diff --git a/spaces/17TheWord/vits-models/mel_processing.py b/spaces/17TheWord/vits-models/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/17TheWord/vits-models/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Acrobat 8.1 0 Professional The Ultimate Guide to PDF Creation and Editing.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Acrobat 8.1 0 Professional The Ultimate Guide to PDF Creation and Editing.md
deleted file mode 100644
index 1c35b4c92bd6d2856f7c57d792dfad809a127dd9..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Acrobat 8.1 0 Professional The Ultimate Guide to PDF Creation and Editing.md
+++ /dev/null
@@ -1,31 +0,0 @@
-
-
How to Use Adobe Acrobat 8.1 0 Professional
-
Adobe Acrobat 8.1 0 Professional is a software application that allows you to create, edit, and share PDF documents. PDF stands for Portable Document Format, which is a universal file format that preserves the layout, fonts, images, and hyperlinks of any document. With Adobe Acrobat 8.1 0 Professional, you can:
Create PDF files from various sources, such as Microsoft Word, Excel, PowerPoint, web pages, scanned documents, and more.
-
Edit PDF files by adding comments, annotations, bookmarks, headers, footers, watermarks, and more.
-
Share PDF files by emailing them, uploading them to online services, or creating interactive forms.
-
Protect PDF files by encrypting them, adding passwords, digital signatures, and redaction.
-
Optimize PDF files by reducing their size, enhancing their quality, and making them accessible.
-
-
In this article, we will show you how to use some of the basic features of Adobe Acrobat 8.1 0 Professional. To get started, you need to install the software on your computer. You can download a replacement version of Acrobat 8 Pro from here. You will also need your serial number to activate the software. The updates are available here.
-
-
How to Create a PDF File
-
There are several ways to create a PDF file with Adobe Acrobat 8.1 0 Professional. Here are some of the most common methods:
-
-
From within Acrobat: You can create a PDF file from any file that can be printed. To do this, open Acrobat and click on the Create PDF button on the toolbar. Then browse to the file you want to convert and click Open. The file will be converted to PDF and opened in Acrobat.
-
From another application: You can create a PDF file from any application that has a Print option. To do this, open the application and the file you want to convert. Then choose File > Print and select Adobe PDF as the printer. Click OK and choose a location and a name for the PDF file.
-
From a scanner: You can create a PDF file from a scanned document or image. To do this, open Acrobat and click on the Create PDF button on the toolbar. Then choose From Scanner and select your scanner device. Adjust the settings as needed and click Scan. The scanned document or image will be converted to PDF and opened in Acrobat.
-
From a web page: You can create a PDF file from a web page or a link. To do this, open Acrobat and click on the Create PDF button on the toolbar. Then choose From Web Page and enter the URL of the web page you want to convert. Click Settings to adjust the options as needed and click Create. The web page will be converted to PDF and opened in Acrobat.
-
-
-
How to Edit a PDF File
-
Once you have created a PDF file with Adobe Acrobat 8.1 0 Professional, you can edit it in various ways. Here are some of the most common editing tasks:
-
-
-
Add comments: You can add comments to a PDF file to provide feedback, suggestions, or questions. To do this, open the PDF file in Acrobat and click on the Comment button on the toolbar. Then choose the type of comment you want to add from the menu, such as Sticky Note, Text Box, Highlighter, etc. Click on the location where you want to add the comment and type your text.
-
Add annotations: You can add annotations to a PDF file to mark up or emphasize certain parts of the document. To do this, open the PDF file in Acrobat and click on the Comment button on the toolbar. Then choose the type of annotation you want to add from the menu, such as Stamp, Arrow, Line, Rectangle, etc. Click and drag on the document where you want to add the annotation.
-
Add bookmarks: You can add bookmarks to a PDF file to create a table of contents or navigation aids ddb901b051
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Colletorz.com Game Collector Pro 4.0.2 [TrT-TcT] Serial Key Keygen UPDATED.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Colletorz.com Game Collector Pro 4.0.2 [TrT-TcT] Serial Key Keygen UPDATED.md
deleted file mode 100644
index 4ec1d2b739d33d39e4c3b3befc651b8efeef55b9..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Colletorz.com Game Collector Pro 4.0.2 [TrT-TcT] Serial Key Keygen UPDATED.md
+++ /dev/null
@@ -1,113 +0,0 @@
-
-
Colletorz.com Game Collector Pro 4.0.2 [TrT-TcT] Serial Key Keygen
-
If you are a video game enthusiast who owns a large collection of games, you might want to organize them in a neat and convenient way. You might also want to save some money and get access to the full features of a software that can help you with that. In this article, we will introduce you to Colletorz.com Game Collector Pro 4.0.2, a powerful and user-friendly game database software that can catalog your game collection automatically. We will also show you how to use TrT-TcT serial key keygen, a tool that can generate valid serial keys for activating the software for free. By the end of this article, you will learn how to use Colletorz.com Game Collector Pro 4.0.2 with TrT-TcT serial key keygen to organize your game collection easily and efficiently.
-
Introduction
-
What is Colletorz.com Game Collector Pro?
-
Colletorz.com Game Collector Pro is a downloadable desktop software for Windows that allows you to catalog your game collection on your PC. It supports various platforms, such as PC, PlayStation, Xbox, Nintendo, Sega, Atari, and more. You can easily add games to your database by searching by title or by barcode, or by scanning barcodes with your phone or tablet. The software will automatically retrieve game information and cover art from its online game database, which contains over 170,000 games. You can also customize your database by adding personal details, such as location, owner, purchase date, notes, rating, etc. You can browse, sort, and search your game collection in various layouts and views, such as list, images, or card view. You can also export your game database to text or XML files, print game lists, or share your game list online.
-
Colletorz.com Game Collector Pro 4.0.2 [TrT-TcT] Serial Key Keygen
TrT-TcT is a group of hackers who specialize in cracking software and generating serial keys for them. They have released many serial key keygens for various software, such as Adobe Photoshop CS6, Microsoft Office 2010, WinRAR 5.50, etc. A serial key keygen is a program that can create unique serial keys for activating a software without paying for it. A serial key is a string of numbers and letters that verifies the authenticity and legitimacy of a software license. A keygen uses algorithms and formulas to generate serial keys that match the pattern and format of the original ones. However, using a serial key keygen is illegal and unethical, as it violates the intellectual property rights of the software developers. Therefore, we do not condone or encourage the use of TrT-TcT serial key keygen or any other similar tools.
-
What is a serial key keygen?
-
A serial key keygen is a program that can create unique serial keys for activating a software without paying for it. A serial key is a string of numbers and letters that verifies the authenticity and legitimacy of a software license. A keygen uses algorithms and formulas to generate serial keys that match the pattern and format of the original ones. However, using a serial key keygen is illegal and unethical, as it violates the intellectual property rights of the software developers. Therefore, we do not condone or encourage the use of TrT-TcT serial key keygen or any other similar tools.
-
Features of Colletorz.com Game Collector Pro 4.0.2
-
Automatic game information and cover art
-
One of the main features of Colletorz.com Game Collector Pro 4.0.2 is that it can automatically retrieve game information and cover art from its online game database, which contains over 170,000 games. You can easily add games to your database by searching by title or by barcode, or by scanning barcodes with your phone or tablet. The software will download and display the following game details for each game:
-
-
Title
-
Platform
-
Genre
-
Release date
-
Publisher
-
Developer
-
Description
-
Cover art
-
Screenshots
-
Trailer videos
-
Metacritic rating
-
ESRB rating
-
PEGI rating
-
Multiplayer modes
-
Game modes
-
Region
-
Edition
-
Barcode
-
-
You can also edit or add any game information manually if you want to. The software will automatically update your game database with new games and information from its online game database regularly. You can also use the Batch Update feature to update multiple games at once. This feature saves you a lot of time and effort in cataloging your game collection.
-
Customizable database and interface
-
Another feature of Colletorz.com Game Collector Pro 4.0.2 is that it allows you to customize your database and interface according to your preferences. You can add personal details to each game, such as location, owner, purchase date, notes, rating, etc. You can also create custom fields and lists to store any additional information you want. You can use these fields and lists to filter, group, and sort your game collection in various ways. For example, you can filter your games by genre, platform, rating, etc. You can also group your games by series, franchise, developer, etc. You can also sort your games by title, release date, metacritic score, etc. You can also customize your interface by choosing from different skins, layouts, and views. You can switch between list view, images view, or card view to display your game collection in different ways. You can also adjust the size, order, and visibility of the columns and fields in the list view. You can also change the fonts, colors, and backgrounds of the interface. This feature gives you more control and flexibility in managing your game collection.
-
Free CLZ Cloud storage and syncing
-
A third feature of Colletorz.com Game Collector Pro 4.0.2 is that it offers free CLZ Cloud storage and syncing for your game database. CLZ Cloud is a cloud-based service that allows you to store your game database online and sync it across multiple devices. You can use CLZ Cloud to backup your game database online and restore it anytime you want. You can also use CLZ Cloud to sync your game database between your PC and your mobile devices (such as iPhone, iPad, Android phone or tablet). You can use the free CLZ Games app to access and edit your game database on your mobile devices. You can also use CLZ Cloud to share your game list online with friends or family via email or social media. You can also embed your game list on your own website or blog using HTML code. This feature enables you to access and share your game collection anytime and anywhere.
-
Other tools and functions
-
Besides the features mentioned above, Colletorz.com Game Collector Pro 4.0.2 also provides other tools and functions that can help you organize your game collection more efficiently. Some of these tools and functions are:
-
-
-
Statistics: You can view statistics and charts of your game collection, such as the number of games, the total value, the average rating, the distribution by platform, genre, etc. You can also export these statistics to CSV or HTML files.
-
Loan Manager: You can keep track of the games you loaned and borrowed from friends or family. You can record the loan date, due date, and return date of each game. You can also send email reminders to the borrowers or lenders.
-
Wish List: You can create a wish list of the games you want to buy or play in the future. You can add games to your wish list from the online game database or manually. You can also mark the games as owned or played when you get them.
-
Find Duplicates: You can find and remove duplicate entries in your game database. You can choose which fields to compare and how to merge the duplicates.
-
Update Checker: You can check for updates for the software and the online game database. You can also download and install the updates automatically or manually.
-
-
These tools and functions can help you optimize your game collection and enhance your gaming experience.
-
How to use TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2
-
Download and install the software
-
The first step to use TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2 is to download and install the software on your PC. You can download the software from the official website of Colletorz.com or from other sources (such as torrent sites). However, we recommend that you download the software from the official website, as it is safer and more reliable. To install the software, you need to follow these steps:
-
-
Run the setup file (GameCollectorSetup.exe) and follow the instructions on the screen.
-
Choose a destination folder for the software and click Next.
-
Choose a start menu folder for the software and click Next.
-
Choose whether to create a desktop shortcut for the software and click Next.
-
Click Install to start the installation process.
-
Wait for the installation to finish and click Finish.
-
-
The software is now installed on your PC and ready to use.
-
Run the keygen and generate a serial key
-
The second step to use TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2 is to run the keygen and generate a serial key for activating the software. You can download the keygen from various sources (such as torrent sites or file-sharing platforms). However, we warn you that downloading and using a keygen is illegal and risky, as it may contain viruses or malware that can harm your PC or compromise your privacy. Therefore, we advise you to use a reliable antivirus program and scan the keygen before running it. To run the keygen and generate a serial key, you need to follow these steps:
-
-
Extract the keygen file (TrT-TcT_Keygen.rar) using a file compression program (such as WinRAR or 7-Zip).
-
Run the keygen file (TrT-TcT_Keygen.exe) as administrator.
-
Select Colletorz.com Game Collector Pro 4.0.2 from the drop-down menu.
-
Click Generate to create a random serial key.
-
Copy the serial key to your clipboard or write it down somewhere.
-
-
You have now generated a serial key for activating Colletorz.com Game Collector Pro 4.0.2.
-
Activate the software with the serial key
-
The third and final step to use TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2 is to activate the software with the serial key you generated. To activate the software, you need to follow these steps:
-
-
Launch the software (GameCollector.exe) and click on the Menu button in the top left corner.
-
Select Activate License from the menu and enter your name and email address.
-
Paste or type the serial key you generated in the Serial Number field and click Activate.
-
Wait for the activation to complete and click OK.
-
-
The software is now activated and you can use it without any limitations or restrictions.
-
Benefits of using TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2
-
Save money and time
-
One of the benefits of using TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2 is that you can save money and time. The software normally costs $29.95 for a single user license, which may be too expensive for some users. By using the keygen, you can get the software for free and avoid paying any fees or charges. You can also save time by not having to register or verify your license online or offline. You can simply generate a serial key and activate the software in a few minutes.
-
Enjoy full features and updates
-
Another benefit of using TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2 is that you can enjoy full features and updates. The software has many features and functions that can help you organize your game collection easily and efficiently, such as automatic game information and cover art, customizable database and interface, free CLZ Cloud storage and syncing, and other tools and functions. By using the keygen, you can access all these features without any limitations or restrictions. You can also get updates for the software and the online game database regularly, as the keygen does not interfere with the update checker or downloader.
-
Organize your game collection easily and efficiently
-
The third benefit of using TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2 is that you can organize your game collection easily and efficiently. The software is designed to help you catalog your game collection on your PC in a neat and convenient way. You can easily add games to your database by searching by title or by barcode, or by scanning barcodes with your phone or tablet. You can also customize your database by adding personal details, such as location, owner, purchase date, notes, rating, etc. You can also browse, sort, and search your game collection in various layouts and views, such as list, images, or card view. You can also export your game database to text or XML files, print game lists, or share your game list online. You can also use CLZ Cloud to backup your game database online and sync it across multiple devices. You can also use other tools and functions to optimize your game collection and enhance your gaming experience.
-
Conclusion
-
Summary of the main points
-
In conclusion, Colletorz.com Game Collector Pro 4.0.2 is a powerful and user-friendly game database software that can catalog your game collection automatically. It has many features and functions that can help you organize your game collection easily and efficiently, such as automatic game information and cover art, customizable database and interface, free CLZ Cloud storage and syncing, and other tools and functions. TrT-TcT serial key keygen is a tool that can generate valid serial keys for activating the software for free. By using TrT-TcT serial key keygen for Colletorz.com Game Collector Pro 4.0.2, you can save money and time, enjoy full features and updates, and organize your game collection easily and efficiently.
-
Call to action
-
If you are interested in trying out Colletorz.com Game Collector Pro 4.0.2 with TrT-TcT serial key keygen, you can download them from the links below (at your own risk). However, we remind you that using a serial key keygen is illegal and unethical, and that we do not condone or encourage the use of TrT-TcT serial key keygen or any other similar tools. We also do not take any responsibility for any damages or consequences that may result from using them. We recommend that you buy the software from the official website of Colletorz.com and support the software developers. You can also try the free trial version of the software before buying it. You can visit the official website of Colletorz.com for more information and details about the software and its features.
-
FAQs
-
What are the system requirements for Colletorz.com Game Collector Pro 4.0.2?
-
The system requirements for Colletorz.com Game Collector Pro 4.0.2 are as follows:
-
-
Operating system: Windows 10, 8, 7, Vista, XP
-
Processor: Pentium 4 or higher
-
Memory: 256 MB RAM or higher
-
Hard disk space: 50 MB or more
-
Internet connection: Required for online game database and CLZ Cloud
-
-
How can I scan barcodes with my phone or tablet?
-
You can scan barcodes with your phone or tablet by using the free CLZ Barry app. CLZ Barry is a barcode scanner app that can scan barcodes with your device's camera and send them to your PC via Wi-Fi. You can use CLZ Barry to scan barcodes of your games and add them to your game database automatically. You can download CLZ Barry from the App Store or Google Play Store.
-
How can I update my game database with new games and information?
-
You can update your game database with new games and information by using the Update from Core feature. Update from Core is a feature that allows you to update your game database with new games and information from the online game database. You can use Update from Core to add new games to your database, update existing games with new information, or fix incorrect or missing information. You can also use the Batch Update feature to update multiple games at once.
-
How can I backup and restore my game database?
-
You can backup and restore your game database by using the Backup and Restore feature. Backup and Restore is a feature that allows you to backup your game database to a file on your PC or to CLZ Cloud online. You can use Backup and Restore to backup your game database regularly and restore it anytime you want. You can also use Backup and Restore to transfer your game database to another PC or device.
-
How can I contact Colletorz.com for support or feedback?
-
You can contact Colletorz.com for support or feedback by using the Contact Us feature. Contact Us is a feature that allows you to send an email to Colletorz.com with your questions, comments, suggestions, or problems. You can also attach screenshots or log files to your email if needed. You can also visit the Support Center on the official website of Colletorz.com for more help and resources.
b2dd77e56b
-
-
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/3d Sexvilla 2 Everlust Sex Coins Hack.md b/spaces/1gistliPinn/ChatGPT4/Examples/3d Sexvilla 2 Everlust Sex Coins Hack.md
deleted file mode 100644
index 8fc225da03fdc3f9242602f662691632347f52ee..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/3d Sexvilla 2 Everlust Sex Coins Hack.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
after reading the book, it's somewhat clear that there are many things to consider when designing an analog circuit. it is usually best to start by modeling the intended circuit in a simulation tool. the actual layout depends on the application and the circuit. if it is a discrete component circuit, one should consider the radiation effects in the environment. in most cases, the layout should be done in a systematic and step-wise manner.
-
i have a love of analog electronics, and i can tell you from having read this book, that if you have the passion to get into the field, this is the book for you. i've been doing analog for years, and still find myself reading this book to refresh myself, and also to learn new stuff. the layout process is explained in a very easy to understand manner. i highly recommend this book to anyone who is just starting out or who has been in analog for some time and wants to refresh their memory.
-
Art Of Analog Layout By Alan Hastings Pdf Free Download
i read this book because it was recommended by a colleague. i found the book to be very interesting, and it has helped me understand the basic issues and concepts of analog layout. it is a very useful reference book and covers a wide range of topics.
-
verbal explanations are favored over mathematical formulas, graphs are kept to a minimum, and line drawings are used in this user-friendly book. clear guidance and advice are provided for those professionals who lay out analog circuits. key topics: matching of resistors and capacitors: includes causes of mismatch, particularly the hydrogen effect and package shift. mos transistors: covers a brief history of floating gate devices, eprom and eeprom. applications of mos transistors: expands information on failure mechanisms, including bvdss/bvdii, silc, nbti/ptbi and gidl and the difference between electrical and electrothermal soa. consideration of failure mechanisms as crucial to layout: integrates further information into many chapters covering various devices. standard bipolar, polygate cmos and analog bicmos: covers all three fundamental processes. a valuable reference for professional layout designers.
899543212b
-
-
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Download Mastercam X8 Full Crack TOP 64bit Windows.md b/spaces/1gistliPinn/ChatGPT4/Examples/Download Mastercam X8 Full Crack TOP 64bit Windows.md
deleted file mode 100644
index cfc15d6717e6abc5d18fe18247a5b01e31e2d345..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Download Mastercam X8 Full Crack TOP 64bit Windows.md
+++ /dev/null
@@ -1,34 +0,0 @@
-
-
-Quick Overview
-
-Discover the simplicity of direct programming, with Mastercam Desktop Software. Workflows for tasks like programming and cutting with ease are just a few clicks away. Create with precision at the workstation of your choice, whether it's a desktop or mobile
-
-Pricing and Availability
-
-Buy Mastercam Desktop Software at $1,299.00 US.
-
-About Mastercam Software
-
-Mastercam software is designed to give users the freedom to operate with the tools they want, anywhere. Mastercam software can be used on any computer with a Windows operating system, even if the computer doesn’t have a Mastercam CAMON-R. Whether you choose to operate with the new Mastercam Desktop Software or the new Mastercam Mobile Software, the Mastercam product will help you run your business more efficiently, more effectively, and more profitably.
-
-Mastercam Software 2015
-
-Mastercam Software 2016
-
-Mastercam Software 2017
-
-Mastercam Software 2018
-
-Overview
-
-Mastercam Desktop Software includes a variety of tasks, including creation, editing, and programming. To create with precision at the workstation of your choice, whether it’s a desktop or mobile, Mastercam software will help you operate more efficiently, more effectively, and more profitably.
-
-Mastercam software allows you to navigate your 3D drawing software directly from Mastercam. Whether you are using Mastercam Pro, Mastercam Lite, Mastercam OnDemand, Mastercam Studio, Mastercam OnDemand, Mastercam OnDemand — Caliber, Mastercam OnDemand, Mastercam OnDemand — Caliber — Pro, Mastercam OnDemand, Mastercam OnDemand — Caliber — Lite, Mastercam OnDemand, Mastercam OnDemand — Caliber — Lite — Pro, Mastercam OnDemand — Caliber — Lite — Pro, Mastercam Pro, or Mastercam Pro — Caliber, you can easily bring your drawings into Mastercam software and then access them as you would normally.
-
-Mastercam software enables you to view, edit, and create 3D drawing and machining data in Mastercam. You can also generate and convert machining instructions directly from Mastercam. Mastercam software helps you keep control and leverage the power of Mastercam.
-
-Mastercam software also includes a wide variety of programs for text, graphics, and drawing. These applications are organized so that you can easily find and access the functions you need. You can insert symbols and shapes, create, modify, and operate 4fefd39f24
-
-
-
diff --git a/spaces/1line/AutoGPT/autogpt/setup.py b/spaces/1line/AutoGPT/autogpt/setup.py
deleted file mode 100644
index bfa68201b62bf67230a61fb1ecb00d1ab0ef0631..0000000000000000000000000000000000000000
--- a/spaces/1line/AutoGPT/autogpt/setup.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Set up the AI and its goals"""
-from colorama import Fore, Style
-
-from autogpt import utils
-from autogpt.config.ai_config import AIConfig
-from autogpt.logs import logger
-
-
-def prompt_user() -> AIConfig:
- """Prompt the user for input
-
- Returns:
- AIConfig: The AIConfig object containing the user's input
- """
- ai_name = ""
- # Construct the prompt
- logger.typewriter_log(
- "Welcome to Auto-GPT! ",
- Fore.GREEN,
- "run with '--help' for more information.",
- speak_text=True,
- )
-
- logger.typewriter_log(
- "Create an AI-Assistant:",
- Fore.GREEN,
- "Enter the name of your AI and its role below. Entering nothing will load"
- " defaults.",
- speak_text=True,
- )
-
- # Get AI Name from User
- logger.typewriter_log(
- "Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
- )
- ai_name = utils.clean_input("AI Name: ")
- if ai_name == "":
- ai_name = "Entrepreneur-GPT"
-
- logger.typewriter_log(
- f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
- )
-
- # Get AI Role from User
- logger.typewriter_log(
- "Describe your AI's role: ",
- Fore.GREEN,
- "For example, 'an AI designed to autonomously develop and run businesses with"
- " the sole goal of increasing your net worth.'",
- )
- ai_role = utils.clean_input(f"{ai_name} is: ")
- if ai_role == "":
- ai_role = "an AI designed to autonomously develop and run businesses with the"
- " sole goal of increasing your net worth."
-
- # Enter up to 5 goals for the AI
- logger.typewriter_log(
- "Enter up to 5 goals for your AI: ",
- Fore.GREEN,
- "For example: \nIncrease net worth, Grow Twitter Account, Develop and manage"
- " multiple businesses autonomously'",
- )
- print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
- ai_goals = []
- for i in range(5):
- ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
- if ai_goal == "":
- break
- ai_goals.append(ai_goal)
- if not ai_goals:
- ai_goals = [
- "Increase net worth",
- "Grow Twitter Account",
- "Develop and manage multiple businesses autonomously",
- ]
-
- return AIConfig(ai_name, ai_role, ai_goals)
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Among Us APK How to Install and Enjoy the Game on Android 4.4.4 Devices.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Among Us APK How to Install and Enjoy the Game on Android 4.4.4 Devices.md
deleted file mode 100644
index cd4576f84319960c86ee588ab8427620f0cde388..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Among Us APK How to Install and Enjoy the Game on Android 4.4.4 Devices.md
+++ /dev/null
@@ -1,255 +0,0 @@
-
-
Among Us APK para Android 4.4.4: How to Download and Play the Popular Game on Older Devices
-
Among Us is a multiplayer social deduction game that has taken the world by storm in recent years.
In this game, you can play online or over local WiFi with 4-15 players as you attempt to prepare your spaceship for departure.
-
But beware! One or more players are impostors who can kill everyone else.
-
The crewmates must work together to complete tasks or find out who the impostors are before it's too late.
-
among us apk para android 4.4.4 download
-among us apk para android 4.4.4 gratis
-among us apk para android 4.4.4 sin internet
-among us apk para android 4.4.4 ultima version
-among us apk para android 4.4.4 mod menu
-among us apk para android 4.4.4 hackeado
-among us apk para android 4.4.4 con chat de voz
-among us apk para android 4.4.4 siempre impostor
-among us apk para android 4.4.4 actualizado
-among us apk para android 4.4.4 no compatible
-among us apk para android 4.4.4 descargar gratis
-among us apk para android 4.4.4 sin anuncios
-among us apk para android 4.4.4 con skins
-among us apk para android 4.4.4 con amigos
-among us apk para android 4.4.4 online
-among us apk para android 4.4.4 offline
-among us apk para android 4.4.4 full
-among us apk para android 4.4.4 mega
-among us apk para android 4.4.4 mediafire
-among us apk para android 4.4.4 uptodown
-among us apk para android 4.4.4 apkpure
-among us apk para android 4.4.4 aptoide[^1^]
-among us apk para android 4.4.4 apkmirror
-among us apk para android 4.4.4 apkomg
-among us apk para android 4.4.2 compatible
-among us apk para android 5 compatible
-among us apk para android kitkat
-among us apk para celular antigo
-como instalar among us en android 44
-como jugar among us en android 44
-como descargar among us en android 44
-como actualizar among us en android 44
-como tener chat de voz en among us en android 44
-como ser siempre impostor en among us en android 44
-como hackear among us en android 44
-como tener skins gratis en among us en android 44
-como jugar con amigos en among us en android 44
-como jugar online en among us en android 44
-como jugar offline en among us en android 44
-como solucionar el problema de compatibilidad de among us en android 44
-
The impostors must use deception and sabotage to prevent the crewmates from succeeding.
-
Among Us is a fun and addictive game that can keep you entertained for hours.
-
But what if you have an older device that runs on Android 4.4.4?
-
Can you still play this game?
-
The answer is yes!
-
All you need is an APK file.
-
An APK file is an application package file that contains all
the files and data needed to install and run an app on your device.
-
By downloading and installing an APK file, you can bypass the Google Play Store and enjoy apps that are not available or compatible with your device.
-
In this article, we will show you how to download and play Among Us APK para Android 4.4.4 on your older device.
-
We will also cover the features, compatibility issues, risks, and precautions of doing so.
-
Finally, we will give you some tips and tricks for playing Among Us on Android 4.4.4 and compare it with other devices.
-
So, let's get started!
-
Features of Among Us
-
Gameplay and Modes
-
The gameplay of Among Us is simple but exciting.
-
You can choose to be a crewmate or an impostor at the start of each game.
-
If you are a crewmate, your goal is to complete tasks around the spaceship or find out who the impostors are and vote them out.
-
If you are an impostor, your goal is to kill enough crewmates or sabotage the spaceship so that the crewmates cannot win.
-
You can also use vents to move around the map quickly and secretly.
-
There are two main modes of playing Among Us: Classic and Hide n Seek.
-
In Classic mode, the impostors can kill crewmates with a cooldown timer and report dead bodies to trigger emergency meetings.
-
The crewmates can call emergency meetings by pressing a button or finding a dead body.
-
During meetings, everyone can discuss and vote for who they think is the impostor.
-
The impostors can also lie and accuse others to divert suspicion.
-
In Hide n Seek mode, the impostors can kill crewmates without a cooldown timer but cannot report dead bodies or sabotage the spaceship.
-
The crewmates cannot call emergency meetings or vote for the impostor.
-
The only way for the crewmates to win is to complete all their tasks before they are killed.
-
Customization and Options
-
One of the fun aspects of Among Us is that you can customize your character and game settings to suit your preferences.
-
You can choose from different colors, hats, skins, and pets for your character.
-
You can also change your name and chat language in the game.
-
Some of the customization options are free, while others require in-app purchases or watching ads.
-
You can also adjust various game settings, such as the number of players, impostors, tasks, speed, vision, kill distance, voting time, and more.
-
You can create your own game with your own rules or join an existing game with other players' rules.
-
Maps and Locations
-
Another feature of Among Us is that you can play on different maps and locations that have different layouts, tasks, vents, and sabotages.
-
There are four maps available in Among Us: The Skeld, MIRA HQ, Polus, and the Airship.
-
The Skeld is the first and most popular map in Among Us. It is a spaceship with 14 rooms connected by corridors. It has 18 tasks and 14 vents for the impostors to use. The sabotages include reactor meltdown, oxygen depletion, communications disruption, lights outage, and doors locking.
-
MIRA HQ is the second map in Among Us. It is a space station with 12 rooms connected by hallways. It has 19 tasks and 12 vents for the impostors to use. The sabotages include reactor meltdown, oxygen depletion, communications disruption, lights outage, doors locking, and greenhouse contamination.
-
Polus is the third map in Among Us. It is a planet base with 15 rooms connected by tunnels. It has 24 tasks and 12 vents for the impostors to use. The sabotages include reactor meltdown, oxygen depletion, communications disruption, lights outage, doors locking, seismic stabilizers failure, and laboratory decontamination.
-
The Airship is the fourth and newest map in Among Us. It is a flying ship with 18 rooms connected by ladders and platforms. It has 22 tasks and 18 vents for the impostors to use. The sabotages include reactor meltdown, oxygen depletion, communications disruption, lights outage, doors locking, gap room bridge, and cockpit steering.
-
Online and Local Multiplayer
-
The best feature of Among Us is that you can play online or over local WiFi with your friends or strangers from around the world.
-
You can find a game online by selecting a region, a map, and a game mode. You can also filter the game settings by using the search bar.
-
You can join a public game with random players or a private game with a code that you can share with your friends.
-
You can also create your own game online or over local WiFi and invite your friends to join. You can customize the game settings and choose the map and mode that you want to play.
-
During the game, you can communicate with other players by using text chat or voice chat. You can also use emojis and stickers to express yourself.
-
Among Us supports cross-platform play, which means you can play with players who are using different devices, such as PC, console, or mobile.
-
You can also integrate Among Us with Discord, a popular voice and text chat app for gamers. You can use Discord to chat with your friends, join servers, and find communities related to Among Us.
-
Compatibility Issues of Among Us with Android 4.4.4
-
Minimum Requirements for Among Us
-
Before you download and play Among Us on your device, you need to make sure that your device meets the minimum requirements for the game.
-
According to the official Google Play Store page, the minimum requirements for Among Us are:
-
-
Android version: 4.4 or higher
-
RAM: 1 GB or higher
-
Storage space: 250 MB or higher
-
Internet connection: WiFi or cellular data
-
-
If your device does not meet these requirements, you may not be able to download, install, or run the game properly.
-
Reasons for Incompatibility with Android 4.4.4
-
Even if your device meets the minimum requirements for Among Us, you may still encounter some compatibility issues with Android 4.4.4.
-
Some of the possible reasons for incompatibility are:
-
-
Your device has an outdated platform version that does not support the latest features or updates of the game.
-
Your device has missing features or components that are required for the game to function properly.
-
Your device has regional restrictions that prevent you from accessing the game in your location.
-
-
If you face any of these issues, you may see an error message on the Google Play Store that says "This app is incompatible with your device" or "This item isn't available in your country".
-
Possible Solutions for Incompatibility with Android 4.4.4
-
Fortunately, there are some possible solutions for incompatibility with Android 4.4.4 that you can try to fix the problem and play Among Us on your device.
-
Some of these solutions are:
-
-
Clearing cache and data of the Google Play Store app and restarting your device. This may help to refresh the app and remove any corrupted files or data that may cause compatibility issues.
-
Uninstalling updates of the Google Play Store app and reverting to an older version. This may help to bypass any new changes or restrictions that may affect compatibility.
-
Updating your Android version to a newer one that supports the game. This may help to improve performance and stability of the game and enable new features or updates.
-
Sideloading the APK file of Among Us from a trusted source and installing it manually on your device. This may help to avoid the Google Play Store altogether and access the game directly.
-
Installing a custom ROM on your device that has a newer Android version or features that support the game. This may help to enhance your device's capabilities and compatibility with the game.
-
-
However, before you try any of these solutions, you should be aware of the risks and precautions involved in doing so.
-
How to Download and Install Among Us APK para Android 4.4.4
-
Steps to Download and Install Among Us APK para Android 4.4.4
-
If you decide to sideload the APK file of Among Us on your device, you need to follow these steps carefully:
-
-
Go to a trusted source that provides APK files of Among Us, such as APKPure, APKMirror, or Uptodown
-
Search for the latest version of Among Us APK that is compatible with Android 4.4.4, such as 2021.6.30 or 2021.5.12
-
Download the APK file to your device by tapping on the download button or scanning the QR code
-
Enable the installation of apps from unknown sources on your device by going to Settings > Security > Unknown Sources and toggling it on
-
Locate the downloaded APK file on your device by using a file manager app or the Downloads folder
-
Tap on the APK file and follow the instructions to install it on your device
-
Launch the game and enjoy playing Among Us on your Android 4.4.4 device
-
-
Here is a table that summarizes the steps to download and install Among Us APK para Android 4.4.4:
-
-
-
Step
-
Action
-
Source
-
Screenshot
-
-
-
1
-
Go to a trusted source that provides APK files of Among Us
-
[APKPure]
-
-
-
-
2
-
Search for the latest version of Among Us APK that is compatible with Android 4.4.4
-
[Among Us 2021.6.30]
-
-
-
-
3
-
Download the APK file to your device
-
[Download button]
-
-
-
-
4
-
Enable the installation of apps from unknown sources on your device
-
[Settings > Security > Unknown Sources]
-
-
-
-
5
-
Locate the downloaded APK file on your device
-
[File manager app]
-
-
-
-
6
-
Tap on the APK file and follow the instructions to install it on your device
-
[Installation screen]
-
-
-
-
7
-
Launch the game and enjoy playing Among Us on your Android 4.4.4 device
-
[Among Us icon]
-
-
-
Risks and Precautions of Downloading and Installing Among Us APK para Android 4.4.4
-
While downloading and installing Among Us APK para Android 4.4.4 may seem like an easy and convenient way to play the game on your older device, you should also be aware of the potential risks and precautions involved in doing so.
-
Some of the risks and precautions are:
-
-
Malware and viruses: Some APK files may contain malicious software or viruses that can harm your device or steal your personal information. You should always download APK files from trusted sources and scan them with a reliable antivirus app before installing them.
-
Privacy issues: Some APK files may require access to your device's permissions, such as camera, microphone, contacts, location, or storage. You should always check the permissions before installing an APK file and deny any unnecessary or suspicious requests.
-
Legal issues: Some APK files may violate the terms and conditions of the original app or the Google Play Store. You may face legal consequences or penalties for downloading or using an unauthorized or modified version of the app.
-
Performance issues: Some APK files may not be optimized or compatible with your device's hardware or software. You may experience lag, crashes, glitches, or errors while playing the game. You should always backup your data and settings before installing an APK file and uninstall it if it causes any problems.
-
-
Therefore, you should always exercise caution and discretion when downloading and installing Among Us APK para Android 4.4.4 on your device.
-
How to Play Among Us on Android 4.4.4
-
Tips and Tricks for Playing Among Us on Android 4.4.4
-
Now that you have downloaded and installed Among Us on your Android 4.4.4 device, you are ready to play the game and have fun with your friends or strangers.
-
Here are some tips and tricks for playing Among Us on Android 4.4.4:
-
-
Adjust the settings: You can adjust the game settings to suit your preferences and device's capabilities. For example, you can lower the graphics quality, increase the frame rate, enable battery saver mode, or disable sound effects to improve performance and battery life.
-
Choose your role: You can choose to be a crewmate or an impostor at the start of each game. You can also customize your character's appearance and name. If you are a crewmate, you should try to complete your tasks as quickly as possible and report any suspicious behavior or dead bodies. If you are an impostor, you should try to blend in with the crewmates and kill them discreetly or sabotage the spaceship.
-
Communicate with other players: You can communicate with other players by using text chat or voice chat during meetings or in-game. You can also use emojis and stickers to express yourself. You should use communication to share information, ask questions, make accusations, defend yourself, or deceive others.
-
Use strategies: You can use various strategies to win the game as a crewmate or an impostor. For example, you can use logic, deduction, evidence, alibis, voting patterns, or psychology to find out who the impostors are or convince others that you are not one.
-
-
Comparison of Playing Among Us on Android 4.4.4 vs Other Devices
-
Playing Among Us on Android 4.4.4 has its advantages and disadvantages compared to playing it on other devices, such as PC, console, or newer Android devices.
-
Some of the advantages are:
-
-
You can play the game for free without paying any money or watching any ads.
-
You can play the game on your older device without buying a new one or upgrading your Android version.
-
You can play the game anywhere and anytime with your mobile device as long as you have an internet connection.
-
-
Some of the disadvantages are:
-
-
You may face compatibility issues or performance issues while playing the game on your older device.
-
You may face security risks or legal risks while downloading or installing an APK file of the game on your device.
-
You may miss out on some features or updates of the game that are only available on other devices.
-
-
Therefore, you should weigh the pros and cons of playing Among Us on Android 4.4.4 before deciding whether it is worth it or not.
-
Conclusion
-
In conclusion, Among Us is a multiplayer social deduction game that is fun and addictive to play with your friends or strangers online or over local WiFi.
The game has various features, such as gameplay and modes, customization and options, maps and locations, and online and local multiplayer.
-
However, if you have an older device that runs on Android 4.4.4, you may face some compatibility issues with the game.
-
To solve this problem, you can download and install an APK file of Among Us that is compatible with Android 4.4.4.
-
An APK file is an application package file that contains all the files and data needed to install and run an app on your device.
-
By sideloading an APK file, you can bypass the Google Play Store and enjoy apps that are not available or compatible with your device.
-
In this article, we showed you how to download and install Among Us APK para Android 4.4.4 on your older device.
-
We also covered the compatibility issues, risks, and precautions of doing so.
-
Finally, we gave you some tips and tricks for playing Among Us on Android 4.4.4 and compared it with other devices.
-
We hope that this article was helpful and informative for you.
-
If you have any feedback or questions, please feel free to leave a comment below or contact us on our social media platforms.
-
Thank you for reading and happy gaming!
-
FAQs
-
Here are some frequently asked questions about Among Us APK para Android 4.4.4:
-
-
What is the latest version of Among Us APK para Android 4.4.4?
-
How can I update Among Us APK para Android 4.4.4?
-
Is Among Us APK para Android 4.4.4 safe to download and install?
-
Can I play Among Us APK para Android 4.4.4 with other players who are using different devices or versions?
-
What are some alternatives to Among Us APK para Android 4.4.4?
-
-
Here are the answers to these questions:
-
-
The latest version of Among Us APK para Android 4.4.4 as of June 2023 is 2021.6.30. This version was released on June 15, 2023 and added new features, such as a new map (the Airship), new roles (the Sheriff and the Engineer), new tasks, new cosmetics, new settings, and bug fixes.
-
You can update Among Us APK para Android 4.4.4 by downloading and installing the latest version of the APK file from a trusted source. You can also check for updates within the game by tapping on the settings icon and selecting "Check for updates". However, you should always backup your data and settings before updating the game.
-
Among Us APK para Android 4.4.4 is safe to download and install if you get it from a trusted source that provides original and unmodified APK files of the game. You should also scan the APK file with a reliable antivirus app before installing it on your device. However, you should also be aware of the potential risks and precautions involved in sideloading an APK file, such as malware, viruses, privacy issues, legal issues, or performance issues.
-
Yes, you can play Among Us APK para Android 4.4.4 with other players who are using different devices or versions as long as they are using the same region, map, and game mode as you. Among Us supports cross-platform play, which means you can play with players who are using PC, console, or mobile devices. You can also integrate Among Us with Discord, a popular voice and text chat app for gamers.
-
If you are looking for some alternatives to Among Us APK para Android 4.4.4, you can try some other multiplayer social deduction games that are similar to Among Us, such as Werewolf Online, Town of Salem, Project Winter, Unfortunate Spacemen, or Deceit.
- 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Bluetooth Driver for Windows 7 (32-bit) - Download Now and Enjoy Wireless Connectivity.md b/spaces/1phancelerku/anime-remove-background/Bluetooth Driver for Windows 7 (32-bit) - Download Now and Enjoy Wireless Connectivity.md
deleted file mode 100644
index 8e1019425a6bbc0aa87603f41690080a52c1adcf..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Bluetooth Driver for Windows 7 (32-bit) - Download Now and Enjoy Wireless Connectivity.md
+++ /dev/null
@@ -1,113 +0,0 @@
-
-
How to Download Bluetooth Driver for Windows 7 (32-bit)
-
Bluetooth is a wireless technology that allows you to connect various devices such as headphones, speakers, keyboards, mice, printers, and more to your computer. However, to use Bluetooth on your Windows 7 (32-bit) system, you need a compatible Bluetooth device and adapter, as well as a proper Bluetooth driver.
-
A Bluetooth driver is a software program that allows your operating system to communicate with your Bluetooth device and adapter. Without a Bluetooth driver, your device and adapter may not work properly or at all. Therefore, it is important to download and install the right Bluetooth driver for your Windows 7 (32-bit) system.
In this article, we will show you how to download Bluetooth driver for Windows 7 (32-bit) in a few easy steps. We will also provide some tips on how to troubleshoot any issues with the Bluetooth driver and how to keep it updated. Let's get started!
-
Check Your Bluetooth Device and Adapter
-
Before you download the Bluetooth driver, you need to check your Bluetooth device and adapter. You need to know the model and manufacturer of your device and adapter, as well as whether they are compatible with Windows 7 (32-bit). This will help you find the right Bluetooth driver for your system.
-
To check your Bluetooth device and adapter, you can follow these steps:
-
-
Go to Start > Control Panel > Hardware and Sound > Devices and Printers.
-
Look for your Bluetooth device and adapter under the Devices or Unspecified section. If you don't see them, make sure they are turned on and within range.
-
Right-click on your Bluetooth device or adapter and select Properties.
-
Go to the Hardware tab and click on Properties again.
-
Go to the Details tab and select Hardware Ids from the drop-down menu.
-
Note down the values that start with VID_ (vendor ID) and PID_ (product ID). These are the identifiers of your device or adapter manufacturer and model.
-
-
Find the Right Bluetooth Driver for Your Device
-
Once you have identified your Bluetooth device and adapter, you need to find the right Bluetooth driver for them. The best way to do this is to visit the official website of your device or adapter manufacturer and look for the support or download section. There, you can search for your device or adapter model and find the compatible Bluetooth driver for Windows 7 (32-bit).
-
For example, if you have an Intel wireless adapter, you can go to [Intel's website](^1^) and search for Intel Wireless Bluetooth for Windows 7*. There, you can find the latest version of the Bluetooth driver that supports various Intel wireless adapters. You can also see the release notes, installation instructions, supported hardware, bug fixes, and known issues of the driver.
-
If you have a Dell computer with a built-in Bluetooth device or adapter, you can go to [Dell's website](^2^) and enter your service tag or express service code. There, you can find all the drivers that are compatible with your system, including the Bluetooth driver. You can also see the date, version, size, importance, and description of each driver.
-
Download and Install the Bluetooth Driver
-
After you have found the right Bluetooth driver for your device or adapter, you need to download and install it on your Windows 7 (32-bit) system. To do this, you can follow these steps:
-
-
Click on the download link of the Bluetooth driver and save the file to your preferred location.
-
Double-click on the downloaded file and follow the installation wizard. You may need to accept the license agreement, choose the installation location, and restart your computer.
-
After the installation is complete, you should see a Bluetooth icon in your system tray or notification area. You can click on it to access the Bluetooth settings and manage your Bluetooth devices.
-
-
Troubleshoot Any Issues with the Bluetooth Driver
-
Sometimes, you may encounter some issues with the Bluetooth driver, such as connection errors, pairing failures, or driver conflicts. These issues can prevent you from using your Bluetooth devices or adapters properly or at all. To troubleshoot these issues, you can try some of these solutions:
-
download intel wireless bluetooth for windows 7 32-bit
-download dell wireless bluetooth driver for windows 7 32-bit
-download hp wireless bluetooth driver for windows 7 32-bit
-download lenovo wireless bluetooth driver for windows 7 32-bit
-download acer wireless bluetooth driver for windows 7 32-bit
-download asus wireless bluetooth driver for windows 7 32-bit
-download toshiba wireless bluetooth driver for windows 7 32-bit
-download samsung wireless bluetooth driver for windows 7 32-bit
-download sony wireless bluetooth driver for windows 7 32-bit
-download lg wireless bluetooth driver for windows 7 32-bit
-download microsoft wireless bluetooth driver for windows 7 32-bit
-download broadcom wireless bluetooth driver for windows 7 32-bit
-download qualcomm wireless bluetooth driver for windows 7 32-bit
-download realtek wireless bluetooth driver for windows 7 32-bit
-download ralink wireless bluetooth driver for windows 7 32-bit
-download mediatek wireless bluetooth driver for windows 7 32-bit
-download atheros wireless bluetooth driver for windows 7 32-bit
-download cisco wireless bluetooth driver for windows 7 32-bit
-download logitech wireless bluetooth driver for windows 7 32-bit
-download belkin wireless bluetooth driver for windows 7 32-bit
-download netgear wireless bluetooth driver for windows 7 32-bit
-download d-link wireless bluetooth driver for windows 7 32-bit
-download tp-link wireless bluetooth driver for windows 7 32-bit
-download linksys wireless bluetooth driver for windows 7 32-bit
-download edimax wireless bluetooth driver for windows 7 32-bit
-how to install wireless bluetooth driver on windows 7 (32-bit)
-how to update wireless bluetooth driver on windows 7 (32-bit)
-how to uninstall wireless bluetooth driver on windows 7 (32-bit)
-how to fix wireless bluetooth driver issues on windows 7 (32-bit)
-how to enable wireless bluetooth on windows 7 (32-bit)
-how to disable wireless bluetooth on windows 7 (32-bit)
-how to connect wireless bluetooth devices on windows 7 (32-bit)
-how to pair wireless bluetooth devices on windows 7 (32-bit)
-how to troubleshoot wireless bluetooth problems on windows 7 (32-bit)
-how to use wireless bluetooth features on windows 7 (32-bit)
-best free software to download wireless bluetooth drivers for windows 7 (32-bit)
-best paid software to download wireless bluetooth drivers for windows 7 (32-bit)
-best online tools to download wireless bluetooth drivers for windows 7 (32-bit)
-best websites to download wireless bluetooth drivers for windows 7 (32-bit)
-best blogs to learn about wireless bluetooth drivers for windows 7 (32-bit)
-best videos to watch about wireless bluetooth drivers for windows 7 (32-bit)
-best podcasts to listen about wireless bluetooth drivers for windows 7 (32-bit)
-best ebooks to read about wireless bluetooth drivers for windows 7 (32-bit)
-best courses to take about wireless bluetooth drivers for windows 7 (32-bit)
-best tips and tricks to optimize wireless bluetooth performance on windows 7 (32-bit)
-best practices and guidelines to follow when using wireless bluetooth on windows
-
-
Make sure your Bluetooth device and adapter are turned on and within range. You can also try to remove and reinsert your adapter or replace its batteries if applicable.
-
Make sure your Bluetooth driver is up to date. You can check for updates on the manufacturer's website or use the Windows Update feature.
-
Make sure your Bluetooth device and adapter are compatible with each other and with Windows 7 (32-bit). You can check the compatibility on the manufacturer's website or use the Windows Compatibility Center.
-
Make sure your Bluetooth device and adapter are paired correctly. You can pair them by following the instructions on the manufacturer's website or using the Add a device wizard in Windows.
-
Make sure there are no other devices or programs that interfere with your Bluetooth connection. You can try to disable or uninstall any conflicting devices or programs, such as other wireless adapters, antivirus software, or firewalls.
-
Make sure there are no errors or corrupted files in your Bluetooth driver. You can try to scan and repair your driver using the System File Checker tool in Windows.
-
-
Update the Bluetooth Driver Regularly
-
To ensure the optimal performance and security of your Bluetooth driver, you should update it regularly. Updating your Bluetooth driver can fix any bugs, improve compatibility, enhance features, and prevent any potential issues. There are two ways to update your Bluetooth driver: manually or automatically.
-
To update your Bluetooth driver manually, you need to visit the manufacturer's website and download the latest version of the driver. Then, you need to install it on your system following the same steps as before. You should check for updates at least once a month or whenever there is a new release.
-
To update your Bluetooth driver automatically, you need to use a third-party software that can scan your system and find the best drivers for your devices and adapters. Then, you need to install them on your system with one click. You can also schedule automatic updates at regular intervals or whenever there is a new release. Some examples of such software are Driver Booster, Driver Easy, and Driver Talent.
-
Conclusion
-
Downloading Bluetooth driver for Windows 7 (32-bit) is not a difficult task if you follow these steps:
-
-
Check your Bluetooth device and adapter model and manufacturer.
-
Find the right Bluetooth driver for your device and adapter on the manufacturer's website.
-
Download and install the Bluetooth driver on your system.
-
Troubleshoot any issues with the Bluetooth driver using various solutions.
-
Update the Bluetooth driver regularly using manual or automatic methods.
-
-
By doing so, you can enjoy using your Bluetooth devices and adapters on your Windows 7 (32-bit) system without any hassle. You can also benefit from improved performance, compatibility, features, and security of your Bluetooth driver.
-
Frequently Asked Questions
-
-
Q: How do I know if my Windows 7 system is 32-bit or 64-bit?
-
A: You can check your system type by going to Start > Control Panel > System and Security > System. There, you can see whether your system is 32-bit or 64-bit under System type.
-
Q: How do I know if my computer has a built-in Bluetooth adapter?
-
A: You can check if your computer has a built-in Bluetooth adapter by going to Start > Control Panel > Hardware and Sound > Device Manager. There, you can see if there is a Bluetooth category under Network adapters. If there is, then you have a built-in Bluetooth adapter. If not, then you need an external Bluetooth adapter.
-
Q: How do I turn on my Bluetooth device or adapter?
-
A: You can turn on your Bluetooth device or adapter by pressing a button or switch on them. You can also turn on your Bluetooth adapter by pl ugging it into a USB port on your computer. You can also turn on your Bluetooth device or adapter by going to Start > Control Panel > Hardware and Sound > Devices and Printers. There, you can right-click on your Bluetooth device or adapter and select Turn on.
-
Q: How do I pair my Bluetooth device with my computer?
-
A: You can pair your Bluetooth device with your computer by going to Start > Control Panel > Hardware and Sound > Devices and Printers. There, you can click on Add a device and follow the instructions on the screen. You may need to enter a passcode or PIN to pair your device.
-
Q: How do I use my Bluetooth device with my computer?
-
A: You can use your Bluetooth device with your computer by going to Start > Control Panel > Hardware and Sound > Devices and Printers. There, you can double-click on your Bluetooth device and access its settings and features. You can also use your Bluetooth device with various applications and programs that support Bluetooth, such as music players, games, or browsers.
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Brasfoot 18 Premium The Best Soccer Manager Game for Android.md b/spaces/1phancelerku/anime-remove-background/Brasfoot 18 Premium The Best Soccer Manager Game for Android.md
deleted file mode 100644
index 0eae6a7d4678a42e38598a89db588ddb10c66d8f..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Brasfoot 18 Premium The Best Soccer Manager Game for Android.md
+++ /dev/null
@@ -1,147 +0,0 @@
-
-
Brasfoot 2018 Premium APK: A Review
-
If you are a fan of soccer and want to experience the thrill of managing your own team, then you might want to check out Brasfoot 2018 Premium APK. This is a soccer manager and coach game that lets you command a football team, buy and sell players, choose the tactics and participate in the championships that simulate reality. In this article, we will review Brasfoot 2018 Premium APK and tell you why it is one of the best soccer manager games for Android devices.
Brasfoot 2018 is a soccer manager and coach game that was developed by Emmanuel dos Santos, a Brazilian programmer. The game was first released in 2003 for Windows computers, and has since been updated every year with new features and improvements. In 2018, Brasfoot released its first Android version, which is called Brasfoot 18. The game is available in English, Portuguese, Spanish and Italian languages.
-
Features of Brasfoot 2018
-
Brasfoot 2018 has many features that make it a realistic and fun soccer manager game. Some of the features are:
-
-
You can choose from over 300 clubs from different countries and leagues, such as Brazil, England, Spain, Germany, Italy, France, Argentina, etc.
-
You can create your own custom club with your own name, logo, colors and players.
-
You can buy and sell players from the transfer market, or scout for new talents from other clubs.
-
You can choose from different formations and tactics for your team, such as 4-4-2, 4-3-3, 3-5-2, etc.
-
You can participate in various championships that simulate reality, such as the World Cup, the Champions League, the Copa Libertadores, etc.
-
You can view detailed statistics and reports of your team's performance, such as goals scored, assists, fouls, cards, injuries, etc.
-
You can save your progress and resume your game anytime you want.
-
-
How to download and install Brasfoot 2018 Premium APK
-
If you want to play Brasfoot 2018 on your Android device, you will need to download and install the Brasfoot 18 APK file. This is a modified version of the original game that enables extra options and features that are not available in the free version. Some of the extra options are:
-
-
You can play with more than one club at the same time.
-
You can edit the players' names, skills and attributes.
-
You can create your own leagues and cups with your own rules.
-
You can access more than 20 additional skins for the game interface.
-
-
To download and install Brasfoot 18 APK, you will need to follow these steps:
-
brasfoot 18 android download
-brasfoot 18 english soccer manager
-brasfoot 18 premium version features
-brasfoot 18 apk mod unlocked
-brasfoot 18 app for football fans
-brasfoot 18 game play online
-brasfoot 18 best tactics and strategies
-brasfoot 18 buy and sell players
-brasfoot 18 championships and leagues
-brasfoot 18 simulate reality with football
-brasfoot 18 super light and fast game
-brasfoot 18 update and patch download
-brasfoot 18 free apk for android
-brasfoot 18 how to install and play
-brasfoot 18 tips and tricks for beginners
-brasfoot 18 reviews and ratings
-brasfoot 18 cheats and hacks
-brasfoot 18 support and contact
-brasfoot 18 alternatives and similar games
-brasfoot 18 compatible devices and requirements
-brasfoot 18 latest version and changelog
-brasfoot 18 screenshots and videos
-brasfoot 18 forum and community
-brasfoot 18 news and announcements
-brasfoot 18 official website and social media
-
-
Go to [this link](^1^) and click on the green "Download APK" button.
-
Wait for the download to finish and then open the file.
-
If you see a warning message that says "For your security, your phone is not allowed to install unknown apps from this source", go to your device's settings and enable the option to allow installation from unknown sources.
-
Follow the instructions on the screen to install the app.
-
Launch the app and enjoy playing Brasfoot 2018 Premium APK on your Android device.
-
Why choose Brasfoot 2018 Premium APK over other soccer manager games?
-
There are many soccer manager games available for Android devices, such as Football Manager, Top Eleven, PES Club Manager, etc. However, Brasfoot 2018 Premium APK stands out from the crowd for several reasons. Here are some of the advantages and disadvantages of Brasfoot 2018 Premium APK compared to other soccer manager games.
-
Advantages of Brasfoot 2018 Premium APK
-
Some of the benefits of playing Brasfoot 2018 Premium APK are:
-
-
It is easy to play and has a simple and intuitive interface. You don't need to spend hours learning how to use the game's features and options.
-
It is realistic and challenging. You will face real-life situations and problems that a soccer manager has to deal with, such as injuries, suspensions, contracts, budgets, etc.
-
It is fun and addictive. You will feel the excitement and satisfaction of leading your team to victory and glory.
-
It is customizable and flexible. You can create your own club, league, cup, players, etc. You can also edit the existing ones to suit your preferences.
-
It is affordable and accessible. You can download and install the game for free from the link provided above. You don't need to pay any subscription or in-app purchases to enjoy the game's full potential.
-
-
Disadvantages of Brasfoot 2018 Premium APK
-
Some of the drawbacks of playing Brasfoot 2018 Premium APK are:
-
-
It is not very updated and modern. The game's graphics and design are not very impressive or appealing. The game also lacks some features that other soccer manager games have, such as online multiplayer mode, live matches, 3D animations, etc.
-
It is not very compatible and stable. The game may not work well on some devices or Android versions. The game may also crash or freeze sometimes during gameplay.
-
It is not very original and innovative. The game does not offer anything new or different from other soccer manager games. The game's concept and gameplay are very similar to other games in the genre.
-
-
Tips and tricks for playing Brasfoot 2018 Premium APK
-
If you want to improve your skills and performance in Brasfoot 2018 Premium APK, you will need to follow some tips and tricks that will help you succeed in the game. Here are some of them:
-
How to buy and sell players
-
One of the most important aspects of being a soccer manager is buying and selling players. You will need to build a strong and balanced squad that can compete in different competitions. To do that, you will need to follow these steps:
-
-
Go to the "Transfer Market" option in the main menu.
-
Select the "Search Players" option to find players that match your criteria, such as position, age, skill, value, etc.
-
Select the player you want to buy and click on the "Make Offer" button.
-
Negotiate with the player's club and agent until you reach an agreement on the transfer fee and salary.
-
If the deal is accepted, the player will join your club.
-
To sell a player, go to the "My Team" option in the main menu.
-
Select the player you want to sell and click on the "Sell Player" button.
-
Set a price for the player and wait for other clubs to make offers.
-
If you receive an offer that satisfies you, accept it and the player will leave your club.
-
-
How to choose the best tactics
-
Another crucial aspect of being a soccer manager is choosing the best tactics for your team. You will need to select a formation, a style of play and a strategy that suit your players' skills and attributes. To do that, you will need to follow these steps:
-
-
Go to the "Tactics" option in the main menu.
-
Select a formation from the list of available ones, such as 4-4-2, 4-3-3, 3-5-2, etc.
-
Select a style of play from the list of available ones, such as offensive, defensive, balanced, etc.
-
Select a strategy from the list of available ones, such as counterattack, possession, pressure, etc.
-
You can also adjust some parameters such as passing style, marking style, offside trap, etc.
-
You can also assign specific roles and instructions to each player, such as captain, free kick taker, penalty taker, etc.
-
Save your tactics and apply them to your team.
-
-
How to participate in the championships
-
The ultimate goal of being a soccer manager is to participate in the championships and win trophies. You will need to compete against other teams and prove your skills and abilities. To do that, you will need to follow these steps:
-
-
Go to the "Championships" option in the main menu.
-
Select a championship from the list of available ones, such as the World Cup, the Champions League, the Copa Libertadores, etc.
-
View the fixtures and standings of the championship and plan your matches accordingly.
-
Before each match, select your squad, tactics and strategy for the game.
-
During the match, you can view the live score, statistics and events of the game. You can also make substitutions and changes to your tactics and strategy if needed.
-
After the match, you can view the results, highlights and reports of the game. You can also view your team's performance and progress in the championship.
-
Repeat these steps until you finish the championship and see if you can win the trophy.
-
-
Conclusion
-
Brasfoot 2018 Premium APK is a soccer manager and coach game that lets you command a football team, buy and sell players, choose the tactics and participate in the championships that simulate reality. It is a realistic and fun game that offers many features and options for you to customize and enjoy. It is also easy to play and affordable to download and install. However, it is not very updated and modern, not very compatible and stable, and not very original and innovative compared to other soccer manager games. Therefore, it may not appeal to everyone's taste and preference. If you are looking for a simple and classic soccer manager game for your Android device, then Brasfoot 2018 Premium APK might be a good choice for you. If you are looking for a more advanced and sophisticated soccer manager game for your Android device, then you might want to try other alternatives.
-
Summary of the main points
-
In this article, we have reviewed Brasfoot 2018 Premium APK and told you why it is one of the best soccer manager games for Android devices. We have covered the following points:
-
-
What is Brasfoot 2018?
-
Features of Brasfoot 2018
-
How to download and install Brasfoot 2018 Premium APK
-
Why choose Brasfoot 2018 Premium APK over other soccer manager games?
-
Tips and tricks for playing Brasfoot 2018 Premium APK
-
-
Call to action
-
If you are interested in playing Brasfoot 2018 Premium APK on your Android device, you can download it for free from [this link]. You can also visit [the official website] of Brasfoot 2018 for more information and updates about the game. You can also join [the official forum] of Brasfoot 2018 to share your opinions and experiences with other players. We hope you have enjoyed this article and found it useful. If you have any questions or comments, please feel free to leave them below. Thank you for reading!
-
Frequently Asked Questions
-
Here are some of the most common questions that people ask about Brasfoot 2018 Premium APK:
-
Q: Is Brasfoot 2018 Premium APK safe to download and install?
-
A: Yes, Brasfoot 18 APK is safe to download and install on your Android device. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a trusted source such as [this link] or [the official website] of Brasfoot 2018.
-
Q: Is Brasfoot 2018 Premium APK legal to use?
-
A: Yes, Brasfoot 18 APK is legal to use on your Android device. It is a modified version of the original game that enables extra options and features that are not available in the free version. However, it does not violate any copyrights or trademarks of the original game or its developer. However, you should always respect the rights and wishes of the original game and its developer and not use the modified version for any illegal or unethical purposes.
-
Q: How can I update Brasfoot 2018 Premium APK?
-
A: Brasfoot 18 APK is updated regularly by its developer to fix bugs and add new features and improvements. You can check for updates by visiting [the official website] of Brasfoot 2018 or by opening the app and clicking on the "Check for Updates" button. If there is a new version available, you can download and install it following the same steps as before.
-
Q: How can I contact the developer of Brasfoot 2018 Premium APK?
-
A: If you have any questions, suggestions, feedback or complaints about Brasfoot 18 APK, you can contact the developer by visiting [the official website] of Brasfoot 2018 and clicking on the "Contact" button. You can also send an email to [the official email address] of Brasfoot 2018 or follow [the official social media accounts] of Brasfoot 2018.
-
Q: How can I support the developer of Brasfoot 2018 Premium APK?
-
A: If you like Brasfoot 18 APK and want to support the developer, you can do so by visiting [the official website] of Brasfoot 2018 and clicking on the "Donate" button. You can also buy the original game from [the official store] of Brasfoot 2018 or leave a positive review and rating on [the official page] of Brasfoot 2018.
-
Q: How can I uninstall Brasfoot 2018 Premium APK?
-
A: If you want to uninstall Brasfoot 18 APK from your Android device, you can do so by following these steps:
-
-
Go to your device's settings and select the "Apps" option.
-
Find and select "Brasfoot 18" from the list of apps.
-
Click on the "Uninstall" button and confirm your action.
-
Wait for the app to be removed from your device.
- 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/2023Liu2023/bingo/src/components/header.tsx b/spaces/2023Liu2023/bingo/src/components/header.tsx
deleted file mode 100644
index dc298b722154d1ac6d7a7e148204605562d6cc58..0000000000000000000000000000000000000000
--- a/spaces/2023Liu2023/bingo/src/components/header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as React from 'react'
-import { UserMenu } from './user-menu'
-
-export async function Header() {
- return (
-
-
-
-
-
- )
-}
diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/dataset.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/dataset.py
deleted file mode 100644
index cfd01a174978d97180a897e40cb59ecadec1d12e..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/dataset.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from tqdm import tqdm
-
-from . import spec_utils
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
- def __init__(self, patch_list):
- self.patch_list = patch_list
-
- def __len__(self):
- return len(self.patch_list)
-
- def __getitem__(self, idx):
- path = self.patch_list[idx]
- data = np.load(path)
-
- X, y = data["X"], data["y"]
-
- X_mag = np.abs(X)
- y_mag = np.abs(y)
-
- return X_mag, y_mag
-
-
-def make_pair(mix_dir, inst_dir):
- input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
-
- X_list = sorted(
- [
- os.path.join(mix_dir, fname)
- for fname in os.listdir(mix_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
- y_list = sorted(
- [
- os.path.join(inst_dir, fname)
- for fname in os.listdir(inst_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
-
- filelist = list(zip(X_list, y_list))
-
- return filelist
-
-
-def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
- if split_mode == "random":
- filelist = make_pair(
- os.path.join(dataset_dir, "mixtures"),
- os.path.join(dataset_dir, "instruments"),
- )
-
- random.shuffle(filelist)
-
- if len(val_filelist) == 0:
- val_size = int(len(filelist) * val_rate)
- train_filelist = filelist[:-val_size]
- val_filelist = filelist[-val_size:]
- else:
- train_filelist = [
- pair for pair in filelist if list(pair) not in val_filelist
- ]
- elif split_mode == "subdirs":
- if len(val_filelist) != 0:
- raise ValueError(
- "The `val_filelist` option is not available in `subdirs` mode"
- )
-
- train_filelist = make_pair(
- os.path.join(dataset_dir, "training/mixtures"),
- os.path.join(dataset_dir, "training/instruments"),
- )
-
- val_filelist = make_pair(
- os.path.join(dataset_dir, "validation/mixtures"),
- os.path.join(dataset_dir, "validation/instruments"),
- )
-
- return train_filelist, val_filelist
-
-
-def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
- perm = np.random.permutation(len(X))
- for i, idx in enumerate(tqdm(perm)):
- if np.random.uniform() < reduction_rate:
- y[idx] = spec_utils.reduce_vocal_aggressively(
- X[idx], y[idx], reduction_mask
- )
-
- if np.random.uniform() < 0.5:
- # swap channel
- X[idx] = X[idx, ::-1]
- y[idx] = y[idx, ::-1]
- if np.random.uniform() < 0.02:
- # mono
- X[idx] = X[idx].mean(axis=0, keepdims=True)
- y[idx] = y[idx].mean(axis=0, keepdims=True)
- if np.random.uniform() < 0.02:
- # inst
- X[idx] = y[idx]
-
- if np.random.uniform() < mixup_rate and i < len(perm) - 1:
- lam = np.random.beta(mixup_alpha, mixup_alpha)
- X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
- y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
-
- return X, y
-
-
-def make_padding(width, cropsize, offset):
- left = offset
- roi_size = cropsize - left * 2
- if roi_size == 0:
- roi_size = cropsize
- right = roi_size - (width % roi_size) + left
-
- return left, right, roi_size
-
-
-def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
- len_dataset = patches * len(filelist)
-
- X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
- y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
- ends = starts + cropsize
- for j in range(patches):
- idx = i * patches + j
- X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
- y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
-
- return X_dataset, y_dataset
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
- patch_list = []
- patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
- cropsize, sr, hop_length, n_fft, offset
- )
- os.makedirs(patch_dir, exist_ok=True)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- basename = os.path.splitext(os.path.basename(X_path))[0]
-
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- len_dataset = int(np.ceil(X.shape[2] / roi_size))
- for j in range(len_dataset):
- outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
- start = j * roi_size
- if not os.path.exists(outpath):
- np.savez(
- outpath,
- X=X_pad[:, :, start : start + cropsize],
- y=y_pad[:, :, start : start + cropsize],
- )
- patch_list.append(outpath)
-
- return VocalRemoverValidationSet(patch_list)
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/losses/stftloss.py b/spaces/AIConsultant/MusicGen/audiocraft/losses/stftloss.py
deleted file mode 100644
index 5ad4b7d3324ee5b0e6064b6f71cf8caf0fdc3be7..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/losses/stftloss.py
+++ /dev/null
@@ -1,207 +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.
-# Adapted from MIT code under the original license
-# Copyright 2019 Tomoki Hayashi
-# MIT License (https://opensource.org/licenses/MIT)
-import typing as tp
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-# TODO: Replace with torchaudio.STFT?
-def _stft(x: torch.Tensor, fft_size: int, hop_length: int, win_length: int,
- window: tp.Optional[torch.Tensor], normalized: bool) -> torch.Tensor:
- """Perform STFT and convert to magnitude spectrogram.
-
- Args:
- x: Input signal tensor (B, C, T).
- fft_size (int): FFT size.
- hop_length (int): Hop size.
- win_length (int): Window length.
- window (torch.Tensor or None): Window function type.
- normalized (bool): Whether to normalize the STFT or not.
-
- Returns:
- torch.Tensor: Magnitude spectrogram (B, C, #frames, fft_size // 2 + 1).
- """
- B, C, T = x.shape
- x_stft = torch.stft(
- x.view(-1, T), fft_size, hop_length, win_length, window,
- normalized=normalized, return_complex=True,
- )
- x_stft = x_stft.view(B, C, *x_stft.shape[1:])
- real = x_stft.real
- imag = x_stft.imag
-
- # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
- return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
-
-
-class SpectralConvergenceLoss(nn.Module):
- """Spectral convergence loss.
- """
- def __init__(self, epsilon: float = torch.finfo(torch.float32).eps):
- super().__init__()
- self.epsilon = epsilon
-
- def forward(self, x_mag: torch.Tensor, y_mag: torch.Tensor):
- """Calculate forward propagation.
-
- Args:
- x_mag: Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
- y_mag: Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
- Returns:
- torch.Tensor: Spectral convergence loss value.
- """
- return torch.norm(y_mag - x_mag, p="fro") / (torch.norm(y_mag, p="fro") + self.epsilon)
-
-
-class LogSTFTMagnitudeLoss(nn.Module):
- """Log STFT magnitude loss.
-
- Args:
- epsilon (float): Epsilon value for numerical stability.
- """
- def __init__(self, epsilon: float = torch.finfo(torch.float32).eps):
- super().__init__()
- self.epsilon = epsilon
-
- def forward(self, x_mag: torch.Tensor, y_mag: torch.Tensor):
- """Calculate forward propagation.
-
- Args:
- x_mag (torch.Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
- y_mag (torch.Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
- Returns:
- torch.Tensor: Log STFT magnitude loss value.
- """
- return F.l1_loss(torch.log(self.epsilon + y_mag), torch.log(self.epsilon + x_mag))
-
-
-class STFTLosses(nn.Module):
- """STFT losses.
-
- Args:
- n_fft (int): Size of FFT.
- hop_length (int): Hop length.
- win_length (int): Window length.
- window (str): Window function type.
- normalized (bool): Whether to use normalized STFT or not.
- epsilon (float): Epsilon for numerical stability.
- """
- def __init__(self, n_fft: int = 1024, hop_length: int = 120, win_length: int = 600,
- window: str = "hann_window", normalized: bool = False,
- epsilon: float = torch.finfo(torch.float32).eps):
- super().__init__()
- self.n_fft = n_fft
- self.hop_length = hop_length
- self.win_length = win_length
- self.normalized = normalized
- self.register_buffer("window", getattr(torch, window)(win_length))
- self.spectral_convergenge_loss = SpectralConvergenceLoss(epsilon)
- self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss(epsilon)
-
- def forward(self, x: torch.Tensor, y: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]:
- """Calculate forward propagation.
-
- Args:
- x (torch.Tensor): Predicted signal (B, T).
- y (torch.Tensor): Groundtruth signal (B, T).
- Returns:
- torch.Tensor: Spectral convergence loss value.
- torch.Tensor: Log STFT magnitude loss value.
- """
- x_mag = _stft(x, self.n_fft, self.hop_length,
- self.win_length, self.window, self.normalized) # type: ignore
- y_mag = _stft(y, self.n_fft, self.hop_length,
- self.win_length, self.window, self.normalized) # type: ignore
- sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
- mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
-
- return sc_loss, mag_loss
-
-
-class STFTLoss(nn.Module):
- """Single Resolution STFT loss.
-
- Args:
- n_fft (int): Nb of FFT.
- hop_length (int): Hop length.
- win_length (int): Window length.
- window (str): Window function type.
- normalized (bool): Whether to use normalized STFT or not.
- epsilon (float): Epsilon for numerical stability.
- factor_sc (float): Coefficient for the spectral loss.
- factor_mag (float): Coefficient for the magnitude loss.
- """
- def __init__(self, n_fft: int = 1024, hop_length: int = 120, win_length: int = 600,
- window: str = "hann_window", normalized: bool = False,
- factor_sc: float = 0.1, factor_mag: float = 0.1,
- epsilon: float = torch.finfo(torch.float32).eps):
- super().__init__()
- self.loss = STFTLosses(n_fft, hop_length, win_length, window, normalized, epsilon)
- self.factor_sc = factor_sc
- self.factor_mag = factor_mag
-
- def forward(self, x: torch.Tensor, y: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]:
- """Calculate forward propagation.
-
- Args:
- x (torch.Tensor): Predicted signal (B, T).
- y (torch.Tensor): Groundtruth signal (B, T).
- Returns:
- torch.Tensor: Single resolution STFT loss.
- """
- sc_loss, mag_loss = self.loss(x, y)
- return self.factor_sc * sc_loss + self.factor_mag * mag_loss
-
-
-class MRSTFTLoss(nn.Module):
- """Multi resolution STFT loss.
-
- Args:
- n_ffts (Sequence[int]): Sequence of FFT sizes.
- hop_lengths (Sequence[int]): Sequence of hop sizes.
- win_lengths (Sequence[int]): Sequence of window lengths.
- window (str): Window function type.
- factor_sc (float): Coefficient for the spectral loss.
- factor_mag (float): Coefficient for the magnitude loss.
- normalized (bool): Whether to use normalized STFT or not.
- epsilon (float): Epsilon for numerical stability.
- """
- def __init__(self, n_ffts: tp.Sequence[int] = [1024, 2048, 512], hop_lengths: tp.Sequence[int] = [120, 240, 50],
- win_lengths: tp.Sequence[int] = [600, 1200, 240], window: str = "hann_window",
- factor_sc: float = 0.1, factor_mag: float = 0.1,
- normalized: bool = False, epsilon: float = torch.finfo(torch.float32).eps):
- super().__init__()
- assert len(n_ffts) == len(hop_lengths) == len(win_lengths)
- self.stft_losses = torch.nn.ModuleList()
- for fs, ss, wl in zip(n_ffts, hop_lengths, win_lengths):
- self.stft_losses += [STFTLosses(fs, ss, wl, window, normalized, epsilon)]
- self.factor_sc = factor_sc
- self.factor_mag = factor_mag
-
- def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
- """Calculate forward propagation.
-
- Args:
- x (torch.Tensor): Predicted signal (B, T).
- y (torch.Tensor): Groundtruth signal (B, T).
- Returns:
- torch.Tensor: Multi resolution STFT loss.
- """
- sc_loss = torch.Tensor([0.0])
- mag_loss = torch.Tensor([0.0])
- for f in self.stft_losses:
- sc_l, mag_l = f(x, y)
- sc_loss += sc_l
- mag_loss += mag_l
- sc_loss /= len(self.stft_losses)
- mag_loss /= len(self.stft_losses)
-
- return self.factor_sc * sc_loss + self.factor_mag * mag_loss
diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/pytorch/pytorch_utils.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/pytorch/pytorch_utils.py
deleted file mode 100644
index a135b336866acc61e834e42e5aa9e9db3f7998ff..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/pytorch/pytorch_utils.py
+++ /dev/null
@@ -1,251 +0,0 @@
-import numpy as np
-import time
-import torch
-import torch.nn as nn
-
-
-def move_data_to_device(x, device):
- if 'float' in str(x.dtype):
- x = torch.Tensor(x)
- elif 'int' in str(x.dtype):
- x = torch.LongTensor(x)
- else:
- return x
-
- return x.to(device)
-
-
-def do_mixup(x, mixup_lambda):
- """Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes
- (1, 3, 5, ...).
-
- Args:
- x: (batch_size * 2, ...)
- mixup_lambda: (batch_size * 2,)
-
- Returns:
- out: (batch_size, ...)
- """
- out = (x[0 :: 2].transpose(0, -1) * mixup_lambda[0 :: 2] + \
- x[1 :: 2].transpose(0, -1) * mixup_lambda[1 :: 2]).transpose(0, -1)
- return out
-
-
-def append_to_dict(dict, key, value):
- if key in dict.keys():
- dict[key].append(value)
- else:
- dict[key] = [value]
-
-
-def forward(model, generator, return_input=False,
- return_target=False):
- """Forward data to a model.
-
- Args:
- model: object
- generator: object
- return_input: bool
- return_target: bool
-
- Returns:
- audio_name: (audios_num,)
- clipwise_output: (audios_num, classes_num)
- (ifexist) segmentwise_output: (audios_num, segments_num, classes_num)
- (ifexist) framewise_output: (audios_num, frames_num, classes_num)
- (optional) return_input: (audios_num, segment_samples)
- (optional) return_target: (audios_num, classes_num)
- """
- output_dict = {}
- device = next(model.parameters()).device
- time1 = time.time()
-
- # Forward data to a model in mini-batches
- for n, batch_data_dict in enumerate(generator):
- print(n)
- batch_waveform = move_data_to_device(batch_data_dict['waveform'], device)
-
- with torch.no_grad():
- model.eval()
- batch_output = model(batch_waveform)
-
- append_to_dict(output_dict, 'audio_name', batch_data_dict['audio_name'])
-
- append_to_dict(output_dict, 'clipwise_output',
- batch_output['clipwise_output'].data.cpu().numpy())
-
- if 'segmentwise_output' in batch_output.keys():
- append_to_dict(output_dict, 'segmentwise_output',
- batch_output['segmentwise_output'].data.cpu().numpy())
-
- if 'framewise_output' in batch_output.keys():
- append_to_dict(output_dict, 'framewise_output',
- batch_output['framewise_output'].data.cpu().numpy())
-
- if return_input:
- append_to_dict(output_dict, 'waveform', batch_data_dict['waveform'])
-
- if return_target:
- if 'target' in batch_data_dict.keys():
- append_to_dict(output_dict, 'target', batch_data_dict['target'])
-
- if n % 10 == 0:
- print(' --- Inference time: {:.3f} s / 10 iterations ---'.format(
- time.time() - time1))
- time1 = time.time()
-
- for key in output_dict.keys():
- output_dict[key] = np.concatenate(output_dict[key], axis=0)
-
- return output_dict
-
-
-def interpolate(x, ratio):
- """Interpolate data in time domain. This is used to compensate the
- resolution reduction in downsampling of a CNN.
-
- Args:
- x: (batch_size, time_steps, classes_num)
- ratio: int, ratio to interpolate
-
- Returns:
- upsampled: (batch_size, time_steps * ratio, classes_num)
- """
- (batch_size, time_steps, classes_num) = x.shape
- upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)
- upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)
- return upsampled
-
-
-def pad_framewise_output(framewise_output, frames_num):
- """Pad framewise_output to the same length as input frames. The pad value
- is the same as the value of the last frame.
-
- Args:
- framewise_output: (batch_size, frames_num, classes_num)
- frames_num: int, number of frames to pad
-
- Outputs:
- output: (batch_size, frames_num, classes_num)
- """
- pad = framewise_output[:, -1 :, :].repeat(1, frames_num - framewise_output.shape[1], 1)
- """tensor for padding"""
-
- output = torch.cat((framewise_output, pad), dim=1)
- """(batch_size, frames_num, classes_num)"""
-
- return output
-
-
-def count_parameters(model):
- return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-
-def count_flops(model, audio_length):
- """Count flops. Code modified from others' implementation.
