_features = hvd.allgather(audio_features)
- all_text_features = hvd.allgather(text_features)
- if mlp_loss:
- all_audio_features_mlp = hvd.allgather(audio_features_mlp)
- all_text_features_mlp = hvd.allgather(text_features_mlp)
- if not local_loss:
- # ensure grads for local rank when all_* features don't have a gradient
- gathered_audio_features = list(
- all_audio_features.chunk(world_size, dim=0)
- )
- gathered_text_features = list(
- all_text_features.chunk(world_size, dim=0)
- )
- gathered_audio_features[rank] = audio_features
- gathered_text_features[rank] = text_features
- all_audio_features = torch.cat(gathered_audio_features, dim=0)
- all_text_features = torch.cat(gathered_text_features, dim=0)
- if mlp_loss:
- gathered_audio_features_mlp = list(
- all_audio_features_mlp.chunk(world_size, dim=0)
- )
- gathered_text_features_mlp = list(
- all_text_features_mlp.chunk(world_size, dim=0)
- )
- gathered_audio_features_mlp[rank] = audio_features_mlp
- gathered_text_features_mlp[rank] = text_features_mlp
- all_audio_features_mlp = torch.cat(
- gathered_audio_features_mlp, dim=0
- )
- all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
- else:
- # We gather tensors from all gpus
- if gather_with_grad:
- all_audio_features = torch.cat(
- torch.distributed.nn.all_gather(audio_features), dim=0
- )
- all_text_features = torch.cat(
- torch.distributed.nn.all_gather(text_features), dim=0
- )
- if mlp_loss:
- all_audio_features_mlp = torch.cat(
- torch.distributed.nn.all_gather(audio_features_mlp), dim=0
- )
- all_text_features_mlp = torch.cat(
- torch.distributed.nn.all_gather(text_features_mlp), dim=0
- )
- else:
- gathered_audio_features = [
- torch.zeros_like(audio_features) for _ in range(world_size)
- ]
- gathered_text_features = [
- torch.zeros_like(text_features) for _ in range(world_size)
- ]
- dist.all_gather(gathered_audio_features, audio_features)
- dist.all_gather(gathered_text_features, text_features)
- if mlp_loss:
- gathered_audio_features_mlp = [
- torch.zeros_like(audio_features_mlp) for _ in range(world_size)
- ]
- gathered_text_features_mlp = [
- torch.zeros_like(text_features_mlp) for _ in range(world_size)
- ]
- dist.all_gather(gathered_audio_features_mlp, audio_features_mlp)
- dist.all_gather(gathered_text_features_mlp, text_features_mlp)
- if not local_loss:
- # ensure grads for local rank when all_* features don't have a gradient
- gathered_audio_features[rank] = audio_features
- gathered_text_features[rank] = text_features
- if mlp_loss:
- gathered_audio_features_mlp[rank] = audio_features_mlp
- gathered_text_features_mlp[rank] = text_features_mlp
-
- all_audio_features = torch.cat(gathered_audio_features, dim=0)
- all_text_features = torch.cat(gathered_text_features, dim=0)
- if mlp_loss:
- all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
- all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
- if mlp_loss:
- return (
- all_audio_features,
- all_text_features,
- all_audio_features_mlp,
- all_text_features_mlp,
- )
- else:
- return all_audio_features, all_text_features
-
-
-class ClipLoss(nn.Module):
- def __init__(
- self,
- local_loss=False,
- gather_with_grad=False,
- cache_labels=False,
- rank=0,
- world_size=1,
- use_horovod=False,
- mlp_loss=False,
- weight_loss_kappa=0,
- ):
- super().__init__()
- self.local_loss = local_loss
- self.gather_with_grad = gather_with_grad
- self.cache_labels = cache_labels
- self.rank = rank
- self.world_size = world_size
- self.use_horovod = use_horovod
- self.mlp_loss = mlp_loss
- self.weighted_loss = bool(weight_loss_kappa != 0)
- self.weight_loss_kappa = weight_loss_kappa
- # cache state
- self.prev_num_logits = 0
- self.labels = {}
-
- def forward(
- self,
- audio_features,
- text_features,
- logit_scale_a,
- logit_scale_t=None,
- audio_features_mlp=None,
- text_features_mlp=None,
- ):
- device = audio_features.device
- if self.mlp_loss:
- if self.world_size > 1:
- (
- all_audio_features,
- all_text_features,
- all_audio_features_mlp,
- all_text_features_mlp,
- ) = gather_features(
- audio_features=audio_features,
- text_features=text_features,
- audio_features_mlp=audio_features_mlp,
- text_features_mlp=text_features_mlp,
- local_loss=self.local_loss,
- gather_with_grad=self.gather_with_grad,
- rank=self.rank,
- world_size=self.world_size,
- use_horovod=self.use_horovod,
- mlp_loss=self.mlp_loss,
- )
- if self.local_loss:
- a_logits_per_audio = (
- logit_scale_a * audio_features @ all_text_features_mlp.T
- )
- a_logits_per_text = (
- logit_scale_a * text_features_mlp @ all_audio_features.T
- )
- t_logits_per_audio = (
- logit_scale_t * audio_features_mlp @ all_text_features.T
- )
- t_logits_per_text = (
- logit_scale_t * text_features @ all_audio_features_mlp.T
- )
- else:
- a_logits_per_audio = (
- logit_scale_a * all_audio_features @ all_text_features_mlp.T
- )
- a_logits_per_text = a_logits_per_audio.T
- t_logits_per_audio = (
- logit_scale_t * all_audio_features_mlp @ all_text_features.T
- )
- t_logits_per_text = t_logits_per_audio.T
- else:
- a_logits_per_audio = (
- logit_scale_a * audio_features @ text_features_mlp.T
- )
- a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T
- t_logits_per_audio = (
- logit_scale_t * audio_features_mlp @ text_features.T
- )
- t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T
-
- # calculated ground-truth and cache if enabled
- num_logits = a_logits_per_audio.shape[0]
- if self.prev_num_logits != num_logits or device not in self.labels:
- labels = torch.arange(num_logits, device=device, dtype=torch.long)
- if self.world_size > 1 and self.local_loss:
- labels = labels + num_logits * self.rank
- if self.cache_labels:
- self.labels[device] = labels
- self.prev_num_logits = num_logits
- else:
- labels = self.labels[device]
-
- if not self.weighted_loss:
- total_loss = (
- F.cross_entropy(a_logits_per_audio, labels)
- + F.cross_entropy(a_logits_per_text, labels)
- + F.cross_entropy(t_logits_per_audio, labels)
- + F.cross_entropy(t_logits_per_text, labels)
- ) / 4
- else:
- audio_weight = (audio_features @ audio_features.T).detach()
- audio_weight = (
- torch.exp(
- torch.sum(audio_weight, axis=1)
- / (self.weight_loss_kappa * len(audio_weight))
- )
- ).detach()
- text_weight = (text_features @ text_features.T).detach()
- text_weight = (
- torch.exp(
- torch.sum(text_weight, axis=1)
- / (self.weight_loss_kappa * len(text_features))
- )
- ).detach()
- total_loss = (
- F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight)
- + F.cross_entropy(a_logits_per_text, labels, weight=audio_weight)
- + F.cross_entropy(t_logits_per_audio, labels, weight=text_weight)
- + F.cross_entropy(t_logits_per_text, labels, weight=text_weight)
- ) / 4
- else:
- if self.world_size > 1:
- all_audio_features, all_text_features = gather_features(
- audio_features=audio_features,
- text_features=text_features,
- local_loss=self.local_loss,
- gather_with_grad=self.gather_with_grad,
- rank=self.rank,
- world_size=self.world_size,
- use_horovod=self.use_horovod,
- mlp_loss=self.mlp_loss,
- )
-
- if self.local_loss:
- logits_per_audio = (
- logit_scale_a * audio_features @ all_text_features.T
- )
- logits_per_text = (
- logit_scale_a * text_features @ all_audio_features.T
- )
- else:
- logits_per_audio = (
- logit_scale_a * all_audio_features @ all_text_features.T
- )
- logits_per_text = logits_per_audio.T
- else:
- logits_per_audio = logit_scale_a * audio_features @ text_features.T
- logits_per_text = logit_scale_a * text_features @ audio_features.T
-
- # calculated ground-truth and cache if enabled
- num_logits = logits_per_audio.shape[0]
- if self.prev_num_logits != num_logits or device not in self.labels:
- labels = torch.arange(num_logits, device=device, dtype=torch.long)
- if self.world_size > 1 and self.local_loss:
- labels = labels + num_logits * self.rank
- if self.cache_labels:
- self.labels[device] = labels
- self.prev_num_logits = num_logits
- else:
- labels = self.labels[device]
- if not self.weighted_loss:
- total_loss = (
- F.cross_entropy(logits_per_audio, labels)
- + F.cross_entropy(logits_per_text, labels)
- ) / 2
- else:
- audio_weight = (all_audio_features @ all_audio_features.T).detach()
- audio_weight = (
- torch.exp(
- torch.sum(audio_weight, axis=1)
- / (self.weight_loss_kappa * len(all_audio_features))
- )
- ).detach()
- text_weight = (all_text_features @ all_text_features.T).detach()
- text_weight = (
- torch.exp(
- torch.sum(text_weight, axis=1)
- / (self.weight_loss_kappa * len(all_text_features))
- )
- ).detach()
- total_loss = (
- F.cross_entropy(logits_per_audio, labels, weight=text_weight)
- + F.cross_entropy(logits_per_text, labels, weight=audio_weight)
- ) / 2
- return total_loss
-
-
-def lp_gather_features(pred, target, world_size=1, use_horovod=False):
- if use_horovod:
- assert hvd is not None, "Please install horovod"
- with torch.no_grad():
- all_preds = hvd.allgather(pred)
- all_targets = hvd.allgath(target)
- else:
- gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)]
- gathered_targets = [torch.zeros_like(target) for _ in range(world_size)]
-
- dist.all_gather(gathered_preds, pred)
- dist.all_gather(gathered_targets, target)
- all_preds = torch.cat(gathered_preds, dim=0)
- all_targets = torch.cat(gathered_targets, dim=0)
-
- return all_preds, all_targets
-
-
-def get_map(pred, target):
- pred = torch.sigmoid(pred).numpy()
- target = target.numpy()
- return np.mean(average_precision_score(target, pred, average=None))
-
-
-def get_acc(pred, target):
- pred = torch.argmax(pred, 1).numpy()
- target = torch.argmax(target, 1).numpy()
- return accuracy_score(target, pred)
-
-
-def get_mauc(pred, target):
- pred = torch.sigmoid(pred).numpy()
- target = target.numpy()
- return np.mean(roc_auc_score(target, pred, average=None))
-
-
-class LPMetrics(object):
- def __init__(self, metric_names=["map", "acc", "mauc"]):
- self.metrics = []
- for name in metric_names:
- self.metrics.append(self.get_metric(name))
- self.metric_names = metric_names
-
- def get_metric(self, name):
- if name == "map":
- return get_map
- elif name == "acc":
- return get_acc
- elif name == "mauc":
- return get_mauc
- else:
- raise ValueError(f"the metric should be at least one of [map, acc, mauc]")
-
- def evaluate_mertics(self, pred, target):
- metric_dict = {}
- for i in range(len(self.metric_names)):
- metric_dict[self.metric_names[i]] = self.metrics[i](pred, target)
- return metric_dict
-
-
-def calc_celoss(pred, target):
- target = torch.argmax(target, 1).long()
- return nn.CrossEntropyLoss()(pred, target)
-
-
-class LPLoss(nn.Module):
- def __init__(self, loss_name):
- super().__init__()
- if loss_name == "bce":
- self.loss_func = nn.BCEWithLogitsLoss()
- elif loss_name == "ce":
- self.loss_func = calc_celoss
- elif loss_name == "mse":
- self.loss_func = nn.MSELoss()
- else:
- raise ValueError(f"the loss func should be at least one of [bce, ce, mse]")
-
- def forward(self, pred, target):
- loss = self.loss_func(pred, target)
- return loss
diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/openai.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/openai.py
deleted file mode 100644
index 3f4eb8b55fe960e1792b3da804b60b3d8f70fe26..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/openai.py
+++ /dev/null
@@ -1,156 +0,0 @@
-""" OpenAI pretrained model functions
-
-Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-
-import os
-import warnings
-from typing import Union, List
-
-import torch
-
-from .model import build_model_from_openai_state_dict
-from .pretrained import (
- get_pretrained_url,
- list_pretrained_tag_models,
- download_pretrained,
-)
-
-__all__ = ["list_openai_models", "load_openai_model"]
-
-
-def list_openai_models() -> List[str]:
- """Returns the names of available CLIP models"""
- return list_pretrained_tag_models("openai")
-
-
-def load_openai_model(
- name: str,
- model_cfg,
- device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
- jit=True,
- cache_dir=os.path.expanduser("~/.cache/clip"),
- enable_fusion: bool = False,
- fusion_type: str = "None",
-):
- """Load a CLIP model, preserve its text pretrained part, and set in the CLAP model
-
- Parameters
- ----------
- name : str
- A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
- device : Union[str, torch.device]
- The device to put the loaded model
- jit : bool
- Whether to load the optimized JIT model (default) or more hackable non-JIT model.
-
- Returns
- -------
- model : torch.nn.Module
- The CLAP model
- preprocess : Callable[[PIL.Image], torch.Tensor]
- A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
- """
- if get_pretrained_url(name, "openai"):
- model_path = download_pretrained(
- get_pretrained_url(name, "openai"), root=cache_dir
- )
- elif os.path.isfile(name):
- model_path = name
- else:
- raise RuntimeError(
- f"Model {name} not found; available models = {list_openai_models()}"
- )
-
- try:
- # loading JIT archive
- model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
- state_dict = None
- except RuntimeError:
- # loading saved state dict
- if jit:
- warnings.warn(
- f"File {model_path} is not a JIT archive. Loading as a state dict instead"
- )
- jit = False
- state_dict = torch.load(model_path, map_location="cpu")
-
- if not jit:
- try:
- model = build_model_from_openai_state_dict(
- state_dict or model.state_dict(), model_cfg, enable_fusion, fusion_type
- ).to(device)
- except KeyError:
- sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
- model = build_model_from_openai_state_dict(
- sd, model_cfg, enable_fusion, fusion_type
- ).to(device)
-
- if str(device) == "cpu":
- model.float()
- return model
-
- # patch the device names
- device_holder = torch.jit.trace(
- lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]
- )
- device_node = [
- n
- for n in device_holder.graph.findAllNodes("prim::Constant")
- if "Device" in repr(n)
- ][-1]
-
- def patch_device(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("prim::Constant"):
- if "value" in node.attributeNames() and str(node["value"]).startswith(
- "cuda"
- ):
- node.copyAttributes(device_node)
-
- model.apply(patch_device)
- patch_device(model.encode_audio)
- patch_device(model.encode_text)
-
- # patch dtype to float32 on CPU
- if str(device) == "cpu":
- float_holder = torch.jit.trace(
- lambda: torch.ones([]).float(), example_inputs=[]
- )
- float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
- float_node = float_input.node()
-
- def patch_float(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("aten::to"):
- inputs = list(node.inputs())
- for i in [
- 1,
- 2,
- ]: # dtype can be the second or third argument to aten::to()
- if inputs[i].node()["value"] == 5:
- inputs[i].node().copyAttributes(float_node)
-
- model.apply(patch_float)
- patch_float(model.encode_audio)
- patch_float(model.encode_text)
- model.float()
-
- model.audio_branch.audio_length = model.audio_cfg.audio_length
- return model
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/os_utils.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/os_utils.py
deleted file mode 100644
index 4567d17c398c535884600cdd86a36a823acb886f..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/os_utils.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-import subprocess
-
-
-def link_file(from_file, to_file):
- subprocess.check_call(
- f'ln -s "`realpath --relative-to="{os.path.dirname(to_file)}" "{from_file}"`" "{to_file}"', shell=True)
-
-
-def move_file(from_file, to_file):
- subprocess.check_call(f'mv "{from_file}" "{to_file}"', shell=True)
-
-
-def copy_file(from_file, to_file):
- subprocess.check_call(f'cp -r "{from_file}" "{to_file}"', shell=True)
-
-
-def remove_file(*fns):
- for f in fns:
- subprocess.check_call(f'rm -rf "{f}"', shell=True)
diff --git a/spaces/ASJMO/freegpt/client/css/options.css b/spaces/ASJMO/freegpt/client/css/options.css
deleted file mode 100644
index fb015a54e0a7f7ac521517357d812c994621592e..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/client/css/options.css
+++ /dev/null
@@ -1,10 +0,0 @@
-.options-container {
- display: flex;
- flex-wrap: wrap;
-}
-
-@media screen and (max-width: 990px) {
- .options-container {
- justify-content: space-between;
- }
-}
diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/builders.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/builders.py
deleted file mode 100644
index 77ee5f96fea2e3c9e475fe961bc1a5ee473ed8eb..0000000000000000000000000000000000000000
--- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/models/builders.py
+++ /dev/null
@@ -1,218 +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.
-
-"""
-All the functions to build the relevant models and modules
-from the Hydra config.
-"""
-
-import typing as tp
-import warnings
-
-import audiocraft
-import omegaconf
-import torch
-
-from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel # noqa
-from .lm import LMModel
-from ..modules.codebooks_patterns import (
- CodebooksPatternProvider,
- DelayedPatternProvider,
- ParallelPatternProvider,
- UnrolledPatternProvider,
- VALLEPattern,
- MusicLMPattern,
-)
-from ..modules.conditioners import (
- BaseConditioner,
- ConditioningProvider,
- LUTConditioner,
- T5Conditioner,
- ConditionFuser,
- ChromaStemConditioner,
-)
-from .. import quantization as qt
-from ..utils.utils import dict_from_config
-
-
-def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
- klass = {
- 'no_quant': qt.DummyQuantizer,
- 'rvq': qt.ResidualVectorQuantizer
- }[quantizer]
- kwargs = dict_from_config(getattr(cfg, quantizer))
- if quantizer != 'no_quant':
- kwargs['dimension'] = dimension
- return klass(**kwargs)
-
-
-def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
- if encoder_name == 'seanet':
- kwargs = dict_from_config(getattr(cfg, 'seanet'))
- encoder_override_kwargs = kwargs.pop('encoder')
- decoder_override_kwargs = kwargs.pop('decoder')
- encoder_kwargs = {**kwargs, **encoder_override_kwargs}
- decoder_kwargs = {**kwargs, **decoder_override_kwargs}
- encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
- decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs)
- return encoder, decoder
- else:
- raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
- """Instantiate a compression model.
- """
- if cfg.compression_model == 'encodec':
- kwargs = dict_from_config(getattr(cfg, 'encodec'))
- encoder_name = kwargs.pop('autoencoder')
- quantizer_name = kwargs.pop('quantizer')
- encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
- quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
- frame_rate = kwargs['sample_rate'] // encoder.hop_length
- renormalize = kwargs.pop('renormalize', None)
- renorm = kwargs.pop('renorm')
- if renormalize is None:
- renormalize = renorm is not None
- warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.")
- return EncodecModel(encoder, decoder, quantizer,
- frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device)
- else:
- raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
- """Instantiate a transformer LM.
- """
- if cfg.lm_model == 'transformer_lm':
- kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
- n_q = kwargs['n_q']
- q_modeling = kwargs.pop('q_modeling', None)
- codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
- attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
- cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
- cfg_prob, cfg_coef = cls_free_guidance["training_dropout"], cls_free_guidance["inference_coef"]
- fuser = get_condition_fuser(cfg)
- condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
- if len(fuser.fuse2cond['cross']) > 0: # enforce cross-att programatically
- kwargs['cross_attention'] = True
- if codebooks_pattern_cfg.modeling is None:
- assert q_modeling is not None, \
- 'LM model should either have a codebook pattern defined or transformer_lm.q_modeling'
- codebooks_pattern_cfg = omegaconf.OmegaConf.create(
- {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
- )
- pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
- return LMModel(
- pattern_provider=pattern_provider,
- condition_provider=condition_provider,
- fuser=fuser,
- cfg_dropout=cfg_prob,
- cfg_coef=cfg_coef,
- attribute_dropout=attribute_dropout,
- dtype=getattr(torch, cfg.dtype),
- device=cfg.device,
- **kwargs
- ).to(cfg.device)
- else:
- raise KeyError(f'Unexpected LM model {cfg.lm_model}')
-
-
-def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
- """Instantiate a conditioning model.
- """
- device = cfg.device
- duration = cfg.dataset.segment_duration
- cfg = getattr(cfg, "conditioners")
- cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg
- conditioners: tp.Dict[str, BaseConditioner] = {}
- with omegaconf.open_dict(cfg):
- condition_provider_args = cfg.pop('args', {})
- for cond, cond_cfg in cfg.items():
- model_type = cond_cfg["model"]
- model_args = cond_cfg[model_type]
- if model_type == "t5":
- conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
- elif model_type == "lut":
- conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
- elif model_type == "chroma_stem":
- model_args.pop('cache_path', None)
- conditioners[str(cond)] = ChromaStemConditioner(
- output_dim=output_dim,
- duration=duration,
- device=device,
- **model_args
- )
- else:
- raise ValueError(f"unrecognized conditioning model: {model_type}")
- conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
- return conditioner
-
-
-def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
- """Instantiate a condition fuser object.
- """
- fuser_cfg = getattr(cfg, "fuser")
- fuser_methods = ["sum", "cross", "prepend", "input_interpolate"]
- fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
- kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
- fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
- return fuser
-
-
-def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
- """Instantiate a codebooks pattern provider object.
- """
- pattern_providers = {
- 'parallel': ParallelPatternProvider,
- 'delay': DelayedPatternProvider,
- 'unroll': UnrolledPatternProvider,
- 'valle': VALLEPattern,
- 'musiclm': MusicLMPattern,
- }
- name = cfg.modeling
- kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
- klass = pattern_providers[name]
- return klass(n_q, **kwargs)
-
-
-def get_debug_compression_model(device='cpu'):
- """Instantiate a debug compression model to be used for unit tests.
- """
- seanet_kwargs = {
- 'n_filters': 4,
- 'n_residual_layers': 1,
- 'dimension': 32,
- 'ratios': [10, 8, 16] # 25 Hz at 32kHz
- }
- encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
- decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
- quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
- init_x = torch.randn(8, 32, 128)
- quantizer(init_x, 1) # initialize kmeans etc.
- compression_model = EncodecModel(
- encoder, decoder, quantizer,
- frame_rate=25, sample_rate=32000, channels=1).to(device)
- return compression_model.eval()
-
-
-def get_debug_lm_model(device='cpu'):
- """Instantiate a debug LM to be used for unit tests.
- """
- pattern = DelayedPatternProvider(n_q=4)
- dim = 16
- providers = {
- 'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
- }
- condition_provider = ConditioningProvider(providers)
- fuser = ConditionFuser(
- {'cross': ['description'], 'prepend': [],
- 'sum': [], 'input_interpolate': []})
- lm = LMModel(
- pattern, condition_provider, fuser,
- n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
- cross_attention=True, causal=True)
- return lm.to(device).eval()
diff --git a/spaces/Abdulkader/Abdulkader-T5-MedRepAnalyzer/app.py b/spaces/Abdulkader/Abdulkader-T5-MedRepAnalyzer/app.py
deleted file mode 100644
index 7002bdb2bbf0aaa2efb06cc0ccf98da3f9ac05da..0000000000000000000000000000000000000000
--- a/spaces/Abdulkader/Abdulkader-T5-MedRepAnalyzer/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-import requests
-import transformers
-from transformers import pipeline
-model="https://huggingface.co/Abdulkader/autotrain-medical-reports-summarizer-2484176581"
-pipe = pipeline( model=model)
-gr.Interface.load("pipe").launch()
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/utils/Yoyo.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/utils/Yoyo.js
deleted file mode 100644
index 7d1430b877a480359f744be55c9236d527ab5dfd..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/utils/Yoyo.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import Yoyo from '../../../plugins/utils/math/Yoyo.js'
-export default Yoyo;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/clickoutside/ClickOutside.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/clickoutside/ClickOutside.d.ts
deleted file mode 100644
index 576f9c7a5f5a5da09443c2fa211303a0c73fcb40..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/clickoutside/ClickOutside.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import ClickOutside from '../../../plugins/clickoutside';
-export default ClickOutside;
\ No newline at end of file
diff --git a/spaces/Alfaxad/BioGalacticModels/model_list.py b/spaces/Alfaxad/BioGalacticModels/model_list.py
deleted file mode 100644
index 886991b59a1049d2cfbd3ba35a54966480c4ee71..0000000000000000000000000000000000000000
--- a/spaces/Alfaxad/BioGalacticModels/model_list.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-import requests
-from huggingface_hub.hf_api import SpaceInfo
-
-url = 'https://docs.google.com/spreadsheets/d/1XH7Jo3LXXfbSJ14z-QrSIQs21ArJMiV6_hMSAwY85PU/edit#gid=0'
-csv_url = url.replace('/edit#gid=', '/export?format=csv&gid=')
-
-class ModelList:
- def __init__(self):
- self.table = pd.read_csv(csv_url)
- self._preprocess_table()
-
- self.table_header = '''
-
-
Model Name
-
Type
-
Year
-
Paper
-
Code on Github
-
Weights on 🤗
-
Other Weights
-
'''
-
- def _preprocess_table(self) -> None:
- self.table['name_lowercase'] = self.table.name.str.lower()
- self.table['year'] = self.table['year'].apply(str)
-
- rows = []
- for row in self.table.itertuples():
- paper = f'Paper' if isinstance(
- row.paper, str) else ''
- github = f'GitHub' if isinstance(
- row.github, str) else ''
- hf_model = f'Hub Model' if isinstance(
- row.hub, str) else ''
- other_model = f'Other Weights' if isinstance(
- row.other, str) else ''
- data_type = f'{row.data_type}' if isinstance(
- row.data_type, str) else ''
- base_model = f'{row.base_model}' if isinstance(
- row.base_model, str) else ''
- year = f'{row.year}' if isinstance(
- row.year, str) else ''
- row = f'''
-
'''
- return html
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py
deleted file mode 100644
index ed61b37171f1d8b9025b392542db02ac1d5f4c31..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py
+++ /dev/null
@@ -1,554 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import warnings
-from typing import Callable, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.utils.checkpoint
-from transformers import (
- CLIPImageProcessor,
- CLIPTextModelWithProjection,
- CLIPTokenizer,
- CLIPVisionModelWithProjection,
-)
-
-from ...image_processor import VaeImageProcessor
-from ...models import AutoencoderKL, DualTransformer2DModel, Transformer2DModel, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from .modeling_text_unet import UNetFlatConditionModel
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
- r"""
- Pipeline for image-text dual-guided generation using Versatile Diffusion.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
- implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
- Parameters:
- vqvae ([`VQModel`]):
- Vector-quantized (VQ) model to encode and decode images to and from latent representations.
- bert ([`LDMBertModel`]):
- Text-encoder model based on [`~transformers.BERT`].
- tokenizer ([`~transformers.BertTokenizer`]):
- A `BertTokenizer` to tokenize text.
- unet ([`UNet2DConditionModel`]):
- A `UNet2DConditionModel` to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- """
- tokenizer: CLIPTokenizer
- image_feature_extractor: CLIPImageProcessor
- text_encoder: CLIPTextModelWithProjection
- image_encoder: CLIPVisionModelWithProjection
- image_unet: UNet2DConditionModel
- text_unet: UNetFlatConditionModel
- vae: AutoencoderKL
- scheduler: KarrasDiffusionSchedulers
-
- _optional_components = ["text_unet"]
-
- def __init__(
- self,
- tokenizer: CLIPTokenizer,
- image_feature_extractor: CLIPImageProcessor,
- text_encoder: CLIPTextModelWithProjection,
- image_encoder: CLIPVisionModelWithProjection,
- image_unet: UNet2DConditionModel,
- text_unet: UNetFlatConditionModel,
- vae: AutoencoderKL,
- scheduler: KarrasDiffusionSchedulers,
- ):
- super().__init__()
- self.register_modules(
- tokenizer=tokenizer,
- image_feature_extractor=image_feature_extractor,
- text_encoder=text_encoder,
- image_encoder=image_encoder,
- image_unet=image_unet,
- text_unet=text_unet,
- vae=vae,
- scheduler=scheduler,
- )
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
- if self.text_unet is not None and (
- "dual_cross_attention" not in self.image_unet.config or not self.image_unet.config.dual_cross_attention
- ):
- # if loading from a universal checkpoint rather than a saved dual-guided pipeline
- self._convert_to_dual_attention()
-
- def remove_unused_weights(self):
- self.register_modules(text_unet=None)
-
- def _convert_to_dual_attention(self):
- """
- Replace image_unet's `Transformer2DModel` blocks with `DualTransformer2DModel` that contains transformer blocks
- from both `image_unet` and `text_unet`
- """
- for name, module in self.image_unet.named_modules():
- if isinstance(module, Transformer2DModel):
- parent_name, index = name.rsplit(".", 1)
- index = int(index)
-
- image_transformer = self.image_unet.get_submodule(parent_name)[index]
- text_transformer = self.text_unet.get_submodule(parent_name)[index]
-
- config = image_transformer.config
- dual_transformer = DualTransformer2DModel(
- num_attention_heads=config.num_attention_heads,
- attention_head_dim=config.attention_head_dim,
- in_channels=config.in_channels,
- num_layers=config.num_layers,
- dropout=config.dropout,
- norm_num_groups=config.norm_num_groups,
- cross_attention_dim=config.cross_attention_dim,
- attention_bias=config.attention_bias,
- sample_size=config.sample_size,
- num_vector_embeds=config.num_vector_embeds,
- activation_fn=config.activation_fn,
- num_embeds_ada_norm=config.num_embeds_ada_norm,
- )
- dual_transformer.transformers[0] = image_transformer
- dual_transformer.transformers[1] = text_transformer
-
- self.image_unet.get_submodule(parent_name)[index] = dual_transformer
- self.image_unet.register_to_config(dual_cross_attention=True)
-
- def _revert_dual_attention(self):
- """
- Revert the image_unet `DualTransformer2DModel` blocks back to `Transformer2DModel` with image_unet weights Call
- this function if you reuse `image_unet` in another pipeline, e.g. `VersatileDiffusionPipeline`
- """
- for name, module in self.image_unet.named_modules():
- if isinstance(module, DualTransformer2DModel):
- parent_name, index = name.rsplit(".", 1)
- index = int(index)
- self.image_unet.get_submodule(parent_name)[index] = module.transformers[0]
-
- self.image_unet.register_to_config(dual_cross_attention=False)
-
- def _encode_text_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `List[str]`):
- prompt to be encoded
- device: (`torch.device`):
- torch device
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- """
-
- def normalize_embeddings(encoder_output):
- embeds = self.text_encoder.text_projection(encoder_output.last_hidden_state)
- embeds_pooled = encoder_output.text_embeds
- embeds = embeds / torch.norm(embeds_pooled.unsqueeze(1), dim=-1, keepdim=True)
- return embeds
-
- batch_size = len(prompt)
-
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
-
- if not torch.equal(text_input_ids, untruncated_ids):
- removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
- logger.warning(
- "The following part of your input was truncated because CLIP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = text_inputs.attention_mask.to(device)
- else:
- attention_mask = None
-
- prompt_embeds = self.text_encoder(
- text_input_ids.to(device),
- attention_mask=attention_mask,
- )
- prompt_embeds = normalize_embeddings(prompt_embeds)
-
- # duplicate text embeddings for each generation per prompt, using mps friendly method
- bs_embed, seq_len, _ = prompt_embeds.shape
- prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
- prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- uncond_tokens = [""] * batch_size
- max_length = text_input_ids.shape[-1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = uncond_input.attention_mask.to(device)
- else:
- attention_mask = None
-
- negative_prompt_embeds = self.text_encoder(
- uncond_input.input_ids.to(device),
- attention_mask=attention_mask,
- )
- negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)
-
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = negative_prompt_embeds.shape[1]
- negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
- negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
- return prompt_embeds
-
- def _encode_image_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `List[str]`):
- prompt to be encoded
- device: (`torch.device`):
- torch device
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- """
-
- def normalize_embeddings(encoder_output):
- embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)
- embeds = self.image_encoder.visual_projection(embeds)
- embeds_pooled = embeds[:, 0:1]
- embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True)
- return embeds
-
- batch_size = len(prompt) if isinstance(prompt, list) else 1
-
- # get prompt text embeddings
- image_input = self.image_feature_extractor(images=prompt, return_tensors="pt")
- pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype)
- image_embeddings = self.image_encoder(pixel_values)
- image_embeddings = normalize_embeddings(image_embeddings)
-
- # duplicate image embeddings for each generation per prompt, using mps friendly method
- bs_embed, seq_len, _ = image_embeddings.shape
- image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
- image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size
- uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt")
- pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)
- negative_prompt_embeds = self.image_encoder(pixel_values)
- negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)
-
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = negative_prompt_embeds.shape[1]
- negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
- negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and conditional embeddings into a single batch
- # to avoid doing two forward passes
- image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
-
- return image_embeddings
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
- def decode_latents(self, latents):
- warnings.warn(
- "The decode_latents method is deprecated and will be removed in a future version. Please"
- " use VaeImageProcessor instead",
- FutureWarning,
- )
- latents = 1 / self.vae.config.scaling_factor * latents
- image = self.vae.decode(latents, return_dict=False)[0]
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- def check_inputs(self, prompt, image, height, width, callback_steps):
- if not isinstance(prompt, str) and not isinstance(prompt, PIL.Image.Image) and not isinstance(prompt, list):
- raise ValueError(f"`prompt` has to be of type `str` `PIL.Image` or `list` but is {type(prompt)}")
- if not isinstance(image, str) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list):
- raise ValueError(f"`image` has to be of type `str` `PIL.Image` or `list` but is {type(image)}")
-
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
- def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
- shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- if latents is None:
- latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
- else:
- latents = latents.to(device)
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
- return latents
-
- def set_transformer_params(self, mix_ratio: float = 0.5, condition_types: Tuple = ("text", "image")):
- for name, module in self.image_unet.named_modules():
- if isinstance(module, DualTransformer2DModel):
- module.mix_ratio = mix_ratio
-
- for i, type in enumerate(condition_types):
- if type == "text":
- module.condition_lengths[i] = self.text_encoder.config.max_position_embeddings
- module.transformer_index_for_condition[i] = 1 # use the second (text) transformer
- else:
- module.condition_lengths[i] = 257
- module.transformer_index_for_condition[i] = 0 # use the first (image) transformer
-
- @torch.no_grad()
- def __call__(
- self,
- prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
- image: Union[str, List[str]],
- text_to_image_strength: float = 0.5,
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: int = 1,
- **kwargs,
- ):
- r"""
- The call function to the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide image generation.
- height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- A higher guidance scale value encourages the model to generate images closely linked to the text
- `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide what to not include in image generation. If not defined, you need to
- pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
- to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
- generation deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor is generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
- callback (`Callable`, *optional*):
- A function that calls every `callback_steps` steps during inference. The function is called with the
- following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function is called. If not specified, the callback is called at
- every step.
-
- Examples:
-
- ```py
- >>> from diffusers import VersatileDiffusionDualGuidedPipeline
- >>> import torch
- >>> import requests
- >>> from io import BytesIO
- >>> from PIL import Image
-
- >>> # let's download an initial image
- >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
-
- >>> response = requests.get(url)
- >>> image = Image.open(BytesIO(response.content)).convert("RGB")
- >>> text = "a red car in the sun"
-
- >>> pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(
- ... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
- ... )
- >>> pipe.remove_unused_weights()
- >>> pipe = pipe.to("cuda")
-
- >>> generator = torch.Generator(device="cuda").manual_seed(0)
- >>> text_to_image_strength = 0.75
-
- >>> image = pipe(
- ... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
- ... ).images[0]
- >>> image.save("./car_variation.png")
- ```
-
- Returns:
- [`~pipelines.ImagePipelineOutput`] or `tuple`:
- If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
- returned where the first element is a list with the generated images.
- """
- # 0. Default height and width to unet
- height = height or self.image_unet.config.sample_size * self.vae_scale_factor
- width = width or self.image_unet.config.sample_size * self.vae_scale_factor
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(prompt, image, height, width, callback_steps)
-
- # 2. Define call parameters
- prompt = [prompt] if not isinstance(prompt, list) else prompt
- image = [image] if not isinstance(image, list) else image
- batch_size = len(prompt)
- device = self._execution_device
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- # 3. Encode input prompts
- prompt_embeds = self._encode_text_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance)
- image_embeddings = self._encode_image_prompt(image, device, num_images_per_prompt, do_classifier_free_guidance)
- dual_prompt_embeddings = torch.cat([prompt_embeds, image_embeddings], dim=1)
- prompt_types = ("text", "image")
-
- # 4. Prepare timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.scheduler.timesteps
-
- # 5. Prepare latent variables
- num_channels_latents = self.image_unet.config.in_channels
- latents = self.prepare_latents(
- batch_size * num_images_per_prompt,
- num_channels_latents,
- height,
- width,
- dual_prompt_embeddings.dtype,
- device,
- generator,
- latents,
- )
-
- # 6. Prepare extra step kwargs.
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 7. Combine the attention blocks of the image and text UNets
- self.set_transformer_params(text_to_image_strength, prompt_types)
-
- # 8. Denoising loop
- for i, t in enumerate(self.progress_bar(timesteps)):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=dual_prompt_embeddings).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- # call the callback, if provided
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- if not output_type == "latent":
- image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
- else:
- image = latents
-
- image = self.image_processor.postprocess(image, output_type=output_type)
-
- if not return_dict:
- return (image,)
-
- return ImagePipelineOutput(images=image)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/detectors/htc_r50_sac_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/detectors/htc_r50_sac_1x_coco.py
deleted file mode 100644
index 72d4db963ffd95851b945911b3db9941426583ab..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/detectors/htc_r50_sac_1x_coco.py
+++ /dev/null
@@ -1,8 +0,0 @@
-_base_ = '../htc/htc_r50_fpn_1x_coco.py'
-
-model = dict(
- backbone=dict(
- type='DetectoRS_ResNet',
- conv_cfg=dict(type='ConvAWS'),
- sac=dict(type='SAC', use_deform=True),
- stage_with_sac=(False, True, True, True)))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/standard_roi_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/standard_roi_head.py
deleted file mode 100644
index c530f2a5ce904439492de12ff7d267cc1e757d3a..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/standard_roi_head.py
+++ /dev/null
@@ -1,295 +0,0 @@
-import torch
-
-from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
-from ..builder import HEADS, build_head, build_roi_extractor
-from .base_roi_head import BaseRoIHead
-from .test_mixins import BBoxTestMixin, MaskTestMixin
-
-
-@HEADS.register_module()
-class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
- """Simplest base roi head including one bbox head and one mask head."""
-
- def init_assigner_sampler(self):
- """Initialize assigner and sampler."""
- self.bbox_assigner = None
- self.bbox_sampler = None
- if self.train_cfg:
- self.bbox_assigner = build_assigner(self.train_cfg.assigner)
- self.bbox_sampler = build_sampler(
- self.train_cfg.sampler, context=self)
-
- def init_bbox_head(self, bbox_roi_extractor, bbox_head):
- """Initialize ``bbox_head``"""
- self.bbox_roi_extractor = build_roi_extractor(bbox_roi_extractor)
- self.bbox_head = build_head(bbox_head)
-
- def init_mask_head(self, mask_roi_extractor, mask_head):
- """Initialize ``mask_head``"""
- if mask_roi_extractor is not None:
- self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor)
- self.share_roi_extractor = False
- else:
- self.share_roi_extractor = True
- self.mask_roi_extractor = self.bbox_roi_extractor
- self.mask_head = build_head(mask_head)
-
- def init_weights(self, pretrained):
- """Initialize the weights in head.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
- if self.with_shared_head:
- self.shared_head.init_weights(pretrained=pretrained)
- if self.with_bbox:
- self.bbox_roi_extractor.init_weights()
- self.bbox_head.init_weights()
- if self.with_mask:
- self.mask_head.init_weights()
- if not self.share_roi_extractor:
- self.mask_roi_extractor.init_weights()
-
- def forward_dummy(self, x, proposals):
- """Dummy forward function."""
- # bbox head
- outs = ()
- rois = bbox2roi([proposals])
- if self.with_bbox:
- bbox_results = self._bbox_forward(x, rois)
- outs = outs + (bbox_results['cls_score'],
- bbox_results['bbox_pred'])
- # mask head
- if self.with_mask:
- mask_rois = rois[:100]
- mask_results = self._mask_forward(x, mask_rois)
- outs = outs + (mask_results['mask_pred'], )
- return outs
-
- def forward_train(self,
- x,
- img_metas,
- proposal_list,
- gt_bboxes,
- gt_labels,
- gt_bboxes_ignore=None,
- gt_masks=None):
- """
- Args:
- x (list[Tensor]): list of multi-level img features.
- img_metas (list[dict]): list of image info dict where each dict
- has: 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- `mmdet/datasets/pipelines/formatting.py:Collect`.
- proposals (list[Tensors]): list of region proposals.
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[Tensor]): class indices corresponding to each box
- gt_bboxes_ignore (None | list[Tensor]): specify which bounding
- boxes can be ignored when computing the loss.
- gt_masks (None | Tensor) : true segmentation masks for each box
- used if the architecture supports a segmentation task.
-
- Returns:
- dict[str, Tensor]: a dictionary of loss components
- """
- # assign gts and sample proposals
- if self.with_bbox or self.with_mask:
- num_imgs = len(img_metas)
- if gt_bboxes_ignore is None:
- gt_bboxes_ignore = [None for _ in range(num_imgs)]
- sampling_results = []
- for i in range(num_imgs):
- assign_result = self.bbox_assigner.assign(
- proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
- gt_labels[i])
- sampling_result = self.bbox_sampler.sample(
- assign_result,
- proposal_list[i],
- gt_bboxes[i],
- gt_labels[i],
- feats=[lvl_feat[i][None] for lvl_feat in x])
- sampling_results.append(sampling_result)
-
- losses = dict()
- # bbox head forward and loss
- if self.with_bbox:
- bbox_results = self._bbox_forward_train(x, sampling_results,
- gt_bboxes, gt_labels,
- img_metas)
- losses.update(bbox_results['loss_bbox'])
-
- # mask head forward and loss
- if self.with_mask:
- mask_results = self._mask_forward_train(x, sampling_results,
- bbox_results['bbox_feats'],
- gt_masks, img_metas)
- losses.update(mask_results['loss_mask'])
-
- return losses
-
- def _bbox_forward(self, x, rois):
- """Box head forward function used in both training and testing."""
- # TODO: a more flexible way to decide which feature maps to use
- bbox_feats = self.bbox_roi_extractor(
- x[:self.bbox_roi_extractor.num_inputs], rois)
- if self.with_shared_head:
- bbox_feats = self.shared_head(bbox_feats)
- cls_score, bbox_pred = self.bbox_head(bbox_feats)
-
- bbox_results = dict(
- cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
- return bbox_results
-
- def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
- img_metas):
- """Run forward function and calculate loss for box head in training."""
- rois = bbox2roi([res.bboxes for res in sampling_results])
- bbox_results = self._bbox_forward(x, rois)
-
- bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
- gt_labels, self.train_cfg)
- loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
- bbox_results['bbox_pred'], rois,
- *bbox_targets)
-
- bbox_results.update(loss_bbox=loss_bbox)
- return bbox_results
-
- def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
- img_metas):
- """Run forward function and calculate loss for mask head in
- training."""
- if not self.share_roi_extractor:
- pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
- mask_results = self._mask_forward(x, pos_rois)
- else:
- pos_inds = []
- device = bbox_feats.device
- for res in sampling_results:
- pos_inds.append(
- torch.ones(
- res.pos_bboxes.shape[0],
- device=device,
- dtype=torch.uint8))
- pos_inds.append(
- torch.zeros(
- res.neg_bboxes.shape[0],
- device=device,
- dtype=torch.uint8))
- pos_inds = torch.cat(pos_inds)
-
- mask_results = self._mask_forward(
- x, pos_inds=pos_inds, bbox_feats=bbox_feats)
-
- mask_targets = self.mask_head.get_targets(sampling_results, gt_masks,
- self.train_cfg)
- pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
- loss_mask = self.mask_head.loss(mask_results['mask_pred'],
- mask_targets, pos_labels)
-
- mask_results.update(loss_mask=loss_mask, mask_targets=mask_targets)
- return mask_results
-
- def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None):
- """Mask head forward function used in both training and testing."""
- assert ((rois is not None) ^
- (pos_inds is not None and bbox_feats is not None))
- if rois is not None:
- mask_feats = self.mask_roi_extractor(
- x[:self.mask_roi_extractor.num_inputs], rois)
- if self.with_shared_head:
- mask_feats = self.shared_head(mask_feats)
- else:
- assert bbox_feats is not None
- mask_feats = bbox_feats[pos_inds]
-
- mask_pred = self.mask_head(mask_feats)
- mask_results = dict(mask_pred=mask_pred, mask_feats=mask_feats)
- return mask_results
-
- async def async_simple_test(self,
- x,
- proposal_list,
- img_metas,
- proposals=None,
- rescale=False):
- """Async test without augmentation."""
- assert self.with_bbox, 'Bbox head must be implemented.'
-
- det_bboxes, det_labels = await self.async_test_bboxes(
- x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
- bbox_results = bbox2result(det_bboxes, det_labels,
- self.bbox_head.num_classes)
- if not self.with_mask:
- return bbox_results
- else:
- segm_results = await self.async_test_mask(
- x,
- img_metas,
- det_bboxes,
- det_labels,
- rescale=rescale,
- mask_test_cfg=self.test_cfg.get('mask'))
- return bbox_results, segm_results
-
- def simple_test(self,
- x,
- proposal_list,
- img_metas,
- proposals=None,
- rescale=False):
- """Test without augmentation."""
- assert self.with_bbox, 'Bbox head must be implemented.'
-
- det_bboxes, det_labels = self.simple_test_bboxes(
- x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
- if torch.onnx.is_in_onnx_export():
- if self.with_mask:
- segm_results = self.simple_test_mask(
- x, img_metas, det_bboxes, det_labels, rescale=rescale)
- return det_bboxes, det_labels, segm_results
- else:
- return det_bboxes, det_labels
-
- bbox_results = [
- bbox2result(det_bboxes[i], det_labels[i],
- self.bbox_head.num_classes)
- for i in range(len(det_bboxes))
- ]
-
- if not self.with_mask:
- return bbox_results
- else:
- segm_results = self.simple_test_mask(
- x, img_metas, det_bboxes, det_labels, rescale=rescale)
- return list(zip(bbox_results, segm_results))
-
- def aug_test(self, x, proposal_list, img_metas, rescale=False):
- """Test with augmentations.
-
- If rescale is False, then returned bboxes and masks will fit the scale
- of imgs[0].
- """
- det_bboxes, det_labels = self.aug_test_bboxes(x, img_metas,
- proposal_list,
- self.test_cfg)
-
- if rescale:
- _det_bboxes = det_bboxes
- else:
- _det_bboxes = det_bboxes.clone()
- _det_bboxes[:, :4] *= det_bboxes.new_tensor(
- img_metas[0][0]['scale_factor'])
- bbox_results = bbox2result(_det_bboxes, det_labels,
- self.bbox_head.num_classes)
-
- # det_bboxes always keep the original scale
- if self.with_mask:
- segm_results = self.aug_test_mask(x, img_metas, det_bboxes,
- det_labels)
- return [(bbox_results, segm_results)]
- else:
- return [bbox_results]
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index 19841547a42315164de547a4121cfd64739cf24b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = [
- '../_base_/models/dmnet_r50-d8.py',
- '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
- decode_head=dict(align_corners=True),
- auxiliary_head=dict(align_corners=True),
- test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_480x480_40k_pascal_context.py b/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_480x480_40k_pascal_context.py
deleted file mode 100644
index 5ff05aa595399d77ee51552c243e489f395a820e..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_480x480_40k_pascal_context.py
+++ /dev/null
@@ -1,8 +0,0 @@
-_base_ = [
- '../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_context.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
- decode_head=dict(num_classes=60),
- test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
-optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/handlers/base.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/handlers/base.py
deleted file mode 100644
index 288878bc57282fbb2f12b32290152ca8e9d3cab0..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/handlers/base.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import ABCMeta, abstractmethod
-
-
-class BaseFileHandler(metaclass=ABCMeta):
- # `str_like` is a flag to indicate whether the type of file object is
- # str-like object or bytes-like object. Pickle only processes bytes-like
- # objects but json only processes str-like object. If it is str-like
- # object, `StringIO` will be used to process the buffer.
- str_like = True
-
- @abstractmethod
- def load_from_fileobj(self, file, **kwargs):
- pass
-
- @abstractmethod
- def dump_to_fileobj(self, obj, file, **kwargs):
- pass
-
- @abstractmethod
- def dump_to_str(self, obj, **kwargs):
- pass
-
- def load_from_path(self, filepath, mode='r', **kwargs):
- with open(filepath, mode) as f:
- return self.load_from_fileobj(f, **kwargs)
-
- def dump_to_path(self, obj, filepath, mode='w', **kwargs):
- with open(filepath, mode) as f:
- self.dump_to_fileobj(obj, f, **kwargs)
diff --git a/spaces/ArkanDash/rvc-models/app.py b/spaces/ArkanDash/rvc-models/app.py
deleted file mode 100644
index 82708cf62d946b368cf5e1af470a1afd1c1735bb..0000000000000000000000000000000000000000
--- a/spaces/ArkanDash/rvc-models/app.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import os
-import json
-import argparse
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import (
- is_half,
- device
-)
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
- def vc_fn(
- input_audio,
- f0_up_key,
- f0_method,
- index_rate,
- tts_mode,
- tts_text,
- tts_voice
- ):
- try:
- if tts_mode:
- if len(tts_text) > 100 and limitation:
- return "Text is too long", None
- if tts_text is None or tts_voice is None:
- return "You need to enter text and select a voice", None
- asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
- audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
- else:
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- if duration > 20 and limitation:
- return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- )
- print(
- f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- )
- return "Success", (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, (None, None)
- return vc_fn
-
-def load_hubert():
- global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(device)
- if is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-def change_to_tts_mode(tts_mode):
- if tts_mode:
- return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
- else:
- return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--api', action="store_true", default=False)
- parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
- args, unknown = parser.parse_known_args()
- load_hubert()
- models = []
- tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
- voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
- with open("weights/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for name, info in models_info.items():
- if not info['enable']:
- continue
- title = info['title']
- author = info.get("author", None)
- cover = f"weights/{name}/{info['cover']}"
- index = f"weights/{name}/{info['feature_retrieval_library']}"
- npy = f"weights/{name}/{info['feature_file']}"
- cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
- net_g.eval().to(device)
- if is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, device, is_half)
- models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
- with gr.Blocks() as app:
- gr.Markdown(
- "#
RVC Models (Outdated)\n"
- "##
The input audio should be clean and pure voice without background music.\n"
- "#
[](https://huggingface.co/spaces/ArkanDash/rvc-models-new)\n\n"
- "[](https://colab.research.google.com/drive/1hx6kKvIuv5XNY1Gai2PEuZhpO5z6xpVh?usp=sharing)\n\n"
- )
- with gr.Tabs():
- for (name, title, author, cover, vc_fn) in models:
- with gr.TabItem(name):
- with gr.Row():
- gr.Markdown(
- '
'
- f'
{title}
\n'+
- (f'
Model author: {author}
' if author else "")+
- (f'' if cover else "")+
- '
'
- )
- with gr.Row():
- with gr.Column():
- vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
- vc_transpose = gr.Number(label="Transpose", value=0)
- vc_f0method = gr.Radio(
- label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- vc_index_ratio = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- value=0.6,
- interactive=True,
- )
- tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
- tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- vc_submit = gr.Button("Generate", variant="primary")
- with gr.Column():
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio")
- vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
- tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
- app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.colab)
\ No newline at end of file
diff --git a/spaces/ArtyomKhyan/Detection/app.py b/spaces/ArtyomKhyan/Detection/app.py
deleted file mode 100644
index c7d5352068a2685842d435ef8d69003ea6d5bb25..0000000000000000000000000000000000000000
--- a/spaces/ArtyomKhyan/Detection/app.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import argparse
-import time
-import gradio as gr
-import cv2
-import matplotlib.pyplot as plt
-import torch
-from PIL import Image
-from torchvision.datasets import ImageFolder
-import matplotlib.pyplot as plt
-import torch.backends.cudnn as cudnn
-import numpy as np
-import matplotlib.pyplot as plt
-import torchvision
-from torchvision.transforms import transforms
-import os
-def class_model():
- model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet101', pretrained=True)
-
- class LinearModel(torch.nn.Module):
-
- def __init__(self):
- super(LinearModel, self).__init__()
- self.activation = torch.nn.ReLU()
- self.linear1 = torch.nn.Linear(1024, 100)
- self.linear2 = torch.nn.Linear(100, 3)
-
- def forward(self, x):
- x = self.activation(x)
- x = self.linear1(x)
- x = self.activation(x)
- x = self.linear2(x)
- return x
- full_c = torch.nn.Linear(in_features = 2048, out_features = 1024)
- full_c.load_state_dict(torch.load('so.pt'))
- model.fc = full_c
- Linear = LinearModel()
- Linear.load_state_dict(torch.load('som.pt'))
- model = torch.nn.Sequential(model, Linear)
- model.eval()
- return model
-
-transform = transforms.Compose([
- transforms.ToPILImage(),
- transforms.Resize((224, 224)),
- transforms.ToTensor(),
- transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
- ])
-
-def box_iou(box1, box2):
- # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
- """
- Return intersection-over-union (Jaccard index) of boxes.
- Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
- Arguments:
- box1 (Tensor[N, 4])
- box2 (Tensor[M, 4])
- Returns:
- iou (Tensor[N, M]): the NxM matrix containing the pairwise
- IoU values for every element in boxes1 and boxes2
- """
-
- def box_area(box):
- # box = 4xn
- return (box[2] - box[0]) * (box[3] - box[1])
-
- area1 = box_area(box1.t())
- area2 = box_area(box2.t())
-
- # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
- inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
- return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
-
-def xywh2xyxy(x):
- # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
- y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
- y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
- y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
- y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
- y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
- return y
-def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.65, merge=False, classes=None, agnostic=False):
- """Performs Non-Maximum Suppression (NMS) on inference results
-
- Returns:
- detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
- """
- if prediction.dtype is torch.float16:
- prediction = prediction.float() # to FP32
-
- nc = prediction[0].shape[1] - 5 # number of classes
- xc = prediction[..., 4] > conf_thres # candidates
-
- # Settings
- min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
- max_det = 300 # maximum number of detections per image
- time_limit = 10.0 # seconds to quit after
- redundant = True # require redundant detections
- multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
-
- t = time.time()
- output = [None] * prediction.shape[0]
- for xi, x in enumerate(prediction): # image index, image inference
- # Apply constraints
- # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
- x = x[xc[xi]] # confidence
-
- # If none remain process next image
- if not x.shape[0]:
- continue
-
- # Compute conf
- x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
-
- # Box (center x, center y, width, height) to (x1, y1, x2, y2)
- box = xywh2xyxy(x[:, :4])
-
- # Detections matrix nx6 (xyxy, conf, cls)
- if multi_label:
- i, j = (x[:, 5:] > conf_thres).nonzero().t()
- x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
- else: # best class only
- conf, j = x[:, 5:].max(1, keepdim=True)
- x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
- # Filter by class
- if classes:
- x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
- # Apply finite constraint
- # if not torch.isfinite(x).all():
- # x = x[torch.isfinite(x).all(1)]
-
- # If none remain process next image
- n = x.shape[0] # number of boxes
- if not n:
- continue
-
- # Sort by confidence
- # x = x[x[:, 4].argsort(descending=True)]
-
- # Batched NMS
- c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
- boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
- i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
- if i.shape[0] > max_det: # limit detections
- i = i[:max_det]
- if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
- try: # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
- iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
- weights = iou * scores[None] # box weights
- x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
- if redundant:
- i = i[iou.sum(1) > 1] # require redundancy
- except: # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139
- print(x, i, x.shape, i.shape)
- pass
-
- output[xi] = x[i]
- if (time.time() - t) > time_limit:
- break # time limit exceeded
-
- return output
-
-
-
-def run_detector(img):
- device = torch.device('cpu')
- print(1)
- model = torch.load('last_yolov5s_results.pt', map_location=device)['model'].float()
- print(2)
- image = img
- res_image = cv2.resize(image, (416, 416))
- image = torch.tensor(res_image).permute(2, 0, 1).unsqueeze(0).float() / 255.
- count_dobri = 0
- count_all = 0
- with torch.no_grad():
- detection = model(image)
- pred = non_max_suppression(detection[0], conf_thres=0.4, iou_thres=0.6)
- main_model = class_model()
- main_model.eval()
-
- for i in pred[0]:
- if i[0] > 0 and i[1] > 0 and i[2] > 0 and i[3] > 0:
- if i[3] > i[1] and i[2] > i[0]:
-
- cropped_image = res_image[int(i[1]):int(i[3]), int(i[0]):int(i[2])]
- input_tensor = transform(cropped_image)
- input_tensor = input_tensor.reshape(1, 3, 224, 224)
- with torch.no_grad():
- input = main_model(input_tensor)
- layer = torch.nn.Softmax(dim=1)
- output_s = layer(input)
- output = torch.argmax(output_s)
- print(output)
- if output == 0 and output_s[0][0] > 0.55:
- count_dobri +=1
- cv2.rectangle(res_image, (int(i[0]), int(i[1])), (int(i[2]), int(i[3])), (0, 255, 0))
- elif output == 1:
- count_all +=1
- cv2.rectangle(res_image, (int(i[0]), int(i[1])), (int(i[2]), int(i[3])), (0, 0, 255))
- elif output == 2:
- cv2.rectangle(res_image, (int(i[0]), int(i[1])), (int(i[2]), int(i[3])), (255, 0, 0))
- count_all += count_dobri
- global dobry_percent
- dobry_percent = count_dobri/count_all
- return res_image
-
-
-def greet(name):
- print(type(name))
- res_image = run_detector(name)
-
- return res_image, dobry_percent*100
-
-
-demo = gr.Interface(fn=greet, inputs="image", outputs=['image', 'text'])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Ashrafb/translate/app.py b/spaces/Ashrafb/translate/app.py
deleted file mode 100644
index 644e3ffae892d8895908745d4bcdf405fc85c86d..0000000000000000000000000000000000000000
--- a/spaces/Ashrafb/translate/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-from transformers import M2M100ForConditionalGeneration
-from tokenization_small100 import SMALL100Tokenizer
-
-langs = """af,am,ar,ast,az,ba,be,bg,bn,br,bs,ca,ceb,cs,cy,da,de,el,en,es,et,fa,ff,fi,fr,fy,ga,gd,gl,gu,ha,he,hi,hr,ht,hu,hy,id,ig,ilo,is,it,ja,jv,ka,kk,km,kn,ko,lb,lg,ln,lo,lt,lv,mg,mk,ml,mn,mr,ms,my,ne,nl,no,ns,oc,or,pa,pl,ps,pt,ro,ru,sd,si,sk,sl,so,sq,sr,ss,su,sv,sw,ta,th,tl,tn,tr,uk,ur,uz,vi,wo,xh,yi,yo,zh,zu"""
-lang_list = langs.split(',')
-
-model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100")
-tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100")
-
-def translate(lang, text):
- tokenizer.tgt_lang = lang
- encoded_text = tokenizer(text, return_tensors="pt")
- generated_tokens = model.generate(**encoded_text)
- return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-
-with gr.Blocks(analytics_enabled=False) as app:
-
- Source = gr.Textbox( label="Source" )
- Language = gr.Dropdown( lang_list, label="Language" )
- Translate = gr.Button( "Translate" )
- Result = gr.Textbox( label="Result" )
-
-
- Translate.click(
- translate,
- inputs=[ Language, Source ],
- outputs=[Result],
- api_name="translate",
- )
-
- app.launch( inline=True )
- block.queue( concurrency_count=2 )
diff --git a/spaces/Awesimo/jojogan/e4e/criteria/lpips/__init__.py b/spaces/Awesimo/jojogan/e4e/criteria/lpips/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/builtin_datasets.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/builtin_datasets.md
deleted file mode 100644
index 0ba82423ad498bdd86274ada56a201134a590d94..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/builtin_datasets.md
+++ /dev/null
@@ -1 +0,0 @@
-../../datasets/README.md
\ No newline at end of file
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/models.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/models.md
deleted file mode 100644
index 3cf918e7a145ee326c6cccf8a88835b7e02a7c30..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/models.md
+++ /dev/null
@@ -1,180 +0,0 @@
-# Use Models
-
-## Build Models from Yacs Config
-From a yacs config object,
-models (and their sub-models) can be built by
-functions such as `build_model`, `build_backbone`, `build_roi_heads`:
-```python
-from detectron2.modeling import build_model
-model = build_model(cfg) # returns a torch.nn.Module
-```
-
-`build_model` only builds the model structure and fills it with random parameters.
-See below for how to load an existing checkpoint to the model and how to use the `model` object.
-
-### Load/Save a Checkpoint
-```python
-from detectron2.checkpoint import DetectionCheckpointer
-DetectionCheckpointer(model).load(file_path_or_url) # load a file, usually from cfg.MODEL.WEIGHTS
-
-checkpointer = DetectionCheckpointer(model, save_dir="output")
-checkpointer.save("model_999") # save to output/model_999.pth
-```
-
-Detectron2's checkpointer recognizes models in pytorch's `.pth` format, as well as the `.pkl` files
-in our model zoo.
-See [API doc](../modules/checkpoint.html#detectron2.checkpoint.DetectionCheckpointer)
-for more details about its usage.
-
-The model files can be arbitrarily manipulated using `torch.{load,save}` for `.pth` files or
-`pickle.{dump,load}` for `.pkl` files.
-
-### Use a Model
-
-A model can be called by `outputs = model(inputs)`, where `inputs` is a `list[dict]`.
-Each dict corresponds to one image and the required keys
-depend on the type of model, and whether the model is in training or evaluation mode.
-For example, in order to do inference,
-all existing models expect the "image" key, and optionally "height" and "width".
-The detailed format of inputs and outputs of existing models are explained below.
-
-__Training__: When in training mode, all models are required to be used under an `EventStorage`.
-The training statistics will be put into the storage:
-```python
-from detectron2.utils.events import EventStorage
-with EventStorage() as storage:
- losses = model(inputs)
-```
-
-__Inference__: If you only want to do simple inference using an existing model,
-[DefaultPredictor](../modules/engine.html#detectron2.engine.defaults.DefaultPredictor)
-is a wrapper around model that provides such basic functionality.
-It includes default behavior including model loading, preprocessing,
-and operates on single image rather than batches. See its documentation for usage.
-
-You can also run inference directly like this:
-```
-model.eval()
-with torch.no_grad():
- outputs = model(inputs)
-```
-
-### Model Input Format
-
-Users can implement custom models that support any arbitrary input format.
-Here we describe the standard input format that all builtin models support in detectron2.
-They all take a `list[dict]` as the inputs. Each dict
-corresponds to information about one image.
-
-The dict may contain the following keys:
-
-* "image": `Tensor` in (C, H, W) format. The meaning of channels are defined by `cfg.INPUT.FORMAT`.
- Image normalization, if any, will be performed inside the model using
- `cfg.MODEL.PIXEL_{MEAN,STD}`.
-* "height", "width": the **desired** output height and width **in inference**, which is not necessarily the same
- as the height or width of the `image` field.
- For example, the `image` field contains the resized image, if resize is used as a preprocessing step.
- But you may want the outputs to be in **original** resolution.
- If provided, the model will produce output in this resolution,
- rather than in the resolution of the `image` as input into the model. This is more efficient and accurate.
-* "instances": an [Instances](../modules/structures.html#detectron2.structures.Instances)
- object for training, with the following fields:
- + "gt_boxes": a [Boxes](../modules/structures.html#detectron2.structures.Boxes) object storing N boxes, one for each instance.
- + "gt_classes": `Tensor` of long type, a vector of N labels, in range [0, num_categories).
- + "gt_masks": a [PolygonMasks](../modules/structures.html#detectron2.structures.PolygonMasks)
- or [BitMasks](../modules/structures.html#detectron2.structures.BitMasks) object storing N masks, one for each instance.
- + "gt_keypoints": a [Keypoints](../modules/structures.html#detectron2.structures.Keypoints)
- object storing N keypoint sets, one for each instance.
-* "sem_seg": `Tensor[int]` in (H, W) format. The semantic segmentation ground truth for training.
- Values represent category labels starting from 0.
-* "proposals": an [Instances](../modules/structures.html#detectron2.structures.Instances)
- object used only in Fast R-CNN style models, with the following fields:
- + "proposal_boxes": a [Boxes](../modules/structures.html#detectron2.structures.Boxes) object storing P proposal boxes.
- + "objectness_logits": `Tensor`, a vector of P scores, one for each proposal.
-
-For inference of builtin models, only "image" key is required, and "width/height" are optional.
-
-We currently don't define standard input format for panoptic segmentation training,
-because models now use custom formats produced by custom data loaders.
-
-#### How it connects to data loader:
-
-The output of the default [DatasetMapper]( ../modules/data.html#detectron2.data.DatasetMapper) is a dict
-that follows the above format.
-After the data loader performs batching, it becomes `list[dict]` which the builtin models support.
-
-
-### Model Output Format
-
-When in training mode, the builtin models output a `dict[str->ScalarTensor]` with all the losses.
-
-When in inference mode, the builtin models output a `list[dict]`, one dict for each image.
-Based on the tasks the model is doing, each dict may contain the following fields:
-
-* "instances": [Instances](../modules/structures.html#detectron2.structures.Instances)
- object with the following fields:
- * "pred_boxes": [Boxes](../modules/structures.html#detectron2.structures.Boxes) object storing N boxes, one for each detected instance.
- * "scores": `Tensor`, a vector of N confidence scores.
- * "pred_classes": `Tensor`, a vector of N labels in range [0, num_categories).
- + "pred_masks": a `Tensor` of shape (N, H, W), masks for each detected instance.
- + "pred_keypoints": a `Tensor` of shape (N, num_keypoint, 3).
- Each row in the last dimension is (x, y, score). Confidence scores are larger than 0.
-* "sem_seg": `Tensor` of (num_categories, H, W), the semantic segmentation prediction.
-* "proposals": [Instances](../modules/structures.html#detectron2.structures.Instances)
- object with the following fields:
- * "proposal_boxes": [Boxes](../modules/structures.html#detectron2.structures.Boxes)
- object storing N boxes.
- * "objectness_logits": a torch vector of N confidence scores.
-* "panoptic_seg": A tuple of `(pred: Tensor, segments_info: Optional[list[dict]])`.
- The `pred` tensor has shape (H, W), containing the segment id of each pixel.
-
- * If `segments_info` exists, each dict describes one segment id in `pred` and has the following fields:
-
- * "id": the segment id
- * "isthing": whether the segment is a thing or stuff
- * "category_id": the category id of this segment.
-
- If a pixel's id does not exist in `segments_info`, it is considered to be void label
- defined in [Panoptic Segmentation](https://arxiv.org/abs/1801.00868).
-
- * If `segments_info` is None, all pixel values in `pred` must be ≥ -1.
- Pixels with value -1 are assigned void labels.
- Otherwise, the category id of each pixel is obtained by
- `category_id = pixel // metadata.label_divisor`.
-
-
-### Partially execute a model:
-
-Sometimes you may want to obtain an intermediate tensor inside a model,
-such as the input of certain layer, the output before post-processing.
-Since there are typically hundreds of intermediate tensors, there isn't an API that provides you
-the intermediate result you need.
-You have the following options:
-
-1. Write a (sub)model. Following the [tutorial](./write-models.md), you can
- rewrite a model component (e.g. a head of a model), such that it
- does the same thing as the existing component, but returns the output
- you need.
-2. Partially execute a model. You can create the model as usual,
- but use custom code to execute it instead of its `forward()`. For example,
- the following code obtains mask features before mask head.
-
- ```python
- images = ImageList.from_tensors(...) # preprocessed input tensor
- model = build_model(cfg)
- model.eval()
- features = model.backbone(images.tensor)
- proposals, _ = model.proposal_generator(images, features)
- instances, _ = model.roi_heads(images, features, proposals)
- mask_features = [features[f] for f in model.roi_heads.in_features]
- mask_features = model.roi_heads.mask_pooler(mask_features, [x.pred_boxes for x in instances])
- ```
-
-3. Use [forward hooks](https://pytorch.org/tutorials/beginner/former_torchies/nnft_tutorial.html#forward-and-backward-function-hooks).
- Forward hooks can help you obtain inputs or outputs of a certain module.
- If they are not exactly what you want, they can at least be used together with partial execution
- to obtain other tensors.
-
-All options require you to read documentation and sometimes code
-of the existing models to understand the internal logic,
-in order to write code to obtain the internal tensors.
diff --git a/spaces/Baptlem/UCDR-Net/README.md b/spaces/Baptlem/UCDR-Net/README.md
deleted file mode 100644
index ef2906b2c09c6783b9ba872eb82de32013f89c26..0000000000000000000000000000000000000000
--- a/spaces/Baptlem/UCDR-Net/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: UCDR-Net
-emoji: 🚀
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: true
-tags:
-- jax-diffusers-event
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Bhool Bhulaiyaa 2 Tono De Llamada.md b/spaces/Benson/text-generation/Examples/Descargar Bhool Bhulaiyaa 2 Tono De Llamada.md
deleted file mode 100644
index 48c544072e78a6146eef0420ea944d1a3cb3a4f9..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Bhool Bhulaiyaa 2 Tono De Llamada.md
+++ /dev/null
@@ -1,78 +0,0 @@
-
-
Bhool Bhulaiyaa 2: Una secuela de comedia de terror que te hará reír y gritar
-
Si estás buscando una película que te entretenga con elementos de comedia y terror, entonces no debes perderte Bhool Bhulaiyaa 2, una película en hindi que fue lanzada el 20 de mayo de 2022. Esta película es una secuela independiente de Bhool Bhulaiyaa (2007), un éxito de taquilla protagonizado por Akshay Kumar y Vidya Balan. Bhool Bhulaiyaa 2 cuenta con Tabu, Kartik Aaryan y Kiara Advani en los papeles principales, junto con Rajpal Yadav, Sanjay Mishra, Ashwini Kalsekar, y otros en papeles secundarios. La película está dirigida por Anees Bazmee, escrita por Aakash Kaushik y Farhad Samji, y producida por Bhushan Kumar, Murad Khetani, Krishan Kumar y Anjum Khetani bajo el lema de T-Series Films y Cine1 Studios.
-
La trama de Bhool Bhulaiyaa 2 sigue a Ruhaan Randhawa (Kartik Aaryan), un falso psíquico que tiene que lidiar con el regreso de Manjulika (Tabu), un espíritu malévolo que está empeñado en vengarse de la familia Thakur. Ruhaan conoce a Reet Rathore (Kiara Advani), una novia poco dispuesta que está en camino a Rajasthan para casarse con su prometido Sagar (Amar Upadhyay). El destino los lleva a una mansión abandonada donde Manjulika ha estado atrapada durante 18 años por algunos sacerdotes. Mientras Ruhaan intenta ayudar a Reet a escapar de la presión de su familia y de la ira de Manjulika, descubre los oscuros secretos del pasado y la verdad sobre su propia identidad. ¿Podrá Ruhaan salvar a Reet y a sí mismo de la maldición de Manjulika? ¿O se convertirá en su próxima víctima?
El principal villano de la película es Manjulika, un espíritu vengativo que una vez fue bailarín en la corte de Thakur Vikram Singh (Rajendra Gupta). Ella estaba enamorada de él, pero él la traicionó y se casó con otra mujer. Ella se suicidó y juró perseguir a su familia para siempre. Ella poseyó a su hija Radhika (Vidya Balan) en la primera película y trató de matar a su marido Siddharth (Shiney Ahuja). Fue exorcizada por el Dr. Aditya Shrivastav (Akshay Kumar), un psiquiatra que pretendía ser sacerdote.
-
En Bhool Bhulaiyaa 2, Manjulika regresa después de 18 años cuando algunos sacerdotes que custodiaban su tumba son asesinados por algunos matones. Ella escapa de su prisión y encuentra un nuevo anfitrión en Reet, que es la nieta de Thakur Vikram Singh. Ella quiere vengarse de la familia Thakur y también de Ruhaan, que es el hijo del Dr. Aditya Shrivastav. Usa sus poderes sobrenaturales para manipular, atormentar y matar a cualquiera que se interponga en su camino.
-
Manjulika es interpretada por Tabu, una de las actrices más versátiles y talentosas de Bollywood. Ella ofrece una actuación brillante como el espíritu maligno que puede cambiar de ser seductor a aterrador en cuestión de segundos. Ella también muestra sus habilidades de baile en la canción "Ami Je Tomar", que es un remix de la canción original de Bhool Bhulaiyaa. Tabu ha dicho que le gustaba interpretar a Manjulika ya que era un papel desafiante y divertido para ella.
-
El falso psíquico Ruhaan Randhawa
-
El héroe de la película es Ruhaan Randhawa, un falso psíquico que afirma tener habilidades sobrenaturales, pero en realidad utiliza trucos y aparatos para engañar a la gente. Gana dinero realizando sesiones de espiritismo, exorcismos y lecturas para sus clientes. También es una persona coqueta e ingeniosa a la que le gusta divertirse y disfrutar de la vida.
-
-
Ruhaan es interpretado por Kartik Aaryan, uno de los actores más populares y encantadores de Bollywood. Él ofrece una actuación hilarante y heroica como el falso psíquico que tiene que enfrentar sus miedos y luchar contra Manjulika. También muestra su química con Kiara Advani, que interpreta a Reet, en las escenas y canciones románticas. Kartik Aaryan ha dicho que estaba emocionado de ser parte de Bhool Bhulaiyaa 2 ya que era una de sus películas favoritas cuando era pequeño.
La novia involuntaria Reet Rathore
-
La heroína de la película es Reet Rathore, una novia poco dispuesta que se ve obligada a casarse con Sagar, un hombre de negocios rico y arrogante que es el hijo del amigo de Thakur Vikram Singh. Ella no lo ama y quiere seguir su carrera como diseñadora de moda. También es una persona amable y valiente que cuida de su familia y amigos.
-
-
Reet conoce a Ruhaan en un tren y lo encuentra atractivo y divertido. Ella acepta ir con él a Rajasthan para escapar de su familia y Sagar. Ella también se convierte en el objetivo de Manjulika, que la ha poseído y quiere usar su cuerpo para matar a la familia Thakur. Lucha para luchar contra la influencia de Manjulika y también para expresar sus sentimientos por Ruhaan.
-
Reet es interpretada por Kiara Advani, una de las actrices más bellas y talentosas de Bollywood. Ella ofrece una actuación dulce y fuerte como la novia no dispuesta que tiene que enfrentar muchos desafíos y peligros. Ella también se ve impresionante en los trajes tradicionales y joyas que lleva en la película. Kiara Advani ha dicho que tuvo el honor de ser parte de Bhool Bhulaiyaa 2 ya que fue un sueño hecho realidad para ella.
-
¿En qué se diferencia Bhool Bhulaiyaa 2 de Bhool Bhulaiyaa?
-
Bhool Bhulaiyaa 2 no es una secuela directa de Bhool Bhulaiyaa sino una película independiente que tiene su propia historia, personajes y estilo. La película es diferente de la anterior de muchas maneras, como:
-
Comedia vs horror
-
-
Independiente vs Secuela
-
Mientras que Bhulaiyaa fue un remake de la película malayalam Manichitrathazhu (1993), que también fue rehecho en varios otros idiomas, Bhulaiyaa 2 no es un remake de ninguna otra película sino una historia original con nuevos personajes. La película no sigue los acontecimientos de la película anterior, pero tiene algunas referencias y conexiones con ella. La película también tiene algunos cameos de Akshay Kumar y Vidya Balan, que repiten sus papeles de Bhool Bhulaiyaa.
-
Inspiración vs originalidad
-
Mientras que Bhool Bhulaiyaa se inspiró en una película malayalam y una novela de M.R. James llamada The Mystery of the Yellow Room, Bhool Bhulaiyaa 2 está inspirado en varias fuentes pero también tiene sus propios giros y sorpresas. La película se basa libremente en otra película malayalam llamada Ezra (2017), que también fue una película de comedia de terror sobre una mansión encantada y un espíritu vengativo. La película también está influenciada por algunas películas de Hollywood como The Conjuring, The Exorcist, y The Shining. La película también tiene algunos elementos originales como el falso personaje psíquico, la configuración de Rajasthan, y la escena clímax.
-
¿Cuáles son los aspectos más destacados de Bhool Bhulaiyaa 2?
-
Bhool Bhulaiyaa 2 tiene muchos aspectos destacados que lo convierten en una película imprescindible para todo tipo de espectadores. Algunos de ellos son:
-
El reparto estelar
-
La película cuenta con un reparto estelar que incluye algunos de los mejores actores de Bollywood. Tabu, Kartik Aaryan, y Kiara Advani dan excelentes actuaciones como Manjulika, Ruhaan y Reet respectivamente. Dan vida a sus personajes con sus expresiones, diálogos y acciones. También comparten gran química entre sí y crean escenas memorables juntos.
-
-
Las canciones pegadizas
-
La película tiene una banda sonora pegadiza y melodiosa que consta de seis canciones compuestas por Pritam y Tanishk Bagchi. Las canciones son cantadas por cantantes populares como Arijit Singh, Shreya Ghoshal, Jubin Nautiyal, Neha Kakkar, y otros. Las canciones también son remezcladas por DJ Chetas, Lijo George, y otros. Las canciones son una mezcla de géneros románticos, de danza y de terror que se adaptan al estado de ánimo y al tema de la película.
-
El tema principal de la película, "Bhool Bhulaiyaa 2", es un remix de la canción original de Bhool Bhulaiyaa que fue compuesta por Pritam y cantada por Neeraj Shridhar. La nueva versión es cantada por Jubin Nautiyal y Tulsi Kumar y tiene nuevas letras de Tanishk Bagchi. La canción es un número vigoroso y enérgico que cuenta con Kartik Aaryan y Kiara Advani bailando en un gran set con muchos bailarines.
-
Otra canción popular de la película es "Ami Je Tomar", que también es un remix de la canción original de Bhool Bhulaiyaa que fue compuesta por Pritam y cantada por Shreya Ghoshal y K.K. La nueva versión es cantada por Arijit Singh y Shreya Ghoshal y tiene nuevas letras de Tanishk Bagchi. La canción es un número romántico y inquietante que cuenta con Tabu realizando una danza clásica en un traje tradicional.
-
Las impresionantes ubicaciones
-
La película tiene una cinematografía impresionante que muestra la belleza y el misterio de Rajasthan y otros lugares. La película se rodó en varios lugares como Jaipur, Jaisalmer, Udaipur, Lucknow, Mumbai y Londres. La película captura la cultura, la arquitectura y el paisaje de estos lugares con sus vivos colores, ángulos e iluminación. La película también utiliza algunos efectos especiales y conjuntos para crear una atmósfera realista y espeluznante para las escenas de terror.