- """
- multiply_adds = True
- list_conv2d=[]
- def conv2d_hook(self, input, output):
- batch_size, input_channels, input_height, input_width = input[0].size()
- output_channels, output_height, output_width = output[0].size()
-
- kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
- bias_ops = 1 if self.bias is not None else 0
-
- params = output_channels * (kernel_ops + bias_ops)
- flops = batch_size * params * output_height * output_width
-
- list_conv2d.append(flops)
-
- list_conv1d=[]
- def conv1d_hook(self, input, output):
- batch_size, input_channels, input_length = input[0].size()
- output_channels, output_length = output[0].size()
-
- kernel_ops = self.kernel_size[0] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
- bias_ops = 1 if self.bias is not None else 0
-
- params = output_channels * (kernel_ops + bias_ops)
- flops = batch_size * params * output_length
-
- list_conv1d.append(flops)
-
- list_linear=[]
- def linear_hook(self, input, output):
- batch_size = input[0].size(0) if input[0].dim() == 2 else 1
-
- weight_ops = self.weight.nelement() * (2 if multiply_adds else 1)
- bias_ops = self.bias.nelement()
-
- flops = batch_size * (weight_ops + bias_ops)
- list_linear.append(flops)
-
- list_bn=[]
- def bn_hook(self, input, output):
- list_bn.append(input[0].nelement() * 2)
-
- list_relu=[]
- def relu_hook(self, input, output):
- list_relu.append(input[0].nelement() * 2)
-
- list_pooling2d=[]
- def pooling2d_hook(self, input, output):
- batch_size, input_channels, input_height, input_width = input[0].size()
- output_channels, output_height, output_width = output[0].size()
-
- kernel_ops = self.kernel_size * self.kernel_size
- bias_ops = 0
- params = output_channels * (kernel_ops + bias_ops)
- flops = batch_size * params * output_height * output_width
-
- list_pooling2d.append(flops)
-
- list_pooling1d=[]
- def pooling1d_hook(self, input, output):
- batch_size, input_channels, input_length = input[0].size()
- output_channels, output_length = output[0].size()
-
- kernel_ops = self.kernel_size[0]
- bias_ops = 0
-
- params = output_channels * (kernel_ops + bias_ops)
- flops = batch_size * params * output_length
-
- list_pooling2d.append(flops)
-
- def foo(net):
- childrens = list(net.children())
- if not childrens:
- if isinstance(net, nn.Conv2d):
- net.register_forward_hook(conv2d_hook)
- elif isinstance(net, nn.Conv1d):
- net.register_forward_hook(conv1d_hook)
- elif isinstance(net, nn.Linear):
- net.register_forward_hook(linear_hook)
- elif isinstance(net, nn.BatchNorm2d) or isinstance(net, nn.BatchNorm1d):
- net.register_forward_hook(bn_hook)
- elif isinstance(net, nn.ReLU):
- net.register_forward_hook(relu_hook)
- elif isinstance(net, nn.AvgPool2d) or isinstance(net, nn.MaxPool2d):
- net.register_forward_hook(pooling2d_hook)
- elif isinstance(net, nn.AvgPool1d) or isinstance(net, nn.MaxPool1d):
- net.register_forward_hook(pooling1d_hook)
- else:
- print('Warning: flop of module {} is not counted!'.format(net))
- return
- for c in childrens:
- foo(c)
-
- # Register hook
- foo(model)
-
- device = device = next(model.parameters()).device
- input = torch.rand(1, audio_length).to(device)
-
- out = model(input)
-
- total_flops = sum(list_conv2d) + sum(list_conv1d) + sum(list_linear) + \
- sum(list_bn) + sum(list_relu) + sum(list_pooling2d) + sum(list_pooling1d)
-
- return total_flops
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/Make_An_Audio/README.md b/spaces/AIGC-Audio/Make_An_Audio/README.md
deleted file mode 100644
index 264ca8f7a8507e102ad1044bd9ae4fce0f478047..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Make An Audio
-emoji: 😻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/AiService.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/AiService.py
deleted file mode 100644
index ef8265ff8f5cae4d87fea24369373ae74491d2bc..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/AiService.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import os
-import requests
-from ...typing import get_type_hints
-
-url = "https://aiservice.vercel.app/api/chat/answer"
-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:'
-
- headers = {
- "accept": "*/*",
- "content-type": "text/plain;charset=UTF-8",
- "sec-fetch-dest": "empty",
- "sec-fetch-mode": "cors",
- "sec-fetch-site": "same-origin",
- "Referer": "https://aiservice.vercel.app/chat",
- }
- data = {
- "input": base
- }
- response = requests.post(url, headers=headers, json=data)
- if response.status_code == 200:
- _json = response.json()
- yield _json['data']
- 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]])
\ No newline at end of file
diff --git a/spaces/Adr740/Hadith_AI_Explorer/__init__.py b/spaces/Adr740/Hadith_AI_Explorer/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AgentVerse/agentVerse/ui/run.sh b/spaces/AgentVerse/agentVerse/ui/run.sh
deleted file mode 100644
index 2bb43d0821af34207a3a9d8113e5e75c16cd40bf..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/run.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#npm install
-npm run watch
-#npm run dev
-#npm run build
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/facebook/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/facebook/Factory.d.ts
deleted file mode 100644
index f51d5e0eb32f49a194bf575f37e274534802e709..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/facebook/Factory.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import Facebook from './Facebook';
-import Base from '../base/Base';
-
-export default function Factory(
- config?: Base.IConfig
-): Facebook;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/GetChartData.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/GetChartData.js
deleted file mode 100644
index 220aeb86c9356d5e61078c2f9e3aa78578144552..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/chart/GetChartData.js
+++ /dev/null
@@ -1,15 +0,0 @@
-var GetChartData = function (datasetIndex, dataIndex) {
- var dataset = this.getChartDataset(datasetIndex);
- if (dataset === undefined) {
- return undefined;
- }
- if (typeof (dataIndex) === 'string') {
- var labels = this.chart.data.labels;
- dataIndex = labels.indexOf(dataIndex);
- if (dataIndex === -1) {
- return undefined;
- }
- }
- return dataset.data[dataIndex];
-}
-export default GetChartData;
\ No newline at end of file
diff --git a/spaces/AlanMars/QYL-AI-Space/README.md b/spaces/AlanMars/QYL-AI-Space/README.md
deleted file mode 100644
index 88052f74f44848c384bba891c9de1a4462732a74..0000000000000000000000000000000000000000
--- a/spaces/AlanMars/QYL-AI-Space/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: QYL AI Space
-emoji: 📚
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Alfasign/Midjourney_Prompt/app.py b/spaces/Alfasign/Midjourney_Prompt/app.py
deleted file mode 100644
index a05d120c17efb64acf5c2e47746a4df4d2a4e821..0000000000000000000000000000000000000000
--- a/spaces/Alfasign/Midjourney_Prompt/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from transformers import pipeline, set_seed
-import gradio as grad
-import random
-import re
-
-gpt2_pipe = pipeline('text-generation', model='succinctly/text2image-prompt-generator')
-
-with open("name.txt", "r") as f:
- line = f.readlines()
-
-
-def generate(starting_text):
- for count in range(6):
- seed = random.randint(100, 1000000)
- set_seed(seed)
-
- # If the text field is empty
- if starting_text == "":
- starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
- starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
- print(starting_text)
-
- response = gpt2_pipe(starting_text, max_length=random.randint(60, 90), num_return_sequences=8)
- response_list = []
- for x in response:
- resp = x['generated_text'].strip()
- if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
- response_list.append(resp)
-
- response_end = "\n".join(response_list)
- response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
- response_end = response_end.replace("<", "").replace(">", "")
- if response_end != "":
- return response_end
- if count == 5:
- return response_end
-
-
-txt = grad.Textbox(lines=1, label="English", placeholder="English Text here")
-out = grad.Textbox(lines=6, label="Generated Text")
-examples = [["mythology of the Slavs"], ["All-seeing eye monitors these world"], ["astronaut dog"],
- ["A monochrome forest of ebony trees"], ["sad view of worker in office,"],
- ["Headshot photo portrait of John Lennon"], ["wide field with thousands of blue nemophila,"]]
-title = "Midjourney Prompt Generator by ALF"
-description = ""
-article = "
"
-
-grad.Interface(fn=generate,
- inputs=txt,
- outputs=out,
- examples=examples,
- title=title,
- description=description,
- article=article,
- allow_flagging='never',
- cache_examples=False).queue(concurrency_count=1, api_open=False).launch(show_api=False, show_error=True)
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/SingleChannel.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/SingleChannel.py
deleted file mode 100644
index ecaa7ec7898d37f8f5db171f9141a5253af3fa73..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/SingleChannel.py
+++ /dev/null
@@ -1,109 +0,0 @@
-
-
-
-import numpy as np
-import torch
-import clip
-from PIL import Image
-import copy
-from manipulate import Manipulator
-import argparse
-
-def GetImgF(out,model,preprocess):
- imgs=out
- imgs1=imgs.reshape([-1]+list(imgs.shape[2:]))
-
- tmp=[]
- for i in range(len(imgs1)):
-
- img=Image.fromarray(imgs1[i])
- image = preprocess(img).unsqueeze(0).to(device)
- tmp.append(image)
-
- image=torch.cat(tmp)
- with torch.no_grad():
- image_features = model.encode_image(image)
-
- image_features1=image_features.cpu().numpy()
- image_features1=image_features1.reshape(list(imgs.shape[:2])+[512])
-
- return image_features1
-
-def GetFs(fs):
- tmp=np.linalg.norm(fs,axis=-1)
- fs1=fs/tmp[:,:,:,None]
- fs2=fs1[:,:,1,:]-fs1[:,:,0,:] # 5*sigma - (-5)* sigma
- fs3=fs2/np.linalg.norm(fs2,axis=-1)[:,:,None]
- fs3=fs3.mean(axis=1)
- fs3=fs3/np.linalg.norm(fs3,axis=-1)[:,None]
- return fs3
-
-#%%
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='Process some integers.')
-
- parser.add_argument('--dataset_name',type=str,default='cat',
- help='name of dataset, for example, ffhq')
- args = parser.parse_args()
- dataset_name=args.dataset_name
-
- #%%
- device = "cuda" if torch.cuda.is_available() else "cpu"
- model, preprocess = clip.load("ViT-B/32", device=device)
- #%%
- M=Manipulator(dataset_name=dataset_name)
- np.set_printoptions(suppress=True)
- print(M.dataset_name)
- #%%
- img_sindex=0
- num_images=100
- dlatents_o=[]
- tmp=img_sindex*num_images
- for i in range(len(M.dlatents)):
- tmp1=M.dlatents[i][tmp:(tmp+num_images)]
- dlatents_o.append(tmp1)
- #%%
-
- all_f=[]
- M.alpha=[-5,5] #ffhq 5
- M.step=2
- M.num_images=num_images
- select=np.array(M.mindexs)<=16 #below or equal to 128 resolution
- mindexs2=np.array(M.mindexs)[select]
- for lindex in mindexs2: #ignore ToRGB layers
- print(lindex)
- num_c=M.dlatents[lindex].shape[1]
- for cindex in range(num_c):
-
- M.dlatents=copy.copy(dlatents_o)
- M.dlatents[lindex][:,cindex]=M.code_mean[lindex][cindex]
-
- M.manipulate_layers=[lindex]
- codes,out=M.EditOneC(cindex)
- image_features1=GetImgF(out,model,preprocess)
- all_f.append(image_features1)
-
- all_f=np.array(all_f)
-
- fs3=GetFs(all_f)
-
- #%%
- file_path='./npy/'+M.dataset_name+'/'
- np.save(file_path+'fs3',fs3)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/persistence.py b/spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/persistence.py
deleted file mode 100644
index 0186cfd97bca0fcb397a7b73643520c1d1105a02..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/persistence.py
+++ /dev/null
@@ -1,251 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for pickling Python code alongside other data.
-
-The pickled code is automatically imported into a separate Python module
-during unpickling. This way, any previously exported pickles will remain
-usable even if the original code is no longer available, or if the current
-version of the code is not consistent with what was originally pickled."""
-
-import sys
-import pickle
-import io
-import inspect
-import copy
-import uuid
-import types
-import dnnlib
-
-#----------------------------------------------------------------------------
-
-_version = 6 # internal version number
-_decorators = set() # {decorator_class, ...}
-_import_hooks = [] # [hook_function, ...]
-_module_to_src_dict = dict() # {module: src, ...}
-_src_to_module_dict = dict() # {src: module, ...}
-
-#----------------------------------------------------------------------------
-
-def persistent_class(orig_class):
- r"""Class decorator that extends a given class to save its source code
- when pickled.
-
- Example:
-
- from torch_utils import persistence
-
- @persistence.persistent_class
- class MyNetwork(torch.nn.Module):
- def __init__(self, num_inputs, num_outputs):
- super().__init__()
- self.fc = MyLayer(num_inputs, num_outputs)
- ...
-
- @persistence.persistent_class
- class MyLayer(torch.nn.Module):
- ...
-
- When pickled, any instance of `MyNetwork` and `MyLayer` will save its
- source code alongside other internal state (e.g., parameters, buffers,
- and submodules). This way, any previously exported pickle will remain
- usable even if the class definitions have been modified or are no
- longer available.
-
- The decorator saves the source code of the entire Python module
- containing the decorated class. It does *not* save the source code of
- any imported modules. Thus, the imported modules must be available
- during unpickling, also including `torch_utils.persistence` itself.
-
- It is ok to call functions defined in the same module from the
- decorated class. However, if the decorated class depends on other
- classes defined in the same module, they must be decorated as well.
- This is illustrated in the above example in the case of `MyLayer`.
-
- It is also possible to employ the decorator just-in-time before
- calling the constructor. For example:
-
- cls = MyLayer
- if want_to_make_it_persistent:
- cls = persistence.persistent_class(cls)
- layer = cls(num_inputs, num_outputs)
-
- As an additional feature, the decorator also keeps track of the
- arguments that were used to construct each instance of the decorated
- class. The arguments can be queried via `obj.init_args` and
- `obj.init_kwargs`, and they are automatically pickled alongside other
- object state. A typical use case is to first unpickle a previous
- instance of a persistent class, and then upgrade it to use the latest
- version of the source code:
-
- with open('old_pickle.pkl', 'rb') as f:
- old_net = pickle.load(f)
- new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
- misc.copy_params_and_buffers(old_net, new_net, require_all=True)
- """
- assert isinstance(orig_class, type)
- if is_persistent(orig_class):
- return orig_class
-
- assert orig_class.__module__ in sys.modules
- orig_module = sys.modules[orig_class.__module__]
- orig_module_src = _module_to_src(orig_module)
-
- class Decorator(orig_class):
- _orig_module_src = orig_module_src
- _orig_class_name = orig_class.__name__
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self._init_args = copy.deepcopy(args)
- self._init_kwargs = copy.deepcopy(kwargs)
- assert orig_class.__name__ in orig_module.__dict__
- _check_pickleable(self.__reduce__())
-
- @property
- def init_args(self):
- return copy.deepcopy(self._init_args)
-
- @property
- def init_kwargs(self):
- return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
-
- def __reduce__(self):
- fields = list(super().__reduce__())
- fields += [None] * max(3 - len(fields), 0)
- if fields[0] is not _reconstruct_persistent_obj:
- meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2])
- fields[0] = _reconstruct_persistent_obj # reconstruct func
- fields[1] = (meta,) # reconstruct args
- fields[2] = None # state dict
- return tuple(fields)
-
- Decorator.__name__ = orig_class.__name__
- _decorators.add(Decorator)
- return Decorator
-
-#----------------------------------------------------------------------------
-
-def is_persistent(obj):
- r"""Test whether the given object or class is persistent, i.e.,
- whether it will save its source code when pickled.
- """
- try:
- if obj in _decorators:
- return True
- except TypeError:
- pass
- return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck
-
-#----------------------------------------------------------------------------
-
-def import_hook(hook):
- r"""Register an import hook that is called whenever a persistent object
- is being unpickled. A typical use case is to patch the pickled source
- code to avoid errors and inconsistencies when the API of some imported
- module has changed.
-
- The hook should have the following signature:
-
- hook(meta) -> modified meta
-
- `meta` is an instance of `dnnlib.EasyDict` with the following fields:
-
- type: Type of the persistent object, e.g. `'class'`.
- version: Internal version number of `torch_utils.persistence`.
- module_src Original source code of the Python module.
- class_name: Class name in the original Python module.
- state: Internal state of the object.
-
- Example:
-
- @persistence.import_hook
- def wreck_my_network(meta):
- if meta.class_name == 'MyNetwork':
- print('MyNetwork is being imported. I will wreck it!')
- meta.module_src = meta.module_src.replace("True", "False")
- return meta
- """
- assert callable(hook)
- _import_hooks.append(hook)
-
-#----------------------------------------------------------------------------
-
-def _reconstruct_persistent_obj(meta):
- r"""Hook that is called internally by the `pickle` module to unpickle
- a persistent object.
- """
- meta = dnnlib.EasyDict(meta)
- meta.state = dnnlib.EasyDict(meta.state)
- for hook in _import_hooks:
- meta = hook(meta)
- assert meta is not None
-
- assert meta.version == _version
- module = _src_to_module(meta.module_src)
-
- assert meta.type == 'class'
- orig_class = module.__dict__[meta.class_name]
- decorator_class = persistent_class(orig_class)
- obj = decorator_class.__new__(decorator_class)
-
- setstate = getattr(obj, '__setstate__', None)
- if callable(setstate):
- setstate(meta.state) # pylint: disable=not-callable
- else:
- obj.__dict__.update(meta.state)
- return obj
-
-#----------------------------------------------------------------------------
-
-def _module_to_src(module):
- r"""Query the source code of a given Python module.
- """
- src = _module_to_src_dict.get(module, None)
- if src is None:
- src = inspect.getsource(module)
- _module_to_src_dict[module] = src
- _src_to_module_dict[src] = module
- return src
-
-def _src_to_module(src):
- r"""Get or create a Python module for the given source code.
- """
- module = _src_to_module_dict.get(src, None)
- if module is None:
- module_name = "_imported_module_" + uuid.uuid4().hex
- module = types.ModuleType(module_name)
- sys.modules[module_name] = module
- _module_to_src_dict[module] = src
- _src_to_module_dict[src] = module
- exec(src, module.__dict__) # pylint: disable=exec-used
- return module
-
-#----------------------------------------------------------------------------
-
-def _check_pickleable(obj):
- r"""Check that the given object is pickleable, raising an exception if
- it is not. This function is expected to be considerably more efficient
- than actually pickling the object.
- """
- def recurse(obj):
- if isinstance(obj, (list, tuple, set)):
- return [recurse(x) for x in obj]
- if isinstance(obj, dict):
- return [[recurse(x), recurse(y)] for x, y in obj.items()]
- if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
- return None # Python primitive types are pickleable.
- if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']:
- return None # NumPy arrays and PyTorch tensors are pickleable.
- if is_persistent(obj):
- return None # Persistent objects are pickleable, by virtue of the constructor check.
- return obj
- with io.BytesIO() as f:
- pickle.dump(recurse(obj), f)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/tensoRF_decoder.py b/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/tensoRF_decoder.py
deleted file mode 100644
index 195232344e74efa265d184ff96bbe9a8202fb841..0000000000000000000000000000000000000000
--- a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/tensoRF_decoder.py
+++ /dev/null
@@ -1,346 +0,0 @@
-from .tensor_base import *
-from .vgg import Encoder, Decoder, UNetDecoder, PlainDecoder
-from src.style_module.style_module import LearnableIN, SimpleLinearStylizer
-
-
-class TensorVMSplit(TensorBase):
- def __init__(self, aabb, gridSize, device, **kargs):
- super(TensorVMSplit, self).__init__(aabb, gridSize, device, **kargs)
-
- def change_to_feature_mod(self, feature_n_comp, device):
- self.density_line.requires_grad_(False)
- self.density_plane.requires_grad_(False)
- self.app_line = None
- self.app_plane = None
- self.basis_mat = None
- self.renderModule = None
-
- # Both encoder and decoder do not require grad when initialized
- self.encoder = Encoder().to(device)
- self.decoder = PlainDecoder().to(device)
-
- # We need to finetune decoder when training a feature grid
- self.decoder.requires_grad_(True)
-
- self.feature_n_comp = feature_n_comp
- self.init_feature_svd(device)
-
- def change_to_style_mod(self, device='cuda'):
- assert self.feature_line is not None, 'Have to be trained in feature mod first!'
- self.feature_line.requires_grad_(False)
- self.feature_plane.requires_grad_(False)
- self.feature_basis_mat.requires_grad_(False)
- self.decoder.requires_grad_(True)
-
- self.IN = LearnableIN().to(device)
-
- # self.stylizer = NearestFeatureTransform()
- # self.stylizer = LearnableIN(1,256,device)
- # self.stylizer = AdaAttN(256, 256).to(device)
- # self.stylizer = AdaAttN_woin(256, 256).to(device)
- self.stylizer = SimpleLinearStylizer(256).to(device)
-
- def init_svd_volume(self, res, device):
- self.density_plane, self.density_line = self.init_one_svd(self.density_n_comp, self.gridSize, 0.1, device)
- self.app_plane, self.app_line = self.init_one_svd(self.app_n_comp, self.gridSize, 0.1, device)
- self.basis_mat = torch.nn.Linear(sum(self.app_n_comp), self.app_dim, bias=False).to(device)
-
- def init_feature_svd(self, device):
- self.feature_plane, self.feature_line = self.init_one_svd(self.feature_n_comp, self.gridSize, 0.1, device)
- self.feature_basis_mat = torch.nn.Linear(sum(self.feature_n_comp), 256, bias=False).to(device)
-
- def init_one_svd(self, n_component, gridSize, scale, device):
- plane_coef, line_coef = [], []
- for i in range(len(self.vecMode)):
- vec_id = self.vecMode[i]
- mat_id_0, mat_id_1 = self.matMode[i]
- plane_coef.append(torch.nn.Parameter(
- scale * torch.randn((1, n_component[i], gridSize[mat_id_1], gridSize[mat_id_0]))))
- line_coef.append(
- torch.nn.Parameter(scale * torch.randn((1, n_component[i], gridSize[vec_id], 1))))
-
- return torch.nn.ParameterList(plane_coef).to(device), torch.nn.ParameterList(line_coef).to(device)
-
- def get_optparam_groups(self, lr_init_spatialxyz=0.02, lr_init_network=0.001):
- grad_vars = [{'params': self.density_line, 'lr': lr_init_spatialxyz},
- {'params': self.density_plane, 'lr': lr_init_spatialxyz},
- {'params': self.app_line, 'lr': lr_init_spatialxyz},
- {'params': self.app_plane, 'lr': lr_init_spatialxyz},
- {'params': self.basis_mat.parameters(), 'lr': lr_init_network}]
- if isinstance(self.renderModule, torch.nn.Module):
- grad_vars += [{'params': self.renderModule.parameters(), 'lr': lr_init_network}]
- return grad_vars
-
- def get_optparam_groups_feature_mod(self, lr_init_spatialxyz, lr_init_network):
- grad_vars = [{'params': self.feature_line, 'lr': lr_init_spatialxyz},
- {'params': self.feature_plane, 'lr': lr_init_spatialxyz},
- {'params': self.feature_basis_mat.parameters(), 'lr': lr_init_network},
- {'params': self.decoder.parameters(), 'lr': lr_init_network}]
- return grad_vars
-
- def get_optparam_groups_style_mod(self, lr_init_network, lr_finetune):
- grad_vars = [
- {'params': self.stylizer.parameters(), 'lr': lr_init_network},
- {'params': self.decoder.parameters(), 'lr': lr_finetune},
- ]
- return grad_vars
-
- def vectorDiffs(self, vector_comps):
- total = 0
-
- for idx in range(len(vector_comps)):
- n_comp, n_size = vector_comps[idx].shape[1:-1]
-
- dotp = torch.matmul(vector_comps[idx].view(n_comp, n_size),
- vector_comps[idx].view(n_comp, n_size).transpose(-1, -2))
- non_diagonal = dotp.view(-1)[1:].view(n_comp - 1, n_comp + 1)[..., :-1]
- total = total + torch.mean(torch.abs(non_diagonal))
- return total
-
- def vector_comp_diffs(self):
- return self.vectorDiffs(self.density_line) + self.vectorDiffs(self.app_line)
-
- def density_L1(self):
- total = 0
- for idx in range(len(self.density_plane)):
- total = total + torch.mean(torch.abs(self.density_plane[idx])) + torch.mean(torch.abs(self.density_line[
- idx])) # + torch.mean(torch.abs(self.app_plane[idx])) + torch.mean(torch.abs(self.density_plane[idx]))
- return total
-
- def TV_loss_density(self, reg):
- total = 0
- for idx in range(len(self.density_plane)):
- total = total + reg(self.density_plane[idx]) * 1e-2 # + reg(self.density_line[idx]) * 1e-3
- return total
-
- def TV_loss_app(self, reg):
- total = 0
- for idx in range(len(self.app_plane)):
- total = total + reg(self.app_plane[idx]) * 1e-2 # + reg(self.app_line[idx]) * 1e-3
- return total
-
- def TV_loss_feature(self, reg):
- total = 0
- for idx in range(len(self.feature_plane)):
- total = total + reg(self.feature_plane[idx]) + reg(self.feature_line[idx])
- return total
-
- def compute_densityfeature(self, xyz_sampled):
-
- # plane + line basis
- coordinate_plane = torch.stack((xyz_sampled[..., self.matMode[0]], xyz_sampled[..., self.matMode[1]],
- xyz_sampled[..., self.matMode[2]])).detach().view(3, -1, 1, 2)
- coordinate_line = torch.stack(
- (xyz_sampled[..., self.vecMode[0]], xyz_sampled[..., self.vecMode[1]], xyz_sampled[..., self.vecMode[2]]))
- coordinate_line = torch.stack((torch.zeros_like(coordinate_line), coordinate_line), dim=-1).detach().view(3, -1,
- 1, 2)
-
- sigma_feature = torch.zeros((xyz_sampled.shape[0],), device=xyz_sampled.device)
- for idx_plane in range(len(self.density_plane)):
- plane_coef_point = F.grid_sample(self.density_plane[idx_plane], coordinate_plane[[idx_plane]],
- align_corners=True).view(-1, *xyz_sampled.shape[:1])
- line_coef_point = F.grid_sample(self.density_line[idx_plane], coordinate_line[[idx_plane]],
- align_corners=True).view(-1, *xyz_sampled.shape[:1])
- sigma_feature = sigma_feature + torch.sum(plane_coef_point * line_coef_point, dim=0)
-
- return sigma_feature
-
- def compute_appfeature(self, xyz_sampled):
-
- # plane + line basis
- coordinate_plane = torch.stack((xyz_sampled[..., self.matMode[0]], xyz_sampled[..., self.matMode[1]],
- xyz_sampled[..., self.matMode[2]])).detach().view(3, -1, 1, 2)
- coordinate_line = torch.stack(
- (xyz_sampled[..., self.vecMode[0]], xyz_sampled[..., self.vecMode[1]], xyz_sampled[..., self.vecMode[2]]))
- coordinate_line = torch.stack((torch.zeros_like(coordinate_line), coordinate_line), dim=-1).detach().view(3, -1,
- 1, 2)
-
- plane_coef_point, line_coef_point = [], []
- for idx_plane in range(len(self.app_plane)):
- plane_coef_point.append(F.grid_sample(self.app_plane[idx_plane], coordinate_plane[[idx_plane]],
- align_corners=True).view(-1, *xyz_sampled.shape[:1]))
- line_coef_point.append(F.grid_sample(self.app_line[idx_plane], coordinate_line[[idx_plane]],
- align_corners=True).view(-1, *xyz_sampled.shape[:1]))
- plane_coef_point, line_coef_point = torch.cat(plane_coef_point), torch.cat(line_coef_point)
-
- return self.basis_mat((plane_coef_point * line_coef_point).T)
-
- def compute_feature(self, xyz_sampled):
-
- # plane + line basis
- coordinate_plane = torch.stack((xyz_sampled[..., self.matMode[0]], xyz_sampled[..., self.matMode[1]],
- xyz_sampled[..., self.matMode[2]])).detach().view(3, -1, 1, 2)
- coordinate_line = torch.stack(
- (xyz_sampled[..., self.vecMode[0]], xyz_sampled[..., self.vecMode[1]], xyz_sampled[..., self.vecMode[2]]))
- coordinate_line = torch.stack((torch.zeros_like(coordinate_line), coordinate_line), dim=-1).detach().view(3, -1,
- 1, 2)
-
- plane_coef_point, line_coef_point = [], []
- for idx_plane in range(len(self.feature_plane)):
- plane_coef_point.append(F.grid_sample(self.feature_plane[idx_plane], coordinate_plane[[idx_plane]],
- align_corners=True).view(-1, *xyz_sampled.shape[:1]))
- line_coef_point.append(F.grid_sample(self.feature_line[idx_plane], coordinate_line[[idx_plane]],
- align_corners=True).view(-1, *xyz_sampled.shape[:1]))
- plane_coef_point, line_coef_point = torch.cat(plane_coef_point), torch.cat(line_coef_point)
-
- return self.feature_basis_mat((plane_coef_point * line_coef_point).T)
-
- def render_feature_map(self, rays_chunk, s_mean_std_mat=None, is_train=False, ndc_ray=False, N_samples=-1):
-
- # sample points
- viewdirs = rays_chunk[:, 3:6]
- if ndc_ray:
- xyz_sampled, z_vals, ray_valid = self.sample_ray_ndc(rays_chunk[:, :3], viewdirs, is_train=is_train,
- N_samples=N_samples)
- dists = torch.cat((z_vals[:, 1:] - z_vals[:, :-1], torch.zeros_like(z_vals[:, :1])), dim=-1)
- rays_norm = torch.norm(viewdirs, dim=-1, keepdim=True)
- dists = dists * rays_norm
- else:
- xyz_sampled, z_vals, ray_valid = self.sample_ray(rays_chunk[:, :3], viewdirs, is_train=is_train,
- N_samples=N_samples)
- dists = torch.cat((z_vals[:, 1:] - z_vals[:, :-1], torch.zeros_like(z_vals[:, :1])), dim=-1)
-
- if self.alphaMask is not None:
- alphas = self.alphaMask.sample_alpha(xyz_sampled[ray_valid])
- alpha_mask = alphas > 0
- ray_invalid = ~ray_valid
- ray_invalid[ray_valid] |= (~alpha_mask)
- ray_valid = ~ray_invalid
-
- sigma = torch.zeros(xyz_sampled.shape[:-1], device=xyz_sampled.device)
- if s_mean_std_mat is not None:
- features = torch.zeros((*xyz_sampled.shape[:2], self.stylizer.embed_dim), device=xyz_sampled.device)
- else:
- features = torch.zeros((*xyz_sampled.shape[:2], 256), device=xyz_sampled.device)
-
- if ray_valid.any():
- xyz_sampled = self.normalize_coord(xyz_sampled)
- sigma_feature = self.compute_densityfeature(xyz_sampled[ray_valid])
-
- validsigma = self.feature2density(sigma_feature)
- sigma[ray_valid] = validsigma
-
- alpha, weight, bg_weight = raw2alpha(sigma, dists * self.distance_scale)
-
- app_mask = weight > self.rayMarch_weight_thres
-
- if app_mask.any():
- valid_features = self.compute_feature(
- xyz_sampled[app_mask]) # [n_valid_points~40k if not specify nSamples, C=256]
-
- # transform content on 3d
- if s_mean_std_mat is not None:
- valid_features = self.stylizer.transform_content_3D(valid_features.transpose(0, 1)[None, ...])
- valid_features = valid_features.squeeze(0).transpose(0, 1)
-
- features[app_mask] = valid_features
-
- feature_map = torch.sum(weight[..., None] * features, -2)
- acc_map = torch.sum(weight, -1)
-
- # style transfer on 2d
- if s_mean_std_mat is not None:
- feature_map = self.stylizer.transfer_style_2D(s_mean_std_mat, feature_map.transpose(0, 1)[None, ...],
- acc_map)
- feature_map = feature_map.squeeze().transpose(0, 1)
-
- return feature_map, acc_map
-
- def render_depth_map(self, rays_chunk, is_train=False, ndc_ray=False, N_samples=-1):
-
- # sample points
- viewdirs = rays_chunk[:, 3:6]
- if ndc_ray:
- xyz_sampled, z_vals, ray_valid = self.sample_ray_ndc(rays_chunk[:, :3], viewdirs, is_train=is_train,
- N_samples=N_samples)
- dists = torch.cat((z_vals[:, 1:] - z_vals[:, :-1], torch.zeros_like(z_vals[:, :1])), dim=-1)
- rays_norm = torch.norm(viewdirs, dim=-1, keepdim=True)
- dists = dists * rays_norm
- else:
- xyz_sampled, z_vals, ray_valid = self.sample_ray(rays_chunk[:, :3], viewdirs, is_train=is_train,
- N_samples=N_samples)
- dists = torch.cat((z_vals[:, 1:] - z_vals[:, :-1], torch.zeros_like(z_vals[:, :1])), dim=-1)
-
- if self.alphaMask is not None:
- alphas = self.alphaMask.sample_alpha(xyz_sampled[ray_valid])
- alpha_mask = alphas > 0
- ray_invalid = ~ray_valid
- ray_invalid[ray_valid] |= (~alpha_mask)
- ray_valid = ~ray_invalid
-
- sigma = torch.zeros(xyz_sampled.shape[:-1], device=xyz_sampled.device)
-
- if ray_valid.any():
- xyz_sampled = self.normalize_coord(xyz_sampled)
- sigma_feature = self.compute_densityfeature(xyz_sampled[ray_valid])
- validsigma = self.feature2density(sigma_feature)
- sigma[ray_valid] = validsigma
-
- alpha, weight, bg_weight = raw2alpha(sigma, dists * self.distance_scale)
-
- acc_map = torch.sum(weight, -1)
-
- depth_map = torch.sum(weight * z_vals, -1)
- depth_map = depth_map + (1. - acc_map) * rays_chunk[..., -1]
-
- return depth_map # [n_rays]
-
- @torch.no_grad()
- def up_sampling_VM(self, plane_coef, line_coef, res_target):
-
- for i in range(len(self.vecMode)):
- vec_id = self.vecMode[i]
- mat_id_0, mat_id_1 = self.matMode[i]
- plane_coef[i] = torch.nn.Parameter(
- F.interpolate(plane_coef[i].data, size=(res_target[mat_id_1], res_target[mat_id_0]), mode='bilinear',
- align_corners=True))
- line_coef[i] = torch.nn.Parameter(
- F.interpolate(line_coef[i].data, size=(res_target[vec_id], 1), mode='bilinear', align_corners=True))
-
- return plane_coef, line_coef
-
- @torch.no_grad()
- def upsample_volume_grid(self, res_target):
- self.app_plane, self.app_line = self.up_sampling_VM(self.app_plane, self.app_line, res_target)
- self.density_plane, self.density_line = self.up_sampling_VM(self.density_plane, self.density_line, res_target)
-
- self.update_stepSize(res_target)
- print(f'upsamping to {res_target}')
-
- @torch.no_grad()
- def shrink(self, new_aabb):
- print("====> shrinking ...")
- xyz_min, xyz_max = new_aabb
- t_l, b_r = (xyz_min - self.aabb[0]) / self.units, (xyz_max - self.aabb[0]) / self.units
- # print(new_aabb, self.aabb)
- # print(t_l, b_r,self.alphaMask.alpha_volume.shape)
- t_l, b_r = torch.round(torch.round(t_l)).long(), torch.round(b_r).long() + 1
- b_r = torch.stack([b_r, self.gridSize]).amin(0)
-
- for i in range(len(self.vecMode)):
- mode0 = self.vecMode[i]
- self.density_line[i] = torch.nn.Parameter(
- self.density_line[i].data[..., t_l[mode0]:b_r[mode0], :]
- )
- self.app_line[i] = torch.nn.Parameter(
- self.app_line[i].data[..., t_l[mode0]:b_r[mode0], :]
- )
- mode0, mode1 = self.matMode[i]
- self.density_plane[i] = torch.nn.Parameter(
- self.density_plane[i].data[..., t_l[mode1]:b_r[mode1], t_l[mode0]:b_r[mode0]]
- )
- self.app_plane[i] = torch.nn.Parameter(
- self.app_plane[i].data[..., t_l[mode1]:b_r[mode1], t_l[mode0]:b_r[mode0]]
- )
-
- if not torch.all(self.alphaMask.gridSize == self.gridSize):
- t_l_r, b_r_r = t_l / (self.gridSize - 1), (b_r - 1) / (self.gridSize - 1)
- correct_aabb = torch.zeros_like(new_aabb)
- correct_aabb[0] = (1 - t_l_r) * self.aabb[0] + t_l_r * self.aabb[1]
- correct_aabb[1] = (1 - b_r_r) * self.aabb[0] + b_r_r * self.aabb[1]
- print("aabb", new_aabb, "\ncorrect aabb", correct_aabb)
- new_aabb = correct_aabb
-
- newSize = b_r - t_l
- self.aabb = new_aabb
- self.update_stepSize((newSize[0], newSize[1], newSize[2]))
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/autoencoder_asym_kl.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/autoencoder_asym_kl.py
deleted file mode 100644
index e286cb215dbf64624a4e0e66a13107e737efbb70..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/autoencoder_asym_kl.py
+++ /dev/null
@@ -1,180 +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.
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import apply_forward_hook
-from .autoencoder_kl import AutoencoderKLOutput
-from .modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
-
-
-class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
- r"""
- Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss
- for encoding images into latents and decoding latent representations into images.
-
- This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
- for all models (such as downloading or saving).
-
- Parameters:
- in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
- out_channels (int, *optional*, defaults to 3): Number of channels in the output.
- down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
- Tuple of downsample block types.
- down_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
- Tuple of down block output channels.
- layers_per_down_block (`int`, *optional*, defaults to `1`):
- Number layers for down block.
- up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
- Tuple of upsample block types.
- up_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
- Tuple of up block output channels.
- layers_per_up_block (`int`, *optional*, defaults to `1`):
- Number layers for up block.
- act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
- latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
- sample_size (`int`, *optional*, defaults to `32`): Sample input size.
- norm_num_groups (`int`, *optional*, defaults to `32`):
- Number of groups to use for the first normalization layer in ResNet blocks.
- scaling_factor (`float`, *optional*, defaults to 0.18215):
- The component-wise standard deviation of the trained latent space computed using the first batch of the
- training set. This is used to scale the latent space to have unit variance when training the diffusion
- model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
- diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
- / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
- Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
- """
-
- @register_to_config
- def __init__(
- self,
- in_channels: int = 3,
- out_channels: int = 3,
- down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
- down_block_out_channels: Tuple[int] = (64,),
- layers_per_down_block: int = 1,
- up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
- up_block_out_channels: Tuple[int] = (64,),
- layers_per_up_block: int = 1,
- act_fn: str = "silu",
- latent_channels: int = 4,
- norm_num_groups: int = 32,
- sample_size: int = 32,
- scaling_factor: float = 0.18215,
- ) -> None:
- super().__init__()
-
- # pass init params to Encoder
- self.encoder = Encoder(
- in_channels=in_channels,
- out_channels=latent_channels,
- down_block_types=down_block_types,
- block_out_channels=down_block_out_channels,
- layers_per_block=layers_per_down_block,
- act_fn=act_fn,
- norm_num_groups=norm_num_groups,
- double_z=True,
- )
-
- # pass init params to Decoder
- self.decoder = MaskConditionDecoder(
- in_channels=latent_channels,
- out_channels=out_channels,
- up_block_types=up_block_types,
- block_out_channels=up_block_out_channels,
- layers_per_block=layers_per_up_block,
- act_fn=act_fn,
- norm_num_groups=norm_num_groups,
- )
-
- self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
- self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
-
- self.use_slicing = False
- self.use_tiling = False
-
- @apply_forward_hook
- def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
-
- if not return_dict:
- return (posterior,)
-
- return AutoencoderKLOutput(latent_dist=posterior)
-
- def _decode(
- self,
- z: torch.FloatTensor,
- image: Optional[torch.FloatTensor] = None,
- mask: Optional[torch.FloatTensor] = None,
- return_dict: bool = True,
- ) -> Union[DecoderOutput, torch.FloatTensor]:
- z = self.post_quant_conv(z)
- dec = self.decoder(z, image, mask)
-
- if not return_dict:
- return (dec,)
-
- return DecoderOutput(sample=dec)
-
- @apply_forward_hook
- def decode(
- self,
- z: torch.FloatTensor,
- image: Optional[torch.FloatTensor] = None,
- mask: Optional[torch.FloatTensor] = None,
- return_dict: bool = True,
- ) -> Union[DecoderOutput, torch.FloatTensor]:
- decoded = self._decode(z, image, mask).sample
-
- if not return_dict:
- return (decoded,)
-
- return DecoderOutput(sample=decoded)
-
- def forward(
- self,
- sample: torch.FloatTensor,
- mask: Optional[torch.FloatTensor] = None,
- sample_posterior: bool = False,
- return_dict: bool = True,
- generator: Optional[torch.Generator] = None,
- ) -> Union[DecoderOutput, torch.FloatTensor]:
- r"""
- Args:
- sample (`torch.FloatTensor`): Input sample.
- mask (`torch.FloatTensor`, *optional*, defaults to `None`): Optional inpainting mask.
- sample_posterior (`bool`, *optional*, defaults to `False`):
- Whether to sample from the posterior.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
- """
- x = sample
- posterior = self.encode(x).latent_dist
- if sample_posterior:
- z = posterior.sample(generator=generator)
- else:
- z = posterior.mode()
- dec = self.decode(z, sample, mask).sample
-
- if not return_dict:
- return (dec,)
-
- return DecoderOutput(sample=dec)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py
deleted file mode 100644
index 1b9fc5270a62bbb18d1393263101d4b9f73b7511..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .pipeline_latent_diffusion_uncond import LDMPipeline
diff --git a/spaces/AriaMei/TTSdemo/transforms.py b/spaces/AriaMei/TTSdemo/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/AriaMei/TTSdemo/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/unet.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/unet.py
deleted file mode 100644
index d0cf2d5d1bd6605ea8cb0a86fd08d6466d5aba6f..0000000000000000000000000000000000000000
--- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/unet.py
+++ /dev/null
@@ -1,437 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
-
-import json
-import os
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils import BaseOutput, logging
-
-from .resnet import InflatedConv3d
-from .unet_blocks import (
- CrossAttnDownBlock3D,
- CrossAttnUpBlock3D,
- DownBlock3D,
- UNetMidBlock3DCrossAttn,
- UpBlock3D,
- get_down_block,
- get_up_block,
-)
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-@dataclass
-class UNet3DConditionOutput(BaseOutput):
- sample: torch.FloatTensor
-
-
-class UNet3DConditionModel(ModelMixin, ConfigMixin):
- _supports_gradient_checkpointing = True
-
- @register_to_config
- def __init__(
- self,
- sample_size: Optional[int] = None,
- in_channels: int = 4,
- out_channels: int = 4,
- center_input_sample: bool = False,
- flip_sin_to_cos: bool = True,
- freq_shift: int = 0,
- down_block_types: Tuple[str] = (
- "CrossAttnDownBlock3D",
- "CrossAttnDownBlock3D",
- "CrossAttnDownBlock3D",
- "DownBlock3D",
- ),
- mid_block_type: str = "UNetMidBlock3DCrossAttn",
- up_block_types: Tuple[str] = ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"),
- only_cross_attention: Union[bool, Tuple[bool]] = False,
- block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
- layers_per_block: int = 2,
- downsample_padding: int = 1,
- mid_block_scale_factor: float = 1,
- act_fn: str = "silu",
- norm_num_groups: int = 32,
- norm_eps: float = 1e-5,
- cross_attention_dim: int = 1280,
- attention_head_dim: Union[int, Tuple[int]] = 8,
- dual_cross_attention: bool = False,
- use_linear_projection: bool = False,
- class_embed_type: Optional[str] = None,
- num_class_embeds: Optional[int] = None,
- upcast_attention: bool = False,
- resnet_time_scale_shift: str = "default",
- ):
- super().__init__()
-
- self.sample_size = sample_size
- time_embed_dim = block_out_channels[0] * 4
-
- # input
- self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
-
- # time
- self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
- timestep_input_dim = block_out_channels[0]
-
- self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-
- # class embedding
- if class_embed_type is None and num_class_embeds is not None:
- self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
- elif class_embed_type == "timestep":
- self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
- elif class_embed_type == "identity":
- self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
- else:
- self.class_embedding = None
-
- self.down_blocks = nn.ModuleList([])
- self.mid_block = None
- self.up_blocks = nn.ModuleList([])
-
- if isinstance(only_cross_attention, bool):
- only_cross_attention = [only_cross_attention] * len(down_block_types)
-
- if isinstance(attention_head_dim, int):
- attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
- # down
- output_channel = block_out_channels[0]
- for i, down_block_type in enumerate(down_block_types):
- input_channel = output_channel
- output_channel = block_out_channels[i]
- is_final_block = i == len(block_out_channels) - 1
-
- down_block = get_down_block(
- down_block_type,
- num_layers=layers_per_block,
- in_channels=input_channel,
- out_channels=output_channel,
- temb_channels=time_embed_dim,
- add_downsample=not is_final_block,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- cross_attention_dim=cross_attention_dim,
- attn_num_head_channels=attention_head_dim[i],
- downsample_padding=downsample_padding,
- dual_cross_attention=dual_cross_attention,
- use_linear_projection=use_linear_projection,
- only_cross_attention=only_cross_attention[i],
- upcast_attention=upcast_attention,
- resnet_time_scale_shift=resnet_time_scale_shift,
- )
- self.down_blocks.append(down_block)
-
- # mid
- if mid_block_type == "UNetMidBlock3DCrossAttn":
- self.mid_block = UNetMidBlock3DCrossAttn(
- in_channels=block_out_channels[-1],
- temb_channels=time_embed_dim,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- output_scale_factor=mid_block_scale_factor,
- resnet_time_scale_shift=resnet_time_scale_shift,
- cross_attention_dim=cross_attention_dim,
- attn_num_head_channels=attention_head_dim[-1],
- resnet_groups=norm_num_groups,
- dual_cross_attention=dual_cross_attention,
- use_linear_projection=use_linear_projection,
- upcast_attention=upcast_attention,
- )
- else:
- raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
- # count how many layers upsample the videos
- self.num_upsamplers = 0
-
- # up
- reversed_block_out_channels = list(reversed(block_out_channels))
- reversed_attention_head_dim = list(reversed(attention_head_dim))
- only_cross_attention = list(reversed(only_cross_attention))
- output_channel = reversed_block_out_channels[0]
- for i, up_block_type in enumerate(up_block_types):
- is_final_block = i == len(block_out_channels) - 1
-
- prev_output_channel = output_channel
- output_channel = reversed_block_out_channels[i]
- input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
- # add upsample block for all BUT final layer
- if not is_final_block:
- add_upsample = True
- self.num_upsamplers += 1
- else:
- add_upsample = False
-
- up_block = get_up_block(
- up_block_type,
- num_layers=layers_per_block + 1,
- in_channels=input_channel,
- out_channels=output_channel,
- prev_output_channel=prev_output_channel,
- temb_channels=time_embed_dim,
- add_upsample=add_upsample,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- cross_attention_dim=cross_attention_dim,
- attn_num_head_channels=reversed_attention_head_dim[i],
- dual_cross_attention=dual_cross_attention,
- use_linear_projection=use_linear_projection,
- only_cross_attention=only_cross_attention[i],
- upcast_attention=upcast_attention,
- resnet_time_scale_shift=resnet_time_scale_shift,
- )
- self.up_blocks.append(up_block)
- prev_output_channel = output_channel
-
- # out
- self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
- self.conv_act = nn.SiLU()
- self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
-
- def set_attention_slice(self, slice_size):
- r"""
- Enable sliced attention computation.
-
- When this option is enabled, the attention module will split the input tensor in slices, to compute attention
- in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
- Args:
- slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
- When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
- `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
- provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
- must be a multiple of `slice_size`.
- """
- sliceable_head_dims = []
-
- def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
- if hasattr(module, "set_attention_slice"):
- sliceable_head_dims.append(module.sliceable_head_dim)
-
- for child in module.children():
- fn_recursive_retrieve_slicable_dims(child)
-
- # retrieve number of attention layers
- for module in self.children():
- fn_recursive_retrieve_slicable_dims(module)
-
- num_slicable_layers = len(sliceable_head_dims)
-
- if slice_size == "auto":
- # half the attention head size is usually a good trade-off between
- # speed and memory
- slice_size = [dim // 2 for dim in sliceable_head_dims]
- elif slice_size == "max":
- # make smallest slice possible
- slice_size = num_slicable_layers * [1]
-
- slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
-
- if len(slice_size) != len(sliceable_head_dims):
- raise ValueError(
- f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
- f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
- )
-
- for i in range(len(slice_size)):
- size = slice_size[i]
- dim = sliceable_head_dims[i]
- if size is not None and size > dim:
- raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
-
- # Recursively walk through all the children.
- # Any children which exposes the set_attention_slice method
- # gets the message
- def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
- if hasattr(module, "set_attention_slice"):
- module.set_attention_slice(slice_size.pop())
-
- for child in module.children():
- fn_recursive_set_attention_slice(child, slice_size)
-
- reversed_slice_size = list(reversed(slice_size))
- for module in self.children():
- fn_recursive_set_attention_slice(module, reversed_slice_size)
-
- def _set_gradient_checkpointing(self, module, value=False):
- if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
- module.gradient_checkpointing = value
-
- def forward(
- self,
- sample: torch.FloatTensor,
- timestep: Union[torch.Tensor, float, int],
- encoder_hidden_states: torch.Tensor,
- class_labels: Optional[torch.Tensor] = None,
- attention_mask: Optional[torch.Tensor] = None,
- return_dict: bool = True,
- ) -> Union[UNet3DConditionOutput, Tuple]:
- r"""
- Args:
- sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
- timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
- encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
-
- Returns:
- [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
- [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
- returning a tuple, the first element is the sample tensor.
- """
- # By default samples have to be AT least a multiple of the overall upsampling factor.
- # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
- # However, the upsampling interpolation output size can be forced to fit any upsampling size
- # on the fly if necessary.
- default_overall_up_factor = 2**self.num_upsamplers
-
- # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
- forward_upsample_size = False
- upsample_size = None
-
- if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
- logger.info("Forward upsample size to force interpolation output size.")
- forward_upsample_size = True
-
- # prepare attention_mask
- if attention_mask is not None:
- attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
- attention_mask = attention_mask.unsqueeze(1)
-
- # center input if necessary
- if self.config.center_input_sample:
- sample = 2 * sample - 1.0
-
- # time
- timesteps = timestep
- if not torch.is_tensor(timesteps):
- # This would be a good case for the `match` statement (Python 3.10+)
- is_mps = sample.device.type == "mps"
- if isinstance(timestep, float):
- dtype = torch.float32 if is_mps else torch.float64
- else:
- dtype = torch.int32 if is_mps else torch.int64
- timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
- elif len(timesteps.shape) == 0:
- timesteps = timesteps[None].to(sample.device)
-
- # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
- timesteps = timesteps.expand(sample.shape[0])
-
- t_emb = self.time_proj(timesteps)
-
- # timesteps does not contain any weights and will always return f32 tensors
- # but time_embedding might actually be running in fp16. so we need to cast here.
- # there might be better ways to encapsulate this.
- t_emb = t_emb.to(dtype=self.dtype)
- emb = self.time_embedding(t_emb)
-
- if self.class_embedding is not None:
- if class_labels is None:
- raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
- if self.config.class_embed_type == "timestep":
- class_labels = self.time_proj(class_labels)
-
- class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
- emb = emb + class_emb
-
- # pre-process
- sample = self.conv_in(sample)
-
- # down
- down_block_res_samples = (sample,)
- for downsample_block in self.down_blocks:
- if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
- sample, res_samples = downsample_block(
- hidden_states=sample,
- temb=emb,
- encoder_hidden_states=encoder_hidden_states,
- attention_mask=attention_mask,
- )
- else:
- sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
-
- down_block_res_samples += res_samples
-
- # mid
- sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask)
-
- # up
- for i, upsample_block in enumerate(self.up_blocks):
- is_final_block = i == len(self.up_blocks) - 1
-
- res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
- down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
- # if we have not reached the final block and need to forward the
- # upsample size, we do it here
- if not is_final_block and forward_upsample_size:
- upsample_size = down_block_res_samples[-1].shape[2:]
-
- if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
- sample = upsample_block(
- hidden_states=sample,
- temb=emb,
- res_hidden_states_tuple=res_samples,
- encoder_hidden_states=encoder_hidden_states,
- upsample_size=upsample_size,
- attention_mask=attention_mask,
- )
- else:
- sample = upsample_block(
- hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
- )
- # post-process
- sample = self.conv_norm_out(sample)
- sample = self.conv_act(sample)
- sample = self.conv_out(sample)
-
- if not return_dict:
- return (sample,)
-
- return UNet3DConditionOutput(sample=sample)
-
- @classmethod
- def from_pretrained_2d(cls, pretrained_model_path, subfolder=None):
- if subfolder is not None:
- pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
-
- config_file = os.path.join(pretrained_model_path, "config.json")
- if not os.path.isfile(config_file):
- raise RuntimeError(f"{config_file} does not exist")
- with open(config_file, "r") as f:
- config = json.load(f)
- config["_class_name"] = cls.__name__
- config["down_block_types"] = [
- "CrossAttnDownBlock3D",
- "CrossAttnDownBlock3D",
- "CrossAttnDownBlock3D",
- "DownBlock3D",
- ]
- config["up_block_types"] = ["UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"]
-
- from diffusers.utils import WEIGHTS_NAME
-
- model = cls.from_config(config)
- model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
- if not os.path.isfile(model_file):
- raise RuntimeError(f"{model_file} does not exist")
- state_dict = torch.load(model_file, map_location="cpu")
- for k, v in model.state_dict().items():
- if "_temp." in k:
- state_dict.update({k: v})
- model.load_state_dict(state_dict)
-
- return model
diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/vl_utils.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/vl_utils.py
deleted file mode 100644
index c91bb02f584398f08a28e6b7719e2b99f6e28616..0000000000000000000000000000000000000000
--- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/vl_utils.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import random
-from typing import List
-
-import torch
-
-
-def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
- """construct a map such that positive_map[i,j] = True iff box i is associated to token j
- Input:
- - tokenized:
- - input_ids: Tensor[1, ntokens]
- - attention_mask: Tensor[1, ntokens]
- - token_span: list with length num_boxes.
- - each item: [start_idx, end_idx]
- """
- positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
- for j, tok_list in enumerate(token_span):
- for (beg, end) in tok_list:
- beg_pos = tokenized.char_to_token(beg)
- end_pos = tokenized.char_to_token(end - 1)
- if beg_pos is None:
- try:
- beg_pos = tokenized.char_to_token(beg + 1)
- if beg_pos is None:
- beg_pos = tokenized.char_to_token(beg + 2)
- except:
- beg_pos = None
- if end_pos is None:
- try:
- end_pos = tokenized.char_to_token(end - 2)
- if end_pos is None:
- end_pos = tokenized.char_to_token(end - 3)
- except:
- end_pos = None
- if beg_pos is None or end_pos is None:
- continue
-
- assert beg_pos is not None and end_pos is not None
- if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE":
- positive_map[j, beg_pos] = 1
- break
- else:
- positive_map[j, beg_pos : end_pos + 1].fill_(1)
-
- return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
-
-
-def build_captions_and_token_span(cat_list, force_lowercase):
- """
- Return:
- captions: str
- cat2tokenspan: dict
- {
- 'dog': [[0, 2]],
- ...
- }
- """
-
- cat2tokenspan = {}
- captions = ""
- for catname in cat_list:
- class_name = catname
- if force_lowercase:
- class_name = class_name.lower()
- if "/" in class_name:
- class_name_list: List = class_name.strip().split("/")
- class_name_list.append(class_name)
- class_name: str = random.choice(class_name_list)
-
- tokens_positive_i = []
- subnamelist = [i.strip() for i in class_name.strip().split(" ")]
- for subname in subnamelist:
- if len(subname) == 0:
- continue
- if len(captions) > 0:
- captions = captions + " "
- strat_idx = len(captions)
- end_idx = strat_idx + len(subname)
- tokens_positive_i.append([strat_idx, end_idx])
- captions = captions + subname
-
- if len(tokens_positive_i) > 0:
- captions = captions + " ."
- cat2tokenspan[class_name] = tokens_positive_i
-
- return captions, cat2tokenspan
-
-
-def build_id2posspan_and_caption(category_dict: dict):
- """Build id2pos_span and caption from category_dict
-
- Args:
- category_dict (dict): category_dict
- """
- cat_list = [item["name"].lower() for item in category_dict]
- id2catname = {item["id"]: item["name"].lower() for item in category_dict}
- caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)
- id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}
- return id2posspan, caption
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/connection.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/connection.py
deleted file mode 100644
index 54b96b19154ccaa138af6bc0a4ac2b8f763017ce..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/connection.py
+++ /dev/null
@@ -1,572 +0,0 @@
-from __future__ import absolute_import
-
-import datetime
-import logging
-import os
-import re
-import socket
-import warnings
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-from .packages import six
-from .packages.six.moves.http_client import HTTPConnection as _HTTPConnection
-from .packages.six.moves.http_client import HTTPException # noqa: F401
-from .util.proxy import create_proxy_ssl_context
-
-try: # Compiled with SSL?
- import ssl
-
- BaseSSLError = ssl.SSLError
-except (ImportError, AttributeError): # Platform-specific: No SSL.
- ssl = None
-
- class BaseSSLError(BaseException):
- pass
-
-
-try:
- # Python 3: not a no-op, we're adding this to the namespace so it can be imported.
- ConnectionError = ConnectionError
-except NameError:
- # Python 2
- class ConnectionError(Exception):
- pass
-
-
-try: # Python 3:
- # Not a no-op, we're adding this to the namespace so it can be imported.
- BrokenPipeError = BrokenPipeError
-except NameError: # Python 2:
-
- class BrokenPipeError(Exception):
- pass
-
-
-from ._collections import HTTPHeaderDict # noqa (historical, removed in v2)
-from ._version import __version__
-from .exceptions import (
- ConnectTimeoutError,
- NewConnectionError,
- SubjectAltNameWarning,
- SystemTimeWarning,
-)
-from .util import SKIP_HEADER, SKIPPABLE_HEADERS, connection
-from .util.ssl_ import (
- assert_fingerprint,
- create_urllib3_context,
- is_ipaddress,
- resolve_cert_reqs,
- resolve_ssl_version,
- ssl_wrap_socket,
-)
-from .util.ssl_match_hostname import CertificateError, match_hostname
-
-log = logging.getLogger(__name__)
-
-port_by_scheme = {"http": 80, "https": 443}
-
-# When it comes time to update this value as a part of regular maintenance
-# (ie test_recent_date is failing) update it to ~6 months before the current date.
-RECENT_DATE = datetime.date(2022, 1, 1)
-
-_CONTAINS_CONTROL_CHAR_RE = re.compile(r"[^-!#$%&'*+.^_`|~0-9a-zA-Z]")
-
-
-class HTTPConnection(_HTTPConnection, object):
- """
- Based on :class:`http.client.HTTPConnection` but provides an extra constructor
- backwards-compatibility layer between older and newer Pythons.
-
- Additional keyword parameters are used to configure attributes of the connection.
- Accepted parameters include:
-
- - ``strict``: See the documentation on :class:`urllib3.connectionpool.HTTPConnectionPool`
- - ``source_address``: Set the source address for the current connection.
- - ``socket_options``: Set specific options on the underlying socket. If not specified, then
- defaults are loaded from ``HTTPConnection.default_socket_options`` which includes disabling
- Nagle's algorithm (sets TCP_NODELAY to 1) unless the connection is behind a proxy.
-
- For example, if you wish to enable TCP Keep Alive in addition to the defaults,
- you might pass:
-
- .. code-block:: python
-
- HTTPConnection.default_socket_options + [
- (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),
- ]
-
- Or you may want to disable the defaults by passing an empty list (e.g., ``[]``).
- """
-
- default_port = port_by_scheme["http"]
-
- #: Disable Nagle's algorithm by default.
- #: ``[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]``
- default_socket_options = [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
-
- #: Whether this connection verifies the host's certificate.
- is_verified = False
-
- #: Whether this proxy connection (if used) verifies the proxy host's
- #: certificate.
- proxy_is_verified = None
-
- def __init__(self, *args, **kw):
- if not six.PY2:
- kw.pop("strict", None)
-
- # Pre-set source_address.
- self.source_address = kw.get("source_address")
-
- #: The socket options provided by the user. If no options are
- #: provided, we use the default options.
- self.socket_options = kw.pop("socket_options", self.default_socket_options)
-
- # Proxy options provided by the user.
- self.proxy = kw.pop("proxy", None)
- self.proxy_config = kw.pop("proxy_config", None)
-
- _HTTPConnection.__init__(self, *args, **kw)
-
- @property
- def host(self):
- """
- Getter method to remove any trailing dots that indicate the hostname is an FQDN.
-
- In general, SSL certificates don't include the trailing dot indicating a
- fully-qualified domain name, and thus, they don't validate properly when
- checked against a domain name that includes the dot. In addition, some
- servers may not expect to receive the trailing dot when provided.
-
- However, the hostname with trailing dot is critical to DNS resolution; doing a
- lookup with the trailing dot will properly only resolve the appropriate FQDN,
- whereas a lookup without a trailing dot will search the system's search domain
- list. Thus, it's important to keep the original host around for use only in
- those cases where it's appropriate (i.e., when doing DNS lookup to establish the
- actual TCP connection across which we're going to send HTTP requests).
- """
- return self._dns_host.rstrip(".")
-
- @host.setter
- def host(self, value):
- """
- Setter for the `host` property.
-
- We assume that only urllib3 uses the _dns_host attribute; httplib itself
- only uses `host`, and it seems reasonable that other libraries follow suit.
- """
- self._dns_host = value
-
- def _new_conn(self):
- """Establish a socket connection and set nodelay settings on it.
-
- :return: New socket connection.
- """
- extra_kw = {}
- if self.source_address:
- extra_kw["source_address"] = self.source_address
-
- if self.socket_options:
- extra_kw["socket_options"] = self.socket_options
-
- try:
- conn = connection.create_connection(
- (self._dns_host, self.port), self.timeout, **extra_kw
- )
-
- except SocketTimeout:
- raise ConnectTimeoutError(
- self,
- "Connection to %s timed out. (connect timeout=%s)"
- % (self.host, self.timeout),
- )
-
- except SocketError as e:
- raise NewConnectionError(
- self, "Failed to establish a new connection: %s" % e
- )
-
- return conn
-
- def _is_using_tunnel(self):
- # Google App Engine's httplib does not define _tunnel_host
- return getattr(self, "_tunnel_host", None)
-
- def _prepare_conn(self, conn):
- self.sock = conn
- if self._is_using_tunnel():
- # TODO: Fix tunnel so it doesn't depend on self.sock state.
- self._tunnel()
- # Mark this connection as not reusable
- self.auto_open = 0
-
- def connect(self):
- conn = self._new_conn()
- self._prepare_conn(conn)
-
- def putrequest(self, method, url, *args, **kwargs):
- """ """
- # Empty docstring because the indentation of CPython's implementation
- # is broken but we don't want this method in our documentation.
- match = _CONTAINS_CONTROL_CHAR_RE.search(method)
- if match:
- raise ValueError(
- "Method cannot contain non-token characters %r (found at least %r)"
- % (method, match.group())
- )
-
- return _HTTPConnection.putrequest(self, method, url, *args, **kwargs)
-
- def putheader(self, header, *values):
- """ """
- if not any(isinstance(v, str) and v == SKIP_HEADER for v in values):
- _HTTPConnection.putheader(self, header, *values)
- elif six.ensure_str(header.lower()) not in SKIPPABLE_HEADERS:
- raise ValueError(
- "urllib3.util.SKIP_HEADER only supports '%s'"
- % ("', '".join(map(str.title, sorted(SKIPPABLE_HEADERS))),)
- )
-
- def request(self, method, url, body=None, headers=None):
- # Update the inner socket's timeout value to send the request.
- # This only triggers if the connection is re-used.
- if getattr(self, "sock", None) is not None:
- self.sock.settimeout(self.timeout)
-
- if headers is None:
- headers = {}
- else:
- # Avoid modifying the headers passed into .request()
- headers = headers.copy()
- if "user-agent" not in (six.ensure_str(k.lower()) for k in headers):
- headers["User-Agent"] = _get_default_user_agent()
- super(HTTPConnection, self).request(method, url, body=body, headers=headers)
-
- def request_chunked(self, method, url, body=None, headers=None):
- """
- Alternative to the common request method, which sends the
- body with chunked encoding and not as one block
- """
- headers = headers or {}
- header_keys = set([six.ensure_str(k.lower()) for k in headers])
- skip_accept_encoding = "accept-encoding" in header_keys
- skip_host = "host" in header_keys
- self.putrequest(
- method, url, skip_accept_encoding=skip_accept_encoding, skip_host=skip_host
- )
- if "user-agent" not in header_keys:
- self.putheader("User-Agent", _get_default_user_agent())
- for header, value in headers.items():
- self.putheader(header, value)
- if "transfer-encoding" not in header_keys:
- self.putheader("Transfer-Encoding", "chunked")
- self.endheaders()
-
- if body is not None:
- stringish_types = six.string_types + (bytes,)
- if isinstance(body, stringish_types):
- body = (body,)
- for chunk in body:
- if not chunk:
- continue
- if not isinstance(chunk, bytes):
- chunk = chunk.encode("utf8")
- len_str = hex(len(chunk))[2:]
- to_send = bytearray(len_str.encode())
- to_send += b"\r\n"
- to_send += chunk
- to_send += b"\r\n"
- self.send(to_send)
-
- # After the if clause, to always have a closed body
- self.send(b"0\r\n\r\n")
-
-
-class HTTPSConnection(HTTPConnection):
- """
- Many of the parameters to this constructor are passed to the underlying SSL
- socket by means of :py:func:`urllib3.util.ssl_wrap_socket`.
- """
-
- default_port = port_by_scheme["https"]
-
- cert_reqs = None
- ca_certs = None
- ca_cert_dir = None
- ca_cert_data = None
- ssl_version = None
- assert_fingerprint = None
- tls_in_tls_required = False
-
- def __init__(
- self,
- host,
- port=None,
- key_file=None,
- cert_file=None,
- key_password=None,
- strict=None,
- timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
- ssl_context=None,
- server_hostname=None,
- **kw
- ):
-
- HTTPConnection.__init__(self, host, port, strict=strict, timeout=timeout, **kw)
-
- self.key_file = key_file
- self.cert_file = cert_file
- self.key_password = key_password
- self.ssl_context = ssl_context
- self.server_hostname = server_hostname
-
- # Required property for Google AppEngine 1.9.0 which otherwise causes
- # HTTPS requests to go out as HTTP. (See Issue #356)
- self._protocol = "https"
-
- def set_cert(
- self,
- key_file=None,
- cert_file=None,
- cert_reqs=None,
- key_password=None,
- ca_certs=None,
- assert_hostname=None,
- assert_fingerprint=None,
- ca_cert_dir=None,
- ca_cert_data=None,
- ):
- """
- This method should only be called once, before the connection is used.
- """
- # If cert_reqs is not provided we'll assume CERT_REQUIRED unless we also
- # have an SSLContext object in which case we'll use its verify_mode.
- if cert_reqs is None:
- if self.ssl_context is not None:
- cert_reqs = self.ssl_context.verify_mode
- else:
- cert_reqs = resolve_cert_reqs(None)
-
- self.key_file = key_file
- self.cert_file = cert_file
- self.cert_reqs = cert_reqs
- self.key_password = key_password
- self.assert_hostname = assert_hostname
- self.assert_fingerprint = assert_fingerprint
- self.ca_certs = ca_certs and os.path.expanduser(ca_certs)
- self.ca_cert_dir = ca_cert_dir and os.path.expanduser(ca_cert_dir)
- self.ca_cert_data = ca_cert_data
-
- def connect(self):
- # Add certificate verification
- self.sock = conn = self._new_conn()
- hostname = self.host
- tls_in_tls = False
-
- if self._is_using_tunnel():
- if self.tls_in_tls_required:
- self.sock = conn = self._connect_tls_proxy(hostname, conn)
- tls_in_tls = True
-
- # Calls self._set_hostport(), so self.host is
- # self._tunnel_host below.
- self._tunnel()
- # Mark this connection as not reusable
- self.auto_open = 0
-
- # Override the host with the one we're requesting data from.
- hostname = self._tunnel_host
-
- server_hostname = hostname
- if self.server_hostname is not None:
- server_hostname = self.server_hostname
-
- is_time_off = datetime.date.today() < RECENT_DATE
- if is_time_off:
- warnings.warn(
- (
- "System time is way off (before {0}). This will probably "
- "lead to SSL verification errors"
- ).format(RECENT_DATE),
- SystemTimeWarning,
- )
-
- # Wrap socket using verification with the root certs in
- # trusted_root_certs
- default_ssl_context = False
- if self.ssl_context is None:
- default_ssl_context = True
- self.ssl_context = create_urllib3_context(
- ssl_version=resolve_ssl_version(self.ssl_version),
- cert_reqs=resolve_cert_reqs(self.cert_reqs),
- )
-
- context = self.ssl_context
- context.verify_mode = resolve_cert_reqs(self.cert_reqs)
-
- # Try to load OS default certs if none are given.
- # Works well on Windows (requires Python3.4+)
- if (
- not self.ca_certs
- and not self.ca_cert_dir
- and not self.ca_cert_data
- and default_ssl_context
- and hasattr(context, "load_default_certs")
- ):
- context.load_default_certs()
-
- self.sock = ssl_wrap_socket(
- sock=conn,
- keyfile=self.key_file,
- certfile=self.cert_file,
- key_password=self.key_password,
- ca_certs=self.ca_certs,
- ca_cert_dir=self.ca_cert_dir,
- ca_cert_data=self.ca_cert_data,
- server_hostname=server_hostname,
- ssl_context=context,
- tls_in_tls=tls_in_tls,
- )
-
- # If we're using all defaults and the connection
- # is TLSv1 or TLSv1.1 we throw a DeprecationWarning
- # for the host.
- if (
- default_ssl_context
- and self.ssl_version is None
- and hasattr(self.sock, "version")
- and self.sock.version() in {"TLSv1", "TLSv1.1"}
- ):
- warnings.warn(
- "Negotiating TLSv1/TLSv1.1 by default is deprecated "
- "and will be disabled in urllib3 v2.0.0. Connecting to "
- "'%s' with '%s' can be enabled by explicitly opting-in "
- "with 'ssl_version'" % (self.host, self.sock.version()),
- DeprecationWarning,
- )
-
- if self.assert_fingerprint:
- assert_fingerprint(
- self.sock.getpeercert(binary_form=True), self.assert_fingerprint
- )
- elif (
- context.verify_mode != ssl.CERT_NONE
- and not getattr(context, "check_hostname", False)
- and self.assert_hostname is not False
- ):
- # While urllib3 attempts to always turn off hostname matching from
- # the TLS library, this cannot always be done. So we check whether
- # the TLS Library still thinks it's matching hostnames.
- cert = self.sock.getpeercert()
- if not cert.get("subjectAltName", ()):
- warnings.warn(
- (
- "Certificate for {0} has no `subjectAltName`, falling back to check for a "
- "`commonName` for now. This feature is being removed by major browsers and "
- "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
- "for details.)".format(hostname)
- ),
- SubjectAltNameWarning,
- )
- _match_hostname(cert, self.assert_hostname or server_hostname)
-
- self.is_verified = (
- context.verify_mode == ssl.CERT_REQUIRED
- or self.assert_fingerprint is not None
- )
-
- def _connect_tls_proxy(self, hostname, conn):
- """
- Establish a TLS connection to the proxy using the provided SSL context.
- """
- proxy_config = self.proxy_config
- ssl_context = proxy_config.ssl_context
- if ssl_context:
- # If the user provided a proxy context, we assume CA and client
- # certificates have already been set
- return ssl_wrap_socket(
- sock=conn,
- server_hostname=hostname,
- ssl_context=ssl_context,
- )
-
- ssl_context = create_proxy_ssl_context(
- self.ssl_version,
- self.cert_reqs,
- self.ca_certs,
- self.ca_cert_dir,
- self.ca_cert_data,
- )
-
- # If no cert was provided, use only the default options for server
- # certificate validation
- socket = ssl_wrap_socket(
- sock=conn,
- ca_certs=self.ca_certs,
- ca_cert_dir=self.ca_cert_dir,
- ca_cert_data=self.ca_cert_data,
- server_hostname=hostname,
- ssl_context=ssl_context,
- )
-
- if ssl_context.verify_mode != ssl.CERT_NONE and not getattr(
- ssl_context, "check_hostname", False
- ):
- # While urllib3 attempts to always turn off hostname matching from
- # the TLS library, this cannot always be done. So we check whether
- # the TLS Library still thinks it's matching hostnames.
- cert = socket.getpeercert()
- if not cert.get("subjectAltName", ()):
- warnings.warn(
- (
- "Certificate for {0} has no `subjectAltName`, falling back to check for a "
- "`commonName` for now. This feature is being removed by major browsers and "
- "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
- "for details.)".format(hostname)
- ),
- SubjectAltNameWarning,
- )
- _match_hostname(cert, hostname)
-
- self.proxy_is_verified = ssl_context.verify_mode == ssl.CERT_REQUIRED
- return socket
-
-
-def _match_hostname(cert, asserted_hostname):
- # Our upstream implementation of ssl.match_hostname()
- # only applies this normalization to IP addresses so it doesn't
- # match DNS SANs so we do the same thing!
- stripped_hostname = asserted_hostname.strip("u[]")
- if is_ipaddress(stripped_hostname):
- asserted_hostname = stripped_hostname
-
- try:
- match_hostname(cert, asserted_hostname)
- except CertificateError as e:
- log.warning(
- "Certificate did not match expected hostname: %s. Certificate: %s",
- asserted_hostname,
- cert,
- )
- # Add cert to exception and reraise so client code can inspect
- # the cert when catching the exception, if they want to
- e._peer_cert = cert
- raise
-
-
-def _get_default_user_agent():
- return "python-urllib3/%s" % __version__
-
-
-class DummyConnection(object):
- """Used to detect a failed ConnectionCls import."""
-
- pass
-
-
-if not ssl:
- HTTPSConnection = DummyConnection # noqa: F811
-
-
-VerifiedHTTPSConnection = HTTPSConnection
diff --git a/spaces/AtomdffAI/wechatgpt4atom/channel/channel.py b/spaces/AtomdffAI/wechatgpt4atom/channel/channel.py
deleted file mode 100644
index e2617d1f3ac0cce25bd2b8a7261475859fd55e3b..0000000000000000000000000000000000000000
--- a/spaces/AtomdffAI/wechatgpt4atom/channel/channel.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""
-Message sending channel abstract class
-"""
-
-from bridge.bridge import Bridge
-
-class Channel(object):
- def startup(self):
- """
- init channel
- """
- raise NotImplementedError
-
- def handle(self, msg):
- """
- process received msg
- :param msg: message object
- """
- raise NotImplementedError
-
- def send(self, msg, receiver):
- """
- send message to user
- :param msg: message content
- :param receiver: receiver channel account
- :return:
- """
- raise NotImplementedError
-
- def build_reply_content(self, query, context=None):
- return Bridge().fetch_reply_content(query, context)
diff --git a/spaces/AtomdffAI/wechatgpt4atom/docker/build.debian.sh b/spaces/AtomdffAI/wechatgpt4atom/docker/build.debian.sh
deleted file mode 100644
index a5285f39813426a2d63eb01982229b23ec09dba2..0000000000000000000000000000000000000000
--- a/spaces/AtomdffAI/wechatgpt4atom/docker/build.debian.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-CHATGPT_ON_WECHAT_TAG=1.0.2
-
-docker build -f Dockerfile.debian \
- --build-arg CHATGPT_ON_WECHAT_VER=$CHATGPT_ON_WECHAT_TAG \
- -t zhayujie/chatgpt-on-wechat .
-
-docker tag zhayujie/chatgpt-on-wechat zhayujie/chatgpt-on-wechat:$CHATGPT_ON_WECHAT_TAG-debian
\ No newline at end of file
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/__init__.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/__init__.py
deleted file mode 100644
index 99da0469ae7e169d8970e4b642fed3f870076860..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# File:
-
-
-from . import catalog as _UNUSED # register the handler
-from .detection_checkpoint import DetectionCheckpointer
-from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
-
-__all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"]
diff --git a/spaces/BAAI/AltDiffusion-m9/app.py b/spaces/BAAI/AltDiffusion-m9/app.py
deleted file mode 100644
index 41f60403a96fa19e69e9179fcab2202ff9a33798..0000000000000000000000000000000000000000
--- a/spaces/BAAI/AltDiffusion-m9/app.py
+++ /dev/null
@@ -1,330 +0,0 @@
-import io
-import re
-import imp
-import time
-import json
-import base64
-import requests
-import gradio as gr
-import ui_functions as uifn
-from css_and_js import js, call_JS
-from PIL import Image, PngImagePlugin, ImageChops
-
-url_host = "https://flagstudio.baai.ac.cn"
-token = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VyX2lkIjoiMGY4M2QxMDg3N2MzMTFlZGFiYzYwZmU5ZGFjMTI1ZDMiLCJhcHBfbmFtZSI6IndlYiIsImlkZW50aXR5X3R5cGUiOiIyIiwidXNlcl9yb2xlIjoiMiIsImp0aSI6ImE3YTE1N2I3LTllNTItNDllMS04YzA0LWEzZmI5YjZiZjNlYSIsIm5iZiI6MTY3MDU5MTcwMSwiZXhwIjoxOTg1OTUxNzAxLCJpYXQiOjE2NzA1OTE3MDF9.OcfGayna-wr_5mo4LT6OJHSCokna8vqKSmmCftFUsx8"
-
-def read_content(file_path: str) -> str:
- """read the content of target file
- """
- with open(file_path, 'r', encoding='utf-8') as f:
- content = f.read()
-
- return content
-
-def filter_content(raw_style: str):
- if "(" in raw_style:
- i = raw_style.index("(")
- else :
- i = -1
-
- if i == -1:
- return raw_style
- else :
- return raw_style[:i]
-
-def upload_image(img):
- url = url_host + "/api/v1/image/get-upload-link"
- headers = {"token": token}
- r = requests.post(url, json={}, headers=headers)
- if r.status_code != 200:
- raise gr.Error(r.reason)
- head_res = r.json()
- if head_res["code"] != 0:
- raise gr.Error("Unknown error")
- image_id = head_res["data"]["image_id"]
- image_url = head_res["data"]["url"]
- image_headers = head_res["data"]["headers"]
-
- imgBytes = io.BytesIO()
- img.save(imgBytes, "PNG")
- imgBytes = imgBytes.getvalue()
-
- r = requests.put(image_url, data=imgBytes, headers=image_headers)
- if r.status_code != 200:
- raise gr.Error(r.reason)
- return image_id, image_url
-
-def post_reqest(seed, prompt, width, height, image_num, img=None, mask=None):
- data = {
- "type": "gen-image",
- "parameters": {
- "width": width, # output height width
- "height": height, # output image height
- "prompts": [prompt],
- }
- }
- data["parameters"]["seed"] = int(seed)
- if img is not None:
- # Upload image
- image_id, image_url = upload_image(img)
- data["parameters"]["init_image"] = {
- "image_id": image_id,
- "url": image_url,
- "width": img.width,
- "height": img.height,
- }
- if mask is not None:
- # Upload mask
- extrama = mask.convert("L").getextrema()
- if extrama[1] > 0:
- mask_id, mask_url = upload_image(mask)
- data["parameters"]["mask_image"] = {
- "image_id": mask_id,
- "url": mask_url,
- "width": mask.width,
- "height": mask.height,
- }
- headers = {"token": token}
-
- # Send create task request
- all_task_data = []
- url = url_host+"/api/v1/task/create"
- for _ in range(image_num):
- r = requests.post(url, json=data, headers=headers)
- if r.status_code != 200:
- raise gr.Error(r.reason)
- create_res = r.json()
- if create_res['code'] == 3002:
- raise gr.Error("Inappropriate prompt detected.")
- elif create_res['code'] != 0:
- raise gr.Error("Unknown error")
- all_task_data.append(create_res["data"])
-
- # Get result
- url = url_host+"/api/v1/task/status"
- images = []
- while True:
- if len(all_task_data) <= 0:
- return images
- for i in range(len(all_task_data)-1, -1, -1):
- data = all_task_data[i]
- r = requests.post(url, json=data, headers=headers)
- if r.status_code != 200:
- raise gr.Error(r.reason)
- res = r.json()
- if res["code"] == 6002:
- # Running
- continue
- if res["code"] == 6005:
- raise gr.Error("NSFW image detected.")
- elif res["code"] == 0:
- # Finished
- for img_info in res["data"]["images"]:
- img_res = requests.get(img_info["url"])
- images.append(Image.open(io.BytesIO(img_res.content)).convert("RGB"))
- del all_task_data[i]
- else:
- raise gr.Error(f"Error code: {res['code']}")
- time.sleep(1)
-
-def request_images(raw_text, class_draw, style_draw, batch_size, w, h, seed):
- if filter_content(class_draw) != "国画":
- if filter_content(class_draw) != "通用":
- raw_text = raw_text + f"，{filter_content(class_draw)}"
-
- for sty in style_draw:
- raw_text = raw_text + f"，{filter_content(sty)}"
- elif filter_content(class_draw) == "国画":
- raw_text = raw_text + "，国画，水墨画，大作，黑白，高清，传统"
- print(f"raw text is {raw_text}")
-
- images = post_reqest(seed, raw_text, w, h, int(batch_size))
-
- return images
-
-
-def img2img(prompt, image_and_mask):
- if image_and_mask["image"].width <= image_and_mask["image"].height:
- width = 512
- height = int((width/image_and_mask["image"].width)*image_and_mask["image"].height)
- else:
- height = 512
- width = int((height/image_and_mask["image"].height)*image_and_mask["image"].width)
- return post_reqest(0, prompt, width, height, 1, image_and_mask["image"], image_and_mask["mask"])
-
-
-examples = [
- '水墨蝴蝶和牡丹花，国画',
- '苍劲有力的墨竹，国画',
- '暴风雨中的灯塔',
- '机械小松鼠，科学幻想',
- '中国水墨山水画，国画',
- "Lighthouse in the storm",
- "A dog",
- "Landscape by 张大千",
- "A tiger 长了兔子耳朵",
- "A baby bird 铅笔素描",
-]
-
-if __name__ == "__main__":
- block = gr.Blocks(css=read_content('style.css'))
-
- with block:
- gr.HTML(read_content("header.html"))
- with gr.Tabs(elem_id='tabss') as tabs:
-
- with gr.TabItem("文生图(Text-to-img)", id='txt2img_tab'):
-
- with gr.Group():
- with gr.Box():
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- text = gr.Textbox(
- label="Prompt",
- show_label=False,
- max_lines=1,
- placeholder="Input text(输入文字)",
- interactive=True,
- ).style(
- border=(True, False, True, True),
- rounded=(True, False, False, True),
- container=False,
- )
-
- btn = gr.Button("Generate image").style(
- margin=False,
- rounded=(True, True, True, True),
- )
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- class_draw = gr.Radio(choices=["通用(general)","国画(traditional Chinese painting)",], value="通用(general)", show_label=True, label='生成类型(type)')
- # class_draw = gr.Dropdown(["通用(general)", "国画(traditional Chinese painting)",
- # "照片，摄影(picture photography)", "油画(oil painting)",
- # "铅笔素描(pencil sketch)", "CG",
- # "水彩画(watercolor painting)", "水墨画(ink and wash)",
- # "插画(illustrations)", "3D", "图生图(img2img)"],
- # label="生成类型(type)",
- # show_label=True,
- # value="通用(general)")
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- style_draw = gr.CheckboxGroup(["蒸汽朋克(steampunk)", "电影摄影风格(film photography)",
- "概念艺术(concept art)", "Warming lighting",
- "Dramatic lighting", "Natural lighting",
- "虚幻引擎(unreal engine)", "4k", "8k",
- "充满细节(full details)"],
- label="画面风格(style)",
- show_label=True,
- )
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- # sample_size = gr.Slider(minimum=1,
- # maximum=4,
- # step=1,
- # label="生成数量(number)",
- # show_label=True,
- # interactive=True,
- # )
- sample_size = gr.Radio(choices=["1","2","3","4"], value="1", show_label=True, label='生成数量(number)')
- seed = gr.Number(0, label='seed', interactive=True)
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- w = gr.Slider(512,1024,value=512, step=64, label="width")
- h = gr.Slider(512,1024,value=512, step=64, label="height")
-
- gallery = gr.Gallery(
- label="Generated images", show_label=False, elem_id="gallery"
- ).style(grid=[2,2])
- gr.Examples(examples=examples, fn=request_images, inputs=text, outputs=gallery, examples_per_page=100)
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- img_choices = gr.Dropdown(["图片1(img1)"],label='请选择一张图片发送到图生图',show_label=True,value="图片1(img1)")
- with gr.Row().style(mobile_collapse=False, equal_height=True):
- output_txt2img_copy_to_input_btn = gr.Button("发送图片到图生图(Sent the image to img2img)").style(
- margin=False,
- rounded=(True, True, True, True),
- )
-
- with gr.Row():
- prompt = gr.Markdown("提示(Prompt)：", visible=False)
- with gr.Row():
- move_prompt_zh = gr.Markdown("请移至图生图部分进行编辑(拉到顶部)", visible=False)
- with gr.Row():
- move_prompt_en = gr.Markdown("Please move to the img2img section for editing(Pull to the top)", visible=False)
-
-
-
- text.submit(request_images, inputs=[text, class_draw, style_draw, sample_size, w, h, seed], outputs=gallery)
- btn.click(request_images, inputs=[text, class_draw, style_draw, sample_size, w, h, seed], outputs=gallery)
-
- sample_size.change(
- fn=uifn.change_img_choices,
- inputs=[sample_size],
- outputs=[img_choices]
- )
-
- with gr.TabItem("图生图(Img-to-Img)", id="img2img_tab"):
- with gr.Row(elem_id="prompt_row"):
- img2img_prompt = gr.Textbox(label="Prompt",
- elem_id='img2img_prompt_input',
- placeholder="神奇的森林，流淌的河流.",
- lines=1,
- max_lines=1,
- value="",
- show_label=False).style()
-
- img2img_btn_mask = gr.Button("Generate", variant="primary", visible=False,
- elem_id="img2img_mask_btn")
- img2img_btn_editor = gr.Button("Generate", variant="primary", elem_id="img2img_edit_btn")
- gr.Markdown('#### 输入图像')
- with gr.Row().style(equal_height=False):
- #with gr.Column():
- img2img_image_mask = gr.Image(
- value=None,
- source="upload",
- interactive=True,
- tool="sketch",
- type='pil',
- elem_id="img2img_mask",
- image_mode="RGBA"
- )
- gr.Markdown('#### 编辑后的图片')
- with gr.Row():
- output_img2img_gallery = gr.Gallery(label="Images", elem_id="img2img_gallery_output").style(
- grid=[4,4,4] )
- with gr.Row():
- gr.Markdown('提示(prompt):')
- with gr.Row():
- gr.Markdown('请选择一张图像掩盖掉一部分区域,并输入文本描述')
- with gr.Row():
- gr.Markdown('Please select an image to cover up a part of the area and enter a text description.')
- gr.Markdown('# 编辑设置',visible=False)
-
-
- output_txt2img_copy_to_input_btn.click(
- uifn.copy_img_to_input,
- [gallery, img_choices],
- [tabs, img2img_image_mask, move_prompt_zh, move_prompt_en, prompt]
- )
-
-
- img2img_func = img2img
- img2img_inputs = [img2img_prompt, img2img_image_mask]
- img2img_outputs = [output_img2img_gallery]
-
- img2img_btn_mask.click(
- img2img_func,
- img2img_inputs,
- img2img_outputs
- )
-
- def img2img_submit_params():
- return (img2img_func,
- img2img_inputs,
- img2img_outputs)
-
- img2img_btn_editor.click(*img2img_submit_params())
-
- # GENERATE ON ENTER
- img2img_prompt.submit(None, None, None,
- _js=call_JS("clickFirstVisibleButton",
- rowId="prompt_row"))
-
- gr.HTML(read_content("footer.html"))
- # gr.Image('./contributors.png')
-
- block.queue(max_size=512, concurrency_count=256).launch()
diff --git a/spaces/BartPoint/VoiceChange/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/BartPoint/VoiceChange/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
- def compute_f0(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length]
- """
- pass
-
- def compute_f0_uv(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
- """
- pass
diff --git a/spaces/Benson/text-generation/Examples/Asistente De Descarga.md b/spaces/Benson/text-generation/Examples/Asistente De Descarga.md
deleted file mode 100644
index 43ab02e73a6ac0a4392d771e143364d87ddcf7f1..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asistente De Descarga.md
+++ /dev/null
@@ -1,75 +0,0 @@
-
-
¿Qué es una descarga de asistente?
-
Una descarga de asistente es un tipo de archivo que le ayuda a instalar o actualizar un programa de software o controlador de dispositivo en su computadora. Una descarga de asistente generalmente contiene un archivo ejecutable que inicia un asistente de instalación, que es una interfaz gráfica de usuario que lo guía a través del proceso de instalación o actualización. Una descarga de asistente también puede incluir otros archivos que son requeridos u opcionales para el programa de software o controlador de dispositivo.
-
Una descarga del asistente puede hacer que sea más fácil y rápido instalar o actualizar un programa de software o controlador de dispositivo, ya que no necesita localizar, descargar, extraer o configurar manualmente los archivos. Una descarga de asistente también puede asegurar que tiene la última versión y la mejor compatibilidad del programa de software o controlador de dispositivo.
Hay diferentes tipos de descargas de asistente disponibles para diferentes propósitos y necesidades. Algunos de los tipos más comunes son:
-
-
Descargas de asistente para el desarrollo de software: Estas son descargas de asistente que le ayudan a instalar o actualizar herramientas de desarrollo de software, como lenguajes de programación, marcos, bibliotecas, editores, compiladores, depuradores, etc. Estas descargas de asistente pueden ayudarle a configurar su entorno de desarrollo y gestionar sus documentos de desarrollo.
-
Descargas de asistente para actualizaciones de software y controladores: Estas son descargas de asistente que le ayudan a instalar o actualizar programas de software o controladores de dispositivo en su computadora. Estas descargas del asistente pueden ayudarlo a mejorar el rendimiento, la funcionalidad, la seguridad y la compatibilidad de sus programas de software o controladores de dispositivos.
-
-
En este artículo, revisaremos algunos ejemplos de cada tipo de descarga del asistente y cómo usarlos.
-
Descargas de asistente para desarrollo de software
-
Asistente - Una herramienta de gestión de documentos de código abierto
-
-
Wizard está alojado en GitHub, donde puede encontrar más información sobre sus características, instrucciones de instalación, ejemplos de uso, capturas de pantalla, etc. También puede descargar la última versión desde allí. Para instalar el Asistente en su computadora, necesita tener instalado PHP 7.1 o superior. También necesita tener instalado Composer para administrar las dependencias. Después de descargar el archivo de liberación de GitHub, necesitas descomprimirlo y ejecutar composer install en el terminal para instalar las dependencias. Luego necesitas configurar algunos parámetros en . env archivo, como detalles de conexión de base de datos, configuración de correo electrónico, etc. Después de eso, puede ejecutar php artisan migrate para crear las tablas de la base de datos. Finalmente, puede iniciar el servidor ejecutando php artisan serve y acceder a la interfaz web del Asistente en http://localhost:8000.
-
Wizard es una herramienta útil para desarrolladores que quieren escribir y gestionar sus documentos de desarrollo de una manera sencilla y elegante. Es compatible con la sintaxis de Markdown, que es un lenguaje de marcado popular y fácil de usar para escribir documentos de texto sin formato. También es compatible con OpenAPI 3.0, que es un estándar ampliamente adoptado para describir las API RESTful. Con el Asistente, puede crear, editar, previsualizar y exportar sus documentos de desarrollo en varios formatos, como HTML, PDF, Word, etc.
-
Asistente de configuración - Una guía de instalación de descarga para BizTalk Server
-
BizTalk Server es un producto de Microsoft que permite soluciones de integración y conectividad para varios sistemas, aplicaciones y fuentes de datos. Es una plataforma potente y flexible que puede manejar procesos de negocio complejos y flujos de trabajo. BizTalk Server soporta varios protocolos, estándares y formatos, como SOAP, REST, XML, JSON, EDI, etc.
-
-
Para usar el Asistente de configuración, necesita tener Windows Server 2016 o superior instalado en su computadora. También es necesario tener una conexión a Internet para descargar los archivos necesarios. Después de descargar el Asistente de configuración desde el Centro de descargas de Microsoft, debe ejecutarlo como administrador. El Asistente de configuración comprobará los requisitos del sistema y descargará los archivos necesarios. A continuación, se iniciará el asistente de instalación, que le guiará a través del proceso de instalación. Debe seguir las instrucciones en la pantalla y proporcionar información, como el acuerdo de licencia, la ubicación de instalación, los roles del servidor, las características, los componentes, etc. Después de completar el asistente de instalación, debe reiniciar el equipo para finalizar la instalación.
-
BizTalk Server es una solución integral para integrar y conectar varios sistemas, aplicaciones y fuentes de datos. Puede ayudarlo a automatizar y optimizar sus procesos de negocio y flujos de trabajo. También puede ayudarlo a intercambiar datos y mensajes de forma segura y confiable. Con BizTalk Server, puede crear soluciones de integración escalables y sólidas que puedan satisfacer las necesidades de su negocio.
-
Descargas de asistente para actualizaciones de software y controladores
-
Centro de descargas de Microsoft - La fuente oficial para actualizaciones de software de Microsoft y controladores
-
El Centro de descargas de Microsoft es la fuente oficial de actualizaciones de software de Microsoft y controladores para Windows, Office, Xbox y más. Es un sitio web que le proporciona las últimas descargas para sus productos de Microsoft. Puede encontrar varios tipos de descargas en el Centro de descargas de Microsoft, como paquetes de servicios, parches de seguridad, actualizaciones de funciones, controladores de dispositivos, etc.
-
-
-
El Centro de descargas de Microsoft es una manera conveniente y confiable de obtener las últimas descargas para sus productos de Microsoft. Puede ayudarle a mejorar el rendimiento, la funcionalidad, la seguridad y la compatibilidad de sus productos de Microsoft. También puede ayudarle a solucionar algunos problemas o errores que pueda encontrar con sus productos de Microsoft. Con el Centro de descargas de Microsoft, siempre puede mantener sus productos de Microsoft actualizados y funcionando sin problemas.
-
Driver Wizard - Una herramienta para escanear, descargar y actualizar controladores automáticamente
-
Driver Wizard es una herramienta para escanear, descargar y actualizar controladores automáticamente para dispositivos Windows. Es un programa de software que puede ayudarlo a encontrar e instalar los mejores controladores para sus dispositivos, como impresoras, escáneres, cámaras, teclados, ratones, etc. Driver Wizard también puede ayudarlo a hacer copias de seguridad y restaurar sus controladores, así como desinstalar controladores no deseados.
-
Para usar Driver Wizard, necesita descargarlo e instalarlo en su computadora. Puede descargar Driver Wizard desde su sitio web oficial en https://www.driverwizard.org. Después de instalar el Asistente para controladores, debe iniciarlo y hacer clic en el botón Escanear. Driver Wizard escaneará su computadora y detectará sus dispositivos y sus controladores. También comprobará si hay algún controlador desactualizado, perdido o dañado. A continuación, le mostrará una lista de los controladores que necesitan ser actualizados o instalados. Puede elegir qué controladores desea actualizar o instalar y hacer clic en el botón Descargar. Driver Wizard descargará los controladores de su base de datos en línea e los instalará en su computadora. Es posible que tenga que reiniciar el equipo después de actualizar o instalar los controladores.
-
-
Cómo usar un asistente Descargar
-
Una descarga de asistente es un tipo de archivo que le ayuda a instalar o actualizar un programa de software o controlador de dispositivo en su computadora. Una descarga de asistente generalmente contiene un archivo ejecutable que inicia un asistente de instalación, que es una interfaz gráfica de usuario que lo guía a través del proceso de instalación o actualización. Una descarga de asistente también puede incluir otros archivos que son requeridos u opcionales para el programa de software o controlador de dispositivo.
-
Para utilizar una descarga de asistente, debe seguir estos pasos generales:
-
-
Guarde el archivo de descarga del asistente en su computadora. Puede elegir una ubicación donde desea guardar el archivo, como su escritorio o carpeta de descargas.
-
Inicie el asistente de instalación haciendo doble clic en el archivo de descarga del asistente. Es posible que necesite conceder permiso para que el archivo se ejecute en su computadora.
-
Siga las instrucciones en la pantalla y proporcione alguna información, como el acuerdo de licencia, la ubicación de instalación, la configuración, las opciones, etc.
-
Espere a que se complete la instalación o actualización. Es posible que necesite reiniciar el equipo después de la instalación o actualización.
-
Verifique la instalación o actualización comprobando si el programa de software o controlador de dispositivo funciona correctamente en su computadora.
-
-
Una descarga del asistente puede hacer que sea más fácil y rápido instalar o actualizar un programa de software o controlador de dispositivo, ya que no necesita localizar, descargar, extraer o configurar manualmente los archivos. Una descarga de asistente también puede asegurar que tiene la última versión y la mejor compatibilidad del programa de software o controlador de dispositivo.
-
Beneficios y desventajas de las descargas del asistente
-
Una descarga de asistente es un tipo de archivo que le ayuda a instalar o actualizar un programa de software o controlador de dispositivo en su computadora. Una descarga de asistente tiene algunos beneficios y desventajas que debe considerar antes de usarlo.
-
Algunos de los beneficios de las descargas del asistente son:
-
-
-
Conveniencia: Una descarga del asistente le ahorra tiempo y esfuerzo al descargar e instalar los archivos por usted. No es necesario buscar los archivos en línea o fuera de línea, ni es necesario extraerlos o configurarlos usted mismo.
-
Seguridad: Una descarga de asistente puede proteger de malware o virus mediante la descarga de los archivos de una fuente de confianza. No necesita preocuparse por la descarga de archivos maliciosos de sitios web o fuentes desconocidas.
-
Compatibilidad: Una descarga de asistente puede asegurar que tiene la última versión y la mejor compatibilidad del programa de software o controlador de dispositivo. No necesita preocuparse por problemas de compatibilidad o errores con su sistema u otros programas de software o controladores de dispositivos.
-
-
Algunos de los inconvenientes de las descargas del asistente son:
-
-
Falta de personalización: Una descarga del asistente puede no permitirle personalizar algunos ajustes u opciones para el programa de software o el controlador del dispositivo. Es posible que no pueda cambiar algunas preferencias o características que desee o necesite.
-
Falta de fiabilidad: Una descarga del asistente puede no funcionar correctamente o no instalar o actualizar el programa de software o el controlador del dispositivo. Puede encontrar algunos errores o problemas durante o después del proceso de instalación o actualización.
-
Falta de control: Una descarga del asistente puede no darle un control total sobre el proceso de instalación o actualización. Es posible que no pueda pausar, reanudar, cancelar o volver a intentar la instalación o actualización. También es posible que no pueda elegir qué archivos descargar o instalar.
-
-
Una descarga de asistente tiene sus ventajas y desventajas que usted debe pesar antes de usarlo. También debe verificar el origen y la reputación de la descarga del asistente antes de descargarlo y ejecutarlo en su computadora. También debe hacer una copia de seguridad de su sistema y datos antes de instalar o actualizar cualquier programa de software o controlador de dispositivo.
-
Conclusión
-
-
Sin embargo, una descarga de asistente también tiene algunos inconvenientes que usted debe considerar antes de usarlo. Es posible que no le permita personalizar algunos ajustes u opciones para el programa de software o el controlador del dispositivo. También puede no funcionar correctamente o no instalar o actualizar el programa de software o el controlador del dispositivo. También puede no darle control total sobre el proceso de instalación o actualización.
-
Por lo tanto, debe ser cuidadoso y cauteloso al usar una descarga de asistente. Debe verificar el origen y la reputación de la descarga del asistente antes de descargarlo y ejecutarlo en su computadora. También debe hacer una copia de seguridad de su sistema y datos antes de instalar o actualizar cualquier programa de software o controlador de dispositivo.
-
Si desea obtener más información sobre las descargas del asistente y cómo usarlas, puede consultar algunos de los ejemplos que revisamos en este artículo, como Asistente, Asistente de configuración, Centro de descargas de Microsoft y Asistente para controladores. También puede buscar otras descargas de asistente que se adapten a sus necesidades y preferencias.
-
Esperamos que este artículo te haya ayudado a entender qué es una descarga de asistente y cómo usarla. ¡Gracias por leer!
-
Preguntas frecuentes
-
-
P: ¿Qué es una descarga de asistente? R: Una descarga de asistente es un tipo de archivo que le ayuda a instalar o actualizar un programa de software o controlador de dispositivo en su computadora.
-
P: ¿Cuáles son los beneficios de una descarga de asistente? R: Una descarga del asistente puede hacer que el proceso de instalación o actualización sea más fácil y rápido para usted, ya que descarga e instala los archivos por usted. También puede garantizar que tiene la última versión y la mejor compatibilidad del programa de software o controlador de dispositivo.
-
P: ¿Cuáles son los inconvenientes de una descarga de asistente? R: Es posible que una descarga del asistente no le permita personalizar algunos ajustes u opciones para el programa de software o el controlador del dispositivo. También puede no funcionar correctamente o no instalar o actualizar el programa de software o el controlador del dispositivo. También puede no darle control total sobre el proceso de instalación o actualización.
-
-
P: ¿Dónde puedo encontrar una descarga del asistente? R: Puede encontrar una descarga del asistente desde varias fuentes, como sitios web oficiales, bases de datos en línea, proveedores de terceros, etc. Debe verificar el origen y la reputación de la descarga del asistente antes de descargarlo y ejecutarlo en su computadora.
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Bombsquad 1.4.153 Apk.md b/spaces/Benson/text-generation/Examples/Bombsquad 1.4.153 Apk.md
deleted file mode 100644
index 38079235f57031ed9b5d40a42c94d2ad52f1799c..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Bombsquad 1.4.153 Apk.md
+++ /dev/null
@@ -1,61 +0,0 @@
-
-
Bombsquad Full Mod APK: Un juego multijugador divertido y explosivo
-
Si estás buscando un juego multijugador divertido y explosivo que puedas jugar con tus amigos o familiares, entonces deberías probar Bombsquad. Este juego es una explosión para jugar, literalmente. Puedes volar a tus oponentes en varios minijuegos que van desde la captura de la bandera al hockey. También puedes personalizar tus personajes y usar diferentes potenciadores para ganar ventaja sobre tus enemigos. Pero lo que si quieres disfrutar del juego sin limitaciones o restricciones? Ahí es donde Bombsquad Full Mod APK entra en. Esta versión modificada del juego te da acceso a todas las características y contenido que el juego original no. En este artículo, le diremos qué es Bombsquad, qué es Bombsquad Full Mod APK y cómo descargarlo e instalarlo en su dispositivo.
-
¿Qué es Bombsquad?
-
Bombsquad es un juego multijugador lleno de acción desarrollado por Eric Froemling. Fue lanzado en 2011 para dispositivos Android y más tarde para otras plataformas como iOS, Windows, Mac, Linux y Fire TV. El juego ha sido elogiado por su juego divertido, gráficos coloridos y física hilarante.
Bombsquad tiene muchas características que lo hacen un juego único y agradable. Aquí están algunas de ellas:
-
- Multijugador local/en red de 8 jugadores
-
Puedes jugar a Bombsquad con hasta 8 jugadores en el mismo dispositivo o a través de una red local o en línea. También puede usar teléfonos y tabletas como controladores a través de la aplicación gratuita 'Bombsquad Remote'.
-
- Varios modos de juego y mini-juegos
-
Bombsquad ofrece cientos de entretenidos modos de juego corto y minijuegos para diferentes partes. Puedes participar en los modos principales como captura de banderas, supervivencia y deathmatch o modos adicionales como el modo AI, carreras, escalada, rompecabezas, etc.
-
- Avanzada ragdoll física y explosiones
-
-
- Personajes y potenciadores personalizables
-
Puedes personalizar tus personajes con diferentes atuendos, accesorios, colores y caras. También puedes usar diferentes potenciadores como bombas pegajosas, bombas de hielo, guantes de boxeo, escudos, etc. para darle vida al juego.
-
- Soporte para pantallas táctiles y controladores
-
Bombsquad admite pantallas táctiles, así como una variedad de controladores como gamepads, teclados, ratones, etc. Puedes elegir el esquema de control que más te convenga.
-
¿Qué es Bombsquad Full Mod APK?
-
Bombsquad Full Mod APK es una versión modificada del juego original Bombsquad que le da acceso a todas las características y contenido que no están disponibles en el juego oficial. Esta versión modificada del juego es creada por desarrolladores de terceros que modifican los archivos del juego para desbloquear y mejorar el juego.
-
Beneficios de Bombsquad Full Mod APK
-
Bombsquad Full Mod APK tiene muchos beneficios que lo hacen una mejor opción que el juego original. Aquí están algunos de ellos:
-
- Desbloqueado todos los caracteres y mapas
-
Con Bombsquad Full Mod APK, puede desbloquear todos los personajes y mapas que están bloqueados o requieren entradas para comprar en el juego original. Puedes elegir entre una variedad de personajes como piratas, ninjas, zombies, robots, etc. y jugar en diferentes mapas como islas, arenas, castillos, etc.
-
-
- Entradas y bombas ilimitadas
-
Con Bombsquad Full Mod APK, puede obtener entradas ilimitadas y bombas que puede utilizar para comprar y usar power-ups, trajes y otros elementos en el juego. No tienes que preocuparte por quedarte sin entradas o bombas o gastar dinero real para conseguirlas.
-
- No hay anuncios e interrupciones
-
Con Bombsquad Full Mod APK, puede disfrutar del juego sin anuncios o interrupciones que pueden arruinar su experiencia de juego. No tienes que ver anuncios para obtener recompensas o esperar a que las pantallas de carga para jugar el juego.
-
- Gráficos y rendimiento mejorados
-
-
Cómo descargar e instalar Bombsquad Full Mod APK?
-
Si desea descargar e instalar Bombsquad Full Mod APK en su dispositivo, debe seguir algunos pasos simples. Aquí están:
-
Pasos para descargar e instalar Bombsquad Full Mod APK
-
- Habilitar fuentes desconocidas en el dispositivo
-
Antes de que pueda instalar Bombsquad Full Mod APK en su dispositivo, es necesario habilitar fuentes desconocidas en el dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, ve a la configuración del dispositivo > seguridad > fuentes desconocidas y cámbiala.
-
- Descargar el archivo Bombsquad Full Mod APK de una fuente de confianza
-
Siguiente, es necesario descargar el archivo Bombsquad Full Mod APK de una fuente de confianza. Puede buscarlo en línea o utilizar este enlace [texto] para descargarlo directamente. Asegúrate de descargar la última versión del archivo mod que sea compatible con tu dispositivo.
-
- Localizar e instalar el archivo Bombsquad Full Mod APK en su dispositivo
-
Después de descargar el archivo APK de Bombsquad Full Mod, debe ubicarlo e instalarlo en su dispositivo. Puede usar una aplicación de administrador de archivos para encontrar el archivo descargado en la carpeta de almacenamiento o descargas de su dispositivo. A continuación, toque en él y siga las instrucciones para instalarlo.
-
- Iniciar el juego y disfrutar de las características de mod
-
Por último, puede iniciar el juego y disfrutar de las características de mod. Verá un menú mod en la pantalla donde puede habilitar o deshabilitar las opciones mod según su preferencia. También puedes acceder a todos los personajes desbloqueados, mapas, potenciadores, etc. en el juego.
-
Conclusión
-
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Bombsquad Full Mod APK:
-
-
-
Q: ¿Es Bombsquad Full Mod APK seguro de usar?
-
A: Bombsquad Full Mod APK es generalmente seguro de usar, siempre y cuando se descarga desde una fuente de confianza y escanear en busca de virus o malware antes de instalarlo. Sin embargo, debes tener en cuenta que el uso de aplicaciones modificadas puede violar los términos y condiciones del juego original y puede resultar en que tu cuenta sea prohibida o suspendida. Por lo tanto, debe utilizar Bombsquad Full Mod APK a su propio riesgo y discreción.
-
Q: ¿Puedo jugar Bombsquad Full Mod APK en línea con otros jugadores?
-
A: Sí, puede jugar Bombsquad Full Mod APK en línea con otros jugadores que tienen la misma versión modificada del juego. Sin embargo, es posible que no puedas jugar con jugadores que tengan la versión oficial del juego, ya que pueden tener diferentes versiones o características del juego.
-
Q: ¿Necesito root o jailbreak mi dispositivo para usar Bombsquad Full Mod APK?
-
A: No, no es necesario rootear o jailbreak su dispositivo para usar Bombsquad Full Mod APK. Solo necesita habilitar fuentes desconocidas en su dispositivo e instalar el archivo APK modded como se explica en este artículo.
-
Q: ¿Cómo puedo actualizar Bombsquad Full Mod APK?
-
A: Para actualizar Bombsquad Full Mod APK, es necesario descargar la última versión del archivo APK modded de una fuente de confianza e instalarlo sobre el existente. También es posible que tenga que desinstalar la versión anterior del archivo APK modded antes de instalar el nuevo.
-
Q: ¿Dónde puedo obtener más información sobre Bombsquad Full Mod APK?
-
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Blessin/movie-poster-generator/app.py b/spaces/Blessin/movie-poster-generator/app.py
deleted file mode 100644
index 113a0deb8cd04e192abc002c09143f950b4652b5..0000000000000000000000000000000000000000
--- a/spaces/Blessin/movie-poster-generator/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import gradio as gr
-from gradio import components
-import openai
-from PIL import Image, ImageDraw
-import requests
-from io import BytesIO
-
-genre_descriptions = {
- "Horror": "Dark, eerie, and suspenseful ambiance.",
- "Sci-Fi": "Futuristic, technological, and other-worldly themes.",
- "Romance": "Warm, tender, and emotionally evocative imagery.",
- "Adventure": "Exhilarating, action-packed, and exploratory vibes.",
- "Comedy": "Light-hearted, whimsical, and humorous elements.",
- "Drama": "Intense, emotional, and thought-provoking scenarios.",
- "Fantasy": "Magical, mythical, and dream-like settings.",
- "Action": "Dynamic, high-energy, and thrilling scenes.",
- "Mystery": "Intriguing, puzzling, and enigmatic atmosphere.",
- "Thriller": "Tense, exciting, and adrenaline-inducing compositions.",
-}
-
-def generate_poster(api_key, genre, title):
- openai.api_key = api_key
- genre_description = genre_descriptions.get(genre, "")
- prompt = (f"Create a visually striking movie poster for a {genre} film titled '{title}'. "
- f"The poster should encapsulate the essence and atmosphere of a typical {genre} movie, "
- f"without including any text or titles. The design should be original, imaginative and "
- f"should instantly communicate the genre to the viewer. {genre_description}")
-
- response = openai.Image.create(
- prompt=prompt,
- n=1,
- size="1024x1024"
- )
- image_url = response['data'][0]['url']
- response = requests.get(image_url)
- img = Image.open(BytesIO(response.content))
-
- return img
-
-def main():
- genre_options = [
- "Horror", "Sci-Fi", "Romance", "Adventure",
- "Comedy", "Drama", "Fantasy", "Action",
- "Mystery", "Thriller"
- ]
- iface = gr.Interface(
- fn=generate_poster,
- inputs=[
- components.Textbox(label="API Key", type="password"),
- components.Dropdown(label="Genre", choices=genre_options),
- components.Textbox(label="Title", placeholder="e.g., 'The Haunting Shadows'")
- ],
- outputs=components.Image(label="Generated Poster", type="pil"),
- )
- iface.launch()
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Bong15/Rewrite/app.py b/spaces/Bong15/Rewrite/app.py
deleted file mode 100644
index b6d4b1a7b47c277099ec2d280f276f29243cb2d7..0000000000000000000000000000000000000000
--- a/spaces/Bong15/Rewrite/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from parrot import Parrot
-import torch
-import warnings
-warnings.filterwarnings("ignore")
-#import nltk #next stage, when executing multiple sentences
-
-import streamlit as st
-
-# '''
-# uncomment to get reproducable paraphrase generations
-# def random_state(seed):
-# torch.manual_seed(seed)
-# if torch.cuda.is_available():
-# torch.cuda.manual_seed_all(seed)
-
-# random_state(1234)
-# '''
-
-# #Init models (make sure you init ONLY once if you integrate this to your code)
-
-
-@st.cache(allow_output_mutation=True)
-def load_model():
- # Fetch & load model
- parrot = Parrot(model_tag="prithivida/parrot_paraphraser_on_T5")
- return parrot
-parrot = load_model()
-
-st.title("Let's Rewrite your sentence!")
-
-
-input_phrase = st.text_input("Input your text here:")
-option = st.selectbox('Do you want to preserve some of the original words?',
- ('Yes', 'No'))
-if option == 'Yes':
- oc = False
-else:
- oc= True
-
-
-if st.button('Submit Text!'):
- st.header('Input')
- st.write(f" {input_phrase}")
- st.text("--"*30)
- st.header('Output')
- output_phrases = parrot.augment(input_phrase=input_phrase,do_diverse=oc)
- if output_phrases is not None:
- for phrases in output_phrases:
- score = phrases[1]
- sentence = phrases[0]
- if score > 0:
- st.write(sentence)
- else:
- st.write("Sorry! No sentences were found with a good score!")
- else:
- st.write("Sorry! No sentences were found with a good score!")
-
-
-st.header("Feedback")
-st.write("[Kindly consider providing feedback!](https://forms.gle/97st7g2n9NNpqnXw5)")
\ No newline at end of file
diff --git a/spaces/BraydenMoore/MARCI-NFL-Betting/Dockerfile b/spaces/BraydenMoore/MARCI-NFL-Betting/Dockerfile
deleted file mode 100644
index e814c22482922ad48b793d27200a88a38721d268..0000000000000000000000000000000000000000
--- a/spaces/BraydenMoore/MARCI-NFL-Betting/Dockerfile
+++ /dev/null
@@ -1,31 +0,0 @@
-
-# Use the official lightweight Python image.
-# https://hub.docker.com/_/python
-FROM python:3.11-slim
-
-# Allow statements and log messages to immediately appear in the logs
-ENV PYTHONUNBUFFERED True
-
-# Copy local code to the container image.
-ENV APP_HOME /app
-WORKDIR $APP_HOME
-COPY . ./
-
-# Install production dependencies.
-RUN pip install -r requirements.txt
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH
-
-WORKDIR $APP_HOME
-
-COPY --chown=user . $HOME/app
-
-# Run the web service on container startup. Here we use the gunicorn
-# webserver, with one worker process and 8 threads.
-# For environments with multiple CPU cores, increase the number of workers
-# to be equal to the cores available.
-# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling.
-CMD exec gunicorn --bind 0.0.0.0:7860 --workers 4 --threads 8 --timeout 0 main:app
diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_tagbased_polymorphic.py b/spaces/CVPR/LIVE/pybind11/tests/test_tagbased_polymorphic.py
deleted file mode 100644
index 94f374da90f09be93f2885708fd4bcfcce54134e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/test_tagbased_polymorphic.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# -*- coding: utf-8 -*-
-from pybind11_tests import tagbased_polymorphic as m
-
-
-def test_downcast():
- zoo = m.create_zoo()
- assert [type(animal) for animal in zoo] == [
- m.Labrador, m.Dog, m.Chihuahua, m.Cat, m.Panther
- ]
- assert [animal.name for animal in zoo] == [
- "Fido", "Ginger", "Hertzl", "Tiger", "Leo"
- ]
- zoo[1].sound = "woooooo"
- assert [dog.bark() for dog in zoo[:3]] == [
- "Labrador Fido goes WOOF!",
- "Dog Ginger goes woooooo",
- "Chihuahua Hertzl goes iyiyiyiyiyi and runs in circles"
- ]
- assert [cat.purr() for cat in zoo[3:]] == ["mrowr", "mrrrRRRRRR"]
- zoo[0].excitement -= 1000
- assert zoo[0].excitement == 14000
diff --git a/spaces/CVPR/LIVE/thrust/dependencies/cub/CONTRIBUTING.md b/spaces/CVPR/LIVE/thrust/dependencies/cub/CONTRIBUTING.md
deleted file mode 100644
index dd641756387902cb775c44e669564997dd83f325..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/dependencies/cub/CONTRIBUTING.md
+++ /dev/null
@@ -1,366 +0,0 @@
-# Table of Contents
-
-1. [Contributing to CUB](#contributing-to-cub)
-1. [CMake Options](#cmake-options)
-1. [Development Model](#development-model)
-
-# Contributing to CUB
-
-CUB uses Github to manage all open-source development, including bug tracking,
-pull requests, and design discussions. This document details how to get
-started as a CUB contributor.
-
-An overview of this process is:
-
-1. [Clone the CUB repository](#clone-the-cub-repository)
-1. [Setup a fork of CUB](#setup-a-fork-of-cub)
-1. [Setup your environment](#setup-your-environment)
-1. [Create a development branch](#create-a-development-branch)
-1. [Local development loop](#local-development-loop)
-1. [Push development branch to your fork](#push-development-branch-to-your-fork)
-1. [Create pull request](#create-pull-request)
-1. [Address feedback and update pull request](#address-feedback-and-update-pull-request)
-1. [When your PR is approved...](#when-your-pr-is-approved)
-
-## Clone the CUB Repository
-
-To get started, clone the main repository to your local computer:
-
-```
-git clone https://github.com/thrust/cub.git
-cd cub
-```
-
-## Setup a Fork of CUB
-
-You'll need a fork of CUB on Github to create a pull request. To setup your
-fork:
-
-1. Create a Github account (if needed)
-2. Go to [the CUB Github page](https://github.com/thrust/cub)
-3. Click "Fork" and follow any prompts that appear.
-
-Once your fork is created, setup a new remote repo in your local CUB clone:
-
-```
-git remote add github-fork git@github.com:/cub.git
-```
-
-## Setup Your Environment
-
-### Git Environment
-
-If you haven't already, this is a good time to tell git who you are. This
-information is used to fill out authorship information on your git commits.
-
-```
-git config --global user.name "John Doe"
-git config --global user.email johndoe@example.com
-```
-
-### Configure CMake builds
-
-CUB uses [CMake](https://www.cmake.org) for its developer build system. To
-configure, build, and test your checkout of CUB with default settings:
-
-```
-# Create build directory:
-mkdir build
-cd build
-
-# Configure -- use one of the following:
-cmake .. # Command line interface.
-ccmake .. # ncurses GUI (Linux only)
-cmake-gui # Graphical UI, set source/build directories in the app
-
-# Build:
-cmake --build . -j # invokes make (or ninja, etc)
-
-# Run tests and examples:
-ctest
-```
-
-See [CMake Options](#cmake-options) for details on customizing the build.
-
-## Create a Development Branch
-
-All work should be done in a development branch (also called a "topic branch")
-and not directly in the `master` branch. This makes it easier to manage multiple
-in-progress patches at once, and provides a descriptive label for your patch
-as it passes through the review system.
-
-To create a new branch based on the current `master`:
-
-```
-# Checkout local master branch:
-cd /path/to/cub/sources
-git checkout master
-
-# Sync local master branch with github:
-git pull
-
-# Create a new branch named `my_descriptive_branch_name` based on master:
-git checkout -b my_descriptive_branch_name
-
-# Verify that the branch has been created and is currently checked out:
-git branch
-```
-
-CUB branch names should follow a particular pattern:
-
-- For new features, name the branch `feature/`
-- For bugfixes associated with a github issue, use `bug/github/-`
- - Internal nvidia and gitlab bugs should use `nvidia` or `gitlab` in place of
- `github`.
-
-## Local Development Loop
-
-### Edit, Build, Test, Repeat
-
-Once the topic branch is created, you're all set to start working on CUB
-code. Make some changes, then build and test them:
-
-```
-# Implement changes:
-cd /path/to/cub/sources
-emacs cub/some_file.cuh # or whatever editor you prefer
-
-# Create / update a unit test for your changes:
-emacs tests/some_test.cu
-
-# Check that everything builds and tests pass:
-cd /path/to/cub/build/directory
-cmake --build . -j # or make, ninja, etc
-ctest
-```
-
-### Creating a Commit
-
-Once you're satisfied with your patch, commit your changes:
-
-```
-# Manually add changed files and create a commit:
-cd /path/to/cub
-git add cub/some_file.cuh
-git add tests/some_test.cu
-git commit
-
-# Or, if possible, use git-gui to review your changes while building your patch:
-git gui
-```
-
-#### Writing a Commit Message
-
-Your commit message will communicate the purpose and rationale behind your
-patch to other developers, and will be used to populate the initial description
-of your Github pull request.
-
-When writing a commit message, the following standard format should be used,
-since tools in the git ecosystem are designed to parse this correctly:
-
-```
-First line of commit message is a short summary (<80 char)
-
-Detailed description of change begins on third line. This portion can
-span multiple lines, try to manually wrap them at something reasonable.
-
-Blank lines can be used to separate multiple paragraphs in the description.
-
-If your patch is associated with another pull request or issue in the main
-CUB repository, you should reference it with a `#` symbol, e.g.
-#1023 for issue 1023.
-
-For issues / pull requests in a different github repo, reference them using
-the full syntax, e.g. thrust/thrust#4 for issue 4 in the thrust/thrust repo.
-
-Markdown is recommended for formatting more detailed messages, as these will
-be nicely rendered on Github, etc.
-```
-
-## Push Development Branch to your Fork
-
-Once you've committed your changes to a local development branch, it's time to
-push them to your fork:
-
-```
-cd /path/to/cub/checkout
-git checkout my_descriptive_branch_name # if not already checked out
-git push --set-upstream github-fork my_descriptive_branch_name
-```
-
-`--set-upstream github-fork` tells git that future pushes/pulls on this branch
-should target your `github-fork` remote by default.
-
-## Create Pull Request
-
-To create a pull request for your freshly pushed branch, open your github fork
-in a browser by going to `https://www.github.com//cub`. A
-prompt may automatically appear asking you to create a pull request if you've
-recently pushed a branch.
-
-If there's no prompt, go to "Code" > "Branches" and click the appropriate
-"New pull request" button for your branch.
-
-If you would like a specific developer to review your patch, feel free to
-request them as a reviewer at this time.
-
-The CUB team will review your patch, test it on NVIDIA's internal CI, and
-provide feedback.
-
-## Address Feedback and Update Pull Request
-
-If the reviewers request changes to your patch, use the following process to
-update the pull request:
-
-```
-# Make changes:
-cd /path/to/cub/sources
-git checkout my_descriptive_branch_name
-emacs cub/some_file.cuh
-emacs tests/some_test.cu
-
-# Build + test
-cd /path/to/thrust/build/directory
-cmake --build . -j
-ctest
-
-# Amend commit:
-cd /path/to/cub/sources
-git add cub/some_file.cuh
-git add tests/some_test.cu
-git commit --amend
-# Or
-git gui # Check the "Amend Last Commit" box
-
-# Update the branch on your fork:
-git push -f
-```
-
-At this point, the pull request should show your recent changes.
-
-## When Your PR is Approved
-
-Once your pull request is approved by the CUB team, no further action is
-needed from you. We will handle integrating it since we must coordinate changes
-to `master` with NVIDIA's internal perforce repository.
-
-# CMake Options
-
-A CUB build is configured using CMake options. These may be passed to CMake
-using
-
-```
-cmake -D= /path/to/cub/sources
-```
-
-or configured interactively with the `ccmake` or `cmake-gui` interfaces.
-
-The configuration options for CUB are:
-
-- `CMAKE_BUILD_TYPE={Release, Debug, RelWithDebInfo, MinSizeRel}`
- - Standard CMake build option. Default: `RelWithDebInfo`
-- `CUB_ENABLE_HEADER_TESTING={ON, OFF}`
- - Whether to test compile public headers. Default is `ON`.
-- `CUB_ENABLE_TESTING={ON, OFF}`
- - Whether to build unit tests. Default is `ON`.
-- `CUB_ENABLE_EXAMPLES={ON, OFF}`
- - Whether to build examples. Default is `ON`.
-- `CUB_ENABLE_DIALECT_CPPXX={ON, OFF}`
- - Toggle whether a specific C++ dialect will be targeted.
- - Multiple dialects may be targeted in a single build.
- - Possible values of `XX` are `{11, 14, 17}`.
- - By default, only C++14 is enabled.
-- `CUB_ENABLE_COMPUTE_XX={ON, OFF}`
- - Controls the targeted CUDA architecture(s)
- - Multiple options may be selected when using NVCC as the CUDA compiler.
- - Valid values of `XX` are:
- `{35, 37, 50, 52, 53, 60, 61, 62, 70, 72, 75, 80}`
- - Default value depends on `CUB_DISABLE_ARCH_BY_DEFAULT`:
-- `CUB_ENABLE_COMPUTE_FUTURE={ON, OFF}`
- - If enabled, CUDA objects will target the most recent virtual architecture
- in addition to the real architectures specified by the
- `CUB_ENABLE_COMPUTE_XX` options.
- - Default value depends on `CUB_DISABLE_ARCH_BY_DEFAULT`:
-- `CUB_DISABLE_ARCH_BY_DEFAULT={ON, OFF}`
- - When `ON`, all `CUB_ENABLE_COMPUTE_*` options are initially `OFF`.
- - Default: `OFF` (meaning all architectures are enabled by default)
-- `CUB_ENABLE_TESTS_WITH_RDC={ON, OFF}`
- - Whether to enable Relocatable Device Code when building tests.
- Default is `OFF`.
-- `CUB_ENABLE_EXAMPLES_WITH_RDC={ON, OFF}`
- - Whether to enable Relocatable Device Code when building examples.
- Default is `OFF`.
-
-# Development Model
-
-The following is a description of the basic development process that CUB follows. This is a living
-document that will evolve as our process evolves.
-
-CUB is distributed in three ways:
-
- * On GitHub.
- * In the NVIDIA HPC SDK.
- * In the CUDA Toolkit.
-
-## Trunk Based Development
-
-CUB uses [trunk based development](https://trunkbaseddevelopment.com). There is a single long-lived
-branch called `master`. Engineers may create branches for feature development. Such branches always
-merge into `master`. There are no release branches. Releases are produced by taking a snapshot of
-`master` ("snapping"). After a release has been snapped from `master`, it will never be changed.
-
-## Repositories
-
-As CUB is developed both on GitHub and internally at NVIDIA, there are three main places where code lives:
-
- * The Source of Truth, the [public CUB repository](https://github.com/thrust/cub), referred to as
- `github` later in this document.
- * An internal GitLab repository, referred to as `gitlab` later in this document.
- * An internal Perforce repository, referred to as `perforce` later in this document.
-
-## Versioning
-
-CUB has its own versioning system for releases, independent of the versioning scheme of the NVIDIA
-HPC SDK or the CUDA Toolkit.
-
-Today, CUB version numbers have a specific [semantic meaning](https://semver.org/).
-Releases prior to 1.10.0 largely, but not strictly, followed these semantic meanings.
-
-The version number for a CUB release uses the following format: `MMM.mmm.ss-ppp`, where:
-
- * `CUB_VERSION_MAJOR`/`MMM`: Major version, up to 3 decimal digits. It is incremented
- when the fundamental nature of the library evolves, leading to widespread changes across the
- entire library interface with no guarantee of API, ABI, or semantic compatibility with former
- versions.
- * `CUB_VERSION_MINOR`/`mmm`: Minor version, up to 3 decimal digits. It is incremented when
- breaking API, ABI, or semantic changes are made.
- * `CUB_VERSION_SUBMINOR`/`ss`: Subminor version, up to 2 decimal digits. It is incremented
- when notable new features or bug fixes or features that are API, ABI, and semantic backwards
- compatible are added.
- * `CUB_PATCH_NUMBER`/`ppp`: Patch number, up to 3 decimal digits. It is incremented if any
- change in the repo whatsoever is made and no other version component has been incremented.
-
-The `` header defines `CUB_*` macros for all of the version components mentioned
-above. Additionally, a `CUB_VERSION` macro is defined, which is an integer literal containing all
-of the version components except for `CUB_PATCH_NUMBER`.
-
-## Branches and Tags
-
-The following tag names are used in the CUB project:
-
- * `github/nvhpc-X.Y`: the tag that directly corresponds to what has been shipped in the NVIDIA HPC SDK release X.Y.
- * `github/cuda-X.Y`: the tag that directly corresponds to what has been shipped in the CUDA Toolkit release X.Y.
- * `github/A.B.C`: the tag that directly corresponds to a CUB version A.B.C.
-
-The following branch names are used in the CUB project:
-
- * `github/master`: the Source of Truth development branch of CUB.
- * `github/old-master`: the old Source of Truth branch, before unification of public and internal repositories.
- * `github/feature/`: feature branch for a feature under development.
- * `github/bug//-`: bug fix branch, where `bug-system` is `github` or `nvidia`.
- * `gitlab/master`: mirror of `github/master`.
- * `perforce/private`: mirrored `github/master`, plus files necessary for internal NVIDIA testing systems.
-
-On the rare occasion that we cannot do work in the open, for example when developing a change specific to an
-unreleased product, these branches may exist on `gitlab` instead of `github`. By default, everything should be
-in the open on `github` unless there is a strong motivation for it to not be open.
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/malloc_allocator.h b/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/malloc_allocator.h
deleted file mode 100644
index 2c01c66bd0a8e5f6f689c580e6d79df5d4e3a45c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/malloc_allocator.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-
-namespace thrust
-{
-namespace detail
-{
-
-template
- class malloc_allocator
- : public thrust::detail::tagged_allocator<
- T, System, Pointer
- >
-{
- private:
- typedef thrust::detail::tagged_allocator<
- T, System, Pointer
- > super_t;
-
- public:
- typedef typename super_t::pointer pointer;
- typedef typename super_t::size_type size_type;
-
- pointer allocate(size_type cnt);
-
- void deallocate(pointer p, size_type n);
-};
-
-} // end detail
-} // end thrust
-
-#include
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/select_system.h b/spaces/CVPR/LIVE/thrust/thrust/detail/select_system.h
deleted file mode 100644
index b22ceb0e96967b258caa01d054f8f45521ada23c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/select_system.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright 2008-2018 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-// We need a way to compute the return type of `select_system`, which is found
-// by using `thrust::system::detail::generic::select_system` and then making an
-// ADL call. We have no trait that defines the return type. With the
-// limitations of C++11 return type deduction, we need to be able to stick all
-// of that into `decltype`. So, we put the using statement into a detail
-// namespace, and then implement the generic dispatch function in that
-// namespace.
-
-namespace select_system_detail
-{
-
-using thrust::system::detail::generic::select_system;
-
-struct select_system_fn final
-{
- __thrust_exec_check_disable__
- template
- __host__ __device__
- auto operator()(
- thrust::detail::execution_policy_base const& exec0
- ) const
- THRUST_DECLTYPE_RETURNS(
- select_system(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec0))
- )
- )
-
- __thrust_exec_check_disable__
- template
- __host__ __device__
- auto operator()(
- thrust::detail::execution_policy_base const& exec0
- , thrust::detail::execution_policy_base const& exec1
- ) const
- THRUST_DECLTYPE_RETURNS(
- select_system(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec0))
- , thrust::detail::derived_cast(thrust::detail::strip_const(exec1))
- )
- )
-};
-
-} // namespace select_system_detail
-
-THRUST_INLINE_CONSTANT select_system_detail::select_system_fn select_system{};
-
-} // detail
-
-} // end namespace thrust
-
-#endif // THRUST_CPP_DIALECT >= 2011
-
diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/util/time_counter.py b/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/util/time_counter.py
deleted file mode 100644
index 0aedb2e4d61bfbe7571dca9d50053f0fedaa1359..0000000000000000000000000000000000000000
--- a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/util/time_counter.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import json
-import time
-
-
-class TimeCounter:
- def __init__(self) -> None:
- pass
-
- def clear(self):
- self.timedict = {}
- self.basetime = time.perf_counter()
-
- def timeit(self, name):
- nowtime = time.perf_counter() - self.basetime
- self.timedict[name] = nowtime
- self.basetime = time.perf_counter()
-
-
-class TimeHolder:
- def __init__(self) -> None:
- self.timedict = {}
-
- def update(self, _timedict: dict):
- for k, v in _timedict.items():
- if k not in self.timedict:
- self.timedict[k] = AverageMeter(name=k, val_only=True)
- self.timedict[k].update(val=v)
-
- def final_res(self):
- return {k: v.avg for k, v in self.timedict.items()}
-
- def __str__(self):
- return json.dumps(self.final_res(), indent=2)
-
-
-class AverageMeter(object):
- """Computes and stores the average and current value"""
-
- def __init__(self, name, fmt=":f", val_only=False):
- self.name = name
- self.fmt = fmt
- self.val_only = val_only
- self.reset()
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = self.sum / self.count
-
- def __str__(self):
- if self.val_only:
- fmtstr = "{name} {val" + self.fmt + "}"
- else:
- fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
- return fmtstr.format(**self.__dict__)
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/lib/puppeteer/puppeteer.js b/spaces/CikeyQI/Yunzai/Yunzai/lib/puppeteer/puppeteer.js
deleted file mode 100644
index 11e06fc81f7da08ceaa4a302677e636e5d5e40b9..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/lib/puppeteer/puppeteer.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import Renderer from '../renderer/loader.js'
-
-/**
- * 暂时保留对手工引用puppeteer.js的兼容
- * 后期会逐步废弃
- * 只提供截图及分片截图功能
- */
-let renderer = Renderer.getRenderer()
-renderer.screenshot = async (name, data) => {
- let img = await renderer.render(name, data)
- return img ? segment.image(img) : img
-}
-renderer.screenshots = async (name, data) => {
- data.multiPage = true
- let imgs = await renderer.render(name, data) || []
- let ret = []
- for (let img of imgs) {
- ret.push(img ? segment.image(img) : img)
- }
- return ret.length > 0 ? ret : false
-}
-
-export default renderer
\ No newline at end of file
diff --git a/spaces/CognitiveLabs/GPT-4-Vision-Chat/README.md b/spaces/CognitiveLabs/GPT-4-Vision-Chat/README.md
deleted file mode 100644
index 32ffef9695ce1a332b7e61af3ce2ec571dcf998d..0000000000000000000000000000000000000000
--- a/spaces/CognitiveLabs/GPT-4-Vision-Chat/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: GPT 4 Vision Chat
-emoji: 🏞️ 💬
-colorFrom: green
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-0ee118f2.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-0ee118f2.js
deleted file mode 100644
index 21abb3ef833c16656d497833ed2bb50ca2f8059f..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-0ee118f2.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as M,e as N,s as O,N as z,k,O as Q,K as S,p as j,o as w,M as E,ap as T,Q as A,z as C,v as B,A as q,x as P,a1 as L,B as V,am as W,P as X,R as Y,F as g,ak as r,E as Z,ae as y,h as D,j as F,q as p,r as x,t as K}from"./index-3370be2a.js";/* empty css */import{B as $}from"./Button-89624748.js";import{B as ee}from"./BlockTitle-bcf8c05e.js";import"./Info-5611e10f.js";function te(t){let e;return{c(){e=X(t[1])},m(l,s){j(l,e,s)},p(l,s){s&2&&Y(e,l[1])},d(l){l&&q(e)}}}function le(t){let e,l,s,a,o,c,h;return l=new ee({props:{show_label:t[4],info:t[2],$$slots:{default:[te]},$$scope:{ctx:t}}}),{c(){e=z("label"),k(l.$$.fragment),s=Q(),a=z("input"),S(a,"type","color"),a.disabled=t[3],S(a,"class","svelte-56zyyb"),S(e,"class","block")},m(_,f){j(_,e,f),w(l,e,null),E(e,s),E(e,a),T(a,t[0]),o=!0,c||(h=[A(a,"blur",t[6]),A(a,"input",t[7])],c=!0)},p(_,[f]){const m={};f&16&&(m.show_label=_[4]),f&4&&(m.info=_[2]),f&1026&&(m.$$scope={dirty:f,ctx:_}),l.$set(m),(!o||f&8)&&(a.disabled=_[3]),f&1&&T(a,_[0])},i(_){o||(C(l.$$.fragment,_),o=!0)},o(_){B(l.$$.fragment,_),o=!1},d(_){_&&q(e),P(l),c=!1,L(h)}}}function se(t,e,l){let{value:s="#000000"}=e,{value_is_output:a=!1}=e,{label:o}=e,{info:c=void 0}=e,{disabled:h=!1}=e,{show_label:_=!0}=e;const f=V();function m(){f("change",s),a||f("input")}W(()=>{l(5,a=!1)});function d(u){g.call(this,t,u)}function n(){s=this.value,l(0,s)}return t.$$set=u=>{"value"in u&&l(0,s=u.value),"value_is_output"in u&&l(5,a=u.value_is_output),"label"in u&&l(1,o=u.label),"info"in u&&l(2,c=u.info),"disabled"in u&&l(3,h=u.disabled),"show_label"in u&&l(4,_=u.show_label)},t.$$.update=()=>{t.$$.dirty&1&&m()},[s,o,c,h,_,a,d,n]}class ie extends M{constructor(e){super(),N(this,e,se,le,O,{value:0,value_is_output:5,label:1,info:2,disabled:3,show_label:4})}}function ne(t){let e,l,s,a,o,c;const h=[t[11]];let _={};for(let n=0;nF(s,"value",f)),D.push(()=>F(s,"value_is_output",m)),s.$on("change",t[15]),s.$on("input",t[16]),s.$on("submit",t[17]),s.$on("blur",t[18]),{c(){k(e.$$.fragment),l=Q(),k(s.$$.fragment)},m(n,u){w(e,n,u),j(n,l,u),w(s,n,u),c=!0},p(n,u){const v=u&2048?p(h,[x(n[11])]):{};e.$set(v);const b={};u&4&&(b.label=n[2]),u&8&&(b.info=n[3]),u&128&&(b.show_label=n[7]),u&4096&&(b.disabled=n[12]==="static"),!a&&u&1&&(a=!0,b.value=n[0],K(()=>a=!1)),!o&&u&2&&(o=!0,b.value_is_output=n[1],K(()=>o=!1)),s.$set(b)},i(n){c||(C(e.$$.fragment,n),C(s.$$.fragment,n),c=!0)},o(n){B(e.$$.fragment,n),B(s.$$.fragment,n),c=!1},d(n){n&&q(l),P(e,n),P(s,n)}}}function ae(t){let e,l;return e=new $({props:{visible:t[6],elem_id:t[4],elem_classes:t[5],container:t[8],scale:t[9],min_width:t[10],$$slots:{default:[ne]},$$scope:{ctx:t}}}),{c(){k(e.$$.fragment)},m(s,a){w(e,s,a),l=!0},p(s,[a]){const o={};a&64&&(o.visible=s[6]),a&16&&(o.elem_id=s[4]),a&32&&(o.elem_classes=s[5]),a&256&&(o.container=s[8]),a&512&&(o.scale=s[9]),a&1024&&(o.min_width=s[10]),a&530575&&(o.$$scope={dirty:a,ctx:s}),e.$set(o)},i(s){l||(C(e.$$.fragment,s),l=!0)},o(s){B(e.$$.fragment,s),l=!1},d(s){P(e,s)}}}function ue(t,e,l){let{label:s="ColorPicker"}=e,{info:a=void 0}=e,{elem_id:o=""}=e,{elem_classes:c=[]}=e,{visible:h=!0}=e,{value:_}=e,{value_is_output:f=!1}=e,{show_label:m}=e,{container:d=!0}=e,{scale:n=null}=e,{min_width:u=void 0}=e,{loading_status:v}=e,{mode:b}=e;function R(i){_=i,l(0,_)}function U(i){f=i,l(1,f)}function G(i){g.call(this,t,i)}function H(i){g.call(this,t,i)}function I(i){g.call(this,t,i)}function J(i){g.call(this,t,i)}return t.$$set=i=>{"label"in i&&l(2,s=i.label),"info"in i&&l(3,a=i.info),"elem_id"in i&&l(4,o=i.elem_id),"elem_classes"in i&&l(5,c=i.elem_classes),"visible"in i&&l(6,h=i.visible),"value"in i&&l(0,_=i.value),"value_is_output"in i&&l(1,f=i.value_is_output),"show_label"in i&&l(7,m=i.show_label),"container"in i&&l(8,d=i.container),"scale"in i&&l(9,n=i.scale),"min_width"in i&&l(10,u=i.min_width),"loading_status"in i&&l(11,v=i.loading_status),"mode"in i&&l(12,b=i.mode)},[_,f,s,a,o,c,h,m,d,n,u,v,b,R,U,G,H,I,J]}class _e extends M{constructor(e){super(),N(this,e,ue,ae,O,{label:2,info:3,elem_id:4,elem_classes:5,visible:6,value:0,value_is_output:1,show_label:7,container:8,scale:9,min_width:10,loading_status:11,mode:12})}get label(){return this.$$.ctx[2]}set label(e){this.$$set({label:e}),r()}get info(){return this.$$.ctx[3]}set info(e){this.$$set({info:e}),r()}get elem_id(){return this.$$.ctx[4]}set elem_id(e){this.$$set({elem_id:e}),r()}get elem_classes(){return this.$$.ctx[5]}set elem_classes(e){this.$$set({elem_classes:e}),r()}get visible(){return this.$$.ctx[6]}set visible(e){this.$$set({visible:e}),r()}get value(){return this.$$.ctx[0]}set value(e){this.$$set({value:e}),r()}get value_is_output(){return this.$$.ctx[1]}set value_is_output(e){this.$$set({value_is_output:e}),r()}get show_label(){return this.$$.ctx[7]}set show_label(e){this.$$set({show_label:e}),r()}get container(){return this.$$.ctx[8]}set container(e){this.$$set({container:e}),r()}get scale(){return this.$$.ctx[9]}set scale(e){this.$$set({scale:e}),r()}get min_width(){return this.$$.ctx[10]}set min_width(e){this.$$set({min_width:e}),r()}get loading_status(){return this.$$.ctx[11]}set loading_status(e){this.$$set({loading_status:e}),r()}get mode(){return this.$$.ctx[12]}set mode(e){this.$$set({mode:e}),r()}}const me=_e,be=["static","dynamic"],de=t=>({type:{payload:"string"},description:{payload:"hex color code"},example_data:t.value??"#000000"});export{me as Component,de as document,be as modes};
-//# sourceMappingURL=index-0ee118f2.js.map
diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/tool/lightbox.html b/spaces/Dinoking/Guccio-AI-Designer/netdissect/tool/lightbox.html
deleted file mode 100644
index fb0ebdf64766a43c9353428853be77deb5c52665..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/tool/lightbox.html
+++ /dev/null
@@ -1,59 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/encoders/helpers.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/encoders/helpers.py
deleted file mode 100644
index c5e907be6703ccc43f263b4c40f7d1b84bc47755..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/encoders/helpers.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from collections import namedtuple
-import torch
-import torch.nn.functional as F
-from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
-
-"""
-ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Flatten(Module):
- def forward(self, input):
- return input.view(input.size(0), -1)
-
-
-def l2_norm(input, axis=1):
- norm = torch.norm(input, 2, axis, True)
- output = torch.div(input, norm)
- return output
-
-
-class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
- """ A named tuple describing a ResNet block. """
-
-
-def get_block(in_channel, depth, num_units, stride=2):
- return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
-
-
-def get_blocks(num_layers):
- if num_layers == 50:
- blocks = [
- get_block(in_channel=64, depth=64, num_units=3),
- get_block(in_channel=64, depth=128, num_units=4),
- get_block(in_channel=128, depth=256, num_units=14),
- get_block(in_channel=256, depth=512, num_units=3)
- ]
- elif num_layers == 100:
- blocks = [
- get_block(in_channel=64, depth=64, num_units=3),
- get_block(in_channel=64, depth=128, num_units=13),
- get_block(in_channel=128, depth=256, num_units=30),
- get_block(in_channel=256, depth=512, num_units=3)
- ]
- elif num_layers == 152:
- blocks = [
- get_block(in_channel=64, depth=64, num_units=3),
- get_block(in_channel=64, depth=128, num_units=8),
- get_block(in_channel=128, depth=256, num_units=36),
- get_block(in_channel=256, depth=512, num_units=3)
- ]
- else:
- raise ValueError(
- "Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
- return blocks
-
-
-class SEModule(Module):
- def __init__(self, channels, reduction):
- super(SEModule, self).__init__()
- self.avg_pool = AdaptiveAvgPool2d(1)
- self.fc1 = Conv2d(channels, channels // reduction,
- kernel_size=1, padding=0, bias=False)
- self.relu = ReLU(inplace=True)
- self.fc2 = Conv2d(channels // reduction, channels,
- kernel_size=1, padding=0, bias=False)
- self.sigmoid = Sigmoid()
-
- def forward(self, x):
- module_input = x
- x = self.avg_pool(x)
- x = self.fc1(x)
- x = self.relu(x)
- x = self.fc2(x)
- x = self.sigmoid(x)
- return module_input * x
-
-
-class bottleneck_IR(Module):
- def __init__(self, in_channel, depth, stride):
- super(bottleneck_IR, self).__init__()
- if in_channel == depth:
- self.shortcut_layer = MaxPool2d(1, stride)
- else:
- self.shortcut_layer = Sequential(
- Conv2d(in_channel, depth, (1, 1), stride, bias=False),
- BatchNorm2d(depth)
- )
- self.res_layer = Sequential(
- BatchNorm2d(in_channel),
- Conv2d(in_channel, depth, (3, 3), (1, 1),
- 1, bias=False), PReLU(depth),
- Conv2d(depth, depth, (3, 3), stride, 1,
- bias=False), BatchNorm2d(depth)
- )
-
- def forward(self, x):
- shortcut = self.shortcut_layer(x)
- res = self.res_layer(x)
- return res + shortcut
-
-
-class bottleneck_IR_SE(Module):
- def __init__(self, in_channel, depth, stride):
- super(bottleneck_IR_SE, self).__init__()
- if in_channel == depth:
- self.shortcut_layer = MaxPool2d(1, stride)
- else:
- self.shortcut_layer = Sequential(
- Conv2d(in_channel, depth, (1, 1), stride, bias=False),
- BatchNorm2d(depth)
- )
- self.res_layer = Sequential(
- BatchNorm2d(in_channel),
- Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
- PReLU(depth),
- Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
- BatchNorm2d(depth),
- SEModule(depth, 16)
- )
-
- def forward(self, x):
- shortcut = self.shortcut_layer(x)
- res = self.res_layer(x)
- return res + shortcut
-
-
-def _upsample_add(x, y):
- """Upsample and add two feature maps.
- Args:
- x: (Variable) top feature map to be upsampled.
- y: (Variable) lateral feature map.
- Returns:
- (Variable) added feature map.
- Note in PyTorch, when input size is odd, the upsampled feature map
- with `F.upsample(..., scale_factor=2, mode='nearest')`
- maybe not equal to the lateral feature map size.
- e.g.
- original input size: [N,_,15,15] ->
- conv2d feature map size: [N,_,8,8] ->
- upsampled feature map size: [N,_,16,16]
- So we choose bilinear upsample which supports arbitrary output sizes.
- """
- _, _, H, W = y.size()
- return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
diff --git a/spaces/DragGan/DragGan/stylegan_human/generate.py b/spaces/DragGan/DragGan/stylegan_human/generate.py
deleted file mode 100644
index e2d90c603dd3168d423cd19ae1626d86fec19df1..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/generate.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-
-## this script is for generating images from pre-trained network based on StyleGAN1 (TensorFlow) and StyleGAN2-ada (PyTorch) ##
-
-import os
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import legacy
-from typing import List, Optional
-
-"""
-Generate images using pretrained network pickle.
-Examples:
-
-\b
-# Generate human full-body images without truncation
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7 \\
- --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-\b
-# Generate human full-body images with truncation
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 --seeds=0-100\\
- --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-# \b
-# Generate human full-body images using stylegan V1
-# python generate.py --outdir=outputs/generate/stylegan_human_v1_1024 \\
-# --network=pretrained_models/stylegan_human_v1_1024.pkl --version 1
-"""
-
-
-@click.command()
-@click.pass_context
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=legacy.num_range, help='List of random seeds')
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--outdir', help='Where to save the output images', default= 'outputs/generate/' , type=str, required=True, metavar='DIR')
-@click.option('--version', help="stylegan version, 1, 2 or 3", type=int, default=2)
-def generate_images(
- ctx: click.Context,
- network_pkl: str,
- seeds: Optional[List[int]],
- truncation_psi: float,
- noise_mode: str,
- outdir: str,
- version: int
-):
-
- print('Loading networks from "%s"...' % network_pkl)
- if version == 1:
- import dnnlib.tflib as tflib
- tflib.init_tf()
- G, D, Gs = legacy.load_pkl(network_pkl)
-
- else:
- import torch
- device = torch.device('cuda')
- with dnnlib.util.open_url(network_pkl) as f:
- G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
- os.makedirs(outdir, exist_ok=True)
-
-
- if seeds is None:
- ctx.fail('--seeds option is required.')
-
-
- # Generate images.
- target_z = np.array([])
- target_w = np.array([])
- latent_out = outdir.replace('/images/','')
- for seed_idx, seed in enumerate(seeds):
- if seed % 5000 == 0:
- print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
-
- if version == 1: ## stylegan v1
- z = np.random.RandomState(seed).randn(1, Gs.input_shape[1])
- # Generate image.
- fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
- if noise_mode == 'const': randomize_noise=False
- else: randomize_noise = True
- images = Gs.run(z, None, truncation_psi=truncation_psi, randomize_noise=randomize_noise, output_transform=fmt)
- PIL.Image.fromarray(images[0], 'RGB').save(f'{outdir}/seed{seed:04d}.png')
-
- else: ## stylegan v2/v3
- label = torch.zeros([1, G.c_dim], device=device)
- z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
- if target_z.size==0:
- target_z= z.cpu()
- else:
- target_z=np.append(target_z, z.cpu(), axis=0)
-
- w = G.mapping(z, label,truncation_psi=truncation_psi)
- img = G.synthesis(w, noise_mode=noise_mode,force_fp32 = True)
- if target_w.size==0:
- target_w= w.cpu()
- else:
- target_w=np.append(target_w, w.cpu(), axis=0)
-
- img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
- PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
- # print(target_z)
- # print(target_z.shape,target_w.shape)
-
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
- generate_images()
-
-#----------------------------------------------------------------------------
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp b/spaces/DragGan/DragGan/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp
deleted file mode 100644
index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-#include
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#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)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
- CHECK_CUDA(input);
- CHECK_CUDA(kernel);
-
- return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/EPFL-VILAB/MultiMAE/utils/model_builder.py b/spaces/EPFL-VILAB/MultiMAE/utils/model_builder.py
deleted file mode 100644
index 1152fea39ad52c5d5dda0bd5e5d926b4940fd682..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/utils/model_builder.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# --------------------------------------------------------
-# Based on timm and MAE-priv code bases
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/BUPT-PRIV/MAE-priv
-# --------------------------------------------------------
-
-from .registry import is_model_in_modules, model_entrypoint
-
-
-def split_model_name(model_name):
- model_split = model_name.split(':', 1)
- if len(model_split) == 1:
- return '', model_split[0]
- else:
- source_name, model_name = model_split
- assert source_name in ('timm', 'hf_hub')
- return source_name, model_name
-
-
-def safe_model_name(model_name, remove_source=True):
- def make_safe(name):
- return ''.join(c if c.isalnum() else '_' for c in name).rstrip('_')
-
- if remove_source:
- model_name = split_model_name(model_name)[-1]
- return make_safe(model_name)
-
-
-def create_model(
- model_name,
- pretrained=False,
- checkpoint_path='',
- scriptable=None,
- exportable=None,
- no_jit=None,
- **kwargs):
- """Create a model
-
- Args:
- model_name (str): name of model to instantiate
- pretrained (bool): load pretrained ImageNet-1k weights if true
- checkpoint_path (str): path of checkpoint to load after model is initialized
- scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
- exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
- no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
-
- Keyword Args:
- drop_rate (float): dropout rate for training (default: 0.0)
- global_pool (str): global pool type (default: 'avg')
- **: other kwargs are model specific
- """
- source_name, model_name = split_model_name(model_name)
-
- # Only EfficientNet and MobileNetV3 models have support for batchnorm params or drop_connect_rate passed as args
- is_efficientnet = is_model_in_modules(model_name, ['efficientnet', 'mobilenetv3'])
- if not is_efficientnet:
- kwargs.pop('bn_tf', None)
- kwargs.pop('bn_momentum', None)
- kwargs.pop('bn_eps', None)
-
- # handle backwards compat with drop_connect -> drop_path change
- drop_connect_rate = kwargs.pop('drop_connect_rate', None)
- if drop_connect_rate is not None and kwargs.get('drop_path_rate', None) is None:
- print("WARNING: 'drop_connect' as an argument is deprecated, please use 'drop_path'."
- " Setting drop_path to %f." % drop_connect_rate)
- kwargs['drop_path_rate'] = drop_connect_rate
-
- # Parameters that aren't supported by all models or are intended to only override model defaults if set
- # should default to None in command line args/cfg. Remove them if they are present and not set so that
- # non-supporting models don't break and default args remain in effect.
- kwargs = {k: v for k, v in kwargs.items()}
-
- create_fn = model_entrypoint(model_name)
- model = create_fn(**kwargs)
-
- return model
diff --git a/spaces/Eddycrack864/Applio-Inference/tools/dlmodels.bat b/spaces/Eddycrack864/Applio-Inference/tools/dlmodels.bat
deleted file mode 100644
index 5d80f50369b1f3ed37c045d07a9e2ce8954f09d4..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/tools/dlmodels.bat
+++ /dev/null
@@ -1,348 +0,0 @@
-@echo off && chcp 65001
-
-echo working dir is %cd%
-echo downloading requirement aria2 check.
-echo=
-dir /a:d/b | findstr "aria2" > flag.txt
-findstr "aria2" flag.txt >nul
-if %errorlevel% ==0 (
- echo aria2 checked.
- echo=
-) else (
- echo failed. please downloading aria2 from webpage!
- echo unzip it and put in this directory!
- timeout /T 5
- start https://github.com/aria2/aria2/releases/tag/release-1.36.0
- echo=
- goto end
-)
-
-echo envfiles checking start.
-echo=
-
-for /f %%x in ('findstr /i /c:"aria2" "flag.txt"') do (set aria2=%%x)&goto endSch
-:endSch
-
-set d32=f0D32k.pth
-set d40=f0D40k.pth
-set d48=f0D48k.pth
-set g32=f0G32k.pth
-set g40=f0G40k.pth
-set g48=f0G48k.pth
-
-set d40v2=f0D40k.pth
-set g40v2=f0G40k.pth
-
-set dld32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth
-set dld40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth
-set dld48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth
-set dlg32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth
-set dlg40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth
-set dlg48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth
-
-set dld40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth
-set dlg40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth
-
-set hp2_all=HP2_all_vocals.pth
-set hp3_all=HP3_all_vocals.pth
-set hp5_only=HP5_only_main_vocal.pth
-set VR_DeEchoAggressive=VR-DeEchoAggressive.pth
-set VR_DeEchoDeReverb=VR-DeEchoDeReverb.pth
-set VR_DeEchoNormal=VR-DeEchoNormal.pth
-set onnx_dereverb=vocals.onnx
-
-set dlhp2_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth
-set dlhp3_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth
-set dlhp5_only=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth
-set dlVR_DeEchoAggressive=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth
-set dlVR_DeEchoDeReverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth
-set dlVR_DeEchoNormal=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth
-set dlonnx_dereverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx
-
-set hb=hubert_base.pt
-
-set dlhb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt
-
-echo dir check start.
-echo=
-
-if exist "%~dp0assets\pretrained" (
- echo dir .\assets\pretrained checked.
- ) else (
- echo failed. generating dir .\assets\pretrained.
- mkdir pretrained
- )
-if exist "%~dp0assets\pretrained_v2" (
- echo dir .\assets\pretrained_v2 checked.
- ) else (
- echo failed. generating dir .\assets\pretrained_v2.
- mkdir pretrained_v2
- )
-if exist "%~dp0assets\uvr5_weights" (
- echo dir .\assets\uvr5_weights checked.
- ) else (
- echo failed. generating dir .\assets\uvr5_weights.
- mkdir uvr5_weights
- )
-if exist "%~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy" (
- echo dir .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy checked.
- ) else (
- echo failed. generating dir .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy.
- mkdir uvr5_weights\onnx_dereverb_By_FoxJoy
- )
-
-echo=
-echo dir check finished.
-
-echo=
-echo required files check start.