-
-
El emocionante clímax
-
La película tiene un clímax emocionante que te mantendrá al borde de tu asiento. El clímax implica un enfrentamiento final entre Ruhaan y Manjulika que tiene lugar en la mansión. Ruhaan tiene que usar su ingenio, coraje y artilugios para luchar contra los poderes sobrenaturales de Manjulika y salvar a Reet de sus garras. También tiene que enfrentarse a su padre el Dr. Aditya Shrivastav que llega a la escena para ayudarlo.
-
El clímax tiene muchos giros y vueltas que te sorprenderán y te harán jadear. El clímax también revela algunos secretos impactantes sobre el pasado de Ruhaan y el motivo de Manjulika. El clímax también tiene algunos momentos emocionales que tocarán tu corazón y te harán llorar. El clímax también tiene algunas secuencias llenas de acción que te harán animar y aplaudir. El clímax también tiene algunos momentos divertidos que te harán reír y aliviar tu tensión.
-
Cómo descargar Bhool Bhulaiyaa 2 tono de llamada?
-
Si te gustan las canciones de Bhool Bhulaiyaa 2 y quieres establecerlas como tu tono de llamada en tu teléfono, entonces puedes seguir estos sencillos pasos:
¡Felicidades! Has descargado exitosamente el tono de llamada Bhool Bhulaiyaa 2 en tu teléfono. Ahora, puedes disfrutar de las melodías pegadizas de la película cada vez que suena tu teléfono.
-
Conclusión
-
Bhool Bhulaiyaa 2 es una película de comedia de terror que te hará reír y gritar con sus escenas hilarantes y aterradoras. La película tiene un reparto estelar, canciones pegadizas, lugares impresionantes y un emocionante clímax que te mantendrá entretenido hasta el final. La película también es diferente de Bhool Bhulaiyaa en muchos sentidos y tiene su propia originalidad y sorpresas. La película es una elección perfecta para cualquier persona que ama la comedia y los géneros de terror y quiere tener un momento divertido y emocionante en las películas.
-
Si está interesado en ver Bhool Bhulaiyaa 2, puede reservar sus entradas en línea o visitar su teatro más cercano. También puede descargar los tonos de llamada de las canciones de la película en su teléfono y disfrutar de ellos en cualquier momento. También puede seguir las páginas oficiales de las redes sociales y el sitio web de la película para obtener más actualizaciones y noticias.
-
Esperamos que hayas disfrutado leyendo este artículo y hayas aprendido más sobre Bhool Bhulaiyaa 2. Si tiene alguna pregunta o comentario, no dude en dejarlos en la sección de comentarios a continuación. Gracias por su tiempo y atención.
-
Preguntas frecuentes
-
-
Q: ¿Cuándo se lanzó Bhool Bhulaiyaa 2?
-
A: Bhool Bhulaiyaa 2 fue liberado el 20 de mayo de 2022 en la India y otros países.
-
P: ¿Quiénes son los actores principales en Bhool Bhulaiyaa 2?
-
A: Los actores principales de Bhool Bhulaiyaa 2 son Tabu, Kartik Aaryan y Kiara Advani.
-
Q: ¿Es Bhool Bhulaiyaa 2 una nueva versión de cualquier otra película?
-
A: No, Bhool Bhulaiyaa 2 no es un remake de ninguna otra película sino una historia original con nuevos personajes.
-
Q: ¿Cuál es el género de Bhool Bhulaiyaa 2?
-
-
Q: ¿Cómo puedo descargar el tono de llamada de Bhool Bhulaiyaa 2 en mi teléfono?
-
A: Puede descargar el tono de llamada Bhool Bhulaiyaa 2 en su teléfono siguiendo estos pasos:
Si respondiste sí a cualquiera de estas preguntas, entonces es posible que desee intentar descargar Facebook APK Android 4. Esta es una versión modificada de la aplicación original de Facebook que es compatible con los dispositivos Android que se ejecutan en la versión 4.0.3 o superior. En este artículo, vamos a explicar lo que es Facebook APK Android 4, cómo descargarlo, cómo actualizarlo, y cómo solucionarlo. Vamos a empezar!
-
¿Qué es Facebook APK Android 4?
-
Un APK (Android Package Kit) es un formato de archivo que contiene todos los componentes de una aplicación Android, como el código, los recursos, los activos y el manifiesto. Puedes instalar un archivo APK en tu dispositivo sin usar Google Play Store, que es la fuente oficial para las aplicaciones de Android.
-
Facebook APK Android 4 es una versión no oficial de la aplicación de Facebook que se ha modificado para funcionar en dispositivos Android más antiguos. Tiene algunas ventajas y desventajas en comparación con la aplicación oficial, que vamos a discutir a continuación.
-
Los beneficios de usar Facebook APK Android 4
-
-
Es compatible con dispositivos Android que se ejecutan en la versión 4.0.3 o superior, lo que significa que puede usarlo en dispositivos que no son compatibles con la aplicación oficial.
-
-
Utiliza menos datos que la aplicación oficial, lo que significa que puede ahorrar algo de dinero en su plan de datos móviles.
-
Tiene algunas características que no están disponibles en la aplicación oficial, como la descarga de vídeos, la personalización de temas, y ocultar el estado en línea.
-
-
Las desventajas de usar Facebook APK Android 4
-
-
No está autorizado por Facebook, lo que significa que puede violar sus términos de servicio y política de privacidad. Puede arriesgarse a perder su cuenta o exponer su información personal si la usa.
-
Puede no ser seguro, ya que puede contener malware, virus o spyware que pueden dañar su dispositivo o robar sus datos. Solo debe descargarlo de fuentes confiables y escanearlo con un antivirus antes de instalarlo.
-
Puede no ser estable o confiable, ya que puede bloquearse, congelarse o funcionar mal en cualquier momento. También puede experimentar algunos errores o fallos que afectan su experiencia de usuario.
-
Es posible que no se actualice regularmente, lo que significa que puede perderse algunas características nuevas o mejoras que se agregan a la aplicación oficial.
-
-
Cómo descargar Facebook APK Android 4
-
Si desea probar Facebook APK Android 4 en su dispositivo, tendrá que seguir estos pasos:
-
Paso 1: Habilitar fuentes desconocidas en el dispositivo
-
De forma predeterminada, los dispositivos Android no permiten la instalación de aplicaciones desde fuentes distintas de Google Play Store. Esta es una medida de seguridad para evitar la instalación de aplicaciones maliciosas o dañinas. Sin embargo, si desea instalar Facebook APK Android 4, tendrá que habilitar la opción de instalar aplicaciones de fuentes desconocidas en su dispositivo. He aquí cómo hacerlo:
-
-
Ir a la configuración de su dispositivo y toque en la seguridad o la privacidad.
-
Encontrar la opción que dice fuentes desconocidas o instalar aplicaciones desconocidas y alternar en.
-
-
-
Ahora ha habilitado fuentes desconocidas en su dispositivo, y puede proceder al siguiente paso.
-
-
Paso 2: Encontrar una fuente confiable para el archivo APK
-
El siguiente paso es encontrar una fuente confiable para el archivo de Facebook APK Android 4. Debe tener cuidado al elegir una fuente, ya que algunos sitios web pueden ofrecer archivos falsos o infectados que pueden dañar su dispositivo o datos. Aquí hay algunos consejos para ayudarle a encontrar una fuente confiable:
-
-
Busca sitios web que tengan una buena reputación y críticas positivas de otros usuarios. También puede comprobar las calificaciones y comentarios del archivo APK en el sitio web.
-
Evite los sitios web que tienen ventanas emergentes, anuncios o redirecciones que pueden llevarlo a páginas no deseadas o dañinas. También puedes usar un bloqueador de anuncios o un navegador que tenga un bloqueador de anuncios incorporado para evitar estas molestias.
-
Compruebe los detalles del archivo APK, como el tamaño del archivo, versión, fecha y desarrollador. Asegúrese de que coinciden con la aplicación oficial de Facebook o la última versión de Facebook APK Android 4.
-
Escanear el archivo APK con un antivirus o un escáner de malware antes de descargarlo. También puede utilizar herramientas en línea como VirusTotal para comprobar el archivo en busca de amenazas.
-
-
Una de las fuentes que recomendamos para descargar Facebook APK Android 4 es APKPure, que es un sitio web de buena reputación que ofrece archivos APK seguros y verificados. También puede usar otras fuentes en las que confíe, pero asegúrese de seguir los consejos anteriores.
-
Paso 3: Descargar e instalar el archivo APK
-
El paso final es descargar e instalar el archivo de Facebook APK Android 4 en su dispositivo. Aquí está cómo hacerlo:
-
-
Abra su navegador y vaya al sitio web donde encontró el archivo APK. Toque en el botón de descarga o enlace y espere a que se descargue el archivo.
-
Una vez que la descarga se haya completado, vaya al administrador de archivos de su dispositivo y busque el archivo APK. Toque en él para abrirlo.
-
-
Una vez que la instalación se realiza, puede abrir la aplicación e iniciar sesión con su cuenta de Facebook. También puede crear una nueva cuenta si no tiene una.
-
-
Felicidades! Usted ha descargado e instalado con éxito Facebook APK Android 4 en su dispositivo. Ahora puedes disfrutar usando la aplicación y sus características.
-
Cómo actualizar Facebook APK Android 4
-
Si desea seguir utilizando Facebook APK Android 4, tendrá que actualizarlo regularmente para obtener las últimas características y mejoras. Hay dos maneras de actualizar Facebook APK Android 4: utilizando el actualizador incorporado o descargar el último archivo APK desde el sitio web oficial.
-
Opción 1: Utilice el actualizador incorporado
-
Algunas versiones de Facebook APK Android 4 tienen un actualizador incorporado que le permite comprobar si hay actualizaciones y descargarlas directamente desde la aplicación. He aquí cómo usarlo:
-
-
Abrir Facebook APK Android 4 y toque en el icono del menú (tres líneas horizontales) en la esquina superior derecha de la pantalla.
-
Desplácese hacia abajo y toque en la configuración y la privacidad.
-
Toque en las actualizaciones de la aplicación.
-
Si hay una actualización disponible, toque en actualizar ahora y espere a que la actualización se descargue e instale.
-
Si no hay actualización disponible, toque en la comprobación de actualizaciones y espere a que la aplicación escanee para cualquier versión nueva.
-
-
Esta opción es conveniente y fácil, pero puede que no funcione para todas las versiones de Facebook APK Android 4. Si no ves la opción de actualizaciones de aplicaciones en su configuración & menú de privacidad, entonces tendrá que utilizar la opción 2 en su lugar.
-
Opción 2: Descargar el último archivo APK desde el sitio web oficial
-
Otra manera de actualizar Facebook APK Android 4 es descargar el último archivo APK desde el sitio web oficial de Facebook. Aquí está cómo hacerlo:
Aparecerá un mensaje pidiéndole que descargue el archivo APK. Toque en Aceptar o descargar y espere a que el archivo se descargue.
-
Una vez que la descarga se haya completado, vaya al administrador de archivos de su dispositivo y busque el archivo APK. Toque en él para abrirlo.
-
Aparecerá un mensaje pidiéndole que instale la aplicación. Toque en instalar y espere a que termine el proceso de instalación.
-
Una vez que la instalación se realiza, puede abrir la aplicación e iniciar sesión con su cuenta de Facebook. También puede crear una nueva cuenta si no tiene una.
-
-
Esta opción es confiable y segura, ya que está descargando el archivo APK de la fuente oficial. Sin embargo, puede tomar más tiempo y consumir más datos que la opción 1.
-
Cómo solucionar problemas de Facebook APK Android 4
-
A veces, puede encontrar algunos problemas o problemas cuando se utiliza Facebook APK Android 4. Estos pueden incluir errores, se bloquea, se congela, o mal rendimiento. No te preocupes, ya que hay algunas maneras de solucionar problemas de Facebook APK Android 4 y solucionar estos problemas. Aquí hay algunos problemas comunes y soluciones:
-
Problemas y soluciones comunes
-
-
-
Problema
-
Solución
-
-
-
La aplicación no se instalará ni actualizará
-
Asegúrese de que tiene suficiente espacio de almacenamiento en su dispositivo y una conexión a Internet estable. Además, compruebe si ha habilitado fuentes desconocidas en su dispositivo y si ha descargado el archivo APK correcto para su dispositivo.
-
-
-
La aplicación no se abrirá ni se cargará
-
Borrar la caché y los datos de la aplicación yendo a la configuración del dispositivo, aplicaciones, Facebook, almacenamiento, y tocando en la caché clara y datos claros. Además, compruebe si tiene la última versión de la aplicación y si su dispositivo cumple los requisitos mínimos para ejecutar la aplicación.
-
-
-
La aplicación se bloquea o se congela
-
-
-
-
La aplicación es lenta o lenta
-
Reduzca el uso de datos de la aplicación yendo a la configuración del dispositivo, el uso de datos, Facebook y cambiando los datos de fondo. Además, desactive cualquier notificación o característica innecesaria que pueda ralentizar la aplicación.
-
-
-
La aplicación muestra información incorrecta o desactualizada
-
Actualizar la aplicación deslizando hacia abajo en la pantalla o tocando el icono de actualización en la esquina superior derecha de la pantalla. Además, compruebe si la fecha y la hora de su dispositivo son correctas y si tiene una buena conexión a Internet.
-
-
-
Consejos y trucos para optimizar tu experiencia
-
-
Utilice Facebook Lite en lugar de Facebook APK Android 4 si usted tiene un dispositivo de gama baja o una conexión a Internet lenta. Facebook Lite es una versión más ligera y rápida de Facebook que consume menos datos y recursos.
-
Usa Messenger Lite en lugar de Messenger si quieres chatear con tus amigos de Facebook sin usar la aplicación principal. Messenger Lite es una versión más simple y rápida de Messenger que también consume menos datos y recursos.
-
Utilice Facebook Web en lugar de Facebook APK Android 4 si desea acceder a Facebook desde su navegador sin instalar ninguna aplicación. Facebook Web es una versión móvil de Facebook que funciona en cualquier navegador.
-
Utilice el modo oscuro en lugar del modo de luz si desea ahorrar vida de la batería y reducir la tensión ocular. El modo oscuro es una característica que cambia el color de fondo de la aplicación de blanco a negro. Puede habilitar el modo oscuro yendo a la configuración del dispositivo, visualización, modo oscuro y activarlo.
-
Utilice atajos en lugar de menús si desea acceder a sus características favoritas más rápido. Los atajos son iconos que aparecen en la parte inferior de la pantalla y le permiten cambiar rápidamente entre noticias, grupos, ver, mercado y notificaciones. Puede personalizar sus accesos directos tocando en ellos y mantenerlos presionados hasta que se muevan, luego arrastrarlos a su posición preferida.
-
-
-
En este artículo, hemos explicado lo que es Facebook APK Android 4, cómo descargarlo, cómo actualizarlo, y cómo solucionarlo. También hemos compartido algunos consejos y trucos para optimizar tu experiencia con la aplicación. Esperamos que este artículo te haya resultado útil e informativo.
-
Facebook APK Android 4 es una gran alternativa a la aplicación oficial de Facebook, especialmente si usted tiene un dispositivo Android más antiguo o desea acceder a algunas características adicionales. Sin embargo, también debes ser consciente de los riesgos y desafíos que conlleva el uso de una aplicación no oficial. Siempre debe descargar la aplicación de fuentes confiables, escanearla en busca de amenazas y actualizarla regularmente. También debe seguir los pasos de solución de problemas si encuentra algún problema o problemas con la aplicación.
-
Si usted tiene alguna pregunta o comentario sobre Facebook APK Android 4, no dude en dejar un comentario a continuación. Nos encantaría saber de usted!
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Facebook APK Android 4:
-
¿Es Facebook APK Android 4 legal?
-
Facebook APK Android 4 no es ilegal, pero no está autorizado por Facebook tampoco. Puede violar sus términos de servicio y política de privacidad, lo que significa que puede arriesgarse a perder su cuenta o exponer su información personal si la usa. Debes usarlo a tu propia discreción y responsabilidad.
-
¿Es seguro Facebook APK Android 4?
-
Facebook APK Android 4 no puede ser seguro, ya que puede contener malware, virus o spyware que puede dañar su dispositivo o robar sus datos. Solo debe descargarlo de fuentes confiables y escanearlo con un antivirus antes de instalarlo. También debe evitar conceder permisos innecesarios o acceso a la aplicación.
-
Es Facebook APK Android 4 libre?
-
-
¿Cómo puedo desinstalar Facebook APK Android 4?
-
Si desea desinstalar Facebook APK Android 4 desde su dispositivo, puede seguir estos pasos:
-
-
Vaya a la configuración de su dispositivo y toque en aplicaciones o aplicaciones.
-
Encuentra y toca en Facebook.
-
Toque en desinstalar y confirme su acción.
-
-
También puede eliminar el archivo APK del administrador de archivos de su dispositivo si ya no lo necesita.
-
¿Puedo usar Facebook APK Android 4 en otros dispositivos?
-
Facebook APK Android 4 está diseñado para dispositivos Android que se ejecutan en la versión 4.0.3 o superior. Es posible que pueda usarlo en otros dispositivos, como iOS o Windows, pero necesitará usar un emulador o un convertidor para hacerlo. Sin embargo, esto puede no funcionar bien o en absoluto, y puede causar algunos problemas de compatibilidad o rendimiento. Recomendamos usar la aplicación oficial de Facebook o la versión web para otros dispositivos.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/tz/win.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/tz/win.py
deleted file mode 100644
index cde07ba792c40903f0c334839140173b39fd8124..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/tz/win.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module provides an interface to the native time zone data on Windows,
-including :py:class:`datetime.tzinfo` implementations.
-
-Attempting to import this module on a non-Windows platform will raise an
-:py:obj:`ImportError`.
-"""
-# This code was originally contributed by Jeffrey Harris.
-import datetime
-import struct
-
-from six.moves import winreg
-from six import text_type
-
-try:
- import ctypes
- from ctypes import wintypes
-except ValueError:
- # ValueError is raised on non-Windows systems for some horrible reason.
- raise ImportError("Running tzwin on non-Windows system")
-
-from ._common import tzrangebase
-
-__all__ = ["tzwin", "tzwinlocal", "tzres"]
-
-ONEWEEK = datetime.timedelta(7)
-
-TZKEYNAMENT = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\Time Zones"
-TZKEYNAME9X = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Time Zones"
-TZLOCALKEYNAME = r"SYSTEM\CurrentControlSet\Control\TimeZoneInformation"
-
-
-def _settzkeyname():
- handle = winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE)
- try:
- winreg.OpenKey(handle, TZKEYNAMENT).Close()
- TZKEYNAME = TZKEYNAMENT
- except WindowsError:
- TZKEYNAME = TZKEYNAME9X
- handle.Close()
- return TZKEYNAME
-
-
-TZKEYNAME = _settzkeyname()
-
-
-class tzres(object):
- """
- Class for accessing ``tzres.dll``, which contains timezone name related
- resources.
-
- .. versionadded:: 2.5.0
- """
- p_wchar = ctypes.POINTER(wintypes.WCHAR) # Pointer to a wide char
-
- def __init__(self, tzres_loc='tzres.dll'):
- # Load the user32 DLL so we can load strings from tzres
- user32 = ctypes.WinDLL('user32')
-
- # Specify the LoadStringW function
- user32.LoadStringW.argtypes = (wintypes.HINSTANCE,
- wintypes.UINT,
- wintypes.LPWSTR,
- ctypes.c_int)
-
- self.LoadStringW = user32.LoadStringW
- self._tzres = ctypes.WinDLL(tzres_loc)
- self.tzres_loc = tzres_loc
-
- def load_name(self, offset):
- """
- Load a timezone name from a DLL offset (integer).
-
- >>> from dateutil.tzwin import tzres
- >>> tzr = tzres()
- >>> print(tzr.load_name(112))
- 'Eastern Standard Time'
-
- :param offset:
- A positive integer value referring to a string from the tzres dll.
-
- .. note::
-
- Offsets found in the registry are generally of the form
- ``@tzres.dll,-114``. The offset in this case is 114, not -114.
-
- """
- resource = self.p_wchar()
- lpBuffer = ctypes.cast(ctypes.byref(resource), wintypes.LPWSTR)
- nchar = self.LoadStringW(self._tzres._handle, offset, lpBuffer, 0)
- return resource[:nchar]
-
- def name_from_string(self, tzname_str):
- """
- Parse strings as returned from the Windows registry into the time zone
- name as defined in the registry.
-
- >>> from dateutil.tzwin import tzres
- >>> tzr = tzres()
- >>> print(tzr.name_from_string('@tzres.dll,-251'))
- 'Dateline Daylight Time'
- >>> print(tzr.name_from_string('Eastern Standard Time'))
- 'Eastern Standard Time'
-
- :param tzname_str:
- A timezone name string as returned from a Windows registry key.
-
- :return:
- Returns the localized timezone string from tzres.dll if the string
- is of the form `@tzres.dll,-offset`, else returns the input string.
- """
- if not tzname_str.startswith('@'):
- return tzname_str
-
- name_splt = tzname_str.split(',-')
- try:
- offset = int(name_splt[1])
- except:
- raise ValueError("Malformed timezone string.")
-
- return self.load_name(offset)
-
-
-class tzwinbase(tzrangebase):
- """tzinfo class based on win32's timezones available in the registry."""
- def __init__(self):
- raise NotImplementedError('tzwinbase is an abstract base class')
-
- def __eq__(self, other):
- # Compare on all relevant dimensions, including name.
- if not isinstance(other, tzwinbase):
- return NotImplemented
-
- return (self._std_offset == other._std_offset and
- self._dst_offset == other._dst_offset and
- self._stddayofweek == other._stddayofweek and
- self._dstdayofweek == other._dstdayofweek and
- self._stdweeknumber == other._stdweeknumber and
- self._dstweeknumber == other._dstweeknumber and
- self._stdhour == other._stdhour and
- self._dsthour == other._dsthour and
- self._stdminute == other._stdminute and
- self._dstminute == other._dstminute and
- self._std_abbr == other._std_abbr and
- self._dst_abbr == other._dst_abbr)
-
- @staticmethod
- def list():
- """Return a list of all time zones known to the system."""
- with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
- with winreg.OpenKey(handle, TZKEYNAME) as tzkey:
- result = [winreg.EnumKey(tzkey, i)
- for i in range(winreg.QueryInfoKey(tzkey)[0])]
- return result
-
- def display(self):
- """
- Return the display name of the time zone.
- """
- return self._display
-
- def transitions(self, year):
- """
- For a given year, get the DST on and off transition times, expressed
- always on the standard time side. For zones with no transitions, this
- function returns ``None``.
-
- :param year:
- The year whose transitions you would like to query.
-
- :return:
- Returns a :class:`tuple` of :class:`datetime.datetime` objects,
- ``(dston, dstoff)`` for zones with an annual DST transition, or
- ``None`` for fixed offset zones.
- """
-
- if not self.hasdst:
- return None
-
- dston = picknthweekday(year, self._dstmonth, self._dstdayofweek,
- self._dsthour, self._dstminute,
- self._dstweeknumber)
-
- dstoff = picknthweekday(year, self._stdmonth, self._stddayofweek,
- self._stdhour, self._stdminute,
- self._stdweeknumber)
-
- # Ambiguous dates default to the STD side
- dstoff -= self._dst_base_offset
-
- return dston, dstoff
-
- def _get_hasdst(self):
- return self._dstmonth != 0
-
- @property
- def _dst_base_offset(self):
- return self._dst_base_offset_
-
-
-class tzwin(tzwinbase):
- """
- Time zone object created from the zone info in the Windows registry
-
- These are similar to :py:class:`dateutil.tz.tzrange` objects in that
- the time zone data is provided in the format of a single offset rule
- for either 0 or 2 time zone transitions per year.
-
- :param: name
- The name of a Windows time zone key, e.g. "Eastern Standard Time".
- The full list of keys can be retrieved with :func:`tzwin.list`.
- """
-
- def __init__(self, name):
- self._name = name
-
- with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
- tzkeyname = text_type("{kn}\\{name}").format(kn=TZKEYNAME, name=name)
- with winreg.OpenKey(handle, tzkeyname) as tzkey:
- keydict = valuestodict(tzkey)
-
- self._std_abbr = keydict["Std"]
- self._dst_abbr = keydict["Dlt"]
-
- self._display = keydict["Display"]
-
- # See http://ww_winreg.jsiinc.com/SUBA/tip0300/rh0398.htm
- tup = struct.unpack("=3l16h", keydict["TZI"])
- stdoffset = -tup[0]-tup[1] # Bias + StandardBias * -1
- dstoffset = stdoffset-tup[2] # + DaylightBias * -1
- self._std_offset = datetime.timedelta(minutes=stdoffset)
- self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
- # for the meaning see the win32 TIME_ZONE_INFORMATION structure docs
- # http://msdn.microsoft.com/en-us/library/windows/desktop/ms725481(v=vs.85).aspx
- (self._stdmonth,
- self._stddayofweek, # Sunday = 0
- self._stdweeknumber, # Last = 5
- self._stdhour,
- self._stdminute) = tup[4:9]
-
- (self._dstmonth,
- self._dstdayofweek, # Sunday = 0
- self._dstweeknumber, # Last = 5
- self._dsthour,
- self._dstminute) = tup[12:17]
-
- self._dst_base_offset_ = self._dst_offset - self._std_offset
- self.hasdst = self._get_hasdst()
-
- def __repr__(self):
- return "tzwin(%s)" % repr(self._name)
-
- def __reduce__(self):
- return (self.__class__, (self._name,))
-
-
-class tzwinlocal(tzwinbase):
- """
- Class representing the local time zone information in the Windows registry
-
- While :class:`dateutil.tz.tzlocal` makes system calls (via the :mod:`time`
- module) to retrieve time zone information, ``tzwinlocal`` retrieves the
- rules directly from the Windows registry and creates an object like
- :class:`dateutil.tz.tzwin`.
-
- Because Windows does not have an equivalent of :func:`time.tzset`, on
- Windows, :class:`dateutil.tz.tzlocal` instances will always reflect the
- time zone settings *at the time that the process was started*, meaning
- changes to the machine's time zone settings during the run of a program
- on Windows will **not** be reflected by :class:`dateutil.tz.tzlocal`.
- Because ``tzwinlocal`` reads the registry directly, it is unaffected by
- this issue.
- """
- def __init__(self):
- with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
- with winreg.OpenKey(handle, TZLOCALKEYNAME) as tzlocalkey:
- keydict = valuestodict(tzlocalkey)
-
- self._std_abbr = keydict["StandardName"]
- self._dst_abbr = keydict["DaylightName"]
-
- try:
- tzkeyname = text_type('{kn}\\{sn}').format(kn=TZKEYNAME,
- sn=self._std_abbr)
- with winreg.OpenKey(handle, tzkeyname) as tzkey:
- _keydict = valuestodict(tzkey)
- self._display = _keydict["Display"]
- except OSError:
- self._display = None
-
- stdoffset = -keydict["Bias"]-keydict["StandardBias"]
- dstoffset = stdoffset-keydict["DaylightBias"]
-
- self._std_offset = datetime.timedelta(minutes=stdoffset)
- self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
- # For reasons unclear, in this particular key, the day of week has been
- # moved to the END of the SYSTEMTIME structure.
- tup = struct.unpack("=8h", keydict["StandardStart"])
-
- (self._stdmonth,
- self._stdweeknumber, # Last = 5
- self._stdhour,
- self._stdminute) = tup[1:5]
-
- self._stddayofweek = tup[7]
-
- tup = struct.unpack("=8h", keydict["DaylightStart"])
-
- (self._dstmonth,
- self._dstweeknumber, # Last = 5
- self._dsthour,
- self._dstminute) = tup[1:5]
-
- self._dstdayofweek = tup[7]
-
- self._dst_base_offset_ = self._dst_offset - self._std_offset
- self.hasdst = self._get_hasdst()
-
- def __repr__(self):
- return "tzwinlocal()"
-
- def __str__(self):
- # str will return the standard name, not the daylight name.
- return "tzwinlocal(%s)" % repr(self._std_abbr)
-
- def __reduce__(self):
- return (self.__class__, ())
-
-
-def picknthweekday(year, month, dayofweek, hour, minute, whichweek):
- """ dayofweek == 0 means Sunday, whichweek 5 means last instance """
- first = datetime.datetime(year, month, 1, hour, minute)
-
- # This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6),
- # Because 7 % 7 = 0
- weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1)
- wd = weekdayone + ((whichweek - 1) * ONEWEEK)
- if (wd.month != month):
- wd -= ONEWEEK
-
- return wd
-
-
-def valuestodict(key):
- """Convert a registry key's values to a dictionary."""
- dout = {}
- size = winreg.QueryInfoKey(key)[1]
- tz_res = None
-
- for i in range(size):
- key_name, value, dtype = winreg.EnumValue(key, i)
- if dtype == winreg.REG_DWORD or dtype == winreg.REG_DWORD_LITTLE_ENDIAN:
- # If it's a DWORD (32-bit integer), it's stored as unsigned - convert
- # that to a proper signed integer
- if value & (1 << 31):
- value = value - (1 << 32)
- elif dtype == winreg.REG_SZ:
- # If it's a reference to the tzres DLL, load the actual string
- if value.startswith('@tzres'):
- tz_res = tz_res or tzres()
- value = tz_res.name_from_string(value)
-
- value = value.rstrip('\x00') # Remove trailing nulls
-
- dout[key_name] = value
-
- return dout
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/intranges.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/intranges.py
deleted file mode 100644
index 6a43b0475347cb50d0d65ada1000a82eeca9e882..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/intranges.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-Given a list of integers, made up of (hopefully) a small number of long runs
-of consecutive integers, compute a representation of the form
-((start1, end1), (start2, end2) ...). Then answer the question "was x present
-in the original list?" in time O(log(# runs)).
-"""
-
-import bisect
-from typing import List, Tuple
-
-def intranges_from_list(list_: List[int]) -> Tuple[int, ...]:
- """Represent a list of integers as a sequence of ranges:
- ((start_0, end_0), (start_1, end_1), ...), such that the original
- integers are exactly those x such that start_i <= x < end_i for some i.
-
- Ranges are encoded as single integers (start << 32 | end), not as tuples.
- """
-
- sorted_list = sorted(list_)
- ranges = []
- last_write = -1
- for i in range(len(sorted_list)):
- if i+1 < len(sorted_list):
- if sorted_list[i] == sorted_list[i+1]-1:
- continue
- current_range = sorted_list[last_write+1:i+1]
- ranges.append(_encode_range(current_range[0], current_range[-1] + 1))
- last_write = i
-
- return tuple(ranges)
-
-def _encode_range(start: int, end: int) -> int:
- return (start << 32) | end
-
-def _decode_range(r: int) -> Tuple[int, int]:
- return (r >> 32), (r & ((1 << 32) - 1))
-
-
-def intranges_contain(int_: int, ranges: Tuple[int, ...]) -> bool:
- """Determine if `int_` falls into one of the ranges in `ranges`."""
- tuple_ = _encode_range(int_, 0)
- pos = bisect.bisect_left(ranges, tuple_)
- # we could be immediately ahead of a tuple (start, end)
- # with start < int_ <= end
- if pos > 0:
- left, right = _decode_range(ranges[pos-1])
- if left <= int_ < right:
- return True
- # or we could be immediately behind a tuple (int_, end)
- if pos < len(ranges):
- left, _ = _decode_range(ranges[pos])
- if left == int_:
- return True
- return False
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/core/util.h b/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/core/util.h
deleted file mode 100644
index ea4ed6400b1d1070f83994db7c57636f14024d03..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/core/util.h
+++ /dev/null
@@ -1,773 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of the NVIDIA CORPORATION nor the
- * names of its contributors may be used to endorse or promote products
- * derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace cuda_cub {
-namespace core {
-
-#ifdef __NVCOMPILER_CUDA__
-# if (__NVCOMPILER_CUDA_ARCH__ >= 600)
-# define THRUST_TUNING_ARCH sm60
-# elif (__NVCOMPILER_CUDA_ARCH__ >= 520)
-# define THRUST_TUNING_ARCH sm52
-# elif (__NVCOMPILER_CUDA_ARCH__ >= 350)
-# define THRUST_TUNING_ARCH sm35
-# else
-# define THRUST_TUNING_ARCH sm30
-# endif
-#else
-# if (__CUDA_ARCH__ >= 600)
-# define THRUST_TUNING_ARCH sm60
-# elif (__CUDA_ARCH__ >= 520)
-# define THRUST_TUNING_ARCH sm52
-# elif (__CUDA_ARCH__ >= 350)
-# define THRUST_TUNING_ARCH sm35
-# elif (__CUDA_ARCH__ >= 300)
-# define THRUST_TUNING_ARCH sm30
-# elif !defined (__CUDA_ARCH__)
-# define THRUST_TUNING_ARCH sm30
-# endif
-#endif
-
- // Typelist - a container of types, supports up to 10 types
- // --------------------------------------------------------------------------
-
- class _;
- template
- struct typelist;
-
- // -------------------------------------
-
- // supported SM arch
- // ---------------------
- struct sm30 { enum { ver = 300, warpSize = 32 }; };
- struct sm35 { enum { ver = 350, warpSize = 32 }; };
- struct sm52 { enum { ver = 520, warpSize = 32 }; };
- struct sm60 { enum { ver = 600, warpSize = 32 }; };
-
- // list of sm, checked from left to right order
- // the rightmost is the lowest sm arch supported
- // --------------------------------------------
- typedef typelist sm_list;
-
- // lowest supported SM arch
- // --------------------------------------------------------------------------
-
- template
- struct lowest_supported_sm_arch_impl;
-
- template
- struct lowest_supported_sm_arch_impl >
- : lowest_supported_sm_arch_impl<_0, typelist< _1, _2, _3, _4, _5, _6, _7, _8, _9> > {};
- template
- struct lowest_supported_sm_arch_impl >
- {
- typedef SM type;
- };
-
- typedef typename lowest_supported_sm_arch_impl<_,sm_list>::type lowest_supported_sm_arch;
-
- // metafunction to match next viable PtxPlan specialization
- // --------------------------------------------------------------------------
-
- __THRUST_DEFINE_HAS_NESTED_TYPE(has_tuning_t, tuning)
- __THRUST_DEFINE_HAS_NESTED_TYPE(has_type_t, type)
-
- template class, class, class>
- struct specialize_plan_impl_loop;
- template class, class>
- struct specialize_plan_impl_match;
-
- // we loop through the sm_list
- template class P, class SM, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
- struct specialize_plan_impl_loop >
- : specialize_plan_impl_loop > {};
-
- // until we find first lowest match
- template class P, class SM, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
- struct specialize_plan_impl_loop >
- : specialize_plan_impl_match > {};
-
- template
- struct has_sm_tuning_impl;
-
- // specializing for Tunig which needs 1 arg
- template class Tuning,
- class _0>
- struct has_sm_tuning_impl > : has_type_t > {};
-
- // specializing for Tunig which needs 2 args
- template class Tuning,
- class _0, class _1>
- struct has_sm_tuning_impl > : has_type_t > {};
-
- template class P, class SM>
- struct has_sm_tuning : has_sm_tuning_impl::tuning > {};
-
- // once first match is found in sm_list, all remaining sm are possible
- // candidate for tuning, so pick the first available
- // if the plan P has SM-level tuning then pick it,
- // otherwise move on to the next sm in the sm_list
- template class P, class SM, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
- struct specialize_plan_impl_match >
- : thrust::detail::conditional<
- has_sm_tuning::value,
- P,
- specialize_plan_impl_match > >::type {};
-
- template class Plan, class SM = THRUST_TUNING_ARCH>
- struct specialize_plan_msvc10_war
- {
- // if Plan has tuning type, this means it has SM-specific tuning
- // so loop through sm_list to find match,
- // otherwise just specialize on provided SM
- typedef thrust::detail::conditional >::value,
- specialize_plan_impl_loop,
- Plan >
- type;
- };
-
- template class Plan, class SM = THRUST_TUNING_ARCH>
- struct specialize_plan : specialize_plan_msvc10_war::type::type {};
-
-
- /////////////////////////
- /////////////////////////
- /////////////////////////
-
- // retrieve temp storage size from an Agent
- // ---------------------------------------------------------------------------
- // metafunction introspects Agent, and if it finds TempStorage type
- // it will return its size
-
- __THRUST_DEFINE_HAS_NESTED_TYPE(has_temp_storage, TempStorage)
-
- template
- struct temp_storage_size_impl;
-
- template
- struct temp_storage_size_impl
- {
- enum
- {
- value = 0
- };
- };
-
- template
- struct temp_storage_size_impl
- {
- enum
- {
- value = sizeof(typename Agent::TempStorage)
- };
- };
-
- template
- struct temp_storage_size
- : temp_storage_size_impl::type>
- {
- };
-
- // check whether all Agents requires < MAX_SHMEM shared memory
- // ---------------------------------------------------------------------------
- // if so, we can use simpler kernel for dispatch, which assumes that all
- // shared memory is on chip.
- // Otherwise, a kernel will be compiled which can also accept virtualized
- // shared memory, in case there is not enough on chip. This kernel is about
- // 10% slower
-
- template
- struct has_enough_shmem_impl;
-
- template
- struct has_enough_shmem_impl >
- : has_enough_shmem_impl<
- V && (temp_storage_size >::value <= S),
- A,
- S,
- typelist<_1, _2, _3, _4, _5, _6, _7, _8, _9> >
- {
- };
- template
- struct has_enough_shmem_impl >
- {
- enum
- {
- value = V
- };
- typedef typename thrust::detail::conditional::type type;
- };
-
- template
- struct has_enough_shmem : has_enough_shmem_impl
- {
- };
-
- /////////////////////////
- /////////////////////////
- /////////////////////////
-
- // AgentPlan structure and helpers
- // --------------------------------
-
- struct AgentPlan
- {
- int block_threads;
- int items_per_thread;
- int items_per_tile;
- int shared_memory_size;
- int grid_size;
-
- THRUST_RUNTIME_FUNCTION
- AgentPlan() {}
-
- THRUST_RUNTIME_FUNCTION
- AgentPlan(int block_threads_,
- int items_per_thread_,
- int shared_memory_size_,
- int grid_size_ = 0)
- : block_threads(block_threads_),
- items_per_thread(items_per_thread_),
- items_per_tile(items_per_thread * block_threads),
- shared_memory_size(shared_memory_size_),
- grid_size(grid_size_)
- {
- }
-
- THRUST_RUNTIME_FUNCTION
- AgentPlan(AgentPlan const& plan)
- : block_threads(plan.block_threads),
- items_per_thread(plan.items_per_thread),
- items_per_tile(plan.items_per_tile),
- shared_memory_size(plan.shared_memory_size),
- grid_size(plan.grid_size) {}
-
- template
- THRUST_RUNTIME_FUNCTION
- AgentPlan(PtxPlan,
- typename thrust::detail::disable_if_convertible<
- PtxPlan,
- AgentPlan>::type* = NULL)
- : block_threads(PtxPlan::BLOCK_THREADS),
- items_per_thread(PtxPlan::ITEMS_PER_THREAD),
- items_per_tile(PtxPlan::ITEMS_PER_TILE),
- shared_memory_size(temp_storage_size::value),
- grid_size(0)
- {
- }
- }; // struct AgentPlan
-
-
- __THRUST_DEFINE_HAS_NESTED_TYPE(has_Plan, Plan)
-
- template
- struct return_Plan
- {
- typedef typename Agent::Plan type;
- };
-
- template
- struct get_plan : thrust::detail::conditional<
- has_Plan::value,
- return_Plan,
- thrust::detail::identity_ >::type
- {
- };
-
- // returns AgentPlan corresponding to a given ptx version
- // ------------------------------------------------------
-
- template
- struct get_agent_plan_impl;
-
- template
- struct get_agent_plan_impl >
- {
- typedef typename get_plan::type Plan;
- Plan THRUST_RUNTIME_FUNCTION
- static get(int ptx_version)
- {
- if (ptx_version >= SM::ver)
- return Plan(specialize_plan());
- else
- return get_agent_plan_impl >::
- get(ptx_version);
- }
- };
-
- template
- struct get_agent_plan_impl >
- {
- typedef typename get_plan::type Plan;
- Plan THRUST_RUNTIME_FUNCTION
- static get(int /* ptx_version */)
- {
- typedef typename get_plan::type Plan;
- return Plan(specialize_plan());
- }
- };
-
- template
- typename get_plan::type THRUST_RUNTIME_FUNCTION
- get_agent_plan(int ptx_version)
- {
- // Use one path, with Agent::ptx_plan, for device code where device-side
- // kernel launches are supported. The other path, with
- // get_agent_plan_impl::get(version), is for host code and for device
- // code without device-side kernel launches. NVCC and Feta check for
- // these situations differently.
- #ifdef __NVCOMPILER_CUDA__
- #ifdef __THRUST_HAS_CUDART__
- if (CUB_IS_DEVICE_CODE) {
- return typename get_plan::type(typename Agent::ptx_plan());
- } else
- #endif
- {
- return get_agent_plan_impl::get(ptx_version);
- }
- #else
- #if (CUB_PTX_ARCH > 0) && defined(__THRUST_HAS_CUDART__)
- typedef typename get_plan::type Plan;
- THRUST_UNUSED_VAR(ptx_version);
- // We're on device, use default policy
- return Plan(typename Agent::ptx_plan());
- #else
- return get_agent_plan_impl::get(ptx_version);
- #endif
- #endif
- }
-
-// XXX keep this dead-code for now as a gentle reminder
-// that kernel luunch which reats plan values is the most robust
-// mechanism to extract sm-specific tuning parameters
-// TODO: since we are unable to afford kernel launch + cudaMemcpy ON EVERY
-// algorithm invocation, we need to design a good caching strategy
-// such that when the algorithm is called multiple times, only the
-// first invocation will invoke kernel launch + cudaMemcpy, but
-// the subsequent invocations, will just read cached values from host mem
-// If launched from device, this is just a device-function call
-// no caching is required.
-// ----------------------------------------------------------------------------
- // if we don't know ptx version, we can call kernel
- // to retrieve AgentPlan from device code. Slower, but guaranteed to work
- // -----------------------------------------------------------------------
-#if 0
- template
- void __global__ get_agent_plan_kernel(AgentPlan *plan);
-
- static __device__ AgentPlan agent_plan_device;
-
- template
- AgentPlan __device__ get_agent_plan_dev()
- {
- AgentPlan plan;
- plan.block_threads = Agent::ptx_plan::BLOCK_THREADS;
- plan.items_per_thread = Agent::ptx_plan::ITEMS_PER_THREAD;
- plan.items_per_tile = Agent::ptx_plan::ITEMS_PER_TILE;
- plan.shared_memory_size = temp_storage_size::value;
- return plan;
- }
-
- template
- AgentPlan __host__ __device__ __forceinline__
- xget_agent_plan_impl(F f, cudaStream_t s, void* d_ptr)
- {
- AgentPlan plan;
-#ifdef __CUDA_ARCH__
- plan = get_agent_plan_dev();
-#else
- static cub::Mutex mutex;
- bool lock = false;
- if (d_ptr == 0)
- {
- lock = true;
- cudaGetSymbolAddress(&d_ptr, agent_plan_device);
- }
- if (lock)
- mutex.Lock();
- f<<<1,1,0,s>>>((AgentPlan*)d_ptr);
- cudaMemcpyAsync((void*)&plan,
- d_ptr,
- sizeof(AgentPlan),
- cudaMemcpyDeviceToHost,
- s);
- if (lock)
- mutex.Unlock();
- cudaStreamSynchronize(s);
-#endif
- return plan;
- }
-
- template
- AgentPlan THRUST_RUNTIME_FUNCTION
- get_agent_plan(cudaStream_t s = 0, void *ptr = 0)
- {
- return xget_agent_plan_impl(get_agent_plan_kernel,
- s,
- ptr);
- }
-
- template
- void __global__ get_agent_plan_kernel(AgentPlan *plan)
- {
- *plan = get_agent_plan_dev();
- }
-#endif
-
- /////////////////////////
- /////////////////////////
- /////////////////////////
-
- THRUST_RUNTIME_FUNCTION
- int get_sm_count()
- {
- int dev_id;
- cuda_cub::throw_on_error(cudaGetDevice(&dev_id),
- "get_sm_count :"
- "failed to cudaGetDevice");
-
- cudaError_t status;
- int i32value;
- status = cudaDeviceGetAttribute(&i32value,
- cudaDevAttrMultiProcessorCount,
- dev_id);
- cuda_cub::throw_on_error(status,
- "get_sm_count:"
- "failed to sm_count");
- return i32value;
- }
-
- size_t THRUST_RUNTIME_FUNCTION
- get_max_shared_memory_per_block()
- {
- int dev_id;
- cuda_cub::throw_on_error(cudaGetDevice(&dev_id),
- "get_max_shared_memory_per_block :"
- "failed to cudaGetDevice");
-
- cudaError_t status;
- int i32value;
- status = cudaDeviceGetAttribute(&i32value,
- cudaDevAttrMaxSharedMemoryPerBlock,
- dev_id);
- cuda_cub::throw_on_error(status,
- "get_max_shared_memory_per_block :"
- "failed to get max shared memory per block");
-
- return static_cast(i32value);
- }
-
- size_t THRUST_RUNTIME_FUNCTION
- virtual_shmem_size(size_t shmem_per_block)
- {
- size_t max_shmem_per_block = core::get_max_shared_memory_per_block();
- if (shmem_per_block > max_shmem_per_block)
- return shmem_per_block;
- else
- return 0;
- }
-
- size_t THRUST_RUNTIME_FUNCTION
- vshmem_size(size_t shmem_per_block, size_t num_blocks)
- {
- size_t max_shmem_per_block = core::get_max_shared_memory_per_block();
- if (shmem_per_block > max_shmem_per_block)
- return shmem_per_block*num_blocks;
- else
- return 0;
- }
-
- // LoadIterator
- // ------------
- // if trivial iterator is passed, wrap loads into LDG
- //
- template
- struct LoadIterator
- {
- typedef typename iterator_traits::value_type value_type;
- typedef typename iterator_traits::difference_type size_type;
-
- typedef typename thrust::detail::conditional<
- is_contiguous_iterator::value,
- cub::CacheModifiedInputIterator,
- It>::type type;
- }; // struct Iterator
-
- template
- typename LoadIterator::type __device__ __forceinline__
- make_load_iterator_impl(It it, thrust::detail::true_type /* is_trivial */)
- {
- return raw_pointer_cast(&*it);
- }
-
- template
- typename LoadIterator::type __device__ __forceinline__
- make_load_iterator_impl(It it, thrust::detail::false_type /* is_trivial */)
- {
- return it;
- }
-
- template
- typename LoadIterator::type __device__ __forceinline__
- make_load_iterator(PtxPlan const&, It it)
- {
- return make_load_iterator_impl(
- it, typename is_contiguous_iterator::type());
- }
-
- template
- struct get_arch;
-
- template class Plan, class Arch>
- struct get_arch > { typedef Arch type; };
-
- // BlockLoad
- // -----------
- // a helper metaprogram that returns type of a block loader
- template ::value_type>
- struct BlockLoad
- {
- typedef cub::BlockLoad::type::ver>
-
-
- type;
- };
-
- // BlockStore
- // -----------
- // a helper metaprogram that returns type of a block loader
- template ::value_type>
- struct BlockStore
- {
- typedef cub::BlockStore::type::ver>
- type;
- };
- // cuda_otional
- // --------------
- // used for function that return cudaError_t along with the result
- //
- template
- class cuda_optional
- {
- cudaError_t status_;
- T value_;
-
- public:
- __host__ __device__
- cuda_optional() : status_(cudaSuccess) {}
-
- __host__ __device__
- cuda_optional(T v, cudaError_t status = cudaSuccess) : status_(status), value_(v) {}
-
- bool __host__ __device__
- isValid() const { return cudaSuccess == status_; }
-
- cudaError_t __host__ __device__
- status() const { return status_; }
-
- __host__ __device__ T const &
- value() const { return value_; }
-
- __host__ __device__ operator T const &() const { return value_; }
- };
-
- cuda_optional THRUST_RUNTIME_FUNCTION
- get_ptx_version()
- {
- int ptx_version = 0;
- cudaError_t status = cub::PtxVersion(ptx_version);
- return cuda_optional(ptx_version, status);
- }
-
- cudaError_t THRUST_RUNTIME_FUNCTION
- sync_stream(cudaStream_t stream)
- {
- return cub::SyncStream(stream);
- }
-
- inline void __device__ sync_threadblock()
- {
- cub::CTA_SYNC();
- }
-
-#define CUDA_CUB_RET_IF_FAIL(e) \
- { \
- auto const error = (e); \
- if (cub::Debug(error, __FILE__, __LINE__)) return error; \
- }
-
- // uninitialized
- // -------
- // stores type in uninitialized form
- //
- template
- struct uninitialized
- {
- typedef typename cub::UnitWord::DeviceWord DeviceWord;
-
- enum
- {
- WORDS = sizeof(T) / sizeof(DeviceWord)
- };
-
- DeviceWord storage[WORDS];
-
- __host__ __device__ __forceinline__ T& get()
- {
- return reinterpret_cast(*this);
- }
-
- __host__ __device__ __forceinline__ operator T&() { return get(); }
- };
-
- // uninitialized_array
- // --------------
- // allocates uninitialized data on stack
- template
- struct array
- {
- typedef T value_type;
- typedef T ref[N];
- enum {SIZE = N};
- private:
- T data_[N];
-
- public:
- __host__ __device__ T* data() { return data_; }
- __host__ __device__ const T* data() const { return data_; }
- __host__ __device__ T& operator[](unsigned int idx) { return ((T*)data_)[idx]; }
- __host__ __device__ T const& operator[](unsigned int idx) const { return ((T*)data_)[idx]; }
- __host__ __device__ unsigned int size() const { return N; }
- __host__ __device__ operator ref&() { return data_; }
- };
-
-
- // uninitialized_array
- // --------------
- // allocates uninitialized data on stack
- template
- struct uninitialized_array
- {
- typedef T value_type;
- typedef T ref[N];
- enum {SIZE = N};
- private:
- char data_[N * sizeof(T)];
-
- public:
- __host__ __device__ T* data() { return data_; }
- __host__ __device__ const T* data() const { return data_; }
- __host__ __device__ T& operator[](unsigned int idx) { return ((T*)data_)[idx]; }
- __host__ __device__ T const& operator[](unsigned int idx) const { return ((T*)data_)[idx]; }
- __host__ __device__ T& operator[](int idx) { return ((T*)data_)[idx]; }
- __host__ __device__ T const& operator[](int idx) const { return ((T*)data_)[idx]; }
- __host__ __device__ unsigned int size() const { return N; }
- __host__ __device__ operator ref&() { return *reinterpret_cast(data_); }
- __host__ __device__ ref& get_ref() { return (ref&)*this; }
- };
-
- __host__ __device__ __forceinline__ size_t align_to(size_t n, size_t align)
- {
- return ((n+align-1)/align) * align;
- }
-
- namespace host {
- inline cuda_optional get_max_shared_memory_per_block()
- {
- cudaError_t status = cudaSuccess;
- int dev_id = 0;
- status = cudaGetDevice(&dev_id);
- if (status != cudaSuccess) return cuda_optional(0, status);
-
- int max_shmem = 0;
- status = cudaDeviceGetAttribute(&max_shmem,
- cudaDevAttrMaxSharedMemoryPerBlock,
- dev_id);
- if (status != cudaSuccess) return cuda_optional(0, status);
- return cuda_optional(max_shmem, status);
- }
- }
-
- template
- THRUST_RUNTIME_FUNCTION cudaError_t
- alias_storage(void* storage_ptr,
- size_t& storage_size,
- void* (&allocations)[ALLOCATIONS],
- size_t (&allocation_sizes)[ALLOCATIONS])
- {
- return cub::AliasTemporaries(storage_ptr,
- storage_size,
- allocations,
- allocation_sizes);
- }
-
-
-} // namespace core
-using core::sm60;
-using core::sm52;
-using core::sm35;
-using core::sm30;
-} // namespace cuda_
-
-} // end namespace thrust
diff --git a/spaces/CVPR/lama-example/bin/predict_inner_features.py b/spaces/CVPR/lama-example/bin/predict_inner_features.py
deleted file mode 100644
index 4f9f7a11a6c4757a4eaa05cf1ac648d372f7e02f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/bin/predict_inner_features.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python3
-
-# Example command:
-# ./bin/predict.py \
-# model.path= \
-# indir= \
-# outdir=
-
-import logging
-import os
-import sys
-import traceback
-
-from saicinpainting.evaluation.utils import move_to_device
-
-os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['OPENBLAS_NUM_THREADS'] = '1'
-os.environ['MKL_NUM_THREADS'] = '1'
-os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
-os.environ['NUMEXPR_NUM_THREADS'] = '1'
-
-import cv2
-import hydra
-import numpy as np
-import torch
-import tqdm
-import yaml
-from omegaconf import OmegaConf
-from torch.utils.data._utils.collate import default_collate
-
-from saicinpainting.training.data.datasets import make_default_val_dataset
-from saicinpainting.training.trainers import load_checkpoint, DefaultInpaintingTrainingModule
-from saicinpainting.utils import register_debug_signal_handlers, get_shape
-
-LOGGER = logging.getLogger(__name__)
-
-
-@hydra.main(config_path='../configs/prediction', config_name='default_inner_features.yaml')
-def main(predict_config: OmegaConf):
- try:
- register_debug_signal_handlers() # kill -10 will result in traceback dumped into log
-
- device = torch.device(predict_config.device)
-
- train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
- with open(train_config_path, 'r') as f:
- train_config = OmegaConf.create(yaml.safe_load(f))
-
- checkpoint_path = os.path.join(predict_config.model.path, 'models', predict_config.model.checkpoint)
- model = load_checkpoint(train_config, checkpoint_path, strict=False)
- model.freeze()
- model.to(device)
-
- assert isinstance(model, DefaultInpaintingTrainingModule), 'Only DefaultInpaintingTrainingModule is supported'
- assert isinstance(getattr(model.generator, 'model', None), torch.nn.Sequential)
-
- if not predict_config.indir.endswith('/'):
- predict_config.indir += '/'
-
- dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
-
- max_level = max(predict_config.levels)
-
- with torch.no_grad():
- for img_i in tqdm.trange(len(dataset)):
- mask_fname = dataset.mask_filenames[img_i]
- cur_out_fname = os.path.join(predict_config.outdir, os.path.splitext(mask_fname[len(predict_config.indir):])[0])
- os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
- batch = move_to_device(default_collate([dataset[img_i]]), device)
-
- img = batch['image']
- mask = batch['mask']
- mask[:] = 0
- mask_h, mask_w = mask.shape[-2:]
- mask[:, :,
- mask_h // 2 - predict_config.hole_radius : mask_h // 2 + predict_config.hole_radius,
- mask_w // 2 - predict_config.hole_radius : mask_w // 2 + predict_config.hole_radius] = 1
-
- masked_img = torch.cat([img * (1 - mask), mask], dim=1)
-
- feats = masked_img
- for level_i, level in enumerate(model.generator.model):
- feats = level(feats)
- if level_i in predict_config.levels:
- cur_feats = torch.cat([f for f in feats if torch.is_tensor(f)], dim=1) \
- if isinstance(feats, tuple) else feats
-
- if predict_config.slice_channels:
- cur_feats = cur_feats[:, slice(*predict_config.slice_channels)]
-
- cur_feat = cur_feats.pow(2).mean(1).pow(0.5).clone()
- cur_feat -= cur_feat.min()
- cur_feat /= cur_feat.std()
- cur_feat = cur_feat.clamp(0, 1) / 1
- cur_feat = cur_feat.cpu().numpy()[0]
- cur_feat *= 255
- cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
- cv2.imwrite(cur_out_fname + f'_lev{level_i:02d}_norm.png', cur_feat)
-
- # for channel_i in predict_config.channels:
- #
- # cur_feat = cur_feats[0, channel_i].clone().detach().cpu().numpy()
- # cur_feat -= cur_feat.min()
- # cur_feat /= cur_feat.max()
- # cur_feat *= 255
- # cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
- # cv2.imwrite(cur_out_fname + f'_lev{level_i}_ch{channel_i}.png', cur_feat)
- elif level_i >= max_level:
- break
- except KeyboardInterrupt:
- LOGGER.warning('Interrupted by user')
- except Exception as ex:
- LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
- sys.exit(1)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Chen-Beer/LLMing/app.py b/spaces/Chen-Beer/LLMing/app.py
deleted file mode 100644
index 291d1bbee9a8362a7cca48491ada2a02ec10984e..0000000000000000000000000000000000000000
--- a/spaces/Chen-Beer/LLMing/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import gradio as gr
-import openai
-from PyPDF2 import PdfReader
-
-def funfunc(Name, CV, Adventurous, Years):
- # Years = 5
- reader = PdfReader(CV.name)
- page = reader.pages[0]
- CV_text = page.extract_text()
- MODEL = "gpt-3.5-turbo-16k"
- response = openai.ChatCompletion.create(
- model=MODEL,
- messages=[
- {"role": "system", "content": f"You are an assistant which helps people to find their best future career "
- f"path based on their current CV. Your job is to answer the question: where do "
- f"you see this person {Years} years from now? by taking into account his\hers "
- f"current CV and suggest a professional trajectory for these upcoming {Years} "
- f"years. The current year is 2023, so your forecasting should be until 2023+{Years}."},
- {"role": "user", "content": f"My name is: {Name}"},
- {"role": "system", "content": "There is a spectrum of adventurousness according to which a career path can "
- "be suggested. The adventurousness parameter ranges from 0 to 5, where 0 is not "
- "adventurous at all (continue on the same expected track, no surprises, "
- "no professional development) and 5 is the most adventurous trajectory you can think of "
- "(explore and suggest fascinating related directions). "},
- {"role": "user", "content": f"My adventurous level is: {Adventurous}. \nMy CV is listed below: \n {CV_text}"},
- {"role": "system", "content": f"Please output the answers to the following questions: \n 1. Where do you "
- f"see {Name} in {Years} years? \n- Use 'they' instead of 'he' or 'she.' "
- f"\n- Mention their title, company, and briefly explain what they will do. "
- f"\n- Consider work-life balance. \n \n2. which roles will they perform during "
- f"these upcoming {Years} years? \n- Pick one leading option and be specific. "
- f"\n- Provide the precise years, title, and company for each role in a resume-like list. "
- f"\n- Each role should last between 6 months and 2.5 years on average."},
- {"role": "system", "content": f"Your response should follow the template: \n {Years} years from now, "
- f"{Name} will ... \nDuring these {Years} years, {Name} will perform the "
- f"following positions: \n - : , \n... "
- f"\n Ensure that you start from the current year (2023) and go {Years} years ahead "
- f"(for example, 5 years from now the year will be 2028. \nMake sure that the last "
- f"position is aligned with the details you provided earlier."}
- # {"role": "system", "content": f"Here's an example for desired output: \nIn 3 years, Emily will be a Software Development Team Lead at ABC Tech, managing a team of developers and overseeing software development projects. They will have a healthy work-life balance, with opportunities for growth and innovation. \n \nDuring these 3 years, Emily will perform the following positions: \n2023-2024: Junior Software Developer, XYZ Company \n2024-2025: Software Developer, XYZ Company \n2025-2026: Senior Software Developer, ABC Tech \n2026-: Software Development Team Lead, ABC Tech"}
- ],
- max_tokens=500,
- temperature=1
- )
-
- return response['choices'][0]['message']['content']
-
-
-iface = gr.Interface(
- fn=funfunc,
- inputs=["text", "file", gr.Slider(minimum=0, maximum=5, step=1), gr.Slider(minimum=1, maximum=10, step=1)],
- # inputs=["text", "file", gr.Slider(minimum=0, maximum=5, step=1)],
- outputs=["text"]
-)
-
-
-iface.launch()
diff --git a/spaces/CikeyQI/meme-api/docker/start.sh b/spaces/CikeyQI/meme-api/docker/start.sh
deleted file mode 100644
index 14748f843f0e205213b6a71209290a5cdd8a15a3..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/docker/start.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#! /usr/bin/env bash
-
-mkdir -p ~/.config/meme_generator
-
-envsubst < /app/config.toml.template > ~/.config/meme_generator/config.toml
-
-exec python -m meme_generator.app
diff --git a/spaces/CofAI/CalculatorUI/index.html b/spaces/CofAI/CalculatorUI/index.html
deleted file mode 100644
index f74fbd5528d26f354fed49f9faacde6c447a6961..0000000000000000000000000000000000000000
--- a/spaces/CofAI/CalculatorUI/index.html
+++ /dev/null
@@ -1,63 +0,0 @@
- CalculatorCofAI
Calculator CofAI
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/CofAI/LengthConverter/index.html b/spaces/CofAI/LengthConverter/index.html
deleted file mode 100644
index b9903e56f7d7379e95e045f65beadabfd0eff883..0000000000000000000000000000000000000000
--- a/spaces/CofAI/LengthConverter/index.html
+++ /dev/null
@@ -1,108 +0,0 @@
-
-
-
- Length Converter
-
-
-
Length Converter
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Xiaor.py b/spaces/CofAI/chat.b4/g4f/Provider/Providers/Xiaor.py
deleted file mode 100644
index 5757f9971157116cbbfabbe5420e3b7e88fed4e7..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Xiaor.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://xiaor.eu.org'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k',
- 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
- headers = {
- 'Content-Type': 'application/json',
- }
- data = {
- 'model': model,
- 'temperature': 0.7,
- 'presence_penalty': 0,
- 'messages': messages,
- }
- response = requests.post(url + '/p1/v1/chat/completions',
- json=data, stream=True)
-
- if stream:
- for chunk in response.iter_content(chunk_size=None):
- chunk = chunk.decode('utf-8')
- if chunk.strip():
- message = json.loads(chunk)['choices'][0]['message']['content']
- yield message
- else:
- message = response.json()['choices'][0]['message']['content']
- yield message
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/CofAI/chat/g4f/Provider/Providers/Wewordle.py b/spaces/CofAI/chat/g4f/Provider/Providers/Wewordle.py
deleted file mode 100644
index 090d0bf3ab2e1f3851880393d43662edfbe9d984..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/g4f/Provider/Providers/Wewordle.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import os
-import requests
-import json
-import random
-import time
-import string
-from ...typing import sha256, Dict, get_type_hints
-
-url = "https://wewordle.org/gptapi/v1/android/turbo"
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- base = ''
- for message in messages:
- base += '%s: %s\n' % (message['role'], message['content'])
- base += 'assistant:'
- # randomize user id and app id
- _user_id = ''.join(random.choices(
- f'{string.ascii_lowercase}{string.digits}', k=16))
- _app_id = ''.join(random.choices(
- f'{string.ascii_lowercase}{string.digits}', k=31))
- # make current date with format utc
- _request_date = time.strftime("%Y-%m-%dT%H:%M:%S.000Z", time.gmtime())
- headers = {
- 'accept': '*/*',
- 'pragma': 'no-cache',
- 'Content-Type': 'application/json',
- 'Connection': 'keep-alive'
- }
- data = {
- "user": _user_id,
- "messages": [
- {"role": "user", "content": base}
- ],
- "subscriber": {
- "originalPurchaseDate": None,
- "originalApplicationVersion": None,
- "allPurchaseDatesMillis": {},
- "entitlements": {
- "active": {},
- "all": {}
- },
- "allPurchaseDates": {},
- "allExpirationDatesMillis": {},
- "allExpirationDates": {},
- "originalAppUserId": f"$RCAnonymousID:{_app_id}",
- "latestExpirationDate": None,
- "requestDate": _request_date,
- "latestExpirationDateMillis": None,
- "nonSubscriptionTransactions": [],
- "originalPurchaseDateMillis": None,
- "managementURL": None,
- "allPurchasedProductIdentifiers": [],
- "firstSeen": _request_date,
- "activeSubscriptions": []
- }
- }
- response = requests.post(url, headers=headers, data=json.dumps(data))
- if response.status_code == 200:
- _json = response.json()
- if 'message' in _json:
- message_content = _json['message']['content']
- message_content = message_content.replace('**assistant:** ', '')
- yield message_content
- else:
- print(f"Error Occurred::{response.status_code}")
- return None
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/CorvaeOboro/gen_ability_icon/dnnlib/__init__.py b/spaces/CorvaeOboro/gen_ability_icon/dnnlib/__init__.py
deleted file mode 100644
index 2f08cf36f11f9b0fd94c1b7caeadf69b98375b04..0000000000000000000000000000000000000000
--- a/spaces/CorvaeOboro/gen_ability_icon/dnnlib/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-from .util import EasyDict, make_cache_dir_path
diff --git a/spaces/Cran-May/ygVI/app.py b/spaces/Cran-May/ygVI/app.py
deleted file mode 100644
index d5be40236716654cca52996de779141be135d5f4..0000000000000000000000000000000000000000
--- a/spaces/Cran-May/ygVI/app.py
+++ /dev/null
@@ -1,250 +0,0 @@
-from typing import Iterator
-
-import gradio as gr
-
-
-from model import run
-
-DEFAULT_SYSTEM_PROMPT = ""
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = 4000
-
-DESCRIPTION = """
-# 玉刚六号改/yugangVI-Chat
-"""
-LICENSE="基于Baichuan-13B-Chat以及https://github.com/ouwei2013/baichuan13b.cpp"
-
-
-
-def clear_and_save_textbox(message: str) -> tuple[str, str]:
- return '', message
-
-
-def display_input(message: str,
- history: list[tuple[str, str]]) -> list[tuple[str, str]]:
- history.append((message, ''))
- return history
-
-
-def delete_prev_fn(
- history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
- try:
- message, _ = history.pop()
- except IndexError:
- message = ''
- return history, message or ''
-
-
-def generate(
- message: str,
- history_with_input: list[tuple[str, str]],
- system_prompt: str,
- max_new_tokens: int,
- temperature: float,
- top_p: float,
- top_k: int,
-) -> Iterator[list[tuple[str, str]]]:
-
- history = history_with_input[:-1]
- generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
- for response in generator:
- yield history + [(message, response)]
-
-
-def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
- generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 8192, 1, 0.95, 50)
- for x in generator:
- pass
- return '', x
-
-
-def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None:
- a = 1
-
-
-with gr.Blocks(css='style.css') as demo:
- gr.Markdown(DESCRIPTION)
- gr.DuplicateButton(value='Duplicate Space for private use',
- elem_id='duplicate-button')
-
- with gr.Group():
- chatbot = gr.Chatbot(label='Chatbot')
- with gr.Row():
- textbox = gr.Textbox(
- container=False,
- show_label=False,
- placeholder='请输入/Type a message...',
- scale=10,
- )
- submit_button = gr.Button('提交/Submit',
- variant='primary',
- scale=1,
- min_width=0)
- with gr.Row():
- retry_button = gr.Button('🔄 重来/Retry', variant='secondary')
- undo_button = gr.Button('↩️ 撤销/Undo', variant='secondary')
- clear_button = gr.Button('🗑️ 清除/Clear', variant='secondary')
-
- saved_input = gr.State()
-
- with gr.Accordion(label='进阶设置/Advanced options', open=False):
- system_prompt = gr.Textbox(label='预设引导词/System prompt',
- value=DEFAULT_SYSTEM_PROMPT,
- lines=6)
- max_new_tokens = gr.Slider(
- label='Max new tokens',
- minimum=1,
- maximum=MAX_MAX_NEW_TOKENS,
- step=1,
- value=DEFAULT_MAX_NEW_TOKENS,
- )
- temperature = gr.Slider(
- label='情感温度/Temperature',
- minimum=0.1,
- maximum=4.0,
- step=0.1,
- value=0.3,
- )
- top_p = gr.Slider(
- label='Top-p (nucleus sampling)',
- minimum=0.05,
- maximum=1.0,
- step=0.05,
- value=0.85,
- )
- top_k = gr.Slider(
- label='Top-k',
- minimum=1,
- maximum=1000,
- step=1,
- value=5,
- )
-
- gr.Examples(
- examples=[
- '中华人民共和国的首都是？',
-
- ],
- inputs=textbox,
- outputs=[textbox, chatbot],
- fn=process_example,
- cache_examples=True,
- )
-
- gr.Markdown(LICENSE)
-
- textbox.submit(
- fn=clear_and_save_textbox,
- inputs=textbox,
- outputs=[textbox, saved_input],
- api_name=False,
- queue=False,
- ).then(
- fn=display_input,
- inputs=[saved_input, chatbot],
- outputs=chatbot,
- api_name=False,
- queue=False,
- ).then(
- fn=check_input_token_length,
- inputs=[saved_input, chatbot, system_prompt],
- api_name=False,
- queue=False,
- ).success(
- fn=generate,
- inputs=[
- saved_input,
- chatbot,
- system_prompt,
- max_new_tokens,
- temperature,
- top_p,
- top_k,
- ],
- outputs=chatbot,
- api_name=False,
- )
-
- button_event_preprocess = submit_button.click(
- fn=clear_and_save_textbox,
- inputs=textbox,
- outputs=[textbox, saved_input],
- api_name=False,
- queue=False,
- ).then(
- fn=display_input,
- inputs=[saved_input, chatbot],
- outputs=chatbot,
- api_name=False,
- queue=False,
- ).then(
- fn=check_input_token_length,
- inputs=[saved_input, chatbot, system_prompt],
- api_name=False,
- queue=False,
- ).success(
- fn=generate,
- inputs=[
- saved_input,
- chatbot,
- system_prompt,
- max_new_tokens,
- temperature,
- top_p,
- top_k,
- ],
- outputs=chatbot,
- api_name=False,
- )
-
- retry_button.click(
- fn=delete_prev_fn,
- inputs=chatbot,
- outputs=[chatbot, saved_input],
- api_name=False,
- queue=False,
- ).then(
- fn=display_input,
- inputs=[saved_input, chatbot],
- outputs=chatbot,
- api_name=False,
- queue=False,
- ).then(
- fn=generate,
- inputs=[
- saved_input,
- chatbot,
- system_prompt,
- max_new_tokens,
- temperature,
- top_p,
- top_k,
- ],
- outputs=chatbot,
- api_name=False,
- )
-
- undo_button.click(
-
- fn=delete_prev_fn,
- inputs=chatbot,
- outputs=[chatbot, saved_input],
- api_name=False,
- queue=False,
- ).then(
- fn=lambda x: x,
- inputs=[saved_input],
- outputs=textbox,
- api_name=False,
- queue=False,
- )
-
- clear_button.click(
- fn=lambda: ([], ''),
- outputs=[chatbot, saved_input],
- queue=False,
- api_name=False,
- )
-
-demo.queue(max_size=20).launch()
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/dtype.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/dtype.py
deleted file mode 100644
index baedb192be4bbddd52bc0105a344a0484c890fe1..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/dtype.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#coding=utf-8
-'''
-Created on 2016年9月27日
-@author: dengdan
-'''
-import numpy as np
-
-float32 = 'float32'
-floatX = float32
-int32 = 'int32'
-uint8 = 'uint8'
-string = 'str'
-
-def cast(obj, dtype):
- if isinstance(obj, list):
- return np.asarray(obj, dtype = floatX)
- return np.cast[dtype](obj)
-
-def int(obj):
- return cast(obj, 'int')
-
-def double(obj):
- return cast(obj, 'double')
-
-def is_number(obj):
- try:
- obj + 1
- except:
- return False
- return True
-
-def is_str(s):
- return type(s) == str
-
-def is_list(s):
- return type(s) == list
-
-def is_tuple(s):
- return type(s) == tuple
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/DdsImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/DdsImagePlugin.py
deleted file mode 100644
index a946daeaa6b9a5946fc5492443dfddbb10881c99..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/DdsImagePlugin.py
+++ /dev/null
@@ -1,291 +0,0 @@
-"""
-A Pillow loader for .dds files (S3TC-compressed aka DXTC)
-Jerome Leclanche
-
-Documentation:
- https://web.archive.org/web/20170802060935/http://oss.sgi.com/projects/ogl-sample/registry/EXT/texture_compression_s3tc.txt
-
-The contents of this file are hereby released in the public domain (CC0)
-Full text of the CC0 license:
- https://creativecommons.org/publicdomain/zero/1.0/
-"""
-
-import struct
-from io import BytesIO
-
-from . import Image, ImageFile
-from ._binary import o32le as o32
-
-# Magic ("DDS ")
-DDS_MAGIC = 0x20534444
-
-# DDS flags
-DDSD_CAPS = 0x1
-DDSD_HEIGHT = 0x2
-DDSD_WIDTH = 0x4
-DDSD_PITCH = 0x8
-DDSD_PIXELFORMAT = 0x1000
-DDSD_MIPMAPCOUNT = 0x20000
-DDSD_LINEARSIZE = 0x80000
-DDSD_DEPTH = 0x800000
-
-# DDS caps
-DDSCAPS_COMPLEX = 0x8
-DDSCAPS_TEXTURE = 0x1000
-DDSCAPS_MIPMAP = 0x400000
-
-DDSCAPS2_CUBEMAP = 0x200
-DDSCAPS2_CUBEMAP_POSITIVEX = 0x400
-DDSCAPS2_CUBEMAP_NEGATIVEX = 0x800
-DDSCAPS2_CUBEMAP_POSITIVEY = 0x1000
-DDSCAPS2_CUBEMAP_NEGATIVEY = 0x2000
-DDSCAPS2_CUBEMAP_POSITIVEZ = 0x4000
-DDSCAPS2_CUBEMAP_NEGATIVEZ = 0x8000
-DDSCAPS2_VOLUME = 0x200000
-
-# Pixel Format
-DDPF_ALPHAPIXELS = 0x1
-DDPF_ALPHA = 0x2
-DDPF_FOURCC = 0x4
-DDPF_PALETTEINDEXED8 = 0x20
-DDPF_RGB = 0x40
-DDPF_LUMINANCE = 0x20000
-
-
-# dds.h
-
-DDS_FOURCC = DDPF_FOURCC
-DDS_RGB = DDPF_RGB
-DDS_RGBA = DDPF_RGB | DDPF_ALPHAPIXELS
-DDS_LUMINANCE = DDPF_LUMINANCE
-DDS_LUMINANCEA = DDPF_LUMINANCE | DDPF_ALPHAPIXELS
-DDS_ALPHA = DDPF_ALPHA
-DDS_PAL8 = DDPF_PALETTEINDEXED8
-
-DDS_HEADER_FLAGS_TEXTURE = DDSD_CAPS | DDSD_HEIGHT | DDSD_WIDTH | DDSD_PIXELFORMAT
-DDS_HEADER_FLAGS_MIPMAP = DDSD_MIPMAPCOUNT
-DDS_HEADER_FLAGS_VOLUME = DDSD_DEPTH
-DDS_HEADER_FLAGS_PITCH = DDSD_PITCH
-DDS_HEADER_FLAGS_LINEARSIZE = DDSD_LINEARSIZE
-
-DDS_HEIGHT = DDSD_HEIGHT
-DDS_WIDTH = DDSD_WIDTH
-
-DDS_SURFACE_FLAGS_TEXTURE = DDSCAPS_TEXTURE
-DDS_SURFACE_FLAGS_MIPMAP = DDSCAPS_COMPLEX | DDSCAPS_MIPMAP
-DDS_SURFACE_FLAGS_CUBEMAP = DDSCAPS_COMPLEX
-
-DDS_CUBEMAP_POSITIVEX = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEX
-DDS_CUBEMAP_NEGATIVEX = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEX
-DDS_CUBEMAP_POSITIVEY = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEY
-DDS_CUBEMAP_NEGATIVEY = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEY
-DDS_CUBEMAP_POSITIVEZ = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_POSITIVEZ
-DDS_CUBEMAP_NEGATIVEZ = DDSCAPS2_CUBEMAP | DDSCAPS2_CUBEMAP_NEGATIVEZ
-
-
-# DXT1
-DXT1_FOURCC = 0x31545844
-
-# DXT3
-DXT3_FOURCC = 0x33545844
-
-# DXT5
-DXT5_FOURCC = 0x35545844
-
-
-# dxgiformat.h
-
-DXGI_FORMAT_R8G8B8A8_TYPELESS = 27
-DXGI_FORMAT_R8G8B8A8_UNORM = 28
-DXGI_FORMAT_R8G8B8A8_UNORM_SRGB = 29
-DXGI_FORMAT_BC5_TYPELESS = 82
-DXGI_FORMAT_BC5_UNORM = 83
-DXGI_FORMAT_BC5_SNORM = 84
-DXGI_FORMAT_BC6H_UF16 = 95
-DXGI_FORMAT_BC6H_SF16 = 96
-DXGI_FORMAT_BC7_TYPELESS = 97
-DXGI_FORMAT_BC7_UNORM = 98
-DXGI_FORMAT_BC7_UNORM_SRGB = 99
-
-
-class DdsImageFile(ImageFile.ImageFile):
- format = "DDS"
- format_description = "DirectDraw Surface"
-
- def _open(self):
- if not _accept(self.fp.read(4)):
- msg = "not a DDS file"
- raise SyntaxError(msg)
- (header_size,) = struct.unpack("
- version: 16.16F
-"""
-
-Silf_hdr_format_3 = """
- >
- version: 16.16F
- compilerVersion: L
- numSilf: H
- x
- x
-"""
-
-Silf_part1_format_v3 = """
- >
- ruleVersion: 16.16F
- passOffset: H
- pseudosOffset: H
-"""
-
-Silf_part1_format = """
- >
- maxGlyphID: H
- extraAscent: h
- extraDescent: h
- numPasses: B
- iSubst: B
- iPos: B
- iJust: B
- iBidi: B
- flags: B
- maxPreContext: B
- maxPostContext: B
- attrPseudo: B
- attrBreakWeight: B
- attrDirectionality: B
- attrMirroring: B
- attrSkipPasses: B
- numJLevels: B
-"""
-
-Silf_justify_format = """
- >
- attrStretch: B
- attrShrink: B
- attrStep: B
- attrWeight: B
- runto: B
- x
- x
- x
-"""
-
-Silf_part2_format = """
- >
- numLigComp: H
- numUserDefn: B
- maxCompPerLig: B
- direction: B
- attCollisions: B
- x
- x
- x
- numCritFeatures: B
-"""
-
-Silf_pseudomap_format = """
- >
- unicode: L
- nPseudo: H
-"""
-
-Silf_pseudomap_format_h = """
- >
- unicode: H
- nPseudo: H
-"""
-
-Silf_classmap_format = """
- >
- numClass: H
- numLinear: H
-"""
-
-Silf_lookupclass_format = """
- >
- numIDs: H
- searchRange: H
- entrySelector: H
- rangeShift: H
-"""
-
-Silf_lookuppair_format = """
- >
- glyphId: H
- index: H
-"""
-
-Silf_pass_format = """
- >
- flags: B
- maxRuleLoop: B
- maxRuleContext: B
- maxBackup: B
- numRules: H
- fsmOffset: H
- pcCode: L
- rcCode: L
- aCode: L
- oDebug: L
- numRows: H
- numTransitional: H
- numSuccess: H
- numColumns: H
-"""
-
-aCode_info = (
- ("NOP", 0),
- ("PUSH_BYTE", "b"),
- ("PUSH_BYTE_U", "B"),
- ("PUSH_SHORT", ">h"),
- ("PUSH_SHORT_U", ">H"),
- ("PUSH_LONG", ">L"),
- ("ADD", 0),
- ("SUB", 0),
- ("MUL", 0),
- ("DIV", 0),
- ("MIN", 0),
- ("MAX", 0),
- ("NEG", 0),
- ("TRUNC8", 0),
- ("TRUNC16", 0),
- ("COND", 0),
- ("AND", 0), # x10
- ("OR", 0),
- ("NOT", 0),
- ("EQUAL", 0),
- ("NOT_EQ", 0),
- ("LESS", 0),
- ("GTR", 0),
- ("LESS_EQ", 0),
- ("GTR_EQ", 0),
- ("NEXT", 0),
- ("NEXT_N", "b"),
- ("COPY_NEXT", 0),
- ("PUT_GLYPH_8BIT_OBS", "B"),
- ("PUT_SUBS_8BIT_OBS", "bBB"),
- ("PUT_COPY", "b"),
- ("INSERT", 0),
- ("DELETE", 0), # x20
- ("ASSOC", -1),
- ("CNTXT_ITEM", "bB"),
- ("ATTR_SET", "B"),
- ("ATTR_ADD", "B"),
- ("ATTR_SUB", "B"),
- ("ATTR_SET_SLOT", "B"),
- ("IATTR_SET_SLOT", "BB"),
- ("PUSH_SLOT_ATTR", "Bb"),
- ("PUSH_GLYPH_ATTR_OBS", "Bb"),
- ("PUSH_GLYPH_METRIC", "Bbb"),
- ("PUSH_FEAT", "Bb"),
- ("PUSH_ATT_TO_GATTR_OBS", "Bb"),
- ("PUSH_ATT_TO_GLYPH_METRIC", "Bbb"),
- ("PUSH_ISLOT_ATTR", "Bbb"),
- ("PUSH_IGLYPH_ATTR", "Bbb"),
- ("POP_RET", 0), # x30
- ("RET_ZERO", 0),
- ("RET_TRUE", 0),
- ("IATTR_SET", "BB"),
- ("IATTR_ADD", "BB"),
- ("IATTR_SUB", "BB"),
- ("PUSH_PROC_STATE", "B"),
- ("PUSH_VERSION", 0),
- ("PUT_SUBS", ">bHH"),
- ("PUT_SUBS2", 0),
- ("PUT_SUBS3", 0),
- ("PUT_GLYPH", ">H"),
- ("PUSH_GLYPH_ATTR", ">Hb"),
- ("PUSH_ATT_TO_GLYPH_ATTR", ">Hb"),
- ("BITOR", 0),
- ("BITAND", 0),
- ("BITNOT", 0), # x40
- ("BITSET", ">HH"),
- ("SET_FEAT", "Bb"),
-)
-aCode_map = dict([(x[0], (i, x[1])) for i, x in enumerate(aCode_info)])
-
-
-def disassemble(aCode):
- codelen = len(aCode)
- pc = 0
- res = []
- while pc < codelen:
- opcode = byteord(aCode[pc : pc + 1])
- if opcode > len(aCode_info):
- instr = aCode_info[0]
- else:
- instr = aCode_info[opcode]
- pc += 1
- if instr[1] != 0 and pc >= codelen:
- return res
- if instr[1] == -1:
- count = byteord(aCode[pc])
- fmt = "%dB" % count
- pc += 1
- elif instr[1] == 0:
- fmt = ""
- else:
- fmt = instr[1]
- if fmt == "":
- res.append(instr[0])
- continue
- parms = struct.unpack_from(fmt, aCode[pc:])
- res.append(instr[0] + "(" + ", ".join(map(str, parms)) + ")")
- pc += struct.calcsize(fmt)
- return res
-
-
-instre = re.compile(r"^\s*([^(]+)\s*(?:\(([^)]+)\))?")
-
-
-def assemble(instrs):
- res = b""
- for inst in instrs:
- m = instre.match(inst)
- if not m or not m.group(1) in aCode_map:
- continue
- opcode, parmfmt = aCode_map[m.group(1)]
- res += struct.pack("B", opcode)
- if m.group(2):
- if parmfmt == 0:
- continue
- parms = [int(x) for x in re.split(r",\s*", m.group(2))]
- if parmfmt == -1:
- l = len(parms)
- res += struct.pack(("%dB" % (l + 1)), l, *parms)
- else:
- res += struct.pack(parmfmt, *parms)
- return res
-
-
-def writecode(tag, writer, instrs):
- writer.begintag(tag)
- writer.newline()
- for l in disassemble(instrs):
- writer.write(l)
- writer.newline()
- writer.endtag(tag)
- writer.newline()
-
-
-def readcode(content):
- res = []
- for e in content_string(content).split("\n"):
- e = e.strip()
- if not len(e):
- continue
- res.append(e)
- return assemble(res)
-
-
-attrs_info = (
- "flags",
- "extraAscent",
- "extraDescent",
- "maxGlyphID",
- "numLigComp",
- "numUserDefn",
- "maxCompPerLig",
- "direction",
- "lbGID",
-)
-attrs_passindexes = ("iSubst", "iPos", "iJust", "iBidi")
-attrs_contexts = ("maxPreContext", "maxPostContext")
-attrs_attributes = (
- "attrPseudo",
- "attrBreakWeight",
- "attrDirectionality",
- "attrMirroring",
- "attrSkipPasses",
- "attCollisions",
-)
-pass_attrs_info = (
- "flags",
- "maxRuleLoop",
- "maxRuleContext",
- "maxBackup",
- "minRulePreContext",
- "maxRulePreContext",
- "collisionThreshold",
-)
-pass_attrs_fsm = ("numRows", "numTransitional", "numSuccess", "numColumns")
-
-
-def writesimple(tag, self, writer, *attrkeys):
- attrs = dict([(k, getattr(self, k)) for k in attrkeys])
- writer.simpletag(tag, **attrs)
- writer.newline()
-
-
-def getSimple(self, attrs, *attr_list):
- for k in attr_list:
- if k in attrs:
- setattr(self, k, int(safeEval(attrs[k])))
-
-
-def content_string(contents):
- res = ""
- for element in contents:
- if isinstance(element, tuple):
- continue
- res += element
- return res.strip()
-
-
-def wrapline(writer, dat, length=80):
- currline = ""
- for d in dat:
- if len(currline) > length:
- writer.write(currline[:-1])
- writer.newline()
- currline = ""
- currline += d + " "
- if len(currline):
- writer.write(currline[:-1])
- writer.newline()
-
-
-class _Object:
- pass
-
-
-class table_S__i_l_f(DefaultTable.DefaultTable):
- """Silf table support"""
-
- def __init__(self, tag=None):
- DefaultTable.DefaultTable.__init__(self, tag)
- self.silfs = []
-
- def decompile(self, data, ttFont):
- sstruct.unpack2(Silf_hdr_format, data, self)
- self.version = float(floatToFixedToStr(self.version, precisionBits=16))
- if self.version >= 5.0:
- (data, self.scheme) = grUtils.decompress(data)
- sstruct.unpack2(Silf_hdr_format_3, data, self)
- base = sstruct.calcsize(Silf_hdr_format_3)
- elif self.version < 3.0:
- self.numSilf = struct.unpack(">H", data[4:6])
- self.scheme = 0
- self.compilerVersion = 0
- base = 8
- else:
- self.scheme = 0
- sstruct.unpack2(Silf_hdr_format_3, data, self)
- base = sstruct.calcsize(Silf_hdr_format_3)
-
- silfoffsets = struct.unpack_from((">%dL" % self.numSilf), data[base:])
- for offset in silfoffsets:
- s = Silf()
- self.silfs.append(s)
- s.decompile(data[offset:], ttFont, self.version)
-
- def compile(self, ttFont):
- self.numSilf = len(self.silfs)
- if self.version < 3.0:
- hdr = sstruct.pack(Silf_hdr_format, self)
- hdr += struct.pack(">HH", self.numSilf, 0)
- else:
- hdr = sstruct.pack(Silf_hdr_format_3, self)
- offset = len(hdr) + 4 * self.numSilf
- data = b""
- for s in self.silfs:
- hdr += struct.pack(">L", offset)
- subdata = s.compile(ttFont, self.version)
- offset += len(subdata)
- data += subdata
- if self.version >= 5.0:
- return grUtils.compress(self.scheme, hdr + data)
- return hdr + data
-
- def toXML(self, writer, ttFont):
- writer.comment("Attributes starting with _ are informative only")
- writer.newline()
- writer.simpletag(
- "version",
- version=self.version,
- compilerVersion=self.compilerVersion,
- compressionScheme=self.scheme,
- )
- writer.newline()
- for s in self.silfs:
- writer.begintag("silf")
- writer.newline()
- s.toXML(writer, ttFont, self.version)
- writer.endtag("silf")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- if name == "version":
- self.scheme = int(safeEval(attrs["compressionScheme"]))
- self.version = float(safeEval(attrs["version"]))
- self.compilerVersion = int(safeEval(attrs["compilerVersion"]))
- return
- if name == "silf":
- s = Silf()
- self.silfs.append(s)
- for element in content:
- if not isinstance(element, tuple):
- continue
- tag, attrs, subcontent = element
- s.fromXML(tag, attrs, subcontent, ttFont, self.version)
-
-
-class Silf(object):
- """A particular Silf subtable"""
-
- def __init__(self):
- self.passes = []
- self.scriptTags = []
- self.critFeatures = []
- self.jLevels = []
- self.pMap = {}
-
- def decompile(self, data, ttFont, version=2.0):
- if version >= 3.0:
- _, data = sstruct.unpack2(Silf_part1_format_v3, data, self)
- self.ruleVersion = float(
- floatToFixedToStr(self.ruleVersion, precisionBits=16)
- )
- _, data = sstruct.unpack2(Silf_part1_format, data, self)
- for jlevel in range(self.numJLevels):
- j, data = sstruct.unpack2(Silf_justify_format, data, _Object())
- self.jLevels.append(j)
- _, data = sstruct.unpack2(Silf_part2_format, data, self)
- if self.numCritFeatures:
- self.critFeatures = struct.unpack_from(
- (">%dH" % self.numCritFeatures), data
- )
- data = data[self.numCritFeatures * 2 + 1 :]
- (numScriptTag,) = struct.unpack_from("B", data)
- if numScriptTag:
- self.scriptTags = [
- struct.unpack("4s", data[x : x + 4])[0].decode("ascii")
- for x in range(1, 1 + 4 * numScriptTag, 4)
- ]
- data = data[1 + 4 * numScriptTag :]
- (self.lbGID,) = struct.unpack(">H", data[:2])
- if self.numPasses:
- self.oPasses = struct.unpack(
- (">%dL" % (self.numPasses + 1)), data[2 : 6 + 4 * self.numPasses]
- )
- data = data[6 + 4 * self.numPasses :]
- (numPseudo,) = struct.unpack(">H", data[:2])
- for i in range(numPseudo):
- if version >= 3.0:
- pseudo = sstruct.unpack(
- Silf_pseudomap_format, data[8 + 6 * i : 14 + 6 * i], _Object()
- )
- else:
- pseudo = sstruct.unpack(
- Silf_pseudomap_format_h, data[8 + 4 * i : 12 + 4 * i], _Object()
- )
- self.pMap[pseudo.unicode] = ttFont.getGlyphName(pseudo.nPseudo)
- data = data[8 + 6 * numPseudo :]
- currpos = (
- sstruct.calcsize(Silf_part1_format)
- + sstruct.calcsize(Silf_justify_format) * self.numJLevels
- + sstruct.calcsize(Silf_part2_format)
- + 2 * self.numCritFeatures
- + 1
- + 1
- + 4 * numScriptTag
- + 6
- + 4 * self.numPasses
- + 8
- + 6 * numPseudo
- )
- if version >= 3.0:
- currpos += sstruct.calcsize(Silf_part1_format_v3)
- self.classes = Classes()
- self.classes.decompile(data, ttFont, version)
- for i in range(self.numPasses):
- p = Pass()
- self.passes.append(p)
- p.decompile(
- data[self.oPasses[i] - currpos : self.oPasses[i + 1] - currpos],
- ttFont,
- version,
- )
-
- def compile(self, ttFont, version=2.0):
- self.numPasses = len(self.passes)
- self.numJLevels = len(self.jLevels)
- self.numCritFeatures = len(self.critFeatures)
- numPseudo = len(self.pMap)
- data = b""
- if version >= 3.0:
- hdroffset = sstruct.calcsize(Silf_part1_format_v3)
- else:
- hdroffset = 0
- data += sstruct.pack(Silf_part1_format, self)
- for j in self.jLevels:
- data += sstruct.pack(Silf_justify_format, j)
- data += sstruct.pack(Silf_part2_format, self)
- if self.numCritFeatures:
- data += struct.pack((">%dH" % self.numCritFeaturs), *self.critFeatures)
- data += struct.pack("BB", 0, len(self.scriptTags))
- if len(self.scriptTags):
- tdata = [struct.pack("4s", x.encode("ascii")) for x in self.scriptTags]
- data += b"".join(tdata)
- data += struct.pack(">H", self.lbGID)
- self.passOffset = len(data)
-
- data1 = grUtils.bininfo(numPseudo, 6)
- currpos = hdroffset + len(data) + 4 * (self.numPasses + 1)
- self.pseudosOffset = currpos + len(data1)
- for u, p in sorted(self.pMap.items()):
- data1 += struct.pack(
- (">LH" if version >= 3.0 else ">HH"), u, ttFont.getGlyphID(p)
- )
- data1 += self.classes.compile(ttFont, version)
- currpos += len(data1)
- data2 = b""
- datao = b""
- for i, p in enumerate(self.passes):
- base = currpos + len(data2)
- datao += struct.pack(">L", base)
- data2 += p.compile(ttFont, base, version)
- datao += struct.pack(">L", currpos + len(data2))
-
- if version >= 3.0:
- data3 = sstruct.pack(Silf_part1_format_v3, self)
- else:
- data3 = b""
- return data3 + data + datao + data1 + data2
-
- def toXML(self, writer, ttFont, version=2.0):
- if version >= 3.0:
- writer.simpletag("version", ruleVersion=self.ruleVersion)
- writer.newline()
- writesimple("info", self, writer, *attrs_info)
- writesimple("passindexes", self, writer, *attrs_passindexes)
- writesimple("contexts", self, writer, *attrs_contexts)
- writesimple("attributes", self, writer, *attrs_attributes)
- if len(self.jLevels):
- writer.begintag("justifications")
- writer.newline()
- jformat, jnames, jfixes = sstruct.getformat(Silf_justify_format)
- for i, j in enumerate(self.jLevels):
- attrs = dict([(k, getattr(j, k)) for k in jnames])
- writer.simpletag("justify", **attrs)
- writer.newline()
- writer.endtag("justifications")
- writer.newline()
- if len(self.critFeatures):
- writer.begintag("critFeatures")
- writer.newline()
- writer.write(" ".join(map(str, self.critFeatures)))
- writer.newline()
- writer.endtag("critFeatures")
- writer.newline()
- if len(self.scriptTags):
- writer.begintag("scriptTags")
- writer.newline()
- writer.write(" ".join(self.scriptTags))
- writer.newline()
- writer.endtag("scriptTags")
- writer.newline()
- if self.pMap:
- writer.begintag("pseudoMap")
- writer.newline()
- for k, v in sorted(self.pMap.items()):
- writer.simpletag("pseudo", unicode=hex(k), pseudo=v)
- writer.newline()
- writer.endtag("pseudoMap")
- writer.newline()
- self.classes.toXML(writer, ttFont, version)
- if len(self.passes):
- writer.begintag("passes")
- writer.newline()
- for i, p in enumerate(self.passes):
- writer.begintag("pass", _index=i)
- writer.newline()
- p.toXML(writer, ttFont, version)
- writer.endtag("pass")
- writer.newline()
- writer.endtag("passes")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont, version=2.0):
- if name == "version":
- self.ruleVersion = float(safeEval(attrs.get("ruleVersion", "0")))
- if name == "info":
- getSimple(self, attrs, *attrs_info)
- elif name == "passindexes":
- getSimple(self, attrs, *attrs_passindexes)
- elif name == "contexts":
- getSimple(self, attrs, *attrs_contexts)
- elif name == "attributes":
- getSimple(self, attrs, *attrs_attributes)
- elif name == "justifications":
- for element in content:
- if not isinstance(element, tuple):
- continue
- (tag, attrs, subcontent) = element
- if tag == "justify":
- j = _Object()
- for k, v in attrs.items():
- setattr(j, k, int(v))
- self.jLevels.append(j)
- elif name == "critFeatures":
- self.critFeatures = []
- element = content_string(content)
- self.critFeatures.extend(map(int, element.split()))
- elif name == "scriptTags":
- self.scriptTags = []
- element = content_string(content)
- for n in element.split():
- self.scriptTags.append(n)
- elif name == "pseudoMap":
- self.pMap = {}
- for element in content:
- if not isinstance(element, tuple):
- continue
- (tag, attrs, subcontent) = element
- if tag == "pseudo":
- k = int(attrs["unicode"], 16)
- v = attrs["pseudo"]
- self.pMap[k] = v
- elif name == "classes":
- self.classes = Classes()
- for element in content:
- if not isinstance(element, tuple):
- continue
- tag, attrs, subcontent = element
- self.classes.fromXML(tag, attrs, subcontent, ttFont, version)
- elif name == "passes":
- for element in content:
- if not isinstance(element, tuple):
- continue
- tag, attrs, subcontent = element
- if tag == "pass":
- p = Pass()
- for e in subcontent:
- if not isinstance(e, tuple):
- continue
- p.fromXML(e[0], e[1], e[2], ttFont, version)
- self.passes.append(p)
-
-
-class Classes(object):
- def __init__(self):
- self.linear = []
- self.nonLinear = []
-
- def decompile(self, data, ttFont, version=2.0):
- sstruct.unpack2(Silf_classmap_format, data, self)
- if version >= 4.0:
- oClasses = struct.unpack(
- (">%dL" % (self.numClass + 1)), data[4 : 8 + 4 * self.numClass]
- )
- else:
- oClasses = struct.unpack(
- (">%dH" % (self.numClass + 1)), data[4 : 6 + 2 * self.numClass]
- )
- for s, e in zip(oClasses[: self.numLinear], oClasses[1 : self.numLinear + 1]):
- self.linear.append(
- ttFont.getGlyphName(x)
- for x in struct.unpack((">%dH" % ((e - s) / 2)), data[s:e])
- )
- for s, e in zip(
- oClasses[self.numLinear : self.numClass],
- oClasses[self.numLinear + 1 : self.numClass + 1],
- ):
- nonLinids = [
- struct.unpack(">HH", data[x : x + 4]) for x in range(s + 8, e, 4)
- ]
- nonLin = dict([(ttFont.getGlyphName(x[0]), x[1]) for x in nonLinids])
- self.nonLinear.append(nonLin)
-
- def compile(self, ttFont, version=2.0):
- data = b""
- oClasses = []
- if version >= 4.0:
- offset = 8 + 4 * (len(self.linear) + len(self.nonLinear))
- else:
- offset = 6 + 2 * (len(self.linear) + len(self.nonLinear))
- for l in self.linear:
- oClasses.append(len(data) + offset)
- gs = [ttFont.getGlyphID(x) for x in l]
- data += struct.pack((">%dH" % len(l)), *gs)
- for l in self.nonLinear:
- oClasses.append(len(data) + offset)
- gs = [(ttFont.getGlyphID(x[0]), x[1]) for x in l.items()]
- data += grUtils.bininfo(len(gs))
- data += b"".join([struct.pack(">HH", *x) for x in sorted(gs)])
- oClasses.append(len(data) + offset)
- self.numClass = len(oClasses) - 1
- self.numLinear = len(self.linear)
- return (
- sstruct.pack(Silf_classmap_format, self)
- + struct.pack(
- ((">%dL" if version >= 4.0 else ">%dH") % len(oClasses)), *oClasses
- )
- + data
- )
-
- def toXML(self, writer, ttFont, version=2.0):
- writer.begintag("classes")
- writer.newline()
- writer.begintag("linearClasses")
- writer.newline()
- for i, l in enumerate(self.linear):
- writer.begintag("linear", _index=i)
- writer.newline()
- wrapline(writer, l)
- writer.endtag("linear")
- writer.newline()
- writer.endtag("linearClasses")
- writer.newline()
- writer.begintag("nonLinearClasses")
- writer.newline()
- for i, l in enumerate(self.nonLinear):
- writer.begintag("nonLinear", _index=i + self.numLinear)
- writer.newline()
- for inp, ind in l.items():
- writer.simpletag("map", glyph=inp, index=ind)
- writer.newline()
- writer.endtag("nonLinear")
- writer.newline()
- writer.endtag("nonLinearClasses")
- writer.newline()
- writer.endtag("classes")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont, version=2.0):
- if name == "linearClasses":
- for element in content:
- if not isinstance(element, tuple):
- continue
- tag, attrs, subcontent = element
- if tag == "linear":
- l = content_string(subcontent).split()
- self.linear.append(l)
- elif name == "nonLinearClasses":
- for element in content:
- if not isinstance(element, tuple):
- continue
- tag, attrs, subcontent = element
- if tag == "nonLinear":
- l = {}
- for e in subcontent:
- if not isinstance(e, tuple):
- continue
- tag, attrs, subsubcontent = e
- if tag == "map":
- l[attrs["glyph"]] = int(safeEval(attrs["index"]))
- self.nonLinear.append(l)
-
-
-class Pass(object):
- def __init__(self):
- self.colMap = {}
- self.rules = []
- self.rulePreContexts = []
- self.ruleSortKeys = []
- self.ruleConstraints = []
- self.passConstraints = b""
- self.actions = []
- self.stateTrans = []
- self.startStates = []
-
- def decompile(self, data, ttFont, version=2.0):
- _, data = sstruct.unpack2(Silf_pass_format, data, self)
- (numRange, _, _, _) = struct.unpack(">4H", data[:8])
- data = data[8:]
- for i in range(numRange):
- (first, last, col) = struct.unpack(">3H", data[6 * i : 6 * i + 6])
- for g in range(first, last + 1):
- self.colMap[ttFont.getGlyphName(g)] = col
- data = data[6 * numRange :]
- oRuleMap = struct.unpack_from((">%dH" % (self.numSuccess + 1)), data)
- data = data[2 + 2 * self.numSuccess :]
- rules = struct.unpack_from((">%dH" % oRuleMap[-1]), data)
- self.rules = [rules[s:e] for (s, e) in zip(oRuleMap, oRuleMap[1:])]
- data = data[2 * oRuleMap[-1] :]
- (self.minRulePreContext, self.maxRulePreContext) = struct.unpack("BB", data[:2])
- numStartStates = self.maxRulePreContext - self.minRulePreContext + 1
- self.startStates = struct.unpack(
- (">%dH" % numStartStates), data[2 : 2 + numStartStates * 2]
- )
- data = data[2 + numStartStates * 2 :]
- self.ruleSortKeys = struct.unpack(
- (">%dH" % self.numRules), data[: 2 * self.numRules]
- )
- data = data[2 * self.numRules :]
- self.rulePreContexts = struct.unpack(
- ("%dB" % self.numRules), data[: self.numRules]
- )
- data = data[self.numRules :]
- (self.collisionThreshold, pConstraint) = struct.unpack(">BH", data[:3])
- oConstraints = list(
- struct.unpack(
- (">%dH" % (self.numRules + 1)), data[3 : 5 + self.numRules * 2]
- )
- )
- data = data[5 + self.numRules * 2 :]
- oActions = list(
- struct.unpack((">%dH" % (self.numRules + 1)), data[: 2 + self.numRules * 2])
- )
- data = data[2 * self.numRules + 2 :]
- for i in range(self.numTransitional):
- a = array(
- "H", data[i * self.numColumns * 2 : (i + 1) * self.numColumns * 2]
- )
- if sys.byteorder != "big":
- a.byteswap()
- self.stateTrans.append(a)
- data = data[self.numTransitional * self.numColumns * 2 + 1 :]
- self.passConstraints = data[:pConstraint]
- data = data[pConstraint:]
- for i in range(len(oConstraints) - 2, -1, -1):
- if oConstraints[i] == 0:
- oConstraints[i] = oConstraints[i + 1]
- self.ruleConstraints = [
- (data[s:e] if (e - s > 1) else b"")
- for (s, e) in zip(oConstraints, oConstraints[1:])
- ]
- data = data[oConstraints[-1] :]
- self.actions = [
- (data[s:e] if (e - s > 1) else "") for (s, e) in zip(oActions, oActions[1:])
- ]
- data = data[oActions[-1] :]
- # not using debug
-
- def compile(self, ttFont, base, version=2.0):
- # build it all up backwards
- oActions = reduce(
- lambda a, x: (a[0] + len(x), a[1] + [a[0]]), self.actions + [b""], (0, [])
- )[1]
- oConstraints = reduce(
- lambda a, x: (a[0] + len(x), a[1] + [a[0]]),
- self.ruleConstraints + [b""],
- (1, []),
- )[1]
- constraintCode = b"\000" + b"".join(self.ruleConstraints)
- transes = []
- for t in self.stateTrans:
- if sys.byteorder != "big":
- t.byteswap()
- transes.append(t.tobytes())
- if sys.byteorder != "big":
- t.byteswap()
- if not len(transes):
- self.startStates = [0]
- oRuleMap = reduce(
- lambda a, x: (a[0] + len(x), a[1] + [a[0]]), self.rules + [[]], (0, [])
- )[1]
- passRanges = []
- gidcolmap = dict([(ttFont.getGlyphID(x[0]), x[1]) for x in self.colMap.items()])
- for e in grUtils.entries(gidcolmap, sameval=True):
- if e[1]:
- passRanges.append((e[0], e[0] + e[1] - 1, e[2][0]))
- self.numRules = len(self.actions)
- self.fsmOffset = (
- sstruct.calcsize(Silf_pass_format)
- + 8
- + len(passRanges) * 6
- + len(oRuleMap) * 2
- + 2 * oRuleMap[-1]
- + 2
- + 2 * len(self.startStates)
- + 3 * self.numRules
- + 3
- + 4 * self.numRules
- + 4
- )
- self.pcCode = (
- self.fsmOffset + 2 * self.numTransitional * self.numColumns + 1 + base
- )
- self.rcCode = self.pcCode + len(self.passConstraints)
- self.aCode = self.rcCode + len(constraintCode)
- self.oDebug = 0
- # now generate output
- data = sstruct.pack(Silf_pass_format, self)
- data += grUtils.bininfo(len(passRanges), 6)
- data += b"".join(struct.pack(">3H", *p) for p in passRanges)
- data += struct.pack((">%dH" % len(oRuleMap)), *oRuleMap)
- flatrules = reduce(lambda a, x: a + x, self.rules, [])
- data += struct.pack((">%dH" % oRuleMap[-1]), *flatrules)
- data += struct.pack("BB", self.minRulePreContext, self.maxRulePreContext)
- data += struct.pack((">%dH" % len(self.startStates)), *self.startStates)
- data += struct.pack((">%dH" % self.numRules), *self.ruleSortKeys)
- data += struct.pack(("%dB" % self.numRules), *self.rulePreContexts)
- data += struct.pack(">BH", self.collisionThreshold, len(self.passConstraints))
- data += struct.pack((">%dH" % (self.numRules + 1)), *oConstraints)
- data += struct.pack((">%dH" % (self.numRules + 1)), *oActions)
- return (
- data
- + b"".join(transes)
- + struct.pack("B", 0)
- + self.passConstraints
- + constraintCode
- + b"".join(self.actions)
- )
-
- def toXML(self, writer, ttFont, version=2.0):
- writesimple("info", self, writer, *pass_attrs_info)
- writesimple("fsminfo", self, writer, *pass_attrs_fsm)
- writer.begintag("colmap")
- writer.newline()
- wrapline(
- writer,
- [
- "{}={}".format(*x)
- for x in sorted(
- self.colMap.items(), key=lambda x: ttFont.getGlyphID(x[0])
- )
- ],
- )
- writer.endtag("colmap")
- writer.newline()
- writer.begintag("staterulemap")
- writer.newline()
- for i, r in enumerate(self.rules):
- writer.simpletag(
- "state",
- number=self.numRows - self.numSuccess + i,
- rules=" ".join(map(str, r)),
- )
- writer.newline()
- writer.endtag("staterulemap")
- writer.newline()
- writer.begintag("rules")
- writer.newline()
- for i in range(len(self.actions)):
- writer.begintag(
- "rule",
- index=i,
- precontext=self.rulePreContexts[i],
- sortkey=self.ruleSortKeys[i],
- )
- writer.newline()
- if len(self.ruleConstraints[i]):
- writecode("constraint", writer, self.ruleConstraints[i])
- writecode("action", writer, self.actions[i])
- writer.endtag("rule")
- writer.newline()
- writer.endtag("rules")
- writer.newline()
- if len(self.passConstraints):
- writecode("passConstraint", writer, self.passConstraints)
- if len(self.stateTrans):
- writer.begintag("fsm")
- writer.newline()
- writer.begintag("starts")
- writer.write(" ".join(map(str, self.startStates)))
- writer.endtag("starts")
- writer.newline()
- for i, s in enumerate(self.stateTrans):
- writer.begintag("row", _i=i)
- # no newlines here
- writer.write(" ".join(map(str, s)))
- writer.endtag("row")
- writer.newline()
- writer.endtag("fsm")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont, version=2.0):
- if name == "info":
- getSimple(self, attrs, *pass_attrs_info)
- elif name == "fsminfo":
- getSimple(self, attrs, *pass_attrs_fsm)
- elif name == "colmap":
- e = content_string(content)
- for w in e.split():
- x = w.split("=")
- if len(x) != 2 or x[0] == "" or x[1] == "":
- continue
- self.colMap[x[0]] = int(x[1])
- elif name == "staterulemap":
- for e in content:
- if not isinstance(e, tuple):
- continue
- tag, a, c = e
- if tag == "state":
- self.rules.append([int(x) for x in a["rules"].split(" ")])
- elif name == "rules":
- for element in content:
- if not isinstance(element, tuple):
- continue
- tag, a, c = element
- if tag != "rule":
- continue
- self.rulePreContexts.append(int(a["precontext"]))
- self.ruleSortKeys.append(int(a["sortkey"]))
- con = b""
- act = b""
- for e in c:
- if not isinstance(e, tuple):
- continue
- tag, a, subc = e
- if tag == "constraint":
- con = readcode(subc)
- elif tag == "action":
- act = readcode(subc)
- self.actions.append(act)
- self.ruleConstraints.append(con)
- elif name == "passConstraint":
- self.passConstraints = readcode(content)
- elif name == "fsm":
- for element in content:
- if not isinstance(element, tuple):
- continue
- tag, a, c = element
- if tag == "row":
- s = array("H")
- e = content_string(c)
- s.extend(map(int, e.split()))
- self.stateTrans.append(s)
- elif tag == "starts":
- s = []
- e = content_string(c)
- s.extend(map(int, e.split()))
- self.startStates = s
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/BlockLabel-66866176.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/BlockLabel-66866176.js
deleted file mode 100644
index 4b182a822f86a08bed119c7dbf8a78286fb317c2..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/BlockLabel-66866176.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as r,e as _,s as h,N as m,k as d,O as w,P as g,K as u,U as o,p as v,M as c,o as I,R as k,z as q,v as B,A as S,x as z}from"./index-1d65707a.js";import"./Button-f155035a.js";function A(s){let e,n,i,b,f,t;return i=new s[1]({}),{c(){e=m("div"),n=m("span"),d(i.$$.fragment),b=w(),f=g(s[0]),u(n,"class","svelte-1mwvhlq"),u(e,"data-testid","block-label"),u(e,"class","svelte-1mwvhlq"),o(e,"hide",!s[2]),o(e,"sr-only",!s[2]),o(e,"float",s[4]),o(e,"hide-label",s[3])},m(a,l){v(a,e,l),c(e,n),I(i,n,null),c(e,b),c(e,f),t=!0},p(a,[l]){(!t||l&1)&&k(f,a[0]),(!t||l&4)&&o(e,"hide",!a[2]),(!t||l&4)&&o(e,"sr-only",!a[2]),(!t||l&16)&&o(e,"float",a[4]),(!t||l&8)&&o(e,"hide-label",a[3])},i(a){t||(q(i.$$.fragment,a),t=!0)},o(a){B(i.$$.fragment,a),t=!1},d(a){a&&S(e),z(i)}}}function C(s,e,n){let{label:i=null}=e,{Icon:b}=e,{show_label:f=!0}=e,{disable:t=!1}=e,{float:a=!0}=e;return s.$$set=l=>{"label"in l&&n(0,i=l.label),"Icon"in l&&n(1,b=l.Icon),"show_label"in l&&n(2,f=l.show_label),"disable"in l&&n(3,t=l.disable),"float"in l&&n(4,a=l.float)},[i,b,f,t,a]}class M extends r{constructor(e){super(),_(this,e,C,A,h,{label:0,Icon:1,show_label:2,disable:3,float:4})}}export{M as B};
-//# sourceMappingURL=BlockLabel-66866176.js.map
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b6ab4199.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b6ab4199.js
deleted file mode 100644
index e90d63c8902c4846c410b992dc24bb71a5566478..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b6ab4199.js
+++ /dev/null
@@ -1,14 +0,0 @@
-import{C as R,E as m,L as C,a as u}from"./index-f8ff95a1.js";import{s as z,t as e,y as n,h as W,L as I,i as E,w as Y,z as A,d as J,f as L,a as N,A as k,b as D,B,C as H,v as K,E as b,I as M,m as F,x as OO}from"./index-3ba00a4a.js";import"./index-1d65707a.js";import"./Blocks-c9e1499d.js";import"./Button-f155035a.js";import"./BlockLabel-66866176.js";import"./Empty-eec13822.js";import"./Copy-9f1657c4.js";import"./Download-daff1959.js";const y=301,j=1,QO=2,d=302,eO=304,aO=305,iO=3,$O=4,tO=[9,10,11,12,13,32,133,160,5760,8192,8193,8194,8195,8196,8197,8198,8199,8200,8201,8202,8232,8233,8239,8287,12288],_=125,rO=59,x=47,SO=42,PO=43,nO=45,oO=new R({start:!1,shift(O,Q){return Q==iO||Q==$O||Q==eO?O:Q==aO},strict:!1}),ZO=new m((O,Q)=>{let{next:i}=O;(i==_||i==-1||Q.context)&&Q.canShift(d)&&O.acceptToken(d)},{contextual:!0,fallback:!0}),lO=new m((O,Q)=>{let{next:i}=O,a;tO.indexOf(i)>-1||i==x&&((a=O.peek(1))==x||a==SO)||i!=_&&i!=rO&&i!=-1&&!Q.context&&Q.canShift(y)&&O.acceptToken(y)},{contextual:!0}),XO=new m((O,Q)=>{let{next:i}=O;if((i==PO||i==nO)&&(O.advance(),i==O.next)){O.advance();let a=!Q.context&&Q.canShift(j);O.acceptToken(a?j:QO)}},{contextual:!0}),cO=z({"get set async static":e.modifier,"for while do if else switch try catch finally return throw break continue default case":e.controlKeyword,"in of await yield void typeof delete instanceof":e.operatorKeyword,"let var const function class extends":e.definitionKeyword,"import export from":e.moduleKeyword,"with debugger as new":e.keyword,TemplateString:e.special(e.string),super:e.atom,BooleanLiteral:e.bool,this:e.self,null:e.null,Star:e.modifier,VariableName:e.variableName,"CallExpression/VariableName TaggedTemplateExpression/VariableName":e.function(e.variableName),VariableDefinition:e.definition(e.variableName),Label:e.labelName,PropertyName:e.propertyName,PrivatePropertyName:e.special(e.propertyName),"CallExpression/MemberExpression/PropertyName":e.function(e.propertyName),"FunctionDeclaration/VariableDefinition":e.function(e.definition(e.variableName)),"ClassDeclaration/VariableDefinition":e.definition(e.className),PropertyDefinition:e.definition(e.propertyName),PrivatePropertyDefinition:e.definition(e.special(e.propertyName)),UpdateOp:e.updateOperator,LineComment:e.lineComment,BlockComment:e.blockComment,Number:e.number,String:e.string,Escape:e.escape,ArithOp:e.arithmeticOperator,LogicOp:e.logicOperator,BitOp:e.bitwiseOperator,CompareOp:e.compareOperator,RegExp:e.regexp,Equals:e.definitionOperator,Arrow:e.function(e.punctuation),": Spread":e.punctuation,"( )":e.paren,"[ ]":e.squareBracket,"{ }":e.brace,"InterpolationStart InterpolationEnd":e.special(e.brace),".":e.derefOperator,", ;":e.separator,"@":e.meta,TypeName:e.typeName,TypeDefinition:e.definition(e.typeName),"type enum interface implements namespace module declare":e.definitionKeyword,"abstract global Privacy readonly override":e.modifier,"is keyof unique infer":e.operatorKeyword,JSXAttributeValue:e.attributeValue,JSXText:e.content,"JSXStartTag JSXStartCloseTag JSXSelfCloseEndTag JSXEndTag":e.angleBracket,"JSXIdentifier JSXNameSpacedName":e.tagName,"JSXAttribute/JSXIdentifier JSXAttribute/JSXNameSpacedName":e.attributeName,"JSXBuiltin/JSXIdentifier":e.standard(e.tagName)}),sO={__proto__:null,export:14,as:19,from:27,default:30,async:35,function:36,extends:46,this:50,true:58,false:58,null:70,void:74,typeof:78,super:96,new:130,delete:146,yield:155,await:159,class:164,public:219,private:219,protected:219,readonly:221,instanceof:240,satisfies:243,in:244,const:246,import:278,keyof:333,unique:337,infer:343,is:379,abstract:399,implements:401,type:403,let:406,var:408,interface:415,enum:419,namespace:425,module:427,declare:431,global:435,for:456,of:465,while:468,with:472,do:476,if:480,else:482,switch:486,case:492,try:498,catch:502,finally:506,return:510,throw:514,break:518,continue:522,debugger:526},pO={__proto__:null,async:117,get:119,set:121,public:181,private:181,protected:181,static:183,abstract:185,override:187,readonly:193,accessor:195,new:383},gO={__proto__:null,"<":137},YO=C.deserialize({version:14,states:"$BhO`QUOOO%QQUOOO'TQWOOP(_OSOOO*mQ(CjO'#CfO*tOpO'#CgO+SO!bO'#CgO+bO07`O'#DZO-sQUO'#DaO.TQUO'#DlO%QQUO'#DvO0[QUO'#EOOOQ(CY'#EW'#EWO0rQSO'#ETOOQO'#I_'#I_O0zQSO'#GjOOQO'#Eh'#EhO1VQSO'#EgO1[QSO'#EgO3^Q(CjO'#JbO5}Q(CjO'#JcO6kQSO'#FVO6pQ#tO'#FnOOQ(CY'#F_'#F_O6{O&jO'#F_O7ZQ,UO'#FuO8qQSO'#FtOOQ(CY'#Jc'#JcOOQ(CW'#Jb'#JbOOQQ'#J|'#J|O8vQSO'#IOO8{Q(C[O'#IPOOQQ'#JO'#JOOOQQ'#IT'#ITQ`QUOOO%QQUO'#DnO9TQUO'#DzO%QQUO'#D|O9[QSO'#GjO9aQ,UO'#ClO9oQSO'#EfO9zQSO'#EqO:PQ,UO'#F^O:nQSO'#GjO:sQSO'#GnO;OQSO'#GnO;^QSO'#GqO;^QSO'#GrO;^QSO'#GtO9[QSO'#GwO;}QSO'#GzO=`QSO'#CbO=pQSO'#HXO=xQSO'#H_O=xQSO'#HaO`QUO'#HcO=xQSO'#HeO=xQSO'#HhO=}QSO'#HnO>SQ(C]O'#HtO%QQUO'#HvO>_Q(C]O'#HxO>jQ(C]O'#HzO8{Q(C[O'#H|O>uQ(CjO'#CfO?wQWO'#DfQOQSOOO@_QSO'#EPO9aQ,UO'#EfO@jQSO'#EfO@uQ`O'#F^OOQQ'#Cd'#CdOOQ(CW'#Dk'#DkOOQ(CW'#Jf'#JfO%QQUO'#JfOBOQWO'#E_OOQ(CW'#E^'#E^OBYQ(C`O'#E_OBtQWO'#ESOOQO'#Ji'#JiOCYQWO'#ESOCgQWO'#E_OC}QWO'#EeODQQWO'#E_O@}QWO'#E_OBtQWO'#E_PDkO?MpO'#C`POOO)CDm)CDmOOOO'#IU'#IUODvOpO,59ROOQ(CY,59R,59ROOOO'#IV'#IVOEUO!bO,59RO%QQUO'#D]OOOO'#IX'#IXOEdO07`O,59uOOQ(CY,59u,59uOErQUO'#IYOFVQSO'#JdOHXQbO'#JdO+pQUO'#JdOH`QSO,59{OHvQSO'#EhOITQSO'#JqOI`QSO'#JpOI`QSO'#JpOIhQSO,5;UOImQSO'#JoOOQ(CY,5:W,5:WOItQUO,5:WOKuQ(CjO,5:bOLfQSO,5:jOLkQSO'#JmOMeQ(C[O'#JnO:sQSO'#JmOMlQSO'#JmOMtQSO,5;TOMyQSO'#JmOOQ(CY'#Cf'#CfO%QQUO'#EOONmQ`O,5:oOOQO'#Jj'#JjOOQO-E<]-E<]O9[QSO,5=UO! TQSO,5=UO! YQUO,5;RO!#]Q,UO'#EcO!$pQSO,5;RO!&YQ,UO'#DpO!&aQUO'#DuO!&kQWO,5;[O!&sQWO,5;[O%QQUO,5;[OOQQ'#E}'#E}OOQQ'#FP'#FPO%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]O%QQUO,5;]OOQQ'#FT'#FTO!'RQUO,5;nOOQ(CY,5;s,5;sOOQ(CY,5;t,5;tO!)UQSO,5;tOOQ(CY,5;u,5;uO%QQUO'#IeO!)^Q(C[O,5jOOQQ'#JW'#JWOOQQ,5>k,5>kOOQQ-EgQWO'#EkOOQ(CW'#Jo'#JoO!>nQ(C[O'#J}O8{Q(C[O,5=YO;^QSO,5=`OOQO'#Cr'#CrO!>yQWO,5=]O!?RQ,UO,5=^O!?^QSO,5=`O!?cQ`O,5=cO=}QSO'#G|O9[QSO'#HOO!?kQSO'#HOO9aQ,UO'#HRO!?pQSO'#HROOQQ,5=f,5=fO!?uQSO'#HSO!?}QSO'#ClO!@SQSO,58|O!@^QSO,58|O!BfQUO,58|OOQQ,58|,58|O!BsQ(C[O,58|O%QQUO,58|O!COQUO'#HZOOQQ'#H['#H[OOQQ'#H]'#H]O`QUO,5=sO!C`QSO,5=sO`QUO,5=yO`QUO,5={O!CeQSO,5=}O`QUO,5>PO!CjQSO,5>SO!CoQUO,5>YOOQQ,5>`,5>`O%QQUO,5>`O8{Q(C[O,5>bOOQQ,5>d,5>dO!GvQSO,5>dOOQQ,5>f,5>fO!GvQSO,5>fOOQQ,5>h,5>hO!G{QWO'#DXO%QQUO'#JfO!HjQWO'#JfO!IXQWO'#DgO!IjQWO'#DgO!K{QUO'#DgO!LSQSO'#JeO!L[QSO,5:QO!LaQSO'#ElO!LoQSO'#JrO!LwQSO,5;VO!L|QWO'#DgO!MZQWO'#EROOQ(CY,5:k,5:kO%QQUO,5:kO!MbQSO,5:kO=}QSO,5;QO!;xQWO,5;QO!tO+pQUO,5>tOOQO,5>z,5>zO#$vQUO'#IYOOQO-EtO$8XQSO1G5jO$8aQSO1G5vO$8iQbO1G5wO:sQSO,5>zO$8sQSO1G5sO$8sQSO1G5sO:sQSO1G5sO$8{Q(CjO1G5tO%QQUO1G5tO$9]Q(C[O1G5tO$9nQSO,5>|O:sQSO,5>|OOQO,5>|,5>|O$:SQSO,5>|OOQO-E<`-E<`OOQO1G0]1G0]OOQO1G0_1G0_O!)XQSO1G0_OOQQ7+([7+([O!#]Q,UO7+([O%QQUO7+([O$:bQSO7+([O$:mQ,UO7+([O$:{Q(CjO,59nO$=TQ(CjO,5UOOQQ,5>U,5>UO%QQUO'#HkO%&qQSO'#HmOOQQ,5>[,5>[O:sQSO,5>[OOQQ,5>^,5>^OOQQ7+)`7+)`OOQQ7+)f7+)fOOQQ7+)j7+)jOOQQ7+)l7+)lO%&vQWO1G5lO%'[Q$IUO1G0rO%'fQSO1G0rOOQO1G/m1G/mO%'qQ$IUO1G/mO=}QSO1G/mO!'RQUO'#DgOOQO,5>u,5>uOOQO-E{,5>{OOQO-E<_-E<_O!;xQWO1G/mOOQO-E<[-E<[OOQ(CY1G0X1G0XOOQ(CY7+%q7+%qO!MeQSO7+%qOOQ(CY7+&W7+&WO=}QSO7+&WO!;xQWO7+&WOOQO7+%t7+%tO$7kQ(CjO7+&POOQO7+&P7+&PO%QQUO7+&PO%'{Q(C[O7+&PO=}QSO7+%tO!;xQWO7+%tO%(WQ(C[O7+&POBtQWO7+%tO%(fQ(C[O7+&PO%(zQ(C`O7+&PO%)UQWO7+%tOBtQWO7+&PO%)cQWO7+&PO%)yQSO7++_O%)yQSO7++_O%*RQ(CjO7++`O%QQUO7++`OOQO1G4h1G4hO:sQSO1G4hO%*cQSO1G4hOOQO7+%y7+%yO!MeQSO<vOOQO-EwO%QQUO,5>wOOQO-ESQ$IUO1G0wO%>ZQ$IUO1G0wO%@RQ$IUO1G0wO%@fQ(CjO<VOOQQ,5>X,5>XO&#WQSO1G3vO:sQSO7+&^O!'RQUO7+&^OOQO7+%X7+%XO&#]Q$IUO1G5wO=}QSO7+%XOOQ(CY<zAN>zO%QQUOAN?VO=}QSOAN>zO&<^Q(C[OAN?VO!;xQWOAN>zO&zO&RO!V+iO^(qX'j(qX~O#W+mO'|%OO~Og+pO!X$yO'|%OO~O!X+rO~Oy+tO!XXO~O!t+yO~Ob,OO~O's#jO!W(sP~Ob%lO~O%a!OO's%|O~PRO!V,yO!W(fa~O!W2SO~P'TO^%^O#W2]O'j%^O~O^%^O!a#rO#W2]O'j%^O~O^%^O!a#rO!h%ZO!l2aO#W2]O'j%^O'|%OO(`'dO~O!]2bO!^2bO't!iO~PBtO![2eO!]2bO!^2bO#S2fO#T2fO't!iO~PBtO![2eO!]2bO!^2bO#P2gO#S2fO#T2fO't!iO~PBtO^%^O!a#rO!l2aO#W2]O'j%^O(`'dO~O^%^O'j%^O~P!3jO!V$^Oo$ja~O!S&|i!V&|i~P!3jO!V'xO!S(Wi~O!V(PO!S(di~O!S(ei!V(ei~P!3jO!V(]O!g(ai~O!V(bi!g(bi^(bi'j(bi~P!3jO#W2kO!V(bi!g(bi^(bi'j(bi~O|%vO!X%wO!x]O#a2nO#b2mO's%eO~O|%vO!X%wO#b2mO's%eO~Og2uO!X'QO%`2tO~Og2uO!X'QO%`2tO'|%OO~O#cvaPvaXva^vakva!eva!fva!hva!lva#fva#gva#hva#iva#jva#kva#lva#mva#nva#pva#rva#tva#uva'jva(Qva(`va!gva!Sva'hvaova!Xva%`va!ava~P#M{O#c$kaP$kaX$ka^$kak$kaz$ka!e$ka!f$ka!h$ka!l$ka#f$ka#g$ka#h$ka#i$ka#j$ka#k$ka#l$ka#m$ka#n$ka#p$ka#r$ka#t$ka#u$ka'j$ka(Q$ka(`$ka!g$ka!S$ka'h$kao$ka!X$ka%`$ka!a$ka~P#NqO#c$maP$maX$ma^$mak$maz$ma!e$ma!f$ma!h$ma!l$ma#f$ma#g$ma#h$ma#i$ma#j$ma#k$ma#l$ma#m$ma#n$ma#p$ma#r$ma#t$ma#u$ma'j$ma(Q$ma(`$ma!g$ma!S$ma'h$mao$ma!X$ma%`$ma!a$ma~P$ dO#c${aP${aX${a^${ak${az${a!V${a!e${a!f${a!h${a!l${a#f${a#g${a#h${a#i${a#j${a#k${a#l${a#m${a#n${a#p${a#r${a#t${a#u${a'j${a(Q${a(`${a!g${a!S${a'h${a#W${ao${a!X${a%`${a!a${a~P#(yO^#Zq!V#Zq'j#Zq'h#Zq!S#Zq!g#Zqo#Zq!X#Zq%`#Zq!a#Zq~P!3jOd'OX!V'OX~P!$uO!V._Od(Za~O!U2}O!V'PX!g'PX~P%QO!V.bO!g([a~O!V.bO!g([a~P!3jO!S3QO~O#x!ja!W!ja~PI{O#x!ba!V!ba!W!ba~P#?dO#x!na!W!na~P!6TO#x!pa!W!pa~P!8nO!X3dO$TfO$^3eO~O!W3iO~Oo3jO~P#(yO^$gq!V$gq'j$gq'h$gq!S$gq!g$gqo$gq!X$gq%`$gq!a$gq~P!3jO!S3kO~Ol.}O'uTO'xUO~Oy)sO|)tO(h)xOg%Wi(g%Wi!V%Wi#W%Wi~Od%Wi#x%Wi~P$HbOy)sO|)tOg%Yi(g%Yi(h%Yi!V%Yi#W%Yi~Od%Yi#x%Yi~P$ITO(`$WO~P#(yO!U3nO's%eO!V'YX!g'YX~O!V/VO!g(ma~O!V/VO!a#rO!g(ma~O!V/VO!a#rO(`'dO!g(ma~Od$ti!V$ti#W$ti#x$ti~P!-jO!U3vO's*UO!S'[X!V'[X~P!.XO!V/_O!S(na~O!V/_O!S(na~P#(yO!a#rO~O!a#rO#n4OO~Ok4RO!a#rO(`'dO~Od(Oi!V(Oi~P!-jO#W4UOd(Oi!V(Oi~P!-jO!g4XO~O^$hq!V$hq'j$hq'h$hq!S$hq!g$hqo$hq!X$hq%`$hq!a$hq~P!3jO!V4]O!X(oX~P#(yO!f#tO~P3zO!X$rX%TYX^$rX!V$rX'j$rX~P!,aO%T4_OghXyhX|hX!XhX(ghX(hhX^hX!VhX'jhX~O%T4_O~O%a4fO's+WO'uTO'xUO!V'eX!W'eX~O!V0_O!W(ua~OX4jO~O]4kO~O!S4oO~O^%^O'j%^O~P#(yO!X$yO~P#(yO!V4tO#W4vO!W(rX~O!W4wO~Ol!kO|4yO![5WO!]4}O!^4}O!x;oO!|5VO!}5UO#O5UO#P5TO#S5SO#T!wO't!iO'uTO'xUO(T!jO(_!nO~O!W5RO~P%#XOg5]O!X0zO%`5[O~Og5]O!X0zO%`5[O'|%OO~O's#jO!V'dX!W'dX~O!V1VO!W(sa~O'uTO'xUO(T5fO~O]5jO~O!g5mO~P%QO^5oO~O^5oO~P%QO#n5qO&Q5rO~PMPO_1mO!W5vO&`1lO~P`O!a5xO~O!a5zO!V(Yi!W(Yi!a(Yi!h(Yi'|(Yi~O!V#`i!W#`i~P#?dO#W5{O!V#`i!W#`i~O!V!Zi!W!Zi~P#?dO^%^O#W6UO'j%^O~O^%^O!a#rO#W6UO'j%^O~O^%^O!a#rO!l6ZO#W6UO'j%^O(`'dO~O!h%ZO'|%OO~P%(fO!]6[O!^6[O't!iO~PBtO![6_O!]6[O!^6[O#S6`O#T6`O't!iO~PBtO!V(]O!g(aq~O!V(bq!g(bq^(bq'j(bq~P!3jO|%vO!X%wO#b6dO's%eO~O!X'QO%`6gO~Og6jO!X'QO%`6gO~O#c%WiP%WiX%Wi^%Wik%Wiz%Wi!e%Wi!f%Wi!h%Wi!l%Wi#f%Wi#g%Wi#h%Wi#i%Wi#j%Wi#k%Wi#l%Wi#m%Wi#n%Wi#p%Wi#r%Wi#t%Wi#u%Wi'j%Wi(Q%Wi(`%Wi!g%Wi!S%Wi'h%Wio%Wi!X%Wi%`%Wi!a%Wi~P$HbO#c%YiP%YiX%Yi^%Yik%Yiz%Yi!e%Yi!f%Yi!h%Yi!l%Yi#f%Yi#g%Yi#h%Yi#i%Yi#j%Yi#k%Yi#l%Yi#m%Yi#n%Yi#p%Yi#r%Yi#t%Yi#u%Yi'j%Yi(Q%Yi(`%Yi!g%Yi!S%Yi'h%Yio%Yi!X%Yi%`%Yi!a%Yi~P$ITO#c$tiP$tiX$ti^$tik$tiz$ti!V$ti!e$ti!f$ti!h$ti!l$ti#f$ti#g$ti#h$ti#i$ti#j$ti#k$ti#l$ti#m$ti#n$ti#p$ti#r$ti#t$ti#u$ti'j$ti(Q$ti(`$ti!g$ti!S$ti'h$ti#W$tio$ti!X$ti%`$ti!a$ti~P#(yOd'Oa!V'Oa~P!-jO!V'Pa!g'Pa~P!3jO!V.bO!g([i~O#x#Zi!V#Zi!W#Zi~P#?dOP$YOy#vOz#wO|#xO!f#tO!h#uO!l$YO(QVOX#eik#ei!e#ei#g#ei#h#ei#i#ei#j#ei#k#ei#l#ei#m#ei#n#ei#p#ei#r#ei#t#ei#u#ei#x#ei(`#ei(g#ei(h#ei!V#ei!W#ei~O#f#ei~P%2xO#f;wO~P%2xOP$YOy#vOz#wO|#xO!f#tO!h#uO!l$YO#f;wO#g;xO#h;xO#i;xO(QVOX#ei!e#ei#j#ei#k#ei#l#ei#m#ei#n#ei#p#ei#r#ei#t#ei#u#ei#x#ei(`#ei(g#ei(h#ei!V#ei!W#ei~Ok#ei~P%5TOk;yO~P%5TOP$YOk;yOy#vOz#wO|#xO!f#tO!h#uO!l$YO#f;wO#g;xO#h;xO#i;xO#j;zO(QVO#p#ei#r#ei#t#ei#u#ei#x#ei(`#ei(g#ei(h#ei!V#ei!W#ei~OX#ei!e#ei#k#ei#l#ei#m#ei#n#ei~P%7`OXbO^#vy!V#vy'j#vy'h#vy!S#vy!g#vyo#vy!X#vy%`#vy!a#vy~P!3jOg=jOy)sO|)tO(g)vO(h)xO~OP#eiX#eik#eiz#ei!e#ei!f#ei!h#ei!l#ei#f#ei#g#ei#h#ei#i#ei#j#ei#k#ei#l#ei#m#ei#n#ei#p#ei#r#ei#t#ei#u#ei#x#ei(Q#ei(`#ei!V#ei!W#ei~P%AYO!f#tOP(PXX(PXg(PXk(PXy(PXz(PX|(PX!e(PX!h(PX!l(PX#f(PX#g(PX#h(PX#i(PX#j(PX#k(PX#l(PX#m(PX#n(PX#p(PX#r(PX#t(PX#u(PX#x(PX(Q(PX(`(PX(g(PX(h(PX!V(PX!W(PX~O#x#yi!V#yi!W#yi~P#?dO#x!ni!W!ni~P$!qO!W6vO~O!V'Xa!W'Xa~P#?dO!a#rO(`'dO!V'Ya!g'Ya~O!V/VO!g(mi~O!V/VO!a#rO!g(mi~Od$tq!V$tq#W$tq#x$tq~P!-jO!S'[a!V'[a~P#(yO!a6}O~O!V/_O!S(ni~P#(yO!V/_O!S(ni~O!S7RO~O!a#rO#n7WO~Ok7XO!a#rO(`'dO~O!S7ZO~Od$vq!V$vq#W$vq#x$vq~P!-jO^$hy!V$hy'j$hy'h$hy!S$hy!g$hyo$hy!X$hy%`$hy!a$hy~P!3jO!V4]O!X(oa~O^#Zy!V#Zy'j#Zy'h#Zy!S#Zy!g#Zyo#Zy!X#Zy%`#Zy!a#Zy~P!3jOX7`O~O!V0_O!W(ui~O]7fO~O!a5zO~O(T(qO!V'aX!W'aX~O!V4tO!W(ra~O!h%ZO'|%OO^(YX!a(YX!l(YX#W(YX'j(YX(`(YX~O's7oO~P.[O!x;oO!|7rO!}7qO#O7qO#P7pO#S'bO#T'bO~PBtO^%^O!a#rO!l'hO#W'fO'j%^O(`'dO~O!W7vO~P%#XOl!kO'uTO'xUO(T!jO(_!nO~O|7wO~P%MdO![7{O!]7zO!^7zO#P7pO#S'bO#T'bO't!iO~PBtO![7{O!]7zO!^7zO!}7|O#O7|O#P7pO#S'bO#T'bO't!iO~PBtO!]7zO!^7zO't!iO(T!jO(_!nO~O!X0zO~O!X0zO%`8OO~Og8RO!X0zO%`8OO~OX8WO!V'da!W'da~O!V1VO!W(si~O!g8[O~O!g8]O~O!g8^O~O!g8^O~P%QO^8`O~O!a8cO~O!g8dO~O!V(ei!W(ei~P#?dO^%^O#W8lO'j%^O~O^%^O!a#rO#W8lO'j%^O~O^%^O!a#rO!l8pO#W8lO'j%^O(`'dO~O!h%ZO'|%OO~P&$QO!]8qO!^8qO't!iO~PBtO!V(]O!g(ay~O!V(by!g(by^(by'j(by~P!3jO!X'QO%`8uO~O#c$tqP$tqX$tq^$tqk$tqz$tq!V$tq!e$tq!f$tq!h$tq!l$tq#f$tq#g$tq#h$tq#i$tq#j$tq#k$tq#l$tq#m$tq#n$tq#p$tq#r$tq#t$tq#u$tq'j$tq(Q$tq(`$tq!g$tq!S$tq'h$tq#W$tqo$tq!X$tq%`$tq!a$tq~P#(yO#c$vqP$vqX$vq^$vqk$vqz$vq!V$vq!e$vq!f$vq!h$vq!l$vq#f$vq#g$vq#h$vq#i$vq#j$vq#k$vq#l$vq#m$vq#n$vq#p$vq#r$vq#t$vq#u$vq'j$vq(Q$vq(`$vq!g$vq!S$vq'h$vq#W$vqo$vq!X$vq%`$vq!a$vq~P#(yO!V'Pi!g'Pi~P!3jO#x#Zq!V#Zq!W#Zq~P#?dOy/yOz/yO|/zOPvaXvagvakva!eva!fva!hva!lva#fva#gva#hva#iva#jva#kva#lva#mva#nva#pva#rva#tva#uva#xva(Qva(`va(gva(hva!Vva!Wva~Oy)sO|)tOP$kaX$kag$kak$kaz$ka!e$ka!f$ka!h$ka!l$ka#f$ka#g$ka#h$ka#i$ka#j$ka#k$ka#l$ka#m$ka#n$ka#p$ka#r$ka#t$ka#u$ka#x$ka(Q$ka(`$ka(g$ka(h$ka!V$ka!W$ka~Oy)sO|)tOP$maX$mag$mak$maz$ma!e$ma!f$ma!h$ma!l$ma#f$ma#g$ma#h$ma#i$ma#j$ma#k$ma#l$ma#m$ma#n$ma#p$ma#r$ma#t$ma#u$ma#x$ma(Q$ma(`$ma(g$ma(h$ma!V$ma!W$ma~OP${aX${ak${az${a!e${a!f${a!h${a!l${a#f${a#g${a#h${a#i${a#j${a#k${a#l${a#m${a#n${a#p${a#r${a#t${a#u${a#x${a(Q${a(`${a!V${a!W${a~P%AYO#x$gq!V$gq!W$gq~P#?dO#x$hq!V$hq!W$hq~P#?dO!W9PO~O#x9QO~P!-jO!a#rO!V'Yi!g'Yi~O!a#rO(`'dO!V'Yi!g'Yi~O!V/VO!g(mq~O!S'[i!V'[i~P#(yO!V/_O!S(nq~O!S9WO~P#(yO!S9WO~Od(Oy!V(Oy~P!-jO!V'_a!X'_a~P#(yO!X%Sq^%Sq!V%Sq'j%Sq~P#(yOX9]O~O!V0_O!W(uq~O#W9aO!V'aa!W'aa~O!V4tO!W(ri~P#?dOPYXXYXkYXyYXzYX|YX!SYX!VYX!eYX!fYX!hYX!lYX#WYX#ccX#fYX#gYX#hYX#iYX#jYX#kYX#lYX#mYX#nYX#pYX#rYX#tYX#uYX#zYX(QYX(`YX(gYX(hYX~O!a%QX#n%QX~P&6lO#S-cO#T-cO~PBtO#P9eO#S-cO#T-cO~PBtO!}9fO#O9fO#P9eO#S-cO#T-cO~PBtO!]9iO!^9iO't!iO(T!jO(_!nO~O![9lO!]9iO!^9iO#P9eO#S-cO#T-cO't!iO~PBtO!X0zO%`9oO~O'uTO'xUO(T9tO~O!V1VO!W(sq~O!g9wO~O!g9wO~P%QO!g9yO~O!g9zO~O#W9|O!V#`y!W#`y~O!V#`y!W#`y~P#?dO^%^O#W:QO'j%^O~O^%^O!a#rO#W:QO'j%^O~O^%^O!a#rO!l:UO#W:QO'j%^O(`'dO~O!X'QO%`:XO~O#x#vy!V#vy!W#vy~P#?dOP$tiX$tik$tiz$ti!e$ti!f$ti!h$ti!l$ti#f$ti#g$ti#h$ti#i$ti#j$ti#k$ti#l$ti#m$ti#n$ti#p$ti#r$ti#t$ti#u$ti#x$ti(Q$ti(`$ti!V$ti!W$ti~P%AYOy)sO|)tO(h)xOP%WiX%Wig%Wik%Wiz%Wi!e%Wi!f%Wi!h%Wi!l%Wi#f%Wi#g%Wi#h%Wi#i%Wi#j%Wi#k%Wi#l%Wi#m%Wi#n%Wi#p%Wi#r%Wi#t%Wi#u%Wi#x%Wi(Q%Wi(`%Wi(g%Wi!V%Wi!W%Wi~Oy)sO|)tOP%YiX%Yig%Yik%Yiz%Yi!e%Yi!f%Yi!h%Yi!l%Yi#f%Yi#g%Yi#h%Yi#i%Yi#j%Yi#k%Yi#l%Yi#m%Yi#n%Yi#p%Yi#r%Yi#t%Yi#u%Yi#x%Yi(Q%Yi(`%Yi(g%Yi(h%Yi!V%Yi!W%Yi~O#x$hy!V$hy!W$hy~P#?dO#x#Zy!V#Zy!W#Zy~P#?dO!a#rO!V'Yq!g'Yq~O!V/VO!g(my~O!S'[q!V'[q~P#(yO!S:`O~P#(yO!V0_O!W(uy~O!V4tO!W(rq~O#S2fO#T2fO~PBtO#P:gO#S2fO#T2fO~PBtO!]:kO!^:kO't!iO(T!jO(_!nO~O!X0zO%`:nO~O!g:qO~O^%^O#W:vO'j%^O~O^%^O!a#rO#W:vO'j%^O~O!X'QO%`:{O~OP$tqX$tqk$tqz$tq!e$tq!f$tq!h$tq!l$tq#f$tq#g$tq#h$tq#i$tq#j$tq#k$tq#l$tq#m$tq#n$tq#p$tq#r$tq#t$tq#u$tq#x$tq(Q$tq(`$tq!V$tq!W$tq~P%AYOP$vqX$vqk$vqz$vq!e$vq!f$vq!h$vq!l$vq#f$vq#g$vq#h$vq#i$vq#j$vq#k$vq#l$vq#m$vq#n$vq#p$vq#r$vq#t$vq#u$vq#x$vq(Q$vq(`$vq!V$vq!W$vq~P%AYOd%[!Z!V%[!Z#W%[!Z#x%[!Z~P!-jO!V'aq!W'aq~P#?dO#S6`O#T6`O~PBtO!V#`!Z!W#`!Z~P#?dO^%^O#W;ZO'j%^O~O#c%[!ZP%[!ZX%[!Z^%[!Zk%[!Zz%[!Z!V%[!Z!e%[!Z!f%[!Z!h%[!Z!l%[!Z#f%[!Z#g%[!Z#h%[!Z#i%[!Z#j%[!Z#k%[!Z#l%[!Z#m%[!Z#n%[!Z#p%[!Z#r%[!Z#t%[!Z#u%[!Z'j%[!Z(Q%[!Z(`%[!Z!g%[!Z!S%[!Z'h%[!Z#W%[!Zo%[!Z!X%[!Z%`%[!Z!a%[!Z~P#(yOP%[!ZX%[!Zk%[!Zz%[!Z!e%[!Z!f%[!Z!h%[!Z!l%[!Z#f%[!Z#g%[!Z#h%[!Z#i%[!Z#j%[!Z#k%[!Z#l%[!Z#m%[!Z#n%[!Z#p%[!Z#r%[!Z#t%[!Z#u%[!Z#x%[!Z(Q%[!Z(`%[!Z!V%[!Z!W%[!Z~P%AYOo(UX~P1dO't!iO~P!'RO!ScX!VcX#WcX~P&6lOPYXXYXkYXyYXzYX|YX!VYX!VcX!eYX!fYX!hYX!lYX#WYX#WcX#ccX#fYX#gYX#hYX#iYX#jYX#kYX#lYX#mYX#nYX#pYX#rYX#tYX#uYX#zYX(QYX(`YX(gYX(hYX~O!acX!gYX!gcX(`cX~P'!sOP;nOQ;nOa=_Ob!fOikOk;nOlkOmkOskOu;nOw;nO|WO!QkO!RkO!XXO!c;qO!hZO!k;nO!l;nO!m;nO!o;rO!q;sO!t!eO$P!hO$TfO's)RO'uTO'xUO(QVO(_[O(l=]O~O!Vv!>v!BnPPP!BuHdPPPPPPPPPPP!FTP!GiPPHd!HyPHdPHdHdHdHdPHd!J`PP!MiP#!nP#!r#!|##Q##QP!MfP##U##UP#&ZP#&_HdHd#&e#)iAQPAQPAQAQP#*sAQAQ#,mAQ#.zAQ#0nAQAQ#1[#3W#3W#3[#3d#3W#3lP#3WPAQ#4hAQ#5pAQAQ6iPPP#6{PP#7e#7eP#7eP#7z#7ePP#8QP#7wP#7w#8d!1p#7w#9O#9U6f(}#9X(}P#9`#9`#9`P(}P(}P(}P(}PP(}P#9f#9iP#9i(}P#9mP#9pP(}P(}P(}P(}P(}P(}(}PP#9v#9|#:W#:^#:d#:j#:p#;O#;U#;[#;f#;l#b#?r#@Q#@W#@^#@d#@j#@t#@z#AQ#A[#An#AtPPPPPPPPPP#AzPPPPPPP#Bn#FYP#Gu#G|#HUPPPP#L`$ U$'t$'w$'z$)w$)z$)}$*UPP$*[$*`$+X$,X$,]$,qPP$,u$,{$-PP$-S$-W$-Z$.P$.g$.l$.o$.r$.x$.{$/P$/TR!yRmpOXr!X#a%]&d&f&g&i,^,c1g1jU!pQ'Q-OQ%ctQ%kwQ%rzQ&[!TS&x!c,vQ'W!f[']!m!r!s!t!u!vS*[$y*aQ+U%lQ+c%tQ+}&UQ,|'PQ-W'XW-`'^'_'`'aQ/p*cQ1U,OU2b-b-d-eS4}0z5QS6[2e2gU7z5U5V5WQ8q6_S9i7{7|Q:k9lR TypeParamList TypeDefinition extends ThisType this LiteralType ArithOp Number BooleanLiteral TemplateType InterpolationEnd Interpolation InterpolationStart NullType null VoidType void TypeofType typeof MemberExpression . ?. PropertyName [ TemplateString Escape Interpolation super RegExp ] ArrayExpression Spread , } { ObjectExpression Property async get set PropertyDefinition Block : NewExpression new TypeArgList CompareOp < ) ( ArgList UnaryExpression delete LogicOp BitOp YieldExpression yield AwaitExpression await ParenthesizedExpression ClassExpression class ClassBody MethodDeclaration Decorator @ MemberExpression PrivatePropertyName CallExpression Privacy static abstract override PrivatePropertyDefinition PropertyDeclaration readonly accessor Optional TypeAnnotation Equals StaticBlock FunctionExpression ArrowFunction ParamList ParamList ArrayPattern ObjectPattern PatternProperty Privacy readonly Arrow MemberExpression BinaryExpression ArithOp ArithOp ArithOp ArithOp BitOp CompareOp instanceof satisfies in const CompareOp BitOp BitOp BitOp LogicOp LogicOp ConditionalExpression LogicOp LogicOp AssignmentExpression UpdateOp PostfixExpression CallExpression TaggedTemplateExpression DynamicImport import ImportMeta JSXElement JSXSelfCloseEndTag JSXStartTag JSXSelfClosingTag JSXIdentifier JSXBuiltin JSXIdentifier JSXNamespacedName JSXMemberExpression JSXSpreadAttribute JSXAttribute JSXAttributeValue JSXEscape JSXEndTag JSXOpenTag JSXFragmentTag JSXText JSXEscape JSXStartCloseTag JSXCloseTag PrefixCast ArrowFunction TypeParamList SequenceExpression KeyofType keyof UniqueType unique ImportType InferredType infer TypeName ParenthesizedType FunctionSignature ParamList NewSignature IndexedType TupleType Label ArrayType ReadonlyType ObjectType MethodType PropertyType IndexSignature PropertyDefinition CallSignature TypePredicate is NewSignature new UnionType LogicOp IntersectionType LogicOp ConditionalType ParameterizedType ClassDeclaration abstract implements type VariableDeclaration let var TypeAliasDeclaration InterfaceDeclaration interface EnumDeclaration enum EnumBody NamespaceDeclaration namespace module AmbientDeclaration declare GlobalDeclaration global ClassDeclaration ClassBody MethodDeclaration AmbientFunctionDeclaration ExportGroup VariableName VariableName ImportDeclaration ImportGroup ForStatement for ForSpec ForInSpec ForOfSpec of WhileStatement while WithStatement with DoStatement do IfStatement if else SwitchStatement switch SwitchBody CaseLabel case DefaultLabel TryStatement try CatchClause catch FinallyClause finally ReturnStatement return ThrowStatement throw BreakStatement break ContinueStatement continue DebuggerStatement debugger LabeledStatement ExpressionStatement SingleExpression SingleClassItem",maxTerm:362,context:oO,nodeProps:[["group",-26,6,14,16,62,198,202,205,206,208,211,214,225,227,233,235,237,239,242,248,254,256,258,260,262,264,265,"Statement",-32,10,11,25,28,29,35,45,48,49,51,56,64,72,76,78,80,81,102,103,112,113,130,133,135,136,137,138,140,141,161,162,164,"Expression",-23,24,26,30,34,36,38,165,167,169,170,172,173,174,176,177,178,180,181,182,192,194,196,197,"Type",-3,84,95,101,"ClassItem"],["openedBy",31,"InterpolationStart",50,"[",54,"{",69,"(",142,"JSXStartTag",154,"JSXStartTag JSXStartCloseTag"],["closedBy",33,"InterpolationEnd",44,"]",55,"}",70,")",143,"JSXSelfCloseEndTag JSXEndTag",159,"JSXEndTag"]],propSources:[cO],skippedNodes:[0,3,4,268],repeatNodeCount:32,tokenData:"$>y(CSR!bOX%ZXY+gYZ-yZ[+g[]%Z]^.c^p%Zpq+gqr/mrs3cst:_tu>PuvBavwDxwxGgxyMvyz! Qz{!![{|!%O|}!&]}!O!%O!O!P!'g!P!Q!1w!Q!R#0t!R![#3T![!]#@T!]!^#Aa!^!_#Bk!_!`#GS!`!a#In!a!b#N{!b!c$$z!c!}>P!}#O$&U#O#P$'`#P#Q$,w#Q#R$.R#R#S>P#S#T$/`#T#o$0j#o#p$4z#p#q$5p#q#r$7Q#r#s$8^#s$f%Z$f$g+g$g#BY>P#BY#BZ$9h#BZ$IS>P$IS$I_$9h$I_$I|>P$I|$I}$P$JT$JU$9h$JU$KV>P$KV$KW$9h$KW&FU>P&FU&FV$9h&FV;'S>P;'S;=`BZ<%l?HT>P?HT?HU$9h?HUO>P(n%d_$c&j'vp'y!bOY%ZYZ&cZr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_#O%Z#O#P&c#P#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z&j&hT$c&jO!^&c!_#o&c#p;'S&c;'S;=`&w<%lO&c&j&zP;=`<%l&c'|'U]$c&j'y!bOY&}YZ&cZw&}wx&cx!^&}!^!_'}!_#O&}#O#P&c#P#o&}#o#p'}#p;'S&};'S;=`(l<%lO&}!b(SU'y!bOY'}Zw'}x#O'}#P;'S'};'S;=`(f<%lO'}!b(iP;=`<%l'}'|(oP;=`<%l&}'[(y]$c&j'vpOY(rYZ&cZr(rrs&cs!^(r!^!_)r!_#O(r#O#P&c#P#o(r#o#p)r#p;'S(r;'S;=`*a<%lO(rp)wU'vpOY)rZr)rs#O)r#P;'S)r;'S;=`*Z<%lO)rp*^P;=`<%l)r'[*dP;=`<%l(r#S*nX'vp'y!bOY*gZr*grs'}sw*gwx)rx#O*g#P;'S*g;'S;=`+Z<%lO*g#S+^P;=`<%l*g(n+dP;=`<%l%Z(CS+rq$c&j'vp'y!b'l(;dOX%ZXY+gYZ&cZ[+g[p%Zpq+gqr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_#O%Z#O#P&c#P#o%Z#o#p*g#p$f%Z$f$g+g$g#BY%Z#BY#BZ+g#BZ$IS%Z$IS$I_+g$I_$JT%Z$JT$JU+g$JU$KV%Z$KV$KW+g$KW&FU%Z&FU&FV+g&FV;'S%Z;'S;=`+a<%l?HT%Z?HT?HU+g?HUO%Z(CS.ST'w#S$c&j'm(;dO!^&c!_#o&c#p;'S&c;'S;=`&w<%lO&c(CS.n_$c&j'vp'y!b'm(;dOY%ZYZ&cZr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_#O%Z#O#P&c#P#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z%#`/x`$c&j!l$Ip'vp'y!bOY%ZYZ&cZr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_!`0z!`#O%Z#O#P&c#P#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z%#S1V`#p$Id$c&j'vp'y!bOY%ZYZ&cZr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_!`2X!`#O%Z#O#P&c#P#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z%#S2d_#p$Id$c&j'vp'y!bOY%ZYZ&cZr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_#O%Z#O#P&c#P#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z$2b3l_'u$(n$c&j'y!bOY4kYZ5qZr4krs7nsw4kwx5qx!^4k!^!_8p!_#O4k#O#P5q#P#o4k#o#p8p#p;'S4k;'S;=`:X<%lO4k*r4r_$c&j'y!bOY4kYZ5qZr4krs7nsw4kwx5qx!^4k!^!_8p!_#O4k#O#P5q#P#o4k#o#p8p#p;'S4k;'S;=`:X<%lO4k)`5vX$c&jOr5qrs6cs!^5q!^!_6y!_#o5q#o#p6y#p;'S5q;'S;=`7h<%lO5q)`6jT$^#t$c&jO!^&c!_#o&c#p;'S&c;'S;=`&w<%lO&c#t6|TOr6yrs7]s;'S6y;'S;=`7b<%lO6y#t7bO$^#t#t7eP;=`<%l6y)`7kP;=`<%l5q*r7w]$^#t$c&j'y!bOY&}YZ&cZw&}wx&cx!^&}!^!_'}!_#O&}#O#P&c#P#o&}#o#p'}#p;'S&};'S;=`(l<%lO&}%W8uZ'y!bOY8pYZ6yZr8prs9hsw8pwx6yx#O8p#O#P6y#P;'S8p;'S;=`:R<%lO8p%W9oU$^#t'y!bOY'}Zw'}x#O'}#P;'S'};'S;=`(f<%lO'}%W:UP;=`<%l8p*r:[P;=`<%l4k#%|:hg$c&j'vp'y!bOY%ZYZ&cZr%Zrs&}st%Ztu`k$c&j'vp'y!b(T!LY's&;d$V#tOY%ZYZ&cZr%Zrs&}st%Ztu>Puw%Zwx(rx}%Z}!O@T!O!Q%Z!Q![>P![!^%Z!^!_*g!_!c%Z!c!}>P!}#O%Z#O#P&c#P#R%Z#R#S>P#S#T%Z#T#o>P#o#p*g#p$g%Z$g;'S>P;'S;=`BZ<%lO>P+d@`k$c&j'vp'y!b$V#tOY%ZYZ&cZr%Zrs&}st%Ztu@Tuw%Zwx(rx}%Z}!O@T!O!Q%Z!Q![@T![!^%Z!^!_*g!_!c%Z!c!}@T!}#O%Z#O#P&c#P#R%Z#R#S@T#S#T%Z#T#o@T#o#p*g#p$g%Z$g;'S@T;'S;=`BT<%lO@T+dBWP;=`<%l@T(CSB^P;=`<%l>P%#SBl`$c&j'vp'y!b#h$IdOY%ZYZ&cZr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_!`Cn!`#O%Z#O#P&c#P#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z%#SCy_$c&j#z$Id'vp'y!bOY%ZYZ&cZr%Zrs&}sw%Zwx(rx!^%Z!^!_*g!_#O%Z#O#P&c#P#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z%DfETa(h%Z![!^%Z!^!_*g!_!c%Z!c!i#>Z!i#O%Z#O#P&c#P#R%Z#R#S#>Z#S#T%Z#T#Z#>Z#Z#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z$/l#>fi$c&j'vp'y!bl$'|OY%ZYZ&cZr%Zrs&}sw%Zwx(rx!Q%Z!Q![#>Z![!^%Z!^!_*g!_!c%Z!c!i#>Z!i#O%Z#O#P&c#P#R%Z#R#S#>Z#S#T%Z#T#Z#>Z#Z#b%Z#b#c#5T#c#o%Z#o#p*g#p;'S%Z;'S;=`+a<%lO%Z%Gh#@b_!a$b$c&j#x%Puw%Zwx(rx}%Z}!O@T!O!Q%Z!Q![>P![!^%Z!^!_*g!_!c%Z!c!}>P!}#O%Z#O#P&c#P#R%Z#R#S>P#S#T%Z#T#o>P#o#p*g#p$f%Z$f$g+g$g#BY>P#BY#BZ$9h#BZ$IS>P$IS$I_$9h$I_$JT>P$JT$JU$9h$JU$KV>P$KV$KW$9h$KW&FU>P&FU&FV$9h&FV;'S>P;'S;=`BZ<%l?HT>P?HT?HU$9h?HUO>P(CS$=Uk$c&j'vp'y!b'm(;d(T!LY's&;d$V#tOY%ZYZ&cZr%Zrs&}st%Ztu>Puw%Zwx(rx}%Z}!O@T!O!Q%Z!Q![>P![!^%Z!^!_*g!_!c%Z!c!}>P!}#O%Z#O#P&c#P#R%Z#R#S>P#S#T%Z#T#o>P#o#p*g#p$g%Z$g;'S>P;'S;=`BZ<%lO>P",tokenizers:[lO,XO,2,3,4,5,6,7,8,9,10,11,12,13,ZO,new u("$S~RRtu[#O#Pg#S#T#|~_P#o#pb~gOq~~jVO#i!P#i#j!U#j#l!P#l#m!q#m;'S!P;'S;=`#v<%lO!P~!UO!O~~!XS!Q![!e!c!i!e#T#Z!e#o#p#Z~!hR!Q![!q!c!i!q#T#Z!q~!tR!Q![!}!c!i!}#T#Z!}~#QR!Q![!P!c!i!P#T#Z!P~#^R!Q![#g!c!i#g#T#Z#g~#jS!Q![#g!c!i#g#T#Z#g#q#r!P~#yP;=`<%l!P~$RO(S~~",141,325),new u("j~RQYZXz{^~^O'p~~aP!P!Qd~iO'q~~",25,307)],topRules:{Script:[0,5],SingleExpression:[1,266],SingleClassItem:[2,267]},dialects:{jsx:13213,ts:13215},dynamicPrecedences:{76:1,78:1,162:1,190:1},specialized:[{term:311,get:O=>sO[O]||-1},{term:327,get:O=>pO[O]||-1},{term:67,get:O=>gO[O]||-1}],tokenPrec:13238}),bO=[n("function ${name}(${params}) {\n ${}\n}",{label:"function",detail:"definition",type:"keyword"}),n("for (let ${index} = 0; ${index} < ${bound}; ${index}++) {\n ${}\n}",{label:"for",detail:"loop",type:"keyword"}),n("for (let ${name} of ${collection}) {\n ${}\n}",{label:"for",detail:"of loop",type:"keyword"}),n("do {\n ${}\n} while (${})",{label:"do",detail:"loop",type:"keyword"}),n("while (${}) {\n ${}\n}",{label:"while",detail:"loop",type:"keyword"}),n(`try {
- \${}
-} catch (\${error}) {
- \${}
-}`,{label:"try",detail:"/ catch block",type:"keyword"}),n("if (${}) {\n ${}\n}",{label:"if",detail:"block",type:"keyword"}),n(`if (\${}) {
- \${}
-} else {
- \${}
-}`,{label:"if",detail:"/ else block",type:"keyword"}),n(`class \${name} {
- constructor(\${params}) {
- \${}
- }
-}`,{label:"class",detail:"definition",type:"keyword"}),n('import {${names}} from "${module}"\n${}',{label:"import",detail:"named",type:"keyword"}),n('import ${name} from "${module}"\n${}',{label:"import",detail:"default",type:"keyword"})],v=new OO,G=new Set(["Script","Block","FunctionExpression","FunctionDeclaration","ArrowFunction","MethodDeclaration","ForStatement"]);function c(O){return(Q,i)=>{let a=Q.node.getChild("VariableDefinition");return a&&i(a,O),!0}}const hO=["FunctionDeclaration"],mO={FunctionDeclaration:c("function"),ClassDeclaration:c("class"),ClassExpression:()=>!0,EnumDeclaration:c("constant"),TypeAliasDeclaration:c("type"),NamespaceDeclaration:c("namespace"),VariableDefinition(O,Q){O.matchContext(hO)||Q(O,"variable")},TypeDefinition(O,Q){Q(O,"type")},__proto__:null};function q(O,Q){let i=v.get(Q);if(i)return i;let a=[],$=!0;function t(r,S){let o=O.sliceString(r.from,r.to);a.push({label:o,type:S})}return Q.cursor(M.IncludeAnonymous).iterate(r=>{if($)$=!1;else if(r.name){let S=mO[r.name];if(S&&S(r,t)||G.has(r.name))return!1}else if(r.to-r.from>8192){for(let S of q(O,r.node))a.push(S);return!1}}),v.set(Q,a),a}const g=/^[\w$\xa1-\uffff][\w$\d\xa1-\uffff]*$/,U=["TemplateString","String","RegExp","LineComment","BlockComment","VariableDefinition","TypeDefinition","Label","PropertyDefinition","PropertyName","PrivatePropertyDefinition","PrivatePropertyName"];function WO(O){let Q=W(O.state).resolveInner(O.pos,-1);if(U.indexOf(Q.name)>-1)return null;let i=Q.name=="VariableName"||Q.to-Q.from<20&&g.test(O.state.sliceDoc(Q.from,Q.to));if(!i&&!O.explicit)return null;let a=[];for(let $=Q;$;$=$.parent)G.has($.name)&&(a=a.concat(q(O.state.doc,$)));return{options:a,from:i?Q.from:O.pos,validFor:g}}function h(O,Q,i){var a;let $=[];for(;;){let t=Q.firstChild,r;if(t?.name=="VariableName")return $.push(O(t)),{path:$.reverse(),name:i};if(t?.name=="MemberExpression"&&((a=r=t.lastChild)===null||a===void 0?void 0:a.name)=="PropertyName")$.push(O(r)),Q=t;else return null}}function UO(O){let Q=a=>O.state.doc.sliceString(a.from,a.to),i=W(O.state).resolveInner(O.pos,-1);return i.name=="PropertyName"?h(Q,i.parent,Q(i)):U.indexOf(i.name)>-1?null:i.name=="VariableName"||i.to-i.from<20&&g.test(Q(i))?{path:[],name:Q(i)}:(i.name=="."||i.name=="?.")&&i.parent.name=="MemberExpression"?h(Q,i.parent,""):i.name=="MemberExpression"?h(Q,i,""):O.explicit?{path:[],name:""}:null}function fO(O,Q){let i=[],a=new Set;for(let $=0;;$++){for(let r of(Object.getOwnPropertyNames||Object.keys)(O)){if(a.has(r))continue;a.add(r);let S;try{S=O[r]}catch{continue}i.push({label:r,type:typeof S=="function"?/^[A-Z]/.test(r)?"class":Q?"function":"method":Q?"variable":"property",boost:-$})}let t=Object.getPrototypeOf(O);if(!t)return i;O=t}}function EO(O){let Q=new Map;return i=>{let a=UO(i);if(!a)return null;let $=O;for(let r of a.path)if($=$[r],!$)return null;let t=Q.get($);return t||Q.set($,t=fO($,!a.path.length)),{from:i.pos-a.name.length,options:t,validFor:g}}}const X=I.define({name:"javascript",parser:YO.configure({props:[E.add({IfStatement:Y({except:/^\s*({|else\b)/}),TryStatement:Y({except:/^\s*({|catch\b|finally\b)/}),LabeledStatement:A,SwitchBody:O=>{let Q=O.textAfter,i=/^\s*\}/.test(Q),a=/^\s*(case|default)\b/.test(Q);return O.baseIndent+(i?0:a?1:2)*O.unit},Block:J({closing:"}"}),ArrowFunction:O=>O.baseIndent+O.unit,"TemplateString BlockComment":()=>null,"Statement Property":Y({except:/^{/}),JSXElement(O){let Q=/^\s*<\//.test(O.textAfter);return O.lineIndent(O.node.from)+(Q?0:O.unit)},JSXEscape(O){let Q=/\s*\}/.test(O.textAfter);return O.lineIndent(O.node.from)+(Q?0:O.unit)},"JSXOpenTag JSXSelfClosingTag"(O){return O.column(O.node.from)+O.unit}}),L.add({"Block ClassBody SwitchBody EnumBody ObjectExpression ArrayExpression":N,BlockComment(O){return{from:O.from+2,to:O.to-2}}})]}),languageData:{closeBrackets:{brackets:["(","[","{","'",'"',"`"]},commentTokens:{line:"//",block:{open:"/*",close:"*/"}},indentOnInput:/^\s*(?:case |default:|\{|\}|<\/)$/,wordChars:"$"}}),T={test:O=>/^JSX/.test(O.name),facet:F({commentTokens:{block:{open:"{/*",close:"*/}"}}})},uO=X.configure({dialect:"ts"},"typescript"),yO=X.configure({dialect:"jsx",props:[k.add(O=>O.isTop?[T]:void 0)]}),jO=X.configure({dialect:"jsx ts",props:[k.add(O=>O.isTop?[T]:void 0)]},"typescript"),dO="break case const continue default delete export extends false finally in instanceof let new return static super switch this throw true typeof var yield".split(" ").map(O=>({label:O,type:"keyword"}));function AO(O={}){let Q=O.jsx?O.typescript?jO:yO:O.typescript?uO:X;return new D(Q,[X.data.of({autocomplete:B(U,H(bO.concat(dO)))}),X.data.of({autocomplete:WO}),O.jsx?wO:[]])}function xO(O){for(;;){if(O.name=="JSXOpenTag"||O.name=="JSXSelfClosingTag"||O.name=="JSXFragmentTag")return O;if(!O.parent)return null;O=O.parent}}function w(O,Q,i=O.length){for(let a=Q?.firstChild;a;a=a.nextSibling)if(a.name=="JSXIdentifier"||a.name=="JSXBuiltin"||a.name=="JSXNamespacedName"||a.name=="JSXMemberExpression")return O.sliceString(a.from,Math.min(a.to,i));return""}const vO=typeof navigator=="object"&&/Android\b/.test(navigator.userAgent),wO=K.inputHandler.of((O,Q,i,a)=>{if((vO?O.composing:O.compositionStarted)||O.state.readOnly||Q!=i||a!=">"&&a!="/"||!X.isActiveAt(O.state,Q,-1))return!1;let{state:$}=O,t=$.changeByRange(r=>{var S,o;let{head:P}=r,Z=W($).resolveInner(P,-1),s;if(Z.name=="JSXStartTag"&&(Z=Z.parent),a==">"&&Z.name=="JSXFragmentTag")return{range:b.cursor(P+1),changes:{from:P,insert:">"}};if(a=="/"&&Z.name=="JSXFragmentTag"){let l=Z.parent,p=l?.parent;if(l.from==P-1&&((S=p.lastChild)===null||S===void 0?void 0:S.name)!="JSXEndTag"&&(s=w($.doc,p?.firstChild,P))){let f=`/${s}>`;return{range:b.cursor(P+f.length),changes:{from:P,insert:f}}}}else if(a==">"){let l=xO(Z);if(l&&((o=l.lastChild)===null||o===void 0?void 0:o.name)!="JSXEndTag"&&$.sliceDoc(P,P+2)!="`}}}return{range:r}});return t.changes.empty?!1:(O.dispatch(t,{userEvent:"input.type",scrollIntoView:!0}),!0)});function JO(O,Q){return Q||(Q={parserOptions:{ecmaVersion:2019,sourceType:"module"},env:{browser:!0,node:!0,es6:!0,es2015:!0,es2017:!0,es2020:!0},rules:{}},O.getRules().forEach((i,a)=>{i.meta.docs.recommended&&(Q.rules[a]=2)})),i=>{let{state:a}=i,$=[];for(let{from:t,to:r}of X.findRegions(a)){let S=a.doc.lineAt(t),o={line:S.number-1,col:t-S.from,pos:t};for(let P of O.verify(a.sliceDoc(t,r),Q))$.push(VO(P,a.doc,o))}return $}}function V(O,Q,i,a){return i.line(O+a.line).from+Q+(O==1?a.col-1:-1)}function VO(O,Q,i){let a=V(O.line,O.column,Q,i),$={from:a,to:O.endLine!=null&&O.endColumn!=1?V(O.endLine,O.endColumn,Q,i):a,message:O.message,source:O.ruleId?"eslint:"+O.ruleId:"eslint",severity:O.severity==1?"warning":"error"};if(O.fix){let{range:t,text:r}=O.fix,S=t[0]+i.pos-a,o=t[1]+i.pos-a;$.actions=[{name:"fix",apply(P,Z){P.dispatch({changes:{from:Z+S,to:Z+o,insert:r},scrollIntoView:!0})}}]}return $}export{wO as autoCloseTags,UO as completionPath,JO as esLint,AO as javascript,X as javascriptLanguage,yO as jsxLanguage,WO as localCompletionSource,EO as scopeCompletionSource,bO as snippets,jO as tsxLanguage,uO as typescriptLanguage};
-//# sourceMappingURL=index-b6ab4199.js.map
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/_snapshot_download.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/_snapshot_download.py
deleted file mode 100644
index aa85d0f4fecac2f62aa12318fcf575f1fe2ceea5..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/_snapshot_download.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import os
-from pathlib import Path
-from typing import Dict, List, Optional, Union
-
-from tqdm.auto import tqdm as base_tqdm
-from tqdm.contrib.concurrent import thread_map
-
-from .constants import (
- DEFAULT_REVISION,
- HF_HUB_ENABLE_HF_TRANSFER,
- HUGGINGFACE_HUB_CACHE,
- REPO_TYPES,
-)
-from .file_download import REGEX_COMMIT_HASH, hf_hub_download, repo_folder_name
-from .hf_api import HfApi
-from .utils import filter_repo_objects, logging, validate_hf_hub_args
-from .utils import tqdm as hf_tqdm
-from .utils._typing import Literal
-
-
-logger = logging.get_logger(__name__)
-
-
-@validate_hf_hub_args
-def snapshot_download(
- repo_id: str,
- *,
- revision: Optional[str] = None,
- repo_type: Optional[str] = None,
- cache_dir: Union[str, Path, None] = None,
- local_dir: Union[str, Path, None] = None,
- local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
- library_name: Optional[str] = None,
- library_version: Optional[str] = None,
- user_agent: Optional[Union[Dict, str]] = None,
- proxies: Optional[Dict] = None,
- etag_timeout: float = 10,
- resume_download: bool = False,
- force_download: bool = False,
- token: Optional[Union[bool, str]] = None,
- local_files_only: bool = False,
- allow_patterns: Optional[Union[List[str], str]] = None,
- ignore_patterns: Optional[Union[List[str], str]] = None,
- max_workers: int = 8,
- tqdm_class: Optional[base_tqdm] = None,
-) -> str:
- """Download repo files.