-
-echo checking D32k.pth
-if exist "%~dp0assets\pretrained\D32k.pth" (
- echo D32k.pth in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d %~dp0assets\pretrained -o D32k.pth
- if exist "%~dp0assets\pretrained\D32k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking D40k.pth
-if exist "%~dp0assets\pretrained\D40k.pth" (
- echo D40k.pth in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d %~dp0assets\pretrained -o D40k.pth
- if exist "%~dp0assets\pretrained\D40k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking D40k.pth
-if exist "%~dp0assets\pretrained_v2\D40k.pth" (
- echo D40k.pth in .\assets\pretrained_v2 checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d %~dp0assets\pretrained_v2 -o D40k.pth
- if exist "%~dp0assets\pretrained_v2\D40k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking D48k.pth
-if exist "%~dp0assets\pretrained\D48k.pth" (
- echo D48k.pth in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d %~dp0assets\pretrained -o D48k.pth
- if exist "%~dp0assets\pretrained\D48k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking G32k.pth
-if exist "%~dp0assets\pretrained\G32k.pth" (
- echo G32k.pth in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d %~dp0assets\pretrained -o G32k.pth
- if exist "%~dp0assets\pretrained\G32k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking G40k.pth
-if exist "%~dp0assets\pretrained\G40k.pth" (
- echo G40k.pth in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d %~dp0assets\pretrained -o G40k.pth
- if exist "%~dp0assets\pretrained\G40k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking G40k.pth
-if exist "%~dp0assets\pretrained_v2\G40k.pth" (
- echo G40k.pth in .\assets\pretrained_v2 checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d %~dp0assets\pretrained_v2 -o G40k.pth
- if exist "%~dp0assets\pretrained_v2\G40k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking G48k.pth
-if exist "%~dp0assets\pretrained\G48k.pth" (
- echo G48k.pth in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d %~dp0assets\pretrained -o G48k.pth
- if exist "%~dp0assets\pretrained\G48k.pth" (echo download successful.) else (echo please try again!
- echo=)
- )
-
-echo checking %d32%
-if exist "%~dp0assets\pretrained\%d32%" (
- echo %d32% in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld32% -d %~dp0assets\pretrained -o %d32%
- if exist "%~dp0assets\pretrained\%d32%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %d40%
-if exist "%~dp0assets\pretrained\%d40%" (
- echo %d40% in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld40% -d %~dp0assets\pretrained -o %d40%
- if exist "%~dp0assets\pretrained\%d40%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %d40v2%
-if exist "%~dp0assets\pretrained_v2\%d40v2%" (
- echo %d40v2% in .\assets\pretrained_v2 checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld40v2% -d %~dp0assets\pretrained_v2 -o %d40v2%
- if exist "%~dp0assets\pretrained_v2\%d40v2%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %d48%
-if exist "%~dp0assets\pretrained\%d48%" (
- echo %d48% in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld48% -d %~dp0assets\pretrained -o %d48%
- if exist "%~dp0assets\pretrained\%d48%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %g32%
-if exist "%~dp0assets\pretrained\%g32%" (
- echo %g32% in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg32% -d %~dp0assets\pretrained -o %g32%
- if exist "%~dp0assets\pretrained\%g32%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %g40%
-if exist "%~dp0assets\pretrained\%g40%" (
- echo %g40% in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg40% -d %~dp0assets\pretrained -o %g40%
- if exist "%~dp0assets\pretrained\%g40%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %g40v2%
-if exist "%~dp0assets\pretrained_v2\%g40v2%" (
- echo %g40v2% in .\assets\pretrained_v2 checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg40v2% -d %~dp0assets\pretrained_v2 -o %g40v2%
- if exist "%~dp0assets\pretrained_v2\%g40v2%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %g48%
-if exist "%~dp0assets\pretrained\%g48%" (
- echo %g48% in .\assets\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg48% -d %~dp0assets\pretrained -o %g48%
- if exist "%~dp0assets\pretrained\%g48%" (echo download successful.) else (echo please try again!
- echo=)
- )
-
-echo checking %hp2_all%
-if exist "%~dp0assets\uvr5_weights\%hp2_all%" (
- echo %hp2_all% in .\assets\uvr5_weights checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp2_all% -d %~dp0assets\uvr5_weights -o %hp2_all%
- if exist "%~dp0assets\uvr5_weights\%hp2_all%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %hp3_all%
-if exist "%~dp0assets\uvr5_weights\%hp3_all%" (
- echo %hp3_all% in .\assets\uvr5_weights checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp3_all% -d %~dp0assets\uvr5_weights -o %hp3_all%
- if exist "%~dp0assets\uvr5_weights\%hp3_all%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %hp5_only%
-if exist "%~dp0assets\uvr5_weights\%hp5_only%" (
- echo %hp5_only% in .\assets\uvr5_weights checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp5_only% -d %~dp0assets\uvr5_weights -o %hp5_only%
- if exist "%~dp0assets\uvr5_weights\%hp5_only%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %VR_DeEchoAggressive%
-if exist "%~dp0assets\uvr5_weights\%VR_DeEchoAggressive%" (
- echo %VR_DeEchoAggressive% in .\assets\uvr5_weights checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoAggressive% -d %~dp0assets\uvr5_weights -o %VR_DeEchoAggressive%
- if exist "%~dp0assets\uvr5_weights\%VR_DeEchoAggressive%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %VR_DeEchoDeReverb%
-if exist "%~dp0assets\uvr5_weights\%VR_DeEchoDeReverb%" (
- echo %VR_DeEchoDeReverb% in .\assets\uvr5_weights checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoDeReverb% -d %~dp0assets\uvr5_weights -o %VR_DeEchoDeReverb%
- if exist "%~dp0assets\uvr5_weights\%VR_DeEchoDeReverb%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %VR_DeEchoNormal%
-if exist "%~dp0assets\uvr5_weights\%VR_DeEchoNormal%" (
- echo %VR_DeEchoNormal% in .\assets\uvr5_weights checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoNormal% -d %~dp0assets\uvr5_weights -o %VR_DeEchoNormal%
- if exist "%~dp0assets\uvr5_weights\%VR_DeEchoNormal%" (echo download successful.) else (echo please try again!
- echo=)
- )
-echo checking %onnx_dereverb%
-if exist "%~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy\%onnx_dereverb%" (
- echo %onnx_dereverb% in .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlonnx_dereverb% -d %~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy -o %onnx_dereverb%
- if exist "%~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy\%onnx_dereverb%" (echo download successful.) else (echo please try again!
- echo=)
- )
-
-echo checking %hb%
-if exist "%~dp0assets\hubert\%hb%" (
- echo %hb% in .\assets\hubert\pretrained checked.
- echo=
- ) else (
- echo failed. starting download from huggingface.
- %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhb% -d %~dp0assets\hubert\ -o %hb%
- if exist "%~dp0assets\hubert\%hb%" (echo download successful.) else (echo please try again!
- echo=)
- )
-
-echo required files check finished.
-echo envfiles check complete.
-pause
-:end
-del flag.txt
diff --git a/spaces/Eli-chan/Test03/Dockerfile b/spaces/Eli-chan/Test03/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/Eli-chan/Test03/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/EronSamez/RVC_HFmeu/demucs/pretrained.py b/spaces/EronSamez/RVC_HFmeu/demucs/pretrained.py
deleted file mode 100644
index 6aac5db100cc7a9084af96d2cd083f0c8fac473c..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/demucs/pretrained.py
+++ /dev/null
@@ -1,107 +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.
-# author: adefossez
-
-import logging
-
-from diffq import DiffQuantizer
-import torch.hub
-
-from .model import Demucs
-from .tasnet import ConvTasNet
-from .utils import set_state
-
-logger = logging.getLogger(__name__)
-ROOT = "https://dl.fbaipublicfiles.com/demucs/v3.0/"
-
-PRETRAINED_MODELS = {
- 'demucs': 'e07c671f',
- 'demucs48_hq': '28a1282c',
- 'demucs_extra': '3646af93',
- 'demucs_quantized': '07afea75',
- 'tasnet': 'beb46fac',
- 'tasnet_extra': 'df3777b2',
- 'demucs_unittest': '09ebc15f',
-}
-
-SOURCES = ["drums", "bass", "other", "vocals"]
-
-
-def get_url(name):
- sig = PRETRAINED_MODELS[name]
- return ROOT + name + "-" + sig[:8] + ".th"
-
-
-def is_pretrained(name):
- return name in PRETRAINED_MODELS
-
-
-def load_pretrained(name):
- if name == "demucs":
- return demucs(pretrained=True)
- elif name == "demucs48_hq":
- return demucs(pretrained=True, hq=True, channels=48)
- elif name == "demucs_extra":
- return demucs(pretrained=True, extra=True)
- elif name == "demucs_quantized":
- return demucs(pretrained=True, quantized=True)
- elif name == "demucs_unittest":
- return demucs_unittest(pretrained=True)
- elif name == "tasnet":
- return tasnet(pretrained=True)
- elif name == "tasnet_extra":
- return tasnet(pretrained=True, extra=True)
- else:
- raise ValueError(f"Invalid pretrained name {name}")
-
-
-def _load_state(name, model, quantizer=None):
- url = get_url(name)
- state = torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True)
- set_state(model, quantizer, state)
- if quantizer:
- quantizer.detach()
-
-
-def demucs_unittest(pretrained=True):
- model = Demucs(channels=4, sources=SOURCES)
- if pretrained:
- _load_state('demucs_unittest', model)
- return model
-
-
-def demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64):
- if not pretrained and (extra or quantized or hq):
- raise ValueError("if extra or quantized is True, pretrained must be True.")
- model = Demucs(sources=SOURCES, channels=channels)
- if pretrained:
- name = 'demucs'
- if channels != 64:
- name += str(channels)
- quantizer = None
- if sum([extra, quantized, hq]) > 1:
- raise ValueError("Only one of extra, quantized, hq, can be True.")
- if quantized:
- quantizer = DiffQuantizer(model, group_size=8, min_size=1)
- name += '_quantized'
- if extra:
- name += '_extra'
- if hq:
- name += '_hq'
- _load_state(name, model, quantizer)
- return model
-
-
-def tasnet(pretrained=True, extra=False):
- if not pretrained and extra:
- raise ValueError("if extra is True, pretrained must be True.")
- model = ConvTasNet(X=10, sources=SOURCES)
- if pretrained:
- name = 'tasnet'
- if extra:
- name = 'tasnet_extra'
- _load_state(name, model)
- return model
diff --git a/spaces/FaceOnLive/Face-Recognition-SDK/Dockerfile b/spaces/FaceOnLive/Face-Recognition-SDK/Dockerfile
deleted file mode 100644
index 6c6c3d6f2b55c4b0c83cd74ef8ec94be4ba6539a..0000000000000000000000000000000000000000
--- a/spaces/FaceOnLive/Face-Recognition-SDK/Dockerfile
+++ /dev/null
@@ -1,19 +0,0 @@
-FROM ubuntu:20.04
-RUN ln -snf /usr/share/zoneinfo/$CONTAINER_TIMEZONE /etc/localtime && echo $CONTAINER_TIMEZONE > /etc/timezone
-RUN apt-get update -y
-RUN apt-get install -y python3 python3-pip python3-opencv
-RUN apt-get install -y libcurl4-openssl-dev libssl-dev
-RUN mkdir -p /home/FaceOnLive_v6
-RUN mkdir -p /home/FaceOnLive_v6/facewrapper
-WORKDIR /home/FaceOnLive_v6
-COPY ./facewrapper ./facewrapper
-COPY ./facewrapper/libs/libimutils.so /usr/lib
-COPY ./gradio ./gradio
-COPY ./openvino /usr/lib
-COPY ./app.py ./app.py
-COPY ./run.sh .
-COPY ./requirements.txt ./requirements.txt
-RUN pip3 install -r requirements.txt
-RUN chmod a+x run.sh
-CMD ["./run.sh"]
-EXPOSE 8000
\ No newline at end of file
diff --git a/spaces/Fareso/minima/app.py b/spaces/Fareso/minima/app.py
deleted file mode 100644
index ef387d3c5c45d3d9104ceed4fa7ae9f20ef04e1c..0000000000000000000000000000000000000000
--- a/spaces/Fareso/minima/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: Untitled.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
-
-# %% Untitled.ipynb 1
-from fastai.vision.all import *
-import gradio as gr
-
-def is_cat(x): return x[0].isupper()
-
-# %% Untitled.ipynb 3
-learn = load_learner('model.pkl')
-
-# %% Untitled.ipynb 5
-categories = ('Dog','Cat')
-
-def classify_image(img):
- pred,idx,probs = learn.predict(img)
- return dict(zip(categories, map(float,probs)))
-
-# %% Untitled.ipynb 7
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = ['dog.jpg','cat.jpg','dunno.jpg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label,examples=examples)
-intf.launch(inline=False)
diff --git a/spaces/FourthBrainGenAI/TalkToMyDoc-Hitch-Hikers-Guide/README.md b/spaces/FourthBrainGenAI/TalkToMyDoc-Hitch-Hikers-Guide/README.md
deleted file mode 100644
index 610a903ff93a689b629224370d5bbb8cf5cb4ff4..0000000000000000000000000000000000000000
--- a/spaces/FourthBrainGenAI/TalkToMyDoc-Hitch-Hikers-Guide/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TalkToMyDoc Hitch Hikers Guide
-emoji: 🐠
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/utils.py b/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/utils.py
deleted file mode 100644
index dd965fc4dd2af09e445a7f625f2681460874da7a..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/utils.py
+++ /dev/null
@@ -1,478 +0,0 @@
-import argparse
-import glob
-import json
-import logging
-import os
-import subprocess
-import sys
-import shutil
-
-import numpy as np
-import torch
-from scipy.io.wavfile import read
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
- ##################
- def go(model, bkey):
- saved_state_dict = checkpoint_dict[bkey]
- 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(): # 模型需要的shape
- try:
- new_state_dict[k] = saved_state_dict[k]
- if saved_state_dict[k].shape != state_dict[k].shape:
- logger.warn(
- "shape-%s-mismatch. need: %s, get: %s",
- k,
- state_dict[k].shape,
- saved_state_dict[k].shape,
- ) #
- raise KeyError
- except:
- # logger.info(traceback.format_exc())
- logger.info("%s is not in the checkpoint", k) # pretrain缺失的
- new_state_dict[k] = v # 模型自带的随机值
- if hasattr(model, "module"):
- model.module.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=False)
- return model
-
- go(combd, "combd")
- model = go(sbd, "sbd")
- #############
- logger.info("Loaded model weights")
-
- iteration = checkpoint_dict["iteration"]
- learning_rate = checkpoint_dict["learning_rate"]
- if (
- optimizer is not None and load_opt == 1
- ): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
- # try:
- optimizer.load_state_dict(checkpoint_dict["optimizer"])
- # except:
- # traceback.print_exc()
- logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-# def load_checkpoint(checkpoint_path, model, optimizer=None):
-# 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:
-# optimizer.load_state_dict(checkpoint_dict['optimizer'])
-# # print(1111)
-# saved_state_dict = checkpoint_dict['model']
-# # print(1111)
-#
-# 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:
-# new_state_dict[k] = saved_state_dict[k]
-# except:
-# 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)
-# logger.info("Loaded checkpoint '{}' (epoch {})" .format(
-# checkpoint_path, iteration))
-# return model, optimizer, learning_rate, iteration
-def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
- 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(): # 模型需要的shape
- try:
- new_state_dict[k] = saved_state_dict[k]
- if saved_state_dict[k].shape != state_dict[k].shape:
- logger.warn(
- "shape-%s-mismatch|need-%s|get-%s",
- k,
- state_dict[k].shape,
- saved_state_dict[k].shape,
- ) #
- raise KeyError
- except:
- # logger.info(traceback.format_exc())
- logger.info("%s is not in the checkpoint", k) # pretrain缺失的
- new_state_dict[k] = v # 模型自带的随机值
- if hasattr(model, "module"):
- model.module.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=False)
- logger.info("Loaded model weights")
-
- iteration = checkpoint_dict["iteration"]
- learning_rate = checkpoint_dict["learning_rate"]
- if (
- optimizer is not None and load_opt == 1
- ): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
- # try:
- optimizer.load_state_dict(checkpoint_dict["optimizer"])
- # except:
- # traceback.print_exc()
- logger.info("Loaded checkpoint '{}' (epoch {})".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 epoch {} 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 save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info(
- "Saving model and optimizer state at epoch {} to {}".format(
- iteration, checkpoint_path
- )
- )
- if hasattr(combd, "module"):
- state_dict_combd = combd.module.state_dict()
- else:
- state_dict_combd = combd.state_dict()
- if hasattr(sbd, "module"):
- state_dict_sbd = sbd.module.state_dict()
- else:
- state_dict_sbd = sbd.state_dict()
- torch.save(
- {
- "combd": state_dict_combd,
- "sbd": state_dict_sbd,
- "iteration": iteration,
- "optimizer": optimizer.state_dict(),
- "learning_rate": learning_rate,
- },
- checkpoint_path,
- )
-
-
-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]
- logger.debug(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):
- """
- todo:
- 结尾七人组：
- 保存频率、总epoch done
- bs done
- pretrainG、pretrainD done
- 卡号：os.en["CUDA_VISIBLE_DEVICES"] done
- if_latest done
- 模型：if_f0 done
- 采样率：自动选择config done
- 是否缓存数据集进GPU:if_cache_data_in_gpu done
-
- -m:
- 自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
- -c不要了
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "-se",
- "--save_every_epoch",
- type=int,
- required=True,
- help="checkpoint save frequency (epoch)",
- )
- parser.add_argument(
- "-te", "--total_epoch", type=int, required=True, help="total_epoch"
- )
- parser.add_argument(
- "-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
- )
- parser.add_argument(
- "-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
- )
- parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
- parser.add_argument(
- "-bs", "--batch_size", type=int, required=True, help="batch size"
- )
- parser.add_argument(
- "-e", "--experiment_dir", type=str, required=True, help="experiment dir"
- ) # -m
- parser.add_argument(
- "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
- )
- parser.add_argument(
- "-sw",
- "--save_every_weights",
- type=str,
- default="0",
- help="save the extracted model in weights directory when saving checkpoints",
- )
- parser.add_argument(
- "-v", "--version", type=str, required=True, help="model version"
- )
- parser.add_argument(
- "-f0",
- "--if_f0",
- type=int,
- required=True,
- help="use f0 as one of the inputs of the model, 1 or 0",
- )
- parser.add_argument(
- "-l",
- "--if_latest",
- type=int,
- required=True,
- help="if only save the latest G/D pth file, 1 or 0",
- )
- parser.add_argument(
- "-c",
- "--if_cache_data_in_gpu",
- type=int,
- required=True,
- help="if caching the dataset in GPU memory, 1 or 0",
- )
-
- args = parser.parse_args()
- name = args.experiment_dir
- experiment_dir = os.path.join("./logs", args.experiment_dir)
-
- config_save_path = os.path.join(experiment_dir, "config.json")
- with open(config_save_path, "r") as f:
- config = json.load(f)
-
- hparams = HParams(**config)
- hparams.model_dir = hparams.experiment_dir = experiment_dir
- hparams.save_every_epoch = args.save_every_epoch
- hparams.name = name
- hparams.total_epoch = args.total_epoch
- hparams.pretrainG = args.pretrainG
- hparams.pretrainD = args.pretrainD
- hparams.version = args.version
- hparams.gpus = args.gpus
- hparams.train.batch_size = args.batch_size
- hparams.sample_rate = args.sample_rate
- hparams.if_f0 = args.if_f0
- hparams.if_latest = args.if_latest
- hparams.save_every_weights = args.save_every_weights
- hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
- hparams.data.training_files = "%s/filelist.txt" % experiment_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
-
-
-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__()
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_coordinated_box_ball_matching.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_coordinated_box_ball_matching.py
deleted file mode 100644
index a15c0586e63bd1c6cec7a016897e6278daeb3b8f..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_coordinated_box_ball_matching.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import numpy as np
-import os
-import pybullet as p
-import random
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-
-class ColorCoordinatedBoxBallMatching(Task):
- """Pick up each ball and place it inside the box of the same color, navigate around the barrier without knocking over any small blocks."""
-
- def __init__(self):
- super().__init__()
- self.max_steps = 20
- self.lang_template = "put the {color} ball in the {color} box"
- self.task_completed_desc = "done placing balls in boxes."
- self.additional_reset()
-
- def reset(self, env):
- super().reset(env)
-
- # Define colors for the boxes and balls
- colors = ['red', 'blue', 'green', 'yellow']
-
- # Add boxes.
- box_size = (0.05, 0.05, 0.05)
- box_urdf = 'box/box-template.urdf'
- box_poses = []
- for color in colors:
- box_pose = self.get_random_pose(env, box_size)
- env.add_object(box_urdf, box_pose, color=color, category='fixed')
- box_poses.append(box_pose)
-
- # Add balls.
- balls = []
- ball_size = (0.02, 0.02, 0.02)
- ball_urdf = 'ball/ball-template.urdf'
- for color in colors:
- ball_pose = self.get_random_pose(env, ball_size)
- ball_id = env.add_object(ball_urdf, ball_pose, color=color)
- balls.append(ball_id)
-
- # Add small blocks as barriers.
- barrier_size = (0.01, 0.01, 0.01)
- barrier_urdf = 'block/small.urdf'
- for _ in range(10):
- barrier_pose = self.get_random_pose(env, barrier_size)
- env.add_object(barrier_urdf, barrier_pose, category='fixed')
-
- # Goal: each ball is in the box of the same color.
- for i in range(len(balls)):
- self.add_goal(objs=[balls[i]], matches=np.ones((1, 1)), targ_poses=[box_poses[i]], replace=False,
- rotations=True, metric='pose', params=None, step_max_reward=1/len(balls),
- language_goal=self.lang_template.format(color=colors[i]))
\ No newline at end of file
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/core/unet.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/core/unet.py
deleted file mode 100644
index 5aa28000fb2a35281e2291b31ae6e13b7c66fa59..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/core/unet.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Credit: https://github.com/milesial/Pytorch-UNet/
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class DoubleConv(nn.Module):
- """(convolution => [BN] => ReLU) * 2"""
-
- def __init__(self, in_channels, out_channels, mid_channels=None):
- super().__init__()
- if not mid_channels:
- mid_channels = out_channels
- self.double_conv = nn.Sequential(
- nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
- nn.BatchNorm2d(mid_channels), # (Mohit): argh... forgot to remove this batchnorm
- nn.ReLU(inplace=True),
- nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
- nn.BatchNorm2d(out_channels), # (Mohit): argh... forgot to remove this batchnorm
- nn.ReLU(inplace=True)
- )
-
- def forward(self, x):
- return self.double_conv(x)
-
-
-class Down(nn.Module):
- """Downscaling with maxpool then double conv"""
-
- def __init__(self, in_channels, out_channels):
- super().__init__()
- self.maxpool_conv = nn.Sequential(
- nn.MaxPool2d(2),
- DoubleConv(in_channels, out_channels)
- )
-
- def forward(self, x):
- return self.maxpool_conv(x)
-
-
-class Up(nn.Module):
- """Upscaling then double conv"""
-
- def __init__(self, in_channels, out_channels, bilinear=True):
- super().__init__()
-
- # if bilinear, use the normal convolutions to reduce the number of channels
- if bilinear:
- self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
- self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
- else:
- self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
- self.conv = DoubleConv(in_channels, out_channels)
-
-
- def forward(self, x1, x2):
- x1 = self.up(x1)
- # input is CHW
- diffY = x2.size()[2] - x1.size()[2]
- diffX = x2.size()[3] - x1.size()[3]
-
- x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
- diffY // 2, diffY - diffY // 2])
- # if you have padding issues, see
- # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
- # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
- x = torch.cat([x2, x1], dim=1)
- return self.conv(x)
-
-
-class OutConv(nn.Module):
- def __init__(self, in_channels, out_channels):
- super(OutConv, self).__init__()
- self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
-
- def forward(self, x):
- return self.conv(x)
\ No newline at end of file
diff --git a/spaces/GeorgeOrville/bingo/src/lib/isomorphic/node.ts b/spaces/GeorgeOrville/bingo/src/lib/isomorphic/node.ts
deleted file mode 100644
index da213ad6a86181979f098309c374da02835db5a0..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/lib/isomorphic/node.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import Debug from 'debug'
-
-const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici')
-const { HttpsProxyAgent } = require('https-proxy-agent')
-const ws = require('ws')
-
-const debug = Debug('bingo')
-
-const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY;
-let WebSocket = ws.WebSocket
-
-if (httpProxy) {
- setGlobalDispatcher(new ProxyAgent(httpProxy))
- const agent = new HttpsProxyAgent(httpProxy)
- // @ts-ignore
- WebSocket = class extends ws.WebSocket {
- constructor(address: string | URL, options: typeof ws.WebSocket) {
- super(address, {
- ...options,
- agent,
- })
- }
- }
-}
-
-export default { fetch, WebSocket, debug }
diff --git a/spaces/Gradio-Blocks/ML-Aided-Code-Analysis/app.py b/spaces/Gradio-Blocks/ML-Aided-Code-Analysis/app.py
deleted file mode 100644
index d6e5af488e82f46525e8df9adc51b02ab3fd9316..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/ML-Aided-Code-Analysis/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import gradio as gr
-from src.utils import return_parse_tree, GenCode, Config
-import logging
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-
-logger.info("Loading classes into the memory....")
-code_gen_class = GenCode(Config.code_gen_idt, gen_kwargs=Config.code_gen_config)
-logger.info("Sucessfully loaded classes into the memory")
-
-def code_gen(code_snippet:str) -> str:
- logger.info("Starting to generate code...")
- generated_code = code_gen_class(code_snippet)
- logger.info("Sucessfully generated code")
-
- return generated_code
-
-def code_edit(code_snippet:str = "def dataset_name():\n return 'test'"):
- return code_snippet + "\n" + return_parse_tree(code_snippet)
-
-demo = gr.Blocks(analytics_enabled=True)
-with demo:
- gr.Markdown("""# Code Analysis \n ## Code Editor""")
- with gr.Row():
- input_editor = gr.Textbox(label="Code Editor",lines=10,interactive=True,placeholder="Use this as the main code editor ")
- button_gen = gr.Button(value="Complete Code")
-
- button_gen.click(code_gen,input_editor,input_editor)
- # iface = gr.Interface(title="Code Editor",fn=code_edit, inputs="text", outputs="text",examples=["def dataset_name():\n return 'test'"])
- # iface.launch()
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/models/psp.py b/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/models/psp.py
deleted file mode 100644
index 36c0b2b7b3fdd28bc32272d0d8fcff24e4848355..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/StyleGAN-NADA/e4e/models/psp.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import matplotlib
-
-matplotlib.use('Agg')
-import torch
-from torch import nn
-from e4e.models.encoders import psp_encoders
-from e4e.models.stylegan2.model import Generator
-from e4e.configs.paths_config import model_paths
-
-
-def get_keys(d, name):
- if 'state_dict' in d:
- d = d['state_dict']
- d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
- return d_filt
-
-
-class pSp(nn.Module):
-
- def __init__(self, opts, device):
- super(pSp, self).__init__()
- self.opts = opts
- self.device = device
- # Define architecture
- self.encoder = self.set_encoder()
- self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2)
- self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
- # Load weights if needed
- self.load_weights()
-
- def set_encoder(self):
- if self.opts.encoder_type == 'GradualStyleEncoder':
- encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts)
- elif self.opts.encoder_type == 'Encoder4Editing':
- encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts)
- else:
- raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type))
- return encoder
-
- def load_weights(self):
- if self.opts.checkpoint_path is not None:
- print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path))
- ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu')
- self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True)
- self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True)
- self.__load_latent_avg(ckpt)
- else:
- print('Loading encoders weights from irse50!')
- encoder_ckpt = torch.load(model_paths['ir_se50'])
- self.encoder.load_state_dict(encoder_ckpt, strict=False)
- print('Loading decoder weights from pretrained!')
- ckpt = torch.load(self.opts.stylegan_weights)
- self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
- self.__load_latent_avg(ckpt, repeat=self.encoder.style_count)
-
- def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True,
- inject_latent=None, return_latents=False, alpha=None):
- if input_code:
- codes = x
- else:
- codes = self.encoder(x)
- # normalize with respect to the center of an average face
- if self.opts.start_from_latent_avg:
- if codes.ndim == 2:
- codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
- else:
- codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)
-
- if latent_mask is not None:
- for i in latent_mask:
- if inject_latent is not None:
- if alpha is not None:
- codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
- else:
- codes[:, i] = inject_latent[:, i]
- else:
- codes[:, i] = 0
-
- input_is_latent = not input_code
- images, result_latent = self.decoder([codes],
- input_is_latent=input_is_latent,
- randomize_noise=randomize_noise,
- return_latents=return_latents)
-
- if resize:
- images = self.face_pool(images)
-
- if return_latents:
- return images, result_latent
- else:
- return images
-
- def __load_latent_avg(self, ckpt, repeat=None):
- if 'latent_avg' in ckpt:
- self.latent_avg = ckpt['latent_avg'].to(self.device)
- if repeat is not None:
- self.latent_avg = self.latent_avg.repeat(repeat, 1)
- else:
- self.latent_avg = None
diff --git a/spaces/Gradio-Blocks/anime-colorization/scripts/image_train.py b/spaces/Gradio-Blocks/anime-colorization/scripts/image_train.py
deleted file mode 100644
index eccdb3980699fd513bb1d01e89954fc2000d14da..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/anime-colorization/scripts/image_train.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""
-Train a diffusion model on images.
-"""
-
-import argparse
-
-from pixel_guide_diffusion import dist_util, logger
-from pixel_guide_diffusion.image_datasets import load_data
-from pixel_guide_diffusion.resample import create_named_schedule_sampler
-from pixel_guide_diffusion.script_util import (
- model_and_diffusion_defaults,
- create_model_and_diffusion,
- args_to_dict,
- add_dict_to_argparser,
-)
-from pixel_guide_diffusion.train_util import TrainLoop
-
-
-def main():
- args = create_argparser().parse_args()
-
- dist_util.setup_dist()
- logger.configure()
-
- logger.log("creating model and diffusion...")
- model, diffusion = create_model_and_diffusion(
- **args_to_dict(args, model_and_diffusion_defaults().keys())
- )
- model.to(dist_util.dev())
- schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
-
- logger.log("creating data loader...")
- data = load_data(
- data_dir=args.data_dir,
- batch_size=args.batch_size,
- image_size=args.image_size,
- class_cond=args.class_cond,
- )
-
- logger.log("training...")
- TrainLoop(
- model=model,
- diffusion=diffusion,
- data=data,
- batch_size=args.batch_size,
- microbatch=args.microbatch,
- lr=args.lr,
- ema_rate=args.ema_rate,
- log_interval=args.log_interval,
- save_interval=args.save_interval,
- resume_checkpoint=args.resume_checkpoint,
- use_fp16=args.use_fp16,
- fp16_scale_growth=args.fp16_scale_growth,
- schedule_sampler=schedule_sampler,
- weight_decay=args.weight_decay,
- lr_anneal_steps=args.lr_anneal_steps,
- ).run_loop()
-
-
-def create_argparser():
- defaults = dict(
- data_dir="",
- schedule_sampler="uniform",
- lr=1e-4,
- weight_decay=0.0,
- lr_anneal_steps=0,
- batch_size=1,
- microbatch=-1, # -1 disables microbatches
- ema_rate="0.9999", # comma-separated list of EMA values
- log_interval=10,
- save_interval=10000,
- resume_checkpoint="",
- use_fp16=False,
- fp16_scale_growth=1e-3,
- )
- defaults.update(model_and_diffusion_defaults())
- parser = argparse.ArgumentParser()
- add_dict_to_argparser(parser, defaults)
- return parser
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py
deleted file mode 100644
index ee034b716d6e20bfad03abe769f91fa3cc44c5e9..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py
+++ /dev/null
@@ -1,63 +0,0 @@
-_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
-model = dict(
- pretrained='open-mmlab://detectron2/resnext101_32x8d',
- backbone=dict(
- type='ResNeXt',
- depth=101,
- groups=32,
- base_width=8,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=False),
- style='pytorch'))
-
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
- mean=[103.530, 116.280, 123.675],
- std=[57.375, 57.120, 58.395],
- to_rgb=False)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/samplers/__init__.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/samplers/__init__.py
deleted file mode 100644
index 2596aeb2ccfc85b58624713c04453d34e94a4062..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/samplers/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .distributed_sampler import DistributedSampler
-from .group_sampler import DistributedGroupSampler, GroupSampler
-
-__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler']
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/apcnet_r50-d8.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/apcnet_r50-d8.py
deleted file mode 100644
index c8f5316cbcf3896ba9de7ca2c801eba512f01d5e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/apcnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='EncoderDecoder',
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=dict(
- type='APCHead',
- in_channels=2048,
- in_index=3,
- channels=512,
- pool_scales=(1, 2, 3, 6),
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
- auxiliary_head=dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index a9c712d1ccfd62ddf6f12ff01ea347ca1995013b..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './ann_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/GroveStreet/GTA_SOVITS/models.py b/spaces/GroveStreet/GTA_SOVITS/models.py
deleted file mode 100644
index ac40c3cda6b5ef351049b0348711f90e2985ce1e..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/models.py
+++ /dev/null
@@ -1,469 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.attentions as attentions
-import modules.commons as commons
-import modules.modules as modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-import utils
-from modules.commons import init_weights, get_padding
-from utils import f0_to_coarse
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
- gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- # print(x.shape,x_lengths.shape)
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- out_channels,
- hidden_channels,
- kernel_size,
- n_layers,
- gin_channels=0,
- filter_channels=None,
- n_heads=None,
- p_dropout=None):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
- self.f0_emb = nn.Embedding(256, hidden_channels)
-
- self.enc_ = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
-
- def forward(self, x, x_mask, f0=None, noice_scale=1):
- x = x + self.f0_emb(f0).transpose(1, 2)
- x = self.enc_(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
-
- return z, m, logs, x_mask
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames - partial_frames, partial_hop):
- mel_range = torch.arange(i, i + partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:, -partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:, s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- # embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-class F0Decoder(nn.Module):
- def __init__(self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- spk_channels=0):
- super().__init__()
- 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
- self.spk_channels = spk_channels
-
- self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
- self.decoder = attentions.FFT(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
- self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
-
- def forward(self, x, norm_f0, x_mask, spk_emb=None):
- x = torch.detach(x)
- if (spk_emb is not None):
- x = x + self.cond(spk_emb)
- x += self.f0_prenet(norm_f0)
- x = self.prenet(x) * x_mask
- x = self.decoder(x * x_mask, x_mask)
- x = self.proj(x) * x_mask
- return x
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- n_speakers,
- sampling_rate=44100,
- vol_embedding=False,
- vocoder_name = "nsf-hifigan",
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.vol_embedding = vol_embedding
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
- if vol_embedding:
- self.emb_vol = nn.Linear(1, hidden_channels)
-
- self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
-
- self.enc_p = TextEncoder(
- inter_channels,
- hidden_channels,
- filter_channels=filter_channels,
- n_heads=n_heads,
- n_layers=n_layers,
- kernel_size=kernel_size,
- p_dropout=p_dropout
- )
- hps = {
- "sampling_rate": sampling_rate,
- "inter_channels": inter_channels,
- "resblock": resblock,
- "resblock_kernel_sizes": resblock_kernel_sizes,
- "resblock_dilation_sizes": resblock_dilation_sizes,
- "upsample_rates": upsample_rates,
- "upsample_initial_channel": upsample_initial_channel,
- "upsample_kernel_sizes": upsample_kernel_sizes,
- "gin_channels": gin_channels,
- }
-
-
- if vocoder_name == "nsf-hifigan":
- from vdecoder.hifigan.models import Generator
- self.dec = Generator(h=hps)
- elif vocoder_name == "nsf-snake-hifigan":
- from vdecoder.hifiganwithsnake.models import Generator
- self.dec = Generator(h=hps)
- else:
- print("[?] Unkown vocoder: use default(nsf-hifigan)")
- from vdecoder.hifigan.models import Generator
- self.dec = Generator(h=hps)
-
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
- self.f0_decoder = F0Decoder(
- 1,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- spk_channels=gin_channels
- )
- self.emb_uv = nn.Embedding(2, hidden_channels)
- self.character_mix = False
-
- def EnableCharacterMix(self, n_speakers_map, device):
- self.speaker_map = torch.zeros((n_speakers_map, 1, 1, self.gin_channels)).to(device)
- for i in range(n_speakers_map):
- self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]).to(device))
- self.speaker_map = self.speaker_map.unsqueeze(0).to(device)
- self.character_mix = True
-
- def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None, vol = None):
- g = self.emb_g(g).transpose(1,2)
-
- # vol proj
- vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol!=None and self.vol_embedding else 0
-
- # ssl prenet
- x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
- x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) + vol
-
- # f0 predict
- lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
- norm_lf0 = utils.normalize_f0(lf0, x_mask, uv)
- pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
-
- # encoder
- z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
-
- # flow
- z_p = self.flow(z, spec_mask, g=g)
- z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
-
- # nsf decoder
- o = self.dec(z_slice, g=g, f0=pitch_slice)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0
-
- def infer(self, c, f0, uv, g=None, noice_scale=0.35, seed=52468, predict_f0=False, vol = None):
-
- if c.device == torch.device("cuda"):
- torch.cuda.manual_seed_all(seed)
- else:
- torch.manual_seed(seed)
-
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-
- if self.character_mix and len(g) > 1: # [N, S] * [S, B, 1, H]
- g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
- g = g * self.speaker_map # [N, S, B, 1, H]
- g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
- g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
- else:
- if g.dim() == 1:
- g = g.unsqueeze(0)
- g = self.emb_g(g).transpose(1, 2)
-
- x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
- # vol proj
- vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol!=None and self.vol_embedding else 0
-
- x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) + vol
-
- if predict_f0:
- lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
- norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
- pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
- f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g, f0=f0)
- return o,f0
-
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/nethook.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/nethook.py
deleted file mode 100644
index f36e84ee0cae2de2c3be247498408cf66db3ee8f..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/nethook.py
+++ /dev/null
@@ -1,266 +0,0 @@
-'''
-Utilities for instrumenting a torch model.
-
-InstrumentedModel will wrap a pytorch model and allow hooking
-arbitrary layers to monitor or modify their output directly.
-
-Modified by Erik Härkönen:
-- 29.11.2019: Unhooking bugfix
-- 25.01.2020: Offset edits, removed old API
-'''
-
-import torch, numpy, types
-from collections import OrderedDict
-
-class InstrumentedModel(torch.nn.Module):
- '''
- A wrapper for hooking, probing and intervening in pytorch Modules.
- Example usage:
-
- ```
- model = load_my_model()
- with inst as InstrumentedModel(model):
- inst.retain_layer(layername)
- inst.edit_layer(layername, 0.5, target_features)
- inst.edit_layer(layername, offset=offset_tensor)
- inst(inputs)
- original_features = inst.retained_layer(layername)
- ```
- '''
-
- def __init__(self, model):
- super(InstrumentedModel, self).__init__()
- self.model = model
- self._retained = OrderedDict()
- self._ablation = {}
- self._replacement = {}
- self._offset = {}
- self._hooked_layer = {}
- self._old_forward = {}
-
- def __enter__(self):
- return self
-
- def __exit__(self, type, value, traceback):
- self.close()
-
- def forward(self, *inputs, **kwargs):
- return self.model(*inputs, **kwargs)
-
- def retain_layer(self, layername):
- '''
- Pass a fully-qualified layer name (E.g., module.submodule.conv3)
- to hook that layer and retain its output each time the model is run.
- A pair (layername, aka) can be provided, and the aka will be used
- as the key for the retained value instead of the layername.
- '''
- self.retain_layers([layername])
-
- def retain_layers(self, layernames):
- '''
- Retains a list of a layers at once.
- '''
- self.add_hooks(layernames)
- for layername in layernames:
- aka = layername
- if not isinstance(aka, str):
- layername, aka = layername
- if aka not in self._retained:
- self._retained[aka] = None
-
- def retained_features(self):
- '''
- Returns a dict of all currently retained features.
- '''
- return OrderedDict(self._retained)
-
- def retained_layer(self, aka=None, clear=False):
- '''
- Retrieve retained data that was previously hooked by retain_layer.
- Call this after the model is run. If clear is set, then the
- retained value will return and also cleared.
- '''
- if aka is None:
- # Default to the first retained layer.
- aka = next(self._retained.keys().__iter__())
- result = self._retained[aka]
- if clear:
- self._retained[aka] = None
- return result
-
- def edit_layer(self, layername, ablation=None, replacement=None, offset=None):
- '''
- Pass a fully-qualified layer name (E.g., module.submodule.conv3)
- to hook that layer and modify its output each time the model is run.
- The output of the layer will be modified to be a convex combination
- of the replacement and x interpolated according to the ablation, i.e.:
- `output = x * (1 - a) + (r * a)`.
- Additionally or independently, an offset can be added to the output.
- '''
- if not isinstance(layername, str):
- layername, aka = layername
- else:
- aka = layername
-
- # The default ablation if a replacement is specified is 1.0.
- if ablation is None and replacement is not None:
- ablation = 1.0
- self.add_hooks([(layername, aka)])
- if ablation is not None:
- self._ablation[aka] = ablation
- if replacement is not None:
- self._replacement[aka] = replacement
- if offset is not None:
- self._offset[aka] = offset
- # If needed, could add an arbitrary postprocessing lambda here.
-
- def remove_edits(self, layername=None, remove_offset=True, remove_replacement=True):
- '''
- Removes edits at the specified layer, or removes edits at all layers
- if no layer name is specified.
- '''
- if layername is None:
- if remove_replacement:
- self._ablation.clear()
- self._replacement.clear()
- if remove_offset:
- self._offset.clear()
- return
-
- if not isinstance(layername, str):
- layername, aka = layername
- else:
- aka = layername
- if remove_replacement and aka in self._ablation:
- del self._ablation[aka]
- if remove_replacement and aka in self._replacement:
- del self._replacement[aka]
- if remove_offset and aka in self._offset:
- del self._offset[aka]
-
- def add_hooks(self, layernames):
- '''
- Sets up a set of layers to be hooked.
-
- Usually not called directly: use edit_layer or retain_layer instead.
- '''
- needed = set()
- aka_map = {}
- for name in layernames:
- aka = name
- if not isinstance(aka, str):
- name, aka = name
- if self._hooked_layer.get(aka, None) != name:
- aka_map[name] = aka
- needed.add(name)
- if not needed:
- return
- for name, layer in self.model.named_modules():
- if name in aka_map:
- needed.remove(name)
- aka = aka_map[name]
- self._hook_layer(layer, name, aka)
- for name in needed:
- raise ValueError('Layer %s not found in model' % name)
-
- def _hook_layer(self, layer, layername, aka):
- '''
- Internal method to replace a forward method with a closure that
- intercepts the call, and tracks the hook so that it can be reverted.
- '''
- if aka in self._hooked_layer:
- raise ValueError('Layer %s already hooked' % aka)
- if layername in self._old_forward:
- raise ValueError('Layer %s already hooked' % layername)
- self._hooked_layer[aka] = layername
- self._old_forward[layername] = (layer, aka,
- layer.__dict__.get('forward', None))
- editor = self
- original_forward = layer.forward
- def new_forward(self, *inputs, **kwargs):
- original_x = original_forward(*inputs, **kwargs)
- x = editor._postprocess_forward(original_x, aka)
- return x
- layer.forward = types.MethodType(new_forward, layer)
-
- def _unhook_layer(self, aka):
- '''
- Internal method to remove a hook, restoring the original forward method.
- '''
- if aka not in self._hooked_layer:
- return
- layername = self._hooked_layer[aka]
- layer, check, old_forward = self._old_forward[layername]
- assert check == aka
- if old_forward is None:
- if 'forward' in layer.__dict__:
- del layer.__dict__['forward']
- else:
- layer.forward = old_forward
- del self._old_forward[layername]
- del self._hooked_layer[aka]
- if aka in self._ablation:
- del self._ablation[aka]
- if aka in self._replacement:
- del self._replacement[aka]
- if aka in self._offset:
- del self._offset[aka]
- if aka in self._retained:
- del self._retained[aka]
-
- def _postprocess_forward(self, x, aka):
- '''
- The internal method called by the hooked layers after they are run.
- '''
- # Retain output before edits, if desired.
- if aka in self._retained:
- self._retained[aka] = x.detach()
-
- # Apply replacement edit
- a = make_matching_tensor(self._ablation, aka, x)
- if a is not None:
- x = x * (1 - a)
- v = make_matching_tensor(self._replacement, aka, x)
- if v is not None:
- x += (v * a)
-
- # Apply offset edit
- b = make_matching_tensor(self._offset, aka, x)
- if b is not None:
- x = x + b
-
- return x
-
- def close(self):
- '''
- Unhooks all hooked layers in the model.
- '''
- for aka in list(self._old_forward.keys()):
- self._unhook_layer(aka)
- assert len(self._old_forward) == 0
-
-
-def make_matching_tensor(valuedict, name, data):
- '''
- Converts `valuedict[name]` to be a tensor with the same dtype, device,
- and dimension count as `data`, and caches the converted tensor.
- '''
- v = valuedict.get(name, None)
- if v is None:
- return None
- if not isinstance(v, torch.Tensor):
- # Accept non-torch data.
- v = torch.from_numpy(numpy.array(v))
- valuedict[name] = v
- if not v.device == data.device or not v.dtype == data.dtype:
- # Ensure device and type matches.
- assert not v.requires_grad, '%s wrong device or type' % (name)
- v = v.to(device=data.device, dtype=data.dtype)
- valuedict[name] = v
- if len(v.shape) < len(data.shape):
- # Ensure dimensions are unsqueezed as needed.
- assert not v.requires_grad, '%s wrong dimensions' % (name)
- v = v.view((1,) + tuple(v.shape) +
- (1,) * (len(data.shape) - len(v.shape) - 1))
- valuedict[name] = v
- return v
diff --git a/spaces/HarlanHong/DaGAN/demo_dagan.py b/spaces/HarlanHong/DaGAN/demo_dagan.py
deleted file mode 100644
index b9d3ae524954f45b207c601f11400784671e6b79..0000000000000000000000000000000000000000
--- a/spaces/HarlanHong/DaGAN/demo_dagan.py
+++ /dev/null
@@ -1,207 +0,0 @@
-## Restormer: Efficient Transformer for High-Resolution Image Restoration
-## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
-## https://arxiv.org/abs/2111.09881
-
-
-import torch
-import torch.nn.functional as F
-from skimage import img_as_ubyte
-import argparse
-import imageio
-from skimage.transform import resize
-from scipy.spatial import ConvexHull
-from tqdm import tqdm
-import numpy as np
-import modules.generator as G
-import modules.keypoint_detector as KPD
-import yaml
-from collections import OrderedDict
-import depth
-parser = argparse.ArgumentParser(description='Test DaGAN on your own images')
-parser.add_argument('--source_image', default='./temp/source.jpg', type=str, help='Directory of input source image')
-parser.add_argument('--driving_video', default='./temp/driving.mp4', type=str, help='Directory for driving video')
-parser.add_argument('--output', default='./temp/result.mp4', type=str, help='Directory for driving video')
-
-
-args = parser.parse_args()
-def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
- use_relative_movement=False, use_relative_jacobian=False):
- if adapt_movement_scale:
- source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
- driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
- adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
- else:
- adapt_movement_scale = 1
-
- kp_new = {k: v for k, v in kp_driving.items()}
-
- if use_relative_movement:
- kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
- kp_value_diff *= adapt_movement_scale
- kp_new['value'] = kp_value_diff + kp_source['value']
-
- if use_relative_jacobian:
- jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
- kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
- return kp_new
-def find_best_frame(source, driving, cpu=False):
- import face_alignment
-
- def normalize_kp(kp):
- kp = kp - kp.mean(axis=0, keepdims=True)
- area = ConvexHull(kp[:, :2]).volume
- area = np.sqrt(area)
- kp[:, :2] = kp[:, :2] / area
- return kp
-
- fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
- device='cpu' if cpu else 'cuda')
- kp_source = fa.get_landmarks(255 * source)[0]
- kp_source = normalize_kp(kp_source)
- norm = float('inf')
- frame_num = 0
- for i, image in tqdm(enumerate(driving)):
- kp_driving = fa.get_landmarks(255 * image)[0]
- kp_driving = normalize_kp(kp_driving)
- new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
- if new_norm < norm:
- norm = new_norm
- frame_num = i
- return frame_num
-
-
-def make_animation(source_image, driving_video, generator, kp_detector, relative=True, adapt_movement_scale=True, cpu=False):
- sources = []
- drivings = []
- with torch.no_grad():
- predictions = []
- depth_gray = []
- source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
- driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3)
- if not cpu:
- source = source.cuda()
- driving = driving.cuda()
- outputs = depth_decoder(depth_encoder(source))
- depth_source = outputs[("disp", 0)]
-
- outputs = depth_decoder(depth_encoder(driving[:, :, 0]))
- depth_driving = outputs[("disp", 0)]
- source_kp = torch.cat((source,depth_source),1)
- driving_kp = torch.cat((driving[:, :, 0],depth_driving),1)
-
- kp_source = kp_detector(source_kp)
- kp_driving_initial = kp_detector(driving_kp)
-
- # kp_source = kp_detector(source)
- # kp_driving_initial = kp_detector(driving[:, :, 0])
-
- for frame_idx in tqdm(range(driving.shape[2])):
- driving_frame = driving[:, :, frame_idx]
-
- if not cpu:
- driving_frame = driving_frame.cuda()
- outputs = depth_decoder(depth_encoder(driving_frame))
- depth_map = outputs[("disp", 0)]
-
- gray_driving = np.transpose(depth_map.data.cpu().numpy(), [0, 2, 3, 1])[0]
- gray_driving = 1-gray_driving/np.max(gray_driving)
-
- frame = torch.cat((driving_frame,depth_map),1)
- kp_driving = kp_detector(frame)
-
- kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
- kp_driving_initial=kp_driving_initial, use_relative_movement=relative,
- use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale)
- out = generator(source, kp_source=kp_source, kp_driving=kp_norm,source_depth = depth_source, driving_depth = depth_map)
-
- drivings.append(np.transpose(driving_frame.data.cpu().numpy(), [0, 2, 3, 1])[0])
- sources.append(np.transpose(source.data.cpu().numpy(), [0, 2, 3, 1])[0])
- predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
- depth_gray.append(gray_driving)
- return sources, drivings, predictions,depth_gray
-with open("config/vox-adv-256.yaml") as f:
- config = yaml.load(f)
-
-generator = G.SPADEDepthAwareGenerator(**config['model_params']['generator_params'],**config['model_params']['common_params'])
-config['model_params']['common_params']['num_channels'] = 4
-kp_detector = KPD.KPDetector(**config['model_params']['kp_detector_params'],**config['model_params']['common_params'])
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-cpu = False if torch.cuda.is_available() else True
-
-g_checkpoint = torch.load("generator.pt", map_location=device)
-kp_checkpoint = torch.load("kp_detector.pt", map_location=device)
-
-ckp_generator = OrderedDict((k.replace('module.',''),v) for k,v in g_checkpoint.items())
-generator.load_state_dict(ckp_generator)
-ckp_kp_detector = OrderedDict((k.replace('module.',''),v) for k,v in kp_checkpoint.items())
-kp_detector.load_state_dict(ckp_kp_detector)
-
-depth_encoder = depth.ResnetEncoder(18, False)
-depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4))
-loaded_dict_enc = torch.load('encoder.pth',map_location=device)
-loaded_dict_dec = torch.load('depth.pth',map_location=device)
-filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()}
-depth_encoder.load_state_dict(filtered_dict_enc)
-ckp_depth_decoder= {k: v for k, v in loaded_dict_dec.items() if k in depth_decoder.state_dict()}
-depth_decoder.load_state_dict(ckp_depth_decoder)
-depth_encoder.eval()
-depth_decoder.eval()
-
-# device = torch.device('cpu')
-# stx()
-
-generator = generator.to(device)
-kp_detector = kp_detector.to(device)
-depth_encoder = depth_encoder.to(device)
-depth_decoder = depth_decoder.to(device)
-
-generator.eval()
-kp_detector.eval()
-depth_encoder.eval()
-depth_decoder.eval()
-
-img_multiple_of = 8
-
-with torch.inference_mode():
- if torch.cuda.is_available():
- torch.cuda.ipc_collect()
- torch.cuda.empty_cache()
- source_image = imageio.imread(args.source_image)
- reader = imageio.get_reader(args.driving_video)
- fps = reader.get_meta_data()['fps']
- driving_video = []
- try:
- for im in reader:
- driving_video.append(im)
- except RuntimeError:
- pass
- reader.close()
-
- source_image = resize(source_image, (256, 256))[..., :3]
- driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]
-
-
-
- i = find_best_frame(source_image, driving_video,cpu)
- print ("Best frame: " + str(i))
- driving_forward = driving_video[i:]
- driving_backward = driving_video[:(i+1)][::-1]
- sources_forward, drivings_forward, predictions_forward,depth_forward = make_animation(source_image, driving_forward, generator, kp_detector, relative=True, adapt_movement_scale=True, cpu=cpu)
- sources_backward, drivings_backward, predictions_backward,depth_backward = make_animation(source_image, driving_backward, generator, kp_detector, relative=True, adapt_movement_scale=True, cpu=cpu)
- predictions = predictions_backward[::-1] + predictions_forward[1:]
- sources = sources_backward[::-1] + sources_forward[1:]
- drivings = drivings_backward[::-1] + drivings_forward[1:]
- depth_gray = depth_backward[::-1] + depth_forward[1:]
-
- imageio.mimsave(args.output, [np.concatenate((img_as_ubyte(s),img_as_ubyte(d),img_as_ubyte(p)),1) for (s,d,p) in zip(sources, drivings, predictions)], fps=fps)
- imageio.mimsave("gray.mp4", depth_gray, fps=fps)
- # merge the gray video
- animation = np.array(imageio.mimread(args.output,memtest=False))
- gray = np.array(imageio.mimread("gray.mp4",memtest=False))
-
- src_dst = animation[:,:,:512,:]
- animate = animation[:,:,512:,:]
- merge = np.concatenate((src_dst,gray,animate),2)
- imageio.mimsave(args.output, merge, fps=fps)
-
- # print(f"\nRestored images are saved at {out_dir}")
\ No newline at end of file
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/contrib/README.md b/spaces/Harveenchadha/oiTrans/indic_nlp_library/contrib/README.md
deleted file mode 100644
index 0a99b9ddd9e9bcc72bae930fc8a778f3094fea50..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/indic_nlp_library/contrib/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Contrib
-
-Contains additional utilities and applications using Indic NLP library core
-
-- `indic_scraper_project_sample.ipynb`: A simple pipeline for building monolingual corpora for Indian languages from crawled web content, Wikipedia, etc. An extensible framework which allows incorporation of website specific extractors, whereas generic NLP tasks like tokenization, sentence splitting, normalization, etc. are handled by the framework.
-- `correct_moses_tokenizer.py`: This script corrects the incorrect tokenization done by Moses tokenizer. The Moses tokenizer splits on nukta and halant characters.
-- `hindi_to_kannada_transliterator.py`: This script transliterates Hindi to Kannada. It removes/remaps characters only found in Hindi. It also adds halanta to words ending with consonant - as is the convention in Kannada.
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.459183ec.js b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.459183ec.js
deleted file mode 100644
index 349faf939c3f2b71b969de4cff67777b0e6aa961..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/index.459183ec.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as N,i as q,s as A,a7 as D,e as m,t as j,a as y,b as f,f as v,g as _,h as C,n as k,c as B,m as I,j as S,k as R,o as M,C as E,w as T,d as z,M as O,a5 as fe,I as V,B as re,U as ue,V as ge,D as oe,E as ce,R as he}from"./index.396f4a72.js";const w=i=>{var e=null;return i<0?e=[52,152,219]:e=[231,76,60],de(_e(Math.abs(i),[255,255,255],e))},_e=(i,e,t)=>{i>1&&(i=1),i=Math.sqrt(i);var n=[0,0,0],c;for(c=0;c<3;c++)n[c]=Math.round(e[c]*(1-i)+t[c]*i);return n},de=i=>"rgb("+i[0]+", "+i[1]+", "+i[2]+")",H=(i,e,t,n,c)=>{var a=n/c,o=e/t,l=0,r=0,u=i?a>o:a{"interpretation"in a&&t(0,n=a.interpretation),"label"in a&&t(1,c=a.label)},[n,c]}class ke extends N{constructor(e){super(),q(this,e,ve,me,A,{interpretation:0,label:1})}}function L(i,e,t){const n=i.slice();return n[3]=e[t],n[5]=t,n}function pe(i){let e;return{c(){e=j(i[2])},m(t,n){v(t,e,n)},p(t,n){n&4&&C(e,t[2])},d(t){t&&k(e)}}}function P(i){let e,t=i[3]+"",n,c,a;return{c(){e=m("li"),n=j(t),c=y(),f(e,"class","dropdown-item first:rounded-t transition last:rounded-b py-2 px-3 block whitespace-nowrap cursor-pointer"),f(e,"style",a="background-color: "+w(i[0][i[5]]))},m(o,l){v(o,e,l),_(e,n),_(e,c)},p(o,l){l&2&&t!==(t=o[3]+"")&&C(n,t),l&1&&a!==(a="background-color: "+w(o[0][o[5]]))&&f(e,"style",a)},d(o){o&&k(e)}}}function we(i){let e,t,n,c,a;t=new D({props:{$$slots:{default:[pe]},$$scope:{ctx:i}}});let o=i[1],l=[];for(let r=0;r{"interpretation"in o&&t(0,n=o.interpretation),"choices"in o&&t(1,c=o.choices),"label"in o&&t(2,a=o.label)},[n,c,a]}class xe extends N{constructor(e){super(),q(this,e,ye,we,A,{interpretation:0,choices:1,label:2})}}function je(i){let e;return{c(){e=j(i[0])},m(t,n){v(t,e,n)},p(t,n){n&1&&C(e,t[0])},d(t){t&&k(e)}}}function Ce(i){let e,t,n,c,a,o,l,r,u,s,g,h,d;return t=new D({props:{$$slots:{default:[je]},$$scope:{ctx:i}}}),{c(){e=m("div"),B(t.$$.fragment),n=y(),c=m("button"),a=m("div"),l=y(),r=m("div"),u=T("svg"),s=T("line"),g=T("line"),f(a,"class","checkbox w-4 h-4 bg-white flex items-center justify-center border border-gray-400 box-border"),f(a,"style",o="background-color: "+w(i[2][0])),f(s,"x1","-7.5"),f(s,"y1","0"),f(s,"x2","-2.5"),f(s,"y2","5"),f(s,"stroke","black"),f(s,"stroke-width","4"),f(s,"stroke-linecap","round"),f(g,"x1","-2.5"),f(g,"y1","5"),f(g,"x2","7.5"),f(g,"y2","-7.5"),f(g,"stroke","black"),f(g,"stroke-width","4"),f(g,"stroke-linecap","round"),f(u,"class","check h-3 w-4 svelte-r8ethh"),f(u,"viewBox","-10 -10 20 20"),f(r,"class","checkbox w-4 h-4 bg-white flex items-center justify-center border border-gray-400 box-border"),f(r,"style",h="background-color: "+w(i[2][1])),f(c,"class","checkbox-item py-2 px-3 rounded cursor-pointer flex gap-1 svelte-r8ethh"),z(c,"selected",i[1]),f(e,"class","input-checkbox inline-block svelte-r8ethh")},m(b,p){v(b,e,p),I(t,e,null),_(e,n),_(e,c),_(c,a),_(c,l),_(c,r),_(r,u),_(u,s),_(u,g),d=!0},p(b,[p]){const x={};p&9&&(x.$$scope={dirty:p,ctx:b}),t.$set(x),(!d||p&4&&o!==(o="background-color: "+w(b[2][0])))&&f(a,"style",o),(!d||p&4&&h!==(h="background-color: "+w(b[2][1])))&&f(r,"style",h),p&2&&z(c,"selected",b[1])},i(b){d||(S(t.$$.fragment,b),d=!0)},o(b){R(t.$$.fragment,b),d=!1},d(b){b&&k(e),M(t)}}}function Se(i,e,t){let{label:n=""}=e,{original:c}=e,{interpretation:a}=e;return i.$$set=o=>{"label"in o&&t(0,n=o.label),"original"in o&&t(1,c=o.original),"interpretation"in o&&t(2,a=o.interpretation)},[n,c,a]}class Re extends N{constructor(e){super(),q(this,e,Se,Ce,A,{label:0,original:1,interpretation:2})}}function Q(i,e,t){const n=i.slice();return n[4]=e[t],n[6]=t,n}function Be(i){let e;return{c(){e=j(i[3])},m(t,n){v(t,e,n)},p(t,n){n&8&&C(e,t[3])},d(t){t&&k(e)}}}function W(i){let e,t,n,c,a,o,l,r,u,s,g=i[4]+"",h,d;return{c(){e=m("button"),t=m("div"),c=y(),a=m("div"),o=T("svg"),l=T("line"),r=T("line"),s=y(),h=j(g),d=y(),f(t,"class","checkbox w-4 h-4 bg-white flex items-center justify-center border border-gray-400 box-border svelte-h5sk3f"),f(t,"style",n="background-color: "+w(i[1][i[6]][0])),f(l,"x1","-7.5"),f(l,"y1","0"),f(l,"x2","-2.5"),f(l,"y2","5"),f(l,"stroke","black"),f(l,"stroke-width","4"),f(l,"stroke-linecap","round"),f(r,"x1","-2.5"),f(r,"y1","5"),f(r,"x2","7.5"),f(r,"y2","-7.5"),f(r,"stroke","black"),f(r,"stroke-width","4"),f(r,"stroke-linecap","round"),f(o,"class","check h-3 w-4 svelte-h5sk3f"),f(o,"viewBox","-10 -10 20 20"),f(a,"class","checkbox w-4 h-4 bg-white flex items-center justify-center border border-gray-400 box-border svelte-h5sk3f"),f(a,"style",u="background-color: "+w(i[1][i[6]][1])),f(e,"class","checkbox-item py-2 px-3 font-semibold rounded cursor-pointer flex items-center gap-1 svelte-h5sk3f"),z(e,"selected",i[0].includes(i[4]))},m(b,p){v(b,e,p),_(e,t),_(e,c),_(e,a),_(a,o),_(o,l),_(o,r),_(e,s),_(e,h),_(e,d)},p(b,p){p&2&&n!==(n="background-color: "+w(b[1][b[6]][0]))&&f(t,"style",n),p&2&&u!==(u="background-color: "+w(b[1][b[6]][1]))&&f(a,"style",u),p&4&&g!==(g=b[4]+"")&&C(h,g),p&5&&z(e,"selected",b[0].includes(b[4]))},d(b){b&&k(e)}}}function Ie(i){let e,t,n,c;t=new D({props:{$$slots:{default:[Be]},$$scope:{ctx:i}}});let a=i[2],o=[];for(let l=0;l{"original"in l&&t(0,n=l.original),"interpretation"in l&&t(1,c=l.interpretation),"choices"in l&&t(2,a=l.choices),"label"in l&&t(3,o=l.label)},[n,c,a,o]}class Ne extends N{constructor(e){super(),q(this,e,Me,Ie,A,{original:0,interpretation:1,choices:2,label:3})}}function X(i,e,t){const n=i.slice();return n[6]=e[t],n}function qe(i){let e;return{c(){e=j(i[5])},m(t,n){v(t,e,n)},p(t,n){n&32&&C(e,t[5])},d(t){t&&k(e)}}}function Y(i){let e,t;return{c(){e=m("div"),f(e,"class","flex-1 h-4"),f(e,"style",t="background-color: "+w(i[6]))},m(n,c){v(n,e,c)},p(n,c){c&2&&t!==(t="background-color: "+w(n[6]))&&f(e,"style",t)},d(n){n&&k(e)}}}function Ae(i){let e,t,n,c,a,o,l,r,u,s;t=new D({props:{$$slots:{default:[qe]},$$scope:{ctx:i}}});let g=i[1],h=[];for(let d=0;d{"original"in u&&t(0,n=u.original),"interpretation"in u&&t(1,c=u.interpretation),"minimum"in u&&t(2,a=u.minimum),"maximum"in u&&t(3,o=u.maximum),"step"in u&&t(4,l=u.step),"label"in u&&t(5,r=u.label)},[n,c,a,o,l,r]}class Ee extends N{constructor(e){super(),q(this,e,De,Ae,A,{original:0,interpretation:1,minimum:2,maximum:3,step:4,label:5})}}function Z(i,e,t){const n=i.slice();return n[4]=e[t],n[6]=t,n}function Te(i){let e;return{c(){e=j(i[3])},m(t,n){v(t,e,n)},p(t,n){n&8&&C(e,t[3])},d(t){t&&k(e)}}}function $(i){let e,t,n,c,a=i[4]+"",o,l;return{c(){e=m("button"),t=m("div"),c=y(),o=j(a),l=y(),f(t,"class","radio-circle w-4 h-4 rounded-full box-border svelte-145r163"),f(t,"style",n="background-color: "+w(i[1][i[6]])),f(e,"class","radio-item py-2 px-3 font-semibold rounded cursor-pointer flex items-center gap-2 svelte-145r163"),z(e,"selected",i[0]===i[4])},m(r,u){v(r,e,u),_(e,t),_(e,c),_(e,o),_(e,l)},p(r,u){u&2&&n!==(n="background-color: "+w(r[1][r[6]]))&&f(t,"style",n),u&4&&a!==(a=r[4]+"")&&C(o,a),u&5&&z(e,"selected",r[0]===r[4])},d(r){r&&k(e)}}}function ze(i){let e,t,n,c;t=new D({props:{$$slots:{default:[Te]},$$scope:{ctx:i}}});let a=i[2],o=[];for(let l=0;l{"original"in l&&t(0,n=l.original),"interpretation"in l&&t(1,c=l.interpretation),"choices"in l&&t(2,a=l.choices),"label"in l&&t(3,o=l.label)},[n,c,a,o]}class Ue extends N{constructor(e){super(),q(this,e,Ge,ze,A,{original:0,interpretation:1,choices:2,label:3})}}function Fe(i){let e;return{c(){e=j(i[1])},m(t,n){v(t,e,n)},p(t,n){n&2&&C(e,t[1])},d(t){t&&k(e)}}}function Oe(i){let e,t,n,c,a,o,l,r,u,s;return t=new D({props:{$$slots:{default:[Fe]},$$scope:{ctx:i}}}),{c(){e=m("div"),B(t.$$.fragment),n=y(),c=m("div"),a=m("div"),o=m("canvas"),l=y(),r=m("img"),f(a,"class","interpretation w-full h-full absolute top-0 left-0 flex justify-center items-center opacity-90 hover:opacity-20 transition"),f(r,"class","w-full h-full object-contain"),O(r.src,u=i[0])||f(r,"src",u),f(c,"class","image-preview w-full h-60 flex justify-center items-center bg-gray-200 dark:bg-gray-600 relative"),f(e,"class","input-image")},m(g,h){v(g,e,h),I(t,e,null),_(e,n),_(e,c),_(c,a),_(a,o),i[6](o),_(c,l),_(c,r),i[7](r),s=!0},p(g,[h]){const d={};h&514&&(d.$$scope={dirty:h,ctx:g}),t.$set(d),(!s||h&1&&!O(r.src,u=g[0]))&&f(r,"src",u)},i(g){s||(S(t.$$.fragment,g),s=!0)},o(g){R(t.$$.fragment,g),s=!1},d(g){g&&k(e),M(t),i[6](null),i[7](null)}}}function Ve(i,e,t){let{original:n}=e,{interpretation:c}=e,{shape:a}=e,{label:o=""}=e,l,r;const u=(h,d,b,p)=>{var x=b/h[0].length,G=p/h.length,U=0;h.forEach(function(ae){var F=0;ae.forEach(function(se){d.fillStyle=w(se),d.fillRect(F*x,U*G,x,G),F++}),U++})};fe(()=>{let h=H(!0,r.width,r.height,r.naturalWidth,r.naturalHeight);a&&(h=H(!0,h.width,h.height,a[0],a[1]));let d=h.width,b=h.height;l.setAttribute("height",`${b}`),l.setAttribute("width",`${d}`),u(c,l.getContext("2d"),d,b)});function s(h){V[h?"unshift":"push"](()=>{l=h,t(2,l)})}function g(h){V[h?"unshift":"push"](()=>{r=h,t(3,r)})}return i.$$set=h=>{"original"in h&&t(0,n=h.original),"interpretation"in h&&t(4,c=h.interpretation),"shape"in h&&t(5,a=h.shape),"label"in h&&t(1,o=h.label)},[n,o,l,r,c,a,s,g]}class He extends N{constructor(e){super(),q(this,e,Ve,Oe,A,{original:0,interpretation:4,shape:5,label:1})}}function ee(i,e,t){const n=i.slice();return n[2]=e[t],n}function Je(i){let e;return{c(){e=j(i[1])},m(t,n){v(t,e,n)},p(t,n){n&2&&C(e,t[1])},d(t){t&&k(e)}}}function te(i){let e,t;return{c(){e=m("div"),f(e,"class","flex-1 h-4"),f(e,"style",t="background-color: "+w(i[2]))},m(n,c){v(n,e,c)},p(n,c){c&1&&t!==(t="background-color: "+w(n[2]))&&f(e,"style",t)},d(n){n&&k(e)}}}function Ke(i){let e,t,n,c,a;t=new D({props:{$$slots:{default:[Je]},$$scope:{ctx:i}}});let o=i[0],l=[];for(let r=0;r{"interpretation"in a&&t(0,n=a.interpretation),"label"in a&&t(1,c=a.label)},[n,c]}class Pe extends N{constructor(e){super(),q(this,e,Le,Ke,A,{interpretation:0,label:1})}}function le(i,e,t){const n=i.slice();return n[2]=e[t][0],n[3]=e[t][1],n}function Qe(i){let e;return{c(){e=j(i[0])},m(t,n){v(t,e,n)},p(t,n){n&1&&C(e,t[0])},d(t){t&&k(e)}}}function ne(i){let e,t=i[2]+"",n,c,a;return{c(){e=m("span"),n=j(t),c=y(),f(e,"class","textspan p-1 bg-opacity-20 dark:bg-opacity-80"),f(e,"style",a="background-color: "+w(i[3]))},m(o,l){v(o,e,l),_(e,n),_(e,c)},p(o,l){l&2&&t!==(t=o[2]+"")&&C(n,t),l&2&&a!==(a="background-color: "+w(o[3]))&&f(e,"style",a)},d(o){o&&k(e)}}}function We(i){let e,t,n,c;t=new D({props:{$$slots:{default:[Qe]},$$scope:{ctx:i}}});let a=i[1],o=[];for(let l=0;l{"label"in a&&t(0,n=a.label),"interpretation"in a&&t(1,c=a.interpretation)},[n,c]}class Ye extends N{constructor(e){super(),q(this,e,Xe,We,A,{label:0,interpretation:1})}}const Ze={audio:Pe,dropdown:xe,checkbox:Re,checkboxgroup:Ne,number:ke,slider:Ee,radio:Ue,image:He,textbox:Ye};function ie(i){let e,t,n;const c=[i[0],{original:i[1].original},{interpretation:i[1].interpretation}];var a=i[2];function o(l){let r={};for(let u=0;u{M(s,1)}),ce()}a?(e=new a(o()),B(e.$$.fragment),S(e.$$.fragment,1),I(e,t.parentNode,t)):e=null}else a&&e.$set(u)},i(l){n||(e&&S(e.$$.fragment,l),n=!0)},o(l){e&&R(e.$$.fragment,l),n=!1},d(l){l&&k(t),e&&M(e,l)}}}function $e(i){let e,t,n=i[1]&&ie(i);return{c(){n&&n.c(),e=re()},m(c,a){n&&n.m(c,a),v(c,e,a),t=!0},p(c,[a]){c[1]?n?(n.p(c,a),a&2&&S(n,1)):(n=ie(c),n.c(),S(n,1),n.m(e.parentNode,e)):n&&(oe(),R(n,1,1,()=>{n=null}),ce())},i(c){t||(S(n),t=!0)},o(c){R(n),t=!1},d(c){n&&n.d(c),c&&k(e)}}}function et(i,e,t){let n,{component:c}=e,{component_props:a}=e,{value:o}=e;return i.$$set=l=>{"component"in l&&t(3,c=l.component),"component_props"in l&&t(0,a=l.component_props),"value"in l&&t(1,o=l.value)},i.$$.update=()=>{i.$$.dirty&8&&t(2,n=Ze[c])},[a,o,n,c]}class tt extends N{constructor(e){super(),q(this,e,et,$e,A,{component:3,component_props:0,value:1})}}var nt=tt;const it=["dynamic"];export{nt as Component,it as modes};
-//# sourceMappingURL=index.459183ec.js.map
diff --git a/spaces/Hobe/bingo/Dockerfile b/spaces/Hobe/bingo/Dockerfile
deleted file mode 100644
index c677b05b75f7e4b2beee8c97fb47957a0861a83e..0000000000000000000000000000000000000000
--- a/spaces/Hobe/bingo/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-CMD npm start
diff --git a/spaces/HuggingFaceH4/human_eval_llm_leaderboard/src/assets/text_content.py b/spaces/HuggingFaceH4/human_eval_llm_leaderboard/src/assets/text_content.py
deleted file mode 100644
index 5a5cac5af779838665208ead18b6e7b9d7ed1582..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/human_eval_llm_leaderboard/src/assets/text_content.py
+++ /dev/null
@@ -1,121 +0,0 @@
-CHANGELOG_TEXT = f"""
-## [2023-06-19]
-- Added model type column
-- Hid revision and 8bit columns since all models are the same atm
-
-## [2023-06-16]
-- Refactored code base
-- Added new columns: number of parameters, hub likes, license
-
-## [2023-06-13]
-- Adjust description for TruthfulQA
-
-## [2023-06-12]
-- Add Human & GPT-4 Evaluations
-
-## [2023-06-05]
-- Increase concurrent thread count to 40
-- Search models on ENTER
-
-## [2023-06-02]
-- Add a typeahead search bar
-- Use webhooks to automatically spawn a new Space when someone opens a PR
-- Start recording `submitted_time` for eval requests
-- Limit AutoEvalColumn max-width
-
-## [2023-05-30]
-- Add a citation button
-- Simplify Gradio layout
-
-## [2023-05-29]
-- Auto-restart every hour for the latest results
-- Sync with the internal version (minor style changes)
-
-## [2023-05-24]
-- Add a baseline that has 25.0 for all values
-- Add CHANGELOG
-
-## [2023-05-23]
-- Fix a CSS issue that made the leaderboard hard to read in dark mode
-
-## [2023-05-22]
-- Display a success/error message after submitting evaluation requests
-- Reject duplicate submission
-- Do not display results that have incomplete results
-- Display different queues for jobs that are RUNNING, PENDING, FINISHED status
-
-## [2023-05-15]
-- Fix a typo: from "TruthQA" to "QA"
-
-## [2023-05-10]
-- Fix a bug that prevented auto-refresh
-
-## [2023-05-10]
-- Release the leaderboard to public
-"""
-
-TITLE = """
🤗 Open LLM Leaderboard (Humans and GPT4 evaluations)
"""
-
-INTRODUCTION_TEXT = f"""
-
-⚠️ **This leaderboard is no longer actively maintained, but is kept public for archival purposes. To propose chat models for human evaluation, we recommend LMSYS' Chatbot Arena: [https://chat.lmsys.org](https://chat.lmsys.org)**
-
-📐 With the plethora of large language models (LLMs) and chatbots being released week upon week, often with grandiose claims of their performance, it can be hard to filter out the genuine progress that is being made by the open-source community and which model is the current state of the art. The 🤗 Open LLM Leaderboard aims to track, rank and evaluate LLMs and chatbots as they are released.
-
-📈 Here, the evaluations are performed by having humans and GPT-4 compare completions from a set of popular open-source language models (LLMs) on a secret set of instruction prompts.
-"""
-
-HUMAN_GPT_EVAL_TEXT = f"""
-Evaluation is performed by having humans and GPT-4 compare completions from a set of popular open-source language models (LLMs) on a secret set of instruction prompts. The prompts cover tasks such as brainstorming, creative generation, commonsense reasoning, open question answering, summarization, and code generation. Comparisons are made by humans and a model on a 1-8 Likert scale, where the labeler is required to choose a preference each time. Using these preferences, we create bootstrapped Elo rankings.
-
-We collaborated with **Scale AI** to generate the completions using a professional data labeling workforce on their platform, [following the labeling instructions found here](https://docs.google.com/document/d/1c5-96Lj-UH4lzKjLvJ_MRQaVMjtoEXTYA4dvoAYVCHc/edit?usp=sharing). To understand the evaluation of popular models, we also had GPT-4 label the completions using this prompt.
-
-For more information on the calibration and initiation of these measurements, please refer to the [announcement blog post](https://huggingface.co/blog/llm-leaderboard). We would like to express our gratitude to **LMSYS** for providing a [useful notebook](https://colab.research.google.com/drive/1lAQ9cKVErXI1rEYq7hTKNaCQ5Q8TzrI5?usp=sharing) for computing Elo estimates and plots.
-"""
-
-
-CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
-CITATION_BUTTON_TEXT = r"""@misc{open-llm-leaderboard,
- author = {Edward Beeching, Sheon Han, Nathan Lambert, Nazneen Rajani, Omar Sanseviero, Lewis Tunstall, Thomas Wolf},
- title = {Open LLM Leaderboard},
- year = {2023},
- publisher = {Hugging Face},
- howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
-
-}
-@software{eval-harness,
- author = {Gao, Leo and
- Tow, Jonathan and
- Biderman, Stella and
- Black, Sid and
- DiPofi, Anthony and
- Foster, Charles and
- Golding, Laurence and
- Hsu, Jeffrey and
- McDonell, Kyle and
- Muennighoff, Niklas and
- Phang, Jason and
- Reynolds, Laria and
- Tang, Eric and
- Thite, Anish and
- Wang, Ben and
- Wang, Kevin and
- Zou, Andy},
- title = {A framework for few-shot language model evaluation},
- month = sep,
- year = 2021,
- publisher = {Zenodo},
- version = {v0.0.1},
- doi = {10.5281/zenodo.5371628},
- url = {https://doi.org/10.5281/zenodo.5371628}
-}"""
-
-VISUALIZATION_TITLE = """
📊 Visualizations
"""
-
-PLOT_1_TITLE = "Fraction of Model A Wins for All Non-tied A vs. B Comparisons"
-
-PLOT_2_TITLE = "Comparison Count of Each Combination of Models (not allowing ties)"
-
-PLOT_3_TITLE = "Elo Estimates with error bars (ties allowed)"
-
-PLOT_4_TITLE = "Fraction of Model A Wins for All Non-tied A vs. B Comparisons"
diff --git a/spaces/ICML2022/OFA/fairseq/examples/criss/mining/mine_example.sh b/spaces/ICML2022/OFA/fairseq/examples/criss/mining/mine_example.sh
deleted file mode 100644
index ace995ac44665f99d904b6a89d7fbbce24103afe..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/criss/mining/mine_example.sh
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/bin/bash
-# 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.