-
- Download a whole snapshot of a repo's files at the specified revision. This is useful when you want all files from
- a repo, because you don't know which ones you will need a priori. All files are nested inside a folder in order
- to keep their actual filename relative to that folder. You can also filter which files to download using
- `allow_patterns` and `ignore_patterns`.
-
- If `local_dir` is provided, the file structure from the repo will be replicated in this location. You can configure
- how you want to move those files:
- - If `local_dir_use_symlinks="auto"` (default), files are downloaded and stored in the cache directory as blob
- files. Small files (<5MB) are duplicated in `local_dir` while a symlink is created for bigger files. The goal
- is to be able to manually edit and save small files without corrupting the cache while saving disk space for
- binary files. The 5MB threshold can be configured with the `HF_HUB_LOCAL_DIR_AUTO_SYMLINK_THRESHOLD`
- environment variable.
- - If `local_dir_use_symlinks=True`, files are downloaded, stored in the cache directory and symlinked in `local_dir`.
- This is optimal in term of disk usage but files must not be manually edited.
- - If `local_dir_use_symlinks=False` and the blob files exist in the cache directory, they are duplicated in the
- local dir. This means disk usage is not optimized.
- - Finally, if `local_dir_use_symlinks=False` and the blob files do not exist in the cache directory, then the
- files are downloaded and directly placed under `local_dir`. This means if you need to download them again later,
- they will be re-downloaded entirely.
-
- An alternative would be to clone the repo but this requires git and git-lfs to be installed and properly
- configured. It is also not possible to filter which files to download when cloning a repository using git.
-
- Args:
- repo_id (`str`):
- A user or an organization name and a repo name separated by a `/`.
- revision (`str`, *optional*):
- An optional Git revision id which can be a branch name, a tag, or a
- commit hash.
- repo_type (`str`, *optional*):
- Set to `"dataset"` or `"space"` if downloading from a dataset or space,
- `None` or `"model"` if downloading from a model. Default is `None`.
- cache_dir (`str`, `Path`, *optional*):
- Path to the folder where cached files are stored.
- local_dir (`str` or `Path`, *optional*:
- If provided, the downloaded files will be placed under this directory, either as symlinks (default) or
- regular files (see description for more details).
- local_dir_use_symlinks (`"auto"` or `bool`, defaults to `"auto"`):
- To be used with `local_dir`. If set to "auto", the cache directory will be used and the file will be either
- duplicated or symlinked to the local directory depending on its size. It set to `True`, a symlink will be
- created, no matter the file size. If set to `False`, the file will either be duplicated from cache (if
- already exists) or downloaded from the Hub and not cached. See description for more details.
- library_name (`str`, *optional*):
- The name of the library to which the object corresponds.
- library_version (`str`, *optional*):
- The version of the library.
- user_agent (`str`, `dict`, *optional*):
- The user-agent info in the form of a dictionary or a string.
- proxies (`dict`, *optional*):
- Dictionary mapping protocol to the URL of the proxy passed to
- `requests.request`.
- etag_timeout (`float`, *optional*, defaults to `10`):
- When fetching ETag, how many seconds to wait for the server to send
- data before giving up which is passed to `requests.request`.
- resume_download (`bool`, *optional*, defaults to `False):
- If `True`, resume a previously interrupted download.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether the file should be downloaded even if it already exists in the local cache.
- token (`str`, `bool`, *optional*):
- A token to be used for the download.
- - If `True`, the token is read from the HuggingFace config
- folder.
- - If a string, it's used as the authentication token.
- local_files_only (`bool`, *optional*, defaults to `False`):
- If `True`, avoid downloading the file and return the path to the
- local cached file if it exists.
- allow_patterns (`List[str]` or `str`, *optional*):
- If provided, only files matching at least one pattern are downloaded.
- ignore_patterns (`List[str]` or `str`, *optional*):
- If provided, files matching any of the patterns are not downloaded.
- max_workers (`int`, *optional*):
- Number of concurrent threads to download files (1 thread = 1 file download).
- Defaults to 8.
- tqdm_class (`tqdm`, *optional*):
- If provided, overwrites the default behavior for the progress bar. Passed
- argument must inherit from `tqdm.auto.tqdm` or at least mimic its behavior.
- Note that the `tqdm_class` is not passed to each individual download.
- Defaults to the custom HF progress bar that can be disabled by setting
- `HF_HUB_DISABLE_PROGRESS_BARS` environment variable.
-
- Returns:
- Local folder path (string) of repo snapshot
-
-
-
- Raises the following errors:
-
- - [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
- if `token=True` and the token cannot be found.
- - [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError) if
- ETag cannot be determined.
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if some parameter value is invalid
-
-
- """
- if cache_dir is None:
- cache_dir = HUGGINGFACE_HUB_CACHE
- if revision is None:
- revision = DEFAULT_REVISION
- if isinstance(cache_dir, Path):
- cache_dir = str(cache_dir)
-
- if repo_type is None:
- repo_type = "model"
- if repo_type not in REPO_TYPES:
- raise ValueError(f"Invalid repo type: {repo_type}. Accepted repo types are: {str(REPO_TYPES)}")
-
- storage_folder = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type))
-
- # if we have no internet connection we will look for an
- # appropriate folder in the cache
- # If the specified revision is a commit hash, look inside "snapshots".
- # If the specified revision is a branch or tag, look inside "refs".
- if local_files_only:
- if REGEX_COMMIT_HASH.match(revision):
- commit_hash = revision
- else:
- # retrieve commit_hash from file
- ref_path = os.path.join(storage_folder, "refs", revision)
- with open(ref_path) as f:
- commit_hash = f.read()
-
- snapshot_folder = os.path.join(storage_folder, "snapshots", commit_hash)
-
- if os.path.exists(snapshot_folder):
- return snapshot_folder
-
- raise ValueError(
- "Cannot find an appropriate cached snapshot folder for the specified"
- " revision on the local disk and outgoing traffic has been disabled. To"
- " enable repo look-ups and downloads online, set 'local_files_only' to"
- " False."
- )
-
- # if we have internet connection we retrieve the correct folder name from the huggingface api
- api = HfApi(library_name=library_name, library_version=library_version, user_agent=user_agent)
- repo_info = api.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision, token=token)
- assert repo_info.sha is not None, "Repo info returned from server must have a revision sha."
-
- filtered_repo_files = list(
- filter_repo_objects(
- items=[f.rfilename for f in repo_info.siblings],
- allow_patterns=allow_patterns,
- ignore_patterns=ignore_patterns,
- )
- )
- commit_hash = repo_info.sha
- snapshot_folder = os.path.join(storage_folder, "snapshots", commit_hash)
- # if passed revision is not identical to commit_hash
- # then revision has to be a branch name or tag name.
- # In that case store a ref.
- if revision != commit_hash:
- ref_path = os.path.join(storage_folder, "refs", revision)
- os.makedirs(os.path.dirname(ref_path), exist_ok=True)
- with open(ref_path, "w") as f:
- f.write(commit_hash)
-
- # we pass the commit_hash to hf_hub_download
- # so no network call happens if we already
- # have the file locally.
- def _inner_hf_hub_download(repo_file: str):
- return hf_hub_download(
- repo_id,
- filename=repo_file,
- repo_type=repo_type,
- revision=commit_hash,
- cache_dir=cache_dir,
- local_dir=local_dir,
- local_dir_use_symlinks=local_dir_use_symlinks,
- library_name=library_name,
- library_version=library_version,
- user_agent=user_agent,
- proxies=proxies,
- etag_timeout=etag_timeout,
- resume_download=resume_download,
- force_download=force_download,
- token=token,
- )
-
- if HF_HUB_ENABLE_HF_TRANSFER:
- # when using hf_transfer we don't want extra parallelism
- # from the one hf_transfer provides
- for file in filtered_repo_files:
- _inner_hf_hub_download(file)
- else:
- thread_map(
- _inner_hf_hub_download,
- filtered_repo_files,
- desc=f"Fetching {len(filtered_repo_files)} files",
- max_workers=max_workers,
- # User can use its own tqdm class or the default one from `huggingface_hub.utils`
- tqdm_class=tqdm_class or hf_tqdm,
- )
-
- if local_dir is not None:
- return str(os.path.realpath(local_dir))
- return snapshot_folder
diff --git a/spaces/DaFujaTyping/hf-Chat-ui/src/routes/+layout.server.ts b/spaces/DaFujaTyping/hf-Chat-ui/src/routes/+layout.server.ts
deleted file mode 100644
index 15977f363cc7eec01fca5f118a239d0bfd07e313..0000000000000000000000000000000000000000
--- a/spaces/DaFujaTyping/hf-Chat-ui/src/routes/+layout.server.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-import { redirect } from "@sveltejs/kit";
-import type { LayoutServerLoad } from "./$types";
-import { collections } from "$lib/server/database";
-import type { Conversation } from "$lib/types/Conversation";
-import { UrlDependency } from "$lib/types/UrlDependency";
-import { defaultModel, models } from "$lib/server/models";
-import { validateModel } from "$lib/utils/models";
-
-export const load: LayoutServerLoad = async ({ locals, depends, url }) => {
- const { conversations } = collections;
- const urlModel = url.searchParams.get("model");
-
- depends(UrlDependency.ConversationList);
-
- if (urlModel) {
- const isValidModel = validateModel(models).safeParse(urlModel).success;
-
- if (isValidModel) {
- await collections.settings.updateOne(
- { sessionId: locals.sessionId },
- { $set: { activeModel: urlModel } },
- { upsert: true }
- );
- }
-
- throw redirect(302, url.pathname);
- }
-
- const settings = await collections.settings.findOne({ sessionId: locals.sessionId });
-
- return {
- conversations: await conversations
- .find({
- sessionId: locals.sessionId,
- })
- .sort({ updatedAt: -1 })
- .project>({
- title: 1,
- model: 1,
- _id: 1,
- updatedAt: 1,
- createdAt: 1,
- })
- .map((conv) => ({
- id: conv._id.toString(),
- title: conv.title,
- model: conv.model ?? defaultModel,
- }))
- .toArray(),
- settings: {
- shareConversationsWithModelAuthors: settings?.shareConversationsWithModelAuthors ?? true,
- ethicsModalAcceptedAt: settings?.ethicsModalAcceptedAt ?? null,
- activeModel: settings?.activeModel ?? defaultModel.id,
- },
- models: models.map((model) => ({
- id: model.id,
- name: model.name,
- websiteUrl: model.websiteUrl,
- datasetName: model.datasetName,
- displayName: model.displayName,
- description: model.description,
- promptExamples: model.promptExamples,
- parameters: model.parameters,
- })),
- };
-};
diff --git a/spaces/Dagfinn1962/stablediffusion-articlera/images.py b/spaces/Dagfinn1962/stablediffusion-articlera/images.py
deleted file mode 100644
index b7eff8335d63acef2be9bd688efcdca123149a50..0000000000000000000000000000000000000000
--- a/spaces/Dagfinn1962/stablediffusion-articlera/images.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import glob
-import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
-%matplotlib inline
-
-images = []
-for img_path in sorted(glob.glob('brain.png'), reverse=True):
- images.append(mpimg.imread(img_path))
-
-images = images[:15]
-
-plt.figure(figsize=(20,10))
-
-columns = 5
-for i, image in enumerate(images):
- ax = plt.subplot(len(images) / columns + 1, columns, i + 1)
- ax.axes.xaxis.set_visible(False)
- ax.axes.yaxis.set_visible(False)
- ax.axis('off')
- plt.imshow(image)
- gc.collect()
-
diff --git a/spaces/Detomo/ai-avatar-frontend/src/index.css b/spaces/Detomo/ai-avatar-frontend/src/index.css
deleted file mode 100644
index 08baf558885bfd2f9ac9f28a57cc830609fcb657..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-avatar-frontend/src/index.css
+++ /dev/null
@@ -1,19 +0,0 @@
-body {
- margin: 0;
- font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',
- 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',
- sans-serif;
- -webkit-font-smoothing: antialiased;
- -moz-osx-font-smoothing: grayscale;
- background: #333333;
-}
-
-code {
- font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New',
- monospace;
-}
-
-canvas{
- height: 100vh;
- width: 100vh;
-}
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/ECCV2022/ECCV2022_papers/README.md b/spaces/ECCV2022/ECCV2022_papers/README.md
deleted file mode 100644
index 732a75d853887896a35183874dcf75960e824ff5..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/ECCV2022_papers/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ECCV2022 Papers
-emoji: 📉
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ECCV2022/bytetrack/yolox/utils/setup_env.py b/spaces/ECCV2022/bytetrack/yolox/utils/setup_env.py
deleted file mode 100644
index f282b1f6dc6f5c2d7a4e8de468e624a721bca94c..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/yolox/utils/setup_env.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
-
-import cv2
-
-import os
-import subprocess
-
-__all__ = ["configure_nccl", "configure_module"]
-
-
-def configure_nccl():
- """Configure multi-machine environment variables of NCCL."""
- os.environ["NCCL_LAUNCH_MODE"] = "PARALLEL"
- os.environ["NCCL_IB_HCA"] = subprocess.getoutput(
- "pushd /sys/class/infiniband/ > /dev/null; for i in mlx5_*; "
- "do cat $i/ports/1/gid_attrs/types/* 2>/dev/null "
- "| grep v >/dev/null && echo $i ; done; popd > /dev/null"
- )
- os.environ["NCCL_IB_GID_INDEX"] = "3"
- os.environ["NCCL_IB_TC"] = "106"
-
-
-def configure_module(ulimit_value=8192):
- """
- Configure pytorch module environment. setting of ulimit and cv2 will be set.
-
- Args:
- ulimit_value(int): default open file number on linux. Default value: 8192.
- """
- # system setting
- try:
- import resource
-
- rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
- resource.setrlimit(resource.RLIMIT_NOFILE, (ulimit_value, rlimit[1]))
- except Exception:
- # Exception might be raised in Windows OS or rlimit reaches max limit number.
- # However, set rlimit value might not be necessary.
- pass
-
- # cv2
- # multiprocess might be harmful on performance of torch dataloader
- os.environ["OPENCV_OPENCL_RUNTIME"] = "disabled"
- try:
- cv2.setNumThreads(0)
- cv2.ocl.setUseOpenCL(False)
- except Exception:
- # cv2 version mismatch might rasie exceptions.
- pass
diff --git a/spaces/Eddycrack864/Applio-Inference/diffq/utils.py b/spaces/Eddycrack864/Applio-Inference/diffq/utils.py
deleted file mode 100644
index be6ab5253c38564140bc202077292bb99f9f397b..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/diffq/utils.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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 inspect
-from typing import Optional, List
-
-
-def simple_repr(obj, attrs: Optional[List[str]] = None, overrides={}):
- """
- Return a simple representation string for `obj`.
- If `attrs` is not None, it should be a list of attributes to include.
- """
- params = inspect.signature(obj.__class__).parameters
- attrs_repr = []
- if attrs is None:
- attrs = params.keys()
- for attr in attrs:
- display = False
- if attr in overrides:
- value = overrides[attr]
- elif hasattr(obj, attr):
- value = getattr(obj, attr)
- else:
- continue
- if attr in params:
- param = params[attr]
- if param.default is inspect._empty or value != param.default:
- display = True
- else:
- display = True
-
- if display:
- attrs_repr.append(f"{attr}={value}")
- return f"{obj.__class__.__name__}({','.join(attrs_repr)})"
diff --git a/spaces/EleutherAI/magma/magma/magma.py b/spaces/EleutherAI/magma/magma/magma.py
deleted file mode 100644
index e646f0b9d770f162abd72c1a77017b9f99045451..0000000000000000000000000000000000000000
--- a/spaces/EleutherAI/magma/magma/magma.py
+++ /dev/null
@@ -1,301 +0,0 @@
-from pathlib import Path
-from os.path import exists
-import torch
-import torch.nn as nn
-from copy import deepcopy
-from typing import Literal, Optional, List
-from torchtyping import TensorType
-from transformers.file_utils import ModelOutput
-from magma.config import MultimodalConfig
-
-from magma.utils import get_tokenizer
-from .language_model import get_gptj
-from .adapters import (
- Adapter,
- ParallelAdapter,
- AdapterWrapper,
- ParallelAdapterWrapper,
-)
-from .image_prefix import ImagePrefix
-from .sampling import generate
-from .utils import build_labels, is_url, print_main, download_checkpoint
-from .image_input import ImageInput
-from .transforms import get_transforms
-
-# ------------------------- Magma main class ----------------------------------
-
-
-class Magma(nn.Module):
- def __init__(self, config, device=None):
- super().__init__()
-
- if isinstance(config, (str, Path)):
- config = MultimodalConfig.from_yml(
- config
- ) # load config from yml file if config is a string
- else:
- assert isinstance(config, MultimodalConfig)
-
- self.device = device or torch.device(
- "cuda" if torch.cuda.is_available() else "cpu"
- )
- self.config = config
- self.lm = get_gptj().to(self.device)
- self.seq_len = self.lm.config.max_position_embeddings
-
- self.tokenizer = get_tokenizer("gpt2", sequence_length=self.seq_len)
-
- self.image_token = self.tokenizer.cls_token_id
- self.eos_token = self.tokenizer.eos_token_id
- self.lm.resize_token_embeddings(len(self.tokenizer))
- self.lm.config.pad_token_id = self.tokenizer.eos_token_id
- self.word_embedding = self.lm.transformer.wte.to(device)
- self.transformer = self.lm.transformer.h
-
- # adapter settings
- self.mlp_adapter_added, self.attn_adapter_added = False, False
-
- self.image_prefix = ImagePrefix(
- config=config,
- out_dim=self.lm.config.hidden_size,
- ).to(self.device)
-
- # might change based on the type of image encoder, so get from prefix instead of config
- self.image_prefix_seq_len = self.image_prefix.out_seq_len
-
- self.transforms = get_transforms(
- config.image_size,
- config.encoder_name,
- input_resolution=self.image_prefix.enc.input_resolution,
- )
-
- # add adapters
- if config.adapter_config:
- mlp_config = deepcopy(config.adapter_config.get("mlp", None))
- if mlp_config:
- assert mlp_config.get("adapter_type") is not None
- self.add_adapters(
- location="mlp",
- adapter_type=mlp_config.pop("adapter_type"),
- downsample_factor=mlp_config.pop("downsample_factor", 4),
- **mlp_config,
- )
- attn_config = deepcopy(config.adapter_config.get("attention", None))
- if attn_config:
- assert attn_config.get("adapter_type") is not None
- self.add_adapters(
- location="attention",
- adapter_type=attn_config.pop("adapter_type"),
- **attn_config,
- )
-
- # freeze parameters
- if config.freeze_lm:
- for name, param in self.lm.named_parameters(): # freeze lm weights
- if config.adapter_config and "adapter" in name:
- param.requires_grad = True
-
- if config.freeze_img_encoder:
- for param in self.image_prefix.enc.parameters():
- param.requires_grad = False
-
- def add_adapters(
- self,
- downsample_factor: int = 4,
- adapter_type: Literal["normal", "parallel", "scaled_parallel"] = "normal",
- location: Literal["mlp", "attention"] = "mlp",
- ff_attr: str = "mlp",
- attn_attr: str = "attn",
- **adapter_kwargs,
- ):
- """
- Adds an adapter layer to `self` at the specified location
- """
- assert adapter_type in [
- "normal",
- "parallel",
- "scaled_parallel",
- ], "adapter_type must be one of 'normal', 'parallel', or 'scaled_parallel'"
- assert location in [
- "mlp",
- "attention",
- ], "location must be one of 'mlp' or 'attention'"
-
- for l in range(len(self.transformer)):
- if location == "mlp":
- if self.mlp_adapter_added:
- raise ValueError("Adapter layer already added")
- mlp = getattr(self.transformer[l], ff_attr)
- if adapter_type in ["parallel", "scaled_parallel"]:
- adapter_layer = ParallelAdapter(
- module=mlp,
- dim=self.lm.config.hidden_size,
- downsample_factor=downsample_factor,
- scaled=adapter_type == "scaled_parallel",
- **adapter_kwargs,
- )
- else:
- adpt = Adapter(
- dim=self.lm.config.hidden_size,
- downsample_factor=downsample_factor,
- **adapter_kwargs,
- )
- adapter_layer = nn.Sequential(
- *[
- mlp,
- adpt,
- ]
- )
- setattr(self.transformer[l], ff_attr, adapter_layer)
- else:
- if self.attn_adapter_added:
- raise ValueError("Adapter layer already added")
- attn = getattr(self.transformer[l], attn_attr)
- if adapter_type in ["parallel", "scaled_parallel"]:
- adapter_layer = ParallelAdapterWrapper(
- module=attn,
- dim=self.lm.config.hidden_size,
- downsample_factor=downsample_factor,
- scaled="scaled" in adapter_type,
- **adapter_kwargs,
- )
- else:
- adapter_layer = AdapterWrapper(
- attn_block=attn,
- dim=self.lm.config.hidden_size,
- downsample_factor=downsample_factor,
- **adapter_kwargs,
- )
- setattr(self.transformer[l], attn_attr, adapter_layer)
-
- if location == "mlp":
- self.mlp_adapter_added = True
- else:
- self.attn_adapter_added = True
-
- def preprocess_inputs(self, input_list: list, embed = True) -> List[torch.Tensor]:
- """
- Expects a list of strings and instances of ImageInput
- Converts them into a list of tensors and then optionally runs self.embed over it
- """
- for i in range(len(input_list)):
- inp = input_list[i]
- if isinstance(inp, str):
- input_list[i] = self.tokenizer.encode(inp, return_tensors="pt")
- elif isinstance(inp, ImageInput):
- input_list[i] = inp.get_transformed_image(transform_fn = self.transforms)
- else:
- raise Exception(f'Invalid input type:{type(inp)}')
-
- if embed == True:
- return self.embed(input_list)
- else:
- return input_list
-
- def embed(self, inputs: List[torch.Tensor]) -> TensorType["b", "s", "d"]:
- """
- Embeds a list of tensors In the correct format to input into the LM (b, s, d).
- For each tensor, if it's 2d assume it's text and use word embedding,
- if it's 4d, assume it's an image, and use image_prefix to embed.
- """
- emb_list = []
- for x in inputs:
- if x.ndim == 2:
- x = x.to(self.device)
- emb_list.append(self.word_embedding(x))
- elif x.ndim == 4:
- x = x.to(self.device).half()
- image_embeddings = self.image_prefix(x)
- emb_list.append(image_embeddings)
- else:
- raise ValueError(f"Expected 2d or 4d tensor, got {x.ndim}d")
- return torch.cat(emb_list, dim=1)
-
- @torch.no_grad()
- def generate(
- self,
- embeddings: TensorType["b", "s", "d"],
- max_steps: int = 100,
- temperature: float = 0.7,
- top_k: int = 0,
- top_p: float = 0.9,
- decode: bool = True,
- ):
- """
- Generates captions for a batch of embeddings.
- """
-
- return generate(
- self,
- embeddings=embeddings,
- max_steps=max_steps,
- temperature=temperature,
- top_k=top_k,
- top_p=top_p,
- decode=decode,
- )
-
- def forward(
- self,
- images: TensorType["b", "c", "h", "w"] = None,
- captions: Optional[TensorType["b", "seq"]] = None,
- output_hidden_states: bool = False,
- input_embeddings: TensorType["b", "s", "d"] = None,
- ) -> ModelOutput:
- assert captions is not None, "Must provide captions in training"
- assert any([i is not None for i in [images, input_embeddings]]) and not all(
- [i is not None for i in [images, input_embeddings]]
- ), "Pass in either images, or input embeddings, not both."
- assert (
- captions.shape[1] == self.seq_len
- ), f"in training, captions should be padded to sequence length ({self.seq_len}), but are length {captions.shape[1]}"
-
- if input_embeddings is None:
- input_embeddings = self.image_prefix(images)
- labels = build_labels(
- input_embeddings, captions, self.eos_token, self.device
- ) # build labels from input_embeddings
- word_embeddings = self.word_embedding(captions)
-
- # join together
- input_embeddings = torch.cat(
- (
- input_embeddings,
- word_embeddings[:, : -input_embeddings.shape[1], :],
- ), # remove padding in the word embedding before concatenating
- dim=1,
- )
-
- # forward joined embeddings through lm
- lm_outputs = self.lm(
- inputs_embeds=input_embeddings,
- labels=labels,
- output_hidden_states=output_hidden_states,
- )
-
- return lm_outputs
-
- @classmethod
- def from_checkpoint(cls, config_path, checkpoint_path, device = 'cpu'):
- """
- Loads a model checkpoint from disk / downlods from url if not present
- """
-
- checkpoint_url = 'https://drive.google.com/u/0/uc?id=1EiAY3IcKWmGADaLDzdG25ykQghUwza6L&export=download'
-
- if exists(checkpoint_path) == False:
- print_main(f'checkpoint: {checkpoint_path} does not exist, downloading model')
- download_checkpoint(checkpoint_url = checkpoint_url, save_as = checkpoint_path)
-
- model = cls(config = config_path)
-
- sd = torch.load(checkpoint_path, map_location=torch.device("cpu"))
- if "module" in sd.keys():
- sd = sd["module"]
-
- print_main('loading checkpoint magma')
- model.load_state_dict(sd, strict=False)
- print_main("magma model successfully loaded")
-
- model.half().to(device)
- return model
\ No newline at end of file
diff --git a/spaces/EricA1/openjourney/README.md b/spaces/EricA1/openjourney/README.md
deleted file mode 100644
index b5a128b9cd7ca63138735bf5134be5a0253cf0c0..0000000000000000000000000000000000000000
--- a/spaces/EricA1/openjourney/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: openjourney
-emoji: 👀
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: akhaliq/openjourney
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/EsoCode/text-generation-webui/modules/monkey_patch_gptq_lora.py b/spaces/EsoCode/text-generation-webui/modules/monkey_patch_gptq_lora.py
deleted file mode 100644
index bf8d478d8b76eae296e1fb80a4266b0475b7d0c2..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/modules/monkey_patch_gptq_lora.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copied from https://github.com/johnsmith0031/alpaca_lora_4bit
-
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path("repositories/alpaca_lora_4bit")))
-
-import autograd_4bit
-from amp_wrapper import AMPWrapper
-from autograd_4bit import (
- Autograd4bitQuantLinear,
- load_llama_model_4bit_low_ram
-)
-from monkeypatch.peft_tuners_lora_monkey_patch import (
- Linear4bitLt,
- replace_peft_model_with_gptq_lora_model
-)
-
-from modules import shared
-from modules.GPTQ_loader import find_quantized_model_file
-
-replace_peft_model_with_gptq_lora_model()
-
-
-def load_model_llama(model_name):
- config_path = str(Path(f'{shared.args.model_dir}/{model_name}'))
- model_path = str(find_quantized_model_file(model_name))
- model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=shared.args.groupsize, is_v1_model=False)
- for n, m in model.named_modules():
- if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
- if m.is_v1_model:
- m.zeros = m.zeros.half()
- m.scales = m.scales.half()
- m.bias = m.bias.half()
-
- autograd_4bit.use_new = True
- autograd_4bit.auto_switch = True
-
- model.half()
- wrapper = AMPWrapper(model)
- wrapper.apply_generate()
-
- return model, tokenizer
diff --git a/spaces/EuroPython2022/Sketch2ColourDemo/examples/__init__.py b/spaces/EuroPython2022/Sketch2ColourDemo/examples/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Falah/female/index.html b/spaces/Falah/female/index.html
deleted file mode 100644
index 84553bfe9908e85fd6cfb3063afb996bf38e8ee8..0000000000000000000000000000000000000000
--- a/spaces/Falah/female/index.html
+++ /dev/null
@@ -1,36 +0,0 @@
-
-
-
- Random Command Generator
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/archs/__init__.py b/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/archs/__init__.py
deleted file mode 100644
index cfb1e4d7bb221c429082bd389d9140e5b1cc07b0..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/archs/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import importlib
-from copy import deepcopy
-from os import path as osp
-
-from basicsr.utils import get_root_logger, scandir
-from basicsr.utils.registry import ARCH_REGISTRY
-
-__all__ = ['build_network']
-
-# automatically scan and import arch modules for registry
-# scan all the files under the 'archs' folder and collect files ending with
-# '_arch.py'
-arch_folder = osp.dirname(osp.abspath(__file__))
-arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
-# import all the arch modules
-_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames]
-
-
-def build_network(opt):
- opt = deepcopy(opt)
- network_type = opt.pop('type')
- net = ARCH_REGISTRY.get(network_type)(**opt)
- logger = get_root_logger()
- logger.info(f'Network [{net.__class__.__name__}] is created.')
- return net
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/options.py b/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/options.py
deleted file mode 100644
index db490e4aa52e26fde31959fd74c2cef3af2ecf76..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/utils/options.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import yaml
-import time
-from collections import OrderedDict
-from os import path as osp
-from basicsr.utils.misc import get_time_str
-
-def ordered_yaml():
- """Support OrderedDict for yaml.
-
- Returns:
- yaml Loader and Dumper.
- """
- try:
- from yaml import CDumper as Dumper
- from yaml import CLoader as Loader
- except ImportError:
- from yaml import Dumper, Loader
-
- _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
-
- def dict_representer(dumper, data):
- return dumper.represent_dict(data.items())
-
- def dict_constructor(loader, node):
- return OrderedDict(loader.construct_pairs(node))
-
- Dumper.add_representer(OrderedDict, dict_representer)
- Loader.add_constructor(_mapping_tag, dict_constructor)
- return Loader, Dumper
-
-
-def parse(opt_path, root_path, is_train=True):
- """Parse option file.
-
- Args:
- opt_path (str): Option file path.
- is_train (str): Indicate whether in training or not. Default: True.
-
- Returns:
- (dict): Options.
- """
- with open(opt_path, mode='r') as f:
- Loader, _ = ordered_yaml()
- opt = yaml.load(f, Loader=Loader)
-
- opt['is_train'] = is_train
-
- # opt['name'] = f"{get_time_str()}_{opt['name']}"
- if opt['path'].get('resume_state', None): # Shangchen added
- resume_state_path = opt['path'].get('resume_state')
- opt['name'] = resume_state_path.split("/")[-3]
- else:
- opt['name'] = f"{get_time_str()}_{opt['name']}"
-
-
- # datasets
- for phase, dataset in opt['datasets'].items():
- # for several datasets, e.g., test_1, test_2
- phase = phase.split('_')[0]
- dataset['phase'] = phase
- if 'scale' in opt:
- dataset['scale'] = opt['scale']
- if dataset.get('dataroot_gt') is not None:
- dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
- if dataset.get('dataroot_lq') is not None:
- dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])
-
- # paths
- for key, val in opt['path'].items():
- if (val is not None) and ('resume_state' in key or 'pretrain_network' in key):
- opt['path'][key] = osp.expanduser(val)
-
- if is_train:
- experiments_root = osp.join(root_path, 'experiments', opt['name'])
- opt['path']['experiments_root'] = experiments_root
- opt['path']['models'] = osp.join(experiments_root, 'models')
- opt['path']['training_states'] = osp.join(experiments_root, 'training_states')
- opt['path']['log'] = experiments_root
- opt['path']['visualization'] = osp.join(experiments_root, 'visualization')
-
- else: # test
- results_root = osp.join(root_path, 'results', opt['name'])
- opt['path']['results_root'] = results_root
- opt['path']['log'] = results_root
- opt['path']['visualization'] = osp.join(results_root, 'visualization')
-
- return opt
-
-
-def dict2str(opt, indent_level=1):
- """dict to string for printing options.
-
- Args:
- opt (dict): Option dict.
- indent_level (int): Indent level. Default: 1.
-
- Return:
- (str): Option string for printing.
- """
- msg = '\n'
- for k, v in opt.items():
- if isinstance(v, dict):
- msg += ' ' * (indent_level * 2) + k + ':['
- msg += dict2str(v, indent_level + 1)
- msg += ' ' * (indent_level * 2) + ']\n'
- else:
- msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
- return msg
diff --git a/spaces/Fernando22/freegpt-webui/client/js/highlight.min.js b/spaces/Fernando22/freegpt-webui/client/js/highlight.min.js
deleted file mode 100644
index d410b45b38119606525a0a7c0c60c428c5ee6eb7..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/client/js/highlight.min.js
+++ /dev/null
@@ -1 +0,0 @@
-var hljs=function(){"use strict";var e={exports:{}};function n(e){return e instanceof Map?e.clear=e.delete=e.set=()=>{throw Error("map is read-only")}:e instanceof Set&&(e.add=e.clear=e.delete=()=>{throw Error("set is read-only")}),Object.freeze(e),Object.getOwnPropertyNames(e).forEach(t=>{var a=e[t];"object"!=typeof a||Object.isFrozen(a)||n(a)}),e}e.exports=n,e.exports.default=n;class t{constructor(e){void 0===e.data&&(e.data={}),this.data=e.data,this.isMatchIgnored=!1}ignoreMatch(){this.isMatchIgnored=!0}}function a(e){return e.replace(/&/g,"&").replace(//g,">").replace(/"/g,""").replace(/'/g,"'")}function i(e,...n){let t=Object.create(null);for(let a in e)t[a]=e[a];return n.forEach(e=>{for(let n in e)t[n]=e[n]}),t}let r=e=>!!e.scope||e.sublanguage&&e.language;class s{constructor(e,n){this.buffer="",this.classPrefix=n.classPrefix,e.walk(this)}addText(e){this.buffer+=a(e)}openNode(e){if(!r(e))return;let n="";n=e.sublanguage?"language-"+e.language:((e,{prefix:n})=>{if(e.includes(".")){let t=e.split(".");return[`${n}${t.shift()}`,...t.map((e,n)=>`${e}${"_".repeat(n+1)}`),].join(" ")}return`${n}${e}`})(e.scope,{prefix:this.classPrefix}),this.span(n)}closeNode(e){r(e)&&(this.buffer+="")}value(){return this.buffer}span(e){this.buffer+=``}}let l=(e={})=>{let n={children:[]};return Object.assign(n,e),n};class o{constructor(){this.rootNode=l(),this.stack=[this.rootNode]}get top(){return this.stack[this.stack.length-1]}get root(){return this.rootNode}add(e){this.top.children.push(e)}openNode(e){let n=l({scope:e});this.add(n),this.stack.push(n)}closeNode(){if(this.stack.length>1)return this.stack.pop()}closeAllNodes(){for(;this.closeNode(););}toJSON(){return JSON.stringify(this.rootNode,null,4)}walk(e){return this.constructor._walk(e,this.rootNode)}static _walk(e,n){return"string"==typeof n?e.addText(n):n.children&&(e.openNode(n),n.children.forEach(n=>this._walk(e,n)),e.closeNode(n)),e}static _collapse(e){"string"!=typeof e&&e.children&&(e.children.every(e=>"string"==typeof e)?e.children=[e.children.join("")]:e.children.forEach(e=>{o._collapse(e)}))}}class c extends o{constructor(e){super(),this.options=e}addKeyword(e,n){""!==e&&(this.openNode(n),this.addText(e),this.closeNode())}addText(e){""!==e&&this.add(e)}addSublanguage(e,n){let t=e.root;t.sublanguage=!0,t.language=n,this.add(t)}toHTML(){return new s(this,this.options).value()}finalize(){return!0}}function d(e){return e?"string"==typeof e?e:e.source:null}function g(e){return m("(?=",e,")")}function u(e){return m("(?:",e,")*")}function b(e){return m("(?:",e,")?")}function m(...e){return e.map(e=>d(e)).join("")}function p(...e){let n=(e=>{let n=e[e.length-1];return"object"==typeof n&&n.constructor===Object?(e.splice(e.length-1,1),n):{}})(e);return"("+(n.capture?"":"?:")+e.map(e=>d(e)).join("|")+")"}function h(e){return RegExp(e.toString()+"|").exec("").length-1}let f=/\[(?:[^\\\]]|\\.)*\]|\(\??|\\([1-9][0-9]*)|\\./;function E(e,{joinWith:n}){let t=0;return e.map(e=>{t+=1;let n=t,a=d(e),i="";for(;a.length>0;){let r=f.exec(a);if(!r){i+=a;break}i+=a.substring(0,r.index),a=a.substring(r.index+r[0].length),"\\"===r[0][0]&&r[1]?i+="\\"+(Number(r[1])+n):(i+=r[0],"("===r[0]&&t++)}return i}).map(e=>`(${e})`).join(n)}let $="[a-zA-Z]\\w*",y="[a-zA-Z_]\\w*",N="\\b\\d+(\\.\\d+)?",w="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",v="\\b(0b[01]+)",x={begin:"\\\\[\\s\\S]",relevance:0},k=(e,n,t={})=>{let a=i({scope:"comment",begin:e,end:n,contains:[]},t);a.contains.push({scope:"doctag",begin:"[ ]*(?=(TODO|FIXME|NOTE|BUG|OPTIMIZE|HACK|XXX):)",end:/(TODO|FIXME|NOTE|BUG|OPTIMIZE|HACK|XXX):/,excludeBegin:!0,relevance:0});let r=p("I","a","is","so","us","to","at","if","in","it","on",/[A-Za-z]+['](d|ve|re|ll|t|s|n)/,/[A-Za-z]+[-][a-z]+/,/[A-Za-z][a-z]{2,}/);return a.contains.push({begin:m(/[ ]+/,"(",r,/[.]?[:]?([.][ ]|[ ])/,"){3}")}),a},M=k("//","$"),O=k("/\\*","\\*/"),S=k("#","$");var A=Object.freeze({__proto__:null,MATCH_NOTHING_RE:/\b\B/,IDENT_RE:$,UNDERSCORE_IDENT_RE:y,NUMBER_RE:N,C_NUMBER_RE:w,BINARY_NUMBER_RE:v,RE_STARTERS_RE:"!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",SHEBANG(e={}){let n=/^#![ ]*\//;return e.binary&&(e.begin=m(n,/.*\b/,e.binary,/\b.*/)),i({scope:"meta",begin:n,end:/$/,relevance:0,"on:begin"(e,n){0!==e.index&&n.ignoreMatch()}},e)},BACKSLASH_ESCAPE:x,APOS_STRING_MODE:{scope:"string",begin:"'",end:"'",illegal:"\\n",contains:[x]},QUOTE_STRING_MODE:{scope:"string",begin:'"',end:'"',illegal:"\\n",contains:[x]},PHRASAL_WORDS_MODE:{begin:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},COMMENT:k,C_LINE_COMMENT_MODE:M,C_BLOCK_COMMENT_MODE:O,HASH_COMMENT_MODE:S,NUMBER_MODE:{scope:"number",begin:N,relevance:0},C_NUMBER_MODE:{scope:"number",begin:w,relevance:0},BINARY_NUMBER_MODE:{scope:"number",begin:v,relevance:0},REGEXP_MODE:{begin:/(?=\/[^/\n]*\/)/,contains:[{scope:"regexp",begin:/\//,end:/\/[gimuy]*/,illegal:/\n/,contains:[x,{begin:/\[/,end:/\]/,relevance:0,contains:[x]},]},]},TITLE_MODE:{scope:"title",begin:$,relevance:0},UNDERSCORE_TITLE_MODE:{scope:"title",begin:y,relevance:0},METHOD_GUARD:{begin:"\\.\\s*[a-zA-Z_]\\w*",relevance:0},END_SAME_AS_BEGIN:e=>Object.assign(e,{"on:begin"(e,n){n.data._beginMatch=e[1]},"on:end"(e,n){n.data._beginMatch!==e[1]&&n.ignoreMatch()}})});function C(e,n){"."===e.input[e.index-1]&&n.ignoreMatch()}function T(e,n){void 0!==e.className&&(e.scope=e.className,delete e.className)}function R(e,n){n&&e.beginKeywords&&(e.begin="\\b("+e.beginKeywords.split(" ").join("|")+")(?!\\.)(?=\\b|\\s)",e.__beforeBegin=C,e.keywords=e.keywords||e.beginKeywords,delete e.beginKeywords,void 0===e.relevance&&(e.relevance=0))}function D(e,n){Array.isArray(e.illegal)&&(e.illegal=p(...e.illegal))}function I(e,n){if(e.match){if(e.begin||e.end)throw Error("begin & end are not supported with match");e.begin=e.match,delete e.match}}function L(e,n){void 0===e.relevance&&(e.relevance=1)}let B=(e,n)=>{if(!e.beforeMatch)return;if(e.starts)throw Error("beforeMatch cannot be used with starts");let t=Object.assign({},e);Object.keys(e).forEach(n=>{delete e[n]}),e.keywords=t.keywords,e.begin=m(t.beforeMatch,g(t.begin)),e.starts={relevance:0,contains:[Object.assign(t,{endsParent:!0})]},e.relevance=0,delete t.beforeMatch},_=["of","and","for","in","not","or","if","then","parent","list","value",],z={},F=e=>{console.error(e)},U=(e,...n)=>{},P=(e,n)=>{z[`${e}/${n}`]||(console.log(`Deprecated as of ${e}. ${n}`),z[`${e}/${n}`]=!0)},j=Error();function K(e,n,{key:t}){let a=0,i=e[t],r={},s={};for(let l=1;l<=n.length;l++)s[l+a]=i[l],r[l+a]=!0,a+=h(n[l-1]);e[t]=s,e[t]._emit=r,e[t]._multi=!0}function q(e){var n;(n=e).scope&&"object"==typeof n.scope&&null!==n.scope&&(n.beginScope=n.scope,delete n.scope),"string"==typeof e.beginScope&&(e.beginScope={_wrap:e.beginScope}),"string"==typeof e.endScope&&(e.endScope={_wrap:e.endScope}),(e=>{if(Array.isArray(e.begin)){if(e.skip||e.excludeBegin||e.returnBegin)throw F("skip, excludeBegin, returnBegin not compatible with beginScope: {}"),j;if("object"!=typeof e.beginScope||null===e.beginScope)throw F("beginScope must be object"),j;K(e,e.begin,{key:"beginScope"}),e.begin=E(e.begin,{joinWith:""})}})(e),(e=>{if(Array.isArray(e.end)){if(e.skip||e.excludeEnd||e.returnEnd)throw F("skip, excludeEnd, returnEnd not compatible with endScope: {}"),j;if("object"!=typeof e.endScope||null===e.endScope)throw F("endScope must be object"),j;K(e,e.end,{key:"endScope"}),e.end=E(e.end,{joinWith:""})}})(e)}class H extends Error{constructor(e,n){super(e),this.name="HTMLInjectionError",this.html=n}}let Z=a,G=i,W=Symbol("nomatch");var Q=(n=>{let a=Object.create(null),r=Object.create(null),s=[],l=!0,o="Could not find the language '{}', did you forget to load/include a language module?",f={disableAutodetect:!0,name:"Plain text",contains:[]},$={ignoreUnescapedHTML:!1,throwUnescapedHTML:!1,noHighlightRe:/^(no-?highlight)$/i,languageDetectRe:/\blang(?:uage)?-([\w-]+)\b/i,classPrefix:"hljs-",cssSelector:"pre code",languages:null,__emitter:c};function y(e){return $.noHighlightRe.test(e)}function N(e,n,t){let a="",i="";"object"==typeof n?(a=e,t=n.ignoreIllegals,i=n.language):(P("10.7.0","highlight(lang, code, ...args) has been deprecated."),P("10.7.0","Please use highlight(code, options) instead.\nhttps://github.com/highlightjs/highlight.js/issues/2277"),i=e,a=n),void 0===t&&(t=!0);let r={code:a,language:i};z("before:highlight",r);let s=r.result?r.result:w(r.language,r.code,t);return s.code=r.code,z("after:highlight",s),s}function w(e,n,r,s){let c=Object.create(null);function g(){var e;if(!M.keywords)return void A.addText(C);let n=0;M.keywordPatternRe.lastIndex=0;let t=M.keywordPatternRe.exec(C),a="";for(;t;){a+=C.substring(n,t.index);let i=N.case_insensitive?t[0].toLowerCase():t[0],r=(e=i,M.keywords[e]);if(r){let[s,l]=r;if(A.addText(a),a="",c[i]=(c[i]||0)+1,c[i]<=7&&(z+=l),s.startsWith("_"))a+=t[0];else{let o=N.classNameAliases[s]||s;A.addKeyword(t[0],o)}}else a+=t[0];n=M.keywordPatternRe.lastIndex,t=M.keywordPatternRe.exec(C)}a+=C.substring(n),A.addText(a)}function u(){null!=M.subLanguage?(()=>{if(""===C)return;let e=null;if("string"==typeof M.subLanguage){if(!a[M.subLanguage])return void A.addText(C);e=w(M.subLanguage,C,!0,S[M.subLanguage]),S[M.subLanguage]=e._top}else e=v(C,M.subLanguage.length?M.subLanguage:null);M.relevance>0&&(z+=e.relevance),A.addSublanguage(e._emitter,e.language)})():g(),C=""}function b(e,n){let t=1,a=n.length-1;for(;t<=a;){if(!e._emit[t]){t++;continue}let i=N.classNameAliases[e[t]]||e[t],r=n[t];i?A.addKeyword(r,i):(C=r,g(),C=""),t++}}function m(e,n){return e.scope&&"string"==typeof e.scope&&A.openNode(N.classNameAliases[e.scope]||e.scope),e.beginScope&&(e.beginScope._wrap?(A.addKeyword(C,N.classNameAliases[e.beginScope._wrap]||e.beginScope._wrap),C=""):e.beginScope._multi&&(b(e.beginScope,n),C="")),M=Object.create(e,{parent:{value:M}})}function p(e){return 0===M.matcher.regexIndex?(C+=e[0],1):(j=!0,0)}let f={};function y(a,i){let s=i&&i[0];if(C+=a,null==s)return u(),0;if("begin"===f.type&&"end"===i.type&&f.index===i.index&&""===s){if(C+=n.slice(i.index,i.index+1),!l){let o=Error(`0 width match regex (${e})`);throw o.languageName=e,o.badRule=f.rule,o}return 1}if(f=i,"begin"===i.type)return(e=>{let n=e[0],a=e.rule,i=new t(a),r=[a.__beforeBegin,a["on:begin"]];for(let s of r)if(s&&(s(e,i),i.isMatchIgnored))return p(n);return a.skip?C+=n:(a.excludeBegin&&(C+=n),u(),a.returnBegin||a.excludeBegin||(C=n)),m(a,e),a.returnBegin?0:n.length})(i);if("illegal"===i.type&&!r){let c=Error('Illegal lexeme "'+s+'" for mode "'+(M.scope||"")+'"');throw c.mode=M,c}if("end"===i.type){let d=function e(a){let i=a[0],r=n.substring(a.index),s=function e(n,a,i){let r=((e,n)=>{let t=e&&e.exec(n);return t&&0===t.index})(n.endRe,i);if(r){if(n["on:end"]){let s=new t(n);n["on:end"](a,s),s.isMatchIgnored&&(r=!1)}if(r){for(;n.endsParent&&n.parent;)n=n.parent;return n}}if(n.endsWithParent)return e(n.parent,a,i)}(M,a,r);if(!s)return W;let l=M;M.endScope&&M.endScope._wrap?(u(),A.addKeyword(i,M.endScope._wrap)):M.endScope&&M.endScope._multi?(u(),b(M.endScope,a)):l.skip?C+=i:(l.returnEnd||l.excludeEnd||(C+=i),u(),l.excludeEnd&&(C=i));do M.scope&&A.closeNode(),M.skip||M.subLanguage||(z+=M.relevance),M=M.parent;while(M!==s.parent);return s.starts&&m(s.starts,a),l.returnEnd?0:i.length}(i);if(d!==W)return d}if("illegal"===i.type&&""===s)return 1;if(P>1e5&&P>3*i.index)throw Error("potential infinite loop, way more iterations than matches");return C+=s,s.length}let N=O(e);if(!N)throw F(o.replace("{}",e)),Error('Unknown language: "'+e+'"');let x=function e(n){function t(e,t){return RegExp(d(e),"m"+(n.case_insensitive?"i":"")+(n.unicodeRegex?"u":"")+(t?"g":""))}class a{constructor(){this.matchIndexes={},this.regexes=[],this.matchAt=1,this.position=0}addRule(e,n){n.position=this.position++,this.matchIndexes[this.matchAt]=n,this.regexes.push([n,e]),this.matchAt+=h(e)+1}compile(){0===this.regexes.length&&(this.exec=()=>null);let e=this.regexes.map(e=>e[1]);this.matcherRe=t(E(e,{joinWith:"|"}),!0),this.lastIndex=0}exec(e){this.matcherRe.lastIndex=this.lastIndex;let n=this.matcherRe.exec(e);if(!n)return null;let t=n.findIndex((e,n)=>n>0&&void 0!==e),a=this.matchIndexes[t];return n.splice(0,t),Object.assign(n,a)}}class r{constructor(){this.rules=[],this.multiRegexes=[],this.count=0,this.lastIndex=0,this.regexIndex=0}getMatcher(e){if(this.multiRegexes[e])return this.multiRegexes[e];let n=new a;return this.rules.slice(e).forEach(([e,t])=>n.addRule(e,t)),n.compile(),this.multiRegexes[e]=n,n}resumingScanAtSamePosition(){return 0!==this.regexIndex}considerAll(){this.regexIndex=0}addRule(e,n){this.rules.push([e,n]),"begin"===n.type&&this.count++}exec(e){let n=this.getMatcher(this.regexIndex);n.lastIndex=this.lastIndex;let t=n.exec(e);if(this.resumingScanAtSamePosition()){if(t&&t.index===this.lastIndex);else{let a=this.getMatcher(0);a.lastIndex=this.lastIndex+1,t=a.exec(e)}}return t&&(this.regexIndex+=t.position+1,this.regexIndex===this.count&&this.considerAll()),t}}if(n.compilerExtensions||(n.compilerExtensions=[]),n.contains&&n.contains.includes("self"))throw Error("ERR: contains `self` is not supported at the top-level of a language. See documentation.");return n.classNameAliases=i(n.classNameAliases||{}),function e(a,s){let l=a;if(a.isCompiled)return l;[T,I,q,B].forEach(e=>e(a,s)),n.compilerExtensions.forEach(e=>e(a,s)),a.__beforeBegin=null,[R,D,L].forEach(e=>e(a,s)),a.isCompiled=!0;let o=null;return"object"==typeof a.keywords&&a.keywords.$pattern&&(a.keywords=Object.assign({},a.keywords),o=a.keywords.$pattern,delete a.keywords.$pattern),o=o||/\w+/,a.keywords&&(a.keywords=function e(n,t,a="keyword"){let i=Object.create(null);return"string"==typeof n?r(a,n.split(" ")):Array.isArray(n)?r(a,n):Object.keys(n).forEach(a=>{Object.assign(i,e(n[a],t,a))}),i;function r(e,n){t&&(n=n.map(e=>e.toLowerCase())),n.forEach(n=>{var t,a,r;let s=n.split("|");i[s[0]]=[e,(t=s[0],a=s[1],a?Number(a):(r=t,_.includes(r.toLowerCase()))?0:1)]})}}(a.keywords,n.case_insensitive)),l.keywordPatternRe=t(o,!0),s&&(a.begin||(a.begin=/\B|\b/),l.beginRe=t(l.begin),a.end||a.endsWithParent||(a.end=/\B|\b/),a.end&&(l.endRe=t(l.end)),l.terminatorEnd=d(l.end)||"",a.endsWithParent&&s.terminatorEnd&&(l.terminatorEnd+=(a.end?"|":"")+s.terminatorEnd)),a.illegal&&(l.illegalRe=t(a.illegal)),a.contains||(a.contains=[]),a.contains=[].concat(...a.contains.map(e=>{var n;return(n="self"===e?a:e).variants&&!n.cachedVariants&&(n.cachedVariants=n.variants.map(e=>i(n,{variants:null},e))),n.cachedVariants?n.cachedVariants:!function e(n){return!!n&&(n.endsWithParent||e(n.starts))}(n)?Object.isFrozen(n)?i(n):n:i(n,{starts:n.starts?i(n.starts):null})})),a.contains.forEach(n=>{e(n,l)}),a.starts&&e(a.starts,s),l.matcher=(e=>{let n=new r;return e.contains.forEach(e=>n.addRule(e.begin,{rule:e,type:"begin"})),e.terminatorEnd&&n.addRule(e.terminatorEnd,{type:"end"}),e.illegal&&n.addRule(e.illegal,{type:"illegal"}),n})(l),l}(n)}(N),k="",M=s||x,S={},A=new $.__emitter($);(()=>{let e=[];for(let n=M;n!==N;n=n.parent)n.scope&&e.unshift(n.scope);e.forEach(e=>A.openNode(e))})();let C="",z=0,U=0,P=0,j=!1;try{for(M.matcher.considerAll();;){P++,j?j=!1:M.matcher.considerAll(),M.matcher.lastIndex=U;let K=M.matcher.exec(n);if(!K)break;let H=y(n.substring(U,K.index),K);U=K.index+H}return y(n.substring(U)),A.closeAllNodes(),A.finalize(),k=A.toHTML(),{language:e,value:k,relevance:z,illegal:!1,_emitter:A,_top:M}}catch(G){if(G.message&&G.message.includes("Illegal"))return{language:e,value:Z(n),illegal:!0,relevance:0,_illegalBy:{message:G.message,index:U,context:n.slice(U-100,U+100),mode:G.mode,resultSoFar:k},_emitter:A};if(l)return{language:e,value:Z(n),illegal:!1,relevance:0,errorRaised:G,_emitter:A,_top:M};throw G}}function v(e,n){n=n||$.languages||Object.keys(a);let t=(e=>{let n={value:Z(e),illegal:!1,relevance:0,_top:f,_emitter:new $.__emitter($)};return n._emitter.addText(e),n})(e),i=n.filter(O).filter(C).map(n=>w(n,e,!1));i.unshift(t);let r=i.sort((e,n)=>{if(e.relevance!==n.relevance)return n.relevance-e.relevance;if(e.language&&n.language){if(O(e.language).supersetOf===n.language)return 1;if(O(n.language).supersetOf===e.language)return -1}return 0}),[s,l]=r,o=s;return o.secondBest=l,o}function x(e){let n=null,t=(e=>{let n=e.className+" ";n+=e.parentNode?e.parentNode.className:"";let t=$.languageDetectRe.exec(n);if(t){let a=O(t[1]);return a||(U(o.replace("{}",t[1])),U("Falling back to no-highlight mode for this block.",e)),a?t[1]:"no-highlight"}return n.split(/\s+/).find(e=>y(e)||O(e))})(e);if(y(t))return;if(z("before:highlightElement",{el:e,language:t}),e.children.length>0&&($.ignoreUnescapedHTML||$.throwUnescapedHTML))throw new H("One of your code blocks includes unescaped HTML.",e.innerHTML);n=e;let a=n.textContent,i=t?N(a,{language:t,ignoreIllegals:!0}):v(a);e.innerHTML=i.value,((e,n,t)=>{let a=n&&r[n]||t;e.classList.add("hljs"),e.classList.add("language-"+a)})(e,t,i.language),e.result={language:i.language,re:i.relevance,relevance:i.relevance},i.secondBest&&(e.secondBest={language:i.secondBest.language,relevance:i.secondBest.relevance}),z("after:highlightElement",{el:e,result:i,text:a})}let k=!1;function M(){"loading"!==document.readyState?document.querySelectorAll($.cssSelector).forEach(x):k=!0}function O(e){return a[e=(e||"").toLowerCase()]||a[r[e]]}function S(e,{languageName:n}){"string"==typeof e&&(e=[e]),e.forEach(e=>{r[e.toLowerCase()]=n})}function C(e){let n=O(e);return n&&!n.disableAutodetect}function z(e,n){let t=e;s.forEach(e=>{e[t]&&e[t](n)})}for(let j in"undefined"!=typeof window&&window.addEventListener&&window.addEventListener("DOMContentLoaded",()=>{k&&M()},!1),Object.assign(n,{highlight:N,highlightAuto:v,highlightAll:M,highlightElement:x,highlightBlock:e=>(P("10.7.0","highlightBlock will be removed entirely in v12.0"),P("10.7.0","Please use highlightElement now."),x(e)),configure(e){$=G($,e)},initHighlighting(){M(),P("10.6.0","initHighlighting() deprecated. Use highlightAll() now.")},initHighlightingOnLoad(){M(),P("10.6.0","initHighlightingOnLoad() deprecated. Use highlightAll() now.")},registerLanguage(e,t){let i=null;try{i=t(n)}catch(r){if(F("Language definition for '{}' could not be registered.".replace("{}",e)),!l)throw r;F(r),i=f}i.name||(i.name=e),a[e]=i,i.rawDefinition=t.bind(null,n),i.aliases&&S(i.aliases,{languageName:e})},unregisterLanguage(e){for(let n of(delete a[e],Object.keys(r)))r[n]===e&&delete r[n]},listLanguages:()=>Object.keys(a),getLanguage:O,registerAliases:S,autoDetection:C,inherit:G,addPlugin(e){var n;(n=e)["before:highlightBlock"]&&!n["before:highlightElement"]&&(n["before:highlightElement"]=e=>{n["before:highlightBlock"](Object.assign({block:e.el},e))}),n["after:highlightBlock"]&&!n["after:highlightElement"]&&(n["after:highlightElement"]=e=>{n["after:highlightBlock"](Object.assign({block:e.el},e))}),s.push(e)}}),n.debugMode=()=>{l=!1},n.safeMode=()=>{l=!0},n.versionString="11.7.0",n.regex={concat:m,lookahead:g,either:p,optional:b,anyNumberOfTimes:u},A)"object"==typeof A[j]&&e.exports(A[j]);return Object.assign(n,A),n})({});let X=e=>({IMPORTANT:{scope:"meta",begin:"!important"},BLOCK_COMMENT:e.C_BLOCK_COMMENT_MODE,HEXCOLOR:{scope:"number",begin:/#(([0-9a-fA-F]{3,4})|(([0-9a-fA-F]{2}){3,4}))\b/},FUNCTION_DISPATCH:{className:"built_in",begin:/[\w-]+(?=\()/},ATTRIBUTE_SELECTOR_MODE:{scope:"selector-attr",begin:/\[/,end:/\]/,illegal:"$",contains:[e.APOS_STRING_MODE,e.QUOTE_STRING_MODE]},CSS_NUMBER_MODE:{scope:"number",begin:e.NUMBER_RE+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",relevance:0},CSS_VARIABLE:{className:"attr",begin:/--[A-Za-z][A-Za-z0-9_-]*/}}),V=["a","abbr","address","article","aside","audio","b","blockquote","body","button","canvas","caption","cite","code","dd","del","details","dfn","div","dl","dt","em","fieldset","figcaption","figure","footer","form","h1","h2","h3","h4","h5","h6","header","hgroup","html","i","iframe","img","input","ins","kbd","label","legend","li","main","mark","menu","nav","object","ol","p","q","quote","samp","section","span","strong","summary","sup","table","tbody","td","textarea","tfoot","th","thead","time","tr","ul","var","video",],J=["any-hover","any-pointer","aspect-ratio","color","color-gamut","color-index","device-aspect-ratio","device-height","device-width","display-mode","forced-colors","grid","height","hover","inverted-colors","monochrome","orientation","overflow-block","overflow-inline","pointer","prefers-color-scheme","prefers-contrast","prefers-reduced-motion","prefers-reduced-transparency","resolution","scan","scripting","update","width","min-width","max-width","min-height","max-height",],Y=["active","any-link","blank","checked","current","default","defined","dir","disabled","drop","empty","enabled","first","first-child","first-of-type","fullscreen","future","focus","focus-visible","focus-within","has","host","host-context","hover","indeterminate","in-range","invalid","is","lang","last-child","last-of-type","left","link","local-link","not","nth-child","nth-col","nth-last-child","nth-last-col","nth-last-of-type","nth-of-type","only-child","only-of-type","optional","out-of-range","past","placeholder-shown","read-only","read-write","required","right","root","scope","target","target-within","user-invalid","valid","visited","where",],ee=["after","backdrop","before","cue","cue-region","first-letter","first-line","grammar-error","marker","part","placeholder","selection","slotted","spelling-error",],en=["align-content","align-items","align-self","all","animation","animation-delay","animation-direction","animation-duration","animation-fill-mode","animation-iteration-count","animation-name","animation-play-state","animation-timing-function","backface-visibility","background","background-attachment","background-blend-mode","background-clip","background-color","background-image","background-origin","background-position","background-repeat","background-size","block-size","border","border-block","border-block-color","border-block-end","border-block-end-color","border-block-end-style","border-block-end-width","border-block-start","border-block-start-color","border-block-start-style","border-block-start-width","border-block-style","border-block-width","border-bottom","border-bottom-color","border-bottom-left-radius","border-bottom-right-radius","border-bottom-style","border-bottom-width","border-collapse","border-color","border-image","border-image-outset","border-image-repeat","border-image-slice","border-image-source","border-image-width","border-inline","border-inline-color","border-inline-end","border-inline-end-color","border-inline-end-style","border-inline-end-width","border-inline-start","border-inline-start-color","border-inline-start-style","border-inline-start-width","border-inline-style","border-inline-width","border-left","border-left-color","border-left-style","border-left-width","border-radius","border-right","border-right-color","border-right-style","border-right-width","border-spacing","border-style","border-top","border-top-color","border-top-left-radius","border-top-right-radius","border-top-style","border-top-width","border-width","bottom","box-decoration-break","box-shadow","box-sizing","break-after","break-before","break-inside","caption-side","caret-color","clear","clip","clip-path","clip-rule","color","column-count","column-fill","column-gap","column-rule","column-rule-color","column-rule-style","column-rule-width","column-span","column-width","columns","contain","content","content-visibility","counter-increment","counter-reset","cue","cue-after","cue-before","cursor","direction","display","empty-cells","filter","flex","flex-basis","flex-direction","flex-flow","flex-grow","flex-shrink","flex-wrap","float","flow","font","font-display","font-family","font-feature-settings","font-kerning","font-language-override","font-size","font-size-adjust","font-smoothing","font-stretch","font-style","font-synthesis","font-variant","font-variant-caps","font-variant-east-asian","font-variant-ligatures","font-variant-numeric","font-variant-position","font-variation-settings","font-weight","gap","glyph-orientation-vertical","grid","grid-area","grid-auto-columns","grid-auto-flow","grid-auto-rows","grid-column","grid-column-end","grid-column-start","grid-gap","grid-row","grid-row-end","grid-row-start","grid-template","grid-template-areas","grid-template-columns","grid-template-rows","hanging-punctuation","height","hyphens","icon","image-orientation","image-rendering","image-resolution","ime-mode","inline-size","isolation","justify-content","left","letter-spacing","line-break","line-height","list-style","list-style-image","list-style-position","list-style-type","margin","margin-block","margin-block-end","margin-block-start","margin-bottom","margin-inline","margin-inline-end","margin-inline-start","margin-left","margin-right","margin-top","marks","mask","mask-border","mask-border-mode","mask-border-outset","mask-border-repeat","mask-border-slice","mask-border-source","mask-border-width","mask-clip","mask-composite","mask-image","mask-mode","mask-origin","mask-position","mask-repeat","mask-size","mask-type","max-block-size","max-height","max-inline-size","max-width","min-block-size","min-height","min-inline-size","min-width","mix-blend-mode","nav-down","nav-index","nav-left","nav-right","nav-up","none","normal","object-fit","object-position","opacity","order","orphans","outline","outline-color","outline-offset","outline-style","outline-width","overflow","overflow-wrap","overflow-x","overflow-y","padding","padding-block","padding-block-end","padding-block-start","padding-bottom","padding-inline","padding-inline-end","padding-inline-start","padding-left","padding-right","padding-top","page-break-after","page-break-before","page-break-inside","pause","pause-after","pause-before","perspective","perspective-origin","pointer-events","position","quotes","resize","rest","rest-after","rest-before","right","row-gap","scroll-margin","scroll-margin-block","scroll-margin-block-end","scroll-margin-block-start","scroll-margin-bottom","scroll-margin-inline","scroll-margin-inline-end","scroll-margin-inline-start","scroll-margin-left","scroll-margin-right","scroll-margin-top","scroll-padding","scroll-padding-block","scroll-padding-block-end","scroll-padding-block-start","scroll-padding-bottom","scroll-padding-inline","scroll-padding-inline-end","scroll-padding-inline-start","scroll-padding-left","scroll-padding-right","scroll-padding-top","scroll-snap-align","scroll-snap-stop","scroll-snap-type","scrollbar-color","scrollbar-gutter","scrollbar-width","shape-image-threshold","shape-margin","shape-outside","speak","speak-as","src","tab-size","table-layout","text-align","text-align-all","text-align-last","text-combine-upright","text-decoration","text-decoration-color","text-decoration-line","text-decoration-style","text-emphasis","text-emphasis-color","text-emphasis-position","text-emphasis-style","text-indent","text-justify","text-orientation","text-overflow","text-rendering","text-shadow","text-transform","text-underline-position","top","transform","transform-box","transform-origin","transform-style","transition","transition-delay","transition-duration","transition-property","transition-timing-function","unicode-bidi","vertical-align","visibility","voice-balance","voice-duration","voice-family","voice-pitch","voice-range","voice-rate","voice-stress","voice-volume","white-space","widows","width","will-change","word-break","word-spacing","word-wrap","writing-mode","z-index",].reverse(),et=Y.concat(ee);var ea="\\.([0-9](_*[0-9])*)",ei="[0-9a-fA-F](_*[0-9a-fA-F])*",er={className:"number",variants:[{begin:`(\\b([0-9](_*[0-9])*)((${ea})|\\.)?|(${ea}))[eE][+-]?([0-9](_*[0-9])*)[fFdD]?\\b`},{begin:`\\b([0-9](_*[0-9])*)((${ea})[fFdD]?\\b|\\.([fFdD]\\b)?)`},{begin:`(${ea})[fFdD]?\\b`},{begin:"\\b([0-9](_*[0-9])*)[fFdD]\\b"},{begin:`\\b0[xX]((${ei})\\.?|(${ei})?\\.(${ei}))[pP][+-]?([0-9](_*[0-9])*)[fFdD]?\\b`},{begin:"\\b(0|[1-9](_*[0-9])*)[lL]?\\b"},{begin:`\\b0[xX](${ei})[lL]?\\b`},{begin:"\\b0(_*[0-7])*[lL]?\\b"},{begin:"\\b0[bB][01](_*[01])*[lL]?\\b"},],relevance:0};let es="[A-Za-z$_][0-9A-Za-z$_]*",el=["as","in","of","if","for","while","finally","var","new","function","do","return","void","else","break","catch","instanceof","with","throw","case","default","try","switch","continue","typeof","delete","let","yield","const","class","debugger","async","await","static","import","from","export","extends",],eo=["true","false","null","undefined","NaN","Infinity"],ec=["Object","Function","Boolean","Symbol","Math","Date","Number","BigInt","String","RegExp","Array","Float32Array","Float64Array","Int8Array","Uint8Array","Uint8ClampedArray","Int16Array","Int32Array","Uint16Array","Uint32Array","BigInt64Array","BigUint64Array","Set","Map","WeakSet","WeakMap","ArrayBuffer","SharedArrayBuffer","Atomics","DataView","JSON","Promise","Generator","GeneratorFunction","AsyncFunction","Reflect","Proxy","Intl","WebAssembly",],ed=["Error","EvalError","InternalError","RangeError","ReferenceError","SyntaxError","TypeError","URIError",],eg=["setInterval","setTimeout","clearInterval","clearTimeout","require","exports","eval","isFinite","isNaN","parseFloat","parseInt","decodeURI","decodeURIComponent","encodeURI","encodeURIComponent","escape","unescape",],eu=["arguments","this","super","console","window","document","localStorage","module","global",],eb=[].concat(eg,ec,ed);function em(e){var n;let t=e.regex,a=es,i={begin:/<[A-Za-z0-9\\._:-]+/,end:/\/[A-Za-z0-9\\._:-]+>|\/>/,isTrulyOpeningTag(e,n){let t=e[0].length+e.index,a=e.input[t];if("<"===a||","===a)return void n.ignoreMatch();let i;">"===a&&(((e,{after:n})=>{let t="",v={match:[/const|var|let/,/\s+/,a,/\s*/,/=\s*/,/(async\s*)?/,t.lookahead(w),],keywords:"async",className:{1:"keyword",3:"title.function"},contains:[f]};return{name:"Javascript",aliases:["js","jsx","mjs","cjs"],keywords:r,exports:{PARAMS_CONTAINS:h,CLASS_REFERENCE:$},illegal:/#(?![$_A-z])/,contains:[e.SHEBANG({label:"shebang",binary:"node",relevance:5}),{label:"use_strict",className:"meta",relevance:10,begin:/^\s*['"]use (strict|asm)['"]/},e.APOS_STRING_MODE,e.QUOTE_STRING_MODE,d,g,u,b,{match:/\$\d+/},o,$,{className:"attr",begin:a+t.lookahead(":"),relevance:0},v,{begin:"("+e.RE_STARTERS_RE+"|\\b(case|return|throw)\\b)\\s*",keywords:"return throw case",relevance:0,contains:[b,e.REGEXP_MODE,{className:"function",begin:w,returnBegin:!0,end:"\\s*=>",contains:[{className:"params",variants:[{begin:e.UNDERSCORE_IDENT_RE,relevance:0},{className:null,begin:/\(\s*\)/,skip:!0},{begin:/\(/,end:/\)/,excludeBegin:!0,excludeEnd:!0,keywords:r,contains:h},]},]},{begin:/,/,relevance:0},{match:/\s+/,relevance:0},{variants:[{begin:"<>",end:""},{match:/<[A-Za-z0-9\\._:-]+\s*\/>/},{begin:i.begin,"on:begin":i.isTrulyOpeningTag,end:i.end},],subLanguage:"xml",contains:[{begin:i.begin,end:i.end,skip:!0,contains:["self"]},]},]},{variants:[{match:[/function/,/\s+/,a,/(?=\s*\()/]},{match:[/function/,/\s*(?=\()/]},],className:{1:"keyword",3:"title.function"},label:"func.def",contains:[f],illegal:/%/},{beginKeywords:"while if switch catch for"},{begin:"\\b(?!function)"+e.UNDERSCORE_IDENT_RE+"\\([^()]*(\\([^()]*(\\([^()]*\\)[^()]*)*\\)[^()]*)*\\)\\s*\\{",returnBegin:!0,label:"func.def",contains:[f,e.inherit(e.TITLE_MODE,{begin:a,className:"title.function"}),]},{match:/\.\.\./,relevance:0},N,{match:"\\$"+a,relevance:0},{match:[/\bconstructor(?=\s*\()/],className:{1:"title.function"},contains:[f]},y,{relevance:0,match:/\b[A-Z][A-Z_0-9]+\b/,className:"variable.constant"},E,{match:[/get|set/,/\s+/,a,/(?=\()/],className:{1:"keyword",3:"title.function"},contains:[{begin:/\(\)/},f]},{match:/\$[(.]/},]}}let ep=e=>m(/\b/,e,/\w$/.test(e)?/\b/:/\B/),e8=["Protocol","Type"].map(ep),eh=["init","self"].map(ep),ef=["Any","Self"],eE=["actor","any","associatedtype","async","await",/as\?/,/as!/,"as","break","case","catch","class","continue","convenience","default","defer","deinit","didSet","distributed","do","dynamic","else","enum","extension","fallthrough",/fileprivate\(set\)/,"fileprivate","final","for","func","get","guard","if","import","indirect","infix",/init\?/,/init!/,"inout",/internal\(set\)/,"internal","in","is","isolated","nonisolated","lazy","let","mutating","nonmutating",/open\(set\)/,"open","operator","optional","override","postfix","precedencegroup","prefix",/private\(set\)/,"private","protocol",/public\(set\)/,"public","repeat","required","rethrows","return","set","some","static","struct","subscript","super","switch","throws","throw",/try\?/,/try!/,"try","typealias",/unowned\(safe\)/,/unowned\(unsafe\)/,"unowned","var","weak","where","while","willSet",],e$=["false","nil","true"],ey=["assignment","associativity","higherThan","left","lowerThan","none","right",],eN=["#colorLiteral","#column","#dsohandle","#else","#elseif","#endif","#error","#file","#fileID","#fileLiteral","#filePath","#function","#if","#imageLiteral","#keyPath","#line","#selector","#sourceLocation","#warn_unqualified_access","#warning",],ew=["abs","all","any","assert","assertionFailure","debugPrint","dump","fatalError","getVaList","isKnownUniquelyReferenced","max","min","numericCast","pointwiseMax","pointwiseMin","precondition","preconditionFailure","print","readLine","repeatElement","sequence","stride","swap","swift_unboxFromSwiftValueWithType","transcode","type","unsafeBitCast","unsafeDowncast","withExtendedLifetime","withUnsafeMutablePointer","withUnsafePointer","withVaList","withoutActuallyEscaping","zip",],ev=p(/[/=\-+!*%<>&|^~?]/,/[\u00A1-\u00A7]/,/[\u00A9\u00AB]/,/[\u00AC\u00AE]/,/[\u00B0\u00B1]/,/[\u00B6\u00BB\u00BF\u00D7\u00F7]/,/[\u2016-\u2017]/,/[\u2020-\u2027]/,/[\u2030-\u203E]/,/[\u2041-\u2053]/,/[\u2055-\u205E]/,/[\u2190-\u23FF]/,/[\u2500-\u2775]/,/[\u2794-\u2BFF]/,/[\u2E00-\u2E7F]/,/[\u3001-\u3003]/,/[\u3008-\u3020]/,/[\u3030]/),ex=p(ev,/[\u0300-\u036F]/,/[\u1DC0-\u1DFF]/,/[\u20D0-\u20FF]/,/[\uFE00-\uFE0F]/,/[\uFE20-\uFE2F]/),ek=m(ev,ex,"*"),eM=p(/[a-zA-Z_]/,/[\u00A8\u00AA\u00AD\u00AF\u00B2-\u00B5\u00B7-\u00BA]/,/[\u00BC-\u00BE\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u00FF]/,/[\u0100-\u02FF\u0370-\u167F\u1681-\u180D\u180F-\u1DBF]/,/[\u1E00-\u1FFF]/,/[\u200B-\u200D\u202A-\u202E\u203F-\u2040\u2054\u2060-\u206F]/,/[\u2070-\u20CF\u2100-\u218F\u2460-\u24FF\u2776-\u2793]/,/[\u2C00-\u2DFF\u2E80-\u2FFF]/,/[\u3004-\u3007\u3021-\u302F\u3031-\u303F\u3040-\uD7FF]/,/[\uF900-\uFD3D\uFD40-\uFDCF\uFDF0-\uFE1F\uFE30-\uFE44]/,/[\uFE47-\uFEFE\uFF00-\uFFFD]/),eO=p(eM,/\d/,/[\u0300-\u036F\u1DC0-\u1DFF\u20D0-\u20FF\uFE20-\uFE2F]/),eS=m(eM,eO,"*"),eA=m(/[A-Z]/,eO,"*"),eC=["autoclosure",m(/convention\(/,p("swift","block","c"),/\)/),"discardableResult","dynamicCallable","dynamicMemberLookup","escaping","frozen","GKInspectable","IBAction","IBDesignable","IBInspectable","IBOutlet","IBSegueAction","inlinable","main","nonobjc","NSApplicationMain","NSCopying","NSManaged",m(/objc\(/,eS,/\)/),"objc","objcMembers","propertyWrapper","requires_stored_property_inits","resultBuilder","testable","UIApplicationMain","unknown","usableFromInline",],eT=["iOS","iOSApplicationExtension","macOS","macOSApplicationExtension","macCatalyst","macCatalystApplicationExtension","watchOS","watchOSApplicationExtension","tvOS","tvOSApplicationExtension","swift",];var eR=Object.freeze({__proto__:null,grmr_bash(e){let n=e.regex,t={};Object.assign(t,{className:"variable",variants:[{begin:n.concat(/\$[\w\d#@][\w\d_]*/,"(?![\\w\\d])(?![$])")},{begin:/\$\{/,end:/\}/,contains:["self",{begin:/:-/,contains:[t]}]},]});let a={className:"subst",begin:/\$\(/,end:/\)/,contains:[e.BACKSLASH_ESCAPE]},i={begin:/<<-?\s*(?=\w+)/,starts:{contains:[e.END_SAME_AS_BEGIN({begin:/(\w+)/,end:/(\w+)/,className:"string"}),]}},r={className:"string",begin:/"/,end:/"/,contains:[e.BACKSLASH_ESCAPE,t,a]};a.contains.push(r);let s={begin:/\$?\(\(/,end:/\)\)/,contains:[{begin:/\d+#[0-9a-f]+/,className:"number"},e.NUMBER_MODE,t,]},l=e.SHEBANG({binary:"(fish|bash|zsh|sh|csh|ksh|tcsh|dash|scsh)",relevance:10}),o={className:"function",begin:/\w[\w\d_]*\s*\(\s*\)\s*\{/,returnBegin:!0,contains:[e.inherit(e.TITLE_MODE,{begin:/\w[\w\d_]*/})],relevance:0};return{name:"Bash",aliases:["sh"],keywords:{$pattern:/\b[a-z][a-z0-9._-]+\b/,keyword:["if","then","else","elif","fi","for","while","in","do","done","case","esac","function",],literal:["true","false"],built_in:["break","cd","continue","eval","exec","exit","export","getopts","hash","pwd","readonly","return","shift","test","times","trap","umask","unset","alias","bind","builtin","caller","command","declare","echo","enable","help","let","local","logout","mapfile","printf","read","readarray","source","type","typeset","ulimit","unalias","set","shopt","autoload","bg","bindkey","bye","cap","chdir","clone","comparguments","compcall","compctl","compdescribe","compfiles","compgroups","compquote","comptags","comptry","compvalues","dirs","disable","disown","echotc","echoti","emulate","fc","fg","float","functions","getcap","getln","history","integer","jobs","kill","limit","log","noglob","popd","print","pushd","pushln","rehash","sched","setcap","setopt","stat","suspend","ttyctl","unfunction","unhash","unlimit","unsetopt","vared","wait","whence","where","which","zcompile","zformat","zftp","zle","zmodload","zparseopts","zprof","zpty","zregexparse","zsocket","zstyle","ztcp","chcon","chgrp","chown","chmod","cp","dd","df","dir","dircolors","ln","ls","mkdir","mkfifo","mknod","mktemp","mv","realpath","rm","rmdir","shred","sync","touch","truncate","vdir","b2sum","base32","base64","cat","cksum","comm","csplit","cut","expand","fmt","fold","head","join","md5sum","nl","numfmt","od","paste","ptx","pr","sha1sum","sha224sum","sha256sum","sha384sum","sha512sum","shuf","sort","split","sum","tac","tail","tr","tsort","unexpand","uniq","wc","arch","basename","chroot","date","dirname","du","echo","env","expr","factor","groups","hostid","id","link","logname","nice","nohup","nproc","pathchk","pinky","printenv","printf","pwd","readlink","runcon","seq","sleep","stat","stdbuf","stty","tee","test","timeout","tty","uname","unlink","uptime","users","who","whoami","yes",]},contains:[l,e.SHEBANG(),o,s,e.HASH_COMMENT_MODE,i,{match:/(\/[a-z._-]+)+/},r,{className:"",begin:/\\"/},{className:"string",begin:/'/,end:/'/},t,]}},grmr_c(e){let n=e.regex,t=e.COMMENT("//","$",{contains:[{begin:/\\\n/}]}),a="[a-zA-Z_]\\w*::",i="(decltype\\(auto\\)|"+n.optional(a)+"[a-zA-Z_]\\w*"+n.optional("<[^<>]+>")+")",r={className:"type",variants:[{begin:"\\b[a-z\\d_]*_t\\b"},{match:/\batomic_[a-z]{3,6}\b/},]},s={className:"string",variants:[{begin:'(u8?|U|L)?"',end:'"',illegal:"\\n",contains:[e.BACKSLASH_ESCAPE]},{begin:"(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)",end:"'",illegal:"."},e.END_SAME_AS_BEGIN({begin:/(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/,end:/\)([^()\\ ]{0,16})"/}),]},l={className:"number",variants:[{begin:"\\b(0b[01']+)"},{begin:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)"},{begin:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"},],relevance:0},o={className:"meta",begin:/#\s*[a-z]+\b/,end:/$/,keywords:{keyword:"if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include"},contains:[{begin:/\\\n/,relevance:0},e.inherit(s,{className:"string"}),{className:"string",begin:/<.*?>/},t,e.C_BLOCK_COMMENT_MODE,]},c={className:"title",begin:n.optional(a)+e.IDENT_RE,relevance:0},d=n.optional(a)+e.IDENT_RE+"\\s*\\(",g={keyword:["asm","auto","break","case","continue","default","do","else","enum","extern","for","fortran","goto","if","inline","register","restrict","return","sizeof","struct","switch","typedef","union","volatile","while","_Alignas","_Alignof","_Atomic","_Generic","_Noreturn","_Static_assert","_Thread_local","alignas","alignof","noreturn","static_assert","thread_local","_Pragma",],type:["float","double","signed","unsigned","int","short","long","char","void","_Bool","_Complex","_Imaginary","_Decimal32","_Decimal64","_Decimal128","const","static","complex","bool","imaginary",],literal:"true false NULL",built_in:"std string wstring cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set pair bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap priority_queue make_pair array shared_ptr abort terminate abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf future isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr"},u=[o,r,t,e.C_BLOCK_COMMENT_MODE,l,s],b={variants:[{begin:/=/,end:/;/},{begin:/\(/,end:/\)/},{beginKeywords:"new throw return else",end:/;/},],keywords:g,contains:u.concat([{begin:/\(/,end:/\)/,keywords:g,contains:u.concat(["self"]),relevance:0},]),relevance:0},m={begin:"("+i+"[\\*&\\s]+)+"+d,returnBegin:!0,end:/[{;=]/,excludeEnd:!0,keywords:g,illegal:/[^\w\s\*&:<>.]/,contains:[{begin:"decltype\\(auto\\)",keywords:g,relevance:0},{begin:d,returnBegin:!0,contains:[e.inherit(c,{className:"title.function"}),],relevance:0},{relevance:0,match:/,/},{className:"params",begin:/\(/,end:/\)/,keywords:g,relevance:0,contains:[t,e.C_BLOCK_COMMENT_MODE,s,l,r,{begin:/\(/,end:/\)/,keywords:g,relevance:0,contains:["self",t,e.C_BLOCK_COMMENT_MODE,s,l,r]},]},r,t,e.C_BLOCK_COMMENT_MODE,o,]};return{name:"C",aliases:["h"],keywords:g,disableAutodetect:!0,illegal:"=]/,contains:[{beginKeywords:"final class struct"},e.TITLE_MODE,]},]),exports:{preprocessor:o,strings:s,keywords:g}}},grmr_cpp(e){let n=e.regex,t=e.COMMENT("//","$",{contains:[{begin:/\\\n/}]}),a="[a-zA-Z_]\\w*::",i="(?!struct)(decltype\\(auto\\)|"+n.optional(a)+"[a-zA-Z_]\\w*"+n.optional("<[^<>]+>")+")",r={className:"type",begin:"\\b[a-z\\d_]*_t\\b"},s={className:"string",variants:[{begin:'(u8?|U|L)?"',end:'"',illegal:"\\n",contains:[e.BACKSLASH_ESCAPE]},{begin:"(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)",end:"'",illegal:"."},e.END_SAME_AS_BEGIN({begin:/(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/,end:/\)([^()\\ ]{0,16})"/}),]},l={className:"number",variants:[{begin:"\\b(0b[01']+)"},{begin:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)"},{begin:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"},],relevance:0},o={className:"meta",begin:/#\s*[a-z]+\b/,end:/$/,keywords:{keyword:"if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include"},contains:[{begin:/\\\n/,relevance:0},e.inherit(s,{className:"string"}),{className:"string",begin:/<.*?>/},t,e.C_BLOCK_COMMENT_MODE,]},c={className:"title",begin:n.optional(a)+e.IDENT_RE,relevance:0},d=n.optional(a)+e.IDENT_RE+"\\s*\\(",g={type:["bool","char","char16_t","char32_t","char8_t","double","float","int","long","short","void","wchar_t","unsigned","signed","const","static",],keyword:["alignas","alignof","and","and_eq","asm","atomic_cancel","atomic_commit","atomic_noexcept","auto","bitand","bitor","break","case","catch","class","co_await","co_return","co_yield","compl","concept","const_cast|10","consteval","constexpr","constinit","continue","decltype","default","delete","do","dynamic_cast|10","else","enum","explicit","export","extern","false","final","for","friend","goto","if","import","inline","module","mutable","namespace","new","noexcept","not","not_eq","nullptr","operator","or","or_eq","override","private","protected","public","reflexpr","register","reinterpret_cast|10","requires","return","sizeof","static_assert","static_cast|10","struct","switch","synchronized","template","this","thread_local","throw","transaction_safe","transaction_safe_dynamic","true","try","typedef","typeid","typename","union","using","virtual","volatile","while","xor","xor_eq",],literal:["NULL","false","nullopt","nullptr","true"],built_in:["_Pragma"],_type_hints:["any","auto_ptr","barrier","binary_semaphore","bitset","complex","condition_variable","condition_variable_any","counting_semaphore","deque","false_type","future","imaginary","initializer_list","istringstream","jthread","latch","lock_guard","multimap","multiset","mutex","optional","ostringstream","packaged_task","pair","promise","priority_queue","queue","recursive_mutex","recursive_timed_mutex","scoped_lock","set","shared_future","shared_lock","shared_mutex","shared_timed_mutex","shared_ptr","stack","string_view","stringstream","timed_mutex","thread","true_type","tuple","unique_lock","unique_ptr","unordered_map","unordered_multimap","unordered_multiset","unordered_set","variant","vector","weak_ptr","wstring","wstring_view",]},u={className:"function.dispatch",relevance:0,keywords:{_hint:["abort","abs","acos","apply","as_const","asin","atan","atan2","calloc","ceil","cerr","cin","clog","cos","cosh","cout","declval","endl","exchange","exit","exp","fabs","floor","fmod","forward","fprintf","fputs","free","frexp","fscanf","future","invoke","isalnum","isalpha","iscntrl","isdigit","isgraph","islower","isprint","ispunct","isspace","isupper","isxdigit","labs","launder","ldexp","log","log10","make_pair","make_shared","make_shared_for_overwrite","make_tuple","make_unique","malloc","memchr","memcmp","memcpy","memset","modf","move","pow","printf","putchar","puts","realloc","scanf","sin","sinh","snprintf","sprintf","sqrt","sscanf","std","stderr","stdin","stdout","strcat","strchr","strcmp","strcpy","strcspn","strlen","strncat","strncmp","strncpy","strpbrk","strrchr","strspn","strstr","swap","tan","tanh","terminate","to_underlying","tolower","toupper","vfprintf","visit","vprintf","vsprintf",]},begin:n.concat(/\b/,/(?!decltype)/,/(?!if)/,/(?!for)/,/(?!switch)/,/(?!while)/,e.IDENT_RE,n.lookahead(/(<[^<>]+>|)\s*\(/))},b=[u,o,r,t,e.C_BLOCK_COMMENT_MODE,l,s],m={variants:[{begin:/=/,end:/;/},{begin:/\(/,end:/\)/},{beginKeywords:"new throw return else",end:/;/},],keywords:g,contains:b.concat([{begin:/\(/,end:/\)/,keywords:g,contains:b.concat(["self"]),relevance:0},]),relevance:0},p={className:"function",begin:"("+i+"[\\*&\\s]+)+"+d,returnBegin:!0,end:/[{;=]/,excludeEnd:!0,keywords:g,illegal:/[^\w\s\*&:<>.]/,contains:[{begin:"decltype\\(auto\\)",keywords:g,relevance:0},{begin:d,returnBegin:!0,contains:[c],relevance:0},{begin:/::/,relevance:0},{begin:/:/,endsWithParent:!0,contains:[s,l]},{relevance:0,match:/,/},{className:"params",begin:/\(/,end:/\)/,keywords:g,relevance:0,contains:[t,e.C_BLOCK_COMMENT_MODE,s,l,r,{begin:/\(/,end:/\)/,keywords:g,relevance:0,contains:["self",t,e.C_BLOCK_COMMENT_MODE,s,l,r]},]},r,t,e.C_BLOCK_COMMENT_MODE,o,]};return{name:"C++",aliases:["cc","c++","h++","hpp","hh","hxx","cxx"],keywords:g,illegal:"",keywords:g,contains:["self",r]},{begin:e.IDENT_RE+"::",keywords:g},{match:[/\b(?:enum(?:\s+(?:class|struct))?|class|struct|union)/,/\s+/,/\w+/,],className:{1:"keyword",3:"title.class"}},])}},grmr_csharp(e){let n={keyword:["abstract","as","base","break","case","catch","class","const","continue","do","else","event","explicit","extern","finally","fixed","for","foreach","goto","if","implicit","in","interface","internal","is","lock","namespace","new","operator","out","override","params","private","protected","public","readonly","record","ref","return","scoped","sealed","sizeof","stackalloc","static","struct","switch","this","throw","try","typeof","unchecked","unsafe","using","virtual","void","volatile","while",].concat(["add","alias","and","ascending","async","await","by","descending","equals","from","get","global","group","init","into","join","let","nameof","not","notnull","on","or","orderby","partial","remove","select","set","unmanaged","value|0","var","when","where","with","yield",]),built_in:["bool","byte","char","decimal","delegate","double","dynamic","enum","float","int","long","nint","nuint","object","sbyte","short","string","ulong","uint","ushort",],literal:["default","false","null","true"]},t=e.inherit(e.TITLE_MODE,{begin:"[a-zA-Z](\\.?\\w)*"}),a={className:"number",variants:[{begin:"\\b(0b[01']+)"},{begin:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{begin:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"},],relevance:0},i={className:"string",begin:'@"',end:'"',contains:[{begin:'""'}]},r=e.inherit(i,{illegal:/\n/}),s={className:"subst",begin:/\{/,end:/\}/,keywords:n},l=e.inherit(s,{illegal:/\n/}),o={className:"string",begin:/\$"/,end:'"',illegal:/\n/,contains:[{begin:/\{\{/},{begin:/\}\}/},e.BACKSLASH_ESCAPE,l,]},c={className:"string",begin:/\$@"/,end:'"',contains:[{begin:/\{\{/},{begin:/\}\}/},{begin:'""'},s,]},d=e.inherit(c,{illegal:/\n/,contains:[{begin:/\{\{/},{begin:/\}\}/},{begin:'""'},l]});s.contains=[c,o,i,e.APOS_STRING_MODE,e.QUOTE_STRING_MODE,a,e.C_BLOCK_COMMENT_MODE,],l.contains=[d,o,r,e.APOS_STRING_MODE,e.QUOTE_STRING_MODE,a,e.inherit(e.C_BLOCK_COMMENT_MODE,{illegal:/\n/}),];let g={variants:[c,o,i,e.APOS_STRING_MODE,e.QUOTE_STRING_MODE]},u={begin:"<",end:">",contains:[{beginKeywords:"in out"},t]},b=e.IDENT_RE+"(<"+e.IDENT_RE+"(\\s*,\\s*"+e.IDENT_RE+")*>)?(\\[\\])?",m={begin:"@"+e.IDENT_RE,relevance:0};return{name:"C#",aliases:["cs","c#"],keywords:n,illegal:/::/,contains:[e.COMMENT("///","$",{returnBegin:!0,contains:[{className:"doctag",variants:[{begin:"///",relevance:0},{begin:""},{begin:""},]},]}),e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,{className:"meta",begin:"#",end:"$",keywords:{keyword:"if else elif endif define undef warning error line region endregion pragma checksum"}},g,a,{beginKeywords:"class interface",relevance:0,end:/[{;=]/,illegal:/[^\s:,]/,contains:[{beginKeywords:"where class"},t,u,e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,]},{beginKeywords:"namespace",relevance:0,end:/[{;=]/,illegal:/[^\s:]/,contains:[t,e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE]},{beginKeywords:"record",relevance:0,end:/[{;=]/,illegal:/[^\s:]/,contains:[t,u,e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE]},{className:"meta",begin:"^\\s*\\[(?=[\\w])",excludeBegin:!0,end:"\\]",excludeEnd:!0,contains:[{className:"string",begin:/"/,end:/"/},]},{beginKeywords:"new return throw await else",relevance:0},{className:"function",begin:"("+b+"\\s+)+"+e.IDENT_RE+"\\s*(<[^=]+>\\s*)?\\(",returnBegin:!0,end:/\s*[{;=]/,excludeEnd:!0,keywords:n,contains:[{beginKeywords:"public private protected static internal protected abstract async extern override unsafe virtual new sealed partial",relevance:0},{begin:e.IDENT_RE+"\\s*(<[^=]+>\\s*)?\\(",returnBegin:!0,contains:[e.TITLE_MODE,u],relevance:0},{match:/\(\)/},{className:"params",begin:/\(/,end:/\)/,excludeBegin:!0,excludeEnd:!0,keywords:n,relevance:0,contains:[g,a,e.C_BLOCK_COMMENT_MODE]},e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,]},m,]}},grmr_css(e){let n=e.regex,t=X(e),a=[e.APOS_STRING_MODE,e.QUOTE_STRING_MODE];return{name:"CSS",case_insensitive:!0,illegal:/[=|'\$]/,keywords:{keyframePosition:"from to"},classNameAliases:{keyframePosition:"selector-tag"},contains:[t.BLOCK_COMMENT,{begin:/-(webkit|moz|ms|o)-(?=[a-z])/},t.CSS_NUMBER_MODE,{className:"selector-id",begin:/#[A-Za-z0-9_-]+/,relevance:0},{className:"selector-class",begin:"\\.[a-zA-Z-][a-zA-Z0-9_-]*",relevance:0},t.ATTRIBUTE_SELECTOR_MODE,{className:"selector-pseudo",variants:[{begin:":("+Y.join("|")+")"},{begin:":(:)?("+ee.join("|")+")"},]},t.CSS_VARIABLE,{className:"attribute",begin:"\\b("+en.join("|")+")\\b"},{begin:/:/,end:/[;}{]/,contains:[t.BLOCK_COMMENT,t.HEXCOLOR,t.IMPORTANT,t.CSS_NUMBER_MODE,...a,{begin:/(url|data-uri)\(/,end:/\)/,relevance:0,keywords:{built_in:"url data-uri"},contains:[...a,{className:"string",begin:/[^)]/,endsWithParent:!0,excludeEnd:!0},]},t.FUNCTION_DISPATCH,]},{begin:n.lookahead(/@/),end:"[{;]",relevance:0,illegal:/:/,contains:[{className:"keyword",begin:/@-?\w[\w]*(-\w+)*/},{begin:/\s/,endsWithParent:!0,excludeEnd:!0,relevance:0,keywords:{$pattern:/[a-z-]+/,keyword:"and or not only",attribute:J.join(" ")},contains:[{begin:/[a-z-]+(?=:)/,className:"attribute"},...a,t.CSS_NUMBER_MODE,]},]},{className:"selector-tag",begin:"\\b("+V.join("|")+")\\b"},]}},grmr_diff(e){let n=e.regex;return{name:"Diff",aliases:["patch"],contains:[{className:"meta",relevance:10,match:n.either(/^@@ +-\d+,\d+ +\+\d+,\d+ +@@/,/^\*\*\* +\d+,\d+ +\*\*\*\*$/,/^--- +\d+,\d+ +----$/)},{className:"comment",variants:[{begin:n.either(/Index: /,/^index/,/={3,}/,/^-{3}/,/^\*{3} /,/^\+{3}/,/^diff --git/),end:/$/},{match:/^\*{15}$/},]},{className:"addition",begin:/^\+/,end:/$/},{className:"deletion",begin:/^-/,end:/$/},{className:"addition",begin:/^!/,end:/$/},]}},grmr_go(e){let n={keyword:["break","case","chan","const","continue","default","defer","else","fallthrough","for","func","go","goto","if","import","interface","map","package","range","return","select","struct","switch","type","var",],type:["bool","byte","complex64","complex128","error","float32","float64","int8","int16","int32","int64","string","uint8","uint16","uint32","uint64","int","uint","uintptr","rune",],literal:["true","false","iota","nil"],built_in:["append","cap","close","complex","copy","imag","len","make","new","panic","print","println","real","recover","delete",]};return{name:"Go",aliases:["golang"],keywords:n,illegal:"e(n,t,a-1))}("(?:<"+t+"~~~(?:\\s*,\\s*"+t+"~~~)*>)?",/~~~/g,2),i={keyword:["synchronized","abstract","private","var","static","if","const ","for","while","strictfp","finally","protected","import","native","final","void","enum","else","break","transient","catch","instanceof","volatile","case","assert","package","default","public","try","switch","continue","throws","protected","public","private","module","requires","exports","do","sealed","yield","permits",],literal:["false","true","null"],type:["char","boolean","long","float","int","byte","short","double",],built_in:["super","this"]},r={className:"meta",begin:"@"+t,contains:[{begin:/\(/,end:/\)/,contains:["self"]},]},s={className:"params",begin:/\(/,end:/\)/,keywords:i,relevance:0,contains:[e.C_BLOCK_COMMENT_MODE],endsParent:!0};return{name:"Java",aliases:["jsp"],keywords:i,illegal:/<\/|#/,contains:[e.COMMENT("/\\*\\*","\\*/",{relevance:0,contains:[{begin:/\w+@/,relevance:0},{className:"doctag",begin:"@[A-Za-z]+"},]}),{begin:/import java\.[a-z]+\./,keywords:"import",relevance:2},e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,{begin:/"""/,end:/"""/,className:"string",contains:[e.BACKSLASH_ESCAPE]},e.APOS_STRING_MODE,e.QUOTE_STRING_MODE,{match:[/\b(?:class|interface|enum|extends|implements|new)/,/\s+/,t,],className:{1:"keyword",3:"title.class"}},{match:/non-sealed/,scope:"keyword"},{begin:[n.concat(/(?!else)/,t),/\s+/,t,/\s+/,/=(?!=)/],className:{1:"type",3:"variable",5:"operator"}},{begin:[/record/,/\s+/,t],className:{1:"keyword",3:"title.class"},contains:[s,e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE]},{beginKeywords:"new throw return else",relevance:0},{begin:["(?:"+a+"\\s+)",e.UNDERSCORE_IDENT_RE,/\s*(?=\()/],className:{2:"title.function"},keywords:i,contains:[{className:"params",begin:/\(/,end:/\)/,keywords:i,relevance:0,contains:[r,e.APOS_STRING_MODE,e.QUOTE_STRING_MODE,er,e.C_BLOCK_COMMENT_MODE,]},e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,]},er,r,]}},grmr_javascript:em,grmr_json(e){let n=["true","false","null"],t={scope:"literal",beginKeywords:n.join(" ")};return{name:"JSON",keywords:{literal:n},contains:[{className:"attr",begin:/"(\\.|[^\\"\r\n])*"(?=\s*:)/,relevance:1.01},{match:/[{}[\],:]/,className:"punctuation",relevance:0},e.QUOTE_STRING_MODE,t,e.C_NUMBER_MODE,e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,],illegal:"\\S"}},grmr_kotlin(e){let n={keyword:"abstract as val var vararg get set class object open private protected public noinline crossinline dynamic final enum if else do while for when throw try catch finally import package is in fun override companion reified inline lateinit init interface annotation data sealed internal infix operator out by constructor super tailrec where const inner suspend typealias external expect actual",built_in:"Byte Short Char Int Long Boolean Float Double Void Unit Nothing",literal:"true false null"},t={className:"symbol",begin:e.UNDERSCORE_IDENT_RE+"@"},a={className:"subst",begin:/\$\{/,end:/\}/,contains:[e.C_NUMBER_MODE]},i={className:"variable",begin:"\\$"+e.UNDERSCORE_IDENT_RE},r={className:"string",variants:[{begin:'"""',end:'"""(?=[^"])',contains:[i,a]},{begin:"'",end:"'",illegal:/\n/,contains:[e.BACKSLASH_ESCAPE]},{begin:'"',end:'"',illegal:/\n/,contains:[e.BACKSLASH_ESCAPE,i,a]},]};a.contains.push(r);let s={className:"meta",begin:"@(?:file|property|field|get|set|receiver|param|setparam|delegate)\\s*:(?:\\s*"+e.UNDERSCORE_IDENT_RE+")?"},l={className:"meta",begin:"@"+e.UNDERSCORE_IDENT_RE,contains:[{begin:/\(/,end:/\)/,contains:[e.inherit(r,{className:"string"}),"self"]},]},o=e.COMMENT("/\\*","\\*/",{contains:[e.C_BLOCK_COMMENT_MODE]}),c={variants:[{className:"type",begin:e.UNDERSCORE_IDENT_RE},{begin:/\(/,end:/\)/,contains:[]},]},d=c;return d.variants[1].contains=[c],c.variants[1].contains=[d],{name:"Kotlin",aliases:["kt","kts"],keywords:n,contains:[e.COMMENT("/\\*\\*","\\*/",{relevance:0,contains:[{className:"doctag",begin:"@[A-Za-z]+"}]}),e.C_LINE_COMMENT_MODE,o,{className:"keyword",begin:/\b(break|continue|return|this)\b/,starts:{contains:[{className:"symbol",begin:/@\w+/}]}},t,s,l,{className:"function",beginKeywords:"fun",end:"[(]|$",returnBegin:!0,excludeEnd:!0,keywords:n,relevance:5,contains:[{begin:e.UNDERSCORE_IDENT_RE+"\\s*\\(",returnBegin:!0,relevance:0,contains:[e.UNDERSCORE_TITLE_MODE]},{className:"type",begin://,keywords:"reified",relevance:0},{className:"params",begin:/\(/,end:/\)/,endsParent:!0,keywords:n,relevance:0,contains:[{begin:/:/,end:/[=,\/]/,endsWithParent:!0,contains:[c,e.C_LINE_COMMENT_MODE,o],relevance:0},e.C_LINE_COMMENT_MODE,o,s,l,r,e.C_NUMBER_MODE,]},o,]},{begin:[/class|interface|trait/,/\s+/,e.UNDERSCORE_IDENT_RE],beginScope:{3:"title.class"},keywords:"class interface trait",end:/[:\{(]|$/,excludeEnd:!0,illegal:"extends implements",contains:[{beginKeywords:"public protected internal private constructor"},e.UNDERSCORE_TITLE_MODE,{className:"type",begin://,excludeBegin:!0,excludeEnd:!0,relevance:0},{className:"type",begin:/[,:]\s*/,end:/[<\(,){\s]|$/,excludeBegin:!0,returnEnd:!0},s,l,]},r,{className:"meta",begin:"^#!/usr/bin/env",end:"$",illegal:"\n"},er,]}},grmr_less(e){let n=X(e),t="([\\w-]+|@\\{[\\w-]+\\})",a=[],i=[],r=e=>({className:"string",begin:"~?"+e+".*?"+e}),s=(e,n,t)=>({className:e,begin:n,relevance:t}),l={$pattern:/[a-z-]+/,keyword:"and or not only",attribute:J.join(" ")};i.push(e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,r("'"),r('"'),n.CSS_NUMBER_MODE,{begin:"(url|data-uri)\\(",starts:{className:"string",end:"[\\)\\n]",excludeEnd:!0}},n.HEXCOLOR,{begin:"\\(",end:"\\)",contains:i,keywords:l,relevance:0},s("variable","@@?[\\w-]+",10),s("variable","@\\{[\\w-]+\\}"),s("built_in","~?`[^`]*?`"),{className:"attribute",begin:"[\\w-]+\\s*:",end:":",returnBegin:!0,excludeEnd:!0},n.IMPORTANT,{beginKeywords:"and not"},n.FUNCTION_DISPATCH);let o=i.concat({begin:/\{/,end:/\}/,contains:a}),c={beginKeywords:"when",endsWithParent:!0,contains:[{beginKeywords:"and not"}].concat(i)},d={begin:t+"\\s*:",returnBegin:!0,end:/[;}]/,relevance:0,contains:[{begin:/-(webkit|moz|ms|o)-/},n.CSS_VARIABLE,{className:"attribute",begin:"\\b("+en.join("|")+")\\b",end:/(?=:)/,starts:{endsWithParent:!0,illegal:"[<=$]",relevance:0,contains:i}},]},g={variants:[{begin:"[\\.#:&\\[>]",end:"[;{}]"},{begin:t,end:/\{/},],returnBegin:!0,returnEnd:!0,illegal:"[<='$\"]",relevance:0,contains:[e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,c,s("keyword","all\\b"),s("variable","@\\{[\\w-]+\\}"),{begin:"\\b("+V.join("|")+")\\b",className:"selector-tag"},n.CSS_NUMBER_MODE,s("selector-tag",t,0),s("selector-id","#"+t),s("selector-class","\\."+t,0),s("selector-tag","&",0),n.ATTRIBUTE_SELECTOR_MODE,{className:"selector-pseudo",begin:":("+Y.join("|")+")"},{className:"selector-pseudo",begin:":(:)?("+ee.join("|")+")"},{begin:/\(/,end:/\)/,relevance:0,contains:o},{begin:"!important"},n.FUNCTION_DISPATCH,]},u={begin:`[\\w-]+:(:)?(${et.join("|")})`,returnBegin:!0,contains:[g]};return a.push(e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,{className:"keyword",begin:"@(import|media|charset|font-face|(-[a-z]+-)?keyframes|supports|document|namespace|page|viewport|host)\\b",starts:{end:"[;{}]",keywords:l,returnEnd:!0,contains:i,relevance:0}},{className:"variable",variants:[{begin:"@[\\w-]+\\s*:",relevance:15},{begin:"@[\\w-]+"},],starts:{end:"[;}]",returnEnd:!0,contains:o}},u,d,g,c,n.FUNCTION_DISPATCH),{name:"Less",case_insensitive:!0,illegal:"[=>'/<($\"]",contains:a}},grmr_lua(e){let n="\\[=*\\[",t="\\]=*\\]",a={begin:n,end:t,contains:["self"]},i=[e.COMMENT("--(?!\\[=*\\[)","$"),e.COMMENT("--\\[=*\\[",t,{contains:[a],relevance:10}),];return{name:"Lua",keywords:{$pattern:e.UNDERSCORE_IDENT_RE,literal:"true false nil",keyword:"and break do else elseif end for goto if in local not or repeat return then until while",built_in:"_G _ENV _VERSION __index __newindex __mode __call __metatable __tostring __len __gc __add __sub __mul __div __mod __pow __concat __unm __eq __lt __le assert collectgarbage dofile error getfenv getmetatable ipairs load loadfile loadstring module next pairs pcall print rawequal rawget rawset require select setfenv setmetatable tonumber tostring type unpack xpcall arg self coroutine resume yield status wrap create running debug getupvalue debug sethook getmetatable gethook setmetatable setlocal traceback setfenv getinfo setupvalue getlocal getregistry getfenv io lines write close flush open output type read stderr stdin input stdout popen tmpfile math log max acos huge ldexp pi cos tanh pow deg tan cosh sinh random randomseed frexp ceil floor rad abs sqrt modf asin min mod fmod log10 atan2 exp sin atan os exit setlocale date getenv difftime remove time clock tmpname rename execute package preload loadlib loaded loaders cpath config path seeall string sub upper len gfind rep find match char dump gmatch reverse byte format gsub lower table setn insert getn foreachi maxn foreach concat sort remove"},contains:i.concat([{className:"function",beginKeywords:"function",end:"\\)",contains:[e.inherit(e.TITLE_MODE,{begin:"([_a-zA-Z]\\w*\\.)*([_a-zA-Z]\\w*:)?[_a-zA-Z]\\w*"}),{className:"params",begin:"\\(",endsWithParent:!0,contains:i},].concat(i)},e.C_NUMBER_MODE,e.APOS_STRING_MODE,e.QUOTE_STRING_MODE,{className:"string",begin:n,end:t,contains:[a],relevance:5},])}},grmr_makefile(e){let n={className:"variable",variants:[{begin:"\\$\\("+e.UNDERSCORE_IDENT_RE+"\\)",contains:[e.BACKSLASH_ESCAPE]},{begin:/\$[@%`]+/},]},]},]};return{name:"HTML, XML",aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist","wsf","svg",],case_insensitive:!0,unicodeRegex:!0,contains:[{className:"meta",begin://,relevance:10,contains:[i,l,s,r,{begin:/\[/,end:/\]/,contains:[{className:"meta",begin://,contains:[i,r,l,s]},]},]},e.COMMENT(//,{relevance:10}),{begin://,relevance:10},a,{className:"meta",end:/\?>/,variants:[{begin:/<\?xml/,relevance:10,contains:[l]},{begin:/<\?[a-z][a-z0-9]+/},]},{className:"tag",begin:/)/,end:/>/,keywords:{name:"style"},contains:[o],starts:{end:/<\/style>/,returnEnd:!0,subLanguage:["css","xml"]}},{className:"tag",begin:/)/,end:/>/,keywords:{name:"script"},contains:[o],starts:{end:/<\/script>/,returnEnd:!0,subLanguage:["javascript","handlebars","xml"]}},{className:"tag",begin:/<>|<\/>/},{className:"tag",begin:n.concat(//,/>/,/\s/)))),end:/\/?>/,contains:[{className:"name",begin:t,relevance:0,starts:o},]},{className:"tag",begin:n.concat(/<\//,n.lookahead(n.concat(t,/>/))),contains:[{className:"name",begin:t,relevance:0},{begin:/>/,relevance:0,endsParent:!0},]},]}},grmr_markdown(e){let n={begin:/<\/?[A-Za-z_]/,end:">",subLanguage:"xml",relevance:0},t={variants:[{begin:/\[.+?\]\[.*?\]/,relevance:0},{begin:/\[.+?\]\(((data|javascript|mailto):|(?:http|ftp)s?:\/\/).*?\)/,relevance:2},{begin:e.regex.concat(/\[.+?\]\(/,/[A-Za-z][A-Za-z0-9+.-]*/,/:\/\/.*?\)/),relevance:2},{begin:/\[.+?\]\([./?&#].*?\)/,relevance:1},{begin:/\[.*?\]\(.*?\)/,relevance:0},],returnBegin:!0,contains:[{match:/\[(?=\])/},{className:"string",relevance:0,begin:"\\[",end:"\\]",excludeBegin:!0,returnEnd:!0},{className:"link",relevance:0,begin:"\\]\\(",end:"\\)",excludeBegin:!0,excludeEnd:!0},{className:"symbol",relevance:0,begin:"\\]\\[",end:"\\]",excludeBegin:!0,excludeEnd:!0},]},a={className:"strong",contains:[],variants:[{begin:/_{2}(?!\s)/,end:/_{2}/},{begin:/\*{2}(?!\s)/,end:/\*{2}/},]},i={className:"emphasis",contains:[],variants:[{begin:/\*(?![*\s])/,end:/\*/},{begin:/_(?![_\s])/,end:/_/,relevance:0},]},r=e.inherit(a,{contains:[]}),s=e.inherit(i,{contains:[]});a.contains.push(s),i.contains.push(r);let l=[n,t];return[a,i,r,s].forEach(e=>{e.contains=e.contains.concat(l)}),{name:"Markdown",aliases:["md","mkdown","mkd"],contains:[{className:"section",variants:[{begin:"^#{1,6}",end:"$",contains:l=l.concat(a,i)},{begin:"(?=^.+?\\n[=-]{2,}$)",contains:[{begin:"^[=-]*$"},{begin:"^",end:"\\n",contains:l},]},]},n,{className:"bullet",begin:"^[ ]*([*+-]|(\\d+\\.))(?=\\s+)",end:"\\s+",excludeEnd:!0},a,i,{className:"quote",begin:"^>\\s+",contains:l,end:"$"},{className:"code",variants:[{begin:"(`{3,})[^`](.|\\n)*?\\1`*[ ]*"},{begin:"(~{3,})[^~](.|\\n)*?\\1~*[ ]*"},{begin:"```",end:"```+[ ]*$"},{begin:"~~~",end:"~~~+[ ]*$"},{begin:"`.+?`"},{begin:"(?=^( {4}|\\t))",contains:[{begin:"^( {4}|\\t)",end:"(\\n)$"}],relevance:0},]},{begin:"^[-\\*]{3,}",end:"$"},t,{begin:/^\[[^\n]+\]:/,returnBegin:!0,contains:[{className:"symbol",begin:/\[/,end:/\]/,excludeBegin:!0,excludeEnd:!0},{className:"link",begin:/:\s*/,end:/$/,excludeBegin:!0},]},]}},grmr_objectivec(e){let n=/[a-zA-Z@][a-zA-Z0-9_]*/,t={$pattern:n,keyword:["@interface","@class","@protocol","@implementation"]};return{name:"Objective-C",aliases:["mm","objc","obj-c","obj-c++","objective-c++"],keywords:{"variable.language":["this","super"],$pattern:n,keyword:["while","export","sizeof","typedef","const","struct","for","union","volatile","static","mutable","if","do","return","goto","enum","else","break","extern","asm","case","default","register","explicit","typename","switch","continue","inline","readonly","assign","readwrite","self","@synchronized","id","typeof","nonatomic","IBOutlet","IBAction","strong","weak","copy","in","out","inout","bycopy","byref","oneway","__strong","__weak","__block","__autoreleasing","@private","@protected","@public","@try","@property","@end","@throw","@catch","@finally","@autoreleasepool","@synthesize","@dynamic","@selector","@optional","@required","@encode","@package","@import","@defs","@compatibility_alias","__bridge","__bridge_transfer","__bridge_retained","__bridge_retain","__covariant","__contravariant","__kindof","_Nonnull","_Nullable","_Null_unspecified","__FUNCTION__","__PRETTY_FUNCTION__","__attribute__","getter","setter","retain","unsafe_unretained","nonnull","nullable","null_unspecified","null_resettable","class","instancetype","NS_DESIGNATED_INITIALIZER","NS_UNAVAILABLE","NS_REQUIRES_SUPER","NS_RETURNS_INNER_POINTER","NS_INLINE","NS_AVAILABLE","NS_DEPRECATED","NS_ENUM","NS_OPTIONS","NS_SWIFT_UNAVAILABLE","NS_ASSUME_NONNULL_BEGIN","NS_ASSUME_NONNULL_END","NS_REFINED_FOR_SWIFT","NS_SWIFT_NAME","NS_SWIFT_NOTHROW","NS_DURING","NS_HANDLER","NS_ENDHANDLER","NS_VALUERETURN","NS_VOIDRETURN",],literal:["false","true","FALSE","TRUE","nil","YES","NO","NULL",],built_in:["dispatch_once_t","dispatch_queue_t","dispatch_sync","dispatch_async","dispatch_once",],type:["int","float","char","unsigned","signed","short","long","double","wchar_t","unichar","void","bool","BOOL","id|0","_Bool",]},illegal:"/,end:/$/,illegal:"\\n"},e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,]},{className:"class",begin:"("+t.keyword.join("|")+")\\b",end:/(\{|$)/,excludeEnd:!0,keywords:t,contains:[e.UNDERSCORE_TITLE_MODE]},{begin:"\\."+e.UNDERSCORE_IDENT_RE,relevance:0},]}},grmr_perl(e){let n=e.regex,t=/[dualxmsipngr]{0,12}/,a={$pattern:/[\w.]+/,keyword:"abs accept alarm and atan2 bind binmode bless break caller chdir chmod chomp chop chown chr chroot close closedir connect continue cos crypt dbmclose dbmopen defined delete die do dump each else elsif endgrent endhostent endnetent endprotoent endpwent endservent eof eval exec exists exit exp fcntl fileno flock for foreach fork format formline getc getgrent getgrgid getgrnam gethostbyaddr gethostbyname gethostent getlogin getnetbyaddr getnetbyname getnetent getpeername getpgrp getpriority getprotobyname getprotobynumber getprotoent getpwent getpwnam getpwuid getservbyname getservbyport getservent getsockname getsockopt given glob gmtime goto grep gt hex if index int ioctl join keys kill last lc lcfirst length link listen local localtime log lstat lt ma map mkdir msgctl msgget msgrcv msgsnd my ne next no not oct open opendir or ord our pack package pipe pop pos print printf prototype push q|0 qq quotemeta qw qx rand read readdir readline readlink readpipe recv redo ref rename require reset return reverse rewinddir rindex rmdir say scalar seek seekdir select semctl semget semop send setgrent sethostent setnetent setpgrp setpriority setprotoent setpwent setservent setsockopt shift shmctl shmget shmread shmwrite shutdown sin sleep socket socketpair sort splice split sprintf sqrt srand stat state study sub substr symlink syscall sysopen sysread sysseek system syswrite tell telldir tie tied time times tr truncate uc ucfirst umask undef unless unlink unpack unshift untie until use utime values vec wait waitpid wantarray warn when while write x|0 xor y|0"},i={className:"subst",begin:"[$@]\\{",end:"\\}",keywords:a},r={begin:/->\{/,end:/\}/},s={variants:[{begin:/\$\d/},{begin:n.concat(/[$%@](\^\w\b|#\w+(::\w+)*|\{\w+\}|\w+(::\w*)*)/,"(?![A-Za-z])(?![@$%])")},{begin:/[$%@][^\s\w{]/,relevance:0},]},l=[e.BACKSLASH_ESCAPE,i,s],o=[/!/,/\//,/\|/,/\?/,/'/,/"/,/#/],c=(e,a,i="\\1")=>{let r="\\1"===i?i:n.concat(i,a);return n.concat(n.concat("(?:",e,")"),a,/(?:\\.|[^\\\/])*?/,r,/(?:\\.|[^\\\/])*?/,i,t)},d=(e,a,i)=>n.concat(n.concat("(?:",e,")"),a,/(?:\\.|[^\\\/])*?/,i,t),g=[s,e.HASH_COMMENT_MODE,e.COMMENT(/^=\w/,/=cut/,{endsWithParent:!0}),r,{className:"string",contains:l,variants:[{begin:"q[qwxr]?\\s*\\(",end:"\\)",relevance:5},{begin:"q[qwxr]?\\s*\\[",end:"\\]",relevance:5},{begin:"q[qwxr]?\\s*\\{",end:"\\}",relevance:5},{begin:"q[qwxr]?\\s*\\|",end:"\\|",relevance:5},{begin:"q[qwxr]?\\s*<",end:">",relevance:5},{begin:"qw\\s+q",end:"q",relevance:5},{begin:"'",end:"'",contains:[e.BACKSLASH_ESCAPE]},{begin:'"',end:'"'},{begin:"`",end:"`",contains:[e.BACKSLASH_ESCAPE]},{begin:/\{\w+\}/,relevance:0},{begin:"-?\\w+\\s*=>",relevance:0},]},{className:"number",begin:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",relevance:0},{begin:"(\\/\\/|"+e.RE_STARTERS_RE+"|\\b(split|return|print|reverse|grep)\\b)\\s*",keywords:"split return print reverse grep",relevance:0,contains:[e.HASH_COMMENT_MODE,{className:"regexp",variants:[{begin:c("s|tr|y",n.either(...o,{capture:!0}))},{begin:c("s|tr|y","\\(","\\)")},{begin:c("s|tr|y","\\[","\\]")},{begin:c("s|tr|y","\\{","\\}")},],relevance:2},{className:"regexp",variants:[{begin:/(m|qr)\/\//,relevance:0},{begin:d("(?:m|qr)?",/\//,/\//)},{begin:d("m|qr",n.either(...o,{capture:!0}),/\1/)},{begin:d("m|qr",/\(/,/\)/)},{begin:d("m|qr",/\[/,/\]/)},{begin:d("m|qr",/\{/,/\}/)},]},]},{className:"function",beginKeywords:"sub",end:"(\\s*\\(.*?\\))?[;{]",excludeEnd:!0,relevance:5,contains:[e.TITLE_MODE]},{begin:"-\\w\\b",relevance:0},{begin:"^__DATA__$",end:"^__END__$",subLanguage:"mojolicious",contains:[{begin:"^@@.*",end:"$",className:"comment"}]},];return i.contains=g,r.contains=g,{name:"Perl",aliases:["pl","pm"],keywords:a,contains:g}},grmr_php(e){let n=e.regex,t=/(?![A-Za-z0-9])(?![$])/,a=n.concat(/[a-zA-Z_\x7f-\xff][a-zA-Z0-9_\x7f-\xff]*/,t),i=n.concat(/(\\?[A-Z][a-z0-9_\x7f-\xff]+|\\?[A-Z]+(?=[A-Z][a-z0-9_\x7f-\xff])){1,}/,t),r={scope:"variable",match:"\\$+"+a},s={scope:"subst",variants:[{begin:/\$\w+/},{begin:/\{\$/,end:/\}/},]},l=e.inherit(e.APOS_STRING_MODE,{illegal:null}),o="[ \n]",c={scope:"string",variants:[e.inherit(e.QUOTE_STRING_MODE,{illegal:null,contains:e.QUOTE_STRING_MODE.contains.concat(s)}),l,e.END_SAME_AS_BEGIN({begin:/<<<[ \t]*(\w+)\n/,end:/[ \t]*(\w+)\b/,contains:e.QUOTE_STRING_MODE.contains.concat(s)}),]},d={scope:"number",variants:[{begin:"\\b0[bB][01]+(?:_[01]+)*\\b"},{begin:"\\b0[oO][0-7]+(?:_[0-7]+)*\\b"},{begin:"\\b0[xX][\\da-fA-F]+(?:_[\\da-fA-F]+)*\\b"},{begin:"(?:\\b\\d+(?:_\\d+)*(\\.(?:\\d+(?:_\\d+)*))?|\\B\\.\\d+)(?:[eE][+-]?\\d+)?"},],relevance:0},g=["false","null","true"],u=["__CLASS__","__DIR__","__FILE__","__FUNCTION__","__COMPILER_HALT_OFFSET__","__LINE__","__METHOD__","__NAMESPACE__","__TRAIT__","die","echo","exit","include","include_once","print","require","require_once","array","abstract","and","as","binary","bool","boolean","break","callable","case","catch","class","clone","const","continue","declare","default","do","double","else","elseif","empty","enddeclare","endfor","endforeach","endif","endswitch","endwhile","enum","eval","extends","final","finally","float","for","foreach","from","global","goto","if","implements","instanceof","insteadof","int","integer","interface","isset","iterable","list","match|0","mixed","new","never","object","or","private","protected","public","readonly","real","return","string","switch","throw","trait","try","unset","use","var","void","while","xor","yield",],b=["Error|0","AppendIterator","ArgumentCountError","ArithmeticError","ArrayIterator","ArrayObject","AssertionError","BadFunctionCallException","BadMethodCallException","CachingIterator","CallbackFilterIterator","CompileError","Countable","DirectoryIterator","DivisionByZeroError","DomainException","EmptyIterator","ErrorException","Exception","FilesystemIterator","FilterIterator","GlobIterator","InfiniteIterator","InvalidArgumentException","IteratorIterator","LengthException","LimitIterator","LogicException","MultipleIterator","NoRewindIterator","OutOfBoundsException","OutOfRangeException","OuterIterator","OverflowException","ParentIterator","ParseError","RangeException","RecursiveArrayIterator","RecursiveCachingIterator","RecursiveCallbackFilterIterator","RecursiveDirectoryIterator","RecursiveFilterIterator","RecursiveIterator","RecursiveIteratorIterator","RecursiveRegexIterator","RecursiveTreeIterator","RegexIterator","RuntimeException","SeekableIterator","SplDoublyLinkedList","SplFileInfo","SplFileObject","SplFixedArray","SplHeap","SplMaxHeap","SplMinHeap","SplObjectStorage","SplObserver","SplPriorityQueue","SplQueue","SplStack","SplSubject","SplTempFileObject","TypeError","UnderflowException","UnexpectedValueException","UnhandledMatchError","ArrayAccess","BackedEnum","Closure","Fiber","Generator","Iterator","IteratorAggregate","Serializable","Stringable","Throwable","Traversable","UnitEnum","WeakReference","WeakMap","Directory","__PHP_Incomplete_Class","parent","php_user_filter","self","static","stdClass",],m={keyword:u,literal:(e=>{let n=[];return e.forEach(e=>{n.push(e),e.toLowerCase()===e?n.push(e.toUpperCase()):n.push(e.toLowerCase())}),n})(g),built_in:b},p=e=>e.map(e=>e.replace(/\|\d+$/,"")),h={variants:[{match:[/new/,n.concat(o,"+"),n.concat("(?!",p(b).join("\\b|"),"\\b)"),i,],scope:{1:"keyword",4:"title.class"}},]},f=n.concat(a,"\\b(?!\\()"),E={variants:[{match:[n.concat(/::/,n.lookahead(/(?!class\b)/)),f],scope:{2:"variable.constant"}},{match:[/::/,/class/],scope:{2:"variable.language"}},{match:[i,n.concat(/::/,n.lookahead(/(?!class\b)/)),f],scope:{1:"title.class",3:"variable.constant"}},{match:[i,n.concat("::",n.lookahead(/(?!class\b)/))],scope:{1:"title.class"}},{match:[i,/::/,/class/],scope:{1:"title.class",3:"variable.language"}},]},$={scope:"attr",match:n.concat(a,n.lookahead(":"),n.lookahead(/(?!::)/))},y={relevance:0,begin:/\(/,end:/\)/,keywords:m,contains:[$,r,E,e.C_BLOCK_COMMENT_MODE,c,d,h]},N={relevance:0,match:[/\b/,n.concat("(?!fn\\b|function\\b|",p(u).join("\\b|"),"|",p(b).join("\\b|"),"\\b)"),a,n.concat(o,"*"),n.lookahead(/(?=\()/),],scope:{3:"title.function.invoke"},contains:[y]};y.contains.push(N);let w=[$,E,e.C_BLOCK_COMMENT_MODE,c,d,h];return{case_insensitive:!1,keywords:m,contains:[{begin:n.concat(/#\[\s*/,i),beginScope:"meta",end:/]/,endScope:"meta",keywords:{literal:g,keyword:["new","array"]},contains:[{begin:/\[/,end:/]/,keywords:{literal:g,keyword:["new","array"]},contains:["self",...w]},...w,{scope:"meta",match:i},]},e.HASH_COMMENT_MODE,e.COMMENT("//","$"),e.COMMENT("/\\*","\\*/",{contains:[{scope:"doctag",match:"@[A-Za-z]+"},]}),{match:/__halt_compiler\(\);/,keywords:"__halt_compiler",starts:{scope:"comment",end:e.MATCH_NOTHING_RE,contains:[{match:/\?>/,scope:"meta",endsParent:!0}]}},{scope:"meta",variants:[{begin:/<\?php/,relevance:10},{begin:/<\?=/},{begin:/<\?/,relevance:.1},{begin:/\?>/},]},{scope:"variable.language",match:/\$this\b/},r,N,E,{match:[/const/,/\s/,a],scope:{1:"keyword",3:"variable.constant"}},h,{scope:"function",relevance:0,beginKeywords:"fn function",end:/[;{]/,excludeEnd:!0,illegal:"[$%\\[]",contains:[{beginKeywords:"use"},e.UNDERSCORE_TITLE_MODE,{begin:"=>",endsParent:!0},{scope:"params",begin:"\\(",end:"\\)",excludeBegin:!0,excludeEnd:!0,keywords:m,contains:["self",r,E,e.C_BLOCK_COMMENT_MODE,c,d]},]},{scope:"class",variants:[{beginKeywords:"enum",illegal:/[($"]/},{beginKeywords:"class interface trait",illegal:/[:($"]/},],relevance:0,end:/\{/,excludeEnd:!0,contains:[{beginKeywords:"extends implements"},e.UNDERSCORE_TITLE_MODE,]},{beginKeywords:"namespace",relevance:0,end:";",illegal:/[.']/,contains:[e.inherit(e.UNDERSCORE_TITLE_MODE,{scope:"title.class"}),]},{beginKeywords:"use",relevance:0,end:";",contains:[{match:/\b(as|const|function)\b/,scope:"keyword"},e.UNDERSCORE_TITLE_MODE,]},c,d,]}},grmr_php_template:e=>({name:"PHP template",subLanguage:"xml",contains:[{begin:/<\?(php|=)?/,end:/\?>/,subLanguage:"php",contains:[{begin:"/\\*",end:"\\*/",skip:!0},{begin:'b"',end:'"',skip:!0},{begin:"b'",end:"'",skip:!0},e.inherit(e.APOS_STRING_MODE,{illegal:null,className:null,contains:null,skip:!0}),e.inherit(e.QUOTE_STRING_MODE,{illegal:null,className:null,contains:null,skip:!0}),]},]}),grmr_plaintext:e=>({name:"Plain text",aliases:["text","txt"],disableAutodetect:!0}),grmr_python(e){let n=e.regex,t=/[\p{XID_Start}_]\p{XID_Continue}*/u,a=["and","as","assert","async","await","break","case","class","continue","def","del","elif","else","except","finally","for","from","global","if","import","in","is","lambda","match","nonlocal|10","not","or","pass","raise","return","try","while","with","yield",],i={$pattern:/[A-Za-z]\w+|__\w+__/,keyword:a,built_in:["__import__","abs","all","any","ascii","bin","bool","breakpoint","bytearray","bytes","callable","chr","classmethod","compile","complex","delattr","dict","dir","divmod","enumerate","eval","exec","filter","float","format","frozenset","getattr","globals","hasattr","hash","help","hex","id","input","int","isinstance","issubclass","iter","len","list","locals","map","max","memoryview","min","next","object","oct","open","ord","pow","print","property","range","repr","reversed","round","set","setattr","slice","sorted","staticmethod","str","sum","super","tuple","type","vars","zip",],literal:["__debug__","Ellipsis","False","None","NotImplemented","True",],type:["Any","Callable","Coroutine","Dict","List","Literal","Generic","Optional","Sequence","Set","Tuple","Type","Union",]},r={className:"meta",begin:/^(>>>|\.\.\.) /},s={className:"subst",begin:/\{/,end:/\}/,keywords:i,illegal:/#/},l={begin:/\{\{/,relevance:0},o={className:"string",contains:[e.BACKSLASH_ESCAPE],variants:[{begin:/([uU]|[bB]|[rR]|[bB][rR]|[rR][bB])?'''/,end:/'''/,contains:[e.BACKSLASH_ESCAPE,r],relevance:10},{begin:/([uU]|[bB]|[rR]|[bB][rR]|[rR][bB])?"""/,end:/"""/,contains:[e.BACKSLASH_ESCAPE,r],relevance:10},{begin:/([fF][rR]|[rR][fF]|[fF])'''/,end:/'''/,contains:[e.BACKSLASH_ESCAPE,r,l,s]},{begin:/([fF][rR]|[rR][fF]|[fF])"""/,end:/"""/,contains:[e.BACKSLASH_ESCAPE,r,l,s]},{begin:/([uU]|[rR])'/,end:/'/,relevance:10},{begin:/([uU]|[rR])"/,end:/"/,relevance:10},{begin:/([bB]|[bB][rR]|[rR][bB])'/,end:/'/},{begin:/([bB]|[bB][rR]|[rR][bB])"/,end:/"/},{begin:/([fF][rR]|[rR][fF]|[fF])'/,end:/'/,contains:[e.BACKSLASH_ESCAPE,l,s]},{begin:/([fF][rR]|[rR][fF]|[fF])"/,end:/"/,contains:[e.BACKSLASH_ESCAPE,l,s]},e.APOS_STRING_MODE,e.QUOTE_STRING_MODE,]},c="[0-9](_?[0-9])*",d=`(\\b(${c}))?\\.(${c})|\\b(${c})\\.`,g="\\b|"+a.join("|"),u={className:"number",relevance:0,variants:[{begin:`(\\b(${c})|(${d}))[eE][+-]?(${c})[jJ]?(?=${g})`},{begin:`(${d})[jJ]?`},{begin:`\\b([1-9](_?[0-9])*|0+(_?0)*)[lLjJ]?(?=${g})`},{begin:`\\b0[bB](_?[01])+[lL]?(?=${g})`},{begin:`\\b0[oO](_?[0-7])+[lL]?(?=${g})`},{begin:`\\b0[xX](_?[0-9a-fA-F])+[lL]?(?=${g})`},{begin:`\\b(${c})[jJ](?=${g})`},]},b={className:"comment",begin:n.lookahead(/# type:/),end:/$/,keywords:i,contains:[{begin:/# type:/},{begin:/#/,end:/\b\B/,endsWithParent:!0},]},m={className:"params",variants:[{className:"",begin:/\(\s*\)/,skip:!0},{begin:/\(/,end:/\)/,excludeBegin:!0,excludeEnd:!0,keywords:i,contains:["self",r,u,o,e.HASH_COMMENT_MODE]},]};return s.contains=[o,u,r],{name:"Python",aliases:["py","gyp","ipython"],unicodeRegex:!0,keywords:i,illegal:/(<\/|->|\?)|=>/,contains:[r,u,{begin:/\bself\b/},{beginKeywords:"if",relevance:0},o,b,e.HASH_COMMENT_MODE,{match:[/\bdef/,/\s+/,t],scope:{1:"keyword",3:"title.function"},contains:[m]},{variants:[{match:[/\bclass/,/\s+/,t,/\s*/,/\(\s*/,t,/\s*\)/]},{match:[/\bclass/,/\s+/,t]},],scope:{1:"keyword",3:"title.class",6:"title.class.inherited"}},{className:"meta",begin:/^[\t ]*@/,end:/(?=#)|$/,contains:[u,m,o]},]}},grmr_python_repl:e=>({aliases:["pycon"],contains:[{className:"meta.prompt",starts:{end:/ |$/,starts:{end:"$",subLanguage:"python"}},variants:[{begin:/^>>>(?=[ ]|$)/},{begin:/^\.\.\.(?=[ ]|$)/},]},]}),grmr_r(e){let n=e.regex,t=/(?:(?:[a-zA-Z]|\.[._a-zA-Z])[._a-zA-Z0-9]*)|\.(?!\d)/,a=n.either(/0[xX][0-9a-fA-F]+\.[0-9a-fA-F]*[pP][+-]?\d+i?/,/0[xX][0-9a-fA-F]+(?:[pP][+-]?\d+)?[Li]?/,/(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][+-]?\d+)?[Li]?/),i=/[=!<>:]=|\|\||&&|:::?|<-|<<-|->>|->|\|>|[-+*\/?!$&|:<=>@^~]|\*\*/,r=n.either(/[()]/,/[{}]/,/\[\[/,/[[\]]/,/\\/,/,/);return{name:"R",keywords:{$pattern:t,keyword:"function if in break next repeat else for while",literal:"NULL NA TRUE FALSE Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10",built_in:"LETTERS letters month.abb month.name pi T F abs acos acosh all any anyNA Arg as.call as.character as.complex as.double as.environment as.integer as.logical as.null.default as.numeric as.raw asin asinh atan atanh attr attributes baseenv browser c call ceiling class Conj cos cosh cospi cummax cummin cumprod cumsum digamma dim dimnames emptyenv exp expression floor forceAndCall gamma gc.time globalenv Im interactive invisible is.array is.atomic is.call is.character is.complex is.double is.environment is.expression is.finite is.function is.infinite is.integer is.language is.list is.logical is.matrix is.na is.name is.nan is.null is.numeric is.object is.pairlist is.raw is.recursive is.single is.symbol lazyLoadDBfetch length lgamma list log max min missing Mod names nargs nzchar oldClass on.exit pos.to.env proc.time prod quote range Re rep retracemem return round seq_along seq_len seq.int sign signif sin sinh sinpi sqrt standardGeneric substitute sum switch tan tanh tanpi tracemem trigamma trunc unclass untracemem UseMethod xtfrm"},contains:[e.COMMENT(/#'/,/$/,{contains:[{scope:"doctag",match:/@examples/,starts:{end:n.lookahead(n.either(/\n^#'\s*(?=@[a-zA-Z]+)/,/\n^(?!#')/)),endsParent:!0}},{scope:"doctag",begin:"@param",end:/$/,contains:[{scope:"variable",variants:[{match:t},{match:/`(?:\\.|[^`\\])+`/}],endsParent:!0},]},{scope:"doctag",match:/@[a-zA-Z]+/},{scope:"keyword",match:/\\[a-zA-Z]+/},]}),e.HASH_COMMENT_MODE,{scope:"string",contains:[e.BACKSLASH_ESCAPE],variants:[e.END_SAME_AS_BEGIN({begin:/[rR]"(-*)\(/,end:/\)(-*)"/}),e.END_SAME_AS_BEGIN({begin:/[rR]"(-*)\{/,end:/\}(-*)"/}),e.END_SAME_AS_BEGIN({begin:/[rR]"(-*)\[/,end:/\](-*)"/}),e.END_SAME_AS_BEGIN({begin:/[rR]'(-*)\(/,end:/\)(-*)'/}),e.END_SAME_AS_BEGIN({begin:/[rR]'(-*)\{/,end:/\}(-*)'/}),e.END_SAME_AS_BEGIN({begin:/[rR]'(-*)\[/,end:/\](-*)'/}),{begin:'"',end:'"',relevance:0},{begin:"'",end:"'",relevance:0},]},{relevance:0,variants:[{scope:{1:"operator",2:"number"},match:[i,a]},{scope:{1:"operator",2:"number"},match:[/%[^%]*%/,a]},{scope:{1:"punctuation",2:"number"},match:[r,a]},{scope:{2:"number"},match:[/[^a-zA-Z0-9._]|^/,a]},]},{scope:{3:"operator"},match:[t,/\s+/,/<-/,/\s+/]},{scope:"operator",relevance:0,variants:[{match:i},{match:/%[^%]*%/},]},{scope:"punctuation",relevance:0,match:r},{begin:"`",end:"`",contains:[{begin:/\\./}]},]}},grmr_ruby(e){let n=e.regex,t="([a-zA-Z_]\\w*[!?=]?|[-+~]@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?)",a=n.either(/\b([A-Z]+[a-z0-9]+)+/,/\b([A-Z]+[a-z0-9]+)+[A-Z]+/),i=n.concat(a,/(::\w+)*/),r={"variable.constant":["__FILE__","__LINE__","__ENCODING__"],"variable.language":["self","super"],keyword:["alias","and","begin","BEGIN","break","case","class","defined","do","else","elsif","end","END","ensure","for","if","in","module","next","not","or","redo","require","rescue","retry","return","then","undef","unless","until","when","while","yield","include","extend","prepend","public","private","protected","raise","throw",],built_in:["proc","lambda","attr_accessor","attr_reader","attr_writer","define_method","private_constant","module_function",],literal:["true","false","nil"]},s={className:"doctag",begin:"@[A-Za-z]+"},l={begin:"#<",end:">"},o=[e.COMMENT("#","$",{contains:[s]}),e.COMMENT("^=begin","^=end",{contains:[s],relevance:10}),e.COMMENT("^__END__",e.MATCH_NOTHING_RE),],c={className:"subst",begin:/#\{/,end:/\}/,keywords:r},d={className:"string",contains:[e.BACKSLASH_ESCAPE,c],variants:[{begin:/'/,end:/'/},{begin:/"/,end:/"/},{begin:/`/,end:/`/},{begin:/%[qQwWx]?\(/,end:/\)/},{begin:/%[qQwWx]?\[/,end:/\]/},{begin:/%[qQwWx]?\{/,end:/\}/},{begin:/%[qQwWx]?/},{begin:/%[qQwWx]?\//,end:/\//},{begin:/%[qQwWx]?%/,end:/%/},{begin:/%[qQwWx]?-/,end:/-/},{begin:/%[qQwWx]?\|/,end:/\|/},{begin:/\B\?(\\\d{1,3})/},{begin:/\B\?(\\x[A-Fa-f0-9]{1,2})/},{begin:/\B\?(\\u\{?[A-Fa-f0-9]{1,6}\}?)/},{begin:/\B\?(\\M-\\C-|\\M-\\c|\\c\\M-|\\M-|\\C-\\M-)[\x20-\x7e]/},{begin:/\B\?\\(c|C-)[\x20-\x7e]/},{begin:/\B\?\\?\S/},{begin:n.concat(/<<[-~]?'?/,n.lookahead(/(\w+)(?=\W)[^\n]*\n(?:[^\n]*\n)*?\s*\1\b/)),contains:[e.END_SAME_AS_BEGIN({begin:/(\w+)/,end:/(\w+)/,contains:[e.BACKSLASH_ESCAPE,c]}),]},]},g="[0-9](_?[0-9])*",u={className:"number",relevance:0,variants:[{begin:`\\b([1-9](_?[0-9])*|0)(\\.(${g}))?([eE][+-]?(${g})|r)?i?\\b`},{begin:"\\b0[dD][0-9](_?[0-9])*r?i?\\b"},{begin:"\\b0[bB][0-1](_?[0-1])*r?i?\\b"},{begin:"\\b0[oO][0-7](_?[0-7])*r?i?\\b"},{begin:"\\b0[xX][0-9a-fA-F](_?[0-9a-fA-F])*r?i?\\b"},{begin:"\\b0(_?[0-7])+r?i?\\b"},]},b={variants:[{match:/\(\)/},{className:"params",begin:/\(/,end:/(?=\))/,excludeBegin:!0,endsParent:!0,keywords:r},]},m=[d,{variants:[{match:[/class\s+/,i,/\s+<\s+/,i]},{match:[/\b(class|module)\s+/,i]},],scope:{2:"title.class",4:"title.class.inherited"},keywords:r},{match:[/(include|extend)\s+/,i],scope:{2:"title.class"},keywords:r},{relevance:0,match:[i,/\.new[. (]/],scope:{1:"title.class"}},{relevance:0,match:/\b[A-Z][A-Z_0-9]+\b/,className:"variable.constant"},{relevance:0,match:a,scope:"title.class"},{match:[/def/,/\s+/,t],scope:{1:"keyword",3:"title.function"},contains:[b]},{begin:e.IDENT_RE+"::"},{className:"symbol",begin:e.UNDERSCORE_IDENT_RE+"(!|\\?)?:",relevance:0},{className:"symbol",begin:":(?!\\s)",contains:[d,{begin:t}],relevance:0},u,{className:"variable",begin:"(\\$\\W)|((\\$|@@?)(\\w+))(?=[^@$?])(?![A-Za-z])(?![@$?'])"},{className:"params",begin:/\|/,end:/\|/,excludeBegin:!0,excludeEnd:!0,relevance:0,keywords:r},{begin:"("+e.RE_STARTERS_RE+"|unless)\\s*",keywords:"unless",contains:[{className:"regexp",contains:[e.BACKSLASH_ESCAPE,c],illegal:/\n/,variants:[{begin:"/",end:"/[a-z]*"},{begin:/%r\{/,end:/\}[a-z]*/},{begin:"%r\\(",end:"\\)[a-z]*"},{begin:"%r!",end:"![a-z]*"},{begin:"%r\\[",end:"\\][a-z]*"},]},].concat(l,o),relevance:0},].concat(l,o);return c.contains=m,b.contains=m,o.unshift(l),{name:"Ruby",aliases:["rb","gemspec","podspec","thor","irb"],keywords:r,illegal:/\/\*/,contains:[e.SHEBANG({binary:"ruby"})].concat([{begin:/^\s*=>/,starts:{end:"$",contains:m}},{className:"meta.prompt",begin:"^([>?]>|[\\w#]+\\(\\w+\\):\\d+:\\d+[>*]|(\\w+-)?\\d+\\.\\d+\\.\\d+(p\\d+)?[^\\d][^>]+>)(?=[ ])",starts:{end:"$",keywords:r,contains:m}},]).concat(o).concat(m)}},grmr_rust(e){let n=e.regex,t={className:"title.function.invoke",relevance:0,begin:n.concat(/\b/,/(?!let\b)/,e.IDENT_RE,n.lookahead(/\s*\(/))},a="([ui](8|16|32|64|128|size)|f(32|64))?",i=["drop ","Copy","Send","Sized","Sync","Drop","Fn","FnMut","FnOnce","ToOwned","Clone","Debug","PartialEq","PartialOrd","Eq","Ord","AsRef","AsMut","Into","From","Default","Iterator","Extend","IntoIterator","DoubleEndedIterator","ExactSizeIterator","SliceConcatExt","ToString","assert!","assert_eq!","bitflags!","bytes!","cfg!","col!","concat!","concat_idents!","debug_assert!","debug_assert_eq!","env!","panic!","file!","format!","format_args!","include_bytes!","include_str!","line!","local_data_key!","module_path!","option_env!","print!","println!","select!","stringify!","try!","unimplemented!","unreachable!","vec!","write!","writeln!","macro_rules!","assert_ne!","debug_assert_ne!",],r=["i8","i16","i32","i64","i128","isize","u8","u16","u32","u64","u128","usize","f32","f64","str","char","bool","Box","Option","Result","String","Vec",];return{name:"Rust",aliases:["rs"],keywords:{$pattern:e.IDENT_RE+"!?",type:r,keyword:["abstract","as","async","await","become","box","break","const","continue","crate","do","dyn","else","enum","extern","false","final","fn","for","if","impl","in","let","loop","macro","match","mod","move","mut","override","priv","pub","ref","return","self","Self","static","struct","super","trait","true","try","type","typeof","unsafe","unsized","use","virtual","where","while","yield",],literal:["true","false","Some","None","Ok","Err"],built_in:i},illegal:""},t,]}},grmr_scss(e){let n=X(e),t="@[a-z-]+",a={className:"variable",begin:"(\\$[a-zA-Z-][a-zA-Z0-9_-]*)\\b",relevance:0};return{name:"SCSS",case_insensitive:!0,illegal:"[=/|']",contains:[e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,n.CSS_NUMBER_MODE,{className:"selector-id",begin:"#[A-Za-z0-9_-]+",relevance:0},{className:"selector-class",begin:"\\.[A-Za-z0-9_-]+",relevance:0},n.ATTRIBUTE_SELECTOR_MODE,{className:"selector-tag",begin:"\\b("+V.join("|")+")\\b",relevance:0},{className:"selector-pseudo",begin:":("+Y.join("|")+")"},{className:"selector-pseudo",begin:":(:)?("+ee.join("|")+")"},a,{begin:/\(/,end:/\)/,contains:[n.CSS_NUMBER_MODE]},n.CSS_VARIABLE,{className:"attribute",begin:"\\b("+en.join("|")+")\\b"},{begin:"\\b(whitespace|wait|w-resize|visible|vertical-text|vertical-ideographic|uppercase|upper-roman|upper-alpha|underline|transparent|top|thin|thick|text|text-top|text-bottom|tb-rl|table-header-group|table-footer-group|sw-resize|super|strict|static|square|solid|small-caps|separate|se-resize|scroll|s-resize|rtl|row-resize|ridge|right|repeat|repeat-y|repeat-x|relative|progress|pointer|overline|outside|outset|oblique|nowrap|not-allowed|normal|none|nw-resize|no-repeat|no-drop|newspaper|ne-resize|n-resize|move|middle|medium|ltr|lr-tb|lowercase|lower-roman|lower-alpha|loose|list-item|line|line-through|line-edge|lighter|left|keep-all|justify|italic|inter-word|inter-ideograph|inside|inset|inline|inline-block|inherit|inactive|ideograph-space|ideograph-parenthesis|ideograph-numeric|ideograph-alpha|horizontal|hidden|help|hand|groove|fixed|ellipsis|e-resize|double|dotted|distribute|distribute-space|distribute-letter|distribute-all-lines|disc|disabled|default|decimal|dashed|crosshair|collapse|col-resize|circle|char|center|capitalize|break-word|break-all|bottom|both|bolder|bold|block|bidi-override|below|baseline|auto|always|all-scroll|absolute|table|table-cell)\\b"},{begin:/:/,end:/[;}{]/,relevance:0,contains:[n.BLOCK_COMMENT,a,n.HEXCOLOR,n.CSS_NUMBER_MODE,e.QUOTE_STRING_MODE,e.APOS_STRING_MODE,n.IMPORTANT,n.FUNCTION_DISPATCH,]},{begin:"@(page|font-face)",keywords:{$pattern:t,keyword:"@page @font-face"}},{begin:"@",end:"[{;]",returnBegin:!0,keywords:{$pattern:/[a-z-]+/,keyword:"and or not only",attribute:J.join(" ")},contains:[{begin:t,className:"keyword"},{begin:/[a-z-]+(?=:)/,className:"attribute"},a,e.QUOTE_STRING_MODE,e.APOS_STRING_MODE,n.HEXCOLOR,n.CSS_NUMBER_MODE,]},n.FUNCTION_DISPATCH,]}},grmr_shell:e=>({name:"Shell Session",aliases:["console","shellsession"],contains:[{className:"meta.prompt",begin:/^\s{0,3}[/~\w\d[\]()@-]*[>%$#][ ]?/,starts:{end:/[^\\](?=\s*$)/,subLanguage:"bash"}},]}),grmr_sql(e){let n=e.regex,t=e.COMMENT("--","$"),a=["true","false","unknown"],i=["bigint","binary","blob","boolean","char","character","clob","date","dec","decfloat","decimal","float","int","integer","interval","nchar","nclob","national","numeric","real","row","smallint","time","timestamp","varchar","varying","varbinary",],r=["abs","acos","array_agg","asin","atan","avg","cast","ceil","ceiling","coalesce","corr","cos","cosh","count","covar_pop","covar_samp","cume_dist","dense_rank","deref","element","exp","extract","first_value","floor","json_array","json_arrayagg","json_exists","json_object","json_objectagg","json_query","json_table","json_table_primitive","json_value","lag","last_value","lead","listagg","ln","log","log10","lower","max","min","mod","nth_value","ntile","nullif","percent_rank","percentile_cont","percentile_disc","position","position_regex","power","rank","regr_avgx","regr_avgy","regr_count","regr_intercept","regr_r2","regr_slope","regr_sxx","regr_sxy","regr_syy","row_number","sin","sinh","sqrt","stddev_pop","stddev_samp","substring","substring_regex","sum","tan","tanh","translate","translate_regex","treat","trim","trim_array","unnest","upper","value_of","var_pop","var_samp","width_bucket",],s=["create table","insert into","primary key","foreign key","not null","alter table","add constraint","grouping sets","on overflow","character set","respect nulls","ignore nulls","nulls first","nulls last","depth first","breadth first",],l=r,o=["abs","acos","all","allocate","alter","and","any","are","array","array_agg","array_max_cardinality","as","asensitive","asin","asymmetric","at","atan","atomic","authorization","avg","begin","begin_frame","begin_partition","between","bigint","binary","blob","boolean","both","by","call","called","cardinality","cascaded","case","cast","ceil","ceiling","char","char_length","character","character_length","check","classifier","clob","close","coalesce","collate","collect","column","commit","condition","connect","constraint","contains","convert","copy","corr","corresponding","cos","cosh","count","covar_pop","covar_samp","create","cross","cube","cume_dist","current","current_catalog","current_date","current_default_transform_group","current_path","current_role","current_row","current_schema","current_time","current_timestamp","current_path","current_role","current_transform_group_for_type","current_user","cursor","cycle","date","day","deallocate","dec","decimal","decfloat","declare","default","define","delete","dense_rank","deref","describe","deterministic","disconnect","distinct","double","drop","dynamic","each","element","else","empty","end","end_frame","end_partition","end-exec","equals","escape","every","except","exec","execute","exists","exp","external","extract","false","fetch","filter","first_value","float","floor","for","foreign","frame_row","free","from","full","function","fusion","get","global","grant","group","grouping","groups","having","hold","hour","identity","in","indicator","initial","inner","inout","insensitive","insert","int","integer","intersect","intersection","interval","into","is","join","json_array","json_arrayagg","json_exists","json_object","json_objectagg","json_query","json_table","json_table_primitive","json_value","lag","language","large","last_value","lateral","lead","leading","left","like","like_regex","listagg","ln","local","localtime","localtimestamp","log","log10","lower","match","match_number","match_recognize","matches","max","member","merge","method","min","minute","mod","modifies","module","month","multiset","national","natural","nchar","nclob","new","no","none","normalize","not","nth_value","ntile","null","nullif","numeric","octet_length","occurrences_regex","of","offset","old","omit","on","one","only","open","or","order","out","outer","over","overlaps","overlay","parameter","partition","pattern","per","percent","percent_rank","percentile_cont","percentile_disc","period","portion","position","position_regex","power","precedes","precision","prepare","primary","procedure","ptf","range","rank","reads","real","recursive","ref","references","referencing","regr_avgx","regr_avgy","regr_count","regr_intercept","regr_r2","regr_slope","regr_sxx","regr_sxy","regr_syy","release","result","return","returns","revoke","right","rollback","rollup","row","row_number","rows","running","savepoint","scope","scroll","search","second","seek","select","sensitive","session_user","set","show","similar","sin","sinh","skip","smallint","some","specific","specifictype","sql","sqlexception","sqlstate","sqlwarning","sqrt","start","static","stddev_pop","stddev_samp","submultiset","subset","substring","substring_regex","succeeds","sum","symmetric","system","system_time","system_user","table","tablesample","tan","tanh","then","time","timestamp","timezone_hour","timezone_minute","to","trailing","translate","translate_regex","translation","treat","trigger","trim","trim_array","true","truncate","uescape","union","unique","unknown","unnest","update","upper","user","using","value","values","value_of","var_pop","var_samp","varbinary","varchar","varying","versioning","when","whenever","where","width_bucket","window","with","within","without","year","add","asc","collation","desc","final","first","last","view",].filter(e=>!r.includes(e)),c={begin:n.concat(/\b/,n.either(...l),/\s*\(/),relevance:0,keywords:{built_in:l}};return{name:"SQL",case_insensitive:!0,illegal:/[{}]|<\//,keywords:{$pattern:/\b[\w\.]+/,keyword:((e,{exceptions:n,when:t}={})=>{let a=t;return n=n||[],e.map(e=>e.match(/\|\d+$/)||n.includes(e)?e:a(e)?e+"|0":e)})(o,{when:e=>e.length<3}),literal:a,type:i,built_in:["current_catalog","current_date","current_default_transform_group","current_path","current_role","current_schema","current_transform_group_for_type","current_user","session_user","system_time","system_user","current_time","localtime","current_timestamp","localtimestamp",]},contains:[{begin:n.either(...s),relevance:0,keywords:{$pattern:/[\w\.]+/,keyword:o.concat(s),literal:a,type:i}},{className:"type",begin:n.either("double precision","large object","with timezone","without timezone")},c,{className:"variable",begin:/@[a-z0-9]+/},{className:"string",variants:[{begin:/'/,end:/'/,contains:[{begin:/''/}]},]},{begin:/"/,end:/"/,contains:[{begin:/""/},]},e.C_NUMBER_MODE,e.C_BLOCK_COMMENT_MODE,t,{className:"operator",begin:/[-+*/=%^~]|&&?|\|\|?|!=?|<(?:=>?|<|>)?|>[>=]?/,relevance:0},]}},grmr_swift(e){let n={match:/\s+/,relevance:0},t=e.COMMENT("/\\*","\\*/",{contains:["self"]}),a=[e.C_LINE_COMMENT_MODE,t],i={match:[/\./,p(...e8,...eh)],className:{2:"keyword"}},r={match:m(/\./,p(...eE)),relevance:0},s=eE.filter(e=>"string"==typeof e).concat(["_|0"]),l={variants:[{className:"keyword",match:p(...eE.filter(e=>"string"!=typeof e).concat(ef).map(ep),...eh)},]},o={$pattern:p(/\b\w+/,/#\w+/),keyword:s.concat(eN),literal:e$},c=[i,r,l],d=[{match:m(/\./,p(...ew)),relevance:0},{className:"built_in",match:m(/\b/,p(...ew),/(?=\()/)},],u={match:/->/,relevance:0},b=[u,{className:"operator",relevance:0,variants:[{match:ek},{match:`\\.(\\.|${ex})+`}]},],h="([0-9a-fA-F]_*)+",f={className:"number",relevance:0,variants:[{match:"\\b(([0-9]_*)+)(\\.(([0-9]_*)+))?([eE][+-]?(([0-9]_*)+))?\\b"},{match:`\\b0x(${h})(\\.(${h}))?([pP][+-]?(([0-9]_*)+))?\\b`},{match:/\b0o([0-7]_*)+\b/},{match:/\b0b([01]_*)+\b/},]},E=(e="")=>({className:"subst",variants:[{match:m(/\\/,e,/[0\\tnr"']/)},{match:m(/\\/,e,/u\{[0-9a-fA-F]{1,8}\}/)},]}),$=(e="")=>({className:"subst",match:m(/\\/,e,/[\t ]*(?:[\r\n]|\r\n)/)}),y=(e="")=>({className:"subst",label:"interpol",begin:m(/\\/,e,/\(/),end:/\)/}),N=(e="")=>({begin:m(e,/"""/),end:m(/"""/,e),contains:[E(e),$(e),y(e)]}),w=(e="")=>({begin:m(e,/"/),end:m(/"/,e),contains:[E(e),y(e)]}),v={className:"string",variants:[N(),N("#"),N("##"),N("###"),w(),w("#"),w("##"),w("###"),]},x={match:m(/`/,eS,/`/)},k=[x,{className:"variable",match:/\$\d+/},{className:"variable",match:`\\$${eO}+`},],M=[{match:/(@|#(un)?)available/,className:"keyword",starts:{contains:[{begin:/\(/,end:/\)/,keywords:eT,contains:[...b,f,v]},]}},{className:"keyword",match:m(/@/,p(...eC))},{className:"meta",match:m(/@/,eS)},],O={match:g(/\b[A-Z]/),relevance:0,contains:[{className:"type",match:m(/(AV|CA|CF|CG|CI|CL|CM|CN|CT|MK|MP|MTK|MTL|NS|SCN|SK|UI|WK|XC)/,eO,"+")},{className:"type",match:eA,relevance:0},{match:/[?!]+/,relevance:0},{match:/\.\.\./,relevance:0},{match:m(/\s+&\s+/,g(eA)),relevance:0},]};O.contains.push({begin://,keywords:o,contains:[...a,...c,...M,u,O]});let S={begin:/\(/,end:/\)/,relevance:0,keywords:o,contains:["self",{match:m(eS,/\s*:/),keywords:"_|0",relevance:0},...a,...c,...d,...b,f,v,...k,...M,O,]},A={begin://,contains:[...a,O]},C={begin:/\(/,end:/\)/,keywords:o,contains:[{begin:p(g(m(eS,/\s*:/)),g(m(eS,/\s+/,eS,/\s*:/))),end:/:/,relevance:0,contains:[{className:"keyword",match:/\b_\b/},{className:"params",match:eS},]},...a,...c,...b,f,v,...M,O,S,],endsParent:!0,illegal:/["']/},T={match:[/func/,/\s+/,p(x.match,eS,ek)],className:{1:"keyword",3:"title.function"},contains:[A,C,n],illegal:[/\[/,/%/]};for(let R of v.variants){let D=R.contains.find(e=>"interpol"===e.label);D.keywords=o;let I=[...c,...d,...b,f,v,...k];D.contains=[...I,{begin:/\(/,end:/\)/,contains:["self",...I]},]}return{name:"Swift",keywords:o,contains:[...a,T,{match:[/\b(?:subscript|init[?!]?)/,/\s*(?=[<(])/],className:{1:"keyword"},contains:[A,C,n],illegal:/\[|%/},{beginKeywords:"struct protocol class extension enum actor",end:"\\{",excludeEnd:!0,keywords:o,contains:[e.inherit(e.TITLE_MODE,{className:"title.class",begin:/[A-Za-z$_][\u00C0-\u02B80-9A-Za-z$_]*/}),...c,]},{match:[/operator/,/\s+/,ek],className:{1:"keyword",3:"title"}},{begin:[/precedencegroup/,/\s+/,eA],className:{1:"keyword",3:"title"},contains:[O],keywords:[...ey,...e$],end:/}/},{beginKeywords:"import",end:/$/,contains:[...a],relevance:0},...c,...d,...b,f,v,...k,...M,O,S,]}},grmr_typescript(e){let n=em(e),t=["any","void","number","boolean","string","object","never","symbol","bigint","unknown",],a={beginKeywords:"namespace",end:/\{/,excludeEnd:!0,contains:[n.exports.CLASS_REFERENCE]},i={beginKeywords:"interface",end:/\{/,excludeEnd:!0,keywords:{keyword:"interface extends",built_in:t},contains:[n.exports.CLASS_REFERENCE]},r={$pattern:es,keyword:el.concat(["type","namespace","interface","public","private","protected","implements","declare","abstract","readonly","enum","override",]),literal:eo,built_in:eb.concat(t),"variable.language":eu},s={className:"meta",begin:"@[A-Za-z$_][0-9A-Za-z$_]*"},l=(e,n,t)=>{let a=e.contains.findIndex(e=>e.label===n);if(-1===a)throw Error("can not find mode to replace");e.contains.splice(a,1,t)};return Object.assign(n.keywords,r),n.exports.PARAMS_CONTAINS.push(s),n.contains=n.contains.concat([s,a,i]),l(n,"shebang",e.SHEBANG()),l(n,"use_strict",{className:"meta",relevance:10,begin:/^\s*['"]use strict['"]/}),n.contains.find(e=>"func.def"===e.label).relevance=0,Object.assign(n,{name:"TypeScript",aliases:["ts","tsx"]}),n},grmr_vbnet(e){let n=e.regex,t=/\d{1,2}\/\d{1,2}\/\d{4}/,a=/\d{4}-\d{1,2}-\d{1,2}/,i=/(\d|1[012])(:\d+){0,2} *(AM|PM)/,r=/\d{1,2}(:\d{1,2}){1,2}/,s={className:"literal",variants:[{begin:n.concat(/# */,n.either(a,t),/ *#/)},{begin:n.concat(/# */,r,/ *#/)},{begin:n.concat(/# */,i,/ *#/)},{begin:n.concat(/# */,n.either(a,t),/ +/,n.either(i,r),/ *#/)},]},l=e.COMMENT(/'''/,/$/,{contains:[{className:"doctag",begin:/<\/?/,end:/>/}]}),o=e.COMMENT(null,/$/,{variants:[{begin:/'/},{begin:/([\t ]|^)REM(?=\s)/}]});return{name:"Visual Basic .NET",aliases:["vb"],case_insensitive:!0,classNameAliases:{label:"symbol"},keywords:{keyword:"addhandler alias aggregate ansi as async assembly auto binary by byref byval call case catch class compare const continue custom declare default delegate dim distinct do each equals else elseif end enum erase error event exit explicit finally for friend from function get global goto group handles if implements imports in inherits interface into iterator join key let lib loop me mid module mustinherit mustoverride mybase myclass namespace narrowing new next notinheritable notoverridable of off on operator option optional order overloads overridable overrides paramarray partial preserve private property protected public raiseevent readonly redim removehandler resume return select set shadows shared skip static step stop structure strict sub synclock take text then throw to try unicode until using when where while widening with withevents writeonly yield",built_in:"addressof and andalso await directcast gettype getxmlnamespace is isfalse isnot istrue like mod nameof new not or orelse trycast typeof xor cbool cbyte cchar cdate cdbl cdec cint clng cobj csbyte cshort csng cstr cuint culng cushort",type:"boolean byte char date decimal double integer long object sbyte short single string uinteger ulong ushort",literal:"true false nothing"},illegal:"//|\\{|\\}|endif|gosub|variant|wend|^\\$ ",contains:[{className:"string",begin:/"(""|[^/n])"C\b/},{className:"string",begin:/"/,end:/"/,illegal:/\n/,contains:[{begin:/""/}]},s,{className:"number",relevance:0,variants:[{begin:/\b\d[\d_]*((\.[\d_]+(E[+-]?[\d_]+)?)|(E[+-]?[\d_]+))[RFD@!#]?/},{begin:/\b\d[\d_]*((U?[SIL])|[%&])?/},{begin:/&H[\dA-F_]+((U?[SIL])|[%&])?/},{begin:/&O[0-7_]+((U?[SIL])|[%&])?/},{begin:/&B[01_]+((U?[SIL])|[%&])?/},]},{className:"label",begin:/^\w+:/},l,o,{className:"meta",begin:/[\t ]*#(const|disable|else|elseif|enable|end|externalsource|if|region)\b/,end:/$/,keywords:{keyword:"const disable else elseif enable end externalsource if region then"},contains:[o]},]}},grmr_wasm(e){e.regex;let n=e.COMMENT(/\(;/,/;\)/);return n.contains.push("self"),{name:"WebAssembly",keywords:{$pattern:/[\w.]+/,keyword:["anyfunc","block","br","br_if","br_table","call","call_indirect","data","drop","elem","else","end","export","func","global.get","global.set","local.get","local.set","local.tee","get_global","get_local","global","if","import","local","loop","memory","memory.grow","memory.size","module","mut","nop","offset","param","result","return","select","set_global","set_local","start","table","tee_local","then","type","unreachable",]},contains:[e.COMMENT(/;;/,/$/),n,{match:[/(?:offset|align)/,/\s*/,/=/],className:{1:"keyword",3:"operator"}},{className:"variable",begin:/\$[\w_]+/},{match:/(\((?!;)|\))+/,className:"punctuation",relevance:0},{begin:[/(?:func|call|call_indirect)/,/\s+/,/\$[^\s)]+/],className:{1:"keyword",3:"title.function"}},e.QUOTE_STRING_MODE,{match:/(i32|i64|f32|f64)(?!\.)/,className:"type"},{className:"keyword",match:/\b(f32|f64|i32|i64)(?:\.(?:abs|add|and|ceil|clz|const|convert_[su]\/i(?:32|64)|copysign|ctz|demote\/f64|div(?:_[su])?|eqz?|extend_[su]\/i32|floor|ge(?:_[su])?|gt(?:_[su])?|le(?:_[su])?|load(?:(?:8|16|32)_[su])?|lt(?:_[su])?|max|min|mul|nearest|neg?|or|popcnt|promote\/f32|reinterpret\/[fi](?:32|64)|rem_[su]|rot[lr]|shl|shr_[su]|store(?:8|16|32)?|sqrt|sub|trunc(?:_[su]\/f(?:32|64))?|wrap\/i64|xor))\b/},{className:"number",relevance:0,match:/[+-]?\b(?:\d(?:_?\d)*(?:\.\d(?:_?\d)*)?(?:[eE][+-]?\d(?:_?\d)*)?|0x[\da-fA-F](?:_?[\da-fA-F])*(?:\.[\da-fA-F](?:_?[\da-fA-D])*)?(?:[pP][+-]?\d(?:_?\d)*)?)\b|\binf\b|\bnan(?::0x[\da-fA-F](?:_?[\da-fA-D])*)?\b/},]}},grmr_yaml(e){let n="true false yes no null",t="[\\w#;/?:@&=+$,.~*'()[\\]]+",a={className:"string",relevance:0,variants:[{begin:/'/,end:/'/},{begin:/"/,end:/"/},{begin:/\S+/},],contains:[e.BACKSLASH_ESCAPE,{className:"template-variable",variants:[{begin:/\{\{/,end:/\}\}/},{begin:/%\{/,end:/\}/},]},]},i=e.inherit(a,{variants:[{begin:/'/,end:/'/},{begin:/"/,end:/"/},{begin:/[^\s,{}[\]]+/},]}),r={end:",",endsWithParent:!0,excludeEnd:!0,keywords:n,relevance:0},s=[{className:"attr",variants:[{begin:"\\w[\\w :\\/.-]*:(?=[ ]|$)"},{begin:'"\\w[\\w :\\/.-]*":(?=[ ]|$)'},{begin:"'\\w[\\w :\\/.-]*':(?=[ ]|$)"},]},{className:"meta",begin:"^---\\s*$",relevance:10},{className:"string",begin:"[\\|>]([1-9]?[+-])?[ ]*\\n( +)[^ ][^\\n]*\\n(\\2[^\\n]+\\n?)*"},{begin:"<%[%=-]?",end:"[%-]?%>",subLanguage:"ruby",excludeBegin:!0,excludeEnd:!0,relevance:0},{className:"type",begin:"!\\w+!"+t},{className:"type",begin:"!<"+t+">"},{className:"type",begin:"!"+t},{className:"type",begin:"!!"+t},{className:"meta",begin:"&"+e.UNDERSCORE_IDENT_RE+"$"},{className:"meta",begin:"\\*"+e.UNDERSCORE_IDENT_RE+"$"},{className:"bullet",begin:"-(?=[ ]|$)",relevance:0},e.HASH_COMMENT_MODE,{beginKeywords:n,keywords:{literal:n}},{className:"number",begin:"\\b[0-9]{4}(-[0-9][0-9]){0,2}([Tt \\t][0-9][0-9]?(:[0-9][0-9]){2})?(\\.[0-9]*)?([ \\t])*(Z|[-+][0-9][0-9]?(:[0-9][0-9])?)?\\b"},{className:"number",begin:e.C_NUMBER_RE+"\\b",relevance:0},{begin:/\{/,end:/\}/,contains:[r],illegal:"\\n",relevance:0},{begin:"\\[",end:"\\]",contains:[r],illegal:"\\n",relevance:0},a,],l=[...s];return l.pop(),l.push(i),r.contains=l,{name:"YAML",case_insensitive:!0,aliases:["yml"],contains:s}}});let eD=Q;for(let eI of Object.keys(eR)){let eL=eI.replace("grmr_","").replace("_","-");eD.registerLanguage(eL,eR[eI])}return eD}();"object"==typeof exports&&"undefined"!=typeof module&&(module.exports=hljs);