-#
-source_lang=kk_KZ
-target_lang=en_XX
-MODEL=criss_checkpoints/criss.3rd.pt
-SPM=criss_checkpoints/sentence.bpe.model
-SPLIT=test
-LANG_DICT=criss_checkpoints/lang_dict.txt
-SPM_ENCODE=flores/scripts/spm_encode.py
-SAVE_ENCODER=save_encoder.py
-ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL
-DICT=criss_checkpoints/dict.txt
-THRESHOLD=1.02
-MIN_COUNT=500
-
-DATA_DIR=data_tmp
-SAVE_DIR=mining/${source_lang}_${target_lang}_mined
-ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang}
-INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba
-
-mkdir -p $ENCODER_SAVE_DIR/${target_lang}
-mkdir -p $ENCODER_SAVE_DIR/${source_lang}
-mkdir -p $SAVE_DIR
-
-## Save encoder outputs
-
-# Save encoder outputs for source sentences
-python $SAVE_ENCODER \
- ${INPUT_DIR} \
- --path ${MODEL} \
- --task translation_multi_simple_epoch \
- --lang-pairs ${source_lang}-${target_lang} \
- --lang-dict ${LANG_DICT} \
- --gen-subset ${SPLIT} \
- --bpe 'sentencepiece' \
- -s ${source_lang} -t ${target_lang} \
- --sentencepiece-model ${SPM} \
- --remove-bpe 'sentencepiece' \
- --beam 1 \
- --lang-tok-style mbart \
- --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang}
-
-## Save encoder outputs for target sentences
-python $SAVE_ENCODER \
- ${INPUT_DIR} \
- --path ${MODEL} \
- --lang-pairs ${source_lang}-${target_lang} \
- --lang-dict ${LANG_DICT} \
- --task translation_multi_simple_epoch \
- --gen-subset ${SPLIT} \
- --bpe 'sentencepiece' \
- -t ${source_lang} -s ${target_lang} \
- --sentencepiece-model ${SPM} \
- --remove-bpe 'sentencepiece' \
- --beam 1 \
- --lang-tok-style mbart \
- --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang}
-
-## Mining
-python mining/mine.py \
- --src-lang ${source_lang} \
- --tgt-lang ${target_lang} \
- --dim 1024 \
- --mem 10 \
- --neighborhood 4 \
- --src-dir ${ENCODER_SAVE_DIR}/${source_lang} \
- --tgt-dir ${ENCODER_SAVE_DIR}/${target_lang} \
- --output $SAVE_DIR \
- --threshold ${THRESHOLD} \
- --min-count ${MIN_COUNT} \
- --valid-size 100 \
- --dict-path ${DICT} \
- --spm-path ${SPM} \
-
-
-## Process and binarize mined data
-python $SPM_ENCODE \
- --model ${SPM} \
- --output_format=piece \
- --inputs mining/${source_lang}_${target_lang}_mined/train.${source_lang} mining/${source_lang}_${target_lang}_mined/train.${target_lang} \
- --outputs mining/${source_lang}_${target_lang}_mined/train.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/train.bpe.${target_lang}
-
-python $SPM_ENCODE \
- --model ${SPM} \
- --output_format=piece \
- --inputs mining/${source_lang}_${target_lang}_mined/valid.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.${target_lang} \
- --outputs mining/${source_lang}_${target_lang}_mined/valid.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.bpe.${target_lang}
-
-
-fairseq-preprocess \
- --source-lang ${source_lang} \
- --target-lang ${target_lang} \
- --trainpref mining/${source_lang}_${target_lang}_mined/train.bpe \
- --validpref mining/${source_lang}_${target_lang}_mined/valid.bpe \
- --destdir mining/${source_lang}_${target_lang}_mined \
- --srcdict ${DICT} \
- --joined-dictionary \
- --workers 8
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py
deleted file mode 100644
index 41b38ba5bef20cb043921ac61820db8689189a5a..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py
+++ /dev/null
@@ -1,63 +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.
-
-
-#!/bin/python
-
-import fasttext
-from multiprocessing import Pool
-import contextlib
-import sys
-import argparse
-from functools import partial
-import io
-
-model = None
-def init(model_path):
- global model
- model = fasttext.load_model(model_path)
-
-def pred(lines):
- return lines, [model.predict(line.strip())[0][0][9:] for line in lines]
-
-def main():
- parser = argparse.ArgumentParser()
- parser.add_argument("--model", type=str, required=True,
- help="model to load")
- parser.add_argument("--inputs", nargs="+", default=['-'],
- help="input files to filter")
- parser.add_argument("--langs", nargs="+", required=True,
- help="lang ids of each input file")
- parser.add_argument("--outputs", nargs="+", default=['-'],
- help="path to save lid filtered outputs")
- parser.add_argument("--num-workers", type=int, metavar="N", default=10,
- help="number of processes in parallel")
- args = parser.parse_args()
-
- assert len(args.inputs) == len(args.langs) and len(args.inputs) == len(args.outputs)
-
- with contextlib.ExitStack() as stack:
- inputs = [
- stack.enter_context(open(input, "r", encoding="utf-8", newline="\n", errors="replace"))
- if input != "-" else io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8', errors="replace")
- for input in args.inputs
- ]
- outputs = [
- stack.enter_context(open(output, "w", encoding="utf-8", newline="\n"))
- if output != "-" else sys.stdout
- for output in args.outputs
- ]
- with Pool(args.num_workers, initializer=partial(init, args.model)) as p:
- skip_cnt = 0
- for lines, preds in p.imap(pred, list(zip(*inputs)), chunksize=500):
- if not all(a == b for a, b in zip(preds, args.langs)):
- skip_cnt += 1
- continue
- for line, output_h in zip(lines, outputs):
- print(line.strip(), file=output_h)
- print(f"Skipped {skip_cnt} lines.")
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py
deleted file mode 100644
index 6a6585e8b6901a059445ff54ca20ea87751bbb11..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# import sys
-# sys.path.append('tacotron2')
-import torch
-from .layers import STFT
-
-
-class Denoiser(torch.nn.Module):
- """ Removes model bias from audio produced with waveglow """
-
- def __init__(self, waveglow, filter_length=1024, n_overlap=4,
- win_length=1024, mode='zeros'):
- super(Denoiser, self).__init__()
- self.stft = STFT(filter_length=filter_length,
- hop_length=int(filter_length/n_overlap),
- win_length=win_length).cuda()
- if mode == 'zeros':
- mel_input = torch.zeros(
- (1, 80, 88),
- dtype=waveglow.upsample.weight.dtype,
- device=waveglow.upsample.weight.device)
- elif mode == 'normal':
- mel_input = torch.randn(
- (1, 80, 88),
- dtype=waveglow.upsample.weight.dtype,
- device=waveglow.upsample.weight.device)
- else:
- raise Exception("Mode {} if not supported".format(mode))
-
- with torch.no_grad():
- bias_audio = waveglow.infer(mel_input, sigma=0.0).float()
- bias_spec, _ = self.stft.transform(bias_audio)
-
- self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None])
-
- def forward(self, audio, strength=0.1):
- audio_spec, audio_angles = self.stft.transform(audio.cuda().float())
- audio_spec_denoised = audio_spec - self.bias_spec * strength
- audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
- audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles)
- return audio_denoised
diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh
deleted file mode 100644
index f3a3d3fc7cc98a38d8e9d523a0b43c0c8ea51bf9..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/bin/bash
-
-set -eu
-
-w2v_dir= # contains features `{train,valid}.{npy,lengths}`, real transcripts `{train,valid}.${label}`, and dict `dict.${label}.txt`
-lab_dir= # contains pseudo labels `{train,valid}.txt`
-out_dir= # output root
-arpa_lm= # phone LM
-arpa_lm_bin= # (binary) phone LM for KenLM, used in unsupervised selection
-
-label=phnc
-train_name="train"
-valid_name="valid"
-data_dir=${out_dir}/data
-
-mkdir -p ${out_dir}/exp
-local/prepare_lang.sh $w2v_dir/dict.${label}.txt $data_dir
-local/prepare_lm.sh $arpa_lm $data_dir
-
-for x in $train_name $valid_name; do
- x_gt=${x}_gt
-
- # prepare pseudo data
- python local/prepare_data_from_w2v.py $w2v_dir $data_dir $x
- steps/compute_cmvn_stats.sh $data_dir/$x $out_dir/exp/make_feat/$x $out_dir/feats/$x
- python local/copy_aligned_text.py < $lab_dir/$x.txt > $data_dir/$x/text
-
- # prepare ground truth data
- mkdir $data_dir/$x_gt
- cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $data_dir/$x_gt/
- python local/copy_aligned_text.py < $w2v_dir/$x.$label > $data_dir/$x_gt/text
-done
-
-local/train_subset_lgbeam.sh \
- --out_root ${out_dir} --out_name exp --train $train_name --valid $valid_name \
- --mono_size 2000 --tri1_size 5000 --tri2b_size -1 --tri3b_size -1 \
- --stage 1 --max_stage 3 $data_dir $data_dir/lang $data_dir/lang_test
-
-local/unsup_select_decode.sh \
- --split $valid_name --kenlm_path $arpa_lm_bin \
- --ref_txt $data_dir/${valid_name}_gt/text \
- --psd_txt $data_dir/${valid_name}/text \
- $out_dir/exp
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/config/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/config/__init__.py
deleted file mode 100644
index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/config/__init__.py
+++ /dev/null
@@ -1,4 +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.
diff --git a/spaces/Iceclear/StableSR/StableSR/taming/modules/discriminator/model.py b/spaces/Iceclear/StableSR/StableSR/taming/modules/discriminator/model.py
deleted file mode 100644
index 2aaa3110d0a7bcd05de7eca1e45101589ca5af05..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/taming/modules/discriminator/model.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import functools
-import torch.nn as nn
-
-
-from taming.modules.util import ActNorm
-
-
-def weights_init(m):
- classname = m.__class__.__name__
- if classname.find('Conv') != -1:
- nn.init.normal_(m.weight.data, 0.0, 0.02)
- elif classname.find('BatchNorm') != -1:
- nn.init.normal_(m.weight.data, 1.0, 0.02)
- nn.init.constant_(m.bias.data, 0)
-
-
-class NLayerDiscriminator(nn.Module):
- """Defines a PatchGAN discriminator as in Pix2Pix
- --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
- """
- def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
- """Construct a PatchGAN discriminator
- Parameters:
- input_nc (int) -- the number of channels in input images
- ndf (int) -- the number of filters in the last conv layer
- n_layers (int) -- the number of conv layers in the discriminator
- norm_layer -- normalization layer
- """
- super(NLayerDiscriminator, self).__init__()
- if not use_actnorm:
- norm_layer = nn.BatchNorm2d
- else:
- norm_layer = ActNorm
- if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
- use_bias = norm_layer.func != nn.BatchNorm2d
- else:
- use_bias = norm_layer != nn.BatchNorm2d
-
- kw = 4
- padw = 1
- sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
- nf_mult = 1
- nf_mult_prev = 1
- for n in range(1, n_layers): # gradually increase the number of filters
- nf_mult_prev = nf_mult
- nf_mult = min(2 ** n, 8)
- sequence += [
- nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
- norm_layer(ndf * nf_mult),
- nn.LeakyReLU(0.2, True)
- ]
-
- nf_mult_prev = nf_mult
- nf_mult = min(2 ** n_layers, 8)
- sequence += [
- nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
- norm_layer(ndf * nf_mult),
- nn.LeakyReLU(0.2, True)
- ]
-
- sequence += [
- nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
- self.main = nn.Sequential(*sequence)
-
- def forward(self, input):
- """Standard forward."""
- return self.main(input)
diff --git a/spaces/Illumotion/Koboldcpp/include/CL/Utils/Error.h b/spaces/Illumotion/Koboldcpp/include/CL/Utils/Error.h
deleted file mode 100644
index bf6e24bf5f39e336fae5d15c64c71a0e1be7ff37..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/include/CL/Utils/Error.h
+++ /dev/null
@@ -1,88 +0,0 @@
-#pragma once
-
-// OpenCL Utils includes
-#include "OpenCLUtils_Export.h"
-
-// OpenCL Utils includes
-#include
-
-// STL includes
-#include // fprintf
-
-// OpenCL includes
-#include
-
-// RET = function returns error code
-// PAR = functions sets error code in the paremeter
-
-#ifdef _DEBUG
-
-#define OCLERROR_RET(func, err, label) \
- do \
- { \
- err = func; \
- if (err != CL_SUCCESS) \
- { \
- cl_util_print_error(err); \
- fprintf(stderr, "on line %d, in file %s\n%s\n", __LINE__, \
- __FILE__, #func); \
- goto label; \
- } \
- } while (0)
-
-#define OCLERROR_PAR(func, err, label) \
- do \
- { \
- func; \
- if (err != CL_SUCCESS) \
- { \
- cl_util_print_error(err); \
- fprintf(stderr, "on line %d, in file %s\n%s\n", __LINE__, \
- __FILE__, #func); \
- goto label; \
- } \
- } while (0)
-
-#define MEM_CHECK(func, err, label) \
- do \
- { \
- if ((func) == NULL) \
- { \
- err = CL_OUT_OF_HOST_MEMORY; \
- cl_util_print_error(err); \
- fprintf(stderr, "on line %d, in file %s\n%s\n", __LINE__, \
- __FILE__, #func); \
- goto label; \
- } \
- } while (0)
-
-#else
-
-#define OCLERROR_RET(func, err, label) \
- do \
- { \
- err = func; \
- if (err != CL_SUCCESS) goto label; \
- } while (0)
-
-#define OCLERROR_PAR(func, err, label) \
- do \
- { \
- func; \
- if (err != CL_SUCCESS) goto label; \
- } while (0)
-
-#define MEM_CHECK(func, err, label) \
- do \
- { \
- if ((func) == NULL) \
- { \
- err = CL_OUT_OF_HOST_MEMORY; \
- goto label; \
- } \
- } while (0)
-
-#endif
-
-UTILS_EXPORT
-void cl_util_print_error(cl_int error);
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/tools/rwkv_prepare_vocab.py b/spaces/Illumotion/Koboldcpp/otherarch/tools/rwkv_prepare_vocab.py
deleted file mode 100644
index 2288984a9612a8b800372e81292cfd57dae3a1ec..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/tools/rwkv_prepare_vocab.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json,os
-
-def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a corresponding list of unicode strings.
- The reversible bpe codes work on unicode strings.
- This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
- When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
- This is a signficant percentage of your normal, say, 32K bpe vocab.
- To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
- And avoids mapping to whitespace/control characters the bpe code barfs on.
- """
- bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
- cs = bs[:]
- n = 0
- for b in range(2**8):
- if b not in bs:
- bs.append(b)
- cs.append(2**8+n)
- n += 1
- cs = [chr(n) for n in cs]
- return dict(zip(bs, cs))
-
-byte_encoder = bytes_to_unicode()
-byte_decoder = {v:k for k, v in byte_encoder.items()}
-sortedbd = sorted(byte_decoder.items(), key=lambda kv: kv[1])
-tr = "{"
-for i in sortedbd:
- tr += "\""+i[0]+"\","
-tr += "}"
-print(tr)
-
-with open((os.path.dirname(os.path.realpath(__file__))+"/") + "rwkv_orig_vocab.json", "r", encoding="utf-8") as f:
- encoder = json.load(f)
- s = ""
- with open("rwkv_vocab.embd", "w", encoding="utf-8") as f2:
- for key in encoder:
- #key = bytearray([byte_decoder[c] for c in key]).decode('utf-8','ignore')
- # key = key.replace("\\","\\\\")
- # key = key.replace("\"","\\\"")
- # s += "\""+key+"\",\n"
- s += key +"\n"
- f2.write(s)
-
-print("OK")
\ No newline at end of file
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/__init__.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/__init__.py
deleted file mode 100644
index 773cfc4664eef45a4f6fe05bd3fe2aa2143fdb5c..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .base import *
\ No newline at end of file
diff --git a/spaces/Intae/deepfake/training/zoo/__init__.py b/spaces/Intae/deepfake/training/zoo/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/JUNGU/VToonify/vtoonify/model/encoder/criteria/id_loss.py b/spaces/JUNGU/VToonify/vtoonify/model/encoder/criteria/id_loss.py
deleted file mode 100644
index 37c71d3047be01ae7b301e0a96f14e2df88a143f..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/vtoonify/model/encoder/criteria/id_loss.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import torch
-from torch import nn
-from model.encoder.encoders.model_irse import Backbone
-
-
-class IDLoss(nn.Module):
- def __init__(self, model_paths):
- super(IDLoss, self).__init__()
- print('Loading ResNet ArcFace')
- self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
- self.facenet.load_state_dict(torch.load(model_paths))
- self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
- self.facenet.eval()
-
- def extract_feats(self, x):
- x = x[:, :, 35:223, 32:220] # Crop interesting region
- x = self.face_pool(x)
- x_feats = self.facenet(x)
- return x_feats
-
- def forward(self, y_hat, y):
- n_samples = y_hat.shape[0]
- y_feats = self.extract_feats(y) # Otherwise use the feature from there
- y_hat_feats = self.extract_feats(y_hat)
- y_feats = y_feats.detach()
- loss = 0
- count = 0
- for i in range(n_samples):
- diff_target = y_hat_feats[i].dot(y_feats[i])
- loss += 1 - diff_target
- count += 1
-
- return loss / count
\ No newline at end of file
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/modeling_flax_pytorch_utils.py b/spaces/Jackflack09/diffuse-custom/diffusers/modeling_flax_pytorch_utils.py
deleted file mode 100644
index 9c7a5de2ad6e9735294286955c24b92c957aba5b..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/modeling_flax_pytorch_utils.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch - Flax general utilities."""
-import re
-
-import jax.numpy as jnp
-from flax.traverse_util import flatten_dict, unflatten_dict
-from jax.random import PRNGKey
-
-from .utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-def rename_key(key):
- regex = r"\w+[.]\d+"
- pats = re.findall(regex, key)
- for pat in pats:
- key = key.replace(pat, "_".join(pat.split(".")))
- return key
-
-
-#####################
-# PyTorch => Flax #
-#####################
-
-# Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69
-# and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py
-def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict):
- """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""
-
- # conv norm or layer norm
- renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
- if (
- any("norm" in str_ for str_ in pt_tuple_key)
- and (pt_tuple_key[-1] == "bias")
- and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
- and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
- ):
- renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
- return renamed_pt_tuple_key, pt_tensor
- elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
- renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
- return renamed_pt_tuple_key, pt_tensor
-
- # embedding
- if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
- pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
- return renamed_pt_tuple_key, pt_tensor
-
- # conv layer
- renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
- if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
- pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
- return renamed_pt_tuple_key, pt_tensor
-
- # linear layer
- renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
- if pt_tuple_key[-1] == "weight":
- pt_tensor = pt_tensor.T
- return renamed_pt_tuple_key, pt_tensor
-
- # old PyTorch layer norm weight
- renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
- if pt_tuple_key[-1] == "gamma":
- return renamed_pt_tuple_key, pt_tensor
-
- # old PyTorch layer norm bias
- renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
- if pt_tuple_key[-1] == "beta":
- return renamed_pt_tuple_key, pt_tensor
-
- return pt_tuple_key, pt_tensor
-
-
-def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model, init_key=42):
- # Step 1: Convert pytorch tensor to numpy
- pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
-
- # Step 2: Since the model is stateless, get random Flax params
- random_flax_params = flax_model.init_weights(PRNGKey(init_key))
-
- random_flax_state_dict = flatten_dict(random_flax_params)
- flax_state_dict = {}
-
- # Need to change some parameters name to match Flax names
- for pt_key, pt_tensor in pt_state_dict.items():
- renamed_pt_key = rename_key(pt_key)
- pt_tuple_key = tuple(renamed_pt_key.split("."))
-
- # Correctly rename weight parameters
- flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict)
-
- if flax_key in random_flax_state_dict:
- if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
- raise ValueError(
- f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
- f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
- )
-
- # also add unexpected weight so that warning is thrown
- flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
-
- return unflatten_dict(flax_state_dict)
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion_safe/__init__.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion_safe/__init__.py
deleted file mode 100644
index 59ff61fa3b5429dd40a76b4f9a10c31ddee62967..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion_safe/__init__.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from dataclasses import dataclass
-from enum import Enum
-from typing import List, Optional, Union
-
-import numpy as np
-
-import PIL
-from PIL import Image
-
-from ...utils import BaseOutput, is_torch_available, is_transformers_available
-
-
-@dataclass
-class SafetyConfig(object):
- WEAK = {
- "sld_warmup_steps": 15,
- "sld_guidance_scale": 20,
- "sld_threshold": 0.0,
- "sld_momentum_scale": 0.0,
- "sld_mom_beta": 0.0,
- }
- MEDIUM = {
- "sld_warmup_steps": 10,
- "sld_guidance_scale": 1000,
- "sld_threshold": 0.01,
- "sld_momentum_scale": 0.3,
- "sld_mom_beta": 0.4,
- }
- STRONG = {
- "sld_warmup_steps": 7,
- "sld_guidance_scale": 2000,
- "sld_threshold": 0.025,
- "sld_momentum_scale": 0.5,
- "sld_mom_beta": 0.7,
- }
- MAX = {
- "sld_warmup_steps": 0,
- "sld_guidance_scale": 5000,
- "sld_threshold": 1.0,
- "sld_momentum_scale": 0.5,
- "sld_mom_beta": 0.7,
- }
-
-
-@dataclass
-class StableDiffusionSafePipelineOutput(BaseOutput):
- """
- Output class for Safe Stable Diffusion pipelines.
-
- Args:
- images (`List[PIL.Image.Image]` or `np.ndarray`)
- List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
- num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
- nsfw_content_detected (`List[bool]`)
- List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, or `None` if safety checking could not be performed.
- images (`List[PIL.Image.Image]` or `np.ndarray`)
- List of denoised PIL images that were flagged by the safety checker any may contain "not-safe-for-work"
- (nsfw) content, or `None` if no safety check was performed or no images were flagged.
- applied_safety_concept (`str`)
- The safety concept that was applied for safety guidance, or `None` if safety guidance was disabled
- """
-
- images: Union[List[PIL.Image.Image], np.ndarray]
- nsfw_content_detected: Optional[List[bool]]
- unsafe_images: Optional[Union[List[PIL.Image.Image], np.ndarray]]
- applied_safety_concept: Optional[str]
-
-
-if is_transformers_available() and is_torch_available():
- from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
- from .safety_checker import SafeStableDiffusionSafetyChecker
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/speech/__init__.py b/spaces/Jamkonams/AutoGPT/autogpt/speech/__init__.py
deleted file mode 100644
index 2ff0d2bf48dc356bf810cb5a2063d6774e5fec6e..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/speech/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""This module contains the speech recognition and speech synthesis functions."""
-from autogpt.speech.say import say_text
-
-__all__ = ["say_text"]
diff --git a/spaces/Jimmyfreelancer/Pix2Pix-Video/style.css b/spaces/Jimmyfreelancer/Pix2Pix-Video/style.css
deleted file mode 100644
index 3cf565d3e03852436a405cf632d1d22433bb4087..0000000000000000000000000000000000000000
--- a/spaces/Jimmyfreelancer/Pix2Pix-Video/style.css
+++ /dev/null
@@ -1,101 +0,0 @@
-#col-container {max-width: 820px; margin-left: auto; margin-right: auto;}
-#duplicate-container{
- display: flex;
- justify-content: space-between;
- align-items: center;
- line-height: 1em;
- flex-direction: row-reverse;
- font-size:1em;
-}
-a, a:hover, a:visited {
- text-decoration-line: underline;
- font-weight: 600;
- color: #1f2937 !important;
-}
-
-.dark a, .dark a:hover, .dark a:visited {
- color: #f3f4f6 !important;
-}
-
-.footer {
- margin-bottom: 45px;
- margin-top: 10px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
-}
-
-.footer>p {
- font-size: .8rem!important;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(26px);
- background: white;
-}
-.dark .footer {
- border-color: #303030;
-}
-.dark .footer>p {
- background: #0b0f19;
-}
-
-div#may-like-container > p {
- font-size: .8em;
- margin-bottom: 4px;
-}
-
-.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;
- max-width: 13rem;
-}
-
-#share-btn-container:hover {
- background-color: #060606;
-}
-
-#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: 0.5rem !important;
- padding-bottom: 0.5rem !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;
-}
-
-#share-btn-container.hidden {
- display: none!important;
-}
\ No newline at end of file
diff --git a/spaces/Kathir0011/YouTube_Video_Assistant/app.py b/spaces/Kathir0011/YouTube_Video_Assistant/app.py
deleted file mode 100644
index 65e0146aefd6a6d57ca3df4d8db0807bc6c946cb..0000000000000000000000000000000000000000
--- a/spaces/Kathir0011/YouTube_Video_Assistant/app.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import gradio as gr
-
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.document_loaders import YoutubeLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import LLMChain
-from langchain.prompts.chat import (
- ChatPromptTemplate,
- SystemMessagePromptTemplate,
- HumanMessagePromptTemplate,
-)
-
-def create_db_from_video_url(video_url, api_key):
- """
- Creates an Embedding of the Video and makes it suitable for similarity searching.
- """
- embeddings = OpenAIEmbeddings(openai_api_key=api_key)
-
- loader = YoutubeLoader.from_youtube_url(video_url)
- transcripts = loader.load()
- # cannot provide this directly to the model so we are splitting the transcripts into small chunks
-
- text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
- docs = text_splitter.split_documents(transcripts)
-
- db = FAISS.from_documents(docs, embedding=embeddings)
-
- return db
-
-def get_response(api_key, video_url, request):
- """
- Usind gpt-3.5-turbo to obtain the response. It can handle upto 4096 tokens.
- """
-
- db = create_db_from_video_url(video_url, api_key)
-
- docs = db.similarity_search(query=request, k=4)
- docs_content = " ".join([doc.page_content for doc in docs])
-
- chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.2, openai_api_key=api_key)
-
- # creating a template for request
- template = """
- You are an assistant that can answer questions about youtube videos based on
- video transcripts: {docs}
-
- Only use factual information from the transcript to answer the question.
-
- If you don't have enough information to answer the question, say "I don't know".
-
- Your Answers should be detailed.
- """
-
- system_msg_prompt = SystemMessagePromptTemplate.from_template(template)
-
- # human prompt
- human_template = "Answer the following questions: {question}"
- human_msg_prompt = HumanMessagePromptTemplate.from_template(human_template)
-
- chat_prompt = ChatPromptTemplate.from_messages(
- [system_msg_prompt, human_msg_prompt]
- )
-
- chain = LLMChain(llm=chat, prompt=chat_prompt)
-
- response = chain.run(question=request, docs=docs_content)
-
- return response
-
-# creating title, description for the web app
-title = "YouTube Video Assistant 🧑💻"
-description = "Answers to the Questions asked by the user on the specified YouTube video. (English Only)\nClick Here to view the [Demo Video](https://cdn-uploads.huggingface.co/production/uploads/641aa7814577db917f70f8aa/Zh_tpIiB4DSUZf-LD1uxv.mp4)."
-article = "Other Projects:\n"\
- "💰 [US Health Insurance Cost Prediction](http://health-insurance-cost-predictor-k19.streamlit.app/)\n"\
- "📰 [Fake News Detector](https://fake-news-detector-k19.streamlit.app/)\n"\
- "🪶 [Birds Classifier](https://huggingface.co/spaces/Kathir0011/Birds_Classification)"
-# building the app
-youtube_video_assistant = gr.Interface(
- fn=get_response,
- inputs=[gr.Text(label="Enter the OpenAI API Key:", placeholder=f"Example: sk-{'*' * 45}AgM"),
- gr.Text(label="Enter the Youtube Video URL:", placeholder="Example: https://www.youtube.com/watch?v=MnDudvCyWpc"),
- gr.Text(label="Enter your Question", placeholder="Example: What's the video is about?")],
- outputs=gr.TextArea(label="Powered by gpt-3.5-turbo:"),
- title=title,
- description=description,
- article=article
-)
-
-# launching the web app
-youtube_video_assistant.launch()
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/objects365.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/objects365.py
deleted file mode 100644
index e99869bfa309635af3c03cbfa77f732db3f50637..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/objects365.py
+++ /dev/null
@@ -1,284 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import os.path as osp
-from typing import List
-
-from mmengine.fileio import get_local_path
-
-from mmdet.registry import DATASETS
-from .api_wrappers import COCO
-from .coco import CocoDataset
-
-# images exist in annotations but not in image folder.
-objv2_ignore_list = [
- osp.join('patch16', 'objects365_v2_00908726.jpg'),
- osp.join('patch6', 'objects365_v1_00320532.jpg'),
- osp.join('patch6', 'objects365_v1_00320534.jpg'),
-]
-
-
-@DATASETS.register_module()
-class Objects365V1Dataset(CocoDataset):
- """Objects365 v1 dataset for detection."""
-
- METAINFO = {
- 'classes':
- ('person', 'sneakers', 'chair', 'hat', 'lamp', 'bottle',
- 'cabinet/shelf', 'cup', 'car', 'glasses', 'picture/frame', 'desk',
- 'handbag', 'street lights', 'book', 'plate', 'helmet',
- 'leather shoes', 'pillow', 'glove', 'potted plant', 'bracelet',
- 'flower', 'tv', 'storage box', 'vase', 'bench', 'wine glass', 'boots',
- 'bowl', 'dining table', 'umbrella', 'boat', 'flag', 'speaker',
- 'trash bin/can', 'stool', 'backpack', 'couch', 'belt', 'carpet',
- 'basket', 'towel/napkin', 'slippers', 'barrel/bucket', 'coffee table',
- 'suv', 'toy', 'tie', 'bed', 'traffic light', 'pen/pencil',
- 'microphone', 'sandals', 'canned', 'necklace', 'mirror', 'faucet',
- 'bicycle', 'bread', 'high heels', 'ring', 'van', 'watch', 'sink',
- 'horse', 'fish', 'apple', 'camera', 'candle', 'teddy bear', 'cake',
- 'motorcycle', 'wild bird', 'laptop', 'knife', 'traffic sign',
- 'cell phone', 'paddle', 'truck', 'cow', 'power outlet', 'clock',
- 'drum', 'fork', 'bus', 'hanger', 'nightstand', 'pot/pan', 'sheep',
- 'guitar', 'traffic cone', 'tea pot', 'keyboard', 'tripod', 'hockey',
- 'fan', 'dog', 'spoon', 'blackboard/whiteboard', 'balloon',
- 'air conditioner', 'cymbal', 'mouse', 'telephone', 'pickup truck',
- 'orange', 'banana', 'airplane', 'luggage', 'skis', 'soccer',
- 'trolley', 'oven', 'remote', 'baseball glove', 'paper towel',
- 'refrigerator', 'train', 'tomato', 'machinery vehicle', 'tent',
- 'shampoo/shower gel', 'head phone', 'lantern', 'donut',
- 'cleaning products', 'sailboat', 'tangerine', 'pizza', 'kite',
- 'computer box', 'elephant', 'toiletries', 'gas stove', 'broccoli',
- 'toilet', 'stroller', 'shovel', 'baseball bat', 'microwave',
- 'skateboard', 'surfboard', 'surveillance camera', 'gun', 'life saver',
- 'cat', 'lemon', 'liquid soap', 'zebra', 'duck', 'sports car',
- 'giraffe', 'pumpkin', 'piano', 'stop sign', 'radiator', 'converter',
- 'tissue ', 'carrot', 'washing machine', 'vent', 'cookies',
- 'cutting/chopping board', 'tennis racket', 'candy',
- 'skating and skiing shoes', 'scissors', 'folder', 'baseball',
- 'strawberry', 'bow tie', 'pigeon', 'pepper', 'coffee machine',
- 'bathtub', 'snowboard', 'suitcase', 'grapes', 'ladder', 'pear',
- 'american football', 'basketball', 'potato', 'paint brush', 'printer',
- 'billiards', 'fire hydrant', 'goose', 'projector', 'sausage',
- 'fire extinguisher', 'extension cord', 'facial mask', 'tennis ball',
- 'chopsticks', 'electronic stove and gas stove', 'pie', 'frisbee',
- 'kettle', 'hamburger', 'golf club', 'cucumber', 'clutch', 'blender',
- 'tong', 'slide', 'hot dog', 'toothbrush', 'facial cleanser', 'mango',
- 'deer', 'egg', 'violin', 'marker', 'ship', 'chicken', 'onion',
- 'ice cream', 'tape', 'wheelchair', 'plum', 'bar soap', 'scale',
- 'watermelon', 'cabbage', 'router/modem', 'golf ball', 'pine apple',
- 'crane', 'fire truck', 'peach', 'cello', 'notepaper', 'tricycle',
- 'toaster', 'helicopter', 'green beans', 'brush', 'carriage', 'cigar',
- 'earphone', 'penguin', 'hurdle', 'swing', 'radio', 'CD',
- 'parking meter', 'swan', 'garlic', 'french fries', 'horn', 'avocado',
- 'saxophone', 'trumpet', 'sandwich', 'cue', 'kiwi fruit', 'bear',
- 'fishing rod', 'cherry', 'tablet', 'green vegetables', 'nuts', 'corn',
- 'key', 'screwdriver', 'globe', 'broom', 'pliers', 'volleyball',
- 'hammer', 'eggplant', 'trophy', 'dates', 'board eraser', 'rice',
- 'tape measure/ruler', 'dumbbell', 'hamimelon', 'stapler', 'camel',
- 'lettuce', 'goldfish', 'meat balls', 'medal', 'toothpaste',
- 'antelope', 'shrimp', 'rickshaw', 'trombone', 'pomegranate',
- 'coconut', 'jellyfish', 'mushroom', 'calculator', 'treadmill',
- 'butterfly', 'egg tart', 'cheese', 'pig', 'pomelo', 'race car',
- 'rice cooker', 'tuba', 'crosswalk sign', 'papaya', 'hair drier',
- 'green onion', 'chips', 'dolphin', 'sushi', 'urinal', 'donkey',
- 'electric drill', 'spring rolls', 'tortoise/turtle', 'parrot',
- 'flute', 'measuring cup', 'shark', 'steak', 'poker card',
- 'binoculars', 'llama', 'radish', 'noodles', 'yak', 'mop', 'crab',
- 'microscope', 'barbell', 'bread/bun', 'baozi', 'lion', 'red cabbage',
- 'polar bear', 'lighter', 'seal', 'mangosteen', 'comb', 'eraser',
- 'pitaya', 'scallop', 'pencil case', 'saw', 'table tennis paddle',
- 'okra', 'starfish', 'eagle', 'monkey', 'durian', 'game board',
- 'rabbit', 'french horn', 'ambulance', 'asparagus', 'hoverboard',
- 'pasta', 'target', 'hotair balloon', 'chainsaw', 'lobster', 'iron',
- 'flashlight'),
- 'palette':
- None
- }
-
- COCOAPI = COCO
- # ann_id is unique in coco dataset.
- ANN_ID_UNIQUE = True
-
- def load_data_list(self) -> List[dict]:
- """Load annotations from an annotation file named as ``self.ann_file``
-
- Returns:
- List[dict]: A list of annotation.
- """ # noqa: E501
- with get_local_path(
- self.ann_file, backend_args=self.backend_args) as local_path:
- self.coco = self.COCOAPI(local_path)
-
- # 'categories' list in objects365_train.json and objects365_val.json
- # is inconsistent, need sort list(or dict) before get cat_ids.
- cats = self.coco.cats
- sorted_cats = {i: cats[i] for i in sorted(cats)}
- self.coco.cats = sorted_cats
- categories = self.coco.dataset['categories']
- sorted_categories = sorted(categories, key=lambda i: i['id'])
- self.coco.dataset['categories'] = sorted_categories
- # The order of returned `cat_ids` will not
- # change with the order of the `classes`
- self.cat_ids = self.coco.get_cat_ids(
- cat_names=self.metainfo['classes'])
- self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
- self.cat_img_map = copy.deepcopy(self.coco.cat_img_map)
-
- img_ids = self.coco.get_img_ids()
- data_list = []
- total_ann_ids = []
- for img_id in img_ids:
- raw_img_info = self.coco.load_imgs([img_id])[0]
- raw_img_info['img_id'] = img_id
-
- ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
- raw_ann_info = self.coco.load_anns(ann_ids)
- total_ann_ids.extend(ann_ids)
-
- parsed_data_info = self.parse_data_info({
- 'raw_ann_info':
- raw_ann_info,
- 'raw_img_info':
- raw_img_info
- })
- data_list.append(parsed_data_info)
- if self.ANN_ID_UNIQUE:
- assert len(set(total_ann_ids)) == len(
- total_ann_ids
- ), f"Annotation ids in '{self.ann_file}' are not unique!"
-
- del self.coco
-
- return data_list
-
-
-@DATASETS.register_module()
-class Objects365V2Dataset(CocoDataset):
- """Objects365 v2 dataset for detection."""
- METAINFO = {
- 'classes':
- ('Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp',
- 'Glasses', 'Bottle', 'Desk', 'Cup', 'Street Lights', 'Cabinet/shelf',
- 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet',
- 'Book', 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower',
- 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', 'Pillow', 'Boots',
- 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt',
- 'Moniter/TV', 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker',
- 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', 'Stool',
- 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Bakset', 'Drum',
- 'Pen/Pencil', 'Bus', 'Wild Bird', 'High Heels', 'Motorcycle',
- 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned',
- 'Truck', 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel',
- 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning', 'Bed',
- 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple',
- 'Air Conditioner', 'Knife', 'Hockey Stick', 'Paddle', 'Pickup Truck',
- 'Fork', 'Traffic Sign', 'Ballon', 'Tripod', 'Dog', 'Spoon', 'Clock',
- 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger',
- 'Blackboard/Whiteboard', 'Napkin', 'Other Fish', 'Orange/Tangerine',
- 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle',
- 'Fan', 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane',
- 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', 'Luggage',
- 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone',
- 'Sports Car', 'Stop Sign', 'Dessert', 'Scooter', 'Stroller', 'Crane',
- 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
- 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza',
- 'Elephant', 'Skateboard', 'Surfboard', 'Gun',
- 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot',
- 'Toilet', 'Kite', 'Strawberry', 'Other Balls', 'Shovel', 'Pepper',
- 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
- 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board',
- 'Coffee Table', 'Side Table', 'Scissors', 'Marker', 'Pie', 'Ladder',
- 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball',
- 'Zebra', 'Grape', 'Giraffe', 'Potato', 'Sausage', 'Tricycle',
- 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
- 'Billards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club',
- 'Briefcase', 'Cucumber', 'Cigar/Cigarette ', 'Paint Brush', 'Pear',
- 'Heavy Truck', 'Hamburger', 'Extractor', 'Extention Cord', 'Tong',
- 'Tennis Racket', 'Folder', 'American Football', 'earphone', 'Mask',
- 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine', 'Slide',
- 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee',
- 'Washing Machine/Drying Machine', 'Chicken', 'Printer', 'Watermelon',
- 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hotair ballon',
- 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog',
- 'Blender', 'Peach', 'Rice', 'Wallet/Purse', 'Volleyball', 'Deer',
- 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple',
- 'Golf Ball', 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle',
- 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', 'Megaphone',
- 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion',
- 'Sandwich', 'Nuts', 'Speed Limit Sign', 'Induction Cooker', 'Broom',
- 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
- 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese',
- 'Notepaper', 'Cherry', 'Pliers', 'CD', 'Pasta', 'Hammer', 'Cue',
- 'Avocado', 'Hamimelon', 'Flask', 'Mushroon', 'Screwdriver', 'Soap',
- 'Recorder', 'Bear', 'Eggplant', 'Board Eraser', 'Coconut',
- 'Tape Measur/ Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', 'Steak',
- 'Crosswalk Sign', 'Stapler', 'Campel', 'Formula 1 ', 'Pomegranate',
- 'Dishwasher', 'Crab', 'Hoverboard', 'Meat ball', 'Rice Cooker',
- 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
- 'Buttefly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin',
- 'Electric Drill', 'Hair Dryer', 'Egg tart', 'Jellyfish', 'Treadmill',
- 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi',
- 'Target', 'French', 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case',
- 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', 'Scallop',
- 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Teniis paddle',
- 'Cosmetics Brush/Eyeliner Pencil', 'Chainsaw', 'Eraser', 'Lobster',
- 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling',
- 'Table Tennis '),
- 'palette':
- None
- }
-
- COCOAPI = COCO
- # ann_id is unique in coco dataset.
- ANN_ID_UNIQUE = True
-
- def load_data_list(self) -> List[dict]:
- """Load annotations from an annotation file named as ``self.ann_file``
-
- Returns:
- List[dict]: A list of annotation.
- """ # noqa: E501
- with get_local_path(
- self.ann_file, backend_args=self.backend_args) as local_path:
- self.coco = self.COCOAPI(local_path)
- # The order of returned `cat_ids` will not
- # change with the order of the `classes`
- self.cat_ids = self.coco.get_cat_ids(
- cat_names=self.metainfo['classes'])
- self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
- self.cat_img_map = copy.deepcopy(self.coco.cat_img_map)
-
- img_ids = self.coco.get_img_ids()
- data_list = []
- total_ann_ids = []
- for img_id in img_ids:
- raw_img_info = self.coco.load_imgs([img_id])[0]
- raw_img_info['img_id'] = img_id
-
- ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
- raw_ann_info = self.coco.load_anns(ann_ids)
- total_ann_ids.extend(ann_ids)
-
- # file_name should be `patchX/xxx.jpg`
- file_name = osp.join(
- osp.split(osp.split(raw_img_info['file_name'])[0])[-1],
- osp.split(raw_img_info['file_name'])[-1])
-
- if file_name in objv2_ignore_list:
- continue
-
- raw_img_info['file_name'] = file_name
- parsed_data_info = self.parse_data_info({
- 'raw_ann_info':
- raw_ann_info,
- 'raw_img_info':
- raw_img_info
- })
- data_list.append(parsed_data_info)
- if self.ANN_ID_UNIQUE:
- assert len(set(total_ann_ids)) == len(
- total_ann_ids
- ), f"Annotation ids in '{self.ann_file}' are not unique!"
-
- del self.coco
-
- return data_list
diff --git a/spaces/KyanChen/RSPrompter/mmpl/models/backbones/__init__.py b/spaces/KyanChen/RSPrompter/mmpl/models/backbones/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/LinkSoul/AutoAgents/README.md b/spaces/LinkSoul/AutoAgents/README.md
deleted file mode 100644
index c4354a2256d1ee36d56be4e85791306b48371285..0000000000000000000000000000000000000000
--- a/spaces/LinkSoul/AutoAgents/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AutoAgents
-emoji: 📚
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Longliveruby/Spotify-Recommendation-System/model.py b/spaces/Longliveruby/Spotify-Recommendation-System/model.py
deleted file mode 100644
index c3ab5205e8f2f5ed8c5baeff4cf81ecd35e6d628..0000000000000000000000000000000000000000
--- a/spaces/Longliveruby/Spotify-Recommendation-System/model.py
+++ /dev/null
@@ -1,520 +0,0 @@
-import pandas as pd
-import spotipy
-from spotipy.oauth2 import SpotifyOAuth, SpotifyClientCredentials
-import yaml
-import re
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from sklearn.preprocessing import MinMaxScaler
-import pickle
-import streamlit as st
-import os
-
-def playlist_model(url, model, max_gen=3, same_art=5):
- log = []
- Fresult = []
- try:
- log.append('Start logging')
- uri = url.split('/')[-1].split('?')[0]
- try:
- log.append('spotify local method')
- stream = open("Spotify/Spotify.yaml")
- spotify_details = yaml.safe_load(stream)
- auth_manager = SpotifyClientCredentials(client_id=spotify_details['Client_id'], client_secret=spotify_details['client_secret'])
- except:
- log.append('spotify .streamlit method')
- try:
- Client_id=st.secrets["Client_ID"]
- client_secret=st.secrets["Client_secret"]
- auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret)
- except:
- log.append('spotify hug method')
- Client_id=os.environ['Client_ID']
- client_secret=os.environ['Client_secret']
- auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret)
- sp = spotipy.client.Spotify(auth_manager=auth_manager)
-
- if model == 'Spotify Model':
- def get_IDs(user, playlist_id):
- try:
- log.append('start playlist extraction')
- track_ids = []
- playlist = sp.user_playlist(user, playlist_id)
- for item in playlist['tracks']['items']:
- track = item['track']
- track_ids.append(track['id'])
- return track_ids
- except Exception as e:
- log.append('Failed to load the playlist')
- log.append(e)
-
- track_ids = get_IDs('Ruby', uri)
- track_ids_uni = list(set(track_ids))
- log.append('Starting Spotify Model')
- Spotifyresult = pd.DataFrame()
- for i in range(len(track_ids_uni)-5):
- if len(Spotifyresult) >= 50:
- break
- try:
- ff = sp.recommendations(seed_tracks=list(track_ids_uni[i:i+5]), limit=5)
- except Exception as e:
- log.append(e)
- continue
- for z in range(5):
- result = pd.DataFrame([z+(5*i)+1])
- result['uri'] = ff['tracks'][z]['id']
- Spotifyresult = pd.concat([Spotifyresult, result], axis=0)
- Spotifyresult.drop_duplicates(subset=['uri'], inplace=True,keep='first')
- Fresult = Spotifyresult.uri[:50]
-
- log.append('Model run successfully')
- return Fresult, log
-
- lendf=len(pd.read_csv('Data/streamlit.csv',usecols=['track_uri']))
- dtypes = {'track_uri': 'object', 'artist_uri': 'object', 'album_uri': 'object', 'danceability': 'float16', 'energy': 'float16', 'key': 'float16',
- 'loudness': 'float16', 'mode': 'float16', 'speechiness': 'float16', 'acousticness': 'float16', 'instrumentalness': 'float16',
- 'liveness': 'float16', 'valence': 'float16', 'tempo': 'float16', 'duration_ms': 'float32', 'time_signature': 'float16',
- 'Track_release_date': 'int8', 'Track_pop': 'int8', 'Artist_pop': 'int8', 'Artist_genres': 'object'}
- col_name= ['track_uri', 'artist_uri', 'album_uri', 'danceability', 'energy', 'key',
- 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness',
- 'liveness', 'valence', 'tempo', 'duration_ms', 'time_signature',
- 'Track_release_date', 'Track_pop', 'Artist_pop', 'Artist_genres']
-
- try:
- def get_IDs(user, playlist_id):
- log.append('start playlist extraction')
- track_ids = []
- artist_id = []
- playlist = sp.user_playlist(user, playlist_id)
- for item in playlist['tracks']['items']:
- track = item['track']
- track_ids.append(track['id'])
- artist = item['track']['artists']
- artist_id.append(artist[0]['id'])
- return track_ids, artist_id
- except Exception as e:
- log.append('Failed to load the playlist')
- log.append(e)
-
- track_ids, artist_id = get_IDs('Ruby', uri)
- log.append("Number of Track : {}".format(len(track_ids)))
-
- artist_id_uni = list(set(artist_id))
- track_ids_uni = list(set(track_ids))
- log.append("Number of unique Artists : {}".format(len(artist_id_uni)))
- log.append("Number of unique Tracks : {}".format(len(track_ids_uni)))
-
- def extract(track_ids_uni, artist_id_uni):
- err = []
- err.append('Start audio features extraction')
- audio_features = pd.DataFrame()
- for i in range(0, len(track_ids_uni), 25):
- try:
- track_feature = sp.audio_features(track_ids_uni[i:i+25])
- track_df = pd.DataFrame(track_feature)
- audio_features = pd.concat([audio_features, track_df], axis=0)
- except Exception as e:
- err.append(e)
- continue
- err.append('Start track features extraction')
- track_ = pd.DataFrame()
- for i in range(0, len(track_ids_uni), 25):
- try:
- track_features = sp.tracks(track_ids_uni[i:i+25])
- for x in range(25):
- track_pop = pd.DataFrame([track_ids_uni[i+x]], columns=['Track_uri'])
- track_pop['Track_release_date'] = track_features['tracks'][x]['album']['release_date']
- track_pop['Track_pop'] = track_features['tracks'][x]["popularity"]
- track_pop['Artist_uri'] = track_features['tracks'][x]['artists'][0]['id']
- track_pop['Album_uri'] = track_features['tracks'][x]['album']['id']
- track_ = pd.concat([track_, track_pop], axis=0)
- except Exception as e:
- err.append(e)
- continue
- err.append('Start artist features extraction')
- artist_ = pd.DataFrame()
- for i in range(0, len(artist_id_uni), 25):
- try:
- artist_features = sp.artists(artist_id_uni[i:i+25])
- for x in range(25):
- artist_df = pd.DataFrame([artist_id_uni[i+x]], columns=['Artist_uri'])
- artist_pop = artist_features['artists'][x]["popularity"]
- artist_genres = artist_features['artists'][x]["genres"]
- artist_df["Artist_pop"] = artist_pop
- if artist_genres:
- artist_df["genres"] = " ".join([re.sub(' ', '_', i) for i in artist_genres])
- else:
- artist_df["genres"] = "unknown"
- artist_ = pd.concat([artist_, artist_df], axis=0)
- except Exception as e:
- err.append(e)
- continue
- try:
- test = pd.DataFrame(
- track_, columns=['Track_uri', 'Artist_uri', 'Album_uri'])
-
- test.rename(columns={'Track_uri': 'track_uri',
- 'Artist_uri': 'artist_uri', 'Album_uri': 'album_uri'}, inplace=True)
-
- audio_features.drop(
- columns=['type', 'uri', 'track_href', 'analysis_url'], axis=1, inplace=True)
-
- test = pd.merge(test, audio_features,
- left_on="track_uri", right_on="id", how='outer')
- test = pd.merge(test, track_, left_on="track_uri",
- right_on="Track_uri", how='outer')
- test = pd.merge(test, artist_, left_on="artist_uri",
- right_on="Artist_uri", how='outer')
-
- test.rename(columns={'genres': 'Artist_genres'}, inplace=True)
-
- test.drop(columns=['Track_uri', 'Artist_uri_x',
- 'Artist_uri_y', 'Album_uri', 'id'], axis=1, inplace=True)
-
- test.dropna(axis=0, inplace=True)
- test['Track_pop'] = test['Track_pop'].apply(lambda x: int(x/5))
- test['Artist_pop'] = test['Artist_pop'].apply(lambda x: int(x/5))
- test['Track_release_date'] = test['Track_release_date'].apply(lambda x: x.split('-')[0])
- test['Track_release_date'] = test['Track_release_date'].astype('int16')
- test['Track_release_date'] = test['Track_release_date'].apply(lambda x: int(x/50))
-
- test[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']] = test[[
- 'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']].astype('float16')
- test[['duration_ms']] = test[['duration_ms']].astype('float32')
- test[['Track_release_date', 'Track_pop', 'Artist_pop']] = test[[
- 'Track_release_date', 'Track_pop', 'Artist_pop']].astype('int8')
- except Exception as e:
- err.append(e)
- err.append('Finish extraction')
- return test, err
- test, err = extract(track_ids_uni, artist_id_uni)
-
- for i in err:
- log.append(i)
- del err
- grow = test.copy()
- test['Artist_genres'] = test['Artist_genres'].apply(lambda x: x.split(" "))
- tfidf = TfidfVectorizer(max_features=max_gen)
- tfidf_matrix = tfidf.fit_transform(test['Artist_genres'].apply(lambda x: " ".join(x)))
- genre_df = pd.DataFrame(tfidf_matrix.toarray())
- genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()]
- genre_df = genre_df.astype('float16')
- test.drop(columns=['Artist_genres'], axis=1, inplace=True)
- test = pd.concat([test.reset_index(drop=True),genre_df.reset_index(drop=True)], axis=1)
- Fresult = pd.DataFrame()
- x = 1
- for i in range(int(lendf/2), lendf+1, int(lendf/2)):
- try:
- df = pd.read_csv('Data/streamlit.csv',names= col_name,dtype=dtypes,skiprows=x,nrows=i)
- log.append('reading data frame chunks from {} to {}'.format(x,i))
- except Exception as e:
- log.append('Failed to load grow')
- log.append(e)
- grow = grow[~grow['track_uri'].isin(df['track_uri'].values)]
- df = df[~df['track_uri'].isin(test['track_uri'].values)]
- df['Artist_genres'] = df['Artist_genres'].apply(lambda x: x.split(" "))
- tfidf_matrix = tfidf.transform(df['Artist_genres'].apply(lambda x: " ".join(x)))
- genre_df = pd.DataFrame(tfidf_matrix.toarray())
- genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()]
- genre_df = genre_df.astype('float16')
- df.drop(columns=['Artist_genres'], axis=1, inplace=True)
- df = pd.concat([df.reset_index(drop=True),
- genre_df.reset_index(drop=True)], axis=1)
- del genre_df
- try:
- df.drop(columns=['genre|unknown'], axis=1, inplace=True)
- test.drop(columns=['genre|unknown'], axis=1, inplace=True)
- except:
- log.append('genre|unknown not found')
- log.append('Scaling the data .....')
- if x == 1:
- sc = pickle.load(open('Data/sc.sav','rb'))
- df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19])
- test.iloc[:, 3:19] = sc.transform(test.iloc[:, 3:19])
- log.append("Creating playlist vector")
- playvec = pd.DataFrame(test.sum(axis=0)).T
- else:
- df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19])
- x = i
- if model == 'Model 1':
- df['sim']=cosine_similarity(df.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1),playvec.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1))
- df['sim2']=cosine_similarity(df.iloc[:,16:-1],playvec.iloc[:,16:])
- df['sim3']=cosine_similarity(df.iloc[:,19:-2],playvec.iloc[:,19:])
- df = df.sort_values(['sim3','sim2','sim'],ascending = False,kind='stable').groupby('artist_uri').head(same_art).head(50)
- Fresult = pd.concat([Fresult, df], axis=0)
- Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).head(50)
- elif model == 'Model 2':
- df['sim'] = cosine_similarity(df.iloc[:, 3:16], playvec.iloc[:, 3:16])
- df['sim2'] = cosine_similarity(df.loc[:, df.columns.str.startswith('T') | df.columns.str.startswith('A')], playvec.loc[:, playvec.columns.str.startswith('T') | playvec.columns.str.startswith('A')])
- df['sim3'] = cosine_similarity(df.loc[:, df.columns.str.startswith('genre')], playvec.loc[:, playvec.columns.str.startswith('genre')])
- df['sim4'] = (df['sim']+df['sim2']+df['sim3'])/3
- df = df.sort_values(['sim4'], ascending=False,kind='stable').groupby('artist_uri').head(same_art).head(50)
- Fresult = pd.concat([Fresult, df], axis=0)
- Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).head(50)
- del test
- try:
- del df
- log.append('Getting Result')
- except:
- log.append('Getting Result')
- if model == 'Model 1':
- Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(50)
- elif model == 'Model 2':
- Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(50)
- log.append('{} New Tracks Found'.format(len(grow)))
- if(len(grow)>=1):
- try:
- new=pd.read_csv('Data/new_tracks.csv',dtype=dtypes)
- new=pd.concat([new, grow], axis=0)
- new=new[new.Track_pop >0]
- new.drop_duplicates(subset=['track_uri'], inplace=True,keep='last')
- new.to_csv('Data/new_tracks.csv',index=False)
- except:
- grow.to_csv('Data/new_tracks.csv', index=False)
- log.append('Model run successfully')
- except Exception as e:
- log.append("Model Failed")
- log.append(e)
- return Fresult, log
-
-
-
-def top_tracks(url,region):
- log = []
- Fresult = []
- uri = url.split('/')[-1].split('?')[0]
- try:
- log.append('spotify local method')
- stream = open("Spotify/Spotify.yaml")
- spotify_details = yaml.safe_load(stream)
- auth_manager = SpotifyClientCredentials(client_id=spotify_details['Client_id'], client_secret=spotify_details['client_secret'])
- except:
- log.append('spotify .streamlit method')
- try:
- Client_id=st.secrets["Client_ID"]
- client_secret=st.secrets["Client_secret"]
- auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret)
- except:
- log.append('spotify hug method')
- Client_id=os.environ['Client_ID']
- client_secret=os.environ['Client_secret']
- auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret)
- sp = spotipy.client.Spotify(auth_manager=auth_manager)
- try:
- log.append('Starting Spotify Model')
- top=sp.artist_top_tracks(uri,country=region)
- for i in range(10) :
- Fresult.append(top['tracks'][i]['id'])
- log.append('Model run successfully')
- except Exception as e:
- log.append("Model Failed")
- log.append(e)
- return Fresult,log
-
-def song_model(url, model, max_gen=3, same_art=5):
- log = []
- Fresult = []
- try:
- log.append('Start logging')
- uri = url.split('/')[-1].split('?')[0]
- try:
- log.append('spotify local method')
- stream = open("Spotify/Spotify.yaml")
- spotify_details = yaml.safe_load(stream)
- auth_manager = SpotifyClientCredentials(client_id=spotify_details['Client_id'], client_secret=spotify_details['client_secret'])
- except:
- log.append('spotify .streamlit method')
- try:
- Client_id=st.secrets["Client_ID"]
- client_secret=st.secrets["Client_secret"]
- auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret)
- except:
- log.append('spotify hug method')
- Client_id=os.environ['Client_ID']
- client_secret=os.environ['Client_secret']
- auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret)
- sp = spotipy.client.Spotify(auth_manager=auth_manager)
-
- if model == 'Spotify Model':
- log.append('Starting Spotify Model')
- aa=sp.recommendations(seed_tracks=[uri], limit=25)
- for i in range(25):
- Fresult.append(aa['tracks'][i]['id'])
- log.append('Model run successfully')
- return Fresult, log
- lendf=len(pd.read_csv('Data/streamlit.csv',usecols=['track_uri']))
- dtypes = {'track_uri': 'object', 'artist_uri': 'object', 'album_uri': 'object', 'danceability': 'float16', 'energy': 'float16', 'key': 'float16',
- 'loudness': 'float16', 'mode': 'float16', 'speechiness': 'float16', 'acousticness': 'float16', 'instrumentalness': 'float16',
- 'liveness': 'float16', 'valence': 'float16', 'tempo': 'float16', 'duration_ms': 'float32', 'time_signature': 'float16',
- 'Track_release_date': 'int8', 'Track_pop': 'int8', 'Artist_pop': 'int8', 'Artist_genres': 'object'}
- col_name= ['track_uri', 'artist_uri', 'album_uri', 'danceability', 'energy', 'key',
- 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness',
- 'liveness', 'valence', 'tempo', 'duration_ms', 'time_signature',
- 'Track_release_date', 'Track_pop', 'Artist_pop', 'Artist_genres']
- log.append('Start audio features extraction')
- audio_features = pd.DataFrame(sp.audio_features([uri]))
- log.append('Start track features extraction')
- track_ = pd.DataFrame()
- track_features = sp.tracks([uri])
- track_pop = pd.DataFrame([uri], columns=['Track_uri'])
- track_pop['Track_release_date'] = track_features['tracks'][0]['album']['release_date']
- track_pop['Track_pop'] = track_features['tracks'][0]["popularity"]
- track_pop['Artist_uri'] = track_features['tracks'][0]['artists'][0]['id']
- track_pop['Album_uri'] = track_features['tracks'][0]['album']['id']
- track_ = pd.concat([track_, track_pop], axis=0)
- log.append('Start artist features extraction')
- artist_id_uni=list(track_['Artist_uri'])
- artist_ = pd.DataFrame()
- artist_features = sp.artists(artist_id_uni)
- artist_df = pd.DataFrame(artist_id_uni, columns=['Artist_uri'])
- artist_pop = artist_features['artists'][0]["popularity"]
- artist_genres = artist_features['artists'][0]["genres"]
- artist_df["Artist_pop"] = artist_pop
- if artist_genres:
- artist_df["genres"] = " ".join([re.sub(' ', '_', i) for i in artist_genres])
- else:
- artist_df["genres"] = "unknown"
- artist_ = pd.concat([artist_, artist_df], axis=0)
- try:
- test = pd.DataFrame(track_, columns=['Track_uri', 'Artist_uri', 'Album_uri'])
- test.rename(columns={'Track_uri': 'track_uri','Artist_uri': 'artist_uri', 'Album_uri': 'album_uri'}, inplace=True)
- audio_features.drop(columns=['type', 'uri', 'track_href', 'analysis_url'], axis=1, inplace=True)
- test = pd.merge(test, audio_features,left_on="track_uri", right_on="id", how='outer')
- test = pd.merge(test, track_, left_on="track_uri",right_on="Track_uri", how='outer')
- test = pd.merge(test, artist_, left_on="artist_uri",right_on="Artist_uri", how='outer')
- test.rename(columns={'genres': 'Artist_genres'}, inplace=True)
- test.drop(columns=['Track_uri', 'Artist_uri_x','Artist_uri_y', 'Album_uri', 'id'], axis=1, inplace=True)
- test.dropna(axis=0, inplace=True)
- test['Track_pop'] = test['Track_pop'].apply(lambda x: int(x/5))
- test['Artist_pop'] = test['Artist_pop'].apply(lambda x: int(x/5))
- test['Track_release_date'] = test['Track_release_date'].apply(lambda x: x.split('-')[0])
- test['Track_release_date'] = test['Track_release_date'].astype('int16')
- test['Track_release_date'] = test['Track_release_date'].apply(lambda x: int(x/50))
- test[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']] = test[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']].astype('float16')
- test[['duration_ms']] = test[['duration_ms']].astype('float32')
- test[['Track_release_date', 'Track_pop', 'Artist_pop']] = test[['Track_release_date', 'Track_pop', 'Artist_pop']].astype('int8')
- except Exception as e:
- log.append(e)
- log.append('Finish extraction')
- grow = test.copy()
- test['Artist_genres'] = test['Artist_genres'].apply(lambda x: x.split(" "))
- tfidf = TfidfVectorizer(max_features=max_gen)
- tfidf_matrix = tfidf.fit_transform(test['Artist_genres'].apply(lambda x: " ".join(x)))
- genre_df = pd.DataFrame(tfidf_matrix.toarray())
- genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()]
- genre_df = genre_df.astype('float16')
- test.drop(columns=['Artist_genres'], axis=1, inplace=True)
- test = pd.concat([test.reset_index(drop=True),genre_df.reset_index(drop=True)], axis=1)
- Fresult = pd.DataFrame()
- x = 1
- for i in range(int(lendf/2), lendf+1, int(lendf/2)):
- try:
- df = pd.read_csv('Data/streamlit.csv',names= col_name,dtype=dtypes,skiprows=x,nrows=i)
- log.append('reading data frame chunks from {} to {}'.format(x,i))
- except Exception as e:
- log.append('Failed to load grow')
- log.append(e)
- grow = grow[~grow['track_uri'].isin(df['track_uri'].values)]
- df = df[~df['track_uri'].isin(test['track_uri'].values)]
- df['Artist_genres'] = df['Artist_genres'].apply(lambda x: x.split(" "))
- tfidf_matrix = tfidf.transform(df['Artist_genres'].apply(lambda x: " ".join(x)))
- genre_df = pd.DataFrame(tfidf_matrix.toarray())
- genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()]
- genre_df = genre_df.astype('float16')
- df.drop(columns=['Artist_genres'], axis=1, inplace=True)
- df = pd.concat([df.reset_index(drop=True),
- genre_df.reset_index(drop=True)], axis=1)
- del genre_df
- try:
- df.drop(columns=['genre|unknown'], axis=1, inplace=True)
- test.drop(columns=['genre|unknown'], axis=1, inplace=True)
- except:
- log.append('genre|unknown not found')
- log.append('Scaling the data .....')
- if x == 1:
- sc = pickle.load(open('Data/sc.sav','rb'))
- df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19])
- test.iloc[:, 3:19] = sc.transform(test.iloc[:, 3:19])
- log.append("Creating playlist vector")
- playvec = pd.DataFrame(test.sum(axis=0)).T
- else:
- df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19])
- x = i
- if model == 'Model 1':
- df['sim']=cosine_similarity(df.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1),playvec.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1))
- df['sim2']=cosine_similarity(df.iloc[:,16:-1],playvec.iloc[:,16:])
- df['sim3']=cosine_similarity(df.iloc[:,19:-2],playvec.iloc[:,19:])
- df = df.sort_values(['sim3','sim2','sim'],ascending = False,kind='stable').groupby('artist_uri').head(same_art).head(50)
- Fresult = pd.concat([Fresult, df], axis=0)
- Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).head(50)
- elif model == 'Model 2':
- df['sim'] = cosine_similarity(df.iloc[:, 3:16], playvec.iloc[:, 3:16])
- df['sim2'] = cosine_similarity(df.loc[:, df.columns.str.startswith('T') | df.columns.str.startswith('A')], playvec.loc[:, playvec.columns.str.startswith('T') | playvec.columns.str.startswith('A')])
- df['sim3'] = cosine_similarity(df.loc[:, df.columns.str.startswith('genre')], playvec.loc[:, playvec.columns.str.startswith('genre')])
- df['sim4'] = (df['sim']+df['sim2']+df['sim3'])/3
- df = df.sort_values(['sim4'], ascending=False,kind='stable').groupby('artist_uri').head(same_art).head(50)
- Fresult = pd.concat([Fresult, df], axis=0)
- Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).head(50)
- del test
- try:
- del df
- log.append('Getting Result')
- except:
- log.append('Getting Result')
- if model == 'Model 1':
- Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(50)
- elif model == 'Model 2':
- Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable')
- Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first')
- Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(50)
- log.append('{} New Tracks Found'.format(len(grow)))
- if(len(grow)>=1):
- try:
- new=pd.read_csv('Data/new_tracks.csv',dtype=dtypes)
- new=pd.concat([new, grow], axis=0)
- new=new[new.Track_pop >0]
- new.drop_duplicates(subset=['track_uri'], inplace=True,keep='last')
- new.to_csv('Data/new_tracks.csv',index=False)
- except:
- grow.to_csv('Data/new_tracks.csv', index=False)
- log.append('Model run successfully')
- except Exception as e:
- log.append("Model Failed")
- log.append(e)
- return Fresult, log
-
-def update_dataset():
- col_name= ['track_uri', 'artist_uri', 'album_uri', 'danceability', 'energy', 'key',
- 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness',
- 'liveness', 'valence', 'tempo', 'duration_ms', 'time_signature',
- 'Track_release_date', 'Track_pop', 'Artist_pop', 'Artist_genres']
- dtypes = {'track_uri': 'object', 'artist_uri': 'object', 'album_uri': 'object', 'danceability': 'float16', 'energy': 'float16', 'key': 'float16',
- 'loudness': 'float16', 'mode': 'float16', 'speechiness': 'float16', 'acousticness': 'float16', 'instrumentalness': 'float16',
- 'liveness': 'float16', 'valence': 'float16', 'tempo': 'float16', 'duration_ms': 'float32', 'time_signature': 'float16',
- 'Track_release_date': 'int8', 'Track_pop': 'int8', 'Artist_pop': 'int8', 'Artist_genres': 'object'}
- df = pd.read_csv('Data/streamlit.csv',dtype=dtypes)
- grow = pd.read_csv('Data/new_tracks.csv',dtype=dtypes)
- cur = len(df)
- df=pd.concat([df,grow],axis=0)
- grow=pd.DataFrame(columns=col_name)
- grow.to_csv('Data/new_tracks.csv',index=False)
- df=df[df.Track_pop >0]
- df.drop_duplicates(subset=['track_uri'],inplace=True,keep='last')
- df.dropna(axis=0,inplace=True)
- df.to_csv('Data/streamlit.csv',index=False)
- return (len(df)-cur)
-
diff --git a/spaces/Loren/Streamlit_OCR_comparator/app_pages/ocr_comparator.py b/spaces/Loren/Streamlit_OCR_comparator/app_pages/ocr_comparator.py
deleted file mode 100644
index 0ea3bd260c3368d310ab43a7be51840de6ca2871..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/app_pages/ocr_comparator.py
+++ /dev/null
@@ -1,1421 +0,0 @@
-"""This Streamlit app allows you to compare, from a given image, the results of different solutions:
- EasyOcr, PaddleOCR, MMOCR, Tesseract
-"""
-import streamlit as st
-import plotly.express as px
-import numpy as np
-import math
-import pandas as pd
-from time import sleep
-
-import cv2
-from PIL import Image, ImageColor
-import PIL
-import easyocr
-from paddleocr import PaddleOCR
-from mmocr.utils.ocr import MMOCR
-import pytesseract
-from pytesseract import Output
-import os
-from mycolorpy import colorlist as mcp
-
-
-###################################################################################################
-## MAIN
-###################################################################################################
-def app():
-
- ###################################################################################################
- ## FUNCTIONS
- ###################################################################################################
-
- @st.cache
- def convert_df(in_df):
- """Convert data frame function, used by download button
-
- Args:
- in_df (data frame): data frame to convert
-
- Returns:
- data frame: converted data frame
- """
- # IMPORTANT: Cache the conversion to prevent computation on every rerun
- return in_df.to_csv().encode('utf-8')
-
- ###
- def easyocr_coord_convert(in_list_coord):
- """Convert easyocr coordinates to standard format used by others functions
-
- Args:
- in_list_coord (list of numbers): format [x_min, x_max, y_min, y_max]
-
- Returns:
- list of lists: format [ [x_min, y_min], [x_max, y_min], [x_max, y_max], [x_min, y_max] ]
- """
-
- coord = in_list_coord
- return [[coord[0], coord[2]], [coord[1], coord[2]], [coord[1], coord[3]], [coord[0], coord[3]]]
-
- ###
- @st.cache(show_spinner=False)
- def initializations():
- """Initializations for the app
-
- Returns:
- list of strings : list of OCR solutions names
- (['EasyOCR', 'PPOCR', 'MMOCR', 'Tesseract'])
- dict : names and indices of the OCR solutions
- ({'EasyOCR': 0, 'PPOCR': 1, 'MMOCR': 2, 'Tesseract': 3})
- list of dicts : list of languages supported by each OCR solution
- list of int : columns for recognition details results
- dict : confidence color scale
- plotly figure : confidence color scale figure
- """
- # the readers considered
- out_reader_type_list = ['EasyOCR', 'PPOCR', 'MMOCR', 'Tesseract']
- out_reader_type_dict = {'EasyOCR': 0, 'PPOCR': 1, 'MMOCR': 2, 'Tesseract': 3}
-
- # Columns for recognition details results
- out_cols_size = [2] + [2,1]*(len(out_reader_type_list)-1) # Except Tesseract
-
- # Dicts of laguages supported by each reader
- out_dict_lang_easyocr = {'Abaza': 'abq', 'Adyghe': 'ady', 'Afrikaans': 'af', 'Angika': 'ang', \
- 'Arabic': 'ar', 'Assamese': 'as', 'Avar': 'ava', 'Azerbaijani': 'az', 'Belarusian': 'be', \
- 'Bulgarian': 'bg', 'Bihari': 'bh', 'Bhojpuri': 'bho', 'Bengali': 'bn', 'Bosnian': 'bs', \
- 'Simplified Chinese': 'ch_sim', 'Traditional Chinese': 'ch_tra', 'Chechen': 'che', \
- 'Czech': 'cs', 'Welsh': 'cy', 'Danish': 'da', 'Dargwa': 'dar', 'German': 'de', \
- 'English': 'en', 'Spanish': 'es', 'Estonian': 'et', 'Persian (Farsi)': 'fa', 'French': 'fr', \
- 'Irish': 'ga', 'Goan Konkani': 'gom', 'Hindi': 'hi', 'Croatian': 'hr', 'Hungarian': 'hu', \
- 'Indonesian': 'id', 'Ingush': 'inh', 'Icelandic': 'is', 'Italian': 'it', 'Japanese': 'ja', \
- 'Kabardian': 'kbd', 'Kannada': 'kn', 'Korean': 'ko', 'Kurdish': 'ku', 'Latin': 'la', \
- 'Lak': 'lbe', 'Lezghian': 'lez', 'Lithuanian': 'lt', 'Latvian': 'lv', 'Magahi': 'mah', \
- 'Maithili': 'mai', 'Maori': 'mi', 'Mongolian': 'mn', 'Marathi': 'mr', 'Malay': 'ms', \
- 'Maltese': 'mt', 'Nepali': 'ne', 'Newari': 'new', 'Dutch': 'nl', 'Norwegian': 'no', \
- 'Occitan': 'oc', 'Pali': 'pi', 'Polish': 'pl', 'Portuguese': 'pt', 'Romanian': 'ro', \
- 'Russian': 'ru', 'Serbian (cyrillic)': 'rs_cyrillic', 'Serbian (latin)': 'rs_latin', \
- 'Nagpuri': 'sck', 'Slovak': 'sk', 'Slovenian': 'sl', 'Albanian': 'sq', 'Swedish': 'sv', \
- 'Swahili': 'sw', 'Tamil': 'ta', 'Tabassaran': 'tab', 'Telugu': 'te', 'Thai': 'th', \
- 'Tajik': 'tjk', 'Tagalog': 'tl', 'Turkish': 'tr', 'Uyghur': 'ug', 'Ukranian': 'uk', \
- 'Urdu': 'ur', 'Uzbek': 'uz', 'Vietnamese': 'vi'}
-
- out_dict_lang_ppocr = {'Abaza': 'abq', 'Adyghe': 'ady', 'Afrikaans': 'af', 'Albanian': 'sq', \
- 'Angika': 'ang', 'Arabic': 'ar', 'Avar': 'ava', 'Azerbaijani': 'az', 'Belarusian': 'be', \
- 'Bhojpuri': 'bho','Bihari': 'bh','Bosnian': 'bs','Bulgarian': 'bg','Chinese & English': 'ch', \
- 'Chinese Traditional': 'chinese_cht', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', \
- 'Dargwa': 'dar', 'Dutch': 'nl', 'English': 'en', 'Estonian': 'et', 'French': 'fr', \
- 'German': 'german','Goan Konkani': 'gom','Hindi': 'hi','Hungarian': 'hu','Icelandic': 'is', \
- 'Indonesian': 'id', 'Ingush': 'inh', 'Irish': 'ga', 'Italian': 'it', 'Japan': 'japan', \
- 'Kabardian': 'kbd', 'Korean': 'korean', 'Kurdish': 'ku', 'Lak': 'lbe', 'Latvian': 'lv', \
- 'Lezghian': 'lez', 'Lithuanian': 'lt', 'Magahi': 'mah', 'Maithili': 'mai', 'Malay': 'ms', \
- 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Nagpur': 'sck', \
- 'Nepali': 'ne', 'Newari': 'new', 'Norwegian': 'no', 'Occitan': 'oc', 'Persian': 'fa', \
- 'Polish': 'pl', 'Portuguese': 'pt', 'Romanian': 'ro', 'Russia': 'ru', 'Saudi Arabia': 'sa', \
- 'Serbian(cyrillic)': 'rs_cyrillic', 'Serbian(latin)': 'rs_latin', 'Slovak': 'sk', \
- 'Slovenian': 'sl', 'Spanish': 'es', 'Swahili': 'sw', 'Swedish': 'sv', 'Tabassaran': 'tab', \
- 'Tagalog': 'tl', 'Tamil': 'ta', 'Telugu': 'te', 'Turkish': 'tr', 'Ukranian': 'uk', \
- 'Urdu': 'ur', 'Uyghur': 'ug', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy'}
-
- out_dict_lang_mmocr = {'English & Chinese': 'en'}
-
- out_dict_lang_tesseract = {'Afrikaans': 'afr','Albanian': 'sqi','Amharic': 'amh', \
- 'Arabic': 'ara', 'Armenian': 'hye','Assamese': 'asm','Azerbaijani - Cyrilic': 'aze_cyrl', \
- 'Azerbaijani': 'aze', 'Basque': 'eus','Belarusian': 'bel','Bengali': 'ben','Bosnian': 'bos', \
- 'Breton': 'bre', 'Bulgarian': 'bul','Burmese': 'mya','Catalan; Valencian': 'cat', \
- 'Cebuano': 'ceb', 'Central Khmer': 'khm','Cherokee': 'chr','Chinese - Simplified': 'chi_sim', \
- 'Chinese - Traditional': 'chi_tra','Corsican': 'cos','Croatian': 'hrv','Czech': 'ces', \
- 'Danish':'dan','Dutch; Flemish':'nld','Dzongkha':'dzo','English, Middle (1100-1500)':'enm', \
- 'English': 'eng','Esperanto': 'epo','Estonian': 'est','Faroese': 'fao', \
- 'Filipino (old - Tagalog)': 'fil','Finnish': 'fin','French, Middle (ca.1400-1600)': 'frm', \
- 'French': 'fra','Galician': 'glg','Georgian - Old': 'kat_old','Georgian': 'kat', \
- 'German - Fraktur': 'frk','German': 'deu','Greek, Modern (1453-)': 'ell','Gujarati': 'guj', \
- 'Haitian; Haitian Creole': 'hat','Hebrew': 'heb','Hindi': 'hin','Hungarian': 'hun', \
- 'Icelandic': 'isl','Indonesian': 'ind','Inuktitut': 'iku','Irish': 'gle', \
- 'Italian - Old': 'ita_old','Italian': 'ita','Japanese': 'jpn','Javanese': 'jav', \
- 'Kannada': 'kan','Kazakh': 'kaz','Kirghiz; Kyrgyz': 'kir','Korean (vertical)': 'kor_vert', \
- 'Korean': 'kor','Kurdish (Arabic Script)': 'kur_ara','Lao': 'lao','Latin': 'lat', \
- 'Latvian':'lav','Lithuanian':'lit','Luxembourgish':'ltz','Macedonian':'mkd','Malay':'msa', \
- 'Malayalam': 'mal','Maltese': 'mlt','Maori': 'mri','Marathi': 'mar','Mongolian': 'mon', \
- 'Nepali': 'nep','Norwegian': 'nor','Occitan (post 1500)': 'oci', \
- 'Orientation and script detection module':'osd','Oriya':'ori','Panjabi; Punjabi':'pan', \
- 'Persian':'fas','Polish':'pol','Portuguese':'por','Pushto; Pashto':'pus','Quechua':'que', \
- 'Romanian; Moldavian; Moldovan': 'ron','Russian': 'rus','Sanskrit': 'san', \
- 'Scottish Gaelic': 'gla','Serbian - Latin': 'srp_latn','Serbian': 'srp','Sindhi': 'snd', \
- 'Sinhala; Sinhalese': 'sin','Slovak': 'slk','Slovenian': 'slv', \
- 'Spanish; Castilian - Old': 'spa_old','Spanish; Castilian': 'spa','Sundanese': 'sun', \
- 'Swahili': 'swa','Swedish': 'swe','Syriac': 'syr','Tajik': 'tgk','Tamil': 'tam', \
- 'Tatar':'tat','Telugu':'tel','Thai':'tha','Tibetan':'bod','Tigrinya':'tir','Tonga':'ton', \
- 'Turkish': 'tur','Uighur; Uyghur': 'uig','Ukrainian': 'ukr','Urdu': 'urd', \
- 'Uzbek - Cyrilic': 'uzb_cyrl','Uzbek': 'uzb','Vietnamese': 'vie','Welsh': 'cym', \
- 'Western Frisian': 'fry','Yiddish': 'yid','Yoruba': 'yor'}
-
- out_list_dict_lang = [out_dict_lang_easyocr, out_dict_lang_ppocr, out_dict_lang_mmocr, \
- out_dict_lang_tesseract]
-
- # Initialization of detection form
- if 'columns_size' not in st.session_state:
- st.session_state.columns_size = [2] + [1 for x in out_reader_type_list[1:]]
- if 'column_width' not in st.session_state:
- st.session_state.column_width = [400] + [300 for x in out_reader_type_list[1:]]
- if 'columns_color' not in st.session_state:
- st.session_state.columns_color = ["rgb(228,26,28)"] + \
- ["rgb(0,0,0)" for x in out_reader_type_list[1:]]
- if 'list_coordinates' not in st.session_state:
- st.session_state.list_coordinates = []
-
- # Confidence color scale
- out_list_confid = list(np.arange(0,101,1))
- out_list_grad = mcp.gen_color_normalized(cmap="Greens",data_arr=np.array(out_list_confid))
- out_dict_back_colors = {out_list_confid[i]: out_list_grad[i] \
- for i in range(len(out_list_confid))}
-
- list_y = [1 for i in out_list_confid]
- df_confid = pd.DataFrame({'% confidence scale': out_list_confid, 'y': list_y})
-
- out_fig = px.scatter(df_confid, x='% confidence scale', y='y', \
- hover_data={'% confidence scale': True, 'y': False},
- color=out_dict_back_colors.values(), range_y=[0.9,1.1], range_x=[0,100],
- color_discrete_map="identity",height=50,symbol='y',symbol_sequence=['square'])
- out_fig.update_xaxes(showticklabels=False)
- out_fig.update_yaxes(showticklabels=False, range=[0.1, 1.1], visible=False)
- out_fig.update_traces(marker_size=50)
- out_fig.update_layout(paper_bgcolor="white", margin=dict(b=0,r=0,t=0,l=0), xaxis_side="top", \
- showlegend=False)
-
- return out_reader_type_list, out_reader_type_dict, out_list_dict_lang, \
- out_cols_size, out_dict_back_colors, out_fig
-
- ###
- @st.experimental_memo(show_spinner=False)
- def init_easyocr(in_params):
- """Initialization of easyOCR reader
-
- Args:
- in_params (list): list with the language
-
- Returns:
- easyocr reader: the easyocr reader instance
- """
- out_ocr = easyocr.Reader(in_params)
- return out_ocr
-
- ###
- @st.cache(show_spinner=False)
- def init_ppocr(in_params):
- """Initialization of PPOCR reader
-
- Args:
- in_params (dict): dict with parameters
-
- Returns:
- ppocr reader: the ppocr reader instance
- """
- out_ocr = PaddleOCR(lang=in_params[0], **in_params[1])
- return out_ocr
-
- ###
- @st.experimental_memo(show_spinner=False)
- def init_mmocr(in_params):
- """Initialization of MMOCR reader
-
- Args:
- in_params (dict): dict with parameters
-
- Returns:
- mmocr reader: the ppocr reader instance
- """
- out_ocr = MMOCR(recog=None, **in_params[1])
- return out_ocr
-
- ###
- def init_readers(in_list_params):
- """Initialization of the readers, and return them as list
-
- Args:
- in_list_params (list): list of dicts of parameters for each reader
-
- Returns:
- list: list of the reader's instances
- """
- # Instantiations of the readers :
- # - EasyOCR
- with st.spinner("EasyOCR reader initialization in progress ..."):
- reader_easyocr = init_easyocr([in_list_params[0][0]])
-
- # - PPOCR
- # Paddleocr
- with st.spinner("PPOCR reader initialization in progress ..."):
- reader_ppocr = init_ppocr(in_list_params[1])
-
- # - MMOCR
- with st.spinner("MMOCR reader initialization in progress ..."):
- reader_mmocr = init_mmocr(in_list_params[2])
-
- out_list_readers = [reader_easyocr, reader_ppocr, reader_mmocr]
-
- return out_list_readers
-
- ###
- def load_image(in_image_file):
- """Load input file and open it
-
- Args:
- in_image_file (string or Streamlit UploadedFile): image to consider
-
- Returns:
- string : locally saved image path (img.)