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/utils.py b/spaces/FrankZxShen/so-vits-svc-models-ba/utils.py
deleted file mode 100644
index 0e3c1702d8160f8d348e8b07596a9d172da624eb..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-ba/utils.py
+++ /dev/null
@@ -1,446 +0,0 @@
-import os
-import glob
-import re
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import warnings
-import random
-import functools
-
-import librosa
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from modules.commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.WARN)
-logger = logging
-
-f0_bin = 256
-f0_max = 1100.0
-f0_min = 50.0
-f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-def normalize_f0(f0, x_mask, uv, random_scale=True):
- # calculate means based on x_mask
- uv_sum = torch.sum(uv, dim=1, keepdim=True)
- uv_sum[uv_sum == 0] = 9999
- means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
-
- if random_scale:
- factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
- else:
- factor = torch.ones(f0.shape[0], 1).to(f0.device)
- # normalize f0 based on means and factor
- f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
- if torch.isnan(f0_norm).any():
- exit(0)
- return f0_norm * x_mask
-
-def plot_data_to_numpy(x, y):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- plt.plot(x)
- plt.plot(y)
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def f0_to_coarse(f0):
- is_torch = isinstance(f0, torch.Tensor)
- f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
-
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
- f0_coarse = (f0_mel + 0.5).int() if is_torch else np.rint(f0_mel).astype(np.int)
- assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
- return f0_coarse
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-def get_f0_predictor(f0_predictor,hop_length,sampling_rate,**kargs):
- if f0_predictor == "pm":
- from modules.F0Predictor.PMF0Predictor import PMF0Predictor
- f0_predictor_object = PMF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
- elif f0_predictor == "crepe":
- from modules.F0Predictor.CrepeF0Predictor import CrepeF0Predictor
- f0_predictor_object = CrepeF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate,device=kargs["device"],threshold=kargs["threshold"])
- elif f0_predictor == "harvest":
- from modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
- f0_predictor_object = HarvestF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
- elif f0_predictor == "dio":
- from modules.F0Predictor.DioF0Predictor import DioF0Predictor
- f0_predictor_object = DioF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
- else:
- raise Exception("Unknown f0 predictor")
- return f0_predictor_object
-
-def get_speech_encoder(speech_encoder,device=None,**kargs):
- if speech_encoder == "vec768l12":
- from vencoder.ContentVec768L12 import ContentVec768L12
- speech_encoder_object = ContentVec768L12(device = device)
- elif speech_encoder == "vec256l9":
- from vencoder.ContentVec256L9 import ContentVec256L9
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec256l9-onnx":
- from vencoder.ContentVec256L9_Onnx import ContentVec256L9_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec256l12-onnx":
- from vencoder.ContentVec256L12_Onnx import ContentVec256L12_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec768l9-onnx":
- from vencoder.ContentVec768L9_Onnx import ContentVec768L9_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "vec768l12-onnx":
- from vencoder.ContentVec768L12_Onnx import ContentVec768L12_Onnx
- speech_encoder_object = ContentVec256L9(device = device)
- elif speech_encoder == "hubertsoft-onnx":
- from vencoder.HubertSoft_Onnx import HubertSoft_Onnx
- speech_encoder_object = HubertSoft(device = device)
- elif speech_encoder == "hubertsoft":
- from vencoder.HubertSoft import HubertSoft
- speech_encoder_object = HubertSoft(device = device)
- elif speech_encoder == "whisper-ppg":
- from vencoder.WhisperPPG import WhisperPPG
- speech_encoder_object = WhisperPPG(device = device)
- else:
- raise Exception("Unknown speech encoder")
- return speech_encoder_object
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- # assert "dec" in k or "disc" in k
- # print("load", k)
- new_state_dict[k] = saved_state_dict[k]
- assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
- except:
- print("error, %s is not in the checkpoint" % k)
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- print("load ")
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
- """Freeing up space by deleting saved ckpts
-
- Arguments:
- path_to_models -- Path to the model directory
- n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
- sort_by_time -- True -> chronologically delete ckpts
- False -> lexicographically delete ckpts
- """
- ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
- name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
- time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
- sort_key = time_key if sort_by_time else name_key
- x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
- to_del = [os.path.join(path_to_models, fn) for fn in
- (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
- del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
- del_routine = lambda x: [os.remove(x), del_info(x)]
- rs = [del_routine(fn) for fn in to_del]
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/config.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-def repeat_expand_2d(content, target_len):
- # content : [h, t]
-
- src_len = content.shape[-1]
- target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
- temp = torch.arange(src_len+1) * target_len / src_len
- current_pos = 0
- for i in range(target_len):
- if i < temp[current_pos+1]:
- target[:, i] = content[:, current_pos]
- else:
- current_pos += 1
- target[:, i] = content[:, current_pos]
-
- return target
-
-
-def mix_model(model_paths,mix_rate,mode):
- mix_rate = torch.FloatTensor(mix_rate)/100
- model_tem = torch.load(model_paths[0])
- models = [torch.load(path)["model"] for path in model_paths]
- if mode == 0:
- mix_rate = F.softmax(mix_rate,dim=0)
- for k in model_tem["model"].keys():
- model_tem["model"][k] = torch.zeros_like(model_tem["model"][k])
- for i,model in enumerate(models):
- model_tem["model"][k] += model[k]*mix_rate[i]
- torch.save(model_tem,os.path.join(os.path.curdir,"output.pth"))
- return os.path.join(os.path.curdir,"output.pth")
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
-
- def get(self,index):
- return self.__dict__.get(index)
-
-class Volume_Extractor:
- def __init__(self, hop_size = 512):
- self.hop_size = hop_size
-
- def extract(self, audio): # audio: 2d tensor array
- if not isinstance(audio,torch.Tensor):
- audio = torch.Tensor(audio)
- n_frames = int(audio.size(-1) // self.hop_size)
- audio2 = audio ** 2
- audio2 = torch.nn.functional.pad(audio2, (int(self.hop_size // 2), int((self.hop_size + 1) // 2)), mode = 'reflect')
- volume = torch.FloatTensor([torch.mean(audio2[:,int(n * self.hop_size) : int((n + 1) * self.hop_size)]) for n in range(n_frames)])
- volume = torch.sqrt(volume)
- return volume
\ No newline at end of file
diff --git a/spaces/GMFTBY/PandaGPT/model/ImageBind/model_card.md b/spaces/GMFTBY/PandaGPT/model/ImageBind/model_card.md
deleted file mode 100644
index c7bb26500b6590b64ffa6350f37be80dc88612d8..0000000000000000000000000000000000000000
--- a/spaces/GMFTBY/PandaGPT/model/ImageBind/model_card.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# Model Card for ImageBind
-
-Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images.
-Input any of the six modalities and get the same sized embedding that can be used for cross-modal and multimodal tasks.
-
-# Model Details
-
-## Model Description
-
-
-Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images
-
-- **Developed by:** Meta AI
-- **Model type:** Multimodal model
-- **Language(s) (NLP):** en
-- **License:** CC BY-NC-SA 4.0
-- **Resources for more information:**
- - [GitHub Repo](https://github.com/facebookresearch/ImageBind)
-
-
-# Uses
-
-
-This model is intended only for research purposes. It provides a joint embedding space for different modalities -- image/video, text, audio, depth, IMU and thermal images.
-We hope that these joint embeddings can be used for a variety of different cross-modal research, e.g., cross-modal retrieval and combining embeddings from different modalities.
-
-## Out-of-Scope Use
-
-
-
-
-This model is *NOT* intended to be used in any real world application -- commercial or otherwise.
-It may produce harmful associations with different inputs.
-The model needs to be investigated and likely re-trained on specific data for any such application.
-The model is expected to work better on web-based visual data since it was trained on such data.
-The text encoder is likely to work only on English language text because of the underlying training datasets.
-
-# Bias, Risks, and Limitations
-
-
-Open-domain joint embedding models are prone to producing specific biases, e.g., study from [CLIP](https://github.com/openai/CLIP/blob/main/model-card.md#bias-and-fairness).
-Since our model uses such models as initialization, it will exhibit such biases too.
-Moreover, for learning joint embeddings for other modalities such as audio, thermal, depth, and IMU we leverage datasets that are relatively small. These joint embeddings are thus limited to the concepts present in the datasets. For example, the thermal datasets we used are limited to outdoor street scenes, while the depth datasets are limited to indoor scenes.
-
-
-
-# Training Details
-
-## Training Data
-
-
-
-ImageBind uses image-paired data for training -- (image, X) where X is one of text, audio, depth, IMU or thermal data.
-In particular, we initialize and freeze the image and text encoders using an OpenCLIP ViT-H encoder.
-We train audio embeddings using Audioset, depth embeddings using the SUN RGB-D dataset, IMU using the Ego4D dataset and thermal embeddings using the LLVIP dataset.
-We provide the exact training data details in the paper.
-
-
-## Training Procedure
-
-
-Please refer to the research paper and github repo for exact details on this.
-
-# Evaluation
-
-## Testing Data, Factors & Metrics
-
-We evaluate the model on a variety of different classification benchmarks for each modality.
-The evaluation details are presented in the paper.
-The models performance is measured using standard classification metrics such as accuracy and mAP.
-
-# Citation
-
-
-
-**BibTeX:**
-```
-@inproceedings{girdhar2023imagebind,
- title={ImageBind: One Embedding Space To Bind Them All},
- author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
-and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
- booktitle={CVPR},
- year={2023}
-}
-```
-
-
-# Model Card Contact
-
-Please reach out to the authors at: rgirdhar@meta.com imisra@meta.com alaaelnouby@gmail.com
-
-# How to Get Started with the Model
-
-Our github repo provides a simple example to extract embeddings from images, audio etc.
diff --git a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/model_download/yolov5_model_p5_n.sh b/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/model_download/yolov5_model_p5_n.sh
deleted file mode 100644
index 5fc6d093f4b92e1ad735f8b513d01d95f4d53d5c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/model_download/yolov5_model_p5_n.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-cd ./yolov5
-
-# 下载YOLOv5模型
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt
diff --git a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/configs/data_configs.py b/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/configs/data_configs.py
deleted file mode 100644
index deccb0b1c266ad4b6abaef53d67ec1ed0ddbd462..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/configs/data_configs.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from configs import transforms_config
-from configs.paths_config import dataset_paths
-
-
-DATASETS = {
- 'ffhq_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['ffhq'],
- 'train_target_root': dataset_paths['ffhq'],
- 'test_source_root': dataset_paths['celeba_test'],
- 'test_target_root': dataset_paths['celeba_test'],
- },
- 'cars_encode': {
- 'transforms': transforms_config.CarsEncodeTransforms,
- 'train_source_root': dataset_paths['cars_train'],
- 'train_target_root': dataset_paths['cars_train'],
- 'test_source_root': dataset_paths['cars_test'],
- 'test_target_root': dataset_paths['cars_test'],
- },
- 'horse_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['horse_train'],
- 'train_target_root': dataset_paths['horse_train'],
- 'test_source_root': dataset_paths['horse_test'],
- 'test_target_root': dataset_paths['horse_test'],
- },
- 'church_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['church_train'],
- 'train_target_root': dataset_paths['church_train'],
- 'test_source_root': dataset_paths['church_test'],
- 'test_target_root': dataset_paths['church_test'],
- },
- 'cats_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['cats_train'],
- 'train_target_root': dataset_paths['cats_train'],
- 'test_source_root': dataset_paths['cats_test'],
- 'test_target_root': dataset_paths['cats_test'],
- }
-}
diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/input_pipeline.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/input_pipeline.py
deleted file mode 100644
index e9a9bc3a8aa15316aa88c3947120883be869331e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/input_pipeline.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# 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.
-
-"""Feature pre-processing input pipeline for AlphaFold."""
-
-from alphafold.model.tf import data_transforms
-from alphafold.model.tf import shape_placeholders
-import tensorflow.compat.v1 as tf
-import tree
-
-# Pylint gets confused by the curry1 decorator because it changes the number
-# of arguments to the function.
-# pylint:disable=no-value-for-parameter
-
-
-NUM_RES = shape_placeholders.NUM_RES
-NUM_MSA_SEQ = shape_placeholders.NUM_MSA_SEQ
-NUM_EXTRA_SEQ = shape_placeholders.NUM_EXTRA_SEQ
-NUM_TEMPLATES = shape_placeholders.NUM_TEMPLATES
-
-
-def nonensembled_map_fns(data_config):
- """Input pipeline functions which are not ensembled."""
- common_cfg = data_config.common
-
- map_fns = [
- data_transforms.correct_msa_restypes,
- data_transforms.add_distillation_flag(False),
- data_transforms.cast_64bit_ints,
- data_transforms.squeeze_features,
- # Keep to not disrupt RNG.
- data_transforms.randomly_replace_msa_with_unknown(0.0),
- data_transforms.make_seq_mask,
- data_transforms.make_msa_mask,
- # Compute the HHblits profile if it's not set. This has to be run before
- # sampling the MSA.
- data_transforms.make_hhblits_profile,
- data_transforms.make_random_crop_to_size_seed,
- ]
- if common_cfg.use_templates:
- map_fns.extend([
- data_transforms.fix_templates_aatype,
- data_transforms.make_template_mask,
- data_transforms.make_pseudo_beta('template_')
- ])
- map_fns.extend([
- data_transforms.make_atom14_masks,
- ])
-
- return map_fns
-
-
-def ensembled_map_fns(data_config):
- """Input pipeline functions that can be ensembled and averaged."""
- common_cfg = data_config.common
- eval_cfg = data_config.eval
-
- map_fns = []
-
- if common_cfg.reduce_msa_clusters_by_max_templates:
- pad_msa_clusters = eval_cfg.max_msa_clusters - eval_cfg.max_templates
- else:
- pad_msa_clusters = eval_cfg.max_msa_clusters
-
- max_msa_clusters = pad_msa_clusters
- max_extra_msa = common_cfg.max_extra_msa
-
- map_fns.append(
- data_transforms.sample_msa(
- max_msa_clusters,
- keep_extra=True))
-
- if 'masked_msa' in common_cfg:
- # Masked MSA should come *before* MSA clustering so that
- # the clustering and full MSA profile do not leak information about
- # the masked locations and secret corrupted locations.
- map_fns.append(
- data_transforms.make_masked_msa(common_cfg.masked_msa,
- eval_cfg.masked_msa_replace_fraction))
-
- if common_cfg.msa_cluster_features:
- map_fns.append(data_transforms.nearest_neighbor_clusters())
- map_fns.append(data_transforms.summarize_clusters())
-
- # Crop after creating the cluster profiles.
- if max_extra_msa:
- map_fns.append(data_transforms.crop_extra_msa(max_extra_msa))
- else:
- map_fns.append(data_transforms.delete_extra_msa)
-
- map_fns.append(data_transforms.make_msa_feat())
-
- crop_feats = dict(eval_cfg.feat)
-
- if eval_cfg.fixed_size:
- map_fns.append(data_transforms.select_feat(list(crop_feats)))
- map_fns.append(data_transforms.random_crop_to_size(
- eval_cfg.crop_size,
- eval_cfg.max_templates,
- crop_feats,
- eval_cfg.subsample_templates))
- map_fns.append(data_transforms.make_fixed_size(
- crop_feats,
- pad_msa_clusters,
- common_cfg.max_extra_msa,
- eval_cfg.crop_size,
- eval_cfg.max_templates))
- else:
- map_fns.append(data_transforms.crop_templates(eval_cfg.max_templates))
-
- return map_fns
-
-
-def process_tensors_from_config(tensors, data_config):
- """Apply filters and maps to an existing dataset, based on the config."""
-
- def wrap_ensemble_fn(data, i):
- """Function to be mapped over the ensemble dimension."""
- d = data.copy()
- fns = ensembled_map_fns(data_config)
- fn = compose(fns)
- d['ensemble_index'] = i
- return fn(d)
-
- eval_cfg = data_config.eval
- tensors = compose(
- nonensembled_map_fns(
- data_config))(
- tensors)
-
- tensors_0 = wrap_ensemble_fn(tensors, tf.constant(0))
- num_ensemble = eval_cfg.num_ensemble
- if data_config.common.resample_msa_in_recycling:
- # Separate batch per ensembling & recycling step.
- num_ensemble *= data_config.common.num_recycle + 1
-
- if isinstance(num_ensemble, tf.Tensor) or num_ensemble > 1:
- fn_output_signature = tree.map_structure(
- tf.TensorSpec.from_tensor, tensors_0)
- tensors = tf.map_fn(
- lambda x: wrap_ensemble_fn(tensors, x),
- tf.range(num_ensemble),
- parallel_iterations=1,
- fn_output_signature=fn_output_signature)
- else:
- tensors = tree.map_structure(lambda x: x[None],
- tensors_0)
- return tensors
-
-
-@data_transforms.curry1
-def compose(x, fs):
- for f in fs:
- x = f(x)
- return x
diff --git a/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/models/encodec.py b/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/models/encodec.py
deleted file mode 100644
index 69621a695887b0b41614c51cae020f6fd0af221d..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/models/encodec.py
+++ /dev/null
@@ -1,302 +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 abc import ABC, abstractmethod
-import typing as tp
-
-from einops import rearrange
-import torch
-from torch import nn
-
-from .. import quantization as qt
-
-
-class CompressionModel(ABC, nn.Module):
-
- @abstractmethod
- def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
- ...
-
- @abstractmethod
- def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- """See `EncodecModel.encode`"""
- ...
-
- @abstractmethod
- def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
- """See `EncodecModel.decode`"""
- ...
-
- @property
- @abstractmethod
- def channels(self) -> int:
- ...
-
- @property
- @abstractmethod
- def frame_rate(self) -> int:
- ...
-
- @property
- @abstractmethod
- def sample_rate(self) -> int:
- ...
-
- @property
- @abstractmethod
- def cardinality(self) -> int:
- ...
-
- @property
- @abstractmethod
- def num_codebooks(self) -> int:
- ...
-
- @property
- @abstractmethod
- def total_codebooks(self) -> int:
- ...
-
- @abstractmethod
- def set_num_codebooks(self, n: int):
- """Set the active number of codebooks used by the quantizer.
- """
- ...
-
-
-class EncodecModel(CompressionModel):
- """Encodec model operating on the raw waveform.
-
- Args:
- encoder (nn.Module): Encoder network.
- decoder (nn.Module): Decoder network.
- quantizer (qt.BaseQuantizer): Quantizer network.
- frame_rate (int): Frame rate for the latent representation.
- sample_rate (int): Audio sample rate.
- channels (int): Number of audio channels.
- causal (bool): Whether to use a causal version of the model.
- renormalize (bool): Whether to renormalize the audio before running the model.
- """
- # we need assignement to override the property in the abstract class,
- # I couldn't find a better way...
- frame_rate: int = 0
- sample_rate: int = 0
- channels: int = 0
-
- def __init__(self,
- encoder: nn.Module,
- decoder: nn.Module,
- quantizer: qt.BaseQuantizer,
- frame_rate: int,
- sample_rate: int,
- channels: int,
- causal: bool = False,
- renormalize: bool = False):
- super().__init__()
- self.encoder = encoder
- self.decoder = decoder
- self.quantizer = quantizer
- self.frame_rate = frame_rate
- self.sample_rate = sample_rate
- self.channels = channels
- self.renormalize = renormalize
- self.causal = causal
- if self.causal:
- # we force disabling here to avoid handling linear overlap of segments
- # as supported in original EnCodec codebase.
- assert not self.renormalize, 'Causal model does not support renormalize'
-
- @property
- def total_codebooks(self):
- """Total number of quantizer codebooks available.
- """
- return self.quantizer.total_codebooks
-
- @property
- def num_codebooks(self):
- """Active number of codebooks used by the quantizer.
- """
- return self.quantizer.num_codebooks
-
- def set_num_codebooks(self, n: int):
- """Set the active number of codebooks used by the quantizer.
- """
- self.quantizer.set_num_codebooks(n)
-
- @property
- def cardinality(self):
- """Cardinality of each codebook.
- """
- return self.quantizer.bins
-
- def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- scale: tp.Optional[torch.Tensor]
- if self.renormalize:
- mono = x.mean(dim=1, keepdim=True)
- volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
- scale = 1e-8 + volume
- x = x / scale
- scale = scale.view(-1, 1)
- else:
- scale = None
- return x, scale
-
- def postprocess(self,
- x: torch.Tensor,
- scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
- if scale is not None:
- assert self.renormalize
- x = x * scale.view(-1, 1, 1)
- return x
-
- def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
- assert x.dim() == 3
- length = x.shape[-1]
- x, scale = self.preprocess(x)
-
- emb = self.encoder(x)
- q_res = self.quantizer(emb, self.frame_rate)
- out = self.decoder(q_res.x)
-
- # remove extra padding added by the encoder and decoder
- assert out.shape[-1] >= length, (out.shape[-1], length)
- out = out[..., :length]
-
- q_res.x = self.postprocess(out, scale)
-
- return q_res
-
- def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- """Encode the given input tensor to quantized representation along with scale parameter.
-
- Args:
- x (torch.Tensor): Float tensor of shape [B, C, T]
-
- Returns:
- codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
- codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
- scale a float tensor containing the scale for audio renormalizealization.
- """
- assert x.dim() == 3
- x, scale = self.preprocess(x)
- emb = self.encoder(x)
- codes = self.quantizer.encode(emb)
- return codes, scale
-
- def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
- """Decode the given codes to a reconstructed representation, using the scale to perform
- audio denormalization if needed.
-
- Args:
- codes (torch.Tensor): Int tensor of shape [B, K, T]
- scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
-
- Returns:
- out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
- """
- emb = self.quantizer.decode(codes)
- out = self.decoder(emb)
- out = self.postprocess(out, scale)
- # out contains extra padding added by the encoder and decoder
- return out
-
-
-class FlattenedCompressionModel(CompressionModel):
- """Wraps a CompressionModel and flatten its codebooks, e.g.
- instead of returning [B, K, T], return [B, S, T * (K // S)] with
- S the number of codebooks per step, and `K // S` the number of 'virtual steps'
- for each real time step.
-
- Args:
- model (CompressionModel): compression model to wrap.
- codebooks_per_step (int): number of codebooks to keep per step,
- this must divide the number of codebooks provided by the wrapped model.
- extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
- if each codebook has a cardinality N, then the first codebook will
- use the range [0, N - 1], and the second [N, 2 N - 1] etc.
- On decoding, this can lead to potentially invalid sequences.
- Any invalid entry will be silently remapped to the proper range
- with a modulo.
- """
- def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
- extend_cardinality: bool = True):
- super().__init__()
- self.model = model
- self.codebooks_per_step = codebooks_per_step
- self.extend_cardinality = extend_cardinality
-
- @property
- def total_codebooks(self):
- return self.model.total_codebooks
-
- @property
- def num_codebooks(self):
- """Active number of codebooks used by the quantizer.
-
- ..Warning:: this reports the number of codebooks after the flattening
- of the codebooks!
- """
- assert self.model.num_codebooks % self.codebooks_per_step == 0
- return self.codebooks_per_step
-
- def set_num_codebooks(self, n: int):
- """Set the active number of codebooks used by the quantizer.
-
- ..Warning:: this sets the number of codebooks **before** the flattening
- of the codebooks.
- """
- assert n % self.codebooks_per_step == 0
- self.model.set_num_codebooks(n)
-
- @property
- def num_virtual_steps(self) -> int:
- """Return the number of virtual steps, e.g. one real step
- will be split into that many steps.
- """
- return self.model.num_codebooks // self.codebooks_per_step
-
- @property
- def frame_rate(self) -> int:
- return self.model.frame_rate * self.num_virtual_steps
-
- @property
- def sample_rate(self) -> int:
- return self.model.sample_rate
-
- @property
- def channels(self) -> int:
- return self.model.channels
-
- @property
- def cardinality(self):
- """Cardinality of each codebook.
- """
- if self.extend_cardinality:
- return self.model.cardinality * self.num_virtual_steps
- else:
- return self.model.cardinality
-
- def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
- raise NotImplementedError("Not supported, use encode and decode.")
-
- def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
- indices, scales = self.model.encode(x)
- B, K, T = indices.shape
- indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
- if self.extend_cardinality:
- for virtual_step in range(1, self.num_virtual_steps):
- indices[..., virtual_step] += self.model.cardinality * virtual_step
- indices = rearrange(indices, 'b k t v -> b k (t v)')
- return (indices, scales)
-
- def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
- B, K, T = codes.shape
- assert T % self.num_virtual_steps == 0
- codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
- # We silently ignore potential errors from the LM when
- # using extend_cardinality.
- codes = codes % self.model.cardinality
- return self.model.decode(codes, scale)
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/zen2/ngram_utils.py b/spaces/HaloMaster/chinesesummary/fengshen/models/zen2/ngram_utils.py
deleted file mode 100644
index dcf474bf6f11c91159b9fdbaaeb2a3f7b6858539..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/zen2/ngram_utils.py
+++ /dev/null
@@ -1,192 +0,0 @@
-# coding: utf-8
-# Copyright 2019 Sinovation Ventures AI Institute
-#
-# 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.
-"""utils for ngram for ZEN2 model."""
-
-import os
-import logging
-import math
-import numpy as np
-import torch
-from transformers import cached_path
-
-NGRAM_DICT_NAME = 'ngram.txt'
-
-logger = logging.getLogger(__name__)
-PRETRAINED_VOCAB_ARCHIVE_MAP = {
- 'IDEA-CCNL/Erlangshen-ZEN2-345M-Chinese': 'https://huggingface.co/IDEA-CCNL/Erlangshen-ZEN2-345M-Chinese/resolve/main/ngram.txt',
- 'IDEA-CCNL/Erlangshen-ZEN2-668M-Chinese': 'https://huggingface.co/IDEA-CCNL/Erlangshen-ZEN2-668M-Chinese/resolve/main/ngram.txt',
-}
-
-
-class ZenNgramDict(object):
- """
- Dict class to store the ngram
- """
-
- def __init__(self, ngram_freq_path, tokenizer=None, max_ngram_in_seq=128):
- """Constructs ZenNgramDict
-
- :param ngram_freq_path: ngrams with frequency
- """
- if os.path.isdir(ngram_freq_path):
- ngram_freq_path = os.path.join(ngram_freq_path, NGRAM_DICT_NAME)
- self.ngram_freq_path = ngram_freq_path
- self.max_ngram_in_seq = max_ngram_in_seq
- self.max_ngram_len = 8
- self.id_to_ngram_list = ["[pad]"]
- self.ngram_to_id_dict = {"[pad]": 0}
- self.ngram_to_freq_dict = {}
-
- logger.info("loading ngram frequency file {}".format(ngram_freq_path))
- with open(ngram_freq_path, "r", encoding="utf-8") as fin:
- for i, line in enumerate(fin):
- items = line.strip().split(",")
- if len(items) != 2:
- continue
- ngram, freq = items
- # self.ngram_to_freq_dict[ngram] = int(freq)
- if tokenizer:
- tokens = tuple(tokenizer.tokenize(ngram))
- if len([token for token in tokens if "[UNK]" in token]) > 0:
- tokens = ngram
- else:
- tokens = tuple(ngram.split(" "))
- self.id_to_ngram_list.append(tokens)
- self.ngram_to_id_dict[tokens] = i + 1
- self.ngram_to_freq_dict[tokens] = int(freq)
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, **kwargs):
- """
- Instantiate a PreTrainedBertModel from a pre-trained model file.
- Download and cache the pre-trained model file if needed.
- """
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
- ngram_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
- if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
- logger.warning("The pre-trained model you are loading is a cased model but you have not set "
- "`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
- "you may want to check this behavior.")
- kwargs['do_lower_case'] = False
- elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
- logger.warning("The pre-trained model you are loading is an uncased model but you have set "
- "`do_lower_case` to False. We are setting `do_lower_case=True` for you "
- "but you may want to check this behavior.")
- kwargs['do_lower_case'] = True
- else:
- ngram_file = pretrained_model_name_or_path
- if os.path.isdir(ngram_file):
- ngram_file = os.path.join(ngram_file, NGRAM_DICT_NAME)
- # redirect to the cache, if necessary
- try:
- resolved_ngram_file = cached_path(ngram_file, cache_dir=cache_dir)
- except EnvironmentError:
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
- logger.error(
- "Couldn't reach server at '{}' to download vocabulary.".format(
- ngram_file))
- else:
- logger.error(
- "Model name '{}' was not found in model name list ({}). "
- "We assumed '{}' was a path or url but couldn't find any file "
- "associated to this path or url.".format(
- pretrained_model_name_or_path,
- ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
- ngram_file))
- return None
- if resolved_ngram_file == ngram_file:
- logger.info("loading vocabulary file {}".format(ngram_file))
- else:
- logger.info("loading vocabulary file {} from cache at {}".format(
- ngram_file, resolved_ngram_file))
- # Instantiate ngram.
- ngram_dict = cls(resolved_ngram_file, **kwargs)
- return ngram_dict
-
- def save(self, ngram_freq_path):
- ngram_freq_path = os.path.join(ngram_freq_path, NGRAM_DICT_NAME)
- with open(ngram_freq_path, "w+", encoding="utf-8") as fout:
- for ngram, freq in self.ngram_to_freq_dict.items():
- fout.write("{},{}\n".format(" ".join(ngram), freq))
-
-
-def extract_ngram_feature(tokens, ngram_dict, max_seq_len, seg_id_limit):
- # ----------- code for ngram BEGIN-----------
- ngram_matches = []
- # Filter the word segment from 2 to max_ngram_len to check whether there is a word
- max_gram_n = ngram_dict.max_ngram_len
- for p in range(2, max_gram_n):
- for q in range(0, len(tokens) - p + 1):
- character_segment = tokens[q:q + p]
- # j is the starting position of the word
- # i is the length of the current word
- character_segment = tuple(character_segment)
- if character_segment in ngram_dict.ngram_to_id_dict:
- ngram_index = ngram_dict.ngram_to_id_dict[character_segment]
- ngram_freq = ngram_dict.ngram_to_freq_dict[character_segment]
- ngram_matches.append([ngram_index, q, p, character_segment, ngram_freq])
-
- # shuffle(ngram_matches)
- ngram_matches = sorted(ngram_matches, key=lambda s: s[0])
- # max_word_in_seq_proportion = max_word_in_seq
- max_word_in_seq_proportion = math.ceil((len(tokens) / max_seq_len) * ngram_dict.max_ngram_in_seq)
- if len(ngram_matches) > max_word_in_seq_proportion:
- ngram_matches = ngram_matches[:max_word_in_seq_proportion]
- ngram_ids = [ngram[0] for ngram in ngram_matches]
- ngram_positions = [ngram[1] for ngram in ngram_matches]
- ngram_lengths = [ngram[2] for ngram in ngram_matches]
- ngram_tuples = [ngram[3] for ngram in ngram_matches]
- ngram_freqs = [ngram[4] for ngram in ngram_matches]
- ngram_seg_ids = [0 if position < seg_id_limit else 1 for position in
- ngram_positions]
-
- ngram_mask_array = np.zeros(ngram_dict.max_ngram_in_seq, dtype=np.bool)
- ngram_mask_array[:len(ngram_ids)] = 1
-
- # Zero-pad up to the max word in seq length.
- padding = [0] * (ngram_dict.max_ngram_in_seq - len(ngram_ids))
- ngram_ids += padding
- ngram_positions += padding
- ngram_lengths += padding
- ngram_seg_ids += padding
- ngram_freqs += padding
-
- # ----------- code for ngram END-----------
-
- return {
- "ngram_ids": ngram_ids,
- "ngram_positions": ngram_positions,
- "ngram_lengths": ngram_lengths,
- "ngram_tuples": ngram_tuples,
- "ngram_seg_ids": ngram_seg_ids,
- "ngram_masks": ngram_mask_array,
- "ngram_freqs": ngram_freqs,
- }
-
-
-def construct_ngram_matrix(ngram_data, max_seq_length):
- max_ngram_in_sequence = len(ngram_data["ngram_ids"])
- ngram_ids_num = len([x for x in ngram_data["ngram_masks"] if x == 1])
-
- ngram_positions_matrix = np.zeros(shape=(max_seq_length, max_ngram_in_sequence), dtype=np.float)
- for i in range(ngram_ids_num):
- ngram_positions_matrix[ngram_data["ngram_positions"][i]:
- ngram_data["ngram_positions"][i] + ngram_data["ngram_lengths"][i], i] = \
- ngram_data["ngram_freqs"][i]
- ngram_positions_matrix_t = torch.from_numpy(ngram_positions_matrix.astype(np.float))
- ngram_positions_matrix_t = torch.div(ngram_positions_matrix_t,
- torch.stack([torch.sum(ngram_positions_matrix_t, 1)] * ngram_positions_matrix_t.size(1)).t() + 1e-10)
-
- return ngram_positions_matrix_t.numpy()
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/datasets/asr_prep_json.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/datasets/asr_prep_json.py
deleted file mode 100644
index b8db8ff16691158fae034a8ab3faad622b351caf..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/datasets/asr_prep_json.py
+++ /dev/null
@@ -1,125 +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.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import argparse
-import concurrent.futures
-import json
-import multiprocessing
-import os
-from collections import namedtuple
-from itertools import chain
-
-import sentencepiece as spm
-from fairseq.data import Dictionary
-
-
-MILLISECONDS_TO_SECONDS = 0.001
-
-
-def process_sample(aud_path, lable, utt_id, sp, tgt_dict):
- import torchaudio
-
- input = {}
- output = {}
- si, ei = torchaudio.info(aud_path)
- input["length_ms"] = int(
- si.length / si.channels / si.rate / MILLISECONDS_TO_SECONDS
- )
- input["path"] = aud_path
-
- token = " ".join(sp.EncodeAsPieces(lable))
- ids = tgt_dict.encode_line(token, append_eos=False)
- output["text"] = lable
- output["token"] = token
- output["tokenid"] = ", ".join(map(str, [t.tolist() for t in ids]))
- return {utt_id: {"input": input, "output": output}}
-
-
-def main():
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--audio-dirs",
- nargs="+",
- default=["-"],
- required=True,
- help="input directories with audio files",
- )
- parser.add_argument(
- "--labels",
- required=True,
- help="aggregated input labels with format per line",
- type=argparse.FileType("r", encoding="UTF-8"),
- )
- parser.add_argument(
- "--spm-model",
- required=True,
- help="sentencepiece model to use for encoding",
- type=argparse.FileType("r", encoding="UTF-8"),
- )
- parser.add_argument(
- "--dictionary",
- required=True,
- help="file to load fairseq dictionary from",
- type=argparse.FileType("r", encoding="UTF-8"),
- )
- parser.add_argument("--audio-format", choices=["flac", "wav"], default="wav")
- parser.add_argument(
- "--output",
- required=True,
- type=argparse.FileType("w"),
- help="path to save json output",
- )
- args = parser.parse_args()
-
- sp = spm.SentencePieceProcessor()
- sp.Load(args.spm_model.name)
-
- tgt_dict = Dictionary.load(args.dictionary)
-
- labels = {}
- for line in args.labels:
- (utt_id, label) = line.split(" ", 1)
- labels[utt_id] = label
- if len(labels) == 0:
- raise Exception("No labels found in ", args.labels_path)
-
- Sample = namedtuple("Sample", "aud_path utt_id")
- samples = []
- for path, _, files in chain.from_iterable(
- os.walk(path) for path in args.audio_dirs
- ):
- for f in files:
- if f.endswith(args.audio_format):
- if len(os.path.splitext(f)) != 2:
- raise Exception("Expect file name. Got: ", f)
- utt_id = os.path.splitext(f)[0]
- if utt_id not in labels:
- continue
- samples.append(Sample(os.path.join(path, f), utt_id))
-
- utts = {}
- num_cpu = multiprocessing.cpu_count()
- with concurrent.futures.ThreadPoolExecutor(max_workers=num_cpu) as executor:
- future_to_sample = {
- executor.submit(
- process_sample, s.aud_path, labels[s.utt_id], s.utt_id, sp, tgt_dict
- ): s
- for s in samples
- }
- for future in concurrent.futures.as_completed(future_to_sample):
- try:
- data = future.result()
- except Exception as exc:
- print("generated an exception: ", exc)
- else:
- utts.update(data)
- json.dump({"utts": utts}, args.output, indent=4)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py
deleted file mode 100644
index 2be848fceae65e3bd5747a2c98106b0215c6a039..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-import shlex
-import subprocess
-import progressbar
-from time import time
-from pathlib import Path
-
-def find_all_files(path_dir, extension):
- out = []
- for root, dirs, filenames in os.walk(path_dir):
- for f in filenames:
- if f.endswith(extension):
- out.append(((str(Path(f).stem)), os.path.join(root, f)))
- return out
-
-def convert16k(inputfile, outputfile16k):
- command = ('sox -c 1 -b 16 {} -t wav {} rate 16k'.format(inputfile, outputfile16k))
- subprocess.call(shlex.split(command))
-
-if __name__ == "__main__":
- import argparse
-
- parser = argparse.ArgumentParser(description='Convert to wav 16k audio using sox.')
- parser.add_argument('input_dir', type=str,
- help='Path to the input dir.')
- parser.add_argument('output_dir', type=str,
- help='Path to the output dir.')
- parser.add_argument('--extension', type=str, default='wav',
- help='Audio file extension in the input. Default: mp3')
- args = parser.parse_args()
-
- # Find all sequences
- print(f"Finding all audio files with extension '{args.extension}' from {args.input_dir}...")
- audio_files = find_all_files(args.input_dir, args.extension)
- print(f"Done! Found {len(audio_files)} files.")
-
- # Convert to relative path
- audio_files = [os.path.relpath(file[-1], start=args.input_dir) for file in audio_files]
-
- # Create all the directories needed
- rel_dirs_set = set([os.path.dirname(file) for file in audio_files])
- for rel_dir in rel_dirs_set:
- Path(os.path.join(args.output_dir, rel_dir)).mkdir(parents=True, exist_ok=True)
-
- # Converting wavs files
- print("Converting the audio to wav files...")
- bar = progressbar.ProgressBar(maxval=len(audio_files))
- bar.start()
- start_time = time()
- for index, file in enumerate(audio_files):
- bar.update(index)
- input_file = os.path.join(args.input_dir, file)
- output_file = os.path.join(args.output_dir, os.path.splitext(file)[0]+".wav")
- convert16k(input_file, output_file)
- bar.finish()
- print(f"...done {len(audio_files)} files in {time()-start_time} seconds.")
\ No newline at end of file
diff --git a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/tts_infer/tts.py b/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/tts_infer/tts.py
deleted file mode 100644
index 57cf6c67f04e7839f526c940f3675e5b56c0f79a..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/tts_infer/tts.py
+++ /dev/null
@@ -1,146 +0,0 @@
-from __future__ import absolute_import, division, print_function, unicode_literals
-from typing import Tuple
-import sys
-from argparse import ArgumentParser
-
-import numpy as np
-import os
-import json
-import torch
-
-sys.path.append(os.path.join(os.path.dirname(__file__), "../src/glow_tts"))
-
-from scipy.io.wavfile import write
-from hifi.env import AttrDict
-from hifi.models import Generator
-
-
-from processtext import text_to_sequence
-import commons
-import models
-import utils
-
-
-def check_directory(dir):
- if not os.path.exists(dir):
- sys.exit("Error: {} directory does not exist".format(dir))
-
-
-class TextToMel:
- def __init__(self, glow_model_dir, device="cuda"):
- self.glow_model_dir = glow_model_dir
- check_directory(self.glow_model_dir)
- self.device = device
- self.hps, self.glow_tts_model = self.load_glow_tts()
- pass
-
- def load_glow_tts(self):
- hps = utils.get_hparams_from_dir(self.glow_model_dir)
- checkpoint_path = utils.latest_checkpoint_path(self.glow_model_dir)
- symbols = list(hps.data.punc) + list(hps.data.chars)
- glow_tts_model = models.FlowGenerator(
- len(symbols) + getattr(hps.data, "add_blank", False),
- out_channels=hps.data.n_mel_channels,
- **hps.model
- ) # .to(self.device)
-
- if self.device == "cuda":
- glow_tts_model.to("cuda")
-
- utils.load_checkpoint(checkpoint_path, glow_tts_model)
- glow_tts_model.decoder.store_inverse()
- _ = glow_tts_model.eval()
-
- return hps, glow_tts_model
-
- def generate_mel(self, text, noise_scale=0.667, length_scale=1.0):
- symbols = list(self.hps.data.punc) + list(self.hps.data.chars)
- cleaner = self.hps.data.text_cleaners
- if getattr(self.hps.data, "add_blank", False):
- text_norm = text_to_sequence(symbols, text, cleaner)
- text_norm = commons.intersperse(text_norm, len(symbols))
- else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality
- text = " " + text.strip() + " "
- text_norm = text_to_sequence(symbols, text, cleaner)
-
- sequence = np.array(text_norm)[None, :]
-
- if self.device == "cuda":
- x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
- x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda()
- else:
- x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).long()
- x_tst_lengths = torch.tensor([x_tst.shape[1]])
-
- with torch.no_grad():
- (y_gen_tst, *_), *_, (attn_gen, *_) = self.glow_tts_model(
- x_tst,
- x_tst_lengths,
- gen=True,
- noise_scale=noise_scale,
- length_scale=length_scale,
- )
-
- return y_gen_tst.cpu().detach().numpy()
-
-
-class MelToWav:
- def __init__(self, hifi_model_dir, device="cuda"):
- self.hifi_model_dir = hifi_model_dir
- check_directory(self.hifi_model_dir)
- self.device = device
- self.h, self.hifi_gan_generator = self.load_hifi_gan()
- pass
-
- def load_hifi_gan(self):
- checkpoint_path = utils.latest_checkpoint_path(self.hifi_model_dir, regex="g_*")
- config_file = os.path.join(self.hifi_model_dir, "config.json")
- data = open(config_file).read()
- json_config = json.loads(data)
- h = AttrDict(json_config)
- torch.manual_seed(h.seed)
-
- generator = Generator(h).to(self.device)
-
- assert os.path.isfile(checkpoint_path)
- print("Loading '{}'".format(checkpoint_path))
- state_dict_g = torch.load(checkpoint_path, map_location=self.device)
- print("Complete.")
-
- generator.load_state_dict(state_dict_g["generator"])
-
- generator.eval()
- generator.remove_weight_norm()
-
- return h, generator
-
- def generate_wav(self, mel):
- mel = torch.FloatTensor(mel).to(self.device)
-
- y_g_hat = self.hifi_gan_generator(mel) # passing through vocoder
- audio = y_g_hat.squeeze()
- audio = audio * 32768.0
- audio = audio.cpu().detach().numpy().astype("int16")
-
- return audio, self.h.sampling_rate
-
-
-if __name__ == "__main__":
-
- parser = ArgumentParser()
- parser.add_argument("-m", "--model", required=True, type=str)
- parser.add_argument("-g", "--gan", required=True, type=str)
- parser.add_argument("-d", "--device", type=str, default="cpu")
- parser.add_argument("-t", "--text", type=str, required=True)
- parser.add_argument("-w", "--wav", type=str, required=True)
- args = parser.parse_args()
-
- text_to_mel = TextToMel(glow_model_dir=args.model, device=args.device)
- mel_to_wav = MelToWav(hifi_model_dir=args.gan, device=args.device)
-
- mel = text_to_mel.generate_mel(args.text)
- audio, sr = mel_to_wav.generate_wav(mel)
-
- write(filename=args.wav, rate=sr, data=audio)
-
- pass
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/__init__.py b/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/__init__.py
deleted file mode 100644
index 1ad075593152cf94d30a903d8add28d8200badbb..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import os
-import sys
-
-try:
- from .version import __version__ # noqa
-except ImportError:
- version_txt = os.path.join(os.path.dirname(__file__), "version.txt")
- with open(version_txt) as f:
- __version__ = f.read().strip()
-
diff --git a/spaces/Harveenchadha/oiTrans/interface/index.html b/spaces/Harveenchadha/oiTrans/interface/index.html
deleted file mode 100644
index dfe553d2c6f321c379641f3a2f464ac8b0ebca29..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/interface/index.html
+++ /dev/null
@@ -1,202 +0,0 @@
-
-
-
-
-
-
- AI4B Translation API
-
-
-
-
-
-
-
-
-
-
-
-
- IndicTrans API
-
-
- Real-time Indian Language Text Translation with IndicTrans!
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- From
-
-
-
-
-
-
- To
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
{{ transcription_time }}
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/HgMenon/Transcribe_V0.2/docs/colab.md b/spaces/HgMenon/Transcribe_V0.2/docs/colab.md
deleted file mode 100644
index 3fcdb835327238764fb643b9bbd2e27b6e14f58c..0000000000000000000000000000000000000000
--- a/spaces/HgMenon/Transcribe_V0.2/docs/colab.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# Running Whisper on Google Colab
-
-If you don't have a decent GPU or any experience in running command-line applications, you might want to try this Google Colab instead:
-
-* [Google Colab - Whisper WebUI GPU](https://colab.research.google.com/drive/1qeTSvi7Bt_5RMm88ipW4fkcsMOKlDDss?usp=sharing)
-* [Screenshots](https://imgur.com/a/ZfY6uBO)
-
-The runtime (Runtime -> Change runtime type -> Hardware accelerator) should already be set top GPU. But if not, change it to GPU.
-
-Then, sign in to Google if you haven't already. Next, click on "Connect" at the top right.
-
-Under "Checking out WebUI from Git", click on the [play icon](https://imgur.com/a/81gOLyD) that appears in "[ ]" at the left. If you get a warning, click "Run anyway".
-
-After this step has completed, it should be get a green check mark. Then move on to the next section under "Installing dependencies", and click in "[ ]" again. This might take approximately 30 seconds.
-
-Once this has completed, scroll down to the "Run WebUI" section, and click on "[ ]". This will launch the WebUI in a shared link (expires in 72 hours). To open the UI, click on the link next to "Running on public URL", which will be something like https://12xxx.gradio.app/
-
-The audio length in this version is not restricted, and it will run much faster as it is backed by a GPU. You can also run it using the "Large" model. Also note that it might take some time to start the model the first time, as it may need to download a 2.8 GB file on Google's servers.
-
-Once you're done, you can close the WebUI session by clicking the animated close button under "Run WebUI". You can also do this if you encounter any errors and need to restart the UI. You should also go to "Manage Sessions" and terminate the session, otherwise you may end up using all your free compute credits.
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/modules/positional_embedding.py b/spaces/ICML2022/OFA/fairseq/fairseq/modules/positional_embedding.py
deleted file mode 100644
index 8e94e35edb46bf9dea911fe74577d8ecbe9b5ff1..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/modules/positional_embedding.py
+++ /dev/null
@@ -1,35 +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 torch.nn as nn
-
-from .learned_positional_embedding import LearnedPositionalEmbedding
-from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
-
-
-def PositionalEmbedding(
- num_embeddings: int,
- embedding_dim: int,
- padding_idx: int,
- learned: bool = False,
-):
- if learned:
- # if padding_idx is specified then offset the embedding ids by
- # this index and adjust num_embeddings appropriately
- # TODO: The right place for this offset would be inside
- # LearnedPositionalEmbedding. Move this there for a cleaner implementation.
- if padding_idx is not None:
- num_embeddings = num_embeddings + padding_idx + 1
- m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
- nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
- if padding_idx is not None:
- nn.init.constant_(m.weight[padding_idx], 0)
- else:
- m = SinusoidalPositionalEmbedding(
- embedding_dim,
- padding_idx,
- init_size=num_embeddings + padding_idx + 1,
- )
- return m
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/scoring/chrf.py b/spaces/ICML2022/OFA/fairseq/fairseq/scoring/chrf.py
deleted file mode 100644
index 0d6cb77383a44d9ac739958b79a30764f1fbf7f3..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/scoring/chrf.py
+++ /dev/null
@@ -1,27 +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.
-
-from fairseq.scoring import BaseScorer, register_scorer
-
-
-@register_scorer("chrf")
-class ChrFScorer(BaseScorer):
- def __init__(self, args):
- super(ChrFScorer, self).__init__(args)
- import sacrebleu
-
- self.sacrebleu = sacrebleu
-
- def add_string(self, ref, pred):
- self.ref.append(ref)
- self.pred.append(pred)
-
- def score(self, order=4):
- return self.result_string(order).score
-
- def result_string(self, order=4):
- if order != 4:
- raise NotImplementedError
- return self.sacrebleu.corpus_chrf(self.pred, [self.ref]).format()
diff --git a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/segment/train.py b/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/segment/train.py
deleted file mode 100644
index 3f32d2100a75c2ecb62b4d9d3e59070b5f8a1be2..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/segment/train.py
+++ /dev/null
@@ -1,658 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Train a YOLOv5 segment model on a segment dataset
-Models and datasets download automatically from the latest YOLOv5 release.
-
-Usage - Single-GPU training:
- $ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended)
- $ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch
-
-Usage - Multi-GPU DDP training:
- $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
-
-Models: https://github.com/ultralytics/yolov5/tree/master/models
-Datasets: https://github.com/ultralytics/yolov5/tree/master/data
-Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
-"""
-
-import argparse
-import math
-import os
-import random
-import sys
-import time
-from copy import deepcopy
-from datetime import datetime
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import yaml
-from torch.optim import lr_scheduler
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-import segment.val as validate # for end-of-epoch mAP
-from models.experimental import attempt_load
-from models.yolo import SegmentationModel
-from utils.autoanchor import check_anchors
-from utils.autobatch import check_train_batch_size
-from utils.callbacks import Callbacks
-from utils.downloads import attempt_download, is_url
-from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info,
- check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr,
- get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
- labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
-from utils.loggers import GenericLogger
-from utils.plots import plot_evolve, plot_labels
-from utils.segment.dataloaders import create_dataloader
-from utils.segment.loss import ComputeLoss
-from utils.segment.metrics import KEYS, fitness
-from utils.segment.plots import plot_images_and_masks, plot_results_with_masks
-from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
- smart_resume, torch_distributed_zero_first)
-
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
-GIT_INFO = check_git_info()
-
-
-def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary
- save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, mask_ratio = \
- Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
- opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze, opt.mask_ratio
- # callbacks.run('on_pretrain_routine_start')
-
- # Directories
- w = save_dir / 'weights' # weights dir
- (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
- last, best = w / 'last.pt', w / 'best.pt'
-
- # Hyperparameters
- if isinstance(hyp, str):
- with open(hyp, errors='ignore') as f:
- hyp = yaml.safe_load(f) # load hyps dict
- LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
- opt.hyp = hyp.copy() # for saving hyps to checkpoints
-
- # Save run settings
- if not evolve:
- yaml_save(save_dir / 'hyp.yaml', hyp)
- yaml_save(save_dir / 'opt.yaml', vars(opt))
-
- # Loggers
- data_dict = None
- if RANK in {-1, 0}:
- logger = GenericLogger(opt=opt, console_logger=LOGGER)
-
- # Config
- plots = not evolve and not opt.noplots # create plots
- overlap = not opt.no_overlap
- cuda = device.type != 'cpu'
- init_seeds(opt.seed + 1 + RANK, deterministic=True)
- with torch_distributed_zero_first(LOCAL_RANK):
- data_dict = data_dict or check_dataset(data) # check if None
- train_path, val_path = data_dict['train'], data_dict['val']
- nc = 1 if single_cls else int(data_dict['nc']) # number of classes
- names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
- is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset
-
- # Model
- check_suffix(weights, '.pt') # check weights
- pretrained = weights.endswith('.pt')
- if pretrained:
- with torch_distributed_zero_first(LOCAL_RANK):
- weights = attempt_download(weights) # download if not found locally
- ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
- model = SegmentationModel(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)
- exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
- csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
- csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
- model.load_state_dict(csd, strict=False) # load
- LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report
- else:
- model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
- amp = check_amp(model) # check AMP
-
- # Freeze
- freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
- for k, v in model.named_parameters():
- v.requires_grad = True # train all layers
- # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
- if any(x in k for x in freeze):
- LOGGER.info(f'freezing {k}')
- v.requires_grad = False
-
- # Image size
- gs = max(int(model.stride.max()), 32) # grid size (max stride)
- imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
-
- # Batch size
- if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
- batch_size = check_train_batch_size(model, imgsz, amp)
- logger.update_params({"batch_size": batch_size})
- # loggers.on_params_update({"batch_size": batch_size})
-
- # Optimizer
- nbs = 64 # nominal batch size
- accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
- hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
- optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])
-
- # Scheduler
- if opt.cos_lr:
- lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
- else:
- lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
- scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
-
- # EMA
- ema = ModelEMA(model) if RANK in {-1, 0} else None
-
- # Resume
- best_fitness, start_epoch = 0.0, 0
- if pretrained:
- if resume:
- best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
- del ckpt, csd
-
- # DP mode
- if cuda and RANK == -1 and torch.cuda.device_count() > 1:
- LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
- 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
- model = torch.nn.DataParallel(model)
-
- # SyncBatchNorm
- if opt.sync_bn and cuda and RANK != -1:
- model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
- LOGGER.info('Using SyncBatchNorm()')
-
- # Trainloader
- train_loader, dataset = create_dataloader(
- train_path,
- imgsz,
- batch_size // WORLD_SIZE,
- gs,
- single_cls,
- hyp=hyp,
- augment=True,
- cache=None if opt.cache == 'val' else opt.cache,
- rect=opt.rect,
- rank=LOCAL_RANK,
- workers=workers,
- image_weights=opt.image_weights,
- quad=opt.quad,
- prefix=colorstr('train: '),
- shuffle=True,
- mask_downsample_ratio=mask_ratio,
- overlap_mask=overlap,
- )
- labels = np.concatenate(dataset.labels, 0)
- mlc = int(labels[:, 0].max()) # max label class
- assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
-
- # Process 0
- if RANK in {-1, 0}:
- val_loader = create_dataloader(val_path,
- imgsz,
- batch_size // WORLD_SIZE * 2,
- gs,
- single_cls,
- hyp=hyp,
- cache=None if noval else opt.cache,
- rect=True,
- rank=-1,
- workers=workers * 2,
- pad=0.5,
- mask_downsample_ratio=mask_ratio,
- overlap_mask=overlap,
- prefix=colorstr('val: '))[0]
-
- if not resume:
- if not opt.noautoanchor:
- check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor
- model.half().float() # pre-reduce anchor precision
-
- if plots:
- plot_labels(labels, names, save_dir)
- # callbacks.run('on_pretrain_routine_end', labels, names)
-
- # DDP mode
- if cuda and RANK != -1:
- model = smart_DDP(model)
-
- # Model attributes
- nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
- hyp['box'] *= 3 / nl # scale to layers
- hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers
- hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
- hyp['label_smoothing'] = opt.label_smoothing
- model.nc = nc # attach number of classes to model
- model.hyp = hyp # attach hyperparameters to model
- model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
- model.names = names
-
- # Start training
- t0 = time.time()
- nb = len(train_loader) # number of batches
- nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
- # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
- last_opt_step = -1
- maps = np.zeros(nc) # mAP per class
- results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
- scheduler.last_epoch = start_epoch - 1 # do not move
- scaler = torch.cuda.amp.GradScaler(enabled=amp)
- stopper, stop = EarlyStopping(patience=opt.patience), False
- compute_loss = ComputeLoss(model, overlap=overlap) # init loss class
- # callbacks.run('on_train_start')
- LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
- f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
- f"Logging results to {colorstr('bold', save_dir)}\n"
- f'Starting training for {epochs} epochs...')
- for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
- # callbacks.run('on_train_epoch_start')
- model.train()
-
- # Update image weights (optional, single-GPU only)
- if opt.image_weights:
- cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
- iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
- dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
-
- # Update mosaic border (optional)
- # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
- # dataset.mosaic_border = [b - imgsz, -b] # height, width borders
-
- mloss = torch.zeros(4, device=device) # mean losses
- if RANK != -1:
- train_loader.sampler.set_epoch(epoch)
- pbar = enumerate(train_loader)
- LOGGER.info(('\n' + '%11s' * 8) %
- ('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
- if RANK in {-1, 0}:
- pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
- optimizer.zero_grad()
- for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------
- # callbacks.run('on_train_batch_start')
- ni = i + nb * epoch # number integrated batches (since train start)
- imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
-
- # Warmup
- if ni <= nw:
- xi = [0, nw] # x interp
- # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
- accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
- for j, x in enumerate(optimizer.param_groups):
- # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
- x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)])
- if 'momentum' in x:
- x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
-
- # Multi-scale
- if opt.multi_scale:
- sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
- sf = sz / max(imgs.shape[2:]) # scale factor
- if sf != 1:
- ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
- imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
-
- # Forward
- with torch.cuda.amp.autocast(amp):
- pred = model(imgs) # forward
- loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float())
- if RANK != -1:
- loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
- if opt.quad:
- loss *= 4.
-
- # Backward
- scaler.scale(loss).backward()
-
- # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
- if ni - last_opt_step >= accumulate:
- scaler.unscale_(optimizer) # unscale gradients
- torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
- scaler.step(optimizer) # optimizer.step
- scaler.update()
- optimizer.zero_grad()
- if ema:
- ema.update(model)
- last_opt_step = ni
-
- # Log
- if RANK in {-1, 0}:
- mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
- mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
- pbar.set_description(('%11s' * 2 + '%11.4g' * 6) %
- (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
- # callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths)
- # if callbacks.stop_training:
- # return
-
- # Mosaic plots
- if plots:
- if ni < 3:
- plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg")
- if ni == 10:
- files = sorted(save_dir.glob('train*.jpg'))
- logger.log_images(files, "Mosaics", epoch)
- # end batch ------------------------------------------------------------------------------------------------
-
- # Scheduler
- lr = [x['lr'] for x in optimizer.param_groups] # for loggers
- scheduler.step()
-
- if RANK in {-1, 0}:
- # mAP
- # callbacks.run('on_train_epoch_end', epoch=epoch)
- ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
- final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
- if not noval or final_epoch: # Calculate mAP
- results, maps, _ = validate.run(data_dict,
- batch_size=batch_size // WORLD_SIZE * 2,
- imgsz=imgsz,
- half=amp,
- model=ema.ema,
- single_cls=single_cls,
- dataloader=val_loader,
- save_dir=save_dir,
- plots=False,
- callbacks=callbacks,
- compute_loss=compute_loss,
- mask_downsample_ratio=mask_ratio,
- overlap=overlap)
-
- # Update best mAP
- fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
- stop = stopper(epoch=epoch, fitness=fi) # early stop check
- if fi > best_fitness:
- best_fitness = fi
- log_vals = list(mloss) + list(results) + lr
- # callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
- # Log val metrics and media
- metrics_dict = dict(zip(KEYS, log_vals))
- logger.log_metrics(metrics_dict, epoch)
-
- # Save model
- if (not nosave) or (final_epoch and not evolve): # if save
- ckpt = {
- 'epoch': epoch,
- 'best_fitness': best_fitness,
- 'model': deepcopy(de_parallel(model)).half(),
- 'ema': deepcopy(ema.ema).half(),
- 'updates': ema.updates,
- 'optimizer': optimizer.state_dict(),
- 'opt': vars(opt),
- 'git': GIT_INFO, # {remote, branch, commit} if a git repo
- 'date': datetime.now().isoformat()}
-
- # Save last, best and delete
- torch.save(ckpt, last)
- if best_fitness == fi:
- torch.save(ckpt, best)
- if opt.save_period > 0 and epoch % opt.save_period == 0:
- torch.save(ckpt, w / f'epoch{epoch}.pt')
- logger.log_model(w / f'epoch{epoch}.pt')
- del ckpt
- # callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
-
- # EarlyStopping
- if RANK != -1: # if DDP training
- broadcast_list = [stop if RANK == 0 else None]
- dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
- if RANK != 0:
- stop = broadcast_list[0]
- if stop:
- break # must break all DDP ranks
-
- # end epoch ----------------------------------------------------------------------------------------------------
- # end training -----------------------------------------------------------------------------------------------------
- if RANK in {-1, 0}:
- LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
- for f in last, best:
- if f.exists():
- strip_optimizer(f) # strip optimizers
- if f is best:
- LOGGER.info(f'\nValidating {f}...')
- results, _, _ = validate.run(
- data_dict,
- batch_size=batch_size // WORLD_SIZE * 2,
- imgsz=imgsz,
- model=attempt_load(f, device).half(),
- iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65
- single_cls=single_cls,
- dataloader=val_loader,
- save_dir=save_dir,
- save_json=is_coco,
- verbose=True,
- plots=plots,
- callbacks=callbacks,
- compute_loss=compute_loss,
- mask_downsample_ratio=mask_ratio,
- overlap=overlap) # val best model with plots
- if is_coco:
- # callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
- metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr))
- logger.log_metrics(metrics_dict, epoch)
-
- # callbacks.run('on_train_end', last, best, epoch, results)
- # on train end callback using genericLogger
- logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs)
- if not opt.evolve:
- logger.log_model(best, epoch)
- if plots:
- plot_results_with_masks(file=save_dir / 'results.csv') # save results.png
- files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
- files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter
- LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
- logger.log_images(files, "Results", epoch + 1)
- logger.log_images(sorted(save_dir.glob('val*.jpg')), "Validation", epoch + 1)
- torch.cuda.empty_cache()
- return results
-
-
-def parse_opt(known=False):
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s-seg.pt', help='initial weights path')
- parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path')
- parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
- parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
- parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
- parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
- parser.add_argument('--rect', action='store_true', help='rectangular training')
- parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
- parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
- parser.add_argument('--noval', action='store_true', help='only validate final epoch')
- parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
- parser.add_argument('--noplots', action='store_true', help='save no plot files')
- parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
- parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
- parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
- parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
- parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
- parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
- parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
- parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
- parser.add_argument('--project', default=ROOT / 'runs/train-seg', help='save to project/name')
- parser.add_argument('--name', default='exp', help='save to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--quad', action='store_true', help='quad dataloader')
- parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
- parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
- parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
- parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
- parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
- parser.add_argument('--seed', type=int, default=0, help='Global training seed')
- parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
-
- # Instance Segmentation Args
- parser.add_argument('--mask-ratio', type=int, default=4, help='Downsample the truth masks to saving memory')
- parser.add_argument('--no-overlap', action='store_true', help='Overlap masks train faster at slightly less mAP')
-
- return parser.parse_known_args()[0] if known else parser.parse_args()
-
-
-def main(opt, callbacks=Callbacks()):
- # Checks
- if RANK in {-1, 0}:
- print_args(vars(opt))
- check_git_status()
- check_requirements()
-
- # Resume
- if opt.resume and not opt.evolve: # resume from specified or most recent last.pt
- last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
- opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml
- opt_data = opt.data # original dataset
- if opt_yaml.is_file():
- with open(opt_yaml, errors='ignore') as f:
- d = yaml.safe_load(f)
- else:
- d = torch.load(last, map_location='cpu')['opt']
- opt = argparse.Namespace(**d) # replace
- opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate
- if is_url(opt_data):
- opt.data = check_file(opt_data) # avoid HUB resume auth timeout
- else:
- opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
- check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks
- assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
- if opt.evolve:
- if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve
- opt.project = str(ROOT / 'runs/evolve')
- opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
- if opt.name == 'cfg':
- opt.name = Path(opt.cfg).stem # use model.yaml as name
- opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
-
- # DDP mode
- device = select_device(opt.device, batch_size=opt.batch_size)
- if LOCAL_RANK != -1:
- msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
- assert not opt.image_weights, f'--image-weights {msg}'
- assert not opt.evolve, f'--evolve {msg}'
- assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
- assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
- assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
- torch.cuda.set_device(LOCAL_RANK)
- device = torch.device('cuda', LOCAL_RANK)
- dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
-
- # Train
- if not opt.evolve:
- train(opt.hyp, opt, device, callbacks)
-
- # Evolve hyperparameters (optional)
- else:
- # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
- meta = {
- 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
- 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
- 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
- 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
- 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
- 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
- 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
- 'box': (1, 0.02, 0.2), # box loss gain
- 'cls': (1, 0.2, 4.0), # cls loss gain
- 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
- 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
- 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
- 'iou_t': (0, 0.1, 0.7), # IoU training threshold
- 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
- 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
- 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
- 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
- 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
- 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
- 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
- 'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
- 'scale': (1, 0.0, 0.9), # image scale (+/- gain)
- 'shear': (1, 0.0, 10.0), # image shear (+/- deg)
- 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
- 'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
- 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
- 'mosaic': (1, 0.0, 1.0), # image mixup (probability)
- 'mixup': (1, 0.0, 1.0), # image mixup (probability)
- 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
-
- with open(opt.hyp, errors='ignore') as f:
- hyp = yaml.safe_load(f) # load hyps dict
- if 'anchors' not in hyp: # anchors commented in hyp.yaml
- hyp['anchors'] = 3
- if opt.noautoanchor:
- del hyp['anchors'], meta['anchors']
- opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
- # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
- evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
- if opt.bucket:
- os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists
-
- for _ in range(opt.evolve): # generations to evolve
- if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
- # Select parent(s)
- parent = 'single' # parent selection method: 'single' or 'weighted'
- x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
- n = min(5, len(x)) # number of previous results to consider
- x = x[np.argsort(-fitness(x))][:n] # top n mutations
- w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0)
- if parent == 'single' or len(x) == 1:
- # x = x[random.randint(0, n - 1)] # random selection
- x = x[random.choices(range(n), weights=w)[0]] # weighted selection
- elif parent == 'weighted':
- x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
-
- # Mutate
- mp, s = 0.8, 0.2 # mutation probability, sigma
- npr = np.random
- npr.seed(int(time.time()))
- g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
- ng = len(meta)
- v = np.ones(ng)
- while all(v == 1): # mutate until a change occurs (prevent duplicates)
- v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
- for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
- hyp[k] = float(x[i + 7] * v[i]) # mutate
-
- # Constrain to limits
- for k, v in meta.items():
- hyp[k] = max(hyp[k], v[1]) # lower limit
- hyp[k] = min(hyp[k], v[2]) # upper limit
- hyp[k] = round(hyp[k], 5) # significant digits
-
- # Train mutation
- results = train(hyp.copy(), opt, device, callbacks)
- callbacks = Callbacks()
- # Write mutation results
- print_mutation(KEYS, results, hyp.copy(), save_dir, opt.bucket)
-
- # Plot results
- plot_evolve(evolve_csv)
- LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
- f"Results saved to {colorstr('bold', save_dir)}\n"
- f'Usage example: $ python train.py --hyp {evolve_yaml}')
-
-
-def run(**kwargs):
- # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
- opt = parse_opt(True)
- for k, v in kwargs.items():
- setattr(opt, k, v)
- main(opt)
- return opt
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/spaces/Illumotion/Koboldcpp/examples/embd-input/minigpt4.py b/spaces/Illumotion/Koboldcpp/examples/embd-input/minigpt4.py
deleted file mode 100644
index 7b13e4a5cc4f8e4c61a5d48fcdffb3871c487fc2..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/embd-input/minigpt4.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import os
-sys.path.insert(0, os.path.dirname(__file__))
-from embd_input import MyModel
-import numpy as np
-from torch import nn
-import torch
-from PIL import Image
-
-minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4")
-sys.path.insert(0, minigpt4_path)
-from minigpt4.models.blip2 import Blip2Base
-from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
-
-
-class MiniGPT4(Blip2Base):
- """
- MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4
- """
- def __init__(self,
- args,
- vit_model="eva_clip_g",
- q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
- img_size=224,
- drop_path_rate=0,
- use_grad_checkpoint=False,
- vit_precision="fp32",
- freeze_vit=True,
- freeze_qformer=True,
- num_query_token=32,
- llama_model="",
- prompt_path="",
- prompt_template="",
- max_txt_len=32,
- end_sym='\n',
- low_resource=False, # use 8 bit and put vit in cpu
- device_8bit=0
- ):
- super().__init__()
- self.img_size = img_size
- self.low_resource = low_resource
- self.preprocessor = Blip2ImageEvalProcessor(img_size)
-
- print('Loading VIT')
- self.visual_encoder, self.ln_vision = self.init_vision_encoder(
- vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
- )
- print('Loading VIT Done')
- print('Loading Q-Former')
- self.Qformer, self.query_tokens = self.init_Qformer(
- num_query_token, self.visual_encoder.num_features
- )
- self.Qformer.cls = None
- self.Qformer.bert.embeddings.word_embeddings = None
- self.Qformer.bert.embeddings.position_embeddings = None
- for layer in self.Qformer.bert.encoder.layer:
- layer.output = None
- layer.intermediate = None
- self.load_from_pretrained(url_or_filename=q_former_model)
- print('Loading Q-Former Done')
- self.llama_proj = nn.Linear(
- self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size
- )
- self.max_txt_len = max_txt_len
- self.end_sym = end_sym
- self.model = MyModel(["main", *args])
- # system prompt
- self.model.eval_string("Give the following image: ImageContent. "
- "You will be able to see the image once I provide it to you. Please answer my questions."
- "###")
-
- def encode_img(self, image):
- image = self.preprocessor(image)
- image = image.unsqueeze(0)
- device = image.device
- if self.low_resource:
- self.vit_to_cpu()
- image = image.to("cpu")
-
- with self.maybe_autocast():
- image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
- image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
-
- query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
- query_output = self.Qformer.bert(
- query_embeds=query_tokens,
- encoder_hidden_states=image_embeds,
- encoder_attention_mask=image_atts,
- return_dict=True,
- )
-
- inputs_llama = self.llama_proj(query_output.last_hidden_state)
- # atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
- return inputs_llama
-
- def load_projection(self, path):
- state = torch.load(path)["model"]
- self.llama_proj.load_state_dict({
- "weight": state["llama_proj.weight"],
- "bias": state["llama_proj.bias"]})
-
- def chat(self, question):
- self.model.eval_string("Human: ")
- self.model.eval_string(question)
- self.model.eval_string("\n### Assistant:")
- return self.model.generate_with_print(end="###")
-
- def chat_with_image(self, image, question):
- with torch.no_grad():
- embd_image = self.encode_img(image)
- embd_image = embd_image.cpu().numpy()[0]
- self.model.eval_string("Human: ")
- self.model.eval_float(embd_image.T)
- self.model.eval_string(" ")
- self.model.eval_string(question)
- self.model.eval_string("\n### Assistant:")
- return self.model.generate_with_print(end="###")
-
-
-if __name__=="__main__":
- a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"])
- a.load_projection(os.path.join(
- os.path.dirname(__file__) ,
- "pretrained_minigpt4.pth"))
- respose = a.chat_with_image(
- Image.open("./media/llama1-logo.png").convert('RGB'),
- "what is the text in the picture?")
- a.chat("what is the color of it?")
diff --git a/spaces/Illumotion/Koboldcpp/include/CL/Utils/File.hpp b/spaces/Illumotion/Koboldcpp/include/CL/Utils/File.hpp
deleted file mode 100644
index 984a323889e9e2141ab27fbef2a5a813d7cbd948..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/include/CL/Utils/File.hpp
+++ /dev/null
@@ -1,49 +0,0 @@
-#pragma once
-
-// OpenCL SDK includes
-#include
-
-// STL includes
-#include
-#include
-
-namespace cl {
-namespace util {
- // Scott Meyers, Effective STL, Addison-Wesley Professional, 2001, Item 29
- // with error handling
- UTILSCPP_EXPORT
- std::string read_text_file(const char* const filename, cl_int* const error)
- {
- std::ifstream in(filename);
- if (in.good())
- {
- try
- {
- std::string red((std::istreambuf_iterator(in)),
- std::istreambuf_iterator());
- if (in.good() && in.eof())
- {
- if (error != nullptr) *error = CL_SUCCESS;
- return red;
- }
- else
- {
- detail::errHandler(CL_UTIL_FILE_OPERATION_ERROR, error,
- "File read error!");
- return std::string();
- }
- } catch (std::bad_alloc& ex)
- {
- detail::errHandler(CL_OUT_OF_RESOURCES, error,
- "Bad allocation!");
- return std::string();
- }
- }
- else
- {
- detail::errHandler(CL_INVALID_VALUE, error, "No file!");
- return std::string();
- }
- }
-}
-}
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/extract_masks.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/extract_masks.py
deleted file mode 100644
index d114e0fe470595f1d2aaeeeb84b36352f65b121e..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/extract_masks.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import PIL.Image as Image
-import numpy as np
-import os
-
-
-def main(args):
- if not args.indir.endswith('/'):
- args.indir += '/'
- os.makedirs(args.outdir, exist_ok=True)
-
- src_images = [
- args.indir+fname for fname in os.listdir(args.indir)]
-
- tgt_masks = [
- args.outdir+fname[:-4] + f'_mask000.png'
- for fname in os.listdir(args.indir)]
-
- for img_name, msk_name in zip(src_images, tgt_masks):
- #print(img)
- #print(msk)
-
- image = Image.open(img_name).convert('RGB')
- image = np.transpose(np.array(image), (2, 0, 1))
-
- mask = (image == 255).astype(int)
-
- print(mask.dtype, mask.shape)
-
-
- Image.fromarray(
- np.clip(mask[0,:,:] * 255, 0, 255).astype('uint8'),mode='L'
- ).save(msk_name)
-
-
-
-
- '''
- for infile in src_images:
- try:
- file_relpath = infile[len(indir):]
- img_outpath = os.path.join(outdir, file_relpath)
- os.makedirs(os.path.dirname(img_outpath), exist_ok=True)
-
- image = Image.open(infile).convert('RGB')
-
- mask =
-
- Image.fromarray(
- np.clip(
- cur_mask * 255, 0, 255).astype('uint8'),
- mode='L'
- ).save(cur_basename + f'_mask{i:03d}.png')
- '''
-
-
-
-if __name__ == '__main__':
- import argparse
- aparser = argparse.ArgumentParser()
- aparser.add_argument('--indir', type=str, help='Path to folder with images')
- aparser.add_argument('--outdir', type=str, help='Path to folder to store aligned images and masks to')
-
- main(aparser.parse_args())
diff --git a/spaces/Izal887/rvc-ram12/lib/infer_pack/onnx_inference.py b/spaces/Izal887/rvc-ram12/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index c78324cbc08414fffcc689f325312de0e51bd6b4..0000000000000000000000000000000000000000
--- a/spaces/Izal887/rvc-ram12/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
- def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
- print("load model(s) from {}".format(vec_path))
- if device == "cpu" or device is None:
- providers = ["CPUExecutionProvider"]
- elif device == "cuda":
- providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
- elif device == "dml":
- providers = ["DmlExecutionProvider"]
- else:
- raise RuntimeError("Unsportted Device")
- self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
- def __call__(self, wav):
- return self.forward(wav)
-
- def forward(self, wav):
- feats = wav
- if feats.ndim == 2: # double channels
- feats = feats.mean(-1)
- assert feats.ndim == 1, feats.ndim
- feats = np.expand_dims(np.expand_dims(feats, 0), 0)
- onnx_input = {self.model.get_inputs()[0].name: feats}
- logits = self.model.run(None, onnx_input)[0]
- return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
- if f0_predictor == "pm":
- from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
- f0_predictor_object = PMF0Predictor(
- hop_length=hop_length, sampling_rate=sampling_rate
- )
- elif f0_predictor == "harvest":
- from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
-
- f0_predictor_object = HarvestF0Predictor(
- hop_length=hop_length, sampling_rate=sampling_rate
- )
- elif f0_predictor == "dio":
- from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
- f0_predictor_object = DioF0Predictor(
- hop_length=hop_length, sampling_rate=sampling_rate
- )
- else:
- raise Exception("Unknown f0 predictor")
- return f0_predictor_object
-
-
-class OnnxRVC:
- def __init__(
- self,
- model_path,
- sr=40000,
- hop_size=512,
- vec_path="vec-768-layer-12",
- device="cpu",
- ):
- vec_path = f"pretrained/{vec_path}.onnx"
- self.vec_model = ContentVec(vec_path, device)
- if device == "cpu" or device is None:
- providers = ["CPUExecutionProvider"]
- elif device == "cuda":
- providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
- elif device == "dml":
- providers = ["DmlExecutionProvider"]
- else:
- raise RuntimeError("Unsportted Device")
- self.model = onnxruntime.InferenceSession(model_path, providers=providers)
- self.sampling_rate = sr
- self.hop_size = hop_size
-
- def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
- onnx_input = {
- self.model.get_inputs()[0].name: hubert,
- self.model.get_inputs()[1].name: hubert_length,
- self.model.get_inputs()[2].name: pitch,
- self.model.get_inputs()[3].name: pitchf,
- self.model.get_inputs()[4].name: ds,
- self.model.get_inputs()[5].name: rnd,
- }
- return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
- def inference(
- self,
- raw_path,
- sid,
- f0_method="dio",
- f0_up_key=0,
- pad_time=0.5,
- cr_threshold=0.02,
- ):
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- f0_predictor = get_f0_predictor(
- f0_method,
- hop_length=self.hop_size,
- sampling_rate=self.sampling_rate,
- threshold=cr_threshold,
- )
- wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
- org_length = len(wav)
- if org_length / sr > 50.0:
- raise RuntimeError("Reached Max Length")
-
- wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
- wav16k = wav16k
-
- hubert = self.vec_model(wav16k)
- hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
- hubert_length = hubert.shape[1]
-
- pitchf = f0_predictor.compute_f0(wav, hubert_length)
- pitchf = pitchf * 2 ** (f0_up_key / 12)
- pitch = pitchf.copy()
- f0_mel = 1127 * np.log(1 + pitch / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- pitch = np.rint(f0_mel).astype(np.int64)
-
- pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
- pitch = pitch.reshape(1, len(pitch))
- ds = np.array([sid]).astype(np.int64)
-
- rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
- hubert_length = np.array([hubert_length]).astype(np.int64)
-
- out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
- out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
- return out_wav[0:org_length]
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/web/__init__.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/web/__init__.py
deleted file mode 100644
index 0b71aa5ded086d398c53ce60604c368a1f703c4f..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/web/__init__.py
+++ /dev/null
@@ -1,135 +0,0 @@
-from web.api import api_blueprint
-from pathlib import Path
-from gevent import pywsgi as wsgi
-from flask import Flask, Response, request, render_template
-from synthesizer.inference import Synthesizer
-from encoder import inference as encoder
-from vocoder.hifigan import inference as gan_vocoder
-from vocoder.wavernn import inference as rnn_vocoder
-import numpy as np
-import re
-from scipy.io.wavfile import write
-import librosa
-import io
-import base64
-from flask_cors import CORS
-from flask_wtf import CSRFProtect
-import webbrowser
-
-def webApp():
- # Init and load config
- app = Flask(__name__, instance_relative_config=True)
- app.config.from_object("web.config.default")
- app.config['RESTPLUS_MASK_SWAGGER'] = False
- app.register_blueprint(api_blueprint)
-
- # CORS(app) #允许跨域，注释掉此行则禁止跨域请求
- csrf = CSRFProtect(app)
- csrf.init_app(app)
-
- syn_models_dirt = "synthesizer/saved_models"
- synthesizers = list(Path(syn_models_dirt).glob("**/*.pt"))
- synthesizers_cache = {}
- encoder.load_model(Path("encoder/saved_models/pretrained.pt"))
- rnn_vocoder.load_model(Path("vocoder/saved_models/pretrained/pretrained.pt"))
- gan_vocoder.load_model(Path("vocoder/saved_models/pretrained/g_hifigan.pt"))
-
- def pcm2float(sig, dtype='float32'):
- """Convert PCM signal to floating point with a range from -1 to 1.
- Use dtype='float32' for single precision.
- Parameters
- ----------
- sig : array_like
- Input array, must have integral type.
- dtype : data type, optional
- Desired (floating point) data type.
- Returns
- -------
- numpy.ndarray
- Normalized floating point data.
- See Also
- --------
- float2pcm, dtype
- """
- sig = np.asarray(sig)
- if sig.dtype.kind not in 'iu':
- raise TypeError("'sig' must be an array of integers")
- dtype = np.dtype(dtype)
- if dtype.kind != 'f':
- raise TypeError("'dtype' must be a floating point type")
-
- i = np.iinfo(sig.dtype)
- abs_max = 2 ** (i.bits - 1)
- offset = i.min + abs_max
- return (sig.astype(dtype) - offset) / abs_max
-
- # Cache for synthesizer
- @csrf.exempt
- @app.route("/api/synthesize", methods=["POST"])
- def synthesize():
- # TODO Implementation with json to support more platform
- # Load synthesizer
- if "synt_path" in request.form:
- synt_path = request.form["synt_path"]
- else:
- synt_path = synthesizers[0]
- print("NO synthsizer is specified, try default first one.")
- if synthesizers_cache.get(synt_path) is None:
- current_synt = Synthesizer(Path(synt_path))
- synthesizers_cache[synt_path] = current_synt
- else:
- current_synt = synthesizers_cache[synt_path]
- print("using synthesizer model: " + str(synt_path))
- # Load input wav
- if "upfile_b64" in request.form:
- wav_base64 = request.form["upfile_b64"]
- wav = base64.b64decode(bytes(wav_base64, 'utf-8'))
- wav = pcm2float(np.frombuffer(wav, dtype=np.int16), dtype=np.float32)
- sample_rate = Synthesizer.sample_rate
- else:
- wav, sample_rate, = librosa.load(request.files['file'])
- write("temp.wav", sample_rate, wav) #Make sure we get the correct wav
-
- encoder_wav = encoder.preprocess_wav(wav, sample_rate)
- embed, _, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
-
- # Load input text
- texts = filter(None, request.form["text"].split("\n"))
- punctuation = '！，。、,' # punctuate and split/clean text
- processed_texts = []
- for text in texts:
- for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
- if processed_text:
- processed_texts.append(processed_text.strip())
- texts = processed_texts
-
- # synthesize and vocode
- embeds = [embed] * len(texts)
- specs = current_synt.synthesize_spectrograms(texts, embeds)
- spec = np.concatenate(specs, axis=1)
- sample_rate = Synthesizer.sample_rate
- if "vocoder" in request.form and request.form["vocoder"] == "WaveRNN":
- wav, sample_rate = rnn_vocoder.infer_waveform(spec)
- else:
- wav, sample_rate = gan_vocoder.infer_waveform(spec)
-
- # Return cooked wav
- out = io.BytesIO()
- write(out, sample_rate, wav.astype(np.float32))
- return Response(out, mimetype="audio/wav")
-
- @app.route('/', methods=['GET'])
- def index():
- return render_template("index.html")
-
- host = app.config.get("HOST")
- port = app.config.get("PORT")
- web_address = 'http://{}:{}'.format(host, port)
- print(f"Web server:" + web_address)
- webbrowser.open(web_address)
- server = wsgi.WSGIServer((host, port), app)
- server.serve_forever()
- return app
-
-if __name__ == "__main__":
- webApp()
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/stanfordcars.py b/spaces/KyanChen/RSPrompter/mmpretrain/datasets/stanfordcars.py
deleted file mode 100644
index 355697943cf693869f35f2a0bd71abdfa0396722..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/stanfordcars.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List
-
-import mat4py
-from mmengine import get_file_backend
-
-from mmpretrain.registry import DATASETS
-from .base_dataset import BaseDataset
-from .categories import STANFORDCARS_CATEGORIES
-
-
-@DATASETS.register_module()
-class StanfordCars(BaseDataset):
- """The Stanford Cars Dataset.
-
- Support the `Stanford Cars Dataset `_ Dataset.
- The official website provides two ways to organize the dataset.
- Therefore, after downloading and decompression, the dataset directory structure is as follows.
-
- Stanford Cars dataset directory: ::
-
- Stanford_Cars
- ├── car_ims
- │ ├── 00001.jpg
- │ ├── 00002.jpg
- │ └── ...
- └── cars_annos.mat
-
- or ::
-
- Stanford_Cars
- ├── cars_train
- │ ├── 00001.jpg
- │ ├── 00002.jpg
- │ └── ...
- ├── cars_test
- │ ├── 00001.jpg
- │ ├── 00002.jpg
- │ └── ...
- └── devkit
- ├── cars_meta.mat
- ├── cars_train_annos.mat
- ├── cars_test_annos.mat
- ├── cars_test_annoswithlabels.mat
- ├── eval_train.m
- └── train_perfect_preds.txt
-
- Args:
- data_root (str): The root directory for Stanford Cars dataset.
- split (str, optional): The dataset split, supports "train"
- and "test". Default to "train".
-
- Examples:
- >>> from mmpretrain.datasets import StanfordCars
- >>> train_dataset = StanfordCars(data_root='data/Stanford_Cars', split='train')
- >>> train_dataset
- Dataset StanfordCars
- Number of samples: 8144
- Number of categories: 196
- Root of dataset: data/Stanford_Cars
- >>> test_dataset = StanfordCars(data_root='data/Stanford_Cars', split='test')
- >>> test_dataset
- Dataset StanfordCars
- Number of samples: 8041
- Number of categories: 196
- Root of dataset: data/Stanford_Cars
- """ # noqa: E501
-
- METAINFO = {'classes': STANFORDCARS_CATEGORIES}
-
- def __init__(self, data_root: str, split: str = 'train', **kwargs):
-
- splits = ['train', 'test']
- assert split in splits, \
- f"The split must be one of {splits}, but get '{split}'"
- self.split = split
-
- test_mode = split == 'test'
- self.backend = get_file_backend(data_root, enable_singleton=True)
-
- anno_file_path = self.backend.join_path(data_root, 'cars_annos.mat')
- if self.backend.exists(anno_file_path):
- ann_file = 'cars_annos.mat'
- data_prefix = ''
- else:
- if test_mode:
- ann_file = self.backend.join_path(
- 'devkit', 'cars_test_annos_withlabels.mat')
- data_prefix = 'cars_test'
- else:
- ann_file = self.backend.join_path('devkit',
- 'cars_train_annos.mat')
- data_prefix = 'cars_train'
-
- if not self.backend.exists(
- self.backend.join_path(data_root, ann_file)):
- doc_url = 'https://mmpretrain.readthedocs.io/en/latest/api/datasets.html#stanfordcars' # noqa: E501
- raise RuntimeError(
- f'The dataset is incorrectly organized, please \
- refer to {doc_url} and reorganize your folders.')
-
- super(StanfordCars, self).__init__(
- ann_file=ann_file,
- data_root=data_root,
- data_prefix=data_prefix,
- test_mode=test_mode,
- **kwargs)
-
- def load_data_list(self):
- data = mat4py.loadmat(self.ann_file)['annotations']
-
- data_list = []
- if 'test' in data.keys():
- # first way
- img_paths, labels, test = data['relative_im_path'], data[
- 'class'], data['test']
- num = len(img_paths)
- assert num == len(labels) == len(test), 'get error ann file'
- for i in range(num):
- if not self.test_mode and test[i] == 1:
- continue
- if self.test_mode and test[i] == 0:
- continue
- img_path = self.backend.join_path(self.img_prefix,
- img_paths[i])
- gt_label = labels[i] - 1
- info = dict(img_path=img_path, gt_label=gt_label)
- data_list.append(info)
- else:
- # second way
- img_names, labels = data['fname'], data['class']
- num = len(img_names)
- assert num == len(labels), 'get error ann file'
- for i in range(num):
- img_path = self.backend.join_path(self.img_prefix,
- img_names[i])
- gt_label = labels[i] - 1
- info = dict(img_path=img_path, gt_label=gt_label)
- data_list.append(info)
-
- return data_list
-
- def extra_repr(self) -> List[str]:
- """The extra repr information of the dataset."""
- body = [
- f'Root of dataset: \t{self.data_root}',
- ]
- return body
diff --git a/spaces/Lamai/LAMAIGPT/autogpt/commands/audio_text.py b/spaces/Lamai/LAMAIGPT/autogpt/commands/audio_text.py
deleted file mode 100644
index cae32d4eb78c4268bf6ef1bae3c15a399af046bf..0000000000000000000000000000000000000000
--- a/spaces/Lamai/LAMAIGPT/autogpt/commands/audio_text.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import json
-
-import requests
-
-from autogpt.config import Config
-from autogpt.workspace import path_in_workspace
-
-cfg = Config()
-
-
-def read_audio_from_file(audio_path):
- audio_path = path_in_workspace(audio_path)
- with open(audio_path, "rb") as audio_file:
- audio = audio_file.read()
- return read_audio(audio)
-
-
-def read_audio(audio):
- model = cfg.huggingface_audio_to_text_model
- api_url = f"https://api-inference.huggingface.co/models/{model}"
- api_token = cfg.huggingface_api_token
- headers = {"Authorization": f"Bearer {api_token}"}
-
- if api_token is None:
- raise ValueError(
- "You need to set your Hugging Face API token in the config file."
- )
-
- response = requests.post(
- api_url,
- headers=headers,
- data=audio,
- )
-
- text = json.loads(response.content.decode("utf-8"))["text"]
- return "The audio says: " + text
diff --git a/spaces/LanguageBind/LanguageBind/a_cls/precision.py b/spaces/LanguageBind/LanguageBind/a_cls/precision.py
deleted file mode 100644
index a63b92256518d13afd57261df1568e26b1622201..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/a_cls/precision.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import torch
-from contextlib import suppress
-
-
-def get_autocast(precision):
- if precision == 'amp':
- return torch.cuda.amp.autocast
- elif precision == 'amp_bfloat16' or precision == 'amp_bf16':
- # amp_bfloat16 is more stable than amp float16 for clip training
- return lambda: torch.cuda.amp.autocast(dtype=torch.bfloat16)
- else:
- return suppress
diff --git a/spaces/Linhao416/Bing/Dockerfile b/spaces/Linhao416/Bing/Dockerfile
deleted file mode 100644
index 3698c7cb7938e025afc53b18a571ae2961fbdffe..0000000000000000000000000000000000000000
--- a/spaces/Linhao416/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="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 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/Lynx1221/rvc-test1/vc_infer_pipeline.py b/spaces/Lynx1221/rvc-test1/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/Lynx1221/rvc-test1/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
- def __init__(self, tgt_sr, device, is_half):
- self.sr = 16000 # hubert输入采样率
- self.window = 160 # 每帧点数
- self.t_pad = self.sr * x_pad # 每条前后pad时间
- self.t_pad_tgt = tgt_sr * x_pad
- self.t_pad2 = self.t_pad * 2
- self.t_query = self.sr * x_query # 查询切点前后查询时间
- self.t_center = self.sr * x_center # 查询切点位置
- self.t_max = self.sr * x_max # 免查询时长阈值
- self.device = device
- self.is_half = is_half
-
- def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
- time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- if f0_method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif f0_method == "harvest":
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- times,
- index,
- big_npy,
- index_rate,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9, # layer 9
- }
- t0 = ttime()
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- feats = model.final_proj(logits[0])
-
- if (
- isinstance(index, type(None)) == False
- and isinstance(big_npy, type(None)) == False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half:
- npy = npy.astype("float32")
- _, I = index.search(npy, 1)
- npy = big_npy[I.squeeze()]
- if self.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
-
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- t1 = ttime()
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch != None and pitchf != None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
- p_len = torch.tensor([p_len], device=self.device).long()
- with torch.no_grad():
- if pitch != None and pitchf != None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- del feats, p_len, padding_mask
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- t2 = ttime()
- times[0] += t1 - t0
- times[2] += t2 - t1
- return audio1
-
- def pipeline(
- self,
- model,
- net_g,
- sid,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- f0_file=None,
- ):
- if (
- file_big_npy != ""
- and file_index != ""
- and os.path.exists(file_big_npy) == True
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
- try:
- index = faiss.read_index(file_index)
- big_npy = np.load(file_big_npy)
- except:
- traceback.print_exc()
- index = big_npy = None
- else:
- index = big_npy = None
- print("Feature retrieval library doesn't exist or ratio is 0")
- audio = signal.filtfilt(bh, ah, audio)
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
- opt_ts = []
- if audio_pad.shape[0] > self.t_max:
- audio_sum = np.zeros_like(audio)
- for i in range(self.window):
- audio_sum += audio_pad[i : i - self.window]
- for t in range(self.t_center, audio.shape[0], self.t_center):
- opt_ts.append(
- t
- - self.t_query
- + np.where(
- np.abs(audio_sum[t - self.t_query : t + self.t_query])
- == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
- )[0][0]
- )
- s = 0
- audio_opt = []
- t = None
- t1 = ttime()
- audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
- p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
- if hasattr(f0_file, "name") == True:
- try:
- with open(f0_file.name, "r") as f:
- lines = f.read().strip("\n").split("\n")
- inp_f0 = []
- for line in lines:
- inp_f0.append([float(i) for i in line.split(",")])
- inp_f0 = np.array(inp_f0, dtype="float32")
- except:
- traceback.print_exc()
- sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
- pitch, pitchf = None, None
- if if_f0 == 1:
- pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
- pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
- t2 = ttime()
- times[1] += t2 - t1
- for t in opt_ts:
- t = t // self.window * self.window
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- pitch[:, s // self.window : (t + self.t_pad2) // self.window],
- pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- s = t
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- pitch[:, t // self.window :] if t is not None else pitch,
- pitchf[:, t // self.window :] if t is not None else pitchf,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- audio_opt = np.concatenate(audio_opt)
- del pitch, pitchf, sid
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return audio_opt
diff --git a/spaces/MAPS-research/GEMRec-Gallery/Home.py b/spaces/MAPS-research/GEMRec-Gallery/Home.py
deleted file mode 100644
index 8667a08be337e3651d29522ca4264f8925fdc02b..0000000000000000000000000000000000000000
--- a/spaces/MAPS-research/GEMRec-Gallery/Home.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import os
-import random
-
-import pymysql.cursors
-import streamlit as st
-
-from datetime import datetime
-from streamlit_extras.switch_page_button import switch_page
-
-
-def login():
- # skip customize user name for debug mode
-
- with st.form("user_login"):
- # st.write('## Enter Your Name to Start the Session')
- st.write(
- '### Getting obsessed with tons of different text-to-image generation models available online? Want to find the most suitable one for your taste?')
- st.write('**GEMRec** is here to help you! Enter your name to try it out👇!')
- user_id = st.text_input(
- "Enter your name 👇",
- label_visibility='collapsed',
- disabled=False,
- placeholder='You can leave it blank to be anonymous'
- )
- # st.write('You can leave it blank to be anonymous.')
-
- # st.session_state.show_NSFW = st.toggle(':orange[show potentially mature content]', help='Inevitably, a few images might be NSFW, even if we tried to elimiate NFSW content in our prompts. We calculate a NSFW score to filter them out. Please check only if you are 18+ and want to take a look at the whole GEMRec-18k dataset', value=False, key='mature_content')
- st.session_state.show_NSFW = False # set to falso by default temporarily
-
- # Every form must have a submit button.
- submitted = st.form_submit_button("Start")
- if submitted:
- save_user_id(user_id)
- switch_page("gallery")
-
-
-def save_user_id(user_id):
- user_id = user_id[:60]
- print(user_id)
- if not user_id:
- user_id = 'anonymous' + str(random.randint(0, 100000))
- st.session_state.user_id = [user_id, datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")]
- st.session_state.assigned_rank_mode = random.choice(['Drag and Sort', 'Battle'])
- st.session_state.epoch = {'gallery': 0, 'ranking': {}, 'summary': {'overall': 0}}
-
-
-def logout():
- st.session_state.pop('user_id', None)
- st.session_state.pop('selected_dict', None)
- st.session_state.pop('epoch', None)
- st.session_state.pop('score_weights', None)
- st.session_state.pop('gallery_state', None)
- st.session_state.pop('edit_state', None)
- st.session_state.pop('progress', None)
- st.session_state.pop('pointer', None)
- st.session_state.pop('counter', None)
- st.session_state.pop('gallery_focus', None)
- st.session_state.pop('assigned_rank_mode', None)
- st.session_state.pop('show_NSFW', None)
- st.session_state.pop('modelVersion_standings', None)
-
-
-def project_info():
- with st.sidebar:
- st.write('## About')
- st.write(
- "This is a web application **for individual users to quickly dig out the most preferable text-to-image models from [civitai](https://civitai.com) for different prompts**. Our research aims to understand personal preference towards generative models and you can contribute by playing with this tool and giving us your feedback! "
- )
-
- st.write(
- "After picking images you liked from Gallery and a Ranking Contest, a summary dashboard will be presented **indicating your preferred models with download links ready to be deployed in [Webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)** !"
- )
-
-
-def connect_to_db():
- conn = pymysql.connect(
- host=os.environ.get('RANKING_DB_HOST'),
- port=3306,
- database='myRanking',
- user=os.environ.get('RANKING_DB_USER'),
- password=os.environ.get('RANKING_DB_PASSWORD'),
- charset='utf8mb4',
- cursorclass=pymysql.cursors.DictCursor
- )
-
- return conn
-
-
-if __name__ == '__main__':
- # print(st.source_util.get_pages('Home.py'))
- st.set_page_config(page_title="Login", page_icon="🏠", layout="wide")
- project_info()
- st.write('A Research by [MAPS Lab](https://whongyi.github.io/MAPS-research), [NYU Shanghai](https://shanghai.nyu.edu)')
- st.title("🙌 Welcome to GEMRec Gallery!")
-
- if 'user_id' not in st.session_state:
- login()
- else:
- st.write(f"You have already logged in as `{st.session_state.user_id[0]}`")
- st.write(f"Assigned ranking mode: `{st.session_state.assigned_rank_mode}`")
- st.button('Log out', on_click=logout)
-
- st.write('---')
- st.write('### FAQ')
- with st.expander(label='**🤔 How to use this webapp?**'):
- st.write('### Check out the demo video below')
- # st.info('Interface shown in this video demo is a bit different from the current webapp because it\'s outdated, but the basic idea is the same.')
- st.video('https://youtu.be/iSVM_yyIwlg')
-
- with st.expander(label='**ℹ️ What is GEMRec project?**'):
- st.write('### About GEMRec')
- st.write("**GE**nerative **M**odel **Rec**ommendation (**GEMRec**) is a research project by [MAPS Lab](https://github.com/MAPS-research), NYU Shanghai.")
- st.write('### Our Task')
- st.write('Navigate hundreds of text-to-image models through various categories of pre-defined prompts and a graph-based interface. Given a user’s preference and interaction data, we aim to recommend the most preferred generative model for the user.')
- st.write('### Our Approach')
- st.write('We propose a two-stage framework, which contains prompt-model retrieval and generative model ranking. :red[Your participation in this web application will help us to improve our framework and to further our research on personalization.]')
- # st.write('### Key Contributions')
- # st.write('1. We propose a two-stage framework to approach the Generative Model Recommendation problem. Our framework allows end-users to effectively explore a diverse set of generative models to understand their expressiveness. It also allows system developers to elicit user preferences for items generated from personalized prompts.')
- # st.write('2. We release GEMRec-18K, a dense prompt-model interaction dataset that consists of 18K images generated by pairing 200 generative models with 90 prompts collected from real-world usages, accompanied by detailed metadata and generation configurations. This dataset builds the cornerstone for exploring Generative Recommendation and can be useful for other tasks related to understanding generative models')
- # st.write('3. We take the first step in examining evaluation metrics for personalized image generations and identify several limitations in existing metrics. We propose a weighted metric that is more suitable for the task and opens up directions for future improvements in model training and evaluations.')
-
- with st.expander(label='**📑 Where can I find the paper and dataset?**'):
- st.write('### Paper')
- st.write('Arxiv: [Towards Personalized Prompt-Model Retrieval for Generative Recommendation](https://arxiv.org/abs/2308.02205)')
- st.write('### GEMRec-18K Dataset')
- st.write('Image dataset: https://huggingface.co/datasets/MAPS-research/GEMRec-PromptBook \n \
- Model dataset: https://huggingface.co/datasets/MAPS-research/GEMRec-Roster')
- st.write('### Code')
- st.write('Github: https://github.com/maps-research/gemrec')
\ No newline at end of file
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/batchnorm_reimpl.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/batchnorm_reimpl.py
deleted file mode 100644
index 18145c3353e13d482c492ae46df91a537669fca0..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/batchnorm_reimpl.py
+++ /dev/null
@@ -1,74 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-# File : batchnorm_reimpl.py
-# Author : acgtyrant
-# Date : 11/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-
-__all__ = ['BatchNorm2dReimpl']
-
-
-class BatchNorm2dReimpl(nn.Module):
- """
- A re-implementation of batch normalization, used for testing the numerical
- stability.
-
- Author: acgtyrant
- See also:
- https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14
- """
- def __init__(self, num_features, eps=1e-5, momentum=0.1):
- super().__init__()
-
- self.num_features = num_features
- self.eps = eps
- self.momentum = momentum
- self.weight = nn.Parameter(torch.empty(num_features))
- self.bias = nn.Parameter(torch.empty(num_features))
- self.register_buffer('running_mean', torch.zeros(num_features))
- self.register_buffer('running_var', torch.ones(num_features))
- self.reset_parameters()
-
- def reset_running_stats(self):
- self.running_mean.zero_()
- self.running_var.fill_(1)
-
- def reset_parameters(self):
- self.reset_running_stats()
- init.uniform_(self.weight)
- init.zeros_(self.bias)
-
- def forward(self, input_):
- batchsize, channels, height, width = input_.size()
- numel = batchsize * height * width
- input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel)
- sum_ = input_.sum(1)
- sum_of_square = input_.pow(2).sum(1)
- mean = sum_ / numel
- sumvar = sum_of_square - sum_ * mean
-
- self.running_mean = (
- (1 - self.momentum) * self.running_mean
- + self.momentum * mean.detach()
- )
- unbias_var = sumvar / (numel - 1)
- self.running_var = (
- (1 - self.momentum) * self.running_var
- + self.momentum * unbias_var.detach()
- )
-
- bias_var = sumvar / numel
- inv_std = 1 / (bias_var + self.eps).pow(0.5)
- output = (
- (input_ - mean.unsqueeze(1)) * inv_std.unsqueeze(1) *
- self.weight.unsqueeze(1) + self.bias.unsqueeze(1))
-
- return output.view(channels, batchsize, height, width).permute(1, 0, 2, 3).contiguous()
-
diff --git a/spaces/MaksMaib/PetGradioStyleTransf/README.md b/spaces/MaksMaib/PetGradioStyleTransf/README.md
deleted file mode 100644
index a4bc5e92ba9c877e6a0e5279fa9b2ed79645058f..0000000000000000000000000000000000000000
--- a/spaces/MaksMaib/PetGradioStyleTransf/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PetGradioStyleTransf
-emoji: 🌍
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/exp/upernet_global_small/test_config_h32.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/exp/upernet_global_small/test_config_h32.py
deleted file mode 100644
index a31e3874f76f9f7b089ac8834d85df2441af9b0e..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/exp/upernet_global_small/test_config_h32.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
- '../../configs/_base_/models/upernet_uniformer.py',
- '../../configs/_base_/datasets/ade20k.py',
- '../../configs/_base_/default_runtime.py',
- '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
- backbone=dict(
- type='UniFormer',
- embed_dim=[64, 128, 320, 512],
- layers=[3, 4, 8, 3],
- head_dim=64,
- drop_path_rate=0.25,
- windows=False,
- hybrid=True,
- window_size=32
- ),
- decode_head=dict(
- in_channels=[64, 128, 320, 512],
- num_classes=150
- ),
- auxiliary_head=dict(
- in_channels=320,
- num_classes=150
- ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
- paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
- 'relative_position_bias_table': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)}))
-
-lr_config = dict(_delete_=True, policy='poly',
- warmup='linear',
- warmup_iters=1500,
- warmup_ratio=1e-6,
- power=1.0, min_lr=0.0, by_epoch=False)
-
-data=dict(samples_per_gpu=2)
\ No newline at end of file
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/engine/test.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/engine/test.py
deleted file mode 100644
index 8dbeef271db634ec2dadfda3bc0b5ef9c7a677ff..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/engine/test.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import pickle
-import shutil
-import tempfile
-import time
-
-import torch
-import torch.distributed as dist
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.runner import get_dist_info
-
-
-def single_gpu_test(model, data_loader):
- """Test model with a single gpu.
-
- This method tests model with a single gpu and displays test progress bar.
-
- Args:
- model (nn.Module): Model to be tested.
- data_loader (nn.Dataloader): Pytorch data loader.
-
- Returns:
- list: The prediction results.
- """
- model.eval()
- results = []
- dataset = data_loader.dataset
- prog_bar = mmcv.ProgressBar(len(dataset))
- for data in data_loader:
- with torch.no_grad():
- result = model(return_loss=False, **data)
- results.extend(result)
-
- # Assume result has the same length of batch_size
- # refer to https://github.com/open-mmlab/mmcv/issues/985
- batch_size = len(result)
- for _ in range(batch_size):
- prog_bar.update()
- return results
-
-
-def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
- """Test model with multiple gpus.
-
- This method tests model with multiple gpus and collects the results
- under two different modes: gpu and cpu modes. By setting
- ``gpu_collect=True``, it encodes results to gpu tensors and use gpu
- communication for results collection. On cpu mode it saves the results on
- different gpus to ``tmpdir`` and collects them by the rank 0 worker.
-
- Args:
- model (nn.Module): Model to be tested.
- data_loader (nn.Dataloader): Pytorch data loader.
- tmpdir (str): Path of directory to save the temporary results from
- different gpus under cpu mode.
- gpu_collect (bool): Option to use either gpu or cpu to collect results.
-
- Returns:
- list: The prediction results.
- """
- model.eval()
- results = []
- dataset = data_loader.dataset
- rank, world_size = get_dist_info()
- if rank == 0:
- prog_bar = mmcv.ProgressBar(len(dataset))
- time.sleep(2) # This line can prevent deadlock problem in some cases.
- for i, data in enumerate(data_loader):
- with torch.no_grad():
- result = model(return_loss=False, **data)
- results.extend(result)
-
- if rank == 0:
- batch_size = len(result)
- batch_size_all = batch_size * world_size
- if batch_size_all + prog_bar.completed > len(dataset):
- batch_size_all = len(dataset) - prog_bar.completed
- for _ in range(batch_size_all):
- prog_bar.update()
-
- # collect results from all ranks
- if gpu_collect:
- results = collect_results_gpu(results, len(dataset))
- else:
- results = collect_results_cpu(results, len(dataset), tmpdir)
- return results
-
-
-def collect_results_cpu(result_part, size, tmpdir=None):
- """Collect results under cpu mode.
-
- On cpu mode, this function will save the results on different gpus to
- ``tmpdir`` and collect them by the rank 0 worker.
-
- Args:
- result_part (list): Result list containing result parts
- to be collected.
- size (int): Size of the results, commonly equal to length of
- the results.
- tmpdir (str | None): temporal directory for collected results to
- store. If set to None, it will create a random temporal directory
- for it.
-
- Returns:
- list: The collected results.
- """
- rank, world_size = get_dist_info()
- # create a tmp dir if it is not specified
- if tmpdir is None:
- MAX_LEN = 512
- # 32 is whitespace
- dir_tensor = torch.full((MAX_LEN, ),
- 32,
- dtype=torch.uint8,
- device='cuda')
- if rank == 0:
- mmcv.mkdir_or_exist('.dist_test')
- tmpdir = tempfile.mkdtemp(dir='.dist_test')
- tmpdir = torch.tensor(
- bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
- dir_tensor[:len(tmpdir)] = tmpdir
- dist.broadcast(dir_tensor, 0)
- tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
- else:
- mmcv.mkdir_or_exist(tmpdir)
- # dump the part result to the dir
- mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
- dist.barrier()
- # collect all parts
- if rank != 0:
- return None
- else:
- # load results of all parts from tmp dir
- part_list = []
- for i in range(world_size):
- part_file = osp.join(tmpdir, f'part_{i}.pkl')
- part_result = mmcv.load(part_file)
- # When data is severely insufficient, an empty part_result
- # on a certain gpu could makes the overall outputs empty.
- if part_result:
- part_list.append(part_result)
- # sort the results
- ordered_results = []
- for res in zip(*part_list):
- ordered_results.extend(list(res))
- # the dataloader may pad some samples
- ordered_results = ordered_results[:size]
- # remove tmp dir
- shutil.rmtree(tmpdir)
- return ordered_results
-
-
-def collect_results_gpu(result_part, size):
- """Collect results under gpu mode.
-
- On gpu mode, this function will encode results to gpu tensors and use gpu
- communication for results collection.
-
- Args:
- result_part (list): Result list containing result parts
- to be collected.
- size (int): Size of the results, commonly equal to length of
- the results.
-
- Returns:
- list: The collected results.
- """
- rank, world_size = get_dist_info()
- # dump result part to tensor with pickle
- part_tensor = torch.tensor(
- bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
- # gather all result part tensor shape
- shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
- shape_list = [shape_tensor.clone() for _ in range(world_size)]
- dist.all_gather(shape_list, shape_tensor)
- # padding result part tensor to max length
- shape_max = torch.tensor(shape_list).max()
- part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
- part_send[:shape_tensor[0]] = part_tensor
- part_recv_list = [
- part_tensor.new_zeros(shape_max) for _ in range(world_size)
- ]
- # gather all result part
- dist.all_gather(part_recv_list, part_send)
-
- if rank == 0:
- part_list = []
- for recv, shape in zip(part_recv_list, shape_list):
- part_result = pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())
- # When data is severely insufficient, an empty part_result
- # on a certain gpu could makes the overall outputs empty.
- if part_result:
- part_list.append(part_result)
- # sort the results
- ordered_results = []
- for res in zip(*part_list):
- ordered_results.extend(list(res))
- # the dataloader may pad some samples
- ordered_results = ordered_results[:size]
- return ordered_results
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/llm_utils.py b/spaces/MetaWabbit/Auto-GPT/autogpt/llm_utils.py
deleted file mode 100644
index 821820ffab07be2753cf385ff1de77820e4206ee..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/llm_utils.py
+++ /dev/null
@@ -1,172 +0,0 @@
-from __future__ import annotations
-
-import time
-from ast import List
-
-import openai
-from colorama import Fore, Style
-from openai.error import APIError, RateLimitError
-
-from autogpt.config import Config
-from autogpt.logs import logger
-
-CFG = Config()
-
-openai.api_key = CFG.openai_api_key
-
-
-def call_ai_function(
- function: str, args: list, description: str, model: str | None = None
-) -> str:
- """Call an AI function
-
- This is a magic function that can do anything with no-code. See
- https://github.com/Torantulino/AI-Functions for more info.
-
- Args:
- function (str): The function to call
- args (list): The arguments to pass to the function
- description (str): The description of the function
- model (str, optional): The model to use. Defaults to None.
-
- Returns:
- str: The response from the function
- """
- if model is None:
- model = CFG.smart_llm_model
- # For each arg, if any are None, convert to "None":
- args = [str(arg) if arg is not None else "None" for arg in args]
- # parse args to comma separated string
- args = ", ".join(args)
- messages = [
- {
- "role": "system",
- "content": f"You are now the following python function: ```# {description}"
- f"\n{function}```\n\nOnly respond with your `return` value.",
- },
- {"role": "user", "content": args},
- ]
-
- return create_chat_completion(model=model, messages=messages, temperature=0)
-
-
-# Overly simple abstraction until we create something better
-# simple retry mechanism when getting a rate error or a bad gateway
-def create_chat_completion(
- messages: list, # type: ignore
- model: str | None = None,
- temperature: float = CFG.temperature,
- max_tokens: int | None = None,
-) -> str:
- """Create a chat completion using the OpenAI API
-
- Args:
- messages (list[dict[str, str]]): The messages to send to the chat completion
- model (str, optional): The model to use. Defaults to None.
- temperature (float, optional): The temperature to use. Defaults to 0.9.
- max_tokens (int, optional): The max tokens to use. Defaults to None.
-
- Returns:
- str: The response from the chat completion
- """
- response = None
- num_retries = 10
- warned_user = False
- if CFG.debug_mode:
- print(
- Fore.GREEN
- + f"Creating chat completion with model {model}, temperature {temperature},"
- f" max_tokens {max_tokens}" + Fore.RESET
- )
- for attempt in range(num_retries):
- backoff = 2 ** (attempt + 2)
- try:
- if CFG.use_azure:
- response = openai.ChatCompletion.create(
- deployment_id=CFG.get_azure_deployment_id_for_model(model),
- model=model,
- messages=messages,
- temperature=temperature,
- max_tokens=max_tokens,
- )
- else:
- response = openai.ChatCompletion.create(
- model=model,
- messages=messages,
- temperature=temperature,
- max_tokens=max_tokens,
- )
- break
- except RateLimitError:
- if CFG.debug_mode:
- print(
- Fore.RED + "Error: ",
- f"Reached rate limit, passing..." + Fore.RESET,
- )
- if not warned_user:
- logger.double_check(
- f"Please double check that you have setup a {Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. "
- + f"You can read more here: {Fore.CYAN}https://github.com/Significant-Gravitas/Auto-GPT#openai-api-keys-configuration{Fore.RESET}"
- )
- warned_user = True
- except APIError as e:
- if e.http_status == 502:
- pass
- else:
- raise
- if attempt == num_retries - 1:
- raise
- if CFG.debug_mode:
- print(
- Fore.RED + "Error: ",
- f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
- )
- time.sleep(backoff)
- if response is None:
- logger.typewriter_log(
- "FAILED TO GET RESPONSE FROM OPENAI",
- Fore.RED,
- "Auto-GPT has failed to get a response from OpenAI's services. "
- + f"Try running Auto-GPT again, and if the problem the persists try running it with `{Fore.CYAN}--debug{Fore.RESET}`.",
- )
- logger.double_check()
- if CFG.debug_mode:
- raise RuntimeError(f"Failed to get response after {num_retries} retries")
- else:
- quit(1)
-
- return response.choices[0].message["content"]
-
-
-def create_embedding_with_ada(text) -> list:
- """Create an embedding with text-ada-002 using the OpenAI SDK"""
- num_retries = 10
- for attempt in range(num_retries):
- backoff = 2 ** (attempt + 2)
- try:
- if CFG.use_azure:
- return openai.Embedding.create(
- input=[text],
- engine=CFG.get_azure_deployment_id_for_model(
- "text-embedding-ada-002"
- ),
- )["data"][0]["embedding"]
- else:
- return openai.Embedding.create(
- input=[text], model="text-embedding-ada-002"
- )["data"][0]["embedding"]
- except RateLimitError:
- pass
- except APIError as e:
- if e.http_status == 502:
- pass
- else:
- raise
- if attempt == num_retries - 1:
- raise
- if CFG.debug_mode:
- print(
- Fore.RED + "Error: ",
- f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
- )
- time.sleep(backoff)
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/layers/satrn_layers.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/layers/satrn_layers.py
deleted file mode 100644
index a8441c1bcf0f98c10ff35ce270578016e003d1e6..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/layers/satrn_layers.py
+++ /dev/null
@@ -1,230 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, List, Optional, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule
-from mmengine.model import BaseModule
-from torch import Tensor
-
-from mmocr.models.common import MultiHeadAttention
-
-
-class SATRNEncoderLayer(BaseModule):
- """Implement encoder layer for SATRN, see `SATRN.
-
- `_.
-
- Args:
- d_model (int): Dimension :math:`D_m` of the input from previous model.
- Defaults to 512.
- d_inner (int): Hidden dimension of feedforward layers. Defaults to 256.
- n_head (int): Number of parallel attention heads. Defaults to 8.
- d_k (int): Dimension of the key vector. Defaults to 64.
- d_v (int): Dimension of the value vector. Defaults to 64.
- dropout (float): Dropout rate. Defaults to 0.1.
- qkv_bias (bool): Whether to use bias. Defaults to False.
- init_cfg (dict or list[dict], optional): Initialization configs.
- Defaults to None.
- """
-
- def __init__(self,
- d_model: int = 512,
- d_inner: int = 512,
- n_head: int = 8,
- d_k: int = 64,
- d_v: int = 64,
- dropout: float = 0.1,
- qkv_bias: bool = False,
- init_cfg: Optional[Union[Dict, List[Dict]]] = None) -> None:
- super().__init__(init_cfg=init_cfg)
- self.norm1 = nn.LayerNorm(d_model)
- self.attn = MultiHeadAttention(
- n_head, d_model, d_k, d_v, qkv_bias=qkv_bias, dropout=dropout)
- self.norm2 = nn.LayerNorm(d_model)
- self.feed_forward = LocalityAwareFeedforward(d_model, d_inner)
-
- def forward(self,
- x: Tensor,
- h: int,
- w: int,
- mask: Optional[Tensor] = None) -> Tensor:
- """Forward propagation of encoder.
-
- Args:
- x (Tensor): Feature tensor of shape :math:`(N, h*w, D_m)`.
- h (int): Height of the original feature.
- w (int): Width of the original feature.
- mask (Tensor, optional): Mask used for masked multi-head attention.
- Defaults to None.
-
- Returns:
- Tensor: A tensor of shape :math:`(N, h*w, D_m)`.
- """
- n, hw, c = x.size()
- residual = x
- x = self.norm1(x)
- x = residual + self.attn(x, x, x, mask)
- residual = x
- x = self.norm2(x)
- x = x.transpose(1, 2).contiguous().view(n, c, h, w)
- x = self.feed_forward(x)
- x = x.view(n, c, hw).transpose(1, 2)
- x = residual + x
- return x
-
-
-class LocalityAwareFeedforward(BaseModule):
- """Locality-aware feedforward layer in SATRN, see `SATRN.
-
- `_
-
- Args:
- d_in (int): Dimension of the input features.
- d_hid (int): Hidden dimension of feedforward layers.
- init_cfg (dict or list[dict], optional): Initialization configs.
- Defaults to [dict(type='Xavier', layer='Conv2d'),
- dict(type='Constant', layer='BatchNorm2d', val=1, bias=0)].
- """
-
- def __init__(
- self,
- d_in: int,
- d_hid: int,
- init_cfg: Optional[Union[Dict, List[Dict]]] = [
- dict(type='Xavier', layer='Conv2d'),
- dict(type='Constant', layer='BatchNorm2d', val=1, bias=0)
- ]
- ) -> None:
- super().__init__(init_cfg=init_cfg)
- self.conv1 = ConvModule(
- d_in,
- d_hid,
- kernel_size=1,
- padding=0,
- bias=False,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'))
-
- self.depthwise_conv = ConvModule(
- d_hid,
- d_hid,
- kernel_size=3,
- padding=1,
- bias=False,
- groups=d_hid,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'))
-
- self.conv2 = ConvModule(
- d_hid,
- d_in,
- kernel_size=1,
- padding=0,
- bias=False,
- norm_cfg=dict(type='BN'),
- act_cfg=dict(type='ReLU'))
-
- def forward(self, x: Tensor) -> Tensor:
- """Forward propagation of Locality Aware Feedforward module.
-
- Args:
- x (Tensor): Feature tensor.
-
- Returns:
- Tensor: Feature tensor after Locality Aware Feedforward.
- """
- x = self.conv1(x)
- x = self.depthwise_conv(x)
- x = self.conv2(x)
- return x
-
-
-class Adaptive2DPositionalEncoding(BaseModule):
- """Implement Adaptive 2D positional encoder for SATRN, see `SATRN.
-
- `_ Modified from
- https://github.com/Media-Smart/vedastr Licensed under the Apache License,
- Version 2.0 (the "License");
-
- Args:
- d_hid (int): Dimensions of hidden layer. Defaults to 512.
- n_height (int): Max height of the 2D feature output. Defaults to 100.
- n_width (int): Max width of the 2D feature output. Defaults to 100.
- dropout (float): Dropout rate. Defaults to 0.1.
- init_cfg (dict or list[dict], optional): Initialization configs.
- Defaults to [dict(type='Xavier', layer='Conv2d')]
- """
-
- def __init__(
- self,
- d_hid: int = 512,
- n_height: int = 100,
- n_width: int = 100,
- dropout: float = 0.1,
- init_cfg: Optional[Union[Dict, List[Dict]]] = [
- dict(type='Xavier', layer='Conv2d')
- ]
- ) -> None:
- super().__init__(init_cfg=init_cfg)
-
- h_position_encoder = self._get_sinusoid_encoding_table(n_height, d_hid)
- h_position_encoder = h_position_encoder.transpose(0, 1)
- h_position_encoder = h_position_encoder.view(1, d_hid, n_height, 1)
-
- w_position_encoder = self._get_sinusoid_encoding_table(n_width, d_hid)
- w_position_encoder = w_position_encoder.transpose(0, 1)
- w_position_encoder = w_position_encoder.view(1, d_hid, 1, n_width)
-
- self.register_buffer('h_position_encoder', h_position_encoder)
- self.register_buffer('w_position_encoder', w_position_encoder)
-
- self.h_scale = self._scale_factor_generate(d_hid)
- self.w_scale = self._scale_factor_generate(d_hid)
- self.pool = nn.AdaptiveAvgPool2d(1)
- self.dropout = nn.Dropout(p=dropout)
-
- @staticmethod
- def _get_sinusoid_encoding_table(n_position: int, d_hid: int) -> Tensor:
- """Generate sinusoid position encoding table."""
- denominator = torch.Tensor([
- 1.0 / np.power(10000, 2 * (hid_j // 2) / d_hid)
- for hid_j in range(d_hid)
- ])
- denominator = denominator.view(1, -1)
- pos_tensor = torch.arange(n_position).unsqueeze(-1).float()
- sinusoid_table = pos_tensor * denominator
- sinusoid_table[:, 0::2] = torch.sin(sinusoid_table[:, 0::2])
- sinusoid_table[:, 1::2] = torch.cos(sinusoid_table[:, 1::2])
-
- return sinusoid_table
-
- @staticmethod
- def _scale_factor_generate(d_hid: int) -> nn.Sequential:
- """Generate scale factor layers."""
- scale_factor = nn.Sequential(
- nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.ReLU(inplace=True),
- nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.Sigmoid())
-
- return scale_factor
-
- def forward(self, x: Tensor) -> Tensor:
- """Forward propagation of Locality Aware Feedforward module.
-
- Args:
- x (Tensor): Feature tensor.
-
- Returns:
- Tensor: Feature tensor after Locality Aware Feedforward.
- """
- _, _, h, w = x.size()
- avg_pool = self.pool(x)
- h_pos_encoding = \
- self.h_scale(avg_pool) * self.h_position_encoder[:, :, :h, :]
- w_pos_encoding = \
- self.w_scale(avg_pool) * self.w_position_encoder[:, :, :, :w]
- out = x + h_pos_encoding + w_pos_encoding
- out = self.dropout(out)
-
- return out
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/clip/model.py b/spaces/NAACL2022/CLIP-Caption-Reward/clip/model.py
deleted file mode 100644
index 049391e9816d7faf00bdab95a08b99a99c3c405a..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/clip/model.py
+++ /dev/null
@@ -1,437 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1):
- super().__init__()
-
- # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
- self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
-
- self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
-
- self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
- self.relu = nn.ReLU(inplace=True)
- self.downsample = None
- self.stride = stride
-
- if stride > 1 or inplanes != planes * Bottleneck.expansion:
- # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
- self.downsample = nn.Sequential(OrderedDict([
- ("-1", nn.AvgPool2d(stride)),
- ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
- ("1", nn.BatchNorm2d(planes * self.expansion))
- ]))
-
- def forward(self, x: torch.Tensor):
- identity = x
-
- out = self.relu(self.bn1(self.conv1(x)))
- out = self.relu(self.bn2(self.conv2(out)))
- out = self.avgpool(out)
- out = self.bn3(self.conv3(out))
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
- return out
-
-
-class AttentionPool2d(nn.Module):
- def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
- super().__init__()
- self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
- self.k_proj = nn.Linear(embed_dim, embed_dim)
- self.q_proj = nn.Linear(embed_dim, embed_dim)
- self.v_proj = nn.Linear(embed_dim, embed_dim)
- self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
- self.num_heads = num_heads
-
- def forward(self, x):
- x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC
- # print(x.shape, self.positional_embedding.shape)
- x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
- x = x + self.positional_embedding[0, :, None, :].to(x.dtype) # (HW+1)NC
- x, _ = F.multi_head_attention_forward(
- query=x, key=x, value=x,
- embed_dim_to_check=x.shape[-1],
- num_heads=self.num_heads,
- q_proj_weight=self.q_proj.weight,
- k_proj_weight=self.k_proj.weight,
- v_proj_weight=self.v_proj.weight,
- in_proj_weight=None,
- in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
- bias_k=None,
- bias_v=None,
- add_zero_attn=False,
- dropout_p=0,
- out_proj_weight=torch.ones_like(self.q_proj.weight),
- out_proj_bias=torch.zeros_like(self.q_proj.bias),
- # out_proj_weight=self.c_proj.weight,
- # out_proj_bias=self.c_proj.bias,
- use_separate_proj_weight=True,
- training=self.training,
- need_weights=False
- )
-
- return x[0]
-
-
-class ModifiedResNet(nn.Module):
- """
- A ResNet class that is similar to torchvision's but contains the following changes:
- - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
- - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
- - The final pooling layer is a QKV attention instead of an average pool
- """
-
- def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
- super().__init__()
- self.output_dim = output_dim
- self.input_resolution = input_resolution
-
- # the 3-layer stem
- self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(width // 2)
- self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(width // 2)
- self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
- self.bn3 = nn.BatchNorm2d(width)
- self.avgpool = nn.AvgPool2d(2)
- self.relu = nn.ReLU(inplace=True)
-
- # residual layers
- self._inplanes = width # this is a *mutable* variable used during construction
- self.layer1 = self._make_layer(width, layers[0])
- self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
- self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
- self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
- embed_dim = width * 32 # the ResNet feature dimension
- self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-
- def _make_layer(self, planes, blocks, stride=1):
- layers = [Bottleneck(self._inplanes, planes, stride)]
-
- self._inplanes = planes * Bottleneck.expansion
- for _ in range(1, blocks):
- layers.append(Bottleneck(self._inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- def stem(x):
- for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
- x = self.relu(bn(conv(x)))
- x = self.avgpool(x)
- return x
-
- x = x.type(self.conv1.weight.dtype)
- x = stem(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- # print(x.shape)
- # x = self.attnpool(x)
- attnpool = self.attnpool(x)
-
- return (x, attnpool)
-
-
-class LayerNorm(nn.LayerNorm):
- """Subclass torch's LayerNorm to handle fp16."""