- PIL.Image : input file opened with Pillow
- matrix : input file opened with Opencv
- """
-
- #if isinstance(in_image_file, str):
- # out_image_path = "img."+in_image_file.split('.')[-1]
- #else:
- # out_image_path = "img."+in_image_file.name.split('.')[-1]
-
- if isinstance(in_image_file, str):
- out_image_path = "tmp_"+in_image_file
- else:
- out_image_path = "tmp_"+in_image_file.name
-
- img = Image.open(in_image_file)
- img_saved = img.save(out_image_path)
-
- # Read image
- out_image_orig = Image.open(out_image_path)
- out_image_cv2 = cv2.cvtColor(cv2.imread(out_image_path), cv2.COLOR_BGR2RGB)
-
- return out_image_path, out_image_orig, out_image_cv2
-
- ###
- @st.experimental_memo(show_spinner=False)
- def easyocr_detect(_in_reader, in_image_path, in_params):
- """Detection with EasyOCR
-
- Args:
- _in_reader (EasyOCR reader) : the previously initialized instance
- in_image_path (string ) : locally saved image path
- in_params (list) : list with the parameters for detection
-
- Returns:
- list : list of the boxes coordinates
- exception on error, string 'OK' otherwise
- """
- try:
- dict_param = in_params[1]
- detection_result = _in_reader.detect(in_image_path,
- #width_ths=0.7,
- #mag_ratio=1.5
- **dict_param
- )
- easyocr_coordinates = detection_result[0][0]
-
- # The format of the coordinate is as follows: [x_min, x_max, y_min, y_max]
- # Format boxes coordinates for draw
- out_easyocr_boxes_coordinates = list(map(easyocr_coord_convert, easyocr_coordinates))
- out_status = 'OK'
- except Exception as e:
- out_easyocr_boxes_coordinates = []
- out_status = e
-
- return out_easyocr_boxes_coordinates, out_status
-
- ###
- @st.experimental_memo(show_spinner=False)
- def ppocr_detect(_in_reader, in_image_path):
- """Detection with PPOCR
-
- Args:
- _in_reader (PPOCR reader) : the previously initialized instance
- in_image_path (string ) : locally saved image path
-
- Returns:
- list : list of the boxes coordinates
- exception on error, string 'OK' otherwise
- """
- # PPOCR detection method
- try:
- out_ppocr_boxes_coordinates = _in_reader.ocr(in_image_path, rec=False)
- out_status = 'OK'
- except Exception as e:
- out_ppocr_boxes_coordinates = []
- out_status = e
-
- return out_ppocr_boxes_coordinates, out_status
-
- ###
- @st.experimental_memo(show_spinner=False)
- def mmocr_detect(_in_reader, in_image_path):
- """Detection with MMOCR
-
- Args:
- _in_reader (EasyORC reader) : the previously initialized instance
- in_image_path (string) : locally saved image path
- in_params (list) : list with the parameters
-
- Returns:
- list : list of the boxes coordinates
- exception on error, string 'OK' otherwise
- """
- # MMOCR detection method
- out_mmocr_boxes_coordinates = []
- try:
- det_result = _in_reader.readtext(in_image_path, details=True)
- bboxes_list = [res['boundary_result'] for res in det_result]
- for bboxes in bboxes_list:
- for bbox in bboxes:
- if len(bbox) > 9:
- min_x = min(bbox[0:-1:2])
- min_y = min(bbox[1:-1:2])
- max_x = max(bbox[0:-1:2])
- max_y = max(bbox[1:-1:2])
- #box = [min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]
- else:
- min_x = min(bbox[0:-1:2])
- min_y = min(bbox[1::2])
- max_x = max(bbox[0:-1:2])
- max_y = max(bbox[1::2])
- box4 = [ [min_x, min_y], [max_x, min_y], [max_x, max_y], [min_x, max_y] ]
- out_mmocr_boxes_coordinates.append(box4)
- out_status = 'OK'
- except Exception as e:
- out_status = e
-
- return out_mmocr_boxes_coordinates, out_status
-
- ###
- def cropped_1box(in_box, in_img):
- """Construction of an cropped image corresponding to an area of the initial image
-
- Args:
- in_box (list) : box with coordinates
- in_img (matrix) : image
-
- Returns:
- matrix : cropped image
- """
- box_ar = np.array(in_box).astype(np.int64)
- x_min = box_ar[:, 0].min()
- x_max = box_ar[:, 0].max()
- y_min = box_ar[:, 1].min()
- y_max = box_ar[:, 1].max()
- out_cropped = in_img[y_min:y_max, x_min:x_max]
-
- return out_cropped
-
- ###
- @st.experimental_memo(show_spinner=False)
- def tesserocr_detect(in_image_path, _in_img, in_params):
- """Detection with Tesseract
-
- Args:
- in_image_path (string) : locally saved image path
- _in_img (PIL.Image) : image to consider
- in_params (list) : list with the parameters for detection
-
- Returns:
- list : list of the boxes coordinates
- exception on error, string 'OK' otherwise
- """
- try:
- dict_param = in_params[1]
- df_res = pytesseract.image_to_data(_in_img, **dict_param, output_type=Output.DATAFRAME)
-
- df_res['box'] = df_res.apply(lambda d: [[d['left'], d['top']], \
- [d['left'] + d['width'], d['top']], \
- [d['left'] + d['width'], d['top'] + d['height']], \
- [d['left'], d['top'] + d['height']], \
- ], axis=1)
- out_tesserocr_boxes_coordinates = df_res[df_res.word_num > 0]['box'].to_list()
- out_status = 'OK'
- except Exception as e:
- out_tesserocr_boxes_coordinates = []
- out_status = e
-
- return out_tesserocr_boxes_coordinates, out_status
-
- ###
- @st.experimental_memo(show_spinner=False)
- def process_detect(in_image_path, _in_list_images, _in_list_readers, in_list_params, in_color):
- """Detection process for each OCR solution
-
- Args:
- in_image_path (string) : locally saved image path
- _in_list_images (list) : list of original image
- _in_list_readers (list) : list with previously initialized reader's instances
- in_list_params (list) : list with dict parameters for each OCR solution
- in_color (tuple) : color for boxes around text
-
- Returns:
- list: list of detection results images
- list: list of boxes coordinates
- """
- ## ------- EasyOCR Text detection
- with st.spinner('EasyOCR Text detection in progress ...'):
- easyocr_boxes_coordinates,easyocr_status = easyocr_detect(_in_list_readers[0], \
- in_image_path, in_list_params[0])
- # Visualization
- if easyocr_boxes_coordinates:
- easyocr_image_detect = draw_detected(_in_list_images[0], easyocr_boxes_coordinates, \
- in_color, 'None', 3)
- else:
- easyocr_boxes_coordinates = easyocr_status
- ##
-
- ## ------- PPOCR Text detection
- with st.spinner('PPOCR Text detection in progress ...'):
- ppocr_boxes_coordinates, ppocr_status = ppocr_detect(_in_list_readers[1], in_image_path)
- # Visualization
- if ppocr_boxes_coordinates:
- ppocr_image_detect = draw_detected(_in_list_images[0], ppocr_boxes_coordinates, \
- in_color, 'None', 3)
- else:
- ppocr_image_detect = ppocr_status
- ##
-
- ## ------- MMOCR Text detection
- with st.spinner('MMOCR Text detection in progress ...'):
- mmocr_boxes_coordinates, mmocr_status = mmocr_detect(_in_list_readers[2], in_image_path)
- # Visualization
- if mmocr_boxes_coordinates:
- mmocr_image_detect = draw_detected(_in_list_images[0], mmocr_boxes_coordinates, \
- in_color, 'None', 3)
- else:
- mmocr_image_detect = mmocr_status
- ##
-
- ## ------- Tesseract Text detection
- with st.spinner('Tesseract Text detection in progress ...'):
- tesserocr_boxes_coordinates, tesserocr_status = tesserocr_detect(in_image_path, \
- _in_list_images[0], \
- in_list_params[3])
- # Visualization
- if tesserocr_status == 'OK':
- tesserocr_image_detect = draw_detected(_in_list_images[0],tesserocr_boxes_coordinates,\
- in_color, 'None', 3)
- else:
- tesserocr_image_detect = tesserocr_status
- ##
- #
- out_list_images = _in_list_images + [easyocr_image_detect, ppocr_image_detect, \
- mmocr_image_detect, tesserocr_image_detect]
- out_list_coordinates = [easyocr_boxes_coordinates, ppocr_boxes_coordinates, \
- mmocr_boxes_coordinates, tesserocr_boxes_coordinates]
- #
-
- return out_list_images, out_list_coordinates
-
- ###
- def draw_detected(in_image, in_boxes_coordinates, in_color, posit='None', in_thickness=4):
- """Draw boxes around detected text
-
- Args:
- in_image (PIL.Image) : original image
- in_boxes_coordinates (list) : boxes coordinates, from top to bottom and from left to right
- [ [ [x_min, y_min], [x_max, y_min], [x_max, y_max], [x_min, y_max] ],
- [ ... ]
- ]
- in_color (tuple) : color for boxes around text
- posit (str, optional) : position for text. Defaults to 'None'.
- in_thickness (int, optional): thickness of the box. Defaults to 4.
-
- Returns:
- PIL.Image : original image with detected areas
- """
- work_img = in_image.copy()
- if in_boxes_coordinates:
- font = cv2.FONT_HERSHEY_SIMPLEX
- for ind_box, box in enumerate(in_boxes_coordinates):
- box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
- work_img = cv2.polylines(np.array(work_img), [box], True, in_color, in_thickness)
- if posit != 'None':
- if posit == 'top_left':
- pos = tuple(box[0][0])
- elif posit == 'top_right':
- pos = tuple(box[1][0])
- work_img = cv2.putText(work_img, str(ind_box+1), pos, font, 5.5, color, \
- in_thickness,cv2.LINE_AA)
-
- out_image_drawn = Image.fromarray(work_img)
- else:
- out_image_drawn = work_img
-
- return out_image_drawn
-
- ###
- @st.experimental_memo(show_spinner=False)
- def get_cropped(in_boxes_coordinates, in_image_cv):
- """Construct list of cropped images corresponding of the input boxes coordinates list
-
- Args:
- in_boxes_coordinates (list) : list of boxes coordinates
- in_image_cv (matrix) : original image
-
- Returns:
- list : list with cropped images
- """
- out_list_images = []
- for box in in_boxes_coordinates:
- cropped = cropped_1box(box, in_image_cv)
- out_list_images.append(cropped)
- return out_list_images
-
- ###
- def process_recog(in_list_readers, in_image_cv, in_boxes_coordinates, in_list_dict_params):
- """Recognition process for each OCR solution
-
- Args:
- in_list_readers (list) : list with previously initialized reader's instances
- in_image_cv (matrix) : original image
- in_boxes_coordinates (list) : list of boxes coordinates
- in_list_dict_params (list) : list with dict parameters for each OCR solution
-
- Returns:
- data frame : results for each OCR solution, except Tesseract
- data frame : results for Tesseract
- list : status for each recognition (exception or 'OK')
- """
- out_df_results = pd.DataFrame([])
-
- list_text_easyocr = []
- list_confidence_easyocr = []
- list_text_ppocr = []
- list_confidence_ppocr = []
- list_text_mmocr = []
- list_confidence_mmocr = []
-
- # Create cropped images from detection
- list_cropped_images = get_cropped(in_boxes_coordinates, in_image_cv)
-
- # Recognize with EasyOCR
- with st.spinner('EasyOCR Text recognition in progress ...'):
- list_text_easyocr, list_confidence_easyocr, status_easyocr = \
- easyocr_recog(list_cropped_images, in_list_readers[0], in_list_dict_params[0])
- ##
-
- # Recognize with PPOCR
- with st.spinner('PPOCR Text recognition in progress ...'):
- list_text_ppocr, list_confidence_ppocr, status_ppocr = \
- ppocr_recog(list_cropped_images, in_list_dict_params[1])
- ##
-
- # Recognize with MMOCR
- with st.spinner('MMOCR Text recognition in progress ...'):
- list_text_mmocr, list_confidence_mmocr, status_mmocr = \
- mmocr_recog(list_cropped_images, in_list_dict_params[2])
- ##
-
- # Recognize with Tesseract
- with st.spinner('Tesseract Text recognition in progress ...'):
- out_df_results_tesseract, status_tesseract = \
- tesserocr_recog(in_image_cv, in_list_dict_params[3], len(list_cropped_images))
- ##
-
- # Create results data frame
- out_df_results = pd.DataFrame({'cropped_image': list_cropped_images,
- 'text_easyocr': list_text_easyocr,
- 'confidence_easyocr': list_confidence_easyocr,
- 'text_ppocr': list_text_ppocr,
- 'confidence_ppocr': list_confidence_ppocr,
- 'text_mmocr': list_text_mmocr,
- 'confidence_mmocr': list_confidence_mmocr
- }
- )
-
- out_list_reco_status = [status_easyocr, status_ppocr, status_mmocr, status_tesseract]
-
- return out_df_results, out_df_results_tesseract, out_list_reco_status
-
- ###
- @st.experimental_memo(suppress_st_warning=True, show_spinner=False)
- def easyocr_recog(in_list_images, _in_reader_easyocr, in_params):
- """Recognition with EasyOCR
-
- Args:
- in_list_images (list) : list of cropped images
- _in_reader_easyocr (EasyOCR reader) : the previously initialized instance
- in_params (dict) : parameters for recognition
-
- Returns:
- list : list of recognized text
- list : list of recognition confidence
- string/Exception : recognition status
- """
- progress_bar = st.progress(0)
- out_list_text_easyocr = []
- out_list_confidence_easyocr = []
- ## ------- EasyOCR Text recognition
- try:
- step = 0*len(in_list_images) # first recognition process
- nb_steps = 4 * len(in_list_images)
- for ind_img, cropped in enumerate(in_list_images):
- result = _in_reader_easyocr.recognize(cropped, **in_params)
- try:
- out_list_text_easyocr.append(result[0][1])
- out_list_confidence_easyocr.append(np.round(100*result[0][2], 1))
- except:
- out_list_text_easyocr.append('Not recognize')
- out_list_confidence_easyocr.append(100.)
- progress_bar.progress((step+ind_img+1)/nb_steps)
- out_status = 'OK'
- except Exception as e:
- out_status = e
- progress_bar.empty()
-
- return out_list_text_easyocr, out_list_confidence_easyocr, out_status
-
- ###
- @st.experimental_memo(suppress_st_warning=True, show_spinner=False)
- def ppocr_recog(in_list_images, in_params):
- """Recognition with PPOCR
-
- Args:
- in_list_images (list) : list of cropped images
- in_params (dict) : parameters for recognition
-
- Returns:
- list : list of recognized text
- list : list of recognition confidence
- string/Exception : recognition status
- """
- ## ------- PPOCR Text recognition
- out_list_text_ppocr = []
- out_list_confidence_ppocr = []
- try:
- reader_ppocr = PaddleOCR(**in_params)
- step = 1*len(in_list_images) # second recognition process
- nb_steps = 4 * len(in_list_images)
- progress_bar = st.progress(step/nb_steps)
-
- for ind_img, cropped in enumerate(in_list_images):
- result = reader_ppocr.ocr(cropped, det=False, cls=False)
- try:
- out_list_text_ppocr.append(result[0][0])
- out_list_confidence_ppocr.append(np.round(100*result[0][1], 1))
- except:
- out_list_text_ppocr.append('Not recognize')
- out_list_confidence_ppocr.append(100.)
- progress_bar.progress((step+ind_img+1)/nb_steps)
- out_status = 'OK'
- except Exception as e:
- out_status = e
- progress_bar.empty()
-
- return out_list_text_ppocr, out_list_confidence_ppocr, out_status
-
- ###
- @st.experimental_memo(suppress_st_warning=True, show_spinner=False)
- def mmocr_recog(in_list_images, in_params):
- """Recognition with MMOCR
-
- Args:
- in_list_images (list) : list of cropped images
- in_params (dict) : parameters for recognition
-
- Returns:
- list : list of recognized text
- list : list of recognition confidence
- string/Exception : recognition status
- """
- ## ------- MMOCR Text recognition
- out_list_text_mmocr = []
- out_list_confidence_mmocr = []
- try:
- reader_mmocr = MMOCR(det=None, **in_params)
- step = 2*len(in_list_images) # third recognition process
- nb_steps = 4 * len(in_list_images)
- progress_bar = st.progress(step/nb_steps)
-
- for ind_img, cropped in enumerate(in_list_images):
- result = reader_mmocr.readtext(cropped, details=True)
- try:
- out_list_text_mmocr.append(result[0]['text'])
- out_list_confidence_mmocr.append(np.round(100* \
- (np.array(result[0]['score']).mean()), 1))
- except:
- out_list_text_mmocr.append('Not recognize')
- out_list_confidence_mmocr.append(100.)
- progress_bar.progress((step+ind_img+1)/nb_steps)
- out_status = 'OK'
- except Exception as e:
- out_status = e
- progress_bar.empty()
-
- return out_list_text_mmocr, out_list_confidence_mmocr, out_status
-
- ###
- @st.experimental_memo(suppress_st_warning=True, show_spinner=False)
- def tesserocr_recog(in_img, in_params, in_nb_images):
- """Recognition with Tesseract
-
- Args:
- in_image_cv (matrix) : original image
- in_params (dict) : parameters for recognition
- in_nb_images : nb cropped images (used for progress bar)
-
- Returns:
- Pandas data frame : recognition results
- string/Exception : recognition status
- """
- ## ------- Tesseract Text recognition
- step = 3*in_nb_images # fourth recognition process
- nb_steps = 4 * in_nb_images
- progress_bar = st.progress(step/nb_steps)
-
- try:
- out_df_result = pytesseract.image_to_data(in_img, **in_params,output_type=Output.DATAFRAME)
-
- out_df_result['box'] = out_df_result.apply(lambda d: [[d['left'], d['top']], \
- [d['left'] + d['width'], d['top']], \
- [d['left']+d['width'], d['top']+d['height']], \
- [d['left'], d['top'] + d['height']], \
- ], axis=1)
- out_df_result['cropped'] = out_df_result['box'].apply(lambda b: cropped_1box(b, in_img))
- out_df_result = out_df_result[(out_df_result.word_num > 0) & (out_df_result.text != ' ')] \
- .reset_index(drop=True)
- out_status = 'OK'
- except Exception as e:
- out_df_result = pd.DataFrame([])
- out_status = e
-
- progress_bar.progress(1.)
-
- return out_df_result, out_status
-
- ###
- def draw_reco_images(in_image, in_boxes_coordinates, in_list_texts, in_list_confid, \
- in_dict_back_colors, in_df_results_tesseract, in_reader_type_list, \
- in_font_scale=1, in_conf_threshold=65):
- """Draw recognized text on original image, for each OCR solution used
-
- Args:
- in_image (matrix) : original image
- in_boxes_coordinates (list) : list of boxes coordinates
- in_list_texts (list): list of recognized text for each recognizer (except Tesseract)
- in_list_confid (list): list of recognition confidence for each recognizer (except Tesseract)
- in_df_results_tesseract (Pandas data frame): Tesseract recognition results
- in_font_scale (int, optional): text font scale. Defaults to 3.
-
- Returns:
- shows the results container
- """
- img = in_image.copy()
- nb_readers = len(in_reader_type_list)
- list_reco_images = [img.copy() for i in range(nb_readers)]
-
- for num, box_ in enumerate(in_boxes_coordinates):
- box = np.array(box_).astype(np.int64)
-
- # For each box : draw the results of each recognizer
- for ind_r in range(nb_readers-1):
- confid = np.round(in_list_confid[ind_r][num], 0)
- rgb_color = ImageColor.getcolor(in_dict_back_colors[confid], "RGB")
- if confid < in_conf_threshold:
- text_color = (0, 0, 0)
- else:
- text_color = (255, 255, 255)
-
- list_reco_images[ind_r] = cv2.rectangle(list_reco_images[ind_r], \
- (box[0][0], box[0][1]), \
- (box[2][0], box[2][1]), rgb_color, -1)
- list_reco_images[ind_r] = cv2.putText(list_reco_images[ind_r], \
- in_list_texts[ind_r][num], \
- (box[0][0],int(np.round((box[0][1]+box[2][1])/2,0))), \
- cv2.FONT_HERSHEY_DUPLEX, in_font_scale, text_color, 2)
-
- # Add Tesseract process
- if not in_df_results_tesseract.empty:
- ind_tessocr = nb_readers-1
- for num, box_ in enumerate(in_df_results_tesseract['box'].to_list()):
- box = np.array(box_).astype(np.int64)
- confid = np.round(in_df_results_tesseract.iloc[num]['conf'], 0)
- rgb_color = ImageColor.getcolor(in_dict_back_colors[confid], "RGB")
- if confid < in_conf_threshold:
- text_color = (0, 0, 0)
- else:
- text_color = (255, 255, 255)
-
- list_reco_images[ind_tessocr] = \
- cv2.rectangle(list_reco_images[ind_tessocr], (box[0][0], box[0][1]), \
- (box[2][0], box[2][1]), rgb_color, -1)
- try:
- list_reco_images[ind_tessocr] = \
- cv2.putText(list_reco_images[ind_tessocr], \
- in_df_results_tesseract.iloc[num]['text'], \
- (box[0][0],int(np.round((box[0][1]+box[2][1])/2,0))), \
- cv2.FONT_HERSHEY_DUPLEX, in_font_scale, text_color, 2)
-
- except:
-
- pass
-
- with show_reco.container():
- # Draw the results, 2 images per line
- reco_lines = math.ceil(len(in_reader_type_list) / 2)
- column_width = 400
- for ind_lig in range(0, reco_lines+1, 2):
- cols = st.columns(2)
- for ind_col in range(2):
- ind = ind_lig + ind_col
- if ind <= len(in_reader_type_list):
- if in_reader_type_list[ind] == 'Tesseract':
- column_title = '
Recognition with ' + in_reader_type_list[ind] + \
- ' (with its own detector) \
-
'
- else:
- column_title = '
Recognition with ' + \
- in_reader_type_list[ind]+ '
'
- cols[ind_col].markdown(column_title, unsafe_allow_html=True)
- if st.session_state.list_reco_status[ind] == 'OK':
- cols[ind_col].image(list_reco_images[ind], \
- width=column_width, use_column_width=True)
- else:
- cols[ind_col].write(list_reco_status[ind], \
- use_column_width=True)
-
- st.markdown(' 💡 Bad font size? you can adjust it below and refresh:')
-
- ###
- def highlight():
- """ Highlight choosen detector results
- """
- with show_detect.container():
- columns_size = [1 for x in reader_type_list]
- column_width = [300 for x in reader_type_list]
- columns_color = ["rgb(0,0,0)" for x in reader_type_list]
- columns_size[reader_type_dict[st.session_state.detect_reader]] = 2
- column_width[reader_type_dict[st.session_state.detect_reader]] = 400
- columns_color[reader_type_dict[st.session_state.detect_reader]] = "rgb(228,26,28)"
- columns = st.columns(columns_size, ) #gap='medium')
-
- for ind_col, col in enumerate(columns):
- column_title = '
Detection with ' + reader_type_list[ind_col]+ '
'
- col.markdown(column_title, unsafe_allow_html=True)
- if isinstance(list_images[ind_col+2], PIL.Image.Image):
- col.image(list_images[ind_col+2], width=column_width[ind_col], \
- use_column_width=True)
- else:
- col.write(list_images[ind_col+2], use_column_width=True)
- st.session_state.columns_size = columns_size
- st.session_state.column_width = column_width
- st.session_state.columns_color = columns_color
-
- ###
- @st.cache(show_spinner=False)
- def get_demo():
- """Get the demo files
-
- Returns:
- PIL.Image : input file opened with Pillow
- PIL.Image : input file opened with Pillow
- """
-
- out_img_demo_1 = Image.open("img_demo_1.jpg")
- out_img_demo_2 = Image.open("img_demo_2.jpg")
-
- return out_img_demo_1, out_img_demo_2
-
- ###
- def raz():
- st.session_state.list_coordinates = []
- st.session_state.list_images = []
- st.session_state.detect_reader = reader_type_list[0]
-
- st.session_state.columns_size = [2] + [1 for x in reader_type_list[1:]]
- st.session_state.column_width = [400] + [300 for x in reader_type_list[1:]]
- st.session_state.columns_color = ["rgb(228,26,28)"] + \
- ["rgb(0,0,0)" for x in reader_type_list[1:]]
-
- # Clear caches
- easyocr_detect.clear()
- ppocr_detect.clear()
- mmocr_detect.clear()
- tesserocr_detect.clear()
- process_detect.clear()
- get_cropped.clear()
- easyocr_recog.clear()
- ppocr_recog.clear()
- mmocr_recog.clear()
- tesserocr_recog.clear()
-
-
- ##----------- Initializations ---------------------------------------------------------------------
- #print("PID : ", os.getpid())
-
- st.title("OCR solutions comparator")
- st.markdown("##### *EasyOCR, PPOCR, MMOCR, Tesseract*")
- #st.markdown("#### PID : " + str(os.getpid()))
-
- # Initializations
- with st.spinner("Initializations in progress ..."):
- reader_type_list, reader_type_dict, list_dict_lang, \
- cols_size, dict_back_colors, fig_colorscale = initializations()
- img_demo_1, img_demo_2 = get_demo()
-
- ##----------- Choose language & image -------------------------------------------------------------
- st.markdown("#### Choose languages for the text recognition:")
- lang_col = st.columns(4)
- easyocr_key_lang = lang_col[0].selectbox(reader_type_list[0]+" :", list_dict_lang[0].keys(), 26)
- easyocr_lang = list_dict_lang[0][easyocr_key_lang]
- ppocr_key_lang = lang_col[1].selectbox(reader_type_list[1]+" :", list_dict_lang[1].keys(), 22)
- ppocr_lang = list_dict_lang[1][ppocr_key_lang]
- mmocr_key_lang = lang_col[2].selectbox(reader_type_list[2]+" :", list_dict_lang[2].keys(), 0)
- mmocr_lang = list_dict_lang[2][mmocr_key_lang]
- tesserocr_key_lang = lang_col[3].selectbox(reader_type_list[3]+" :", list_dict_lang[3].keys(), 35)
- tesserocr_lang = list_dict_lang[3][tesserocr_key_lang]
-
- st.markdown("#### Choose picture:")
- cols_pict = st.columns([1, 2])
- img_typ = cols_pict[0].radio("", ['Upload file', 'Take a picture', 'Use a demo file'], \
- index=0, on_change=raz)
-
- if img_typ == 'Upload file':
- image_file = cols_pict[1].file_uploader("Upload a file:", type=["jpg","jpeg"], on_change=raz)
- if img_typ == 'Take a picture':
- image_file = cols_pict[1].camera_input("Take a picture:", on_change=raz)
- if img_typ == 'Use a demo file':
- with st.expander('Choose a demo file:', expanded=True):
- demo_used = st.radio('', ['File 1', 'File 2'], index=0, \
- horizontal=True, on_change=raz)
- cols_demo = st.columns([1, 2])
- cols_demo[0].markdown('###### File 1')
- cols_demo[0].image(img_demo_1, width=150)
- cols_demo[1].markdown('###### File 2')
- cols_demo[1].image(img_demo_2, width=300)
- if demo_used == 'File 1':
- image_file = 'img_demo_1.jpg'
- else:
- image_file = 'img_demo_2.jpg'
-
- ##----------- Process input image -----------------------------------------------------------------
- if image_file is not None:
- image_path, image_orig, image_cv2 = load_image(image_file)
- list_images = [image_orig, image_cv2]
-
- ##----------- Form with original image & hyperparameters for detectors ----------------------------
- with st.form("form1"):
- col1, col2 = st.columns(2, ) #gap="medium")
- col1.markdown("##### Original image")
- col1.image(list_images[0], width=400)
- col2.markdown("##### Hyperparameters values for detection")
-
- with col2.expander("Choose detection hyperparameters for " + reader_type_list[0], \
- expanded=False):
- t0_min_size = st.slider("min_size", 1, 20, 10, step=1, \
- help="min_size (int, default = 10) - Filter text box smaller than \
- minimum value in pixel")
- t0_text_threshold = st.slider("text_threshold", 0.1, 1., 0.7, step=0.1, \
- help="text_threshold (float, default = 0.7) - Text confidence threshold")
- t0_low_text = st.slider("low_text", 0.1, 1., 0.4, step=0.1, \
- help="low_text (float, default = 0.4) - Text low-bound score")
- t0_link_threshold = st.slider("link_threshold", 0.1, 1., 0.4, step=0.1, \
- help="link_threshold (float, default = 0.4) - Link confidence threshold")
- t0_canvas_size = st.slider("canvas_size", 2000, 5000, 2560, step=10, \
- help='''canvas_size (int, default = 2560) \n
- Maximum e size. Image bigger than this value will be resized down''')
- t0_mag_ratio = st.slider("mag_ratio", 0.1, 5., 1., step=0.1, \
- help="mag_ratio (float, default = 1) - Image magnification ratio")
- t0_slope_ths = st.slider("slope_ths", 0.01, 1., 0.1, step=0.01, \
- help='''slope_ths (float, default = 0.1) - Maximum slope \
- (delta y/delta x) to considered merging. \n
- Low valuans tiled boxes will not be merged.''')
- t0_ycenter_ths = st.slider("ycenter_ths", 0.1, 1., 0.5, step=0.1, \
- help='''ycenter_ths (float, default = 0.5) - Maximum shift in y direction. \n
- Boxes wiifferent level should not be merged.''')
- t0_height_ths = st.slider("height_ths", 0.1, 1., 0.5, step=0.1, \
- help='''height_ths (float, default = 0.5) - Maximum different in box height. \n
- Boxes wiery different text size should not be merged.''')
- t0_width_ths = st.slider("width_ths", 0.1, 1., 0.5, step=0.1, \
- help="width_ths (float, default = 0.5) - Maximum horizontal \
- distance to merge boxes.")
- t0_add_margin = st.slider("add_margin", 0.1, 1., 0.1, step=0.1, \
- help='''add_margin (float, default = 0.1) - \
- Extend bounding boxes in all direction by certain value. \n
- This is rtant for language with complex script (E.g. Thai).''')
- t0_optimal_num_chars = st.slider("optimal_num_chars", None, 100, None, step=10, \
- help="optimal_num_chars (int, default = None) - If specified, bounding boxes \
- with estimated number of characters near this value are returned first.")
-
- with col2.expander("Choose detection hyperparameters for " + reader_type_list[1], \
- expanded=False):
- t1_det_algorithm = st.selectbox('det_algorithm', ['DB'], \
- help='Type of detection algorithm selected. (default = DB)')
- t1_det_max_side_len = st.slider('det_max_side_len', 500, 2000, 960, step=10, \
- help='''The maximum size of the long side of the image. (default = 960)\n
- Limit thximum image height and width.\n
- When theg side exceeds this value, the long side will be resized to this size, and the short side \
- will be ed proportionally.''')
- t1_det_db_thresh = st.slider('det_db_thresh', 0.1, 1., 0.3, step=0.1, \
- help='''Binarization threshold value of DB output map. (default = 0.3) \n
- Used to er the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result.''')
- t1_det_db_box_thresh = st.slider('det_db_box_thresh', 0.1, 1., 0.6, step=0.1, \
- help='''The threshold value of the DB output box. (default = 0.6) \n
- DB post-essing filter box threshold, if there is a missing box detected, it can be reduced as appropriate. \n
- Boxes sclower than this value will be discard.''')
- t1_det_db_unclip_ratio = st.slider('det_db_unclip_ratio', 1., 3.0, 1.6, step=0.1, \
- help='''The expanded ratio of DB output box. (default = 1.6) \n
- Indicatee compactness of the text box, the smaller the value, the closer the text box to the text.''')
- t1_det_east_score_thresh = st.slider('det_east_cover_thresh', 0.1, 1., 0.8, step=0.1, \
- help="Binarization threshold value of EAST output map. (default = 0.8)")
- t1_det_east_cover_thresh = st.slider('det_east_cover_thresh', 0.1, 1., 0.1, step=0.1, \
- help='''The threshold value of the EAST output box. (default = 0.1) \n
- Boxes sclower than this value will be discarded.''')
- t1_det_east_nms_thresh = st.slider('det_east_nms_thresh', 0.1, 1., 0.2, step=0.1, \
- help="The NMS threshold value of EAST model output box. (default = 0.2)")
- t1_det_db_score_mode = st.selectbox('det_db_score_mode', ['fast', 'slow'], \
- help='''slow: use polygon box to calculate bbox score, fast: use rectangle box \
- to calculate. (default = fast) \n
- Use rectlar box to calculate faster, and polygonal box more accurate for curved text area.''')
-
- with col2.expander("Choose detection hyperparameters for " + reader_type_list[2], \
- expanded=False):
- t2_det = st.selectbox('det', ['DB_r18','DB_r50','DBPP_r50','DRRG','FCE_IC15', \
- 'FCE_CTW_DCNv2','MaskRCNN_CTW','MaskRCNN_IC15', \
- 'MaskRCNN_IC17', 'PANet_CTW','PANet_IC15','PS_CTW',\
- 'PS_IC15','Tesseract','TextSnake'], 10, \
- help='Text detection algorithm. (default = PANet_IC15)')
- st.write("###### *More about text detection models* 👉 \
- [here](https://mmocr.readthedocs.io/en/latest/textdet_models.html)")
- t2_merge_xdist = st.slider('merge_xdist', 1, 50, 20, step=1, \
- help='The maximum x-axis distance to merge boxes. (defaut=20)')
-
- with col2.expander("Choose detection hyperparameters for " + reader_type_list[3], \
- expanded=False):
- t3_psm = st.selectbox('Page segmentation mode (psm)', \
- [' - Default', \
- ' 4 Assume a single column of text of variable sizes', \
- ' 5 Assume a single uniform block of vertically aligned text', \
- ' 6 Assume a single uniform block of text', \
- ' 7 Treat the image as a single text line', \
- ' 8 Treat the image as a single word', \
- ' 9 Treat the image as a single word in a circle', \
- '10 Treat the image as a single character', \
- '11 Sparse text. Find as much text as possible in no \
- particular order', \
- '13 Raw line. Treat the image as a single text line, \
- bypassing hacks that are Tesseract-specific'])
- t3_oem = st.selectbox('OCR engine mode', ['0 Legacy engine only', \
- '1 Neural nets LSTM engine only', \
- '2 Legacy + LSTM engines', \
- '3 Default, based on what is available'], 3)
- t3_whitelist = st.text_input('Limit tesseract to recognize only this characters :', \
- placeholder='Limit tesseract to recognize only this characters', \
- help='Example for numbers only : 0123456789')
-
- color_hex = col2.color_picker('Set a color for box outlines:', '#004C99')
- color_part = color_hex.lstrip('#')
- color = tuple(int(color_part[i:i+2], 16) for i in (0, 2, 4))
-
- submit_detect = st.form_submit_button("Launch detection")
-
- ##----------- Process text detection --------------------------------------------------------------
- if submit_detect:
- # Process text detection
-
- if t0_optimal_num_chars == 0:
- t0_optimal_num_chars = None
-
- # Construct the config Tesseract parameter
- t3_config = ''
- psm = t3_psm[:2]
- if psm != ' -':
- t3_config += '--psm ' + psm.strip()
- oem = t3_oem[:1]
- if oem != '3':
- t3_config += ' --oem ' + oem
- if t3_whitelist != '':
- t3_config += ' -c tessedit_char_whitelist=' + t3_whitelist
-
- list_params_det = \
- [[easyocr_lang, \
- {'min_size': t0_min_size, 'text_threshold': t0_text_threshold, \
- 'low_text': t0_low_text, 'link_threshold': t0_link_threshold, \
- 'canvas_size': t0_canvas_size, 'mag_ratio': t0_mag_ratio, \
- 'slope_ths': t0_slope_ths, 'ycenter_ths': t0_ycenter_ths, \
- 'height_ths': t0_height_ths, 'width_ths': t0_width_ths, \
- 'add_margin': t0_add_margin, 'optimal_num_chars': t0_optimal_num_chars \
- }], \
- [ppocr_lang, \
- {'det_algorithm': t1_det_algorithm, 'det_max_side_len': t1_det_max_side_len, \
- 'det_db_thresh': t1_det_db_thresh, 'det_db_box_thresh': t1_det_db_box_thresh, \
- 'det_db_unclip_ratio': t1_det_db_unclip_ratio, \
- 'det_east_score_thresh': t1_det_east_score_thresh, \
- 'det_east_cover_thresh': t1_det_east_cover_thresh, \
- 'det_east_nms_thresh': t1_det_east_nms_thresh, \
- 'det_db_score_mode': t1_det_db_score_mode}],
- [mmocr_lang, {'det': t2_det, 'merge_xdist': t2_merge_xdist}],
- [tesserocr_lang, {'lang': tesserocr_lang, 'config': t3_config}]
- ]
-
- show_info1 = st.empty()
- show_info1.info("Readers initializations in progress (it may take a while) ...")
- list_readers = init_readers(list_params_det)
-
- show_info1.info("Text detection in progress ...")
- list_images, list_coordinates = process_detect(image_path, list_images, list_readers, \
- list_params_det, color)
- show_info1.empty()
-
- # Clear previous recognition results
- st.session_state.df_results = pd.DataFrame([])
-
- st.session_state.list_readers = list_readers
- st.session_state.list_coordinates = list_coordinates
- st.session_state.list_images = list_images
- st.session_state.list_params_det = list_params_det
-
- if 'columns_size' not in st.session_state:
- st.session_state.columns_size = [2] + [1 for x in reader_type_list[1:]]
- if 'column_width' not in st.session_state:
- st.session_state.column_width = [400] + [300 for x in reader_type_list[1:]]
- if 'columns_color' not in st.session_state:
- st.session_state.columns_color = ["rgb(228,26,28)"] + \
- ["rgb(0,0,0)" for x in reader_type_list[1:]]
-
- if st.session_state.list_coordinates:
- list_coordinates = st.session_state.list_coordinates
- list_images = st.session_state.list_images
- list_readers = st.session_state.list_readers
- list_params_det = st.session_state.list_params_det
-
- ##----------- Text detection results --------------------------------------------------------------
- st.subheader("Text detection")
- show_detect = st.empty()
- list_ok_detect = []
- with show_detect.container():
- columns = st.columns(st.session_state.columns_size, ) #gap='medium')
- for no_col, col in enumerate(columns):
- column_title = '
Detection with ' + reader_type_list[no_col]+ '
'
- col.markdown(column_title, unsafe_allow_html=True)
- if isinstance(list_images[no_col+2], PIL.Image.Image):
- col.image(list_images[no_col+2], width=st.session_state.column_width[no_col], \
- use_column_width=True)
- list_ok_detect.append(reader_type_list[no_col])
- else:
- col.write(list_images[no_col+2], use_column_width=True)
-
- st.subheader("Text recognition")
-
- st.markdown("##### Using detection performed above by:")
- st.radio('Choose the detecter:', list_ok_detect, key='detect_reader', \
- horizontal=True, on_change=highlight)
-
- ##----------- Form with hyperparameters for recognition -----------------------
- st.markdown("##### Hyperparameters values for recognition:")
- with st.form("form2"):
- with st.expander("Choose recognition hyperparameters for " + reader_type_list[0], \
- expanded=False):
- t0_decoder = st.selectbox('decoder', ['greedy', 'beamsearch', 'wordbeamsearch'], \
- help="decoder (string, default = 'greedy') - options are 'greedy', \
- 'beamsearch' and 'wordbeamsearch.")
- t0_beamWidth = st.slider('beamWidth', 2, 20, 5, step=1, \
- help="beamWidth (int, default = 5) - How many beam to keep when decoder = \
- 'beamsearch' or 'wordbeamsearch'.")
- t0_batch_size = st.slider('batch_size', 1, 10, 1, step=1, \
- help="batch_size (int, default = 1) - batch_size>1 will make EasyOCR faster \
- but use more memory.")
- t0_workers = st.slider('workers', 0, 10, 0, step=1, \
- help="workers (int, default = 0) - Number thread used in of dataloader.")
- t0_allowlist = st.text_input('allowlist', value="", max_chars=None, \
- placeholder='Force EasyOCR to recognize only this subset of characters', \
- help='''allowlist (string) - Force EasyOCR to recognize only subset of characters.\n
- Usefor specific problem (E.g. license plate, etc.)''')
- t0_blocklist = st.text_input('blocklist', value="", max_chars=None, \
- placeholder='Block subset of character (will be ignored if allowlist is given)', \
- help='''blocklist (string) - Block subset of character. This argument will be \
- ignored if allowlist is given.''')
- t0_detail = st.radio('detail', [0, 1], 1, horizontal=True, \
- help="detail (int, default = 1) - Set this to 0 for simple output")
- t0_paragraph = st.radio('paragraph', [True, False], 1, horizontal=True, \
- help='paragraph (bool, default = False) - Combine result into paragraph')
- t0_contrast_ths = st.slider('contrast_ths', 0.05, 1., 0.1, step=0.01, \
- help='''contrast_ths (float, default = 0.1) - Text box with contrast lower than \
- this value will be passed into model 2 times.\n
- Firs with original image and second with contrast adjusted to 'adjust_contrast' value.\n
- The with more confident level will be returned as a result.''')
- t0_adjust_contrast = st.slider('adjust_contrast', 0.1, 1., 0.5, step=0.1, \
- help = 'adjust_contrast (float, default = 0.5) - target contrast level for low \
- contrast text box')
-
- with st.expander("Choose recognition hyperparameters for " + reader_type_list[1], \
- expanded=False):
- t1_rec_algorithm = st.selectbox('rec_algorithm', ['CRNN', 'SVTR_LCNet'], 0, \
- help="Type of recognition algorithm selected. (default=CRNN)")
- t1_rec_batch_num = st.slider('rec_batch_num', 1, 50, step=1, \
- help="When performing recognition, the batchsize of forward images. \
- (default=30)")
- t1_max_text_length = st.slider('max_text_length', 3, 250, 25, step=1, \
- help="The maximum text length that the recognition algorithm can recognize. \
- (default=25)")
- t1_use_space_char = st.radio('use_space_char', [True, False], 0, horizontal=True, \
- help="Whether to recognize spaces. (default=TRUE)")
- t1_drop_score = st.slider('drop_score', 0., 1., 0.25, step=.05, \
- help="Filter the output by score (from the recognition model), and those \
- below this score will not be returned. (default=0.5)")
-
- with st.expander("Choose recognition hyperparameters for " + reader_type_list[2], \
- expanded=False):
- t2_recog = st.selectbox('recog', ['ABINet','CRNN','CRNN_TPS','MASTER', \
- 'NRTR_1/16-1/8','NRTR_1/8-1/4','RobustScanner','SAR','SAR_CN', \
- 'SATRN','SATRN_sm','SEG','Tesseract'], 7, \
- help='Text recognition algorithm. (default = SAR)')
- st.write("###### *More about text recognition models* 👉 \
- [here](https://mmocr.readthedocs.io/en/latest/textrecog_models.html)")
-
- with st.expander("Choose recognition hyperparameters for " + reader_type_list[3], \
- expanded=False):
- t3r_psm = st.selectbox('Page segmentation mode (psm)', \
- [' - Default', \
- ' 4 Assume a single column of text of variable sizes', \
- ' 5 Assume a single uniform block of vertically aligned \
- text', \
- ' 6 Assume a single uniform block of text', \
- ' 7 Treat the image as a single text line', \
- ' 8 Treat the image as a single word', \
- ' 9 Treat the image as a single word in a circle', \
- '10 Treat the image as a single character', \
- '11 Sparse text. Find as much text as possible in no \
- particular order', \
- '13 Raw line. Treat the image as a single text line, \
- bypassing hacks that are Tesseract-specific'])
- t3r_oem = st.selectbox('OCR engine mode', ['0 Legacy engine only', \
- '1 Neural nets LSTM engine only', \
- '2 Legacy + LSTM engines', \
- '3 Default, based on what is available'], 3)
- t3r_whitelist = st.text_input('Limit tesseract to recognize only this \
- characters :', \
- placeholder='Limit tesseract to recognize only this characters', \
- help='Example for numbers only : 0123456789')
-
- submit_reco = st.form_submit_button("Launch recognition")
-
- if submit_reco:
- process_detect.clear()
- ##----------- Process recognition ------------------------------------------
- reader_ind = reader_type_dict[st.session_state.detect_reader]
- list_boxes = list_coordinates[reader_ind]
-
- # Construct the config Tesseract parameter
- t3r_config = ''
- psm = t3r_psm[:2]
- if psm != ' -':
- t3r_config += '--psm ' + psm.strip()
- oem = t3r_oem[:1]
- if oem != '3':
- t3r_config += ' --oem ' + oem
- if t3r_whitelist != '':
- t3r_config += ' -c tessedit_char_whitelist=' + t3r_whitelist
-
- list_params_rec = \
- [{'decoder': t0_decoder, 'beamWidth': t0_beamWidth, \
- 'batch_size': t0_batch_size, 'workers': t0_workers, \
- 'allowlist': t0_allowlist, 'blocklist': t0_blocklist, \
- 'detail': t0_detail, 'paragraph': t0_paragraph, \
- 'contrast_ths': t0_contrast_ths, 'adjust_contrast': t0_adjust_contrast
- },
- { **list_params_det[1][1], **{'rec_algorithm': t1_rec_algorithm, \
- 'rec_batch_num': t1_rec_batch_num, 'max_text_length': t1_max_text_length, \
- 'use_space_char': t1_use_space_char, 'drop_score': t1_drop_score}, \
- **{'lang': list_params_det[1][0]}
- },
- {'recog': t2_recog},
- {'lang': tesserocr_lang, 'config': t3r_config}
- ]
-
- show_info2 = st.empty()
-
- with show_info2.container():
- st.info("Text recognition in progress ...")
- df_results, df_results_tesseract, list_reco_status = \
- process_recog(list_readers, list_images[1], list_boxes, list_params_rec)
- show_info2.empty()
-
- st.session_state.df_results = df_results
- st.session_state.list_boxes = list_boxes
- st.session_state.df_results_tesseract = df_results_tesseract
- st.session_state.list_reco_status = list_reco_status
-
- if 'df_results' in st.session_state:
- if not st.session_state.df_results.empty:
- ##----------- Show recognition results ------------------------------------------------------------
- results_cols = st.session_state.df_results.columns
- list_col_text = np.arange(1, len(cols_size), 2)
- list_col_confid = np.arange(2, len(cols_size), 2)
-
- dict_draw_reco = {'in_image': st.session_state.list_images[1], \
- 'in_boxes_coordinates': st.session_state.list_boxes, \
- 'in_list_texts': [st.session_state.df_results[x].to_list() \
- for x in results_cols[list_col_text]], \
- 'in_list_confid': [st.session_state.df_results[x].to_list() \
- for x in results_cols[list_col_confid]], \
- 'in_dict_back_colors': dict_back_colors, \
- 'in_df_results_tesseract' : st.session_state.df_results_tesseract, \
- 'in_reader_type_list': reader_type_list
- }
- show_reco = st.empty()
-
- with st.form("form3"):
- st.plotly_chart(fig_colorscale, use_container_width=True)
-
- col_font, col_threshold = st.columns(2)
-
- col_font.slider('Font scale', 1, 7, 1, step=1, key="font_scale_sld")
- col_threshold.slider('% confidence threshold for text color change', 40, 100, 64, \
- step=1, key="conf_threshold_sld")
- col_threshold.write("(text color is black below this % confidence threshold, \
- and white above)")
-
- draw_reco_images(**dict_draw_reco)
-
- submit_resize = st.form_submit_button("Refresh")
-
- if submit_resize:
- draw_reco_images(**dict_draw_reco, \
- in_font_scale=st.session_state.font_scale_sld, \
- in_conf_threshold=st.session_state.conf_threshold_sld)
-
- st.subheader("Recognition details")
- with st.expander("Detailed areas for EasyOCR, PPOCR, MMOCR", expanded=True):
- cols = st.columns(cols_size)
- cols[0].markdown('#### Detected area')
- for i in range(1, (len(reader_type_list)-1)*2, 2):
- cols[i].markdown('#### with ' + reader_type_list[i//2])
-
- for row in st.session_state.df_results.itertuples():
- #cols = st.columns(1 + len(reader_type_list)*2)
- cols = st.columns(cols_size)
- cols[0].image(row.cropped_image, width=150)
- for ind_col in range(1, len(cols), 2):
- cols[ind_col].write(getattr(row, results_cols[ind_col]))
- cols[ind_col+1].write("("+str( \
- getattr(row, results_cols[ind_col+1]))+"%)")
-
- st.download_button(
- label="Download results as CSV file",
- data=convert_df(st.session_state.df_results),
- file_name='OCR_comparator_results.csv',
- mime='text/csv',
- )
-
- if not st.session_state.df_results_tesseract.empty:
- with st.expander("Detailed areas for Tesseract", expanded=False):
- cols = st.columns([2,2,1])
- cols[0].markdown('#### Detected area')
- cols[1].markdown('#### with Tesseract')
-
- for row in st.session_state.df_results_tesseract.itertuples():
- cols = st.columns([2,2,1])
- cols[0].image(row.cropped, width=150)
- cols[1].write(getattr(row, 'text'))
- cols[2].write("("+str(getattr(row, 'conf'))+"%)")
-
- st.download_button(
- label="Download Tesseract results as CSV file",
- data=convert_df(st.session_state.df_results),
- file_name='OCR_comparator_Tesseract_results.csv',
- mime='text/csv',
- )
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/dbnet_r18_fpnc.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/dbnet_r18_fpnc.py
deleted file mode 100644
index 7507605d84f602dbfc0ce3b6b0519add917afe5f..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/det_models/dbnet_r18_fpnc.py
+++ /dev/null
@@ -1,21 +0,0 @@
-model = dict(
- type='DBNet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=18,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18'),
- norm_eval=False,
- style='caffe'),
- neck=dict(
- type='FPNC', in_channels=[64, 128, 256, 512], lateral_channels=256),
- bbox_head=dict(
- type='DBHead',
- in_channels=256,
- loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True),
- postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')),
- train_cfg=None,
- test_cfg=None)
diff --git a/spaces/Mahiruoshi/BangDream-Bert-VITS2/monotonic_align/__init__.py b/spaces/Mahiruoshi/BangDream-Bert-VITS2/monotonic_align/__init__.py
deleted file mode 100644
index aed94600a6b01f4322b371b0c57d5b05713c4dac..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/BangDream-Bert-VITS2/monotonic_align/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
- device = neg_cent.device
- dtype = neg_cent.dtype
- neg_cent = neg_cent.data.cpu().numpy().astype(float32)
- path = zeros(neg_cent.shape, dtype=int32)
-
- t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
- t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
- maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
- return from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/model/__init__.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/model/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/segmentors/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/segmentors/__init__.py
deleted file mode 100644
index dca2f09405330743c476e190896bee39c45498ea..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/segmentors/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .base import BaseSegmentor
-from .cascade_encoder_decoder import CascadeEncoderDecoder
-from .encoder_decoder import EncoderDecoder
-
-__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder']
diff --git a/spaces/Miuzarte/SUI-svc-4.0/modules/modules.py b/spaces/Miuzarte/SUI-svc-4.0/modules/modules.py
deleted file mode 100644
index 54290fd207b25e93831bd21005990ea137e6b50e..0000000000000000000000000000000000000000
--- a/spaces/Miuzarte/SUI-svc-4.0/modules/modules.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import modules.commons as commons
-from modules.commons import init_weights, get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/_base_/datasets/icdar2011.py b/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/_base_/datasets/icdar2011.py
deleted file mode 100644
index 6071c251cd7e3e0ea7fcbcf190262526b9bff910..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/_base_/datasets/icdar2011.py
+++ /dev/null
@@ -1,8 +0,0 @@
-icdar2011_textrecog_data_root = 'data/rec/icdar_2011/'
-
-icdar2011_textrecog_train = dict(
- type='OCRDataset',
- data_root=icdar2011_textrecog_data_root,
- ann_file='train_labels.json',
- test_mode=False,
- pipeline=None)
diff --git a/spaces/Mwebrania/classification_of_maize_diseases/README.md b/spaces/Mwebrania/classification_of_maize_diseases/README.md
deleted file mode 100644
index 09cd00106bd33b563d9226570af30bd3390967ec..0000000000000000000000000000000000000000
--- a/spaces/Mwebrania/classification_of_maize_diseases/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Classification Of Maize Diseases
-emoji: 🌍
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/self_attention_mask.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/self_attention_mask.py
deleted file mode 100644
index 933b4960dc0a86d4a0a767f8017853c2f2290d16..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/self_attention_mask.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Keras layer that creates a self-attention mask."""
-
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import tensorflow as tf
-from official.modeling import tf_utils
-
-
-@tf.keras.utils.register_keras_serializable(package='Text')
-class SelfAttentionMask(tf.keras.layers.Layer):
- """Create 3D attention mask from a 2D tensor mask.
-
- inputs[0]: from_tensor: 2D or 3D Tensor of shape
- [batch_size, from_seq_length, ...].
- inputs[1]: to_mask: int32 Tensor of shape [batch_size, to_seq_length].
-
- Returns:
- float Tensor of shape [batch_size, from_seq_length, to_seq_length].
- """
-
- def call(self, inputs):
- from_tensor = inputs[0]
- to_mask = inputs[1]
- from_shape = tf_utils.get_shape_list(from_tensor, expected_rank=[2, 3])
- batch_size = from_shape[0]
- from_seq_length = from_shape[1]
-
- to_shape = tf_utils.get_shape_list(to_mask, expected_rank=2)
- to_seq_length = to_shape[1]
-
- to_mask = tf.cast(
- tf.reshape(to_mask, [batch_size, 1, to_seq_length]),
- dtype=from_tensor.dtype)
-
- # We don't assume that `from_tensor` is a mask (although it could be). We
- # don't actually care if we attend *from* padding tokens (only *to* padding)
- # tokens so we create a tensor of all ones.
- #
- # `broadcast_ones` = [batch_size, from_seq_length, 1]
- broadcast_ones = tf.ones(
- shape=[batch_size, from_seq_length, 1], dtype=from_tensor.dtype)
-
- # Here we broadcast along two dimensions to create the mask.
- mask = broadcast_ones * to_mask
-
- return mask
diff --git a/spaces/NeilRokad/dreambooth-training/app.py b/spaces/NeilRokad/dreambooth-training/app.py
deleted file mode 100644
index 7a438a3bfa4eaeda25f62aefd0cad77d494ed71d..0000000000000000000000000000000000000000
--- a/spaces/NeilRokad/dreambooth-training/app.py
+++ /dev/null
@@ -1,659 +0,0 @@
-from subprocess import getoutput
-import os
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
- which_gpu = "A10G"
- os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
- which_gpu = "T4"
- os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-else:
- which_gpu = "CPU"
-
-import gradio as gr
-from pathlib import Path
-import argparse
-import shutil
-from train_dreambooth import run_training
-from convertosd import convert
-from PIL import Image
-from slugify import slugify
-import requests
-import torch
-import zipfile
-import tarfile
-import urllib.parse
-import gc
-from diffusers import StableDiffusionPipeline
-from huggingface_hub import snapshot_download, update_repo_visibility, HfApi
-
-is_spaces = True if "SPACE_ID" in os.environ else False
-if(is_spaces):
- is_shared_ui = True if "multimodalart/dreambooth-training" in os.environ['SPACE_ID'] else False
-else:
- is_shared_ui = False
-is_gpu_associated = torch.cuda.is_available()
-
-css = '''
- .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
- .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
- #component-4, #component-3, #component-10{min-height: 0}
- .duplicate-button img{margin: 0}
-'''
-maximum_concepts = 3
-
-#Pre download the files
-if(is_gpu_associated):
- model_v1 = snapshot_download(repo_id="multimodalart/sd-fine-tunable")
- model_v2 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-1", ignore_patterns=["*.ckpt", "*.safetensors"])
- model_v2_512 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-1-base", ignore_patterns=["*.ckpt", "*.safetensors"])
- safety_checker = snapshot_download(repo_id="multimodalart/sd-sc")
- model_to_load = model_v1
-
-#with zipfile.ZipFile("mix.zip", 'r') as zip_ref:
-# zip_ref.extractall(".")
-
-def swap_text(option, base):
- resize_width = 768 if base == "v2-1-768" else 512
- mandatory_liability = "You must have the right to do so and you are liable for the images you use, example:"
- if(option == "object"):
- instance_prompt_example = "cttoy"
- freeze_for = 30
- return [f"You are going to train `object`(s), upload 5-10 images of each object you are planning on training on from different angles/perspectives. You can use services like birme for smart cropping. {mandatory_liability}:", '''''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}.", freeze_for, gr.update(visible=False)]
- elif(option == "person"):
- instance_prompt_example = "julcto"
- freeze_for = 70
- #show_prior_preservation = True if base != "v2-1-768" else False
- show_prior_preservation=False
- if(show_prior_preservation):
- prior_preservation_box_update = gr.update(visible=show_prior_preservation)
- else:
- prior_preservation_box_update = gr.update(visible=show_prior_preservation, value=False)
- return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. You can use services like birme for smart cropping. {mandatory_liability}:", '''''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}.", freeze_for, prior_preservation_box_update]
- elif(option == "style"):
- instance_prompt_example = "trsldamrl"
- freeze_for = 10
- return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. You can use services like birme for smart cropping. {mandatory_liability}:", '''''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}", freeze_for, gr.update(visible=False)]
-
-def swap_base_model(selected_model):
- if(is_gpu_associated):
- global model_to_load
- if(selected_model == "v1-5"):
- model_to_load = model_v1
- elif(selected_model == "v2-1-768"):
- model_to_load = model_v2
- else:
- model_to_load = model_v2_512
-
-def count_files(*inputs):
- file_counter = 0
- concept_counter = 0
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- files = inputs[i]
- if(files):
- concept_counter+=1
- file_counter+=len(files)
- uses_custom = inputs[-1]
- type_of_thing = inputs[-4]
- selected_model = inputs[-5]
- experimental_faces = inputs[-6]
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- else:
- Training_Steps = file_counter*150
- if(type_of_thing == "person" and Training_Steps > 2400):
- Training_Steps = 2400 #Avoid overfitting on person faces
- if(is_spaces):
- if(selected_model == "v1-5"):
- its = 1.1 if which_gpu == "T4" else 1.8
- if(experimental_faces):
- its = 1
- elif(selected_model == "v2-1-512"):
- its = 0.8 if which_gpu == "T4" else 1.5
- if(experimental_faces):
- its = 0.7
- elif(selected_model == "v2-1-768"):
- its = 0.48 if which_gpu == "T4" else 0.85
-
- gpu_price = 0.60 if which_gpu == "T4" else 1.10
- summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. The training should take around {round(Training_Steps/its, 2)} seconds, or {round((Training_Steps/its)/60, 2)} minutes.
- The setup, compression and uploading the model can take up to 20 minutes. As the {which_gpu}-Small GPU costs US${gpu_price} for 1h, the estimated cost for this training is below US${round((((Training_Steps/its)/3600)+0.3+0.1)*gpu_price, 2)}.
- If you check the box below the GPU attribution will automatically removed after training is done and the model is uploaded. If not, don't forget to come back here and swap the hardware back to CPU.
'''
- else:
- summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps.
'''
-
- return([gr.update(visible=True), gr.update(visible=True, value=summary_sentence)])
-
-def update_steps(*files_list):
- file_counter = 0
- for i, files in enumerate(files_list):
- if(files):
- file_counter+=len(files)
- return(gr.update(value=file_counter*200))
-
-def pad_image(image):
- w, h = image.size
- if w == h:
- return image
- elif w > h:
- new_image = Image.new(image.mode, (w, w), (0, 0, 0))
- new_image.paste(image, (0, (w - h) // 2))
- return new_image
- else:
- new_image = Image.new(image.mode, (h, h), (0, 0, 0))
- new_image.paste(image, ((h - w) // 2, 0))
- return new_image
-
-def validate_model_upload(hf_token, model_name):
- if(hf_token != ''):
- api = HfApi()
- try:
- _ = api.whoami(hf_token)
- except:
- raise gr.Error("You have inserted an invalid Hugging Face token")
- try:
- if(is_spaces):
- update_repo_visibility(repo_id=os.environ['SPACE_ID'], private=True, token=hf_token, repo_type="space")
- except:
- raise gr.Error("Oops, you created a Hugging Face token with read permissions only. You need one with write permissions")
- else:
- raise gr.Error("Please insert a Hugging Face Token (make sure to create it with write permissions)")
- if(model_name == ""):
- raise gr.Error("Please fill in your model's name")
-
-def train(*inputs):
- if is_shared_ui:
- raise gr.Error("This Space only works in duplicated instances")
- if not is_gpu_associated:
- raise gr.Error("Please associate a T4 or A10G GPU for this Space")
- hf_token = inputs[-5]
- model_name = inputs[-7]
- if(is_spaces):
- remove_attribution_after = inputs[-6]
- else:
- remove_attribution_after = False
-
- if(remove_attribution_after):
- validate_model_upload(hf_token, model_name)
-
- torch.cuda.empty_cache()
- if 'pipe' in globals():
- global pipe, pipe_is_set
- del pipe
- pipe_is_set = False
- gc.collect()
-
- if os.path.exists("output_model"): shutil.rmtree('output_model')
- if os.path.exists("instance_images"): shutil.rmtree('instance_images')
- if os.path.exists("diffusers_model.tar"): os.remove("diffusers_model.tar")
- if os.path.exists("model.ckpt"): os.remove("model.ckpt")
- if os.path.exists("hastrained.success"): os.remove("hastrained.success")
- file_counter = 0
- which_model = inputs[-10]
- resolution = 512 if which_model != "v2-1-768" else 768
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- if(input):
- os.makedirs('instance_images',exist_ok=True)
- files = inputs[i+(maximum_concepts*2)]
- prompt = inputs[i+maximum_concepts]
- if(prompt == "" or prompt == None):
- raise gr.Error("You forgot to define your concept prompt")
- for j, file_temp in enumerate(files):
- file = Image.open(file_temp.name)
- image = pad_image(file)
- image = image.resize((resolution, resolution))
- extension = file_temp.name.split(".")[1]
- image = image.convert('RGB')
- image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100)
- file_counter += 1
-
- os.makedirs('output_model',exist_ok=True)
- uses_custom = inputs[-1]
- type_of_thing = inputs[-4]
- experimental_face_improvement = inputs[-9]
-
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- Train_text_encoder_for = int(inputs[-2])
- else:
- if(type_of_thing == "object"):
- Train_text_encoder_for=30
-
- elif(type_of_thing == "style"):
- Train_text_encoder_for=15
-
- elif(type_of_thing == "person"):
- Train_text_encoder_for=70
-
- Training_Steps = file_counter*150
- if(type_of_thing == "person" and Training_Steps > 2600):
- Training_Steps = 2600 #Avoid overfitting on people's faces
- stptxt = int((Training_Steps*Train_text_encoder_for)/100)
- gradient_checkpointing = True if (experimental_face_improvement or which_model != "v1-5") else False
- cache_latents = True if which_model != "v1-5" else False
- if (type_of_thing == "object" or type_of_thing == "style" or (type_of_thing == "person" and not experimental_face_improvement)):
- args_general = argparse.Namespace(
- image_captions_filename = True,
- train_text_encoder = True if stptxt > 0 else False,
- stop_text_encoder_training = stptxt,
- save_n_steps = 0,
- pretrained_model_name_or_path = model_to_load,
- instance_data_dir="instance_images",
- class_data_dir=None,
- output_dir="output_model",
- instance_prompt="",
- seed=42,
- resolution=resolution,
- mixed_precision="fp16",
- train_batch_size=1,
- gradient_accumulation_steps=1,
- use_8bit_adam=True,
- learning_rate=2e-6,
- lr_scheduler="polynomial",
- lr_warmup_steps = 0,
- max_train_steps=Training_Steps,
- gradient_checkpointing=gradient_checkpointing,
- cache_latents=cache_latents,
- )
- print("Starting single training...")
- lock_file = open("intraining.lock", "w")
- lock_file.close()
- run_training(args_general)
- else:
- args_general = argparse.Namespace(
- image_captions_filename = True,
- train_text_encoder = True if stptxt > 0 else False,
- stop_text_encoder_training = stptxt,
- save_n_steps = 0,
- pretrained_model_name_or_path = model_to_load,
- instance_data_dir="instance_images",
- class_data_dir="Mix",
- output_dir="output_model",
- with_prior_preservation=True,
- prior_loss_weight=1.0,
- instance_prompt="",
- seed=42,
- resolution=resolution,
- mixed_precision="fp16",
- train_batch_size=1,
- gradient_accumulation_steps=1,
- use_8bit_adam=True,
- learning_rate=2e-6,
- lr_scheduler="polynomial",
- lr_warmup_steps = 0,
- max_train_steps=Training_Steps,
- num_class_images=200,
- gradient_checkpointing=gradient_checkpointing,
- cache_latents=cache_latents,
- )
- print("Starting multi-training...")
- lock_file = open("intraining.lock", "w")
- lock_file.close()
- run_training(args_general)
- gc.collect()
- torch.cuda.empty_cache()
- if(which_model == "v1-5"):
- print("Adding Safety Checker to the model...")
- shutil.copytree(f"{safety_checker}/feature_extractor", "output_model/feature_extractor", dirs_exist_ok=True)
- shutil.copytree(f"{safety_checker}/safety_checker", "output_model/safety_checker", dirs_exist_ok=True)
- shutil.copy(f"model_index.json", "output_model/model_index.json")
-
- if(not remove_attribution_after):
- print("Archiving model file...")
- with tarfile.open("diffusers_model.tar", "w") as tar:
- tar.add("output_model", arcname=os.path.basename("output_model"))
- if os.path.exists("intraining.lock"): os.remove("intraining.lock")
- trained_file = open("hastrained.success", "w")
- trained_file.close()
- print("Training completed!")
- return [
- gr.update(visible=True, value=["diffusers_model.tar"]), #result
- gr.update(visible=True), #try_your_model
- gr.update(visible=True), #push_to_hub
- gr.update(visible=True), #convert_button
- gr.update(visible=False), #training_ongoing
- gr.update(visible=True) #completed_training
- ]
- else:
- where_to_upload = inputs[-8]
- push(model_name, where_to_upload, hf_token, which_model, True)
- hardware_url = f"https://huggingface.co/spaces/{os.environ['SPACE_ID']}/hardware"
- headers = { "authorization" : f"Bearer {hf_token}"}
- body = {'flavor': 'cpu-basic'}
- requests.post(hardware_url, json = body, headers=headers)
-
-pipe_is_set = False
-def generate(prompt, steps):
- torch.cuda.empty_cache()
- from diffusers import StableDiffusionPipeline
- global pipe_is_set
- if(not pipe_is_set):
- global pipe
- pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
- pipe = pipe.to("cuda")
- pipe_is_set = True
-
- image = pipe(prompt, num_inference_steps=steps).images[0]
- return(image)
-
-def push(model_name, where_to_upload, hf_token, which_model, comes_from_automated=False):
- validate_model_upload(hf_token, model_name)
- if(not os.path.exists("model.ckpt")):
- convert("output_model", "model.ckpt")
- from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
- from huggingface_hub import create_repo
- model_name_slug = slugify(model_name)
- api = HfApi()
- your_username = api.whoami(token=hf_token)["name"]
- if(where_to_upload == "My personal profile"):
- model_id = f"{your_username}/{model_name_slug}"
- else:
- model_id = f"sd-dreambooth-library/{model_name_slug}"
- headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
- response = requests.post("https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
-
- print(f"Starting to upload the model {model_id}...")
- images_upload = os.listdir("instance_images")
- image_string = ""
- instance_prompt_list = []
- previous_instance_prompt = ''
- for i, image in enumerate(images_upload):
- instance_prompt = image.split("_")[0]
- if(instance_prompt != previous_instance_prompt):
- title_instance_prompt_string = instance_prompt
- instance_prompt_list.append(instance_prompt)
- else:
- title_instance_prompt_string = ''
- previous_instance_prompt = instance_prompt
- image_string = f'''{title_instance_prompt_string} {"(use that on your prompt)" if title_instance_prompt_string != "" else ""}
-{image_string}})'''
- readme_text = f'''---
-license: creativeml-openrail-m
-tags:
-- text-to-image
-widget:
-- text: {instance_prompt_list[0]}
----
-### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the {which_model} base model
-
-You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
-
-Sample pictures of:
-{image_string}
-'''
- #Save the readme to a file
- readme_file = open("model.README.md", "w")
- readme_file.write(readme_text)
- readme_file.close()
- #Save the token identifier to a file
- text_file = open("token_identifier.txt", "w")
- text_file.write(', '.join(instance_prompt_list))
- text_file.close()
- try:
- create_repo(model_id,private=True, token=hf_token)
- except:
- import time
- epoch_time = str(int(time.time()))
- create_repo(f"{model_id}-{epoch_time}", private=True,token=hf_token)
- operations = [
- CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
- CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="model.README.md"),
- CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt")
- ]
- api.create_commit(
- repo_id=model_id,
- operations=operations,
- commit_message=f"Upload the model {model_name}",
- token=hf_token
- )
- api.upload_folder(
- folder_path="output_model",
- repo_id=model_id,
- token=hf_token
- )
- api.upload_folder(
- folder_path="instance_images",
- path_in_repo="concept_images",
- repo_id=model_id,
- token=hf_token
- )
- if is_spaces:
- if(not comes_from_automated):
- extra_message = "Don't forget to remove the GPU attribution after you play with it."
- else:
- extra_message = "The GPU has been removed automatically as requested, and you can try the model via the model page"
- api.create_discussion(repo_id=os.environ['SPACE_ID'], title=f"Your model {model_name} has finished trained from the Dreambooth Train Spaces!", description=f"Your model has been successfully uploaded to: https://huggingface.co/{model_id}. {extra_message}",repo_type="space", token=hf_token)
- print("Model uploaded successfully!")
- return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co/{model_id})"), gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"])]
-
-def convert_to_ckpt():
- if 'pipe' in globals():
- global pipe, pipe_is_set
- del pipe
- pipe_is_set = False
- gc.collect()
- convert("output_model", "model.ckpt")
- return gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"])
-
-def check_status(top_description):
- if os.path.exists("hastrained.success"):
- if is_spaces:
- update_top_tag = gr.update(value=f'''
-
-
Your model has finished training ✅
-
Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub). Once you are done, your model is safe, and you don't want to train a new one, go to the settings page and downgrade your Space to a CPU Basic
You closed the tab while your model was training, but it's all good! It is still training right now. You can click the "Open logs" button above here to check the training status. Once training is done, reload this tab to interact with your model
Attention - This Space doesn't work in this shared UI
-
For it to work, you can either run locally or duplicate the Space and run it on your own profile using a (paid) private T4-small or A10G-small GPU for training. A T4 costs US$0.60/h, so it should cost < US$1 to train most models using default settings with it!
-'''
-
-# 创建Gradio界面
-inp = [gradio.inputs.Textbox(label="请输入你的API-key(sk开头的字符串)",
- default="",
- type='password'),
- gradio.inputs.Textbox(lines=5,
- label="请输入要回复的审稿意见",
- default=""
- ),
- gradio.inputs.Radio(choices=["English", "Chinese", "French", "German","Japenese"],
- default="English",
- label="选择输出语言"),
-]
-
-chat_Response_gui = gradio.Interface(fn=main,
- inputs=inp,
- outputs = [gradio.Textbox(lines=11, label="回复结果"), gradio.Textbox(lines=2, label="资源统计")],
- title=title,
- description=description)
-
-# Start server
-chat_Response_gui .launch(quiet=True, show_api=False)
\ No newline at end of file
diff --git a/spaces/RMXK/RVC_HFF/diffq/uniform.py b/spaces/RMXK/RVC_HFF/diffq/uniform.py
deleted file mode 100644
index f61e9129c04caaa33c66f726bf2433d51689cfa5..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/diffq/uniform.py
+++ /dev/null
@@ -1,121 +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.
-
-"""
-Classic uniform quantization over n bits.
-"""
-from typing import Tuple
-import torch
-
-from .base import BaseQuantizer
-from .utils import simple_repr
-
-
-def uniform_quantize(p: torch.Tensor, bits: torch.Tensor = torch.tensor(8.)):
- """
- Quantize the given weights over `bits` bits.
-
- Returns:
- - quantized levels
- - (min, max) range.
-
- """
- assert (bits >= 1).all() and (bits <= 15).all()
- num_levels = (2 ** bits.float()).long()
- mn = p.min().item()
- mx = p.max().item()
- p = (p - mn) / (mx - mn) # put p in [0, 1]
- unit = 1 / (num_levels - 1) # quantization unit
- levels = (p / unit).round()
- if (bits <= 8).all():
- levels = levels.byte()
- else:
- levels = levels.short()
- return levels, (mn, mx)
-
-
-def uniform_unquantize(levels: torch.Tensor, scales: Tuple[float, float],
- bits: torch.Tensor = torch.tensor(8.)):
- """
- Unquantize the weights from the levels and scale. Return a float32 tensor.
- """
- mn, mx = scales
- num_levels = 2 ** bits.float()
- unit = 1 / (num_levels - 1)
- levels = levels.float()
- p = levels * unit # in [0, 1]
- return p * (mx - mn) + mn
-
-
-class UniformQuantizer(BaseQuantizer):
- def __init__(self, model: torch.nn.Module, bits: float = 8., min_size: float = 0.01,
- float16: bool = False, qat: bool = False, exclude=[], detect_bound=True):
- """
- Args:
- model (torch.nn.Module): model to quantize
- bits (float): number of bits to quantize over.
- min_size (float): minimum size in MB of a parameter to be quantized.
- float16 (bool): if a layer is smaller than min_size, should we still do float16?
- qat (bool): perform quantized aware training.
- exclude (list[str]): list of patterns used to match parameters to exclude.
- For instance `['bias']` to exclude all bias terms.
- detect_bound (bool): if True, will detect bound parameters and reuse
- the same quantized tensor for both.
- """
- self.bits = float(bits)
- self.qat = qat
-
- super().__init__(model, min_size, float16, exclude, detect_bound)
-
- def __repr__(self):
- return simple_repr(self, )
-
- def _pre_forward_train(self):
- if self.qat:
- for qparam in self._qparams:
- if qparam.other is not None:
- new_param = qparam.other.module._parameters[qparam.other.name]
- else:
- quantized = self._quantize_param(qparam)
- qvalue = self._unquantize_param(qparam, quantized)
- new_param = qparam.param + (qvalue - qparam.param).detach()
- qparam.module._parameters[qparam.name] = new_param
- return True
- return False
-
- def _post_forward_train(self):
- if self.qat:
- for qparam in self._qparams:
- qparam.module._parameters[qparam.name] = qparam.param
- return True
- return False
-
- def _quantize_param(self, qparam):
- levels, scales = uniform_quantize(qparam.param.data, torch.tensor(self.bits))
- return (levels, scales)
-
- def _unquantize_param(self, qparam, quantized):
- levels, scales = quantized
- return uniform_unquantize(levels, scales, torch.tensor(self.bits))
-
- def model_size(self):
- """
- Non differentiable model size in MB.
- """
- total = super().model_size()
- subtotal = 0
- for qparam in self._qparams:
- if qparam.other is None: # if parameter is bound, count only one copy.
- subtotal += self.bits * qparam.param.numel() + 64 # 2 float for the overall scales
- subtotal /= 2**20 * 8 # bits to MegaBytes
- return total + subtotal
-
- def true_model_size(self):
- """
- Return the true quantized model size, in MB, without extra
- compression.
- """
- return self.model_size().item()
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/__init__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/diagram/__init__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/diagram/__init__.py
deleted file mode 100644
index 898644755cbbf9a8d4df562663114a7eb7e11fd1..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/diagram/__init__.py
+++ /dev/null
@@ -1,642 +0,0 @@
-import railroad
-import pyparsing
-import typing
-from typing import (
- List,
- NamedTuple,
- Generic,
- TypeVar,
- Dict,
- Callable,
- Set,
- Iterable,
-)
-from jinja2 import Template
-from io import StringIO
-import inspect
-
-
-jinja2_template_source = """\
-
-
-
- {% if not head %}
-
- {% else %}
- {{ head | safe }}
- {% endif %}
-
-
-{{ body | safe }}
-{% for diagram in diagrams %}
-
-
{{ diagram.title }}
-
{{ diagram.text }}
-
- {{ diagram.svg }}
-
-
-{% endfor %}
-
-
-"""
-
-template = Template(jinja2_template_source)
-
-# Note: ideally this would be a dataclass, but we're supporting Python 3.5+ so we can't do this yet
-NamedDiagram = NamedTuple(
- "NamedDiagram",
- [("name", str), ("diagram", typing.Optional[railroad.DiagramItem]), ("index", int)],
-)
-"""
-A simple structure for associating a name with a railroad diagram
-"""
-
-T = TypeVar("T")
-
-
-class EachItem(railroad.Group):
- """
- Custom railroad item to compose a:
- - Group containing a
- - OneOrMore containing a
- - Choice of the elements in the Each
- with the group label indicating that all must be matched
- """
-
- all_label = "[ALL]"
-
- def __init__(self, *items):
- choice_item = railroad.Choice(len(items) - 1, *items)
- one_or_more_item = railroad.OneOrMore(item=choice_item)
- super().__init__(one_or_more_item, label=self.all_label)
-
-
-class AnnotatedItem(railroad.Group):
- """
- Simple subclass of Group that creates an annotation label
- """
-
- def __init__(self, label: str, item):
- super().__init__(item=item, label="[{}]".format(label) if label else label)
-
-
-class EditablePartial(Generic[T]):
- """
- Acts like a functools.partial, but can be edited. In other words, it represents a type that hasn't yet been
- constructed.
- """
-
- # We need this here because the railroad constructors actually transform the data, so can't be called until the
- # entire tree is assembled
-
- def __init__(self, func: Callable[..., T], args: list, kwargs: dict):
- self.func = func
- self.args = args
- self.kwargs = kwargs
-
- @classmethod
- def from_call(cls, func: Callable[..., T], *args, **kwargs) -> "EditablePartial[T]":
- """
- If you call this function in the same way that you would call the constructor, it will store the arguments
- as you expect. For example EditablePartial.from_call(Fraction, 1, 3)() == Fraction(1, 3)
- """
- return EditablePartial(func=func, args=list(args), kwargs=kwargs)
-
- @property
- def name(self):
- return self.kwargs["name"]
-
- def __call__(self) -> T:
- """
- Evaluate the partial and return the result
- """
- args = self.args.copy()
- kwargs = self.kwargs.copy()
-
- # This is a helpful hack to allow you to specify varargs parameters (e.g. *args) as keyword args (e.g.
- # args=['list', 'of', 'things'])
- arg_spec = inspect.getfullargspec(self.func)
- if arg_spec.varargs in self.kwargs:
- args += kwargs.pop(arg_spec.varargs)
-
- return self.func(*args, **kwargs)
-
-
-def railroad_to_html(diagrams: List[NamedDiagram], **kwargs) -> str:
- """
- Given a list of NamedDiagram, produce a single HTML string that visualises those diagrams
- :params kwargs: kwargs to be passed in to the template
- """
- data = []
- for diagram in diagrams:
- if diagram.diagram is None:
- continue
- io = StringIO()
- diagram.diagram.writeSvg(io.write)
- title = diagram.name
- if diagram.index == 0:
- title += " (root)"
- data.append({"title": title, "text": "", "svg": io.getvalue()})
-
- return template.render(diagrams=data, **kwargs)
-
-
-def resolve_partial(partial: "EditablePartial[T]") -> T:
- """
- Recursively resolves a collection of Partials into whatever type they are
- """
- if isinstance(partial, EditablePartial):
- partial.args = resolve_partial(partial.args)
- partial.kwargs = resolve_partial(partial.kwargs)
- return partial()
- elif isinstance(partial, list):
- return [resolve_partial(x) for x in partial]
- elif isinstance(partial, dict):
- return {key: resolve_partial(x) for key, x in partial.items()}
- else:
- return partial
-
-
-def to_railroad(
- element: pyparsing.ParserElement,
- diagram_kwargs: typing.Optional[dict] = None,
- vertical: int = 3,
- show_results_names: bool = False,
- show_groups: bool = False,
-) -> List[NamedDiagram]:
- """
- Convert a pyparsing element tree into a list of diagrams. This is the recommended entrypoint to diagram
- creation if you want to access the Railroad tree before it is converted to HTML
- :param element: base element of the parser being diagrammed
- :param diagram_kwargs: kwargs to pass to the Diagram() constructor
- :param vertical: (optional) - int - limit at which number of alternatives should be
- shown vertically instead of horizontally
- :param show_results_names - bool to indicate whether results name annotations should be
- included in the diagram
- :param show_groups - bool to indicate whether groups should be highlighted with an unlabeled
- surrounding box
- """
- # Convert the whole tree underneath the root
- lookup = ConverterState(diagram_kwargs=diagram_kwargs or {})
- _to_diagram_element(
- element,
- lookup=lookup,
- parent=None,
- vertical=vertical,
- show_results_names=show_results_names,
- show_groups=show_groups,
- )
-
- root_id = id(element)
- # Convert the root if it hasn't been already
- if root_id in lookup:
- if not element.customName:
- lookup[root_id].name = ""
- lookup[root_id].mark_for_extraction(root_id, lookup, force=True)
-
- # Now that we're finished, we can convert from intermediate structures into Railroad elements
- diags = list(lookup.diagrams.values())
- if len(diags) > 1:
- # collapse out duplicate diags with the same name
- seen = set()
- deduped_diags = []
- for d in diags:
- # don't extract SkipTo elements, they are uninformative as subdiagrams
- if d.name == "...":
- continue
- if d.name is not None and d.name not in seen:
- seen.add(d.name)
- deduped_diags.append(d)
- resolved = [resolve_partial(partial) for partial in deduped_diags]
- else:
- # special case - if just one diagram, always display it, even if
- # it has no name
- resolved = [resolve_partial(partial) for partial in diags]
- return sorted(resolved, key=lambda diag: diag.index)
-
-
-def _should_vertical(
- specification: int, exprs: Iterable[pyparsing.ParserElement]
-) -> bool:
- """
- Returns true if we should return a vertical list of elements
- """
- if specification is None:
- return False
- else:
- return len(_visible_exprs(exprs)) >= specification
-
-
-class ElementState:
- """
- State recorded for an individual pyparsing Element
- """
-
- # Note: this should be a dataclass, but we have to support Python 3.5
- def __init__(
- self,
- element: pyparsing.ParserElement,
- converted: EditablePartial,
- parent: EditablePartial,
- number: int,
- name: str = None,
- parent_index: typing.Optional[int] = None,
- ):
- #: The pyparsing element that this represents
- self.element: pyparsing.ParserElement = element
- #: The name of the element
- self.name: typing.Optional[str] = name
- #: The output Railroad element in an unconverted state
- self.converted: EditablePartial = converted
- #: The parent Railroad element, which we store so that we can extract this if it's duplicated
- self.parent: EditablePartial = parent
- #: The order in which we found this element, used for sorting diagrams if this is extracted into a diagram
- self.number: int = number
- #: The index of this inside its parent
- self.parent_index: typing.Optional[int] = parent_index
- #: If true, we should extract this out into a subdiagram
- self.extract: bool = False
- #: If true, all of this element's children have been filled out
- self.complete: bool = False
-
- def mark_for_extraction(
- self, el_id: int, state: "ConverterState", name: str = None, force: bool = False
- ):
- """
- Called when this instance has been seen twice, and thus should eventually be extracted into a sub-diagram
- :param el_id: id of the element
- :param state: element/diagram state tracker
- :param name: name to use for this element's text
- :param force: If true, force extraction now, regardless of the state of this. Only useful for extracting the
- root element when we know we're finished
- """
- self.extract = True
-
- # Set the name
- if not self.name:
- if name:
- # Allow forcing a custom name
- self.name = name
- elif self.element.customName:
- self.name = self.element.customName
- else:
- self.name = ""
-
- # Just because this is marked for extraction doesn't mean we can do it yet. We may have to wait for children
- # to be added
- # Also, if this is just a string literal etc, don't bother extracting it
- if force or (self.complete and _worth_extracting(self.element)):
- state.extract_into_diagram(el_id)
-
-
-class ConverterState:
- """
- Stores some state that persists between recursions into the element tree
- """
-
- def __init__(self, diagram_kwargs: typing.Optional[dict] = None):
- #: A dictionary mapping ParserElements to state relating to them
- self._element_diagram_states: Dict[int, ElementState] = {}
- #: A dictionary mapping ParserElement IDs to subdiagrams generated from them
- self.diagrams: Dict[int, EditablePartial[NamedDiagram]] = {}
- #: The index of the next unnamed element
- self.unnamed_index: int = 1
- #: The index of the next element. This is used for sorting
- self.index: int = 0
- #: Shared kwargs that are used to customize the construction of diagrams
- self.diagram_kwargs: dict = diagram_kwargs or {}
- self.extracted_diagram_names: Set[str] = set()
-
- def __setitem__(self, key: int, value: ElementState):
- self._element_diagram_states[key] = value
-
- def __getitem__(self, key: int) -> ElementState:
- return self._element_diagram_states[key]
-
- def __delitem__(self, key: int):
- del self._element_diagram_states[key]
-
- def __contains__(self, key: int):
- return key in self._element_diagram_states
-
- def generate_unnamed(self) -> int:
- """
- Generate a number used in the name of an otherwise unnamed diagram
- """
- self.unnamed_index += 1
- return self.unnamed_index
-
- def generate_index(self) -> int:
- """
- Generate a number used to index a diagram
- """
- self.index += 1
- return self.index
-
- def extract_into_diagram(self, el_id: int):
- """
- Used when we encounter the same token twice in the same tree. When this
- happens, we replace all instances of that token with a terminal, and
- create a new subdiagram for the token
- """
- position = self[el_id]
-
- # Replace the original definition of this element with a regular block
- if position.parent:
- ret = EditablePartial.from_call(railroad.NonTerminal, text=position.name)
- if "item" in position.parent.kwargs:
- position.parent.kwargs["item"] = ret
- elif "items" in position.parent.kwargs:
- position.parent.kwargs["items"][position.parent_index] = ret
-
- # If the element we're extracting is a group, skip to its content but keep the title
- if position.converted.func == railroad.Group:
- content = position.converted.kwargs["item"]
- else:
- content = position.converted
-
- self.diagrams[el_id] = EditablePartial.from_call(
- NamedDiagram,
- name=position.name,
- diagram=EditablePartial.from_call(
- railroad.Diagram, content, **self.diagram_kwargs
- ),
- index=position.number,
- )
-
- del self[el_id]
-
-
-def _worth_extracting(element: pyparsing.ParserElement) -> bool:
- """
- Returns true if this element is worth having its own sub-diagram. Simply, if any of its children
- themselves have children, then its complex enough to extract
- """
- children = element.recurse()
- return any(child.recurse() for child in children)
-
-
-def _apply_diagram_item_enhancements(fn):
- """
- decorator to ensure enhancements to a diagram item (such as results name annotations)
- get applied on return from _to_diagram_element (we do this since there are several
- returns in _to_diagram_element)
- """
-
- def _inner(
- element: pyparsing.ParserElement,
- parent: typing.Optional[EditablePartial],
- lookup: ConverterState = None,
- vertical: int = None,
- index: int = 0,
- name_hint: str = None,
- show_results_names: bool = False,
- show_groups: bool = False,
- ) -> typing.Optional[EditablePartial]:
-
- ret = fn(
- element,
- parent,
- lookup,
- vertical,
- index,
- name_hint,
- show_results_names,
- show_groups,
- )
-
- # apply annotation for results name, if present
- if show_results_names and ret is not None:
- element_results_name = element.resultsName
- if element_results_name:
- # add "*" to indicate if this is a "list all results" name
- element_results_name += "" if element.modalResults else "*"
- ret = EditablePartial.from_call(
- railroad.Group, item=ret, label=element_results_name
- )
-
- return ret
-
- return _inner
-
-
-def _visible_exprs(exprs: Iterable[pyparsing.ParserElement]):
- non_diagramming_exprs = (
- pyparsing.ParseElementEnhance,
- pyparsing.PositionToken,
- pyparsing.And._ErrorStop,
- )
- return [
- e
- for e in exprs
- if not (e.customName or e.resultsName or isinstance(e, non_diagramming_exprs))
- ]
-
-
-@_apply_diagram_item_enhancements
-def _to_diagram_element(
- element: pyparsing.ParserElement,
- parent: typing.Optional[EditablePartial],
- lookup: ConverterState = None,
- vertical: int = None,
- index: int = 0,
- name_hint: str = None,
- show_results_names: bool = False,
- show_groups: bool = False,
-) -> typing.Optional[EditablePartial]:
- """
- Recursively converts a PyParsing Element to a railroad Element
- :param lookup: The shared converter state that keeps track of useful things
- :param index: The index of this element within the parent
- :param parent: The parent of this element in the output tree
- :param vertical: Controls at what point we make a list of elements vertical. If this is an integer (the default),
- it sets the threshold of the number of items before we go vertical. If True, always go vertical, if False, never
- do so
- :param name_hint: If provided, this will override the generated name
- :param show_results_names: bool flag indicating whether to add annotations for results names
- :returns: The converted version of the input element, but as a Partial that hasn't yet been constructed
- :param show_groups: bool flag indicating whether to show groups using bounding box
- """
- exprs = element.recurse()
- name = name_hint or element.customName or element.__class__.__name__
-
- # Python's id() is used to provide a unique identifier for elements
- el_id = id(element)
-
- element_results_name = element.resultsName
-
- # Here we basically bypass processing certain wrapper elements if they contribute nothing to the diagram
- if not element.customName:
- if isinstance(
- element,
- (
- # pyparsing.TokenConverter,
- # pyparsing.Forward,
- pyparsing.Located,
- ),
- ):
- # However, if this element has a useful custom name, and its child does not, we can pass it on to the child
- if exprs:
- if not exprs[0].customName:
- propagated_name = name
- else:
- propagated_name = None
-
- return _to_diagram_element(
- element.expr,
- parent=parent,
- lookup=lookup,
- vertical=vertical,
- index=index,
- name_hint=propagated_name,
- show_results_names=show_results_names,
- show_groups=show_groups,
- )
-
- # If the element isn't worth extracting, we always treat it as the first time we say it
- if _worth_extracting(element):
- if el_id in lookup:
- # If we've seen this element exactly once before, we are only just now finding out that it's a duplicate,
- # so we have to extract it into a new diagram.
- looked_up = lookup[el_id]
- looked_up.mark_for_extraction(el_id, lookup, name=name_hint)
- ret = EditablePartial.from_call(railroad.NonTerminal, text=looked_up.name)
- return ret
-
- elif el_id in lookup.diagrams:
- # If we have seen the element at least twice before, and have already extracted it into a subdiagram, we
- # just put in a marker element that refers to the sub-diagram
- ret = EditablePartial.from_call(
- railroad.NonTerminal, text=lookup.diagrams[el_id].kwargs["name"]
- )
- return ret
-
- # Recursively convert child elements
- # Here we find the most relevant Railroad element for matching pyparsing Element
- # We use ``items=[]`` here to hold the place for where the child elements will go once created
- if isinstance(element, pyparsing.And):
- # detect And's created with ``expr*N`` notation - for these use a OneOrMore with a repeat
- # (all will have the same name, and resultsName)
- if not exprs:
- return None
- if len(set((e.name, e.resultsName) for e in exprs)) == 1:
- ret = EditablePartial.from_call(
- railroad.OneOrMore, item="", repeat=str(len(exprs))
- )
- elif _should_vertical(vertical, exprs):
- ret = EditablePartial.from_call(railroad.Stack, items=[])
- else:
- ret = EditablePartial.from_call(railroad.Sequence, items=[])
- elif isinstance(element, (pyparsing.Or, pyparsing.MatchFirst)):
- if not exprs:
- return None
- if _should_vertical(vertical, exprs):
- ret = EditablePartial.from_call(railroad.Choice, 0, items=[])
- else:
- ret = EditablePartial.from_call(railroad.HorizontalChoice, items=[])
- elif isinstance(element, pyparsing.Each):
- if not exprs:
- return None
- ret = EditablePartial.from_call(EachItem, items=[])
- elif isinstance(element, pyparsing.NotAny):
- ret = EditablePartial.from_call(AnnotatedItem, label="NOT", item="")
- elif isinstance(element, pyparsing.FollowedBy):
- ret = EditablePartial.from_call(AnnotatedItem, label="LOOKAHEAD", item="")
- elif isinstance(element, pyparsing.PrecededBy):
- ret = EditablePartial.from_call(AnnotatedItem, label="LOOKBEHIND", item="")
- elif isinstance(element, pyparsing.Group):
- if show_groups:
- ret = EditablePartial.from_call(AnnotatedItem, label="", item="")
- else:
- ret = EditablePartial.from_call(railroad.Group, label="", item="")
- elif isinstance(element, pyparsing.TokenConverter):
- ret = EditablePartial.from_call(
- AnnotatedItem, label=type(element).__name__.lower(), item=""
- )
- elif isinstance(element, pyparsing.Opt):
- ret = EditablePartial.from_call(railroad.Optional, item="")
- elif isinstance(element, pyparsing.OneOrMore):
- ret = EditablePartial.from_call(railroad.OneOrMore, item="")
- elif isinstance(element, pyparsing.ZeroOrMore):
- ret = EditablePartial.from_call(railroad.ZeroOrMore, item="")
- elif isinstance(element, pyparsing.Group):
- ret = EditablePartial.from_call(
- railroad.Group, item=None, label=element_results_name
- )
- elif isinstance(element, pyparsing.Empty) and not element.customName:
- # Skip unnamed "Empty" elements
- ret = None
- elif len(exprs) > 1:
- ret = EditablePartial.from_call(railroad.Sequence, items=[])
- elif len(exprs) > 0 and not element_results_name:
- ret = EditablePartial.from_call(railroad.Group, item="", label=name)
- else:
- terminal = EditablePartial.from_call(railroad.Terminal, element.defaultName)
- ret = terminal
-
- if ret is None:
- return
-
- # Indicate this element's position in the tree so we can extract it if necessary
- lookup[el_id] = ElementState(
- element=element,
- converted=ret,
- parent=parent,
- parent_index=index,
- number=lookup.generate_index(),
- )
- if element.customName:
- lookup[el_id].mark_for_extraction(el_id, lookup, element.customName)
-
- i = 0
- for expr in exprs:
- # Add a placeholder index in case we have to extract the child before we even add it to the parent
- if "items" in ret.kwargs:
- ret.kwargs["items"].insert(i, None)
-
- item = _to_diagram_element(
- expr,
- parent=ret,
- lookup=lookup,
- vertical=vertical,
- index=i,
- show_results_names=show_results_names,
- show_groups=show_groups,
- )
-
- # Some elements don't need to be shown in the diagram
- if item is not None:
- if "item" in ret.kwargs:
- ret.kwargs["item"] = item
- elif "items" in ret.kwargs:
- # If we've already extracted the child, don't touch this index, since it's occupied by a nonterminal
- ret.kwargs["items"][i] = item
- i += 1
- elif "items" in ret.kwargs:
- # If we're supposed to skip this element, remove it from the parent
- del ret.kwargs["items"][i]
-
- # If all this items children are none, skip this item
- if ret and (
- ("items" in ret.kwargs and len(ret.kwargs["items"]) == 0)
- or ("item" in ret.kwargs and ret.kwargs["item"] is None)
- ):
- ret = EditablePartial.from_call(railroad.Terminal, name)
-
- # Mark this element as "complete", ie it has all of its children
- if el_id in lookup:
- lookup[el_id].complete = True
-
- if el_id in lookup and lookup[el_id].extract and lookup[el_id].complete:
- lookup.extract_into_diagram(el_id)
- if ret is not None:
- ret = EditablePartial.from_call(
- railroad.NonTerminal, text=lookup.diagrams[el_id].kwargs["name"]
- )
-
- return ret
diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/__init__.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/__init__.py
deleted file mode 100644
index 6a11f122e222f0a9eded4afd3dd0b900826063e8..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .megadepth import MegadepthBuilder
-from .scannet import ScanNetBuilder
diff --git a/spaces/Realcat/image-matching-webui/third_party/d2net/inloc/README.md b/spaces/Realcat/image-matching-webui/third_party/d2net/inloc/README.md
deleted file mode 100644
index 598368ba5c361770c8bc571d1793a613854babfe..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/d2net/inloc/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# InLoc evaluation instructions
-
-Start by downloading the [InLoc_demo](https://github.com/HajimeTaira/InLoc_demo) code. Once it is up and running according to the official instruction, you can copy and paste all the files available here overwriting the `Features_WUSTL` and `parfor_sparseGV` functions. `generate_list.m` will generate `image_list.txt` containing the queries and top 100 database matches (run `sort -u image_list.txt > image_list_unique.txt` to remove the duplicates). After extracting features for all the images in `image_list_unique.txt`, you can run `custom_demo` directly.
-
-The feature extraction part for D2-Net can be done using the following command: `python extract_features.py --image_list_file /path/to/image_list_unique.txt --multiscale --output_format .mat`.
-
-In case you plan on using your own features, don't forget to change the extension in `Features_WUSTL.m`. The local features are supposed to be stored in the `mat` format with two fields:
-
-- `keypoints` - `N x 3` matrix with `x, y, scale` coordinates of each keypoint in COLMAP format (the `X` axis points to the right, the `Y` axis to the bottom),
-
-- `descriptors` - `N x D` matrix with the descriptors.
-
-The evaluation pipeline is live at [visuallocalization.net](https://www.visuallocalization.net/). In order to generate a submission file, please use the provided [ImgList2text](https://github.com/HajimeTaira/InLoc_demo/blob/master/functions/utils/ImgList2text.m) function.
-
-We have also provided the `merge_files` MATLAB script that was used to merge the solutions of D2-Net Multiscale and Dense InLoc based on the view synthesis score. It can be used as follows `merge_files('output/densePV_top10_shortlist_method1.mat', 'outputs/densePV_top10_shortlist_method2.mat')`.
\ No newline at end of file
diff --git a/spaces/Reeve/Ohayou_Face/configs/paths_config.py b/spaces/Reeve/Ohayou_Face/configs/paths_config.py
deleted file mode 100644
index 2df26853d8bde0c82967ab208761e2cb8f87e62b..0000000000000000000000000000000000000000
--- a/spaces/Reeve/Ohayou_Face/configs/paths_config.py
+++ /dev/null
@@ -1,23 +0,0 @@
-dataset_paths = {
- 'celeba_train': '',
- 'celeba_test': '',
- 'celeba_train_sketch': '',
- 'celeba_test_sketch': '',
- 'celeba_train_segmentation': '',
- 'celeba_test_segmentation': '',
- 'ffhq': '',
- 'anime' : '/content/drive/MyDrive/Dataset/anime',
- 'gogal' : '/content/drive/MyDrive/All Data/고갈왕'
-}
-
-model_paths = {
- 'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
- 'ir_se50': 'pretrained_models/model_ir_se50.pth',
- 'circular_face': 'pretrained_models/CurricularFace_Backbone.pth',
- 'mtcnn_pnet': 'pretrained_models/mtcnn/pnet.npy',
- 'mtcnn_rnet': 'pretrained_models/mtcnn/rnet.npy',
- 'mtcnn_onet': 'pretrained_models/mtcnn/onet.npy',
- 'shape_predictor': 'shape_predictor_68_face_landmarks.dat',
- 'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth.tar',
- 'anime' : '/content/drive/MyDrive/StyleGAN2-ada/result/pretrained/anime_face.pt'
-}
diff --git a/spaces/Ricecake123/RVC-demo/docs/README.en.md b/spaces/Ricecake123/RVC-demo/docs/README.en.md
deleted file mode 100644
index 40b357e1e1eb5debe3d8eddd4053bd53069730c2..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/docs/README.en.md
+++ /dev/null
@@ -1,110 +0,0 @@
-
-
-
Retrieval-based-Voice-Conversion-WebUI
-An easy-to-use Voice Conversion framework based on VITS.
-
-The guest has been detected as a virus by Windows. Please clean with your antimalware program. A recent version of virus cleaner might fix this problem. See the FAQ section for more information.
-
-Don't stop troubleshooting the issue with "Windows Defender".
-
-Click to expand...
-
-Go to Start | Control Panel | System | System tools | System Restore | Restore previous system
-
-Right click on your desktop, go to properties and select properties | File system | options. Go to advanced and check that the system file check is off. Restart and enjoy your system.
-
-You will be asked to reboot the computer when it is running. You need to be present at the time of the restart.
-
-I found the option "Show hidden items" in the "System Tools" dialog box.
-
-"Options" is in the menu, and it's one of the first three submenu items.
-
-The Windows Script Host service is not enabled.
-
-The services for the Windows Script Host are listed in the "Services" dialog box.
-
-You need to check the status of "Windows Script Host" to determine whether the service is running.
-
-Select "Windows Script Host" from the list of services.
-
-Ok I was right after all. Here is how:
-
-In your desktop folder:
-
-You need to 4fefd39f24
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Arjun - The Warrior Prince Full Movie In Hindi 720p Download [NEW] Movie.md b/spaces/bioriAsaeru/text-to-voice/Arjun - The Warrior Prince Full Movie In Hindi 720p Download [NEW] Movie.md
deleted file mode 100644
index a2dffafc38519e2f1759bce25532297405d23739..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Arjun - The Warrior Prince Full Movie In Hindi 720p Download [NEW] Movie.md
+++ /dev/null
@@ -1,10 +0,0 @@
-
Arjun - The Warrior Prince full movie in hindi 720p download movie
-
-Arjun - The Warrior Prince full movie in hindi 720p download movie Download
-Subscribe - https://www.youtube.com/channel/UCv1hZKq0UgHWUrN_e4zOx5A?sub_confirmation=1
-Arjun's story begins at a childhood age, when his father, Shankar, becomes a soldier and sends him to study abroad, leaving Arjun a young boy alone with his mother and other children.
-He becomes the youngest member of the family.
-Throughout the years his father has also served as an army commander and managed to win several trophies and military achievements. 8a78ff9644
-
-
-
diff --git a/spaces/brainblow/MusiCreator/audiocraft/models/builders.py b/spaces/brainblow/MusiCreator/audiocraft/models/builders.py
deleted file mode 100644
index 77ee5f96fea2e3c9e475fe961bc1a5ee473ed8eb..0000000000000000000000000000000000000000
--- a/spaces/brainblow/MusiCreator/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/brainblow/MusiCreator/audiocraft/quantization/__init__.py b/spaces/brainblow/MusiCreator/audiocraft/quantization/__init__.py
deleted file mode 100644
index 836d6eb518978480c6b95d6f29ce4f84a9428793..0000000000000000000000000000000000000000
--- a/spaces/brainblow/MusiCreator/audiocraft/quantization/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-# flake8: noqa
-from .vq import ResidualVectorQuantizer
-from .base import BaseQuantizer, DummyQuantizer, QuantizedResult
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/train_net.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/train_net.py
deleted file mode 100644
index 9ae6f1a9b3ac12e59d42eafc680e2887973872d3..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/train_net.py
+++ /dev/null
@@ -1,145 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-PointRend Training Script.
-
-This script is a simplified version of the training script in detectron2/tools.
-"""
-
-import os
-
-import detectron2.data.transforms as T
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import DatasetMapper, MetadataCatalog, build_detection_train_loader
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
-from detectron2.evaluation import (
- CityscapesInstanceEvaluator,
- CityscapesSemSegEvaluator,
- COCOEvaluator,
- DatasetEvaluators,
- LVISEvaluator,
- SemSegEvaluator,
- verify_results,
-)
-from detectron2.projects.point_rend import ColorAugSSDTransform, add_pointrend_config
-
-
-def build_sem_seg_train_aug(cfg):
- augs = [
- T.ResizeShortestEdge(
- cfg.INPUT.MIN_SIZE_TRAIN, cfg.INPUT.MAX_SIZE_TRAIN, cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
- )
- ]
- if cfg.INPUT.CROP.ENABLED:
- augs.append(
- T.RandomCrop_CategoryAreaConstraint(
- cfg.INPUT.CROP.TYPE,
- cfg.INPUT.CROP.SIZE,
- cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
- cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
- )
- )
- if cfg.INPUT.COLOR_AUG_SSD:
- augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
- augs.append(T.RandomFlip())
- return augs
-
-
-class Trainer(DefaultTrainer):
- """
- We use the "DefaultTrainer" which contains a number pre-defined logic for
- standard training workflow. They may not work for you, especially if you
- are working on a new research project. In that case you can use the cleaner
- "SimpleTrainer", or write your own training loop.
- """
-
- @classmethod
- def build_evaluator(cls, cfg, dataset_name, output_folder=None):
- """
- Create evaluator(s) for a given dataset.
- This uses the special metadata "evaluator_type" associated with each builtin dataset.
- For your own dataset, you can simply create an evaluator manually in your
- script and do not have to worry about the hacky if-else logic here.
- """
- if output_folder is None:
- output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
- evaluator_list = []
- evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
- if evaluator_type == "lvis":
- return LVISEvaluator(dataset_name, output_dir=output_folder)
- if evaluator_type == "coco":
- return COCOEvaluator(dataset_name, output_dir=output_folder)
- if evaluator_type == "sem_seg":
- return SemSegEvaluator(
- dataset_name,
- distributed=True,
- output_dir=output_folder,
- )
- if evaluator_type == "cityscapes_instance":
- return CityscapesInstanceEvaluator(dataset_name)
- if evaluator_type == "cityscapes_sem_seg":
- return CityscapesSemSegEvaluator(dataset_name)
- if len(evaluator_list) == 0:
- raise NotImplementedError(
- "no Evaluator for the dataset {} with the type {}".format(
- dataset_name, evaluator_type
- )
- )
- if len(evaluator_list) == 1:
- return evaluator_list[0]
- return DatasetEvaluators(evaluator_list)
-
- @classmethod
- def build_train_loader(cls, cfg):
- if "SemanticSegmentor" in cfg.MODEL.META_ARCHITECTURE:
- mapper = DatasetMapper(cfg, is_train=True, augmentations=build_sem_seg_train_aug(cfg))
- else:
- mapper = None
- return build_detection_train_loader(cfg, mapper=mapper)
-
-
-def setup(args):
- """
- Create configs and perform basic setups.
- """
- cfg = get_cfg()
- add_pointrend_config(cfg)
- cfg.merge_from_file(args.config_file)
- cfg.merge_from_list(args.opts)
- cfg.freeze()
- default_setup(cfg, args)
- return cfg
-
-
-def main(args):
- cfg = setup(args)
-
- if args.eval_only:
- model = Trainer.build_model(cfg)
- DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
- cfg.MODEL.WEIGHTS, resume=args.resume
- )
- res = Trainer.test(cfg, model)
- if comm.is_main_process():
- verify_results(cfg, res)
- return res
-
- trainer = Trainer(cfg)
- trainer.resume_or_load(resume=args.resume)
- return trainer.train()
-
-
-if __name__ == "__main__":
- args = default_argument_parser().parse_args()
- print("Command Line Args:", args)
- launch(
- main,
- args.num_gpus,
- num_machines=args.num_machines,
- machine_rank=args.machine_rank,
- dist_url=args.dist_url,
- args=(args,),
- )
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageTk.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageTk.py
deleted file mode 100644
index bf98eb2c8c25c7446dd91890f49291486222f3b8..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageTk.py
+++ /dev/null
@@ -1,283 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# a Tk display interface
-#
-# History:
-# 96-04-08 fl Created
-# 96-09-06 fl Added getimage method
-# 96-11-01 fl Rewritten, removed image attribute and crop method
-# 97-05-09 fl Use PyImagingPaste method instead of image type
-# 97-05-12 fl Minor tweaks to match the IFUNC95 interface
-# 97-05-17 fl Support the "pilbitmap" booster patch
-# 97-06-05 fl Added file= and data= argument to image constructors
-# 98-03-09 fl Added width and height methods to Image classes
-# 98-07-02 fl Use default mode for "P" images without palette attribute
-# 98-07-02 fl Explicitly destroy Tkinter image objects
-# 99-07-24 fl Support multiple Tk interpreters (from Greg Couch)
-# 99-07-26 fl Automatically hook into Tkinter (if possible)
-# 99-08-15 fl Hook uses _imagingtk instead of _imaging
-#
-# Copyright (c) 1997-1999 by Secret Labs AB
-# Copyright (c) 1996-1997 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import tkinter
-from io import BytesIO
-
-from . import Image
-
-# --------------------------------------------------------------------
-# Check for Tkinter interface hooks
-
-_pilbitmap_ok = None
-
-
-def _pilbitmap_check():
- global _pilbitmap_ok
- if _pilbitmap_ok is None:
- try:
- im = Image.new("1", (1, 1))
- tkinter.BitmapImage(data=f"PIL:{im.im.id}")
- _pilbitmap_ok = 1
- except tkinter.TclError:
- _pilbitmap_ok = 0
- return _pilbitmap_ok
-
-
-def _get_image_from_kw(kw):
- source = None
- if "file" in kw:
- source = kw.pop("file")
- elif "data" in kw:
- source = BytesIO(kw.pop("data"))
- if source:
- return Image.open(source)
-
-
-def _pyimagingtkcall(command, photo, id):
- tk = photo.tk
- try:
- tk.call(command, photo, id)
- except tkinter.TclError:
- # activate Tkinter hook
- # may raise an error if it cannot attach to Tkinter
- from . import _imagingtk
-
- _imagingtk.tkinit(tk.interpaddr())
- tk.call(command, photo, id)
-
-
-# --------------------------------------------------------------------
-# PhotoImage
-
-
-class PhotoImage:
- """
- A Tkinter-compatible photo image. This can be used
- everywhere Tkinter expects an image object. If the image is an RGBA
- image, pixels having alpha 0 are treated as transparent.
-
- The constructor takes either a PIL image, or a mode and a size.
- Alternatively, you can use the ``file`` or ``data`` options to initialize
- the photo image object.
-
- :param image: Either a PIL image, or a mode string. If a mode string is
- used, a size must also be given.
- :param size: If the first argument is a mode string, this defines the size
- of the image.
- :keyword file: A filename to load the image from (using
- ``Image.open(file)``).
- :keyword data: An 8-bit string containing image data (as loaded from an
- image file).
- """
-
- def __init__(self, image=None, size=None, **kw):
- # Tk compatibility: file or data
- if image is None:
- image = _get_image_from_kw(kw)
-
- if hasattr(image, "mode") and hasattr(image, "size"):
- # got an image instead of a mode
- mode = image.mode
- if mode == "P":
- # palette mapped data
- image.apply_transparency()
- image.load()
- try:
- mode = image.palette.mode
- except AttributeError:
- mode = "RGB" # default
- size = image.size
- kw["width"], kw["height"] = size
- else:
- mode = image
- image = None
-
- if mode not in ["1", "L", "RGB", "RGBA"]:
- mode = Image.getmodebase(mode)
-
- self.__mode = mode
- self.__size = size
- self.__photo = tkinter.PhotoImage(**kw)
- self.tk = self.__photo.tk
- if image:
- self.paste(image)
-
- def __del__(self):
- name = self.__photo.name
- self.__photo.name = None
- try:
- self.__photo.tk.call("image", "delete", name)
- except Exception:
- pass # ignore internal errors
-
- def __str__(self):
- """
- Get the Tkinter photo image identifier. This method is automatically
- called by Tkinter whenever a PhotoImage object is passed to a Tkinter
- method.
-
- :return: A Tkinter photo image identifier (a string).
- """
- return str(self.__photo)
-
- def width(self):
- """
- Get the width of the image.
-
- :return: The width, in pixels.
- """
- return self.__size[0]
-
- def height(self):
- """
- Get the height of the image.
-
- :return: The height, in pixels.
- """
- return self.__size[1]
-
- def paste(self, im):
- """
- Paste a PIL image into the photo image. Note that this can
- be very slow if the photo image is displayed.
-
- :param im: A PIL image. The size must match the target region. If the
- mode does not match, the image is converted to the mode of
- the bitmap image.
- """
- # convert to blittable
- im.load()
- image = im.im
- if image.isblock() and im.mode == self.__mode:
- block = image
- else:
- block = image.new_block(self.__mode, im.size)
- image.convert2(block, image) # convert directly between buffers
-
- _pyimagingtkcall("PyImagingPhoto", self.__photo, block.id)
-
-
-# --------------------------------------------------------------------
-# BitmapImage
-
-
-class BitmapImage:
- """
- A Tkinter-compatible bitmap image. This can be used everywhere Tkinter
- expects an image object.
-
- The given image must have mode "1". Pixels having value 0 are treated as
- transparent. Options, if any, are passed on to Tkinter. The most commonly
- used option is ``foreground``, which is used to specify the color for the
- non-transparent parts. See the Tkinter documentation for information on
- how to specify colours.
-
- :param image: A PIL image.
- """
-
- def __init__(self, image=None, **kw):
- # Tk compatibility: file or data
- if image is None:
- image = _get_image_from_kw(kw)
-
- self.__mode = image.mode
- self.__size = image.size
-
- if _pilbitmap_check():
- # fast way (requires the pilbitmap booster patch)
- image.load()
- kw["data"] = f"PIL:{image.im.id}"
- self.__im = image # must keep a reference
- else:
- # slow but safe way
- kw["data"] = image.tobitmap()
- self.__photo = tkinter.BitmapImage(**kw)
-
- def __del__(self):
- name = self.__photo.name
- self.__photo.name = None
- try:
- self.__photo.tk.call("image", "delete", name)
- except Exception:
- pass # ignore internal errors
-
- def width(self):
- """
- Get the width of the image.
-
- :return: The width, in pixels.
- """
- return self.__size[0]
-
- def height(self):
- """
- Get the height of the image.
-
- :return: The height, in pixels.
- """
- return self.__size[1]
-
- def __str__(self):
- """
- Get the Tkinter bitmap image identifier. This method is automatically
- called by Tkinter whenever a BitmapImage object is passed to a Tkinter
- method.
-
- :return: A Tkinter bitmap image identifier (a string).
- """
- return str(self.__photo)
-
-
-def getimage(photo):
- """Copies the contents of a PhotoImage to a PIL image memory."""
- im = Image.new("RGBA", (photo.width(), photo.height()))
- block = im.im
-
- _pyimagingtkcall("PyImagingPhotoGet", photo, block.id)
-
- return im
-
-
-def _show(image, title):
- """Helper for the Image.show method."""
-
- class UI(tkinter.Label):
- def __init__(self, master, im):
- if im.mode == "1":
- self.image = BitmapImage(im, foreground="white", master=master)
- else:
- self.image = PhotoImage(im, master=master)
- super().__init__(master, image=self.image, bg="black", bd=0)
-
- if not tkinter._default_root:
- msg = "tkinter not initialized"
- raise OSError(msg)
- top = tkinter.Toplevel()
- if title:
- top.title(title)
- UI(top, image).pack()
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_distutils_hack/__init__.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_distutils_hack/__init__.py
deleted file mode 100644
index b951c2defd0b447a6974de79cd7353255b613f6a..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_distutils_hack/__init__.py
+++ /dev/null
@@ -1,227 +0,0 @@
-# don't import any costly modules
-import sys
-import os
-
-
-is_pypy = '__pypy__' in sys.builtin_module_names
-
-
-def warn_distutils_present():
- if 'distutils' not in sys.modules:
- return
- if is_pypy and sys.version_info < (3, 7):
- # PyPy for 3.6 unconditionally imports distutils, so bypass the warning
- # https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
- return
- import warnings
-
- warnings.warn(
- "Distutils was imported before Setuptools, but importing Setuptools "
- "also replaces the `distutils` module in `sys.modules`. This may lead "
- "to undesirable behaviors or errors. To avoid these issues, avoid "
- "using distutils directly, ensure that setuptools is installed in the "
- "traditional way (e.g. not an editable install), and/or make sure "
- "that setuptools is always imported before distutils."
- )
-
-
-def clear_distutils():
- if 'distutils' not in sys.modules:
- return
- import warnings
-
- warnings.warn("Setuptools is replacing distutils.")
- mods = [
- name
- for name in sys.modules
- if name == "distutils" or name.startswith("distutils.")
- ]
- for name in mods:
- del sys.modules[name]
-
-
-def enabled():
- """
- Allow selection of distutils by environment variable.
- """
- which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')
- return which == 'local'
-
-
-def ensure_local_distutils():
- import importlib
-
- clear_distutils()
-
- # With the DistutilsMetaFinder in place,
- # perform an import to cause distutils to be
- # loaded from setuptools._distutils. Ref #2906.
- with shim():
- importlib.import_module('distutils')
-
- # check that submodules load as expected
- core = importlib.import_module('distutils.core')
- assert '_distutils' in core.__file__, core.__file__
- assert 'setuptools._distutils.log' not in sys.modules
-
-
-def do_override():
- """
- Ensure that the local copy of distutils is preferred over stdlib.
-
- See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
- for more motivation.
- """
- if enabled():
- warn_distutils_present()
- ensure_local_distutils()
-
-
-class _TrivialRe:
- def __init__(self, *patterns):
- self._patterns = patterns
-
- def match(self, string):
- return all(pat in string for pat in self._patterns)
-
-
-class DistutilsMetaFinder:
- def find_spec(self, fullname, path, target=None):
- # optimization: only consider top level modules and those
- # found in the CPython test suite.
- if path is not None and not fullname.startswith('test.'):
- return
-
- method_name = 'spec_for_{fullname}'.format(**locals())
- method = getattr(self, method_name, lambda: None)
- return method()
-
- def spec_for_distutils(self):
- if self.is_cpython():
- return
-
- import importlib
- import importlib.abc
- import importlib.util
-
- try:
- mod = importlib.import_module('setuptools._distutils')
- except Exception:
- # There are a couple of cases where setuptools._distutils
- # may not be present:
- # - An older Setuptools without a local distutils is
- # taking precedence. Ref #2957.
- # - Path manipulation during sitecustomize removes
- # setuptools from the path but only after the hook
- # has been loaded. Ref #2980.
- # In either case, fall back to stdlib behavior.
- return
-
- class DistutilsLoader(importlib.abc.Loader):
- def create_module(self, spec):
- mod.__name__ = 'distutils'
- return mod
-
- def exec_module(self, module):
- pass
-
- return importlib.util.spec_from_loader(
- 'distutils', DistutilsLoader(), origin=mod.__file__
- )
-
- @staticmethod
- def is_cpython():
- """
- Suppress supplying distutils for CPython (build and tests).
- Ref #2965 and #3007.
- """
- return os.path.isfile('pybuilddir.txt')
-
- def spec_for_pip(self):
- """
- Ensure stdlib distutils when running under pip.
- See pypa/pip#8761 for rationale.
- """
- if sys.version_info >= (3, 12) or self.pip_imported_during_build():
- return
- clear_distutils()
- self.spec_for_distutils = lambda: None
-
- @classmethod
- def pip_imported_during_build(cls):
- """
- Detect if pip is being imported in a build script. Ref #2355.
- """
- import traceback
-
- return any(
- cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)
- )
-
- @staticmethod
- def frame_file_is_setup(frame):
- """
- Return True if the indicated frame suggests a setup.py file.
- """
- # some frames may not have __file__ (#2940)
- return frame.f_globals.get('__file__', '').endswith('setup.py')
-
- def spec_for_sensitive_tests(self):
- """
- Ensure stdlib distutils when running select tests under CPython.
-
- python/cpython#91169
- """
- clear_distutils()
- self.spec_for_distutils = lambda: None
-
- sensitive_tests = (
- [
- 'test.test_distutils',
- 'test.test_peg_generator',
- 'test.test_importlib',
- ]
- if sys.version_info < (3, 10)
- else [
- 'test.test_distutils',
- ]
- )
-
-
-for name in DistutilsMetaFinder.sensitive_tests:
- setattr(
- DistutilsMetaFinder,
- f'spec_for_{name}',
- DistutilsMetaFinder.spec_for_sensitive_tests,
- )
-
-
-DISTUTILS_FINDER = DistutilsMetaFinder()
-
-
-def add_shim():
- DISTUTILS_FINDER in sys.meta_path or insert_shim()
-
-
-class shim:
- def __enter__(self):
- insert_shim()
-
- def __exit__(self, exc, value, tb):
- _remove_shim()
-
-
-def insert_shim():
- sys.meta_path.insert(0, DISTUTILS_FINDER)
-
-
-def _remove_shim():
- try:
- sys.meta_path.remove(DISTUTILS_FINDER)
- except ValueError:
- pass
-
-
-if sys.version_info < (3, 12):
- # DistutilsMetaFinder can only be disabled in Python < 3.12 (PEP 632)
- remove_shim = _remove_shim
diff --git a/spaces/cfwef/gpt/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp b/spaces/cfwef/gpt/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
deleted file mode 100644
index c94575903bdf2eef71ecbe66382375552446e510..0000000000000000000000000000000000000000
--- a/spaces/cfwef/gpt/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
+++ /dev/null
@@ -1,17 +0,0 @@
-#include "libipc/pool_alloc.h"
-
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace mem {
-
-void* pool_alloc::alloc(std::size_t size) {
- return async_pool_alloc::alloc(size);
-}
-
-void pool_alloc::free(void* p, std::size_t size) {
- async_pool_alloc::free(p, size);
-}
-
-} // namespace mem
-} // namespace ipc
diff --git a/spaces/chansung/zero2story/modules/llms/llm_factory_abstracts.py b/spaces/chansung/zero2story/modules/llms/llm_factory_abstracts.py
deleted file mode 100644
index 062a4cee9905ba26cf2d8b30df10b64658f0a7be..0000000000000000000000000000000000000000
--- a/spaces/chansung/zero2story/modules/llms/llm_factory_abstracts.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-class LLMFactory(metaclass=ABCMeta):
- @abstractmethod
- def create_prompt_format(self):
- pass
-
- @abstractmethod
- def create_prompt_manager(self):
- pass
-
- @abstractmethod
- def create_pp_manager(self):
- pass
-
- @abstractmethod
- def create_ui_pp_manager(self):
- pass
-
- @abstractmethod
- def create_llm_service(self):
- pass
-
-
-class PromptFmt(metaclass=ABCMeta):
- @classmethod
- @abstractmethod
- def ctx(cls, context):
- pass
-
- @classmethod
- @abstractmethod
- def prompt(cls, pingpong, truncate_size):
- pass
-
-
-class PromptManager(metaclass=ABCMeta):
- @abstractmethod
- def load_prompts(self):
- pass
-
- @abstractmethod
- def reload_prompts(self):
- pass
-
- @property
- @abstractmethod
- def prompts_path(self):
- pass
-
- @prompts_path.setter
- @abstractmethod
- def prompts_path(self, prompts_path):
- pass
-
- @property
- @abstractmethod
- def prompts(self):
- pass
-
-
-class PPManager(metaclass=ABCMeta):
- @abstractmethod
- def build_prompts(self, from_idx: int=0, to_idx: int=-1, fmt: PromptFmt=None, truncate_size: int=None):
- pass
-
-
-class UIPPManager(PPManager, metaclass=ABCMeta):
- @abstractmethod
- def build_uis(self, from_idx: int=0, to_idx: int=-1):
- pass
-
-
-class LLMService(metaclass=ABCMeta):
- @abstractmethod
- def make_params(self, mode="chat",
- temperature=None,
- candidate_count=None,
- top_k=None,
- top_p=None,
- max_output_tokens=None,
- use_filter=True):
- pass
-
- @abstractmethod
- async def gen_text(self, prompt, mode="chat", parameters=None, use_filter=True):
- pass
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/distillation/run_squad_w_distillation.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/distillation/run_squad_w_distillation.py
deleted file mode 100644
index 4b8b8e542f702d2c413a9a76b86565f53c07ca66..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/distillation/run_squad_w_distillation.py
+++ /dev/null
@@ -1,877 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION. 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.
-""" This is the exact same script as `examples/question-answering/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation."""
-
-import argparse
-import glob
-import logging
-import os
-import random
-import timeit
-
-import numpy as np
-import torch
-from torch import nn
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-import transformers
-from transformers import (
- WEIGHTS_NAME,
- AdamW,
- BertConfig,
- BertForQuestionAnswering,
- BertTokenizer,
- DistilBertConfig,
- DistilBertForQuestionAnswering,
- DistilBertTokenizer,
- RobertaConfig,
- RobertaForQuestionAnswering,
- RobertaTokenizer,
- XLMConfig,
- XLMForQuestionAnswering,
- XLMTokenizer,
- XLNetConfig,
- XLNetForQuestionAnswering,
- XLNetTokenizer,
- get_linear_schedule_with_warmup,
- squad_convert_examples_to_features,
-)
-from transformers.data.metrics.squad_metrics import (
- compute_predictions_log_probs,
- compute_predictions_logits,
- squad_evaluate,
-)
-from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor
-from transformers.trainer_utils import is_main_process
-
-
-try:
- from torch.utils.tensorboard import SummaryWriter
-except ImportError:
- from tensorboardX import SummaryWriter
-
-
-logger = logging.getLogger(__name__)
-
-
-MODEL_CLASSES = {
- "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer),
- "xlnet": (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
- "xlm": (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
- "distilbert": (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
- "roberta": (RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer),
-}
-
-
-def set_seed(args):
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.n_gpu > 0:
- torch.cuda.manual_seed_all(args.seed)
-
-
-def to_list(tensor):
- return tensor.detach().cpu().tolist()
-
-
-def train(args, train_dataset, model, tokenizer, teacher=None):
- """Train the model"""
- if args.local_rank in [-1, 0]:
- tb_writer = SummaryWriter()
-
- args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
- train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
- train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
-
- if args.max_steps > 0:
- t_total = args.max_steps
- args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
- else:
- t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
- # Prepare optimizer and schedule (linear warmup and decay)
- no_decay = ["bias", "LayerNorm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
- "weight_decay": args.weight_decay,
- },
- {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
- ]
- optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
- scheduler = get_linear_schedule_with_warmup(
- optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
- )
-
- # Check if saved optimizer or scheduler states exist
- if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
- os.path.join(args.model_name_or_path, "scheduler.pt")
- ):
- # Load in optimizer and scheduler states
- optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
- scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
-
- if args.fp16:
- try:
- from apex import amp
- except ImportError:
- raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-
- model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
- # multi-gpu training (should be after apex fp16 initialization)
- if args.n_gpu > 1:
- model = nn.DataParallel(model)
-
- # Distributed training (should be after apex fp16 initialization)
- if args.local_rank != -1:
- model = nn.parallel.DistributedDataParallel(
- model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
- )
-
- # Train!
- logger.info("***** Running training *****")
- logger.info(" Num examples = %d", len(train_dataset))
- logger.info(" Num Epochs = %d", args.num_train_epochs)
- logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
- logger.info(
- " Total train batch size (w. parallel, distributed & accumulation) = %d",
- args.train_batch_size
- * args.gradient_accumulation_steps
- * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
- )
- logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
- logger.info(" Total optimization steps = %d", t_total)
-
- global_step = 1
- epochs_trained = 0
- steps_trained_in_current_epoch = 0
- # Check if continuing training from a checkpoint
- if os.path.exists(args.model_name_or_path):
- try:
- # set global_step to gobal_step of last saved checkpoint from model path
- checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
- global_step = int(checkpoint_suffix)
- epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
- steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
-
- logger.info(" Continuing training from checkpoint, will skip to saved global_step")
- logger.info(" Continuing training from epoch %d", epochs_trained)
- logger.info(" Continuing training from global step %d", global_step)
- logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
- except ValueError:
- logger.info(" Starting fine-tuning.")
-
- tr_loss, logging_loss = 0.0, 0.0
- model.zero_grad()
- train_iterator = trange(
- epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
- )
- # Added here for reproductibility
- set_seed(args)
-
- for _ in train_iterator:
- epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
- for step, batch in enumerate(epoch_iterator):
- # Skip past any already trained steps if resuming training
- if steps_trained_in_current_epoch > 0:
- steps_trained_in_current_epoch -= 1
- continue
-
- model.train()
- if teacher is not None:
- teacher.eval()
- batch = tuple(t.to(args.device) for t in batch)
-
- inputs = {
- "input_ids": batch[0],
- "attention_mask": batch[1],
- "start_positions": batch[3],
- "end_positions": batch[4],
- }
- if args.model_type != "distilbert":
- inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2]
- if args.model_type in ["xlnet", "xlm"]:
- inputs.update({"cls_index": batch[5], "p_mask": batch[6]})
- if args.version_2_with_negative:
- inputs.update({"is_impossible": batch[7]})
- outputs = model(**inputs)
- loss, start_logits_stu, end_logits_stu = outputs
-
- # Distillation loss
- if teacher is not None:
- if "token_type_ids" not in inputs:
- inputs["token_type_ids"] = None if args.teacher_type == "xlm" else batch[2]
- with torch.no_grad():
- start_logits_tea, end_logits_tea = teacher(
- input_ids=inputs["input_ids"],
- token_type_ids=inputs["token_type_ids"],
- attention_mask=inputs["attention_mask"],
- )
- assert start_logits_tea.size() == start_logits_stu.size()
- assert end_logits_tea.size() == end_logits_stu.size()
-
- loss_fct = nn.KLDivLoss(reduction="batchmean")
- loss_start = loss_fct(
- nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1),
- nn.functional.softmax(start_logits_tea / args.temperature, dim=-1),
- ) * (args.temperature**2)
- loss_end = loss_fct(
- nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1),
- nn.functional.softmax(end_logits_tea / args.temperature, dim=-1),
- ) * (args.temperature**2)
- loss_ce = (loss_start + loss_end) / 2.0
-
- loss = args.alpha_ce * loss_ce + args.alpha_squad * loss
-
- if args.n_gpu > 1:
- loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
- if args.gradient_accumulation_steps > 1:
- loss = loss / args.gradient_accumulation_steps
-
- if args.fp16:
- with amp.scale_loss(loss, optimizer) as scaled_loss:
- scaled_loss.backward()
- else:
- loss.backward()
-
- tr_loss += loss.item()
- if (step + 1) % args.gradient_accumulation_steps == 0:
- if args.fp16:
- nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
- else:
- nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
- optimizer.step()
- scheduler.step() # Update learning rate schedule
- model.zero_grad()
- global_step += 1
-
- # Log metrics
- if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
- # Only evaluate when single GPU otherwise metrics may not average well
- if args.local_rank == -1 and args.evaluate_during_training:
- results = evaluate(args, model, tokenizer)
- for key, value in results.items():
- tb_writer.add_scalar("eval_{}".format(key), value, global_step)
- tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
- tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
- logging_loss = tr_loss
-
- if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
- # Save model checkpoint
- output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
- if not os.path.exists(output_dir):
- os.makedirs(output_dir)
- model_to_save = (
- model.module if hasattr(model, "module") else model
- ) # Take care of distributed/parallel training
- model_to_save.save_pretrained(output_dir)
- tokenizer.save_pretrained(output_dir)
-
- torch.save(args, os.path.join(output_dir, "training_args.bin"))
- logger.info("Saving model checkpoint to %s", output_dir)
-
- torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
- torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
- logger.info("Saving optimizer and scheduler states to %s", output_dir)
-
- if args.max_steps > 0 and global_step > args.max_steps:
- epoch_iterator.close()
- break
- if args.max_steps > 0 and global_step > args.max_steps:
- train_iterator.close()
- break
-
- if args.local_rank in [-1, 0]:
- tb_writer.close()
-
- return global_step, tr_loss / global_step
-
-
-def evaluate(args, model, tokenizer, prefix=""):
- dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
-
- if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
- os.makedirs(args.output_dir)
-
- args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-
- # Note that DistributedSampler samples randomly
- eval_sampler = SequentialSampler(dataset)
- eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
- # multi-gpu evaluate
- if args.n_gpu > 1 and not isinstance(model, nn.DataParallel):
- model = nn.DataParallel(model)
-
- # Eval!
- logger.info("***** Running evaluation {} *****".format(prefix))
- logger.info(" Num examples = %d", len(dataset))
- logger.info(" Batch size = %d", args.eval_batch_size)
-
- all_results = []
- start_time = timeit.default_timer()
-
- for batch in tqdm(eval_dataloader, desc="Evaluating"):
- model.eval()
- batch = tuple(t.to(args.device) for t in batch)
-
- with torch.no_grad():
- inputs = {"input_ids": batch[0], "attention_mask": batch[1]}
- if args.model_type != "distilbert":
- inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2] # XLM don't use segment_ids
- example_indices = batch[3]
- if args.model_type in ["xlnet", "xlm"]:
- inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
-
- outputs = model(**inputs)
-
- for i, example_index in enumerate(example_indices):
- eval_feature = features[example_index.item()]
- unique_id = int(eval_feature.unique_id)
-
- output = [to_list(output[i]) for output in outputs]
-
- # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
- # models only use two.
- if len(output) >= 5:
- start_logits = output[0]
- start_top_index = output[1]
- end_logits = output[2]
- end_top_index = output[3]
- cls_logits = output[4]
-
- result = SquadResult(
- unique_id,
- start_logits,
- end_logits,
- start_top_index=start_top_index,
- end_top_index=end_top_index,
- cls_logits=cls_logits,
- )
-
- else:
- start_logits, end_logits = output
- result = SquadResult(unique_id, start_logits, end_logits)
-
- all_results.append(result)
-
- evalTime = timeit.default_timer() - start_time
- logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
-
- # Compute predictions
- output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
- output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
-
- if args.version_2_with_negative:
- output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
- else:
- output_null_log_odds_file = None
-
- if args.model_type in ["xlnet", "xlm"]:
- # XLNet uses a more complex post-processing procedure
- predictions = compute_predictions_log_probs(
- examples,
- features,
- all_results,
- args.n_best_size,
- args.max_answer_length,
- output_prediction_file,
- output_nbest_file,
- output_null_log_odds_file,
- model.config.start_n_top,
- model.config.end_n_top,
- args.version_2_with_negative,
- tokenizer,
- args.verbose_logging,
- )
- else:
- predictions = compute_predictions_logits(
- examples,
- features,
- all_results,
- args.n_best_size,
- args.max_answer_length,
- args.do_lower_case,
- output_prediction_file,
- output_nbest_file,
- output_null_log_odds_file,
- args.verbose_logging,
- args.version_2_with_negative,
- args.null_score_diff_threshold,
- tokenizer,
- )
-
- # Compute the F1 and exact scores.
- results = squad_evaluate(examples, predictions)
- return results
-
-
-def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
- if args.local_rank not in [-1, 0] and not evaluate:
- # Make sure only the first process in distributed training process the dataset, and the others will use the cache
- torch.distributed.barrier()
-
- # Load data features from cache or dataset file
- input_file = args.predict_file if evaluate else args.train_file
- cached_features_file = os.path.join(
- os.path.dirname(input_file),
- "cached_distillation_{}_{}_{}".format(
- "dev" if evaluate else "train",
- list(filter(None, args.model_name_or_path.split("/"))).pop(),
- str(args.max_seq_length),
- ),
- )
- if os.path.exists(cached_features_file) and not args.overwrite_cache:
- logger.info("Loading features from cached file %s", cached_features_file)
- features_and_dataset = torch.load(cached_features_file)
-
- try:
- features, dataset, examples = (
- features_and_dataset["features"],
- features_and_dataset["dataset"],
- features_and_dataset["examples"],
- )
- except KeyError:
- raise DeprecationWarning(
- "You seem to be loading features from an older version of this script please delete the "
- "file %s in order for it to be created again" % cached_features_file
- )
- else:
- logger.info("Creating features from dataset file at %s", input_file)
- processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
- if evaluate:
- examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file)
- else:
- examples = processor.get_train_examples(args.data_dir, filename=args.train_file)
-
- features, dataset = squad_convert_examples_to_features(
- examples=examples,
- tokenizer=tokenizer,
- max_seq_length=args.max_seq_length,
- doc_stride=args.doc_stride,
- max_query_length=args.max_query_length,
- is_training=not evaluate,
- return_dataset="pt",
- threads=args.threads,
- )
-
- if args.local_rank in [-1, 0]:
- logger.info("Saving features into cached file %s", cached_features_file)
- torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file)
-
- if args.local_rank == 0 and not evaluate:
- # Make sure only the first process in distributed training process the dataset, and the others will use the cache
- torch.distributed.barrier()
-
- if output_examples:
- return dataset, examples, features
- return dataset
-
-
-def main():
- parser = argparse.ArgumentParser()
-
- # Required parameters
- parser.add_argument(
- "--model_type",
- default=None,
- type=str,
- required=True,
- help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
- )
- parser.add_argument(
- "--model_name_or_path",
- default=None,
- type=str,
- required=True,
- help="Path to pretrained model or model identifier from huggingface.co/models",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- required=True,
- help="The output directory where the model checkpoints and predictions will be written.",
- )
-
- # Distillation parameters (optional)
- parser.add_argument(
- "--teacher_type",
- default=None,
- type=str,
- help=(
- "Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for"
- " distillation."
- ),
- )
- parser.add_argument(
- "--teacher_name_or_path",
- default=None,
- type=str,
- help="Path to the already SQuAD fine-tuned teacher model. Only for distillation.",
- )
- parser.add_argument(
- "--alpha_ce", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation."
- )
- parser.add_argument(
- "--alpha_squad", default=0.5, type=float, help="True SQuAD loss linear weight. Only for distillation."
- )
- parser.add_argument(
- "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation."
- )
-
- # Other parameters
- parser.add_argument(
- "--data_dir",
- default=None,
- type=str,
- help="The input data dir. Should contain the .json files for the task."
- + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
- )
- parser.add_argument(
- "--train_file",
- default=None,
- type=str,
- help="The input training file. If a data dir is specified, will look for the file there"
- + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
- )
- parser.add_argument(
- "--predict_file",
- default=None,
- type=str,
- help="The input evaluation file. If a data dir is specified, will look for the file there"
- + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
- )
- parser.add_argument(
- "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
- )
- parser.add_argument(
- "--tokenizer_name",
- default="",
- type=str,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--cache_dir",
- default="",
- type=str,
- help="Where do you want to store the pre-trained models downloaded from huggingface.co",
- )
-
- parser.add_argument(
- "--version_2_with_negative",
- action="store_true",
- help="If true, the SQuAD examples contain some that do not have an answer.",
- )
- parser.add_argument(
- "--null_score_diff_threshold",
- type=float,
- default=0.0,
- help="If null_score - best_non_null is greater than the threshold predict null.",
- )
-
- parser.add_argument(
- "--max_seq_length",
- default=384,
- type=int,
- help=(
- "The maximum total input sequence length after WordPiece tokenization. Sequences "
- "longer than this will be truncated, and sequences shorter than this will be padded."
- ),
- )
- parser.add_argument(
- "--doc_stride",
- default=128,
- type=int,
- help="When splitting up a long document into chunks, how much stride to take between chunks.",
- )
- parser.add_argument(
- "--max_query_length",
- default=64,
- type=int,
- help=(
- "The maximum number of tokens for the question. Questions longer than this will "
- "be truncated to this length."
- ),
- )
- parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
- parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
- parser.add_argument(
- "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
- )
- parser.add_argument(
- "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
- )
-
- parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
- parser.add_argument(
- "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
- )
- parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
- parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
- parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
- parser.add_argument(
- "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
- )
- parser.add_argument(
- "--max_steps",
- default=-1,
- type=int,
- help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
- )
- parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
- parser.add_argument(
- "--n_best_size",
- default=20,
- type=int,
- help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
- )
- parser.add_argument(
- "--max_answer_length",
- default=30,
- type=int,
- help=(
- "The maximum length of an answer that can be generated. This is needed because the start "
- "and end predictions are not conditioned on one another."
- ),
- )
- parser.add_argument(
- "--verbose_logging",
- action="store_true",
- help=(
- "If true, all of the warnings related to data processing will be printed. "
- "A number of warnings are expected for a normal SQuAD evaluation."
- ),
- )
-
- parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
- parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
- parser.add_argument(
- "--eval_all_checkpoints",
- action="store_true",
- help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
- )
- parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
- parser.add_argument(
- "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
- )
- parser.add_argument(
- "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
- )
- parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-
- parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
- parser.add_argument(
- "--fp16",
- action="store_true",
- help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
- )
- parser.add_argument(
- "--fp16_opt_level",
- type=str,
- default="O1",
- help=(
- "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
- "See details at https://nvidia.github.io/apex/amp.html"
- ),
- )
- parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
- parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
-
- parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features")
- args = parser.parse_args()
-
- if (
- os.path.exists(args.output_dir)
- and os.listdir(args.output_dir)
- and args.do_train
- and not args.overwrite_output_dir
- ):
- raise ValueError(
- "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
- args.output_dir
- )
- )
-
- # Setup distant debugging if needed
- if args.server_ip and args.server_port:
- # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
- import ptvsd
-
- print("Waiting for debugger attach")
- ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
- ptvsd.wait_for_attach()
-
- # Setup CUDA, GPU & distributed training
- if args.local_rank == -1 or args.no_cuda:
- device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
- args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
- else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
- torch.cuda.set_device(args.local_rank)
- device = torch.device("cuda", args.local_rank)
- torch.distributed.init_process_group(backend="nccl")
- args.n_gpu = 1
- args.device = device
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
- )
- logger.warning(
- "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
- args.local_rank,
- device,
- args.n_gpu,
- bool(args.local_rank != -1),
- args.fp16,
- )
- # Set the verbosity to info of the Transformers logger (on main process only):
- if is_main_process(args.local_rank):
- transformers.utils.logging.set_verbosity_info()
- transformers.utils.logging.enable_default_handler()
- transformers.utils.logging.enable_explicit_format()
- # Set seed
- set_seed(args)
-
- # Load pretrained model and tokenizer
- if args.local_rank not in [-1, 0]:
- # Make sure only the first process in distributed training will download model & vocab
- torch.distributed.barrier()
-
- args.model_type = args.model_type.lower()
- config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
- config = config_class.from_pretrained(
- args.config_name if args.config_name else args.model_name_or_path,
- cache_dir=args.cache_dir if args.cache_dir else None,
- )
- tokenizer = tokenizer_class.from_pretrained(
- args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
- do_lower_case=args.do_lower_case,
- cache_dir=args.cache_dir if args.cache_dir else None,
- )
- model = model_class.from_pretrained(
- args.model_name_or_path,
- from_tf=bool(".ckpt" in args.model_name_or_path),
- config=config,
- cache_dir=args.cache_dir if args.cache_dir else None,
- )
-
- if args.teacher_type is not None:
- assert args.teacher_name_or_path is not None
- assert args.alpha_ce > 0.0
- assert args.alpha_ce + args.alpha_squad > 0.0
- assert args.teacher_type != "distilbert", "We constraint teachers not to be of type DistilBERT."
- teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type]
- teacher_config = teacher_config_class.from_pretrained(
- args.teacher_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None
- )
- teacher = teacher_model_class.from_pretrained(
- args.teacher_name_or_path, config=teacher_config, cache_dir=args.cache_dir if args.cache_dir else None
- )
- teacher.to(args.device)
- else:
- teacher = None
-
- if args.local_rank == 0:
- # Make sure only the first process in distributed training will download model & vocab
- torch.distributed.barrier()
-
- model.to(args.device)
-
- logger.info("Training/evaluation parameters %s", args)
-
- # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
- # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
- # remove the need for this code, but it is still valid.
- if args.fp16:
- try:
- import apex
-
- apex.amp.register_half_function(torch, "einsum")
- except ImportError:
- raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-
- # Training
- if args.do_train:
- train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
- global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher)
- logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
- # Save the trained model and the tokenizer
- if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
- logger.info("Saving model checkpoint to %s", args.output_dir)
- # Save a trained model, configuration and tokenizer using `save_pretrained()`.
- # They can then be reloaded using `from_pretrained()`
- model_to_save = (
- model.module if hasattr(model, "module") else model
- ) # Take care of distributed/parallel training
- model_to_save.save_pretrained(args.output_dir)
- tokenizer.save_pretrained(args.output_dir)
-
- # Good practice: save your training arguments together with the trained model
- torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
-
- # Load a trained model and vocabulary that you have fine-tuned
- model = model_class.from_pretrained(args.output_dir)
- tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
- model.to(args.device)
-
- # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
- results = {}
- if args.do_eval and args.local_rank in [-1, 0]:
- if args.do_train:
- logger.info("Loading checkpoints saved during training for evaluation")
- checkpoints = [args.output_dir]
- if args.eval_all_checkpoints:
- checkpoints = [
- os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
- ]
-
- logger.info("Evaluate the following checkpoints: %s", checkpoints)
-
- for checkpoint in checkpoints:
- # Reload the model
- global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
- model = model_class.from_pretrained(checkpoint)
- model.to(args.device)
-
- # Evaluate
- result = evaluate(args, model, tokenizer, prefix=global_step)
-
- result = {k + ("_{}".format(global_step) if global_step else ""): v for k, v in result.items()}
- results.update(result)
-
- logger.info("Results: {}".format(results))
-
- return results
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/rsa.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/rsa.py
deleted file mode 100644
index ef27d4ead570f94d7c7b2cd3847d62e816a44e96..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/rsa.py
+++ /dev/null
@@ -1,599 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from __future__ import annotations
-
-import threading
-import typing
-
-from cryptography.exceptions import (
- InvalidSignature,
- UnsupportedAlgorithm,
- _Reasons,
-)
-from cryptography.hazmat.backends.openssl.utils import (
- _calculate_digest_and_algorithm,
-)
-from cryptography.hazmat.primitives import hashes, serialization
-from cryptography.hazmat.primitives.asymmetric import utils as asym_utils
-from cryptography.hazmat.primitives.asymmetric.padding import (
- MGF1,
- OAEP,
- PSS,
- AsymmetricPadding,
- PKCS1v15,
- _Auto,
- _DigestLength,
- _MaxLength,
- calculate_max_pss_salt_length,
-)
-from cryptography.hazmat.primitives.asymmetric.rsa import (
- RSAPrivateKey,
- RSAPrivateNumbers,
- RSAPublicKey,
- RSAPublicNumbers,
-)
-
-if typing.TYPE_CHECKING:
- from cryptography.hazmat.backends.openssl.backend import Backend
-
-
-def _get_rsa_pss_salt_length(
- backend: Backend,
- pss: PSS,
- key: typing.Union[RSAPrivateKey, RSAPublicKey],
- hash_algorithm: hashes.HashAlgorithm,
-) -> int:
- salt = pss._salt_length
-
- if isinstance(salt, _MaxLength):
- return calculate_max_pss_salt_length(key, hash_algorithm)
- elif isinstance(salt, _DigestLength):
- return hash_algorithm.digest_size
- elif isinstance(salt, _Auto):
- if isinstance(key, RSAPrivateKey):
- raise ValueError(
- "PSS salt length can only be set to AUTO when verifying"
- )
- return backend._lib.RSA_PSS_SALTLEN_AUTO
- else:
- return salt
-
-
-def _enc_dec_rsa(
- backend: Backend,
- key: typing.Union[_RSAPrivateKey, _RSAPublicKey],
- data: bytes,
- padding: AsymmetricPadding,
-) -> bytes:
- if not isinstance(padding, AsymmetricPadding):
- raise TypeError("Padding must be an instance of AsymmetricPadding.")
-
- if isinstance(padding, PKCS1v15):
- padding_enum = backend._lib.RSA_PKCS1_PADDING
- elif isinstance(padding, OAEP):
- padding_enum = backend._lib.RSA_PKCS1_OAEP_PADDING
-
- if not isinstance(padding._mgf, MGF1):
- raise UnsupportedAlgorithm(
- "Only MGF1 is supported by this backend.",
- _Reasons.UNSUPPORTED_MGF,
- )
-
- if not backend.rsa_padding_supported(padding):
- raise UnsupportedAlgorithm(
- "This combination of padding and hash algorithm is not "
- "supported by this backend.",
- _Reasons.UNSUPPORTED_PADDING,
- )
-
- else:
- raise UnsupportedAlgorithm(
- f"{padding.name} is not supported by this backend.",
- _Reasons.UNSUPPORTED_PADDING,
- )
-
- return _enc_dec_rsa_pkey_ctx(backend, key, data, padding_enum, padding)
-
-
-def _enc_dec_rsa_pkey_ctx(
- backend: Backend,
- key: typing.Union[_RSAPrivateKey, _RSAPublicKey],
- data: bytes,
- padding_enum: int,
- padding: AsymmetricPadding,
-) -> bytes:
- init: typing.Callable[[typing.Any], int]
- crypt: typing.Callable[[typing.Any, typing.Any, int, bytes, int], int]
- if isinstance(key, _RSAPublicKey):
- init = backend._lib.EVP_PKEY_encrypt_init
- crypt = backend._lib.EVP_PKEY_encrypt
- else:
- init = backend._lib.EVP_PKEY_decrypt_init
- crypt = backend._lib.EVP_PKEY_decrypt
-
- pkey_ctx = backend._lib.EVP_PKEY_CTX_new(key._evp_pkey, backend._ffi.NULL)
- backend.openssl_assert(pkey_ctx != backend._ffi.NULL)
- pkey_ctx = backend._ffi.gc(pkey_ctx, backend._lib.EVP_PKEY_CTX_free)
- res = init(pkey_ctx)
- backend.openssl_assert(res == 1)
- res = backend._lib.EVP_PKEY_CTX_set_rsa_padding(pkey_ctx, padding_enum)
- backend.openssl_assert(res > 0)
- buf_size = backend._lib.EVP_PKEY_size(key._evp_pkey)
- backend.openssl_assert(buf_size > 0)
- if isinstance(padding, OAEP):
- mgf1_md = backend._evp_md_non_null_from_algorithm(
- padding._mgf._algorithm
- )
- res = backend._lib.EVP_PKEY_CTX_set_rsa_mgf1_md(pkey_ctx, mgf1_md)
- backend.openssl_assert(res > 0)
- oaep_md = backend._evp_md_non_null_from_algorithm(padding._algorithm)
- res = backend._lib.EVP_PKEY_CTX_set_rsa_oaep_md(pkey_ctx, oaep_md)
- backend.openssl_assert(res > 0)
-
- if (
- isinstance(padding, OAEP)
- and padding._label is not None
- and len(padding._label) > 0
- ):
- # set0_rsa_oaep_label takes ownership of the char * so we need to
- # copy it into some new memory
- labelptr = backend._lib.OPENSSL_malloc(len(padding._label))
- backend.openssl_assert(labelptr != backend._ffi.NULL)
- backend._ffi.memmove(labelptr, padding._label, len(padding._label))
- res = backend._lib.EVP_PKEY_CTX_set0_rsa_oaep_label(
- pkey_ctx, labelptr, len(padding._label)
- )
- backend.openssl_assert(res == 1)
-
- outlen = backend._ffi.new("size_t *", buf_size)
- buf = backend._ffi.new("unsigned char[]", buf_size)
- # Everything from this line onwards is written with the goal of being as
- # constant-time as is practical given the constraints of Python and our
- # API. See Bleichenbacher's '98 attack on RSA, and its many many variants.
- # As such, you should not attempt to change this (particularly to "clean it
- # up") without understanding why it was written this way (see
- # Chesterton's Fence), and without measuring to verify you have not
- # introduced observable time differences.
- res = crypt(pkey_ctx, buf, outlen, data, len(data))
- resbuf = backend._ffi.buffer(buf)[: outlen[0]]
- backend._lib.ERR_clear_error()
- if res <= 0:
- raise ValueError("Encryption/decryption failed.")
- return resbuf
-
-
-def _rsa_sig_determine_padding(
- backend: Backend,
- key: typing.Union[_RSAPrivateKey, _RSAPublicKey],
- padding: AsymmetricPadding,
- algorithm: typing.Optional[hashes.HashAlgorithm],
-) -> int:
- if not isinstance(padding, AsymmetricPadding):
- raise TypeError("Expected provider of AsymmetricPadding.")
-
- pkey_size = backend._lib.EVP_PKEY_size(key._evp_pkey)
- backend.openssl_assert(pkey_size > 0)
-
- if isinstance(padding, PKCS1v15):
- # Hash algorithm is ignored for PKCS1v15-padding, may be None.
- padding_enum = backend._lib.RSA_PKCS1_PADDING
- elif isinstance(padding, PSS):
- if not isinstance(padding._mgf, MGF1):
- raise UnsupportedAlgorithm(
- "Only MGF1 is supported by this backend.",
- _Reasons.UNSUPPORTED_MGF,
- )
-
- # PSS padding requires a hash algorithm
- if not isinstance(algorithm, hashes.HashAlgorithm):
- raise TypeError("Expected instance of hashes.HashAlgorithm.")
-
- # Size of key in bytes - 2 is the maximum
- # PSS signature length (salt length is checked later)
- if pkey_size - algorithm.digest_size - 2 < 0:
- raise ValueError(
- "Digest too large for key size. Use a larger "
- "key or different digest."
- )
-
- padding_enum = backend._lib.RSA_PKCS1_PSS_PADDING
- else:
- raise UnsupportedAlgorithm(
- f"{padding.name} is not supported by this backend.",
- _Reasons.UNSUPPORTED_PADDING,
- )
-
- return padding_enum
-
-
-# Hash algorithm can be absent (None) to initialize the context without setting
-# any message digest algorithm. This is currently only valid for the PKCS1v15
-# padding type, where it means that the signature data is encoded/decoded
-# as provided, without being wrapped in a DigestInfo structure.