-
- def forward(self, x: torch.Tensor):
- orig_type = x.dtype
- ret = super().forward(x.type(torch.float32))
- return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
- def forward(self, x: torch.Tensor):
- return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
- def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
- super().__init__()
-
- self.attn = nn.MultiheadAttention(d_model, n_head)
- self.ln_1 = LayerNorm(d_model)
- self.mlp = nn.Sequential(OrderedDict([
- ("c_fc", nn.Linear(d_model, d_model * 4)),
- ("gelu", QuickGELU()),
- ("c_proj", nn.Linear(d_model * 4, d_model))
- ]))
- self.ln_2 = LayerNorm(d_model)
- self.attn_mask = attn_mask
-
- def attention(self, x: torch.Tensor):
- self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
- return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
- def forward(self, x: torch.Tensor):
- x = x + self.attention(self.ln_1(x))
- x = x + self.mlp(self.ln_2(x))
- return x
-
-
-class Transformer(nn.Module):
- def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
- super().__init__()
- self.width = width
- self.layers = layers
- self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
- def forward(self, x: torch.Tensor):
- return self.resblocks(x)
-
-
-class VisualTransformer(nn.Module):
- def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
- super().__init__()
- self.input_resolution = input_resolution
- self.output_dim = output_dim
- self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
- scale = width ** -0.5
- self.class_embedding = nn.Parameter(scale * torch.randn(width))
- self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
- self.ln_pre = LayerNorm(width)
-
- self.transformer = Transformer(width, layers, heads)
-
- self.ln_post = LayerNorm(width)
- self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
- def forward(self, x: torch.Tensor):
- x = self.conv1(x) # shape = [*, width, grid, grid]
- x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
- x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
- x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
- x = x + self.positional_embedding.to(x.dtype)
- x = self.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
-
- # x = self.ln_post(x[:, 0, :])
-
- x = self.ln_post(x)
- # if self.proj is not None:
- # x = x @ self.proj
-
- return x
-
-
-class CLIP(nn.Module):
- def __init__(self,
- embed_dim: int,
- # vision
- image_resolution: int,
- vision_layers: Union[Tuple[int, int, int, int], int],
- vision_width: int,
- vision_patch_size: int,
- # text
- context_length: int,
- vocab_size: int,
- transformer_width: int,
- transformer_heads: int,
- transformer_layers: int
- ):
- super().__init__()
-
- self.context_length = context_length
-
- if isinstance(vision_layers, (tuple, list)):
- vision_heads = vision_width * 32 // 64
- self.visual = ModifiedResNet(
- layers=vision_layers,
- output_dim=embed_dim,
- heads=vision_heads,
- input_resolution=image_resolution,
- width=vision_width
- )
- else:
- vision_heads = vision_width // 64
- self.visual = VisualTransformer(
- input_resolution=image_resolution,
- patch_size=vision_patch_size,
- width=vision_width,
- layers=vision_layers,
- heads=vision_heads,
- output_dim=embed_dim
- )
-
- self.transformer = Transformer(
- width=transformer_width,
- layers=transformer_layers,
- heads=transformer_heads,
- attn_mask=self.build_attention_mask()
- )
-
- self.vocab_size = vocab_size
- self.token_embedding = nn.Embedding(vocab_size, transformer_width)
- self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
- self.ln_final = LayerNorm(transformer_width)
-
- self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
- self.logit_scale = nn.Parameter(torch.ones([]))
-
- self.initialize_parameters()
-
- def initialize_parameters(self):
- nn.init.normal_(self.token_embedding.weight, std=0.02)
- nn.init.normal_(self.positional_embedding, std=0.01)
-
- if isinstance(self.visual, ModifiedResNet):
- if self.visual.attnpool is not None:
- std = self.visual.attnpool.c_proj.in_features ** -0.5
- nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
- for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
- for name, param in resnet_block.named_parameters():
- if name.endswith("bn3.weight"):
- nn.init.zeros_(param)
-
- proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
- attn_std = self.transformer.width ** -0.5
- fc_std = (2 * self.transformer.width) ** -0.5
- for block in self.transformer.resblocks:
- nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
- nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
- nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
- nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
- if self.text_projection is not None:
- nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
- def build_attention_mask(self):
- # lazily create causal attention mask, with full attention between the vision tokens
- # pytorch uses additive attention mask; fill with -inf
- mask = torch.empty(self.context_length, self.context_length)
- mask.fill_(float("-inf"))
- mask.triu_(1) # zero out the lower diagonal
- return mask
-
- @property
- def dtype(self):
- return self.visual.conv1.weight.dtype
-
- def encode_image(self, image):
- return self.visual(image.type(self.dtype))
-
- def encode_text(self, text):
- x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
-
- x = x + self.positional_embedding.type(self.dtype)
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.ln_final(x).type(self.dtype)
-
- # x.shape = [batch_size, n_ctx, transformer.width]
- # take features from the eot embedding (eot_token is the highest number in each sequence)
- x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
- return x
-
- def forward(self, image, text):
- image_features = self.encode_image(image)
- text_features = self.encode_text(text)
-
- # normalized features
- image_features = image_features / image_features.norm(dim=-1, keepdim=True)
- text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-
- # cosine similarity as logits
- logit_scale = self.logit_scale.exp()
- logits_per_image = logit_scale * image_features @ text_features.t()
- logits_per_text = logit_scale * text_features @ image_features.t()
-
- # shape = [global_batch_size, global_batch_size]
- return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
- """Convert applicable model parameters to fp16"""
-
- def _convert_weights_to_fp16(l):
- if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
- l.weight.data = l.weight.data.half()
- if l.bias is not None:
- l.bias.data = l.bias.data.half()
-
- if isinstance(l, nn.MultiheadAttention):
- for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
- tensor = getattr(l, attr)
- if tensor is not None:
- tensor.data = tensor.data.half()
-
- for name in ["text_projection", "proj"]:
- if hasattr(l, name):
- attr = getattr(l, name)
- if attr is not None:
- attr.data = attr.data.half()
-
- model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
- vit = "visual.proj" in state_dict
-
- if vit:
- vision_width = state_dict["visual.conv1.weight"].shape[0]
- vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
- vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
- grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
- image_resolution = vision_patch_size * grid_size
- else:
- counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
- vision_layers = tuple(counts)
- vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
- output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
- vision_patch_size = None
- assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
- image_resolution = output_width * 32
-
- embed_dim = state_dict["text_projection"].shape[1]
- context_length = state_dict["positional_embedding"].shape[0]
- vocab_size = state_dict["token_embedding.weight"].shape[0]
- transformer_width = state_dict["ln_final.weight"].shape[0]
- transformer_heads = transformer_width // 64
- transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
- model = CLIP(
- embed_dim,
- image_resolution, vision_layers, vision_width, vision_patch_size,
- context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
- )
-
- for key in ["input_resolution", "context_length", "vocab_size"]:
- if key in state_dict:
- del state_dict[key]
-
- convert_weights(model)
- model.load_state_dict(state_dict)
- return model.eval()
diff --git a/spaces/NCSOFT/harim_plus/harim_scorer.py b/spaces/NCSOFT/harim_plus/harim_scorer.py
deleted file mode 100644
index 608c644ff58a8dc434b3feeb2991bf1c01d1bfa6..0000000000000000000000000000000000000000
--- a/spaces/NCSOFT/harim_plus/harim_scorer.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import torch
-import torch.nn.functional as F
-from transformers import (AutoModelForSeq2SeqLM,
- AutoTokenizer,
- PreTrainedTokenizer,
- PreTrainedTokenizerFast)
-import evaluate
-
-from fire import Fire
-import pandas as pd
-from tqdm import tqdm
-import json
-
-from typing import List, Dict, Union
-from collections import defaultdict
-from functools import partial
-from pprint import pprint
-
-from ipdb import set_trace
-
-class Harimplus_Scorer:
- def __init__(self,
- pretrained_name:str='none',
- tokenizer:Union[PreTrainedTokenizer, PreTrainedTokenizerFast]=None,
- mixing_factor:float=7., # same as lambda in the paper
- device:str='cuda',
-
- src_maxlen=1024,
- tgt_maxlen=110,
- ):
- self._pretrained_name = pretrained_name
- self._lambda = mixing_factor
-
- self._device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
- self._encdec_model = AutoModelForSeq2SeqLM.from_pretrained(self._pretrained_name)
- if tokenizer is None:
- self._tokenizer = AutoTokenizer.from_pretrained(self._pretrained_name)
- else:
- self._tokenizer = tokenizer
- self._encdec_model.to(self._device)
- self._encdec_model.eval()
-
- self._src_maxlen = src_maxlen
- self._tgt_maxlen = tgt_maxlen
-
-
-
- def _prep_input(self, src_tgt_txts, src_or_tgt='src'):
- L = self._src_maxlen if src_or_tgt=='src' else self._tgt_maxlen
- if isinstance(src_tgt_txts, pd.Series):
- src_tgt_txts=src_tgt_txts.tolist()
- if src_or_tgt == 'src':
- src_tgt_txts = [ s.replace("\n", " ") for s in src_tgt_txts ]
- return self._tokenizer(src_tgt_txts, padding=True, truncation=True, max_length=L, return_tensors='pt') # ModelInput dataclass
-
-
- '''below are helper functions w/o dependency to the self, but included inside the class for ease of use'''
- def likelihoods(self, logits, force_decode_indices, tgt_mask):
- probs = F.softmax(logits, dim=-1)
- probs_force_decode_ = probs.gather(-1, force_decode_indices.unsqueeze(-1)).squeeze()
- probs_force_decode= probs_force_decode_ * tgt_mask
- assert probs_force_decode.shape == force_decode_indices.shape
- return probs_force_decode
-
- def log_likelihoods(self, logits, force_decode_indices, tgt_mask):
- ll = F.log_softmax(logits, dim=-1)
- ll_force_decode_ = ll.gather(-1, force_decode_indices.unsqueeze(-1)).squeeze()
- ll_force_decode = ll_force_decode_ * tgt_mask
-
- return ll_force_decode
-
- def harim(self, s2s_logits, lm_logits, force_decode_indices, tgt_mask ):
- p_s2s, p_lm = self.likelihoods(s2s_logits, force_decode_indices, tgt_mask), \
- self.likelihoods(lm_logits, force_decode_indices, tgt_mask)
-
- delta = p_s2s - p_lm
- margin_linear = (1-delta) / 2
- harim = -(1-p_s2s) * margin_linear + 1
- return harim # this is -1 * hallucination risk
-
- def make_minibatches(self, exs:List[str], bsz:int=32):
- idx=0
- minibatches = []
- while True:
- start = idx
- end = idx+bsz
- if start >= len(exs):
- break
-
- minibatches.append( exs[start:end] )
- idx += bsz
- return minibatches
-
- def make_empty_minibatches(self, minibatches:List[List[str]]):
- e_minibatches = minibatches.copy()
- for i, mb in enumerate(e_minibatches):
- e_minibatches[i] = ['' for ex in mb]
- return e_minibatches
-
-
- def compute(self, predictions:List[str],
- references:List[str],
- bsz:int=32,
- use_aggregator:bool=False,
- return_details:bool=False,
- tokenwise_score:bool=False,
- ):
- '''
- returns harim+ score (List[float]) for predictions (summaries) and references (articles)
- **Note**
- - here, predictions = generated summaries to be evaluated, references = article to be summarized (but to follow the convention of the evaluate, we named kwarg as "references")
- - log_ppl equals to bartscore (yuan et al., neurips 2021)
-
- if tokenwise_score:
- returns minibatch chunks of harim+ scores and log-likelihoods with tokenized predictions (List[str])
- if use_aggregator:
- returning scores are aggregated (mean) over given test set
- '''
-
- # tokenize/prep src/tgts
- make_minibatches_bsz = partial(self.make_minibatches, bsz=bsz)
- summaries = predictions
- articles = references
- b_srcs, b_tgts = map(make_minibatches_bsz, [articles, summaries])
- b_emps = self.make_empty_minibatches(b_srcs)
-
- scores=defaultdict(list)
- for mini_s, mini_e, mini_t in tqdm(zip(b_srcs, b_emps, b_tgts), total=len(b_tgts), desc=f"computing HaRiM+ {bsz=}, core={self._pretrained_name}"):
- src_in = self._prep_input(mini_s, src_or_tgt='src')
- emp_in = self._prep_input(mini_e, src_or_tgt='src')
- tgt_in = self._prep_input(mini_t, src_or_tgt='tgt')
- if emp_in.input_ids.shape[-1]==0: # emp_in.input_ids.shape == (32,0)
- boseos = f"{self._tokenizer.bos_token}{self._tokenizer.eos_token}"
- mini_e_ = [boseos for _ in range(len(mini_e))]
- emp_in = self._prep_input( mini_e_, src_or_tgt='src' )
-
-
- tgt_mask = tgt_in.attention_mask
-
- src_in = src_in.to(self._device)
- emp_in = emp_in.to(self._device)
- tgt_in = tgt_in.to(self._device)
- tgt_mask = tgt_mask.to(self._device)
- fill_ignore_mask = ~(tgt_mask.bool())
-
- with torch.no_grad():
- # token_type_ids attribute causes error
- s2s_logits = self._encdec_model.forward(
- input_ids = src_in.input_ids,
- attention_mask = src_in.attention_mask,
- labels = tgt_in.input_ids.masked_fill(fill_ignore_mask, -100),
- return_dict=True).logits
- lm_logits = self._encdec_model.forward(
- input_ids = emp_in.input_ids,
- attention_mask = emp_in.attention_mask,
- labels = tgt_in.input_ids.masked_fill(fill_ignore_mask, -100),
- return_dict=True).logits
- sent_lengths = tgt_mask.sum(-1)
- ll_tok = self.log_likelihoods(s2s_logits, tgt_in.input_ids, tgt_mask)
- ll = ll_tok.sum(-1) / sent_lengths
-
- harim_tok = self.harim(s2s_logits, lm_logits, tgt_in.input_ids, tgt_mask)
- harim = harim_tok.sum(-1) / sent_lengths
-
- harim_plus_normalized = ll + self._lambda * harim # loglikelihood + lambda * negative_harim (negative harim=-1* risk)
-
- scores['harim+'].extend(harim_plus_normalized.tolist())
- scores['harim'].extend(harim.tolist())
- scores['log_ppl'].extend(ll.tolist())
-
- if tokenwise_score:
- scores['tok_harim+'].append(harim_tok*self._lambda + ll_tok)
- scores['tok_predictions'].append( [self._tokenizer.convert_ids_to_token(idxs) for idxs in src_in.labels] )
-
- if use_aggregator: # after
- for k, v in scores.items():
- if not k.startswith('tok_'):
- scores[k] = sum(v)/len(v) # aggregate (mean)
- scores['lambda'] = self._lambda
- if not return_details:
- scores = scores['harim+']
- return scores
diff --git a/spaces/NagaSaiAbhinay/UnCLIP_Image_Interpolation_Demo/app.py b/spaces/NagaSaiAbhinay/UnCLIP_Image_Interpolation_Demo/app.py
deleted file mode 100644
index 27a0447ce3337f114ef86471b00018e36bffa9f9..0000000000000000000000000000000000000000
--- a/spaces/NagaSaiAbhinay/UnCLIP_Image_Interpolation_Demo/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from diffusers import DiffusionPipeline
-import gradio as gr
-import torch
-import math
-import PIL
-
-if torch.cuda.is_available():
- device = "cuda"
- dtype = torch.float16
-else:
- device = "cpu"
- dtype = torch.bfloat16
-
-pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=dtype, custom_pipeline='unclip_image_interpolation')
-pipe.to(device)
-
-def unclip_image_interpolation(
- start_image,
- end_image,
- steps,
- seed
-):
- generator = torch.Generator()
- generator.manual_seed(seed)
-
- images = [start_image, end_image]
- output = pipe(image=images, steps=steps, generator=generator)
- return output.images
-
-inputs = [
- gr.Image(type="pil"),
- gr.Image(type="pil"),
- gr.Slider(minimum=2, maximum=12, default=5, step=1, label="Steps"),
- gr.Number(0, label="Seed", precision=0)
-]
-
-output = gr.Gallery(
- label="Generated images", show_label=False, elem_id="gallery"
- ).style(grid=[2], height="auto")
-
-examples = [
- ["starry_night.jpg","dogs.jpg", 5, 20],
- ["flowers.jpg", "dogs.jpg", 5, 42],
- ["starry_night.jpg","flowers.jpg", 6, 9011]
-]
-
-title = "UnClip Image Interpolation Pipeline"
-
-demo_app = gr.Interface(
- fn=unclip_image_interpolation,
- inputs=inputs,
- outputs=output,
- title=title,
- theme='huggingface',
- examples=examples,
- cache_examples=False
-)
-demo_app.launch(debug=True, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Nahrawy/ControlLight/README.md b/spaces/Nahrawy/ControlLight/README.md
deleted file mode 100644
index 93abfced0c3287cd1d21565c5da9f938ca423748..0000000000000000000000000000000000000000
--- a/spaces/Nahrawy/ControlLight/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: ControlLight
-emoji: 📊
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.28.2
-app_file: app.py
-pinned: false
-license: cc-by-4.0
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- controlnet
-- jax-diffusers-event
----
-
-# ControlLight: Light control through ControlNet and Depth Maps conditioning
-We propose a ControlNet using depth maps conditioning that is capable of controlling the light direction in a scene while trying to maintain the scene integrity.
-The model was trained on [VIDIT dataset](https://huggingface.co/datasets/Nahrawy/VIDIT-Depth-ControlNet) and [
-A Dataset of Flash and Ambient Illumination Pairs from the Crowd](https://huggingface.co/datasets/Nahrawy/FAID-Depth-ControlNet) as a part of the [Jax Diffusers Event](https://huggingface.co/jax-diffusers-event).
-
-Due to the limited available data the model is clearly overfit, but it serves as a proof of concept to what can be further achieved using enough data.
-
-A large part of the training data is synthetic so we encourage further training using synthetically generated scenes, using Unreal engine for example.
-
-The WandB training logs can be found [here](https://wandb.ai/hassanelnahrawy/controlnet-VIDIT-FAID), it's worth noting that the model was left to overfit for experimentation and it's advised to use the 8K steps weights or prior weights.
-
-This project is a joint work between [ParityError](https://huggingface.co/ParityError) and [Nahrawy](https://huggingface.co/Nahrawy).
-
-
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/fast_noisy_channel/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/fast_noisy_channel/README.md
deleted file mode 100644
index f2631a8c34d11bdf7d351c6807b6fe415f5715e1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/fast_noisy_channel/README.md
+++ /dev/null
@@ -1,345 +0,0 @@
-# Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling
-
-## Introduction
-- [Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) introduce a simple and effective noisy channel modeling approach for neural machine translation. However, the noisy channel online decoding approach introduced in this paper is too slow to be practical.
-- To address this, [Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 simple approximations to make this approach very fast and practical without much loss in accuracy.
-- This README provides intructions on how to run online decoding or generation with the noisy channel modeling approach, including ways to make it very fast without much loss in accuracy.
-
-## Noisy Channel Modeling
-
-[Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) applies the Bayes Rule to predict `P(y|x)`, the probability of the target `y` given the source `x`.
-```P(y|x) = P(x|y) * P(y) / P(x)```
-- `P(x|y)` predicts the source `x` given the target `y` and is referred to as the **channel model**
-- `P(y)` is a **language model** over the target `y`
-- `P(x)` is generally not modeled since it is constant for all `y`.
-
-We use Transformer models to parameterize the direct model `P(y|x)`, the channel model `P(x|y)` and the language model `P(y)`.
-
-During online decoding with beam search, we generate the top `K2` candidates per beam and score them with the following linear combination of the channel model, the language model as well as the direct model scores.
-
-```(1 / t) * log(P(y|x) + (1 / s) * ( λ1 * log(P(x|y)) + λ2 * log(P(y) ) )```
-- `t` - Target Prefix Length
-- `s` - Source Length
-- `λ1` - Channel Model Weight
-- `λ2` - Language Model Weight
-
-The top `beam_size` candidates based on the above combined scores are chosen to continue the beams in beam search. In beam search with a direct model alone, the scores from the direct model `P(y|x)` are used to choose the top candidates in beam search.
-
-This framework provides a great way to utlize strong target language models trained on large amounts of unlabeled data. Language models can prefer targets unrelated to the source, so we also need a channel model whose role is to ensure that the target preferred by the language model also translates back to the source.
-
-### Training Translation Models and Language Models
-
-For training Transformer models in fairseq for machine translation, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/translation)
-
-For training Transformer models in fairseq for language modeling, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model)
-
-### Generation with Language Model for German-English translation with fairseq
-
-Here are instructions to generate using a direct model and a target-side language model.
-
-Note:
-- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq)
-- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing)
-
-```sh
-binarized_data=data_dir/binarized
-direct_model=de_en_seed4.pt
-lm_model=en_lm.pt
-lm_data=lm_data
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model}
-mkdir -p ${lm_data}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt
-
-k2=10
-lenpen=0.16
-lm_wt=0.14
-fairseq-generate ${binarized_data} \
- --user-dir examples/fast_noisy_channel \
- --beam 5 \
- --path ${direct_model} \
- --lm-model ${lm_model} \
- --lm-data ${lm_data} \
- --k2 ${k2} \
- --combine-method lm_only \
- --task noisy_channel_translation \
- --lenpen ${lenpen} \
- --lm-wt ${lm_wt} \
- --gen-subset valid \
- --remove-bpe \
- --fp16 \
- --batch-size 10
-```
-### Noisy Channel Generation for German-English translation with fairseq
-
-Here are instructions for noisy channel generation with a direct model, channel model and language model as explained in section [Noisy Channel Modeling](#noisy-channel-modeling).
-
-Note:
-- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq)
-- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing)
-
-```sh
-binarized_data=data_dir/binarized
-direct_model=de_en_seed4.pt
-lm_model=en_lm.pt
-lm_data=lm_data
-ch_model=en_de.big.seed4.pt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model}
-mkdir -p ${lm_data}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt -O ${ch_model}
-
-k2=10
-lenpen=0.21
-lm_wt=0.50
-bw_wt=0.30
-fairseq-generate ${binarized_data} \
- --user-dir examples/fast_noisy_channel \
- --beam 5 \
- --path ${direct_model} \
- --lm-model ${lm_model} \
- --lm-data ${lm_data} \
- --channel-model ${ch_model} \
- --k2 ${k2} \
- --combine-method noisy_channel \
- --task noisy_channel_translation \
- --lenpen ${lenpen} \
- --lm-wt ${lm_wt} \
- --ch-wt ${bw_wt} \
- --gen-subset test \
- --remove-bpe \
- --fp16 \
- --batch-size 1
-```
-## Fast Noisy Channel Modeling
-
-[Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 approximations that speed up online noisy channel decoding -
-- Smaller channel models (`Tranformer Base` with 1 encoder and decoder layer each vs. `Transformer Big`)
- - This involves training a channel model that is possibly smaller and less accurate in terms of BLEU than a channel model of the same size as the direct model.
- - Since the role of the channel model is mainly to assign low scores to generations from the language model if they don't translate back to the source, we may not need the most accurate channel model for this purpose.
-- Smaller output vocabulary size for the channel model (~30,000 -> ~1000)
- - The channel model doesn't need to score the full output vocabulary, it just needs to score the source tokens, which are completely known.
- - This is specified using the arguments `--channel-scoring-type src_vocab --top-k-vocab 500`
- - This means that the output vocabulary for the channel model will be the source tokens for all examples in the batch and the top-K most frequent tokens in the vocabulary
- - This reduces the memory consumption needed to store channel model scores significantly
-- Smaller number of candidates (`k2`) scored per beam
- - This is specified by reducing the argument `--k2`
-
-
-### Fast Noisy Channel Generation for German-English translation with fairseq
-
-Here are instructions for **fast** noisy channel generation with a direct model, channel model and language model as explained in section [Fast Noisy Channel Modeling](#fast-noisy-channel-modeling). The main differences are that we use a smaller channel model, reduce `--k2`, set `--channel-scoring-type src_vocab --top-k-vocab 500` and increase the `--batch-size`.
-
-Note:
-- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq)
-- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing)
-
-```sh
-binarized_data=data_dir/binarized
-direct_model=de_en_seed4.pt
-lm_model=en_lm.pt
-lm_data=lm_data
-small_ch_model=en_de.base_1_1.seed4.pt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model}
-mkdir -p ${lm_data}
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt
-wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt -O ${small_ch_model}
-
-k2=3
-lenpen=0.23
-lm_wt=0.58
-bw_wt=0.26
-fairseq-generate ${binarized_data} \
- --user-dir examples/fast_noisy_channel \
- --beam 5 \
- --path ${direct_model} \
- --lm-model ${lm_model} \
- --lm-data ${lm_data} \
- --channel-model ${small_ch_model} \
- --k2 ${k2} \
- --combine-method noisy_channel \
- --task noisy_channel_translation \
- --lenpen ${lenpen} \
- --lm-wt ${lm_wt} \
- --ch-wt ${bw_wt} \
- --gen-subset test \
- --remove-bpe \
- --fp16 \
- --batch-size 50 \
- --channel-scoring-type src_vocab --top-k-vocab 500
-```
-
-## Test Data Preprocessing
-
-For preprocessing and binarizing the test sets for Romanian-English and German-English translation, we use the following script -
-
-```sh
-FAIRSEQ=/path/to/fairseq
-cd $FAIRSEQ
-SCRIPTS=$FAIRSEQ/mosesdecoder/scripts
-if [ ! -d "${SCRIPTS}" ]; then
- echo 'Cloning Moses github repository (for tokenization scripts)...'
- git clone https://github.com/moses-smt/mosesdecoder.git
-fi
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-NORMALIZE=$SCRIPTS/tokenizer/normalize-punctuation.perl
-
-s=de
-t=en
-test=wmt18
-
-mkdir -p data_dir
-
-# Tokenization
-if [ $s == "ro" ] ; then
- # Note: Get normalise-romanian.py and remove-diacritics.py from
- # https://github.com/rsennrich/wmt16-scripts/tree/master/preprocess
- sacrebleu -t $test -l $s-$t --echo src | \
- $NORMALIZE -l $s | \
- python normalise-romanian.py | \
- python remove-diacritics.py | \
- $TOKENIZER -l $s -a -q > data_dir/$test.$s-$t.$s
-else
- sacrebleu -t $test -l $s-$t --echo src | perl $NORMALIZE -l $s | perl $TOKENIZER -threads 8 -a -l $s > data_dir/$test.$s-$t.$s
-fi
-
-sacrebleu -t $test -l $s-$t --echo ref | perl $NORMALIZE -l $t | perl $TOKENIZER -threads 8 -a -l $t > data_dir/$test.$s-$t.$t
-
-
-# Applying BPE
-src_bpe_code=/path/to/source/language/bpe/code
-tgt_bpe_code=/path/to/target/language/bpe/code
-src_dict=/path/to/source/language/dict
-tgt_dict=/path/to/target/language/dict
-
-FASTBPE=$FAIRSEQ/fastBPE
-if [ ! -d "${FASTBPE}" ] ; then
- git clone https://github.com/glample/fastBPE.git
- # Follow compilation instructions at https://github.com/glample/fastBPE
- g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast
-fi
-
-${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${src_bpe_code}
-${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${tgt_bpe_code}
-
-fairseq-preprocess -s $s -t $t \
- --testpref data_dir/bpe.$test.$s-$t \
- --destdir data_dir/binarized \
- --srcdict ${src_dict} \
- --tgtdict ${tgt_dict}
-```
-
-## Calculating BLEU
-
-```sh
-DETOKENIZER=$SCRIPTS/tokenizer/detokenizer.perl
-cat ${generation_output} | grep -P "^H" | sort -V | cut -f 3- | $DETOKENIZER -l $t -q -a | sacrebleu -t $test -l $s-$t
-```
-
-
-## Romanian-English Translation
-
-The direct and channel models are trained using bitext data (WMT16) combined with backtranslated data (The monolingual data used for backtranslation comes from http://data.statmt.org/rsennrich/wmt16_backtranslations/ (Sennrich et al., 2016c))
-
-The backtranslated data is generated using an ensemble of 3 English-Romanian models trained on bitext training data (WMT16) with unrestricted sampling.
-
-### BPE Codes and Dictionary
-
-We learn a joint BPE vocabulary of 18K types on the bitext training data which is used for both the source and target.
-||Path|
-|----------|------|
-| BPE Code | [joint_bpe_18k](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/bpe_18k) |
-| Dictionary | [dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/dict) |
-
-### Direct Models
-For Ro-En with backtranslation, the direct and channel models use a Transformer-Big architecture.
-
-| Seed | Model |
-|----|----|
-| 2 | [ro_en_seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed2.pt)
-| 4 | [ro_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed4.pt)
-| 6 | [ro_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed6.pt)
-
-### Channel Models
-For channel models, we follow the same steps as for the direct models. But backtranslated data is generated in the opposite direction using [this Romanian monolingual data](http://data.statmt.org/rsennrich/wmt16_backtranslations/).
-The best lenpen, LM weight and CH weight are obtained by sweeping over the validation set (wmt16/dev) using beam 5.
-| Model Size | Lenpen | LM Weight | CH Weight | Seed 2 | Seed 4 | Seed 6 |
-|----|----|----|----|----|----|----|
-| `big` | 0.84 | 0.64 | 0.56 | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) |
-| `base_1_1` | 0.63 | 0.40 | 0.37 | [base_1_1.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed2.pt) | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed6.pt) |
-
-### Language Model
-The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization.
-| | Path |
-|----|----|
-| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/transformer_lm.pt) |
-| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/lm_dict)
-
-## German-English Translation
-
-### BPE Codes and Dictionaries
-
-| | Path|
-|----------|------|
-| Source BPE Code | [de_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_bpe_code_24K) |
-| Target BPE Code | [en_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_bpe_code_24K)
-| Source Dictionary | [de_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_dict) |
-| Target Dictionary | [en_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_dict) |
-
-### Direct Models
-We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs.
-We use the Transformer-Big architecture for the direct model.
-
-| Seed | Model |
-|:----:|----|
-| 4 | [de_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt)
-| 5 | [de_en_seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed5.pt)
-| 6 | [de_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed6.pt)
-
-### Channel Models
-
-We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs.
-
-| Model Size | Seed 4 | Seed 5 | Seed 6 |
-|----|----|----|----|
-| `big` | [big.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt) | [big.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed5.pt) | [big.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed6.pt) |
-| `big_1_1` | [big_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed4.pt) | [big_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed5.pt) | [big_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed6.pt) |
-| `base` | [base.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed4.pt) | [base.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed5.pt) | [base.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed6.pt) |
-| `base_1_1` | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed5.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed6.pt) |
-| `half` | [half.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed4.pt) | [half.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed5.pt) | [half.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed6.pt) |
-| `half_1_1` | [half_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed4.pt) | [half_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed5.pt) | [half_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed6.pt) |
-| `quarter` | [quarter.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed4.pt) | [quarter.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed5.pt) | [quarter.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed6.pt) |
-| `quarter_1_1` | [quarter_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed4.pt) | [quarter_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed5.pt) | [quarter_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed6.pt) |
-| `8th` | [8th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed4.pt) | [8th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed5.pt) | [8th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed6.pt) |
-| `8th_1_1` | [8th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed4.pt) | [8th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed5.pt) | [8th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed6.pt) |
-| `16th` | [16th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed4.pt) | [16th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed5.pt) | [16th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed6.pt) |
-| `16th_1_1` | [16th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed4.pt) | [16th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed5.pt) | [16th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed6.pt) |
-
-### Language Model
-The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization.
-| | Path |
-|----|----|
-| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt) |
-| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/)
-
-
-## Citation
-
-```bibtex
-@inproceedings{bhosale2020language,
- title={Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling},
- author={Shruti Bhosale and Kyra Yee and Sergey Edunov and Michael Auli},
- booktitle={Proceedings of the Fifth Conference on Machine Translation (WMT)},
- year={2020},
-}
-
-@inproceedings{yee2019simple,
- title={Simple and Effective Noisy Channel Modeling for Neural Machine Translation},
- author={Yee, Kyra and Dauphin, Yann and Auli, Michael},
- booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
- pages={5700--5705},
- year={2019}
-}
-```
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/language_model/README.conv.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/language_model/README.conv.md
deleted file mode 100644
index 1ff8635906cf278208be4714e0ef805a6a6b4da1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/language_model/README.conv.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
-
-## Example usage
-
-First download and preprocess the data following the main [language modeling README](README.md).
-
-Then to train a convolutional LM using the `fconv_lm_dauphin_wikitext103`
-architecture:
-```bash
-fairseq-train --task language_modeling \
- data-bin/wikitext-103 \
- --save-dir checkpoints/fconv_wikitext-103 \
- --arch fconv_lm_dauphin_wikitext103 \
- --adaptive-softmax-cutoff 10000,20000,200000 \
- --dropout 0.2 \
- --criterion adaptive_loss \
- --optimizer nag --clip-norm 0.1 --weight-decay 5e-06 \
- --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \
- --max-tokens 1024 --tokens-per-sample 1024 \
- --ddp-backend legacy_ddp \
- --max-epoch 35
-```
-
-And evaluate with:
-```bash
-fairseq-eval-lm data-bin/wikitext-103 --path checkpoints/fconv_wiki103/checkpoint_best.pt
-```
-
-## Citation
-
-```bibtex
-@inproceedings{dauphin2017language,
- title={Language Modeling with Gated Convolutional Networks},
- author={Dauphin, Yann N and Fan, Angela and Auli, Michael and Grangier, David},
- booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
- pages={933--941},
- year={2017},
- organization={JMLR}
-}
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp
deleted file mode 100644
index 707219105a17a691e43de1296a72bbaffa0c7fe9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
-Copyright (c) Microsoft Corporation.
-Licensed under the MIT License.
-*/
-
-#include
-#include
-
-/*
-CPP Binding for CUDA OP
-*/
-
-// CUDA forward declarations
-torch::Tensor ngram_repeat_block_cuda_forward(
- torch::Tensor tokens,
- torch::Tensor lprobs,
- int bsz,
- int step,
- int beam_size,
- int no_repeat_ngram_size);
-
-#define CHECK_CUDA(x) \
- TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) \
- TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) \
- CHECK_CUDA(x); \
- CHECK_CONTIGUOUS(x)
-
-// Input check and call to CUDA OP
-// Backward method not required
-torch::Tensor ngram_repeat_block_forward(
- torch::Tensor tokens,
- torch::Tensor lprobs,
- int bsz,
- int step,
- int beam_size,
- int no_repeat_ngram_size) {
- CHECK_INPUT(tokens);
- CHECK_INPUT(lprobs);
- assert(bsz > 0);
- assert(step >= 0);
- assert(beam_size > 0);
- assert(no_repeat_ngram_size > 0);
-
- return ngram_repeat_block_cuda_forward(
- tokens, lprobs, bsz, step, beam_size, no_repeat_ngram_size);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def(
- "forward",
- &ngram_repeat_block_forward,
- "No Repeat Ngram Block forward (CUDA)");
-}
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/hub_utils.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/hub_utils.py
deleted file mode 100644
index d74470d2ecba2825221a2efa2ce21a9b698340df..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/hub_utils.py
+++ /dev/null
@@ -1,303 +0,0 @@
-#!/usr/bin/env python3 -u
-# 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 argparse
-import copy
-import logging
-import os
-from typing import Any, Dict, Iterator, List
-
-import torch
-from fairseq import utils
-from fairseq.data import encoders
-from omegaconf import open_dict
-from torch import nn
-
-
-logger = logging.getLogger(__name__)
-
-
-def from_pretrained(
- model_name_or_path,
- checkpoint_file="model.pt",
- data_name_or_path=".",
- archive_map=None,
- **kwargs
-):
- from fairseq import checkpoint_utils, file_utils
-
- if archive_map is not None:
- if model_name_or_path in archive_map:
- model_name_or_path = archive_map[model_name_or_path]
- if data_name_or_path is not None and data_name_or_path in archive_map:
- data_name_or_path = archive_map[data_name_or_path]
-
- # allow archive_map to set default arg_overrides (e.g., tokenizer, bpe)
- # for each model
- if isinstance(model_name_or_path, dict):
- for k, v in model_name_or_path.items():
- if k == "checkpoint_file":
- checkpoint_file = v
- elif (
- k != "path"
- # only set kwargs that don't already have overrides
- and k not in kwargs
- ):
- kwargs[k] = v
- model_name_or_path = model_name_or_path["path"]
-
- model_path = file_utils.load_archive_file(model_name_or_path)
-
- # convenience hack for loading data and BPE codes from model archive
- if data_name_or_path.startswith("."):
- kwargs["data"] = os.path.abspath(os.path.join(model_path, data_name_or_path))
- else:
- kwargs["data"] = file_utils.load_archive_file(data_name_or_path)
- for file, arg in {
- "code": "bpe_codes",
- "bpecodes": "bpe_codes",
- "sentencepiece.bpe.model": "sentencepiece_model",
- "merges.txt": "bpe_merges",
- "vocab.json": "bpe_vocab",
- }.items():
- path = os.path.join(model_path, file)
- if os.path.exists(path):
- kwargs[arg] = path
-
- if "user_dir" in kwargs:
- utils.import_user_module(argparse.Namespace(user_dir=kwargs["user_dir"]))
-
- models, args, task = checkpoint_utils.load_model_ensemble_and_task(
- [os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
- arg_overrides=kwargs,
- )
-
- return {
- "args": args,
- "task": task,
- "models": models,
- }
-
-
-class GeneratorHubInterface(nn.Module):
- """
- PyTorch Hub interface for generating sequences from a pre-trained
- translation or language model.
- """
-
- def __init__(self, cfg, task, models):
- super().__init__()
- self.cfg = cfg
- self.task = task
- self.models = nn.ModuleList(models)
- self.src_dict = task.source_dictionary
- self.tgt_dict = task.target_dictionary
-
- # optimize model for generation
- for model in self.models:
- model.prepare_for_inference_(cfg)
-
- # Load alignment dictionary for unknown word replacement
- # (None if no unknown word replacement, empty if no path to align dictionary)
- self.align_dict = utils.load_align_dict(cfg.generation.replace_unk)
-
- self.tokenizer = encoders.build_tokenizer(cfg.tokenizer)
- self.bpe = encoders.build_bpe(cfg.bpe)
-
- self.max_positions = utils.resolve_max_positions(
- self.task.max_positions(), *[model.max_positions() for model in models]
- )
-
- # this is useful for determining the device
- self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float))
-
- @property
- def device(self):
- return self._float_tensor.device
-
- def translate(
- self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs
- ) -> List[str]:
- return self.sample(sentences, beam, verbose, **kwargs)
-
- def sample(
- self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs
- ) -> List[str]:
- if isinstance(sentences, str):
- return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0]
- tokenized_sentences = [self.encode(sentence) for sentence in sentences]
- batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs)
- return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos]
-
- def score(self, sentences: List[str], **kwargs):
- if isinstance(sentences, str):
- return self.score([sentences], **kwargs)[0]
- # NOTE: this doesn't support translation tasks currently
- tokenized_sentences = [self.encode(sentence) for sentence in sentences]
- return [
- hypos[0]
- for hypos in self.generate(
- tokenized_sentences, score_reference=True, **kwargs
- )
- ]
-
- def generate(
- self,
- tokenized_sentences: List[torch.LongTensor],
- beam: int = 5,
- verbose: bool = False,
- skip_invalid_size_inputs=False,
- inference_step_args=None,
- prefix_allowed_tokens_fn=None,
- **kwargs
- ) -> List[List[Dict[str, torch.Tensor]]]:
- if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1:
- return self.generate(
- tokenized_sentences.unsqueeze(0), beam=beam, verbose=verbose, **kwargs
- )[0]
-
- # build generator using current args as well as any kwargs
- gen_args = copy.deepcopy(self.cfg.generation)
- with open_dict(gen_args):
- gen_args.beam = beam
- for k, v in kwargs.items():
- setattr(gen_args, k, v)
- generator = self.task.build_generator(
- self.models,
- gen_args,
- prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
- )
-
- inference_step_args = inference_step_args or {}
- results = []
- for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
- batch = utils.apply_to_sample(lambda t: t.to(self.device), batch)
- translations = self.task.inference_step(
- generator, self.models, batch, **inference_step_args
- )
- for id, hypos in zip(batch["id"].tolist(), translations):
- results.append((id, hypos))
-
- # sort output to match input order
- outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])]
-
- if verbose:
-
- def getarg(name, default):
- return getattr(gen_args, name, getattr(self.cfg, name, default))
-
- for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs):
- src_str_with_unk = self.string(source_tokens)
- logger.info("S\t{}".format(src_str_with_unk))
- for hypo in target_hypotheses:
- hypo_str = self.decode(hypo["tokens"])
- logger.info("H\t{}\t{}".format(hypo["score"], hypo_str))
- logger.info(
- "P\t{}".format(
- " ".join(
- map(
- lambda x: "{:.4f}".format(x),
- hypo["positional_scores"].tolist(),
- )
- )
- )
- )
- if hypo["alignment"] is not None and getarg(
- "print_alignment", False
- ):
- logger.info(
- "A\t{}".format(
- " ".join(
- [
- "{}-{}".format(src_idx, tgt_idx)
- for src_idx, tgt_idx in hypo["alignment"]
- ]
- )
- )
- )
- return outputs
-
- def encode(self, sentence: str) -> torch.LongTensor:
- sentence = self.tokenize(sentence)
- sentence = self.apply_bpe(sentence)
- return self.binarize(sentence)
-
- def decode(self, tokens: torch.LongTensor) -> str:
- sentence = self.string(tokens)
- sentence = self.remove_bpe(sentence)
- return self.detokenize(sentence)
-
- def tokenize(self, sentence: str) -> str:
- if self.tokenizer is not None:
- sentence = self.tokenizer.encode(sentence)
- return sentence
-
- def detokenize(self, sentence: str) -> str:
- if self.tokenizer is not None:
- sentence = self.tokenizer.decode(sentence)
- return sentence
-
- def apply_bpe(self, sentence: str) -> str:
- if self.bpe is not None:
- sentence = self.bpe.encode(sentence)
- return sentence
-
- def remove_bpe(self, sentence: str) -> str:
- if self.bpe is not None:
- sentence = self.bpe.decode(sentence)
- return sentence
-
- def binarize(self, sentence: str) -> torch.LongTensor:
- return self.src_dict.encode_line(sentence, add_if_not_exist=False).long()
-
- def string(self, tokens: torch.LongTensor) -> str:
- return self.tgt_dict.string(tokens)
-
- def _build_batches(
- self, tokens: List[List[int]], skip_invalid_size_inputs: bool
- ) -> Iterator[Dict[str, Any]]:
- lengths = torch.LongTensor([t.numel() for t in tokens])
- batch_iterator = self.task.get_batch_iterator(
- dataset=self.task.build_dataset_for_inference(tokens, lengths),
- max_tokens=self.cfg.dataset.max_tokens,
- max_sentences=self.cfg.dataset.batch_size,
- max_positions=self.max_positions,
- ignore_invalid_inputs=skip_invalid_size_inputs,
- disable_iterator_cache=True,
- ).next_epoch_itr(shuffle=False)
- return batch_iterator
-
-
-class BPEHubInterface(object):
- """PyTorch Hub interface for Byte-Pair Encoding (BPE)."""
-
- def __init__(self, bpe, **kwargs):
- super().__init__()
- args = argparse.Namespace(bpe=bpe, **kwargs)
- self.bpe = encoders.build_bpe(args)
- assert self.bpe is not None
-
- def encode(self, sentence: str) -> str:
- return self.bpe.encode(sentence)
-
- def decode(self, sentence: str) -> str:
- return self.bpe.decode(sentence)
-
-
-class TokenizerHubInterface(object):
- """PyTorch Hub interface for tokenization."""
-
- def __init__(self, tokenizer, **kwargs):
- super().__init__()
- args = argparse.Namespace(tokenizer=tokenizer, **kwargs)
- self.tokenizer = encoders.build_tokenizer(args)
- assert self.tokenizer is not None
-
- def encode(self, sentence: str) -> str:
- return self.tokenizer.encode(sentence)
-
- def decode(self, sentence: str) -> str:
- return self.tokenizer.decode(sentence)
diff --git a/spaces/OIUGLK/bingo/src/components/ui/tooltip.tsx b/spaces/OIUGLK/bingo/src/components/ui/tooltip.tsx
deleted file mode 100644
index af1d48beb90dd5ae311796539843700871052cae..0000000000000000000000000000000000000000
--- a/spaces/OIUGLK/bingo/src/components/ui/tooltip.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as TooltipPrimitive from '@radix-ui/react-tooltip'
-
-import { cn } from '@/lib/utils'
-
-const TooltipProvider = TooltipPrimitive.Provider
-
-const Tooltip = TooltipPrimitive.Root
-
-const TooltipTrigger = TooltipPrimitive.Trigger
-
-const TooltipContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, sideOffset = 4, ...props }, ref) => (
-
-))
-TooltipContent.displayName = TooltipPrimitive.Content.displayName
-
-export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider }
diff --git a/spaces/ORI-Muchim/BarKeYaeTTS/models.py b/spaces/ORI-Muchim/BarKeYaeTTS/models.py
deleted file mode 100644
index fe004e94bbe9074ec736f14325268f4515a53420..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BarKeYaeTTS/models.py
+++ /dev/null
@@ -1,540 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
- super().__init__()
- filter_channels = in_channels # it needs to be removed from future version.
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.log_flow = modules.Log()
- self.flows = nn.ModuleList()
- self.flows.append(modules.ElementwiseAffine(2))
- for i in range(n_flows):
- self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.flows.append(modules.Flip())
-
- self.post_pre = nn.Conv1d(1, filter_channels, 1)
- self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- self.post_flows = nn.ModuleList()
- self.post_flows.append(modules.ElementwiseAffine(2))
- for i in range(4):
- self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.post_flows.append(modules.Flip())
-
- self.pre = nn.Conv1d(in_channels, filter_channels, 1)
- self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
- def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
- x = torch.detach(x)
- x = self.pre(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.convs(x, x_mask)
- x = self.proj(x) * x_mask
-
- if not reverse:
- flows = self.flows
- assert w is not None
-
- logdet_tot_q = 0
- h_w = self.post_pre(w)
- h_w = self.post_convs(h_w, x_mask)
- h_w = self.post_proj(h_w) * x_mask
- e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
- z_q = e_q
- for flow in self.post_flows:
- z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
- logdet_tot_q += logdet_q
- z_u, z1 = torch.split(z_q, [1, 1], 1)
- u = torch.sigmoid(z_u) * x_mask
- z0 = (w - u) * x_mask
- logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
- logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
- logdet_tot = 0
- z0, logdet = self.log_flow(z0, x_mask)
- logdet_tot += logdet
- z = torch.cat([z0, z1], 1)
- for flow in flows:
- z, logdet = flow(z, x_mask, g=x, reverse=reverse)
- logdet_tot = logdet_tot + logdet
- nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
- return nll + logq # [b]
- else:
- flows = list(reversed(self.flows))
- flows = flows[:-2] + [flows[-1]] # remove a useless vflow
- z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
- for flow in flows:
- z = flow(z, x_mask, g=x, reverse=reverse)
- z0, z1 = torch.split(z, [1, 1], 1)
- logw = z0
- return logw
-
-
-class DurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.proj = nn.Conv1d(filter_channels, 1, 1)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- def forward(self, x, x_mask, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
- x = self.proj(x * x_mask)
- return x * x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- n_vocab,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout):
- super().__init__()
- self.n_vocab = n_vocab
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
-
- if self.n_vocab != 0:
- self.emb = nn.Embedding(n_vocab, hidden_channels)
- nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-
- self.encoder = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths):
- if self.n_vocab != 0:
- x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
- gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
- upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
- k, u, padding=(k - u) // 2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- n_vocab,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- n_speakers=0,
- gin_channels=0,
- use_sdp=True,
- **kwargs):
-
- super().__init__()
- self.n_vocab = n_vocab
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.n_speakers = n_speakers
- self.gin_channels = gin_channels
-
- self.use_sdp = use_sdp
-
- self.enc_p = TextEncoder(n_vocab,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
- upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
- gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if use_sdp:
- self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
- else:
- self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
- if n_speakers > 1:
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
- def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 1:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
-
- with torch.no_grad():
- # negative cross-entropy
- s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
- neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
- neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2),
- s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
- neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
- w = attn.sum(2)
- if self.use_sdp:
- l_length = self.dp(x, x_mask, w, g=g)
- l_length = l_length / torch.sum(x_mask)
- else:
- logw_ = torch.log(w + 1e-6) * x_mask
- logw = self.dp(x, x_mask, g=g)
- l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging
-
- # expand prior
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
- return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 1:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- if self.use_sdp:
- logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
- else:
- logw = self.dp(x, x_mask, g=g)
- w = torch.exp(logw) * x_mask * length_scale
- w_ceil = torch.ceil(w)
- y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
- y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = commons.generate_path(w_ceil, attn_mask)
-
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
- 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
- z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
- z = self.flow(z_p, y_mask, g=g, reverse=True)
- o = self.dec((z * y_mask)[:, :, :max_len], g=g)
- return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
- def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
- assert self.n_speakers > 1, "n_speakers have to be larger than 1."
- g_src = self.emb_g(sid_src).unsqueeze(-1)
- g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
- z_p = self.flow(z, y_mask, g=g_src)
- z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
- o_hat = self.dec(z_hat * y_mask, g=g_tgt)
- return o_hat, y_mask, (z, z_p, z_hat)
diff --git a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN/README.md b/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN/README.md
deleted file mode 100644
index 2ef46239c882ee54c8471566e300ae5b0267b4ef..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN/README.md
+++ /dev/null
@@ -1,112 +0,0 @@
----
-title: LLMRiddles-ChatGPT-EN
-emoji: 🚀
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-python_version: 3.8
----
-
-# LLM Riddles
-
-
-
-English | [简体中文](https://github.com/opendilab/LLMRiddles/blob/main/README_zh.md)
-
-## :thinking: What's This
-Welcome to LLM Riddles! This is a game of wits and courage with language models. In the game, you need to construct questions that interact with the language model to get answers that meet the requirements. In this process, you can use your brain and use all the methods you can think of to get the model to output the results required by the answer.
-
-## :space_invader: How to Play
-We provide an online version for players to directly access and try out.
-- [Hugging Face][ChatGPT + English(w/o key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGPTEN)
-- [Hugging Face][ChatGPT + Chinese(w/o key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN)
-- [Hugging Face][ChatGLM + Chinese(w/ key)](https://huggingface.co/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN)
-- [OpenXLab][ChatGPT + Chinese(w/o key)](https://openxlab.org.cn/apps/detail/OpenDILab/LLMRiddlesChatGPTCN)
-- [OpenXLab][ChatGLM + Chinese(w/ key)](https://openxlab.org.cn/apps/detail/OpenDILab/LLMRiddlesChatGLMCN)
-- [OpenXLab][ChatGLM + English(w/ key)](https://openxlab.org.cn/apps/detail/OpenDILab/LLMRiddlesChatGLMEN)
-- [Private Server][Mistral + English(w/ key)](https://d9b451a97791dd8ef3.gradio.live)
-- [Private Server][ChatGPT + Chinese(w/ key)](http://llmriddles.opendilab.net/)
-
-Local deployment can be done in the following ways:
-## Installation
-### Use ChatGPT / ChatGLM API
-```shell
-pip3 install -r requirements.txt
-```
-### Deploy Mistral-7B-Instruct-v0.1 for local inference
-```shell
-pip3 install -r requirements-dev.txt
-```
-## Launch
-### ChatGPT + Chinese
-```shell
-QUESTION_LANG=cn QUESTION_LLM='chatgpt' QUESTION_LLM_KEY= python3 -u app.py
-```
-### ChatGPT + English
-```shell
-QUESTION_LANG=en QUESTION_LLM='chatgpt' QUESTION_LLM_KEY= python3 -u app.py
-```
-### ChatGLM + Chinese
-```shell
-QUESTION_LANG=cn QUESTION_LLM='chatglm' QUESTION_LLM_KEY= python3 -u app.py
-```
-### ChatGLM + English
-```shell
-QUESTION_LANG=en QUESTION_LLM='chatglm' QUESTION_LLM_KEY= python3 -u app.py
-```
-### Mistral-7B-Instruct-v0.1 + English
-```shell
-QUESTION_LANG=en QUESTION_LLM='mistral-7b' python3 -u app.py
-```
-## :technologist: Why Doing This
-
-Our goal is to use this game to give participants a deeper understanding of the fascinating aspects of prompt engineering and natural language processing. This process will show players how to cleverly construct prompts and how to use them to trigger surprising responses from artificial intelligence systems, while also helping them better understand the incredible power of deep learning and natural language processing technologies. .
-
-## :raising_hand: How to Submit a Custom Level
-If you have interesting questions or ideas, players are welcome to submit their own ideas. You can [Initiate a Pull Request](https://github.com/opendilab/LLMRiddles/compare) and submit it to us. We will include it in the level after approval.
-The question format should include the following points:
-- Pull Request title, example: feature(username): Chapter X-Level Design
-- The ID you want to be mentioned
-- Modify the corresponding chapter question files
-- Modification of \__init__.py
-
-For a complete example, please refer to: [Submit your own level design](https://github.com/opendilab/LLMRiddles/pull/6)
-
-## :writing_hand: Roadmap
-
-- [x] Support custom levels
-- [x] Online trial link
-- [x] Hugging Face Space link
-- [x] Support Mistral-7B（English version）
-- [x] Support ChatGLM（Chinese and English version）
-- [ ] Support Baichuan2-7B（Chinese version）
-- [ ] Support LLaMA2-7B（English version）
-- [ ] LLM inference speed optimization
-- [ ] More question levels and solution blogs
-
-## :speech_balloon: Feedback and Contribution
-- [Start an Issue](https://github.com/opendilab/CodeMorpheus/issues/new/choose) on GitHub
-- Contact us by email (opendilab@pjlab.org.cn)
-- Discuss on OpenDILab's WeChat group (i.e. add us on WeChat: ding314assist)
-
-
-## :star2: Special Thanks
-- Thanks to [Haoqiang Fan](https://www.zhihu.com/people/haoqiang-fan) for his original idea and title, which provided inspiration and motivation for the development and expansion of this project.
-- Thanks to [HuggingFace](https://huggingface.co) for supporting and assisting the game.
-- Thanks to [ChatGLM](https://chatglm.cn) for supporting and assisting the game, especially sufficient inference token support.
-- Thanks to [LLM Riddles contributors](https://github.com/opendilab/LLMRiddles/graphs/contributors) for their implementation and support.
-
-## :label: License
-All code within this repository is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
-
-
-
-## Comparisons:
-
-* Faster R-CNN: Detectron's AP is 36.7, similar to ours.
-* Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's
- [bug](https://github.com/facebookresearch/Detectron/issues/459) lead to a drop in box AP, and can be
- compensated back by some parameter tuning.
-* Mask R-CNN: Detectron's AP is box 37.7, mask 33.9. We're 1 AP better in mask AP, due to more correct implementation.
- See [this article](https://ppwwyyxx.com/blog/2021/Where-are-Pixels/) for details.
-
-For speed comparison, see [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html).
diff --git a/spaces/Tonic/greenblast/theme_dropdown.py b/spaces/Tonic/greenblast/theme_dropdown.py
deleted file mode 100644
index 6235388fd00549553df44028f3ccf03e946994ea..0000000000000000000000000000000000000000
--- a/spaces/Tonic/greenblast/theme_dropdown.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import os
-import pathlib
-
-from gradio.themes.utils import ThemeAsset
-
-
-def create_theme_dropdown():
- import gradio as gr
-
- asset_path = pathlib.Path(__file__).parent / "themes"
- themes = []
- for theme_asset in os.listdir(str(asset_path)):
- themes.append(
- (ThemeAsset(theme_asset), gr.Theme.load(str(asset_path / theme_asset)))
- )
-
- def make_else_if(theme_asset):
- return f"""
- else if (theme == '{str(theme_asset[0].version)}') {{
- var theme_css = `{theme_asset[1]._get_theme_css()}`
- }}"""
-
- head, tail = themes[0], themes[1:]
- if_statement = f"""
- if (theme == "{str(head[0].version)}") {{
- var theme_css = `{head[1]._get_theme_css()}`
- }} {" ".join(make_else_if(t) for t in tail)}
- """
-
- latest_to_oldest = sorted([t[0] for t in themes], key=lambda asset: asset.version)[
- ::-1
- ]
- latest_to_oldest = [str(t.version) for t in latest_to_oldest]
-
- component = gr.Dropdown(
- choices=latest_to_oldest,
- value=latest_to_oldest[0],
- render=False,
- label="Select Version",
- ).style(container=False)
-
- return (
- component,
- f"""
- (theme) => {{
- if (!document.querySelector('.theme-css')) {{
- var theme_elem = document.createElement('style');
- theme_elem.classList.add('theme-css');
- document.head.appendChild(theme_elem);
- }} else {{
- var theme_elem = document.querySelector('.theme-css');
- }}
- {if_statement}
- theme_elem.innerHTML = theme_css;
- }}
- """,
- )
diff --git a/spaces/ViktorTsoi13/ABA_Test/README.md b/spaces/ViktorTsoi13/ABA_Test/README.md
deleted file mode 100644
index ba3bf5d350e4f133d179affe17c04af42086c5ec..0000000000000000000000000000000000000000
--- a/spaces/ViktorTsoi13/ABA_Test/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: ABA Test
-emoji: 👁
-colorFrom: pink
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/__init__.py b/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/__init__.py
deleted file mode 100644
index 48ecd705c29bb437b28499f24110a3af16103e88..0000000000000000000000000000000000000000
--- a/spaces/VoiceHero69/changer/setup_tools/magicinstaller/requirements/__init__.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from .packaging_package import Packaging
-from .torch_package import Torch
-from .huggingface_package import Transformers, Diffusers, Gradio
-from .audio2numpy_package import AudioToNumpy
-from .bark_package import Bark, SoundFileOrSox
-from .audiolm_package import AudioLM, JobLib, FairSeq
-from .rvc_package import Praat, PyWorld, FaissCpu, TorchCrepe, FfmpegPython, NoiseReduce, LibRosa, Demucs
-from .tts_package import TTS
-from .pytube_package import PyTube
-from .whisper_package import Whisper
-from .audiocraft_package import AudioCraft
-from setup_tools.magicinstaller.requirement import SimpleRequirementInit, CompareAction
-
-requirements = [
- Packaging(), # Allows for version checks
-
- SimpleRequirementInit('numpy', CompareAction.EQ, '1.23.5'),
-
- Torch(),
-
- Transformers(),
- Diffusers(),
- Gradio(),
-
- AudioToNumpy(),
-
- Bark(),
- SoundFileOrSox(),
-
- AudioLM(),
- JobLib(),
- FairSeq(),
-
- Praat(),
- PyWorld(),
- FaissCpu(),
- TorchCrepe(),
- FfmpegPython(),
- NoiseReduce(),
- LibRosa(),
- Demucs(),
-
- TTS(),
-
- PyTube(),
-
- Whisper(),
-
- AudioCraft()
-]
diff --git a/spaces/Wauplin/space_to_dataset_saver/app.py b/spaces/Wauplin/space_to_dataset_saver/app.py
deleted file mode 100644
index d1d791106b68c1c7bfe98272d4ae06081fe2bab2..0000000000000000000000000000000000000000
--- a/spaces/Wauplin/space_to_dataset_saver/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# Start by setting token and debug mode before starting schedulers
-import os
-
-from huggingface_hub import logging, login
-
-login(token=os.environ.get("HF_TOKEN"), write_permission=True)
-logging.set_verbosity_debug()
-
-# Start apps
-from pathlib import Path
-
-import gradio as gr
-
-from app_1M_image import get_demo as get_demo_1M_image
-from app_image import get_demo as get_demo_image
-from app_json import get_demo as get_demo_json
-from app_parquet import get_demo as get_demo_parquet
-
-
-def _get_demo_code(path: str) -> str:
- code = Path(path).read_text()
- code = code.replace("def get_demo():", "with gr.Blocks() as demo:")
- code += "\n\ndemo.launch()"
- return code
-
-
-DEMO_EXPLANATION = """
-
How to persist data from a Space to a Dataset?
-
-This demo shows how to leverage `gradio` and `huggingface_hub` to save data from a Space to a Dataset on the Hub.
-When doing so, a few things must be taken care of: file formats, concurrent writes, name collision, number of commits,
-number of files, and more. The tabs below show different ways of implementing a "save to dataset" feature. Depending on the
-complexity and usage of your app, you might want to use one or the other.
-
-This Space comes as a demo for this `huggingface_hub` [guide](https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads). Please check it out if you need more technical details.
-"""
-
-JSON_DEMO_EXPLANATION = """
-## Use case
-
-- Save inputs and outputs
-- Build an annotation platform
-
-## Data
-
-Json-able only: text and numeric but no binaries.
-
-## Robustness
-
-Works with concurrent users and replicas.
-
-## Limitations
-
-If you expect millions of lines, you must split the local JSON file into multiple files to avoid getting your file tracked as LFS (5MB) on the Hub.
-
-## Demo
-"""
-
-IMAGE_DEMO_EXPLANATION = """
-## Use case
-
-Save images with metadata (caption, parameters, datetime, etc.).
-
-## Robustness
-
-Works with concurrent users and replicas.
-
-## Limitations
-
- - only 10k images/folder are supported on the Hub. If you expect more usage, you must save data in subfolders.
- - only 1M images/repo supported on the Hub. If you expect more usage, you can zip your data before uploading. See the _1M images Dataset_ demo.
-
-## Demo
-"""
-
-IMAGE_1M_DEMO_EXPLANATION = """
-## Use case:
-
-Save 1M images with metadata (caption, parameters, datetime, etc.).
-
-## Robustness
-
-Works with concurrent users and replicas.
-
-## Limitations
-
-Only 1 image per row. This is fine for most image datasets. However in some cases you might want to save multiple images per row
-(e.g. generate 4 images and select the preferred one). In this case, you must encode how the dataset must be saved, as
-a parquet file. Please have a look to the Parquet example for more details.
-
-## Demo
-"""
-
-PARQUET_DEMO_EXPLANATION = """
-## Use case:
-
-Save any arbitrary dataset, no matter its size or format. If well configured, your dataset will be directly loadable with the `datasets` library
-and benefit from the dataset-preview on the Hub.
-
-Each row can contain metadata (text, numbers, datetimes,...) as well as binary data (images, audio, video,...).
-This is particularly useful for datasets with multiple binary files for each row:
-
-- Generate multiple images and select preferred one.
-- Take audio as input, generate a translated audio as output.
-
-## Robustness
-
-Works with concurrent users and replicas.
-
-## Limitations
-
-None. Implementation of the ParquetScheduler requires slightly more work but you get full control over the data that is
-pushed to the Hub.
-
-## Demo
-"""
-
-with gr.Blocks() as demo:
- gr.Markdown(DEMO_EXPLANATION)
-
- with gr.Tab("JSON Dataset"):
- gr.Markdown(JSON_DEMO_EXPLANATION)
- get_demo_json()
- gr.Markdown(
- "## Result\n\nhttps://huggingface.co/datasets/Wauplin/example-space-to-dataset-json\n\n## Code"
- )
- with gr.Accordion("Source code", open=True):
- gr.Code(_get_demo_code("app_json.py"), language="python")
-
- with gr.Tab("Image Dataset"):
- gr.Markdown(IMAGE_DEMO_EXPLANATION)
- get_demo_image()
- gr.Markdown(
- "## Result\n\nhttps://huggingface.co/datasets/Wauplin/example-space-to-dataset-image\n\n## Code"
- )
- with gr.Accordion("Source code", open=True):
- gr.Code(_get_demo_code("app_image.py"), language="python")
-
- with gr.Tab("1M images Dataset"):
- gr.Markdown(IMAGE_1M_DEMO_EXPLANATION)
- get_demo_1M_image()
- gr.Markdown(
- "## Result\n\nhttps://huggingface.co/datasets/Wauplin/example-space-to-dataset-image-zip\n\n## Code"
- )
- with gr.Accordion("Source code", open=True):
- gr.Code(_get_demo_code("app_1M_image.py"), language="python")
-
- with gr.Tab("Parquet Dataset (e.g. save user preferences)"):
- gr.Markdown(PARQUET_DEMO_EXPLANATION)
- get_demo_parquet()
- gr.Markdown(
- "## Result\n\nhttps://huggingface.co/datasets/Wauplin/example-space-to-dataset-parquet\n\n## Code"
- )
- with gr.Accordion("Source code", open=True):
- gr.Code(_get_demo_code("app_parquet.py"), language="python")
-demo.launch()
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/README.md b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/README.md
deleted file mode 100644
index b14ee7879e936ebbe612eff653bd077cf6fb2557..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: BW IMAGE VIDEO COLORIZER
-emoji: 🏃
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XingHe0127/Chatbot/modules/overwrites.py b/spaces/XingHe0127/Chatbot/modules/overwrites.py
deleted file mode 100644
index 035a4a52722d66ee28af1c05231ad1cea3339ef5..0000000000000000000000000000000000000000
--- a/spaces/XingHe0127/Chatbot/modules/overwrites.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from __future__ import annotations
-import logging
-
-from llama_index import Prompt
-from typing import List, Tuple
-import mdtex2html
-from gradio_client import utils as client_utils
-
-from modules.presets import *
-from modules.llama_func import *
-
-
-def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
- logging.debug("Compacting text chunks...🚀🚀🚀")
- combined_str = [c.strip() for c in text_chunks if c.strip()]
- combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
- combined_str = "\n\n".join(combined_str)
- # resplit based on self.max_chunk_overlap
- text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
- return text_splitter.split_text(combined_str)
-
-
-def postprocess(
- self,
- y: List[List[str | Tuple[str] | Tuple[str, str] | None] | Tuple],
- ) -> List[List[str | Dict | None]]:
- """
- Parameters:
- y: List of lists representing the message and response pairs. Each message and response should be a string, which may be in Markdown format. It can also be a tuple whose first element is a string filepath or URL to an image/video/audio, and second (optional) element is the alt text, in which case the media file is displayed. It can also be None, in which case that message is not displayed.
- Returns:
- List of lists representing the message and response. Each message and response will be a string of HTML, or a dictionary with media information. Or None if the message is not to be displayed.
- """
- if y is None:
- return []
- processed_messages = []
- for message_pair in y:
- assert isinstance(
- message_pair, (tuple, list)
- ), f"Expected a list of lists or list of tuples. Received: {message_pair}"
- assert (
- len(message_pair) == 2
- ), f"Expected a list of lists of length 2 or list of tuples of length 2. Received: {message_pair}"
-
- processed_messages.append(
- [
- self._postprocess_chat_messages(message_pair[0], "user"),
- self._postprocess_chat_messages(message_pair[1], "bot"),
- ]
- )
- return processed_messages
-
-def postprocess_chat_messages(
- self, chat_message: str | Tuple | List | None, message_type: str
- ) -> str | Dict | None:
- if chat_message is None:
- return None
- elif isinstance(chat_message, (tuple, list)):
- filepath = chat_message[0]
- mime_type = client_utils.get_mimetype(filepath)
- filepath = self.make_temp_copy_if_needed(filepath)
- return {
- "name": filepath,
- "mime_type": mime_type,
- "alt_text": chat_message[1] if len(chat_message) > 1 else None,
- "data": None, # These last two fields are filled in by the frontend
- "is_file": True,
- }
- elif isinstance(chat_message, str):
- if message_type == "bot":
- if not detect_converted_mark(chat_message):
- chat_message = convert_mdtext(chat_message)
- elif message_type == "user":
- if not detect_converted_mark(chat_message):
- chat_message = convert_asis(chat_message)
- return chat_message
- else:
- raise ValueError(f"Invalid message for Chatbot component: {chat_message}")
-
-with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
- customJS = f.read()
- kelpyCodos = f2.read()
-
-def reload_javascript():
- print("Reloading javascript...")
- js = f''
- def template_response(*args, **kwargs):
- res = GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'
' if cover else "")+
- '- -
- 
-
-## :star2: Special Thanks
-- Thanks to [Haoqiang Fan](https://www.zhihu.com/people/haoqiang-fan) for his original idea and title, which provided inspiration and motivation for the development and expansion of this project.
-- Thanks to [HuggingFace](https://huggingface.co) for supporting and assisting the game.
-- Thanks to [ChatGLM](https://chatglm.cn) for supporting and assisting the game, especially sufficient inference token support.
-- Thanks to [LLM Riddles contributors](https://github.com/opendilab/LLMRiddles/graphs/contributors) for their implementation and support.
-
-## :label: License
-All code within this repository is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
-
-
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pascal_context.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pascal_context.py
deleted file mode 100644
index 541a63c66a13fb16fd52921e755715ad8d078fdd..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pascal_context.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class PascalContextDataset(CustomDataset):
- """PascalContext dataset.
-
- In segmentation map annotation for PascalContext, 0 stands for background,
- which is included in 60 categories. ``reduce_zero_label`` is fixed to
- False. The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is
- fixed to '.png'.
-
- Args:
- split (str): Split txt file for PascalContext.
- """
-
- CLASSES = ('background', 'aeroplane', 'bag', 'bed', 'bedclothes', 'bench',
- 'bicycle', 'bird', 'boat', 'book', 'bottle', 'building', 'bus',
- 'cabinet', 'car', 'cat', 'ceiling', 'chair', 'cloth',
- 'computer', 'cow', 'cup', 'curtain', 'dog', 'door', 'fence',
- 'floor', 'flower', 'food', 'grass', 'ground', 'horse',
- 'keyboard', 'light', 'motorbike', 'mountain', 'mouse', 'person',
- 'plate', 'platform', 'pottedplant', 'road', 'rock', 'sheep',
- 'shelves', 'sidewalk', 'sign', 'sky', 'snow', 'sofa', 'table',
- 'track', 'train', 'tree', 'truck', 'tvmonitor', 'wall', 'water',
- 'window', 'wood')
-
- PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255]]
-
- def __init__(self, split, **kwargs):
- super(PascalContextDataset, self).__init__(
- img_suffix='.jpg',
- seg_map_suffix='.png',
- split=split,
- reduce_zero_label=False,
- **kwargs)
- assert osp.exists(self.img_dir) and self.split is not None
-
-
-@DATASETS.register_module()
-class PascalContextDataset59(CustomDataset):
- """PascalContext dataset.
-
- In segmentation map annotation for PascalContext, 0 stands for background,
- which is included in 60 categories. ``reduce_zero_label`` is fixed to
- False. The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is
- fixed to '.png'.
-
- Args:
- split (str): Split txt file for PascalContext.
- """
-
- CLASSES = ('aeroplane', 'bag', 'bed', 'bedclothes', 'bench', 'bicycle',
- 'bird', 'boat', 'book', 'bottle', 'building', 'bus', 'cabinet',
- 'car', 'cat', 'ceiling', 'chair', 'cloth', 'computer', 'cow',
- 'cup', 'curtain', 'dog', 'door', 'fence', 'floor', 'flower',
- 'food', 'grass', 'ground', 'horse', 'keyboard', 'light',
- 'motorbike', 'mountain', 'mouse', 'person', 'plate', 'platform',
- 'pottedplant', 'road', 'rock', 'sheep', 'shelves', 'sidewalk',
- 'sign', 'sky', 'snow', 'sofa', 'table', 'track', 'train',
- 'tree', 'truck', 'tvmonitor', 'wall', 'water', 'window', 'wood')
-
- PALETTE = [[180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3],
- [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230],
- [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61],
- [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140],
- [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200],
- [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71],
- [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92],
- [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6],
- [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8],
- [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8],
- [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255],
- [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140],
- [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0],
- [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0],
- [0, 235, 255], [0, 173, 255], [31, 0, 255]]
-
- def __init__(self, split, **kwargs):
- super(PascalContextDataset59, self).__init__(
- img_suffix='.jpg',
- seg_map_suffix='.png',
- split=split,
- reduce_zero_label=True,
- **kwargs)
- assert osp.exists(self.img_dir) and self.split is not None
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/dsd.py b/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/dsd.py
deleted file mode 100644
index a21eb5fdd34bc30b1adc52145f1d9eff50d301b2..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/dsd.py
+++ /dev/null
@@ -1,194 +0,0 @@
-from functools import partial
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn as nn
-import utils.util as util
-from models.dips import KernelDIP
-from models.dsd.spherical_optimizer import SphericalOptimizer
-from torch.optim.lr_scheduler import StepLR
-from tqdm import tqdm
-
-
-class DSD(torch.nn.Module):
- def __init__(self, opt, cache_dir):
- super(DSD, self).__init__()
-
- self.opt = opt
-
- self.verbose = opt["verbose"]
- cache_dir = Path(cache_dir)
- cache_dir.mkdir(parents=True, exist_ok=True)
-
- # Initialize synthesis network
- if self.verbose:
- print("Loading Synthesis Network")
- self.load_synthesis_network()
- if self.verbose:
- print("Synthesis Network loaded!")
-
- self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2)
-
- self.initialize_mapping_network()
-
- def initialize_dip(self):
- self.dip_zk = util.get_noise(64, "noise", (64, 64)).cuda().detach()
- self.k_dip = KernelDIP(self.opt["KernelDIP"]).cuda()
-
- def initialize_latent_space(self):
- pass
-
- def initialize_optimizers(self):
- # Optimizer for k
- self.optimizer_k = torch.optim.Adam(self.k_dip.parameters(), lr=self.opt["k_lr"])
- self.scheduler_k = StepLR(
- self.optimizer_k, step_size=self.opt["num_epochs"] * self.opt["num_k_iters"] // 5, gamma=0.7
- )
-
- # Optimizer for x
- optimizer_dict = {
- "sgd": torch.optim.SGD,
- "adam": torch.optim.Adam,
- "sgdm": partial(torch.optim.SGD, momentum=0.9),
- "adamax": torch.optim.Adamax,
- }
- optimizer_func = optimizer_dict[self.opt["optimizer_name"]]
- self.optimizer_x = SphericalOptimizer(optimizer_func, self.latent_x_var_list, lr=self.opt["x_lr"])
-
- steps = self.opt["num_epochs"] * self.opt["num_x_iters"]
- schedule_dict = {
- "fixed": lambda x: 1,
- "linear1cycle": lambda x: (9 * (1 - np.abs(x / steps - 1 / 2) * 2) + 1) / 10,
- "linear1cycledrop": lambda x: (9 * (1 - np.abs(x / (0.9 * steps) - 1 / 2) * 2) + 1) / 10
- if x < 0.9 * steps
- else 1 / 10 + (x - 0.9 * steps) / (0.1 * steps) * (1 / 1000 - 1 / 10),
- }
- schedule_func = schedule_dict[self.opt["lr_schedule"]]
- self.scheduler_x = torch.optim.lr_scheduler.LambdaLR(self.optimizer_x.opt, schedule_func)
-
- def warmup_dip(self):
- self.reg_noise_std = self.opt["reg_noise_std"]
- warmup_k = torch.load("experiments/pretrained/kernel.pth")
-
- mse = nn.MSELoss().cuda()
-
- print("Warming up k DIP")
- for step in tqdm(range(self.opt["num_warmup_iters"])):
- self.optimizer_k.zero_grad()
- dip_zk_rand = self.dip_zk + self.reg_noise_std * torch.randn_like(self.dip_zk).cuda()
- k = self.k_dip(dip_zk_rand)
-
- loss = mse(k, warmup_k)
- loss.backward()
- self.optimizer_k.step()
-
- def optimize_k_step(self, epoch):
- # Optimize k
- tq_k = tqdm(range(self.opt["num_k_iters"]))
- for j in tq_k:
- for p in self.k_dip.parameters():
- p.requires_grad = True
- for p in self.latent_x_var_list:
- p.requires_grad = False
-
- self.optimizer_k.zero_grad()
-
- # Duplicate latent in case tile_latent = True
- if self.opt["tile_latent"]:
- latent_in = self.latent.expand(-1, 14, -1)
- else:
- latent_in = self.latent
-
- dip_zk_rand = self.dip_zk + self.reg_noise_std * torch.randn_like(self.dip_zk).cuda()
- # Apply learned linear mapping to match latent distribution to that of the mapping network
- latent_in = self.lrelu(latent_in * self.gaussian_fit["std"] + self.gaussian_fit["mean"])
-
- # Normalize image to [0,1] instead of [-1,1]
- self.gen_im = self.get_gen_im(latent_in)
- self.gen_ker = self.k_dip(dip_zk_rand)
-
- # Calculate Losses
- loss, loss_dict = self.loss_builder(latent_in, self.gen_im, self.gen_ker, epoch)
- self.cur_loss = loss.cpu().detach().numpy()
-
- loss.backward()
- self.optimizer_k.step()
- self.scheduler_k.step()
-
- msg = " | ".join("{}: {:.4f}".format(k, v) for k, v in loss_dict.items())
- tq_k.set_postfix(loss=msg)
-
- def optimize_x_step(self, epoch):
- tq_x = tqdm(range(self.opt["num_x_iters"]))
- for j in tq_x:
- for p in self.k_dip.parameters():
- p.requires_grad = False
- for p in self.latent_x_var_list:
- p.requires_grad = True
-
- self.optimizer_x.opt.zero_grad()
-
- # Duplicate latent in case tile_latent = True
- if self.opt["tile_latent"]:
- latent_in = self.latent.expand(-1, 14, -1)
- else:
- latent_in = self.latent
-
- dip_zk_rand = self.dip_zk + self.reg_noise_std * torch.randn_like(self.dip_zk).cuda()
- # Apply learned linear mapping to match latent distribution to that of the mapping network
- latent_in = self.lrelu(latent_in * self.gaussian_fit["std"] + self.gaussian_fit["mean"])
-
- # Normalize image to [0,1] instead of [-1,1]
- self.gen_im = self.get_gen_im(latent_in)
- self.gen_ker = self.k_dip(dip_zk_rand)
-
- # Calculate Losses
- loss, loss_dict = self.loss_builder(latent_in, self.gen_im, self.gen_ker, epoch)
- self.cur_loss = loss.cpu().detach().numpy()
-
- loss.backward()
- self.optimizer_x.step()
- self.scheduler_x.step()
-
- msg = " | ".join("{}: {:.4f}".format(k, v) for k, v in loss_dict.items())
- tq_x.set_postfix(loss=msg)
-
- def log(self):
- if self.cur_loss < self.min_loss:
- self.min_loss = self.cur_loss
- self.best_im = self.gen_im.clone()
- self.best_ker = self.gen_ker.clone()
-
- def forward(self, ref_im):
- if self.opt["seed"]:
- seed = self.opt["seed"]
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- torch.backends.cudnn.deterministic = True
-
- self.initialize_dip()
- self.initialize_latent_space()
- self.initialize_optimizers()
- self.warmup_dip()
-
- self.min_loss = np.inf
- self.gen_im = None
- self.initialize_loss(ref_im)
-
- if self.verbose:
- print("Optimizing")
-
- for epoch in range(self.opt["num_epochs"]):
- print("Step: {}".format(epoch + 1))
-
- self.optimize_x_step(epoch)
- self.log()
- self.optimize_k_step(epoch)
- self.log()
-
- if self.opt["save_intermediate"]:
- yield (
- self.best_im.cpu().detach().clamp(0, 1),
- self.loss_builder.get_blur_img(self.best_im, self.best_ker),
- )
diff --git a/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/classifier.py b/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/classifier.py
deleted file mode 100644
index 67e98b9d8ffb96a150b517497ace0a242d7163ef..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/classifier.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from torch.nn import functional as F
-from torch.optim import AdamW
-from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
-
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {
- 'class_label': EncoderUNetModel,
- 'segmentation': UNetModel
-}
-
-
-def disabled_train(self, mode=True):
- """Overwrite model.train with this function to make sure train/eval mode
- does not change anymore."""
- return self
-
-
-class NoisyLatentImageClassifier(pl.LightningModule):
-
- def __init__(self,
- diffusion_path,
- num_classes,
- ckpt_path=None,
- pool='attention',
- label_key=None,
- diffusion_ckpt_path=None,
- scheduler_config=None,
- weight_decay=1.e-2,
- log_steps=10,
- monitor='val/loss',
- *args,
- **kwargs):
- super().__init__(*args, **kwargs)
- self.num_classes = num_classes
- # get latest config of diffusion model
- diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
- self.diffusion_config = OmegaConf.load(diffusion_config).model
- self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
- self.load_diffusion()
-
- self.monitor = monitor
- self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
- self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
- self.log_steps = log_steps
-
- self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
- else self.diffusion_model.cond_stage_key
-
- assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
-
- if self.label_key not in __models__:
- raise NotImplementedError()
-
- self.load_classifier(ckpt_path, pool)
-
- self.scheduler_config = scheduler_config
- self.use_scheduler = self.scheduler_config is not None
- self.weight_decay = weight_decay
-
- def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
- sd = torch.load(path, map_location="cpu")
- if "state_dict" in list(sd.keys()):
- sd = sd["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
- sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- if len(unexpected) > 0:
- print(f"Unexpected Keys: {unexpected}")
-
- def load_diffusion(self):
- model = instantiate_from_config(self.diffusion_config)
- self.diffusion_model = model.eval()
- self.diffusion_model.train = disabled_train
- for param in self.diffusion_model.parameters():
- param.requires_grad = False
-
- def load_classifier(self, ckpt_path, pool):
- model_config = deepcopy(self.diffusion_config.params.unet_config.params)
- model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
- model_config.out_channels = self.num_classes
- if self.label_key == 'class_label':
- model_config.pool = pool
-
- self.model = __models__[self.label_key](**model_config)
- if ckpt_path is not None:
- print('#####################################################################')
- print(f'load from ckpt "{ckpt_path}"')
- print('#####################################################################')
- self.init_from_ckpt(ckpt_path)
-
- @torch.no_grad()
- def get_x_noisy(self, x, t, noise=None):
- noise = default(noise, lambda: torch.randn_like(x))
- continuous_sqrt_alpha_cumprod = None
- if self.diffusion_model.use_continuous_noise:
- continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
- # todo: make sure t+1 is correct here
-
- return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
- continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
-
- def forward(self, x_noisy, t, *args, **kwargs):
- return self.model(x_noisy, t)
-
- @torch.no_grad()
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = rearrange(x, 'b h w c -> b c h w')
- x = x.to(memory_format=torch.contiguous_format).float()
- return x
-
- @torch.no_grad()
- def get_conditioning(self, batch, k=None):
- if k is None:
- k = self.label_key
- assert k is not None, 'Needs to provide label key'
-
- targets = batch[k].to(self.device)
-
- if self.label_key == 'segmentation':
- targets = rearrange(targets, 'b h w c -> b c h w')
- for down in range(self.numd):
- h, w = targets.shape[-2:]
- targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
-
- # targets = rearrange(targets,'b c h w -> b h w c')
-
- return targets
-
- def compute_top_k(self, logits, labels, k, reduction="mean"):
- _, top_ks = torch.topk(logits, k, dim=1)
- if reduction == "mean":
- return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
- elif reduction == "none":
- return (top_ks == labels[:, None]).float().sum(dim=-1)
-
- def on_train_epoch_start(self):
- # save some memory
- self.diffusion_model.model.to('cpu')
-
- @torch.no_grad()
- def write_logs(self, loss, logits, targets):
- log_prefix = 'train' if self.training else 'val'
- log = {}
- log[f"{log_prefix}/loss"] = loss.mean()
- log[f"{log_prefix}/acc@1"] = self.compute_top_k(
- logits, targets, k=1, reduction="mean"
- )
- log[f"{log_prefix}/acc@5"] = self.compute_top_k(
- logits, targets, k=5, reduction="mean"
- )
-
- self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
- self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
- self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
- lr = self.optimizers().param_groups[0]['lr']
- self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
-
- def shared_step(self, batch, t=None):
- x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
- targets = self.get_conditioning(batch)
- if targets.dim() == 4:
- targets = targets.argmax(dim=1)
- if t is None:
- t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
- else:
- t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
- x_noisy = self.get_x_noisy(x, t)
- logits = self(x_noisy, t)
-
- loss = F.cross_entropy(logits, targets, reduction='none')
-
- self.write_logs(loss.detach(), logits.detach(), targets.detach())
-
- loss = loss.mean()
- return loss, logits, x_noisy, targets
-
- def training_step(self, batch, batch_idx):
- loss, *_ = self.shared_step(batch)
- return loss
-
- def reset_noise_accs(self):
- self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
- range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
-
- def on_validation_start(self):
- self.reset_noise_accs()
-
- @torch.no_grad()
- def validation_step(self, batch, batch_idx):
- loss, *_ = self.shared_step(batch)
-
- for t in self.noisy_acc:
- _, logits, _, targets = self.shared_step(batch, t)
- self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
- self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
-
- return loss
-
- def configure_optimizers(self):
- optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
-
- if self.use_scheduler:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- }]
- return [optimizer], scheduler
-
- return optimizer
-
- @torch.no_grad()
- def log_images(self, batch, N=8, *args, **kwargs):
- log = dict()
- x = self.get_input(batch, self.diffusion_model.first_stage_key)
- log['inputs'] = x
-
- y = self.get_conditioning(batch)
-
- if self.label_key == 'class_label':
- y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
- log['labels'] = y
-
- if ismap(y):
- log['labels'] = self.diffusion_model.to_rgb(y)
-
- for step in range(self.log_steps):
- current_time = step * self.log_time_interval
-
- _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
-
- log[f'inputs@t{current_time}'] = x_noisy
-
- pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
- pred = rearrange(pred, 'b h w c -> b c h w')
-
- log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
-
- for key in log:
- log[key] = log[key][:N]
-
- return log
diff --git a/spaces/PeepDaSlan9/stabilityai-stable-diffusion-xl-base-1.0/app.py b/spaces/PeepDaSlan9/stabilityai-stable-diffusion-xl-base-1.0/app.py
deleted file mode 100644
index 9520517f687cf7229ddfab9d8c5f8af7f76b0bd4..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/stabilityai-stable-diffusion-xl-base-1.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-xl-base-1.0").launch()
\ No newline at end of file
diff --git a/spaces/Plachta/VITS-Umamusume-voice-synthesizer/monotonic_align/setup.py b/spaces/Plachta/VITS-Umamusume-voice-synthesizer/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/Plachta/VITS-Umamusume-voice-synthesizer/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/Plutanico/PlutanicoTeste2/README.md b/spaces/Plutanico/PlutanicoTeste2/README.md
deleted file mode 100644
index ab40e93137f9bac4dafdb806483381d2a668c7de..0000000000000000000000000000000000000000
--- a/spaces/Plutanico/PlutanicoTeste2/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: PlutanicoTeste2
-emoji: 🐨
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/train.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/train.py
deleted file mode 100644
index 22dd117830bb403829d0a60b1b95e120d1e6978b..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/train.py
+++ /dev/null
@@ -1,157 +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
-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}. "
- f"Overrides used: {xp.argv}")
- cfg = xp.cfg
- if override_cfg is not None:
- cfg = omegaconf.OmegaConf.merge(cfg, omegaconf.DictConfig(override_cfg))
- if disable_fsdp and cfg.fsdp.use:
- cfg.fsdp.use = False
- assert load_best is True
- # ignoring some keys that were FSDP sharded like model, ema, and best_state.
- # fsdp_best_state will be used in that case. 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)
-
-
-@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=
-