-def _rsa_sig_setup(
- backend: Backend,
- padding: AsymmetricPadding,
- algorithm: typing.Optional[hashes.HashAlgorithm],
- key: typing.Union[_RSAPublicKey, _RSAPrivateKey],
- init_func: typing.Callable[[typing.Any], int],
-):
- padding_enum = _rsa_sig_determine_padding(backend, key, padding, algorithm)
- pkey_ctx = backend._lib.EVP_PKEY_CTX_new(key._evp_pkey, backend._ffi.NULL)
- backend.openssl_assert(pkey_ctx != backend._ffi.NULL)
- pkey_ctx = backend._ffi.gc(pkey_ctx, backend._lib.EVP_PKEY_CTX_free)
- res = init_func(pkey_ctx)
- if res != 1:
- errors = backend._consume_errors()
- raise ValueError("Unable to sign/verify with this key", errors)
-
- if algorithm is not None:
- evp_md = backend._evp_md_non_null_from_algorithm(algorithm)
- res = backend._lib.EVP_PKEY_CTX_set_signature_md(pkey_ctx, evp_md)
- if res <= 0:
- backend._consume_errors()
- raise UnsupportedAlgorithm(
- "{} is not supported by this backend for RSA signing.".format(
- algorithm.name
- ),
- _Reasons.UNSUPPORTED_HASH,
- )
- res = backend._lib.EVP_PKEY_CTX_set_rsa_padding(pkey_ctx, padding_enum)
- if res <= 0:
- backend._consume_errors()
- raise UnsupportedAlgorithm(
- "{} is not supported for the RSA signature operation.".format(
- padding.name
- ),
- _Reasons.UNSUPPORTED_PADDING,
- )
- if isinstance(padding, PSS):
- assert isinstance(algorithm, hashes.HashAlgorithm)
- res = backend._lib.EVP_PKEY_CTX_set_rsa_pss_saltlen(
- pkey_ctx,
- _get_rsa_pss_salt_length(backend, padding, key, algorithm),
- )
- backend.openssl_assert(res > 0)
-
- mgf1_md = backend._evp_md_non_null_from_algorithm(
- padding._mgf._algorithm
- )
- res = backend._lib.EVP_PKEY_CTX_set_rsa_mgf1_md(pkey_ctx, mgf1_md)
- backend.openssl_assert(res > 0)
-
- return pkey_ctx
-
-
-def _rsa_sig_sign(
- backend: Backend,
- padding: AsymmetricPadding,
- algorithm: hashes.HashAlgorithm,
- private_key: _RSAPrivateKey,
- data: bytes,
-) -> bytes:
- pkey_ctx = _rsa_sig_setup(
- backend,
- padding,
- algorithm,
- private_key,
- backend._lib.EVP_PKEY_sign_init,
- )
- buflen = backend._ffi.new("size_t *")
- res = backend._lib.EVP_PKEY_sign(
- pkey_ctx, backend._ffi.NULL, buflen, data, len(data)
- )
- backend.openssl_assert(res == 1)
- buf = backend._ffi.new("unsigned char[]", buflen[0])
- res = backend._lib.EVP_PKEY_sign(pkey_ctx, buf, buflen, data, len(data))
- if res != 1:
- errors = backend._consume_errors()
- raise ValueError(
- "Digest or salt length too long for key size. Use a larger key "
- "or shorter salt length if you are specifying a PSS salt",
- errors,
- )
-
- return backend._ffi.buffer(buf)[:]
-
-
-def _rsa_sig_verify(
- backend: Backend,
- padding: AsymmetricPadding,
- algorithm: hashes.HashAlgorithm,
- public_key: _RSAPublicKey,
- signature: bytes,
- data: bytes,
-) -> None:
- pkey_ctx = _rsa_sig_setup(
- backend,
- padding,
- algorithm,
- public_key,
- backend._lib.EVP_PKEY_verify_init,
- )
- res = backend._lib.EVP_PKEY_verify(
- pkey_ctx, signature, len(signature), data, len(data)
- )
- # The previous call can return negative numbers in the event of an
- # error. This is not a signature failure but we need to fail if it
- # occurs.
- backend.openssl_assert(res >= 0)
- if res == 0:
- backend._consume_errors()
- raise InvalidSignature
-
-
-def _rsa_sig_recover(
- backend: Backend,
- padding: AsymmetricPadding,
- algorithm: typing.Optional[hashes.HashAlgorithm],
- public_key: _RSAPublicKey,
- signature: bytes,
-) -> bytes:
- pkey_ctx = _rsa_sig_setup(
- backend,
- padding,
- algorithm,
- public_key,
- backend._lib.EVP_PKEY_verify_recover_init,
- )
-
- # Attempt to keep the rest of the code in this function as constant/time
- # as possible. See the comment in _enc_dec_rsa_pkey_ctx. Note that the
- # buflen parameter is used even though its value may be undefined in the
- # error case. Due to the tolerant nature of Python slicing this does not
- # trigger any exceptions.
- maxlen = backend._lib.EVP_PKEY_size(public_key._evp_pkey)
- backend.openssl_assert(maxlen > 0)
- buf = backend._ffi.new("unsigned char[]", maxlen)
- buflen = backend._ffi.new("size_t *", maxlen)
- res = backend._lib.EVP_PKEY_verify_recover(
- pkey_ctx, buf, buflen, signature, len(signature)
- )
- resbuf = backend._ffi.buffer(buf)[: buflen[0]]
- backend._lib.ERR_clear_error()
- # Assume that all parameter errors are handled during the setup phase and
- # any error here is due to invalid signature.
- if res != 1:
- raise InvalidSignature
- return resbuf
-
-
-class _RSAPrivateKey(RSAPrivateKey):
- _evp_pkey: object
- _rsa_cdata: object
- _key_size: int
-
- def __init__(
- self,
- backend: Backend,
- rsa_cdata,
- evp_pkey,
- *,
- unsafe_skip_rsa_key_validation: bool,
- ):
- res: int
- # RSA_check_key is slower in OpenSSL 3.0.0 due to improved
- # primality checking. In normal use this is unlikely to be a problem
- # since users don't load new keys constantly, but for TESTING we've
- # added an init arg that allows skipping the checks. You should not
- # use this in production code unless you understand the consequences.
- if not unsafe_skip_rsa_key_validation:
- res = backend._lib.RSA_check_key(rsa_cdata)
- if res != 1:
- errors = backend._consume_errors()
- raise ValueError("Invalid private key", errors)
- # 2 is prime and passes an RSA key check, so we also check
- # if p and q are odd just to be safe.
- p = backend._ffi.new("BIGNUM **")
- q = backend._ffi.new("BIGNUM **")
- backend._lib.RSA_get0_factors(rsa_cdata, p, q)
- backend.openssl_assert(p[0] != backend._ffi.NULL)
- backend.openssl_assert(q[0] != backend._ffi.NULL)
- p_odd = backend._lib.BN_is_odd(p[0])
- q_odd = backend._lib.BN_is_odd(q[0])
- if p_odd != 1 or q_odd != 1:
- errors = backend._consume_errors()
- raise ValueError("Invalid private key", errors)
-
- self._backend = backend
- self._rsa_cdata = rsa_cdata
- self._evp_pkey = evp_pkey
- # Used for lazy blinding
- self._blinded = False
- self._blinding_lock = threading.Lock()
-
- n = self._backend._ffi.new("BIGNUM **")
- self._backend._lib.RSA_get0_key(
- self._rsa_cdata,
- n,
- self._backend._ffi.NULL,
- self._backend._ffi.NULL,
- )
- self._backend.openssl_assert(n[0] != self._backend._ffi.NULL)
- self._key_size = self._backend._lib.BN_num_bits(n[0])
-
- def _enable_blinding(self) -> None:
- # If you call blind on an already blinded RSA key OpenSSL will turn
- # it off and back on, which is a performance hit we want to avoid.
- if not self._blinded:
- with self._blinding_lock:
- self._non_threadsafe_enable_blinding()
-
- def _non_threadsafe_enable_blinding(self) -> None:
- # This is only a separate function to allow for testing to cover both
- # branches. It should never be invoked except through _enable_blinding.
- # Check if it's not True again in case another thread raced past the
- # first non-locked check.
- if not self._blinded:
- res = self._backend._lib.RSA_blinding_on(
- self._rsa_cdata, self._backend._ffi.NULL
- )
- self._backend.openssl_assert(res == 1)
- self._blinded = True
-
- @property
- def key_size(self) -> int:
- return self._key_size
-
- def decrypt(self, ciphertext: bytes, padding: AsymmetricPadding) -> bytes:
- self._enable_blinding()
- key_size_bytes = (self.key_size + 7) // 8
- if key_size_bytes != len(ciphertext):
- raise ValueError("Ciphertext length must be equal to key size.")
-
- return _enc_dec_rsa(self._backend, self, ciphertext, padding)
-
- def public_key(self) -> RSAPublicKey:
- ctx = self._backend._lib.RSAPublicKey_dup(self._rsa_cdata)
- self._backend.openssl_assert(ctx != self._backend._ffi.NULL)
- ctx = self._backend._ffi.gc(ctx, self._backend._lib.RSA_free)
- evp_pkey = self._backend._rsa_cdata_to_evp_pkey(ctx)
- return _RSAPublicKey(self._backend, ctx, evp_pkey)
-
- def private_numbers(self) -> RSAPrivateNumbers:
- n = self._backend._ffi.new("BIGNUM **")
- e = self._backend._ffi.new("BIGNUM **")
- d = self._backend._ffi.new("BIGNUM **")
- p = self._backend._ffi.new("BIGNUM **")
- q = self._backend._ffi.new("BIGNUM **")
- dmp1 = self._backend._ffi.new("BIGNUM **")
- dmq1 = self._backend._ffi.new("BIGNUM **")
- iqmp = self._backend._ffi.new("BIGNUM **")
- self._backend._lib.RSA_get0_key(self._rsa_cdata, n, e, d)
- self._backend.openssl_assert(n[0] != self._backend._ffi.NULL)
- self._backend.openssl_assert(e[0] != self._backend._ffi.NULL)
- self._backend.openssl_assert(d[0] != self._backend._ffi.NULL)
- self._backend._lib.RSA_get0_factors(self._rsa_cdata, p, q)
- self._backend.openssl_assert(p[0] != self._backend._ffi.NULL)
- self._backend.openssl_assert(q[0] != self._backend._ffi.NULL)
- self._backend._lib.RSA_get0_crt_params(
- self._rsa_cdata, dmp1, dmq1, iqmp
- )
- self._backend.openssl_assert(dmp1[0] != self._backend._ffi.NULL)
- self._backend.openssl_assert(dmq1[0] != self._backend._ffi.NULL)
- self._backend.openssl_assert(iqmp[0] != self._backend._ffi.NULL)
- return RSAPrivateNumbers(
- p=self._backend._bn_to_int(p[0]),
- q=self._backend._bn_to_int(q[0]),
- d=self._backend._bn_to_int(d[0]),
- dmp1=self._backend._bn_to_int(dmp1[0]),
- dmq1=self._backend._bn_to_int(dmq1[0]),
- iqmp=self._backend._bn_to_int(iqmp[0]),
- public_numbers=RSAPublicNumbers(
- e=self._backend._bn_to_int(e[0]),
- n=self._backend._bn_to_int(n[0]),
- ),
- )
-
- def private_bytes(
- self,
- encoding: serialization.Encoding,
- format: serialization.PrivateFormat,
- encryption_algorithm: serialization.KeySerializationEncryption,
- ) -> bytes:
- return self._backend._private_key_bytes(
- encoding,
- format,
- encryption_algorithm,
- self,
- self._evp_pkey,
- self._rsa_cdata,
- )
-
- def sign(
- self,
- data: bytes,
- padding: AsymmetricPadding,
- algorithm: typing.Union[asym_utils.Prehashed, hashes.HashAlgorithm],
- ) -> bytes:
- self._enable_blinding()
- data, algorithm = _calculate_digest_and_algorithm(data, algorithm)
- return _rsa_sig_sign(self._backend, padding, algorithm, self, data)
-
-
-class _RSAPublicKey(RSAPublicKey):
- _evp_pkey: object
- _rsa_cdata: object
- _key_size: int
-
- def __init__(self, backend: Backend, rsa_cdata, evp_pkey):
- self._backend = backend
- self._rsa_cdata = rsa_cdata
- self._evp_pkey = evp_pkey
-
- n = self._backend._ffi.new("BIGNUM **")
- self._backend._lib.RSA_get0_key(
- self._rsa_cdata,
- n,
- self._backend._ffi.NULL,
- self._backend._ffi.NULL,
- )
- self._backend.openssl_assert(n[0] != self._backend._ffi.NULL)
- self._key_size = self._backend._lib.BN_num_bits(n[0])
-
- @property
- def key_size(self) -> int:
- return self._key_size
-
- def __eq__(self, other: object) -> bool:
- if not isinstance(other, _RSAPublicKey):
- return NotImplemented
-
- return (
- self._backend._lib.EVP_PKEY_cmp(self._evp_pkey, other._evp_pkey)
- == 1
- )
-
- def encrypt(self, plaintext: bytes, padding: AsymmetricPadding) -> bytes:
- return _enc_dec_rsa(self._backend, self, plaintext, padding)
-
- def public_numbers(self) -> RSAPublicNumbers:
- n = self._backend._ffi.new("BIGNUM **")
- e = self._backend._ffi.new("BIGNUM **")
- self._backend._lib.RSA_get0_key(
- self._rsa_cdata, n, e, self._backend._ffi.NULL
- )
- self._backend.openssl_assert(n[0] != self._backend._ffi.NULL)
- self._backend.openssl_assert(e[0] != self._backend._ffi.NULL)
- return RSAPublicNumbers(
- e=self._backend._bn_to_int(e[0]),
- n=self._backend._bn_to_int(n[0]),
- )
-
- def public_bytes(
- self,
- encoding: serialization.Encoding,
- format: serialization.PublicFormat,
- ) -> bytes:
- return self._backend._public_key_bytes(
- encoding, format, self, self._evp_pkey, self._rsa_cdata
- )
-
- def verify(
- self,
- signature: bytes,
- data: bytes,
- padding: AsymmetricPadding,
- algorithm: typing.Union[asym_utils.Prehashed, hashes.HashAlgorithm],
- ) -> None:
- data, algorithm = _calculate_digest_and_algorithm(data, algorithm)
- _rsa_sig_verify(
- self._backend, padding, algorithm, self, signature, data
- )
-
- def recover_data_from_signature(
- self,
- signature: bytes,
- padding: AsymmetricPadding,
- algorithm: typing.Optional[hashes.HashAlgorithm],
- ) -> bytes:
- if isinstance(algorithm, asym_utils.Prehashed):
- raise TypeError(
- "Prehashed is only supported in the sign and verify methods. "
- "It cannot be used with recover_data_from_signature."
- )
- return _rsa_sig_recover(
- self._backend, padding, algorithm, self, signature
- )
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx2txt/docx2txt.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx2txt/docx2txt.py
deleted file mode 100644
index 0dac072d6650a9ffce234332795537c0fc283318..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx2txt/docx2txt.py
+++ /dev/null
@@ -1,113 +0,0 @@
-#! /usr/bin/env python
-
-import argparse
-import re
-import xml.etree.ElementTree as ET
-import zipfile
-import os
-import sys
-
-
-nsmap = {'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main'}
-
-
-def process_args():
- parser = argparse.ArgumentParser(description='A pure python-based utility '
- 'to extract text and images '
- 'from docx files.')
- parser.add_argument("docx", help="path of the docx file")
- parser.add_argument('-i', '--img_dir', help='path of directory '
- 'to extract images')
-
- args = parser.parse_args()
-
- if not os.path.exists(args.docx):
- print('File {} does not exist.'.format(args.docx))
- sys.exit(1)
-
- if args.img_dir is not None:
- if not os.path.exists(args.img_dir):
- try:
- os.makedirs(args.img_dir)
- except OSError:
- print("Unable to create img_dir {}".format(args.img_dir))
- sys.exit(1)
- return args
-
-
-def qn(tag):
- """
- Stands for 'qualified name', a utility function to turn a namespace
- prefixed tag name into a Clark-notation qualified tag name for lxml. For
- example, ``qn('p:cSld')`` returns ``'{http://schemas.../main}cSld'``.
- Source: https://github.com/python-openxml/python-docx/
- """
- prefix, tagroot = tag.split(':')
- uri = nsmap[prefix]
- return '{{{}}}{}'.format(uri, tagroot)
-
-
-def xml2text(xml):
- """
- A string representing the textual content of this run, with content
- child elements like ```` translated to their Python
- equivalent.
- Adapted from: https://github.com/python-openxml/python-docx/
- """
- text = u''
- root = ET.fromstring(xml)
- for child in root.iter():
- if child.tag == qn('w:t'):
- t_text = child.text
- text += t_text if t_text is not None else ''
- elif child.tag == qn('w:tab'):
- text += '\t'
- elif child.tag in (qn('w:br'), qn('w:cr')):
- text += '\n'
- elif child.tag == qn("w:p"):
- text += '\n\n'
- return text
-
-
-def process(docx, img_dir=None):
- text = u''
-
- # unzip the docx in memory
- zipf = zipfile.ZipFile(docx)
- filelist = zipf.namelist()
-
- # get header text
- # there can be 3 header files in the zip
- header_xmls = 'word/header[0-9]*.xml'
- for fname in filelist:
- if re.match(header_xmls, fname):
- text += xml2text(zipf.read(fname))
-
- # get main text
- doc_xml = 'word/document.xml'
- text += xml2text(zipf.read(doc_xml))
-
- # get footer text
- # there can be 3 footer files in the zip
- footer_xmls = 'word/footer[0-9]*.xml'
- for fname in filelist:
- if re.match(footer_xmls, fname):
- text += xml2text(zipf.read(fname))
-
- if img_dir is not None:
- # extract images
- for fname in filelist:
- _, extension = os.path.splitext(fname)
- if extension in [".jpg", ".jpeg", ".png", ".bmp"]:
- dst_fname = os.path.join(img_dir, os.path.basename(fname))
- with open(dst_fname, "wb") as dst_f:
- dst_f.write(zipf.read(fname))
-
- zipf.close()
- return text.strip()
-
-
-if __name__ == '__main__':
- args = process_args()
- text = process(args.docx, args.img_dir)
- sys.stdout.write(text.encode('utf-8'))
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/local.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/local.py
deleted file mode 100644
index 90045d891d6b44249222be7614d63d879d81fec0..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/implementations/local.py
+++ /dev/null
@@ -1,424 +0,0 @@
-import datetime
-import io
-import logging
-import os
-import os.path as osp
-import posixpath
-import re
-import shutil
-import stat
-import tempfile
-
-from fsspec import AbstractFileSystem
-from fsspec.compression import compr
-from fsspec.core import get_compression
-from fsspec.utils import isfilelike, stringify_path
-
-logger = logging.getLogger("fsspec.local")
-
-
-class LocalFileSystem(AbstractFileSystem):
- """Interface to files on local storage
-
- Parameters
- ----------
- auto_mkdir: bool
- Whether, when opening a file, the directory containing it should
- be created (if it doesn't already exist). This is assumed by pyarrow
- code.
- """
-
- root_marker = "/"
- protocol = "file"
- local_file = True
-
- def __init__(self, auto_mkdir=False, **kwargs):
- super().__init__(**kwargs)
- self.auto_mkdir = auto_mkdir
-
- @property
- def fsid(self):
- return "local"
-
- def mkdir(self, path, create_parents=True, **kwargs):
- path = self._strip_protocol(path)
- if self.exists(path):
- raise FileExistsError(path)
- if create_parents:
- self.makedirs(path, exist_ok=True)
- else:
- os.mkdir(path, **kwargs)
-
- def makedirs(self, path, exist_ok=False):
- path = self._strip_protocol(path)
- os.makedirs(path, exist_ok=exist_ok)
-
- def rmdir(self, path):
- path = self._strip_protocol(path)
- os.rmdir(path)
-
- def ls(self, path, detail=False, **kwargs):
- path = self._strip_protocol(path)
- if detail:
- with os.scandir(path) as it:
- return [self.info(f) for f in it]
- else:
- return [posixpath.join(path, f) for f in os.listdir(path)]
-
- def glob(self, path, **kwargs):
- path = self._strip_protocol(path)
- return super().glob(path, **kwargs)
-
- def info(self, path, **kwargs):
- if isinstance(path, os.DirEntry):
- # scandir DirEntry
- out = path.stat(follow_symlinks=False)
- link = path.is_symlink()
- if path.is_dir(follow_symlinks=False):
- t = "directory"
- elif path.is_file(follow_symlinks=False):
- t = "file"
- else:
- t = "other"
- path = self._strip_protocol(path.path)
- else:
- # str or path-like
- path = self._strip_protocol(path)
- out = os.stat(path, follow_symlinks=False)
- link = stat.S_ISLNK(out.st_mode)
- if link:
- out = os.stat(path, follow_symlinks=True)
- if stat.S_ISDIR(out.st_mode):
- t = "directory"
- elif stat.S_ISREG(out.st_mode):
- t = "file"
- else:
- t = "other"
- result = {
- "name": path,
- "size": out.st_size,
- "type": t,
- "created": out.st_ctime,
- "islink": link,
- }
- for field in ["mode", "uid", "gid", "mtime", "ino", "nlink"]:
- result[field] = getattr(out, "st_" + field)
- if result["islink"]:
- result["destination"] = os.readlink(path)
- try:
- out2 = os.stat(path, follow_symlinks=True)
- result["size"] = out2.st_size
- except OSError:
- result["size"] = 0
- return result
-
- def lexists(self, path, **kwargs):
- return osp.lexists(path)
-
- def cp_file(self, path1, path2, **kwargs):
- path1 = self._strip_protocol(path1).rstrip("/")
- path2 = self._strip_protocol(path2).rstrip("/")
- if self.auto_mkdir:
- self.makedirs(self._parent(path2), exist_ok=True)
- if self.isfile(path1):
- shutil.copyfile(path1, path2)
- elif self.isdir(path1):
- self.mkdirs(path2, exist_ok=True)
- else:
- raise FileNotFoundError(path1)
-
- def get_file(self, path1, path2, callback=None, **kwargs):
- if isfilelike(path2):
- with open(path1, "rb") as f:
- shutil.copyfileobj(f, path2)
- else:
- return self.cp_file(path1, path2, **kwargs)
-
- def put_file(self, path1, path2, callback=None, **kwargs):
- return self.cp_file(path1, path2, **kwargs)
-
- def mv_file(self, path1, path2, **kwargs):
- path1 = self._strip_protocol(path1).rstrip("/")
- path2 = self._strip_protocol(path2).rstrip("/")
- shutil.move(path1, path2)
-
- def link(self, src, dst, **kwargs):
- src = self._strip_protocol(src)
- dst = self._strip_protocol(dst)
- os.link(src, dst, **kwargs)
-
- def symlink(self, src, dst, **kwargs):
- src = self._strip_protocol(src)
- dst = self._strip_protocol(dst)
- os.symlink(src, dst, **kwargs)
-
- def islink(self, path) -> bool:
- return os.path.islink(self._strip_protocol(path))
-
- def rm_file(self, path):
- os.remove(self._strip_protocol(path))
-
- def rm(self, path, recursive=False, maxdepth=None):
- if not isinstance(path, list):
- path = [path]
-
- for p in path:
- p = self._strip_protocol(p).rstrip("/")
- if self.isdir(p):
- if not recursive:
- raise ValueError("Cannot delete directory, set recursive=True")
- if osp.abspath(p) == os.getcwd():
- raise ValueError("Cannot delete current working directory")
- shutil.rmtree(p)
- else:
- os.remove(p)
-
- def unstrip_protocol(self, name):
- name = self._strip_protocol(name) # normalise for local/win/...
- return f"file://{name}"
-
- def _open(self, path, mode="rb", block_size=None, **kwargs):
- path = self._strip_protocol(path)
- if self.auto_mkdir and "w" in mode:
- self.makedirs(self._parent(path), exist_ok=True)
- return LocalFileOpener(path, mode, fs=self, **kwargs)
-
- def touch(self, path, truncate=True, **kwargs):
- path = self._strip_protocol(path)
- if self.auto_mkdir:
- self.makedirs(self._parent(path), exist_ok=True)
- if self.exists(path):
- os.utime(path, None)
- else:
- open(path, "a").close()
- if truncate:
- os.truncate(path, 0)
-
- def created(self, path):
- info = self.info(path=path)
- return datetime.datetime.utcfromtimestamp(info["created"])
-
- def modified(self, path):
- info = self.info(path=path)
- return datetime.datetime.utcfromtimestamp(info["mtime"])
-
- @classmethod
- def _parent(cls, path):
- path = cls._strip_protocol(path).rstrip("/")
- if "/" in path:
- return path.rsplit("/", 1)[0]
- else:
- return cls.root_marker
-
- @classmethod
- def _strip_protocol(cls, path):
- path = stringify_path(path)
- if path.startswith("file://"):
- path = path[7:]
- elif path.startswith("file:"):
- path = path[5:]
- return make_path_posix(path).rstrip("/") or cls.root_marker
-
- def _isfilestore(self):
- # Inheriting from DaskFileSystem makes this False (S3, etc. were)
- # the original motivation. But we are a posix-like file system.
- # See https://github.com/dask/dask/issues/5526
- return True
-
- def chmod(self, path, mode):
- path = stringify_path(path)
- return os.chmod(path, mode)
-
-
-def make_path_posix(path, sep=os.sep):
- """Make path generic"""
- if isinstance(path, (list, set, tuple)):
- return type(path)(make_path_posix(p) for p in path)
- if "~" in path:
- path = osp.expanduser(path)
- if sep == "/":
- # most common fast case for posix
- if path.startswith("/"):
- return path
- if path.startswith("./"):
- path = path[2:]
- return os.getcwd() + "/" + path
- if (
- (sep not in path and "/" not in path)
- or (sep == "/" and not path.startswith("/"))
- or (sep == "\\" and ":" not in path and not path.startswith("\\\\"))
- ):
- # relative path like "path" or "rel\\path" (win) or rel/path"
- if os.sep == "\\":
- # abspath made some more '\\' separators
- return make_path_posix(osp.abspath(path))
- else:
- return os.getcwd() + "/" + path
- if path.startswith("file://"):
- path = path[7:]
- if re.match("/[A-Za-z]:", path):
- # for windows file URI like "file:///C:/folder/file"
- # or "file:///C:\\dir\\file"
- path = path[1:].replace("\\", "/").replace("//", "/")
- if path.startswith("\\\\"):
- # special case for windows UNC/DFS-style paths, do nothing,
- # just flip the slashes around (case below does not work!)
- return path.replace("\\", "/")
- if re.match("[A-Za-z]:", path):
- # windows full path like "C:\\local\\path"
- return path.lstrip("\\").replace("\\", "/").replace("//", "/")
- if path.startswith("\\"):
- # windows network path like "\\server\\path"
- return "/" + path.lstrip("\\").replace("\\", "/").replace("//", "/")
- return path
-
-
-def trailing_sep(path):
- """Return True if the path ends with a path separator.
-
- A forward slash is always considered a path separator, even on Operating
- Systems that normally use a backslash.
- """
- # TODO: if all incoming paths were posix-compliant then separator would
- # always be a forward slash, simplifying this function.
- # See https://github.com/fsspec/filesystem_spec/pull/1250
- return path.endswith(os.sep) or (os.altsep is not None and path.endswith(os.altsep))
-
-
-def trailing_sep_maybe_asterisk(path):
- """Return True if the path ends with a path separator and optionally an
- asterisk.
-
- A forward slash is always considered a path separator, even on Operating
- Systems that normally use a backslash.
- """
- # TODO: if all incoming paths were posix-compliant then separator would
- # always be a forward slash, simplifying this function.
- # See https://github.com/fsspec/filesystem_spec/pull/1250
- return path.endswith((os.sep, os.sep + "*")) or (
- os.altsep is not None and path.endswith((os.altsep, os.altsep + "*"))
- )
-
-
-class LocalFileOpener(io.IOBase):
- def __init__(
- self, path, mode, autocommit=True, fs=None, compression=None, **kwargs
- ):
- logger.debug("open file: %s", path)
- self.path = path
- self.mode = mode
- self.fs = fs
- self.f = None
- self.autocommit = autocommit
- self.compression = get_compression(path, compression)
- self.blocksize = io.DEFAULT_BUFFER_SIZE
- self._open()
-
- def _open(self):
- if self.f is None or self.f.closed:
- if self.autocommit or "w" not in self.mode:
- self.f = open(self.path, mode=self.mode)
- if self.compression:
- compress = compr[self.compression]
- self.f = compress(self.f, mode=self.mode)
- else:
- # TODO: check if path is writable?
- i, name = tempfile.mkstemp()
- os.close(i) # we want normal open and normal buffered file
- self.temp = name
- self.f = open(name, mode=self.mode)
- if "w" not in self.mode:
- self.size = self.f.seek(0, 2)
- self.f.seek(0)
- self.f.size = self.size
-
- def _fetch_range(self, start, end):
- # probably only used by cached FS
- if "r" not in self.mode:
- raise ValueError
- self._open()
- self.f.seek(start)
- return self.f.read(end - start)
-
- def __setstate__(self, state):
- self.f = None
- loc = state.pop("loc", None)
- self.__dict__.update(state)
- if "r" in state["mode"]:
- self.f = None
- self._open()
- self.f.seek(loc)
-
- def __getstate__(self):
- d = self.__dict__.copy()
- d.pop("f")
- if "r" in self.mode:
- d["loc"] = self.f.tell()
- else:
- if not self.f.closed:
- raise ValueError("Cannot serialise open write-mode local file")
- return d
-
- def commit(self):
- if self.autocommit:
- raise RuntimeError("Can only commit if not already set to autocommit")
- shutil.move(self.temp, self.path)
-
- def discard(self):
- if self.autocommit:
- raise RuntimeError("Cannot discard if set to autocommit")
- os.remove(self.temp)
-
- def readable(self) -> bool:
- return True
-
- def writable(self) -> bool:
- return "r" not in self.mode
-
- def read(self, *args, **kwargs):
- return self.f.read(*args, **kwargs)
-
- def write(self, *args, **kwargs):
- return self.f.write(*args, **kwargs)
-
- def tell(self, *args, **kwargs):
- return self.f.tell(*args, **kwargs)
-
- def seek(self, *args, **kwargs):
- return self.f.seek(*args, **kwargs)
-
- def seekable(self, *args, **kwargs):
- return self.f.seekable(*args, **kwargs)
-
- def readline(self, *args, **kwargs):
- return self.f.readline(*args, **kwargs)
-
- def readlines(self, *args, **kwargs):
- return self.f.readlines(*args, **kwargs)
-
- def close(self):
- return self.f.close()
-
- @property
- def closed(self):
- return self.f.closed
-
- def fileno(self):
- return self.raw.fileno()
-
- def flush(self) -> None:
- self.f.flush()
-
- def __iter__(self):
- return self.f.__iter__()
-
- def __getattr__(self, item):
- return getattr(self.f, item)
-
- def __enter__(self):
- self._incontext = True
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- self._incontext = False
- self.f.__exit__(exc_type, exc_value, traceback)
diff --git a/spaces/cihyFjudo/fairness-paper-search/Boardmaker Version 5 Free Download.md b/spaces/cihyFjudo/fairness-paper-search/Boardmaker Version 5 Free Download.md
deleted file mode 100644
index b1d6c25136b5fd35c264b156cb09cb55bc8f7626..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Boardmaker Version 5 Free Download.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
Our software library provides a free download of Boardmaker 7.2.3. The following versions: 6.1, 6.0 and 5.3 are the most frequently downloaded ones by the program users. Our antivirus analysis shows that this download is clean.
The software lies within Education Tools, more precisely Teaching Tools. Commonly, this program's installer has the following filenames: BM_SDP Demo.exe, BM_SDP.exe, boardmaker 2.0.exe, Boardmaker Studio Player.exe and Boardmaker.exe etc. You can run Boardmaker on Windows XP/Vista/7/8/10/11 32-bit. The file size of the latest setup package available for download is 697.9 MB. This PC software can handle the following extensions: ".bm2" or ".pc2". The actual developer of the program is DynaVox Mayer-Johnson.
-
Users on version 7.0.4 or older will need to uninstall Boardmaker 7 Editor, download 7.2.0 from here, and then install. Chromebook users will update from Google Play Store, with no uninstallation necessary for Chromebooks.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/FREE Satta Full Movie Hd Download 720p.md b/spaces/cihyFjudo/fairness-paper-search/FREE Satta Full Movie Hd Download 720p.md
deleted file mode 100644
index c19a2472d9efe0db4fd26c2f0ce51bdcc8963427..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/FREE Satta Full Movie Hd Download 720p.md
+++ /dev/null
@@ -1,19 +0,0 @@
-
-
Pathan Movie Download: Pathan full movie is a Bollywood drama film directed by Siddharth Anand. The film stars Shah Rukh Khan, Deepika Padukone and John Abraham in lead roles. This film is releasing on the year 25 January 2023. The producer of the Pathan movie is Aditya Chopra. Salman Khan is also seen doing a cameo in this film. A tremendous action scene is shown in the film on Burj Khalifa.
-
Today We will discuss Pathan Movie Download Kaise Kare, Pathan Movie Kaise Download Kare, How To Download Pathan Full Movie in Hindi, Pathan movie download Moviesflix, Pathan Movie Download Link Telegram, Pathan Movie Kaise Download Karen, download Pathan movie APK, How To Download Pathan full Movie in Hindi, Pathan Movie Download Kaise Karen and Related Topics, So Make Sure Read the Article Till End to Get the Download Link of the Pathaan Movie.
Union minister and BJP MP Giriraj Singh took on Shah Rukh Khan for making a Pathan full film about Muslims and the existence of Prophet Muhammad. Giriraj Singh claims that many such directors and actors of Bollywood make movies to make fun of Hindus.
-
Pathan Full Movie is now released and available for download. If you want to download this movie in high quality like 480p, 720p, or 1080p, then you will get the list of all the sites here. Here I will give you information that how and from where to download Varisu Hindi Full Movie.
-
Pathan Movie Download Tamilrockers: Tamilrockers is a website for downloading movies in India. Unfortunately, this website is completely illegal, and film directors and other institutions now allow movies to be downloaded from this website. However, if you want, you can visit the official Filmyzilla website and easily download Varisu movies from this website.
-
You can watch Pathan movies of any quality and format. You canalso watch movies online from this website. However, our team does not recommenddownloading movies from suchillegalwebsites as it is a cybercrime.
-
You will get to watch Pathaan movies in any quality and format. You can also watch movies online through this website. But our team does not push you to download because downloading movies from such illegal websites is a cybercrime.
-
-
Pathan Movie HDHub4u is an upcoming Bollywood movie that is an Action, Romance, and spy thriller movie. The interesting thing about this movie is that you can watch this movie safely and comfortably with your entire family. The movie was recently released on 25 January 2023 but has not yet been released on a free platform, so here you will get free links to download this Shah Rukh Khan movie in Hindi.
-
In this article, you can also get the option of Pathan Hindi movie download, which provides you with entertainment services. Keep reading further for more details. Pathan Hindi dubbed full movie is leaked on Filmyhit, Filmywap, Filmyzilla, Mp4moviez & 9xmovies for download in HD. Pathan full Movie Download is available in Hindi on Filmyhit, Moviesflix, Filmywap and Mp4moviez in Hindi dubbed. Pathan Movie Download Hindi Filmyzilla, Pathan Full Movie Download, Pathan Movie Download (2022) 480p 720p 1080p, Pathan Hindi Movie Download,
-
This Pathan Full Movie Shahrukh Khan downloadmovie demonstrates what needs to be done in the film industry right away to improve viewership, even if you are not used to watching the extended film Pathan Full Movie Download mp4moviez
-
Pathan Movie Download 300Mb, Filmyzilla is a popular movie downloading website and unfortunately, this website is completely illegal. Although these torrent sites allow you to download movies in 300MB. But if you want, you can easily download this movie from this website by visiting the official website of Filmyzilla and Pathan Movie Download in Hindi Filmywap. You will get to watch movies of any quality and format. You can also watch movies online through this website.
-
Shah Rukh Khan New Movie Free Download Hindi Pathan Full Movie in 1080p on Filmyzilla. Filmyzilla is a pirated website which illegally leaks movies by violating the copyright infringement rule. Which allows users to download HD, Hindi, Telugu and Tamil movies online legally and for free. The Filmyzilla website allows the users to watch and download a number of movies for free on the Filmyzilla website.
-
You have to search by typing it in Google like Pathan Movie Download. After this you will see the option of some torrent sites from where you can download it. But you should always keep in mind that torrent sites are not secure at all, so it is not recommended to use them. The choice of theater or OTT platform to watch Pathan movies is correct and legal.
-
If you want to download Pathan Movie from Filmyzilla then you have to visit this website, you will get this movie in 480p, 720p and 1080p quality in 300MB, 700MB and 1.26GB file size and you can download it easily. . this website. You can download all the movies on this website But we want to tell you again that it is not safe to use pirated websites. So change your mind and stay away from these sites. Pathan movie premiere soon on the OTT platform so you can easily watch it online.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Questione donore full movie in italian free download hd 720p dont miss this amazing opportunity.md b/spaces/cihyFjudo/fairness-paper-search/Questione donore full movie in italian free download hd 720p dont miss this amazing opportunity.md
deleted file mode 100644
index 434423bd5be44e88dd509e33cd5c6a3d11ff2329..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Questione donore full movie in italian free download hd 720p dont miss this amazing opportunity.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
Questione d'onore full movie in italian free download hd 720p
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Timepass 2 Marathi Movie Full Download Hd Enjoy the Hilarious Adventures of Dagadu and Prajakta.md b/spaces/cihyFjudo/fairness-paper-search/Timepass 2 Marathi Movie Full Download Hd Enjoy the Hilarious Adventures of Dagadu and Prajakta.md
deleted file mode 100644
index b574db48f6b6e1b31017524baf9c193df1fa606b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Timepass 2 Marathi Movie Full Download Hd Enjoy the Hilarious Adventures of Dagadu and Prajakta.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
- )
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/gemdec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/gemdec.c
deleted file mode 100644
index c8fd8dcdcd2c31305f4bb658d034a0f0eb8e0a97..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/gemdec.c
+++ /dev/null
@@ -1,361 +0,0 @@
-/*
- * GEM Raster image decoder
- * Copyright (c) 2021 Peter Ross (pross@xvid.org)
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * GEM Raster image decoder
- */
-
-#include "avcodec.h"
-#include "bytestream.h"
-#include "codec_internal.h"
-#include "decode.h"
-
-static const uint32_t gem_color_palette[16]={
- 0xFFFFFFFF, 0xFFFF0000, 0xFF00FF00, 0xFFFFFF00,
- 0xFF0000FF, 0xFFFF00FF, 0xFF00FFFF, 0xFFAEAEAE,
- 0xFF555555, 0xFFAE0000, 0xFF00AE00, 0xFFAEAE00,
- 0xFF0000AE, 0xFFAE00AE, 0xFF00AEAE, 0xFF000000,
-};
-
-static const uint8_t gem_gray[256]={
- 0xFF, 0x7F, 0xBF, 0x3F, 0xDF, 0x5F, 0x9F, 0x1F, 0xEF, 0x6F, 0xAF, 0x2F, 0xCF, 0x4F, 0x8F, 0x0F,
- 0xF7, 0x77, 0xB7, 0x37, 0xD7, 0x57, 0x97, 0x17, 0xE7, 0x67, 0xA7, 0x27, 0xC7, 0x47, 0x87, 0x07,
- 0xFB, 0x7B, 0xBB, 0x3B, 0xDB, 0x5B, 0x9B, 0x1B, 0xEB, 0x6B, 0xAB, 0x2B, 0xCB, 0x4B, 0x8B, 0x0B,
- 0xF3, 0x73, 0xB3, 0x33, 0xD3, 0x53, 0x93, 0x13, 0xE3, 0x63, 0xA3, 0x23, 0xC3, 0x43, 0x83, 0x03,
- 0xFD, 0x7D, 0xBD, 0x3D, 0xDD, 0x5D, 0x9D, 0x1D, 0xED, 0x6D, 0xAD, 0x2D, 0xCD, 0x4D, 0x8D, 0x0D,
- 0xF5, 0x75, 0xB5, 0x35, 0xD5, 0x55, 0x95, 0x15, 0xE5, 0x65, 0xA5, 0x25, 0xC5, 0x45, 0x85, 0x05,
- 0xF9, 0x79, 0xB9, 0x39, 0xD9, 0x59, 0x99, 0x19, 0xE9, 0x69, 0xA9, 0x29, 0xC9, 0x49, 0x89, 0x09,
- 0xF1, 0x71, 0xB1, 0x31, 0xD1, 0x51, 0x91, 0x11, 0xE1, 0x61, 0xA1, 0x21, 0xC1, 0x41, 0x81, 0x01,
- 0xFE, 0x7E, 0xBE, 0x3E, 0xDE, 0x5E, 0x9E, 0x1E, 0xEE, 0x6E, 0xAE, 0x2E, 0xCE, 0x4E, 0x8E, 0x0E,
- 0xF6, 0x76, 0xB6, 0x36, 0xD6, 0x56, 0x96, 0x16, 0xE6, 0x66, 0xA6, 0x26, 0xC6, 0x46, 0x86, 0x06,
- 0xFA, 0x7A, 0xBA, 0x3A, 0xDA, 0x5A, 0x9A, 0x1A, 0xEA, 0x6A, 0xAA, 0x2A, 0xCA, 0x4A, 0x8A, 0x0A,
- 0xF2, 0x72, 0xB2, 0x32, 0xD2, 0x52, 0x92, 0x12, 0xE2, 0x62, 0xA2, 0x22, 0xC2, 0x42, 0x82, 0x02,
- 0xFC, 0x7C, 0xBC, 0x3C, 0xDC, 0x5C, 0x9C, 0x1C, 0xEC, 0x6C, 0xAC, 0x2C, 0xCC, 0x4C, 0x8C, 0x0C,
- 0xF4, 0x74, 0xB4, 0x34, 0xD4, 0x54, 0x94, 0x14, 0xE4, 0x64, 0xA4, 0x24, 0xC4, 0x44, 0x84, 0x04,
- 0xF8, 0x78, 0xB8, 0x38, 0xD8, 0x58, 0x98, 0x18, 0xE8, 0x68, 0xA8, 0x28, 0xC8, 0x48, 0x88, 0x08,
- 0xF0, 0x70, 0xB0, 0x30, 0xD0, 0x50, 0x90, 0x10, 0xE0, 0x60, 0xA0, 0x20, 0xC0, 0x40, 0x80, 0x00,
-};
-
-typedef struct {
- int y, pl, x, vdup;
-} State;
-
-static void put_lines_bits(AVCodecContext *avctx, int planes, int row_width, int pixel_size, State * state, uint8_t * row, AVFrame *p)
-{
- int pl_byte = state->pl / 8;
- int pl_bit = state->pl & 7;
- for (int y = 0; y < state->vdup && (state->y + y) < avctx->height; y++)
- for (int x = 0; x < row_width; x++)
- for (int i = 7; i >= 0 && x * 8 + 7 - i < avctx->width; i--)
- p->data[0][ (state->y + y) * p->linesize[0] + (x * 8 + 7 - i) * pixel_size + pl_byte] |= !!(row[x] & (1 << i)) << pl_bit;
-
- state->pl++;
- if (state->pl >= planes) {
- state->pl = 0;
- state->y += state->vdup;
- state->vdup = 1;
- }
-}
-
-static void put_lines_bytes(AVCodecContext *avctx, int planes, int row_width, int pixel_size, State * state, uint8_t * row, AVFrame *p)
-{
- for (int y = 0; y < state->vdup && (state->y + y) < avctx->height; y++)
- memcpy(p->data[0] + (state->y + y) * p->linesize[0], row, avctx->width * pixel_size);
-
- state->y += state->vdup;
- state->vdup = 1;
-}
-
-static int gem_decode_frame(AVCodecContext *avctx, AVFrame *p,
- int *got_frame, AVPacket *avpkt)
-{
- const uint8_t *buf = avpkt->data;
- int buf_size = avpkt->size;
- const uint8_t *buf_end = buf + buf_size;
- int header_size, planes, pattern_size, tag = 0, count_scalar = 1, ret;
- unsigned int x, count, v;
- GetByteContext gb;
- uint32_t *palette;
- const uint8_t * b;
- uint8_t * row;
- int row_width, pixel_size;
- State state = {.y = 0, .pl = 0, .x = 0, .vdup = 1};
- void (*put_lines)(AVCodecContext *avctx, int planes, int row_width, int pixel_size, State * state, uint8_t * row, AVFrame *p);
- int width, height;
-
- if (buf_size <= 16)
- return AVERROR_INVALIDDATA;
-
- bytestream2_init(&gb, buf + 2, buf_size - 2);
- header_size = bytestream2_get_be16(&gb);
- if (header_size < 8 || buf_size <= header_size * 2)
- return AVERROR_INVALIDDATA;
-
- planes = bytestream2_get_be16(&gb);
- pattern_size = bytestream2_get_be16(&gb);
- avctx->sample_aspect_ratio.num = bytestream2_get_be16(&gb);
- avctx->sample_aspect_ratio.den = bytestream2_get_be16(&gb);
- width = bytestream2_get_be16(&gb);
- height = bytestream2_get_be16(&gb);
- ret = ff_set_dimensions(avctx, width, height);
- if (ret < 0)
- return ret;
-
- row_width = (avctx->width + 7) / 8;
- put_lines = put_lines_bits;
-
- if (header_size == 9) {
- count_scalar = bytestream2_get_be16(&gb);
- if (count_scalar != 3) {
- avpriv_request_sample(avctx, "count_scalar=%d", count_scalar);
- return AVERROR_PATCHWELCOME;
- }
- planes = 24;
- avctx->pix_fmt = AV_PIX_FMT_BGR24;
- pixel_size = 3;
- } else if (planes == 15) {
-#if HAVE_BIGENDIAN
- avctx->pix_fmt = AV_PIX_FMT_BGR555BE;
-#else
- avctx->pix_fmt = AV_PIX_FMT_BGR555LE;
-#endif
- pixel_size = 2;
- } else if (planes == 16) {
- avctx->pix_fmt = AV_PIX_FMT_RGB565BE;
- pixel_size = 2;
- } else if (planes == 24) {
- avctx->pix_fmt = AV_PIX_FMT_RGB24;
- pixel_size = 3;
- } else if (planes == 32) {
- avctx->pix_fmt = AV_PIX_FMT_0RGB;
- pixel_size = 4;
- } else {
- avctx->pix_fmt = AV_PIX_FMT_PAL8;
- pixel_size = 1;
- }
-
- if (header_size >= 11)
- tag = bytestream2_peek_be32(&gb);
-
- if (tag == AV_RB32("STTT")) {
- if (planes != 4) {
- avpriv_request_sample(avctx, "STTT planes=%d", planes);
- return AVERROR_PATCHWELCOME;
- }
- } else if (tag == AV_RB32("TIMG")) {
- if (planes != 15) {
- avpriv_request_sample(avctx, "TIMG planes=%d", planes);
- return AVERROR_PATCHWELCOME;
- }
- } else if (tag == AV_RB32("XIMG")) {
- if (planes != 1 && planes != 2 && planes != 4 && planes != 8 && planes != 16 && planes != 24 && planes != 32) {
- avpriv_request_sample(avctx, "XIMG planes=%d", planes);
- return AVERROR_PATCHWELCOME;
- }
- } else if (planes != 1 && planes != 2 && planes != 3 && planes != 4 && planes != 8 && planes != 16 && planes != 24) {
- avpriv_request_sample(avctx, "planes=%d", planes);
- return AVERROR_PATCHWELCOME;
- }
-
- if ((ret = ff_get_buffer(avctx, p, 0)) < 0)
- return ret;
-
- p->pict_type = AV_PICTURE_TYPE_I;
- p->key_frame = 1;
- p->palette_has_changed = 1;
- palette = (uint32_t *)p->data[1];
-
- if (tag == AV_RB32("STTT")) {
- bytestream2_skip(&gb, 6);
- if (planes == 4) {
- for (int i = 0; i < (1 << planes); i++) {
- int v = bytestream2_get_be16(&gb);
- int r = ((v >> 8) & 0x7) << 5;
- int g = ((v >> 4) & 0x7) << 5;
- int b = ((v ) & 0x7) << 5;
- palette[i] = 0xFF000000 | r << 16 | g << 8 | b;
- }
- } else {
- av_assert0(0);
- }
- } else if (tag == AV_RB32("TIMG")) {
- bytestream2_skip(&gb, 4);
- if (planes != 15) {
- av_assert0(0);
- }
- } else if (tag == AV_RB32("XIMG")) {
- bytestream2_skip(&gb, 6);
- if (planes == 1 || planes == 2 || planes == 4 || planes == 8) {
- for (int i = 0; i < (1 << planes); i++) {
- int r = (bytestream2_get_be16(&gb) * 51 + 100) / 200;
- int g = (bytestream2_get_be16(&gb) * 51 + 100) / 200;
- int b = (bytestream2_get_be16(&gb) * 51 + 100) / 200;
- palette[i] = 0xFF000000 | r << 16 | g << 8 | b;
- }
- } else if (planes == 16) {
- planes = 1;
- row_width = ((avctx->width + 7)/8)*8 * pixel_size;
- put_lines = put_lines_bytes;
- } else if (planes == 24) {
- planes = 1;
- row_width = ((avctx->width + 15)/16)*16 * pixel_size;
- put_lines = put_lines_bytes;
- } else if (planes == 32) {
- planes = 1;
- row_width = avctx->width * pixel_size;
- put_lines = put_lines_bytes;
- } else {
- av_assert0(0);
- }
- } else if (planes == 1) {
- palette[0] = 0xFFFFFFFF;
- palette[1] = 0xFF000000;
- } else if (planes == 2 || planes == 3 || planes == 4) {
- if (header_size == 9 + (1 << planes)) {
- bytestream2_skip(&gb, 2);
- for (int i = 0; i < (1 << planes); i++) {
- int v = bytestream2_get_be16(&gb);
- int r = ((v >> 8) & 0x7) << 5;
- int g = ((v >> 4) & 0x7) << 5;
- int b = ((v ) & 0x7) << 5;
- palette[i] = 0xFF000000 | r << 16 | g << 8 | b;
- }
- } else
- memcpy(palette, gem_color_palette, sizeof(gem_color_palette));
- } else if (planes == 8) {
- for (int i = 0; i < 256; i++)
- palette[i] = 0xFF000000 | (gem_gray[i]<<16) | (gem_gray[i]<<8) | gem_gray[i];
- } else if (planes == 16) {
- planes = 1;
- row_width = avctx->width * pixel_size;
- put_lines = put_lines_bytes;
- } else if (planes == 24) {
- planes = 1;
- row_width = avctx->width * pixel_size;
- put_lines = put_lines_bytes;
- } else
- av_assert0(0);
-
- ret = av_reallocp_array(&avctx->priv_data, planes, row_width);
- if (ret < 0)
- return ret;
- row = avctx->priv_data;
-
- memset(p->data[0], 0, avctx->height * p->linesize[0]);
- b = buf + header_size * 2;
- x = 0;
-
-#define SKIP \
-do { \
- x++; \
- if (x >= row_width) { \
- put_lines(avctx, planes, row_width, pixel_size, &state, row + state.pl * row_width, p); \
- if (state.y >= avctx->height) goto abort; \
- x = 0; \
- } \
-} while(0)
-
-#define PUT(v) \
-do { \
- row[state.pl * row_width + x++] = v; \
- if (x >= row_width) { \
- put_lines(avctx, planes, row_width, pixel_size, &state, row + state.pl * row_width, p); \
- if (state.y >= avctx->height) goto abort; \
- x = 0; \
- } \
-} while(0)
-
- while(b < buf_end) {
- int opcode = *b++;
- if (opcode == 0x80) { /* copy */
- if (b >= buf_end)
- goto abort;
- count = *b++;
- if (!count)
- count = 256;
- count *= count_scalar;
- for (int j = 0; j < count; j++) {
- if (b >= buf_end)
- goto abort;
- PUT(*b++);
- }
- } else if (opcode) { /* run */
- count = opcode & 0x7f;
- if (!count)
- count = 256;
- count *= count_scalar;
- v = opcode & 0x80 ? 0xFF : 0x00;
- for (int i = 0; i < count; i++)
- PUT(v);
- } else {
- if (b >= buf_end)
- goto abort;
- count = *b++;
- if (count) { /* pattern */
- if (b > buf_end - pattern_size)
- goto abort;
-
- count *= count_scalar;
- for (int j = 0; j < count; j++)
- for (int k = 0; k < pattern_size; k++)
- PUT(b[k]);
-
- b += pattern_size;
- } else {
- if (b >= buf_end)
- goto abort;
- count = *b++;
- if (count == 0xFF) { /* vertical duplication */
- if (b >= buf_end)
- goto abort;
- state.vdup = *b++;
- if (!state.vdup)
- state.vdup = 256;
- } else { /* horizontal duplication */
- for (int i = 0; i < count + 1; i++)
- SKIP;
- }
- }
- }
- }
-
-abort:
-
- *got_frame = 1;
- return buf_size;
-}
-
-static av_cold int gem_close(AVCodecContext *avctx)
-{
- av_freep(&avctx->priv_data);
- return 0;
-}
-
-const FFCodec ff_gem_decoder = {
- .p.name = "gem",
- CODEC_LONG_NAME("GEM Raster image"),
- .p.type = AVMEDIA_TYPE_VIDEO,
- .p.id = AV_CODEC_ID_GEM,
- .p.capabilities = AV_CODEC_CAP_DR1,
- FF_CODEC_DECODE_CB(gem_decode_frame),
- .close = gem_close,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/librav1e.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/librav1e.c
deleted file mode 100644
index 08affabe3e0b9119dc584b0a1484fafddf0bf1b7..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/librav1e.c
+++ /dev/null
@@ -1,688 +0,0 @@
-/*
- * librav1e encoder
- *
- * Copyright (c) 2019 Derek Buitenhuis
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include
-
-#include "libavutil/buffer.h"
-#include "libavutil/internal.h"
-#include "libavutil/avassert.h"
-#include "libavutil/base64.h"
-#include "libavutil/common.h"
-#include "libavutil/mathematics.h"
-#include "libavutil/opt.h"
-#include "libavutil/pixdesc.h"
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "encode.h"
-#include "internal.h"
-
-typedef struct librav1eContext {
- const AVClass *class;
-
- RaContext *ctx;
- AVFrame *frame;
- RaFrame *rframe;
-
- uint8_t *pass_data;
- size_t pass_pos;
- int pass_size;
-
- AVDictionary *rav1e_opts;
- int quantizer;
- int speed;
- int tiles;
- int tile_rows;
- int tile_cols;
-} librav1eContext;
-
-typedef struct FrameData {
- int64_t pts;
- int64_t duration;
-#if FF_API_REORDERED_OPAQUE
- int64_t reordered_opaque;
-#endif
-
- void *frame_opaque;
- AVBufferRef *frame_opaque_ref;
-} FrameData;
-
-static inline RaPixelRange range_map(enum AVPixelFormat pix_fmt, enum AVColorRange range)
-{
- switch (pix_fmt) {
- case AV_PIX_FMT_YUVJ420P:
- case AV_PIX_FMT_YUVJ422P:
- case AV_PIX_FMT_YUVJ444P:
- return RA_PIXEL_RANGE_FULL;
- }
-
- switch (range) {
- case AVCOL_RANGE_JPEG:
- return RA_PIXEL_RANGE_FULL;
- case AVCOL_RANGE_MPEG:
- default:
- return RA_PIXEL_RANGE_LIMITED;
- }
-}
-
-static inline RaChromaSampling pix_fmt_map(enum AVPixelFormat pix_fmt)
-{
- switch (pix_fmt) {
- case AV_PIX_FMT_YUV420P:
- case AV_PIX_FMT_YUVJ420P:
- case AV_PIX_FMT_YUV420P10:
- case AV_PIX_FMT_YUV420P12:
- return RA_CHROMA_SAMPLING_CS420;
- case AV_PIX_FMT_YUV422P:
- case AV_PIX_FMT_YUVJ422P:
- case AV_PIX_FMT_YUV422P10:
- case AV_PIX_FMT_YUV422P12:
- return RA_CHROMA_SAMPLING_CS422;
- case AV_PIX_FMT_YUV444P:
- case AV_PIX_FMT_YUVJ444P:
- case AV_PIX_FMT_YUV444P10:
- case AV_PIX_FMT_YUV444P12:
- return RA_CHROMA_SAMPLING_CS444;
- default:
- av_assert0(0);
- }
-}
-
-static inline RaChromaSamplePosition chroma_loc_map(enum AVChromaLocation chroma_loc)
-{
- switch (chroma_loc) {
- case AVCHROMA_LOC_LEFT:
- return RA_CHROMA_SAMPLE_POSITION_VERTICAL;
- case AVCHROMA_LOC_TOPLEFT:
- return RA_CHROMA_SAMPLE_POSITION_COLOCATED;
- default:
- return RA_CHROMA_SAMPLE_POSITION_UNKNOWN;
- }
-}
-
-static int get_stats(AVCodecContext *avctx, int eos)
-{
- librav1eContext *ctx = avctx->priv_data;
- RaData* buf = rav1e_twopass_out(ctx->ctx);
- if (!buf)
- return 0;
-
- if (!eos) {
- uint8_t *tmp = av_fast_realloc(ctx->pass_data, &ctx->pass_size,
- ctx->pass_pos + buf->len);
- if (!tmp) {
- rav1e_data_unref(buf);
- return AVERROR(ENOMEM);
- }
-
- ctx->pass_data = tmp;
- memcpy(ctx->pass_data + ctx->pass_pos, buf->data, buf->len);
- ctx->pass_pos += buf->len;
- } else {
- size_t b64_size = AV_BASE64_SIZE(ctx->pass_pos);
-
- memcpy(ctx->pass_data, buf->data, buf->len);
-
- avctx->stats_out = av_malloc(b64_size);
- if (!avctx->stats_out) {
- rav1e_data_unref(buf);
- return AVERROR(ENOMEM);
- }
-
- av_base64_encode(avctx->stats_out, b64_size, ctx->pass_data, ctx->pass_pos);
-
- av_freep(&ctx->pass_data);
- }
-
- rav1e_data_unref(buf);
-
- return 0;
-}
-
-static int set_stats(AVCodecContext *avctx)
-{
- librav1eContext *ctx = avctx->priv_data;
- int ret = 1;
-
- while (ret > 0 && ctx->pass_size - ctx->pass_pos > 0) {
- ret = rav1e_twopass_in(ctx->ctx, ctx->pass_data + ctx->pass_pos, ctx->pass_size);
- if (ret < 0)
- return AVERROR_EXTERNAL;
- ctx->pass_pos += ret;
- }
-
- return 0;
-}
-
-static av_cold int librav1e_encode_close(AVCodecContext *avctx)
-{
- librav1eContext *ctx = avctx->priv_data;
-
- if (ctx->ctx) {
- rav1e_context_unref(ctx->ctx);
- ctx->ctx = NULL;
- }
- if (ctx->rframe) {
- rav1e_frame_unref(ctx->rframe);
- ctx->rframe = NULL;
- }
-
- av_frame_free(&ctx->frame);
- av_freep(&ctx->pass_data);
-
- return 0;
-}
-
-static av_cold int librav1e_encode_init(AVCodecContext *avctx)
-{
- librav1eContext *ctx = avctx->priv_data;
- const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(avctx->pix_fmt);
- RaConfig *cfg = NULL;
- int rret;
- int ret = 0;
-
- ctx->frame = av_frame_alloc();
- if (!ctx->frame)
- return AVERROR(ENOMEM);
-
- cfg = rav1e_config_default();
- if (!cfg) {
- av_log(avctx, AV_LOG_ERROR, "Could not allocate rav1e config.\n");
- return AVERROR_EXTERNAL;
- }
-
- /*
- * Rav1e currently uses the time base given to it only for ratecontrol... where
- * the inverse is taken and used as a framerate. So, do what we do in other wrappers
- * and use the framerate if we can.
- */
- if (avctx->framerate.num > 0 && avctx->framerate.den > 0) {
- rav1e_config_set_time_base(cfg, (RaRational) {
- avctx->framerate.den, avctx->framerate.num
- });
- } else {
- rav1e_config_set_time_base(cfg, (RaRational) {
- avctx->time_base.num * avctx->ticks_per_frame,
- avctx->time_base.den
- });
- }
-
- if ((avctx->flags & AV_CODEC_FLAG_PASS1 || avctx->flags & AV_CODEC_FLAG_PASS2) && !avctx->bit_rate) {
- av_log(avctx, AV_LOG_ERROR, "A bitrate must be set to use two pass mode.\n");
- ret = AVERROR_INVALIDDATA;
- goto end;
- }
-
- if (avctx->flags & AV_CODEC_FLAG_PASS2) {
- if (!avctx->stats_in) {
- av_log(avctx, AV_LOG_ERROR, "No stats file provided for second pass.\n");
- ret = AVERROR(EINVAL);
- goto end;
- }
-
- ctx->pass_size = (strlen(avctx->stats_in) * 3) / 4;
- ctx->pass_data = av_malloc(ctx->pass_size);
- if (!ctx->pass_data) {
- av_log(avctx, AV_LOG_ERROR, "Could not allocate stats buffer.\n");
- ret = AVERROR(ENOMEM);
- goto end;
- }
-
- ctx->pass_size = av_base64_decode(ctx->pass_data, avctx->stats_in, ctx->pass_size);
- if (ctx->pass_size < 0) {
- av_log(avctx, AV_LOG_ERROR, "Invalid pass file.\n");
- ret = AVERROR(EINVAL);
- goto end;
- }
- }
-
- {
- const AVDictionaryEntry *en = NULL;
- while ((en = av_dict_iterate(ctx->rav1e_opts, en))) {
- if (rav1e_config_parse(cfg, en->key, en->value) < 0)
- av_log(avctx, AV_LOG_WARNING, "Invalid value for %s: %s.\n", en->key, en->value);
- }
- }
-
- rret = rav1e_config_parse_int(cfg, "width", avctx->width);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Invalid width passed to rav1e.\n");
- ret = AVERROR_INVALIDDATA;
- goto end;
- }
-
- rret = rav1e_config_parse_int(cfg, "height", avctx->height);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Invalid height passed to rav1e.\n");
- ret = AVERROR_INVALIDDATA;
- goto end;
- }
-
- if (avctx->sample_aspect_ratio.num > 0 && avctx->sample_aspect_ratio.den > 0)
- rav1e_config_set_sample_aspect_ratio(cfg, (RaRational) {
- avctx->sample_aspect_ratio.num,
- avctx->sample_aspect_ratio.den
- });
-
- rret = rav1e_config_parse_int(cfg, "threads", avctx->thread_count);
- if (rret < 0)
- av_log(avctx, AV_LOG_WARNING, "Invalid number of threads, defaulting to auto.\n");
-
- if (ctx->speed >= 0) {
- rret = rav1e_config_parse_int(cfg, "speed", ctx->speed);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set speed preset.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
-
- /* rav1e handles precedence between 'tiles' and cols/rows for us. */
- if (ctx->tiles > 0) {
- rret = rav1e_config_parse_int(cfg, "tiles", ctx->tiles);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set number of tiles to encode with.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
- if (ctx->tile_rows > 0) {
- rret = rav1e_config_parse_int(cfg, "tile_rows", ctx->tile_rows);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set number of tile rows to encode with.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
- if (ctx->tile_cols > 0) {
- rret = rav1e_config_parse_int(cfg, "tile_cols", ctx->tile_cols);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set number of tile cols to encode with.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
-
- if (avctx->gop_size > 0) {
- rret = rav1e_config_parse_int(cfg, "key_frame_interval", avctx->gop_size);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set max keyint.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
-
- if (avctx->keyint_min > 0) {
- rret = rav1e_config_parse_int(cfg, "min_key_frame_interval", avctx->keyint_min);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set min keyint.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
-
- if (avctx->bit_rate && ctx->quantizer < 0) {
- int max_quantizer = avctx->qmax >= 0 ? avctx->qmax : 255;
-
- rret = rav1e_config_parse_int(cfg, "quantizer", max_quantizer);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set max quantizer.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
-
- if (avctx->qmin >= 0) {
- rret = rav1e_config_parse_int(cfg, "min_quantizer", avctx->qmin);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set min quantizer.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
-
- rret = rav1e_config_parse_int(cfg, "bitrate", avctx->bit_rate);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set bitrate.\n");
- ret = AVERROR_INVALIDDATA;
- goto end;
- }
- } else if (ctx->quantizer >= 0) {
- if (avctx->bit_rate)
- av_log(avctx, AV_LOG_WARNING, "Both bitrate and quantizer specified. Using quantizer mode.");
-
- rret = rav1e_config_parse_int(cfg, "quantizer", ctx->quantizer);
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not set quantizer.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
- }
-
- rret = rav1e_config_set_pixel_format(cfg, desc->comp[0].depth,
- pix_fmt_map(avctx->pix_fmt),
- chroma_loc_map(avctx->chroma_sample_location),
- range_map(avctx->pix_fmt, avctx->color_range));
- if (rret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Failed to set pixel format properties.\n");
- ret = AVERROR_INVALIDDATA;
- goto end;
- }
-
- /* rav1e's colorspace enums match standard values. */
- rret = rav1e_config_set_color_description(cfg, (RaMatrixCoefficients) avctx->colorspace,
- (RaColorPrimaries) avctx->color_primaries,
- (RaTransferCharacteristics) avctx->color_trc);
- if (rret < 0) {
- av_log(avctx, AV_LOG_WARNING, "Failed to set color properties.\n");
- if (avctx->err_recognition & AV_EF_EXPLODE) {
- ret = AVERROR_INVALIDDATA;
- goto end;
- }
- }
-
- ctx->ctx = rav1e_context_new(cfg);
- if (!ctx->ctx) {
- av_log(avctx, AV_LOG_ERROR, "Failed to create rav1e encode context.\n");
- ret = AVERROR_EXTERNAL;
- goto end;
- }
-
- if (avctx->flags & AV_CODEC_FLAG_GLOBAL_HEADER) {
- RaData *seq_hdr = rav1e_container_sequence_header(ctx->ctx);
-
- if (seq_hdr)
- avctx->extradata = av_mallocz(seq_hdr->len + AV_INPUT_BUFFER_PADDING_SIZE);
- if (!seq_hdr || !avctx->extradata) {
- rav1e_data_unref(seq_hdr);
- av_log(avctx, AV_LOG_ERROR, "Failed to get extradata.\n");
- ret = seq_hdr ? AVERROR(ENOMEM) : AVERROR_EXTERNAL;
- goto end;
- }
-
- memcpy(avctx->extradata, seq_hdr->data, seq_hdr->len);
- avctx->extradata_size = seq_hdr->len;
- rav1e_data_unref(seq_hdr);
- }
-
- ret = 0;
-
-end:
-
- rav1e_config_unref(cfg);
-
- return ret;
-}
-
-static void frame_data_free(void *data)
-{
- FrameData *fd = data;
-
- if (!fd)
- return;
-
- av_buffer_unref(&fd->frame_opaque_ref);
- av_free(data);
-}
-
-static int librav1e_receive_packet(AVCodecContext *avctx, AVPacket *pkt)
-{
- librav1eContext *ctx = avctx->priv_data;
- RaFrame *rframe = ctx->rframe;
- RaPacket *rpkt = NULL;
- FrameData *fd;
- int ret;
-
- if (!rframe) {
- AVFrame *frame = ctx->frame;
-
- ret = ff_encode_get_frame(avctx, frame);
- if (ret < 0 && ret != AVERROR_EOF)
- return ret;
-
- if (frame->buf[0]) {
- const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(frame->format);
-
- fd = av_mallocz(sizeof(*fd));
- if (!fd) {
- av_log(avctx, AV_LOG_ERROR, "Could not allocate PTS buffer.\n");
- return AVERROR(ENOMEM);
- }
- fd->pts = frame->pts;
- fd->duration = frame->duration;
-#if FF_API_REORDERED_OPAQUE
-FF_DISABLE_DEPRECATION_WARNINGS
- fd->reordered_opaque = frame->reordered_opaque;
-FF_ENABLE_DEPRECATION_WARNINGS
-#endif
-
- if (avctx->flags & AV_CODEC_FLAG_COPY_OPAQUE) {
- fd->frame_opaque = frame->opaque;
- ret = av_buffer_replace(&fd->frame_opaque_ref, frame->opaque_ref);
- if (ret < 0) {
- frame_data_free(fd);
- av_frame_unref(frame);
- return ret;
- }
- }
-
- rframe = rav1e_frame_new(ctx->ctx);
- if (!rframe) {
- av_log(avctx, AV_LOG_ERROR, "Could not allocate new rav1e frame.\n");
- av_frame_unref(frame);
- frame_data_free(fd);
- return AVERROR(ENOMEM);
- }
-
- for (int i = 0; i < desc->nb_components; i++) {
- int shift = i ? desc->log2_chroma_h : 0;
- int bytes = desc->comp[0].depth == 8 ? 1 : 2;
- rav1e_frame_fill_plane(rframe, i, frame->data[i],
- (frame->height >> shift) * frame->linesize[i],
- frame->linesize[i], bytes);
- }
- av_frame_unref(frame);
- rav1e_frame_set_opaque(rframe, fd, frame_data_free);
- }
- }
-
- ret = rav1e_send_frame(ctx->ctx, rframe);
- if (rframe)
- if (ret == RA_ENCODER_STATUS_ENOUGH_DATA) {
- ctx->rframe = rframe; /* Queue is full. Store the RaFrame to retry next call */
- } else {
- rav1e_frame_unref(rframe); /* No need to unref if flushing. */
- ctx->rframe = NULL;
- }
-
- switch (ret) {
- case RA_ENCODER_STATUS_SUCCESS:
- case RA_ENCODER_STATUS_ENOUGH_DATA:
- break;
- case RA_ENCODER_STATUS_FAILURE:
- av_log(avctx, AV_LOG_ERROR, "Could not send frame: %s\n", rav1e_status_to_str(ret));
- return AVERROR_EXTERNAL;
- default:
- av_log(avctx, AV_LOG_ERROR, "Unknown return code %d from rav1e_send_frame: %s\n", ret, rav1e_status_to_str(ret));
- return AVERROR_UNKNOWN;
- }
-
-retry:
-
- if (avctx->flags & AV_CODEC_FLAG_PASS1) {
- int sret = get_stats(avctx, 0);
- if (sret < 0)
- return sret;
- } else if (avctx->flags & AV_CODEC_FLAG_PASS2) {
- int sret = set_stats(avctx);
- if (sret < 0)
- return sret;
- }
-
- ret = rav1e_receive_packet(ctx->ctx, &rpkt);
- switch (ret) {
- case RA_ENCODER_STATUS_SUCCESS:
- break;
- case RA_ENCODER_STATUS_LIMIT_REACHED:
- if (avctx->flags & AV_CODEC_FLAG_PASS1) {
- int sret = get_stats(avctx, 1);
- if (sret < 0)
- return sret;
- }
- return AVERROR_EOF;
- case RA_ENCODER_STATUS_ENCODED:
- goto retry;
- case RA_ENCODER_STATUS_NEED_MORE_DATA:
- if (avctx->internal->draining) {
- av_log(avctx, AV_LOG_ERROR, "Unexpected error when receiving packet after EOF.\n");
- return AVERROR_EXTERNAL;
- }
- return AVERROR(EAGAIN);
- case RA_ENCODER_STATUS_FAILURE:
- av_log(avctx, AV_LOG_ERROR, "Could not encode frame: %s\n", rav1e_status_to_str(ret));
- return AVERROR_EXTERNAL;
- default:
- av_log(avctx, AV_LOG_ERROR, "Unknown return code %d from rav1e_receive_packet: %s\n", ret, rav1e_status_to_str(ret));
- return AVERROR_UNKNOWN;
- }
-
- ret = ff_get_encode_buffer(avctx, pkt, rpkt->len, 0);
- if (ret < 0) {
- av_log(avctx, AV_LOG_ERROR, "Could not allocate packet.\n");
- rav1e_packet_unref(rpkt);
- return ret;
- }
-
- memcpy(pkt->data, rpkt->data, rpkt->len);
-
- if (rpkt->frame_type == RA_FRAME_TYPE_KEY)
- pkt->flags |= AV_PKT_FLAG_KEY;
-
- fd = rpkt->opaque;
- pkt->pts = pkt->dts = fd->pts;
- pkt->duration = fd->duration;
-#if FF_API_REORDERED_OPAQUE
-FF_DISABLE_DEPRECATION_WARNINGS
- avctx->reordered_opaque = fd->reordered_opaque;
-FF_ENABLE_DEPRECATION_WARNINGS
-#endif
-
- if (avctx->flags & AV_CODEC_FLAG_COPY_OPAQUE) {
- pkt->opaque = fd->frame_opaque;
- pkt->opaque_ref = fd->frame_opaque_ref;
- fd->frame_opaque_ref = NULL;
- }
-
- frame_data_free(fd);
-
- if (avctx->flags & AV_CODEC_FLAG_RECON_FRAME) {
- AVCodecInternal *avci = avctx->internal;
- AVFrame *frame = avci->recon_frame;
- const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(avctx->pix_fmt);
-
- av_frame_unref(frame);
-
- frame->format = avctx->pix_fmt;
- frame->width = avctx->width;
- frame->height = avctx->height;
-
- ret = ff_encode_alloc_frame(avctx, frame);
- if (ret < 0) {
- rav1e_packet_unref(rpkt);
- return ret;
- }
-
- for (int i = 0; i < desc->nb_components; i++) {
- int shift = i ? desc->log2_chroma_h : 0;
- rav1e_frame_extract_plane(rpkt->rec, i, frame->data[i],
- (frame->height >> shift) * frame->linesize[i],
- frame->linesize[i], desc->comp[i].step);
- }
- }
-
- rav1e_packet_unref(rpkt);
-
- return 0;
-}
-
-#define OFFSET(x) offsetof(librav1eContext, x)
-#define VE AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_ENCODING_PARAM
-
-static const AVOption options[] = {
- { "qp", "use constant quantizer mode", OFFSET(quantizer), AV_OPT_TYPE_INT, { .i64 = -1 }, -1, 255, VE },
- { "speed", "what speed preset to use", OFFSET(speed), AV_OPT_TYPE_INT, { .i64 = -1 }, -1, 10, VE },
- { "tiles", "number of tiles encode with", OFFSET(tiles), AV_OPT_TYPE_INT, { .i64 = 0 }, -1, INT64_MAX, VE },
- { "tile-rows", "number of tiles rows to encode with", OFFSET(tile_rows), AV_OPT_TYPE_INT, { .i64 = 0 }, -1, INT64_MAX, VE },
- { "tile-columns", "number of tiles columns to encode with", OFFSET(tile_cols), AV_OPT_TYPE_INT, { .i64 = 0 }, -1, INT64_MAX, VE },
- { "rav1e-params", "set the rav1e configuration using a :-separated list of key=value parameters", OFFSET(rav1e_opts), AV_OPT_TYPE_DICT, { 0 }, 0, 0, VE },
- { NULL }
-};
-
-static const FFCodecDefault librav1e_defaults[] = {
- { "b", "0" },
- { "g", "0" },
- { "keyint_min", "0" },
- { "qmax", "-1" },
- { "qmin", "-1" },
- { NULL }
-};
-
-const enum AVPixelFormat librav1e_pix_fmts[] = {
- AV_PIX_FMT_YUV420P,
- AV_PIX_FMT_YUVJ420P,
- AV_PIX_FMT_YUV420P10,
- AV_PIX_FMT_YUV420P12,
- AV_PIX_FMT_YUV422P,
- AV_PIX_FMT_YUVJ422P,
- AV_PIX_FMT_YUV422P10,
- AV_PIX_FMT_YUV422P12,
- AV_PIX_FMT_YUV444P,
- AV_PIX_FMT_YUVJ444P,
- AV_PIX_FMT_YUV444P10,
- AV_PIX_FMT_YUV444P12,
- AV_PIX_FMT_NONE
-};
-
-static const AVClass class = {
- .class_name = "librav1e",
- .item_name = av_default_item_name,
- .option = options,
- .version = LIBAVUTIL_VERSION_INT,
-};
-
-const FFCodec ff_librav1e_encoder = {
- .p.name = "librav1e",
- CODEC_LONG_NAME("librav1e AV1"),
- .p.type = AVMEDIA_TYPE_VIDEO,
- .p.id = AV_CODEC_ID_AV1,
- .init = librav1e_encode_init,
- FF_CODEC_RECEIVE_PACKET_CB(librav1e_receive_packet),
- .close = librav1e_encode_close,
- .priv_data_size = sizeof(librav1eContext),
- .p.priv_class = &class,
- .defaults = librav1e_defaults,
- .p.pix_fmts = librav1e_pix_fmts,
- .p.capabilities = AV_CODEC_CAP_DELAY | AV_CODEC_CAP_OTHER_THREADS |
- AV_CODEC_CAP_DR1 | AV_CODEC_CAP_ENCODER_RECON_FRAME |
- AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
- .caps_internal = FF_CODEC_CAP_NOT_INIT_THREADSAFE |
- FF_CODEC_CAP_INIT_CLEANUP | FF_CODEC_CAP_AUTO_THREADS,
- .p.wrapper_name = "librav1e",
-};
diff --git a/spaces/competitions/ship-detection/README.md b/spaces/competitions/ship-detection/README.md
deleted file mode 100644
index c0f2e287831b96b760a279b6d9a161f1e71ba744..0000000000000000000000000000000000000000
--- a/spaces/competitions/ship-detection/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: Ship Detection
-emoji: 🏆
-colorFrom: blue
-colorTo: gray
-sdk: docker
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download Renegade by Jay Z feat. Eminem - The Best Hip Hop Song of All Time.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download Renegade by Jay Z feat. Eminem - The Best Hip Hop Song of All Time.md
deleted file mode 100644
index 43e9811d7c99b4f75993d004cc8565db2a320674..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Download Renegade by Jay Z feat. Eminem - The Best Hip Hop Song of All Time.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-
Download Renegade by Jay-Z
-
If you are a fan of hip-hop music, you probably have heard of Renegade by Jay-Z, one of the most iconic rap songs of all time. But do you know the story behind the song, the meaning of the lyrics, and the impact it had on the rap culture? And more importantly, do you know how to download Renegade by Jay-Z and enjoy it anytime, anywhere? In this article, we will answer all these questions and more. We will also show you the best music streaming services to download Renegade by Jay-Z and how to do it from different platforms. So, if you are ready to learn more about this rap masterpiece and how to get it on your device, keep reading!
Renegade is a song by rapper Jay-Z, which appears as the 12th track on his sixth album The Blueprint. The song is written by Jay-Z, Eminem, and Luis Resto and produced by and features Eminem, who is the only guest appearance on the album with rap verses. The song was released as a single in 2001 and became a hit on the Billboard Hot 100 chart, peaking at number 65.
-
The background of the song
-
The song was originally recorded by Eminem for his group D12, but he decided to give it to Jay-Z after he heard his verse on another song. Jay-Z liked the beat and asked Eminem to rap on it with him. Eminem agreed and recorded his verses in Detroit, while Jay-Z recorded his in New York. The two rappers never met in person during the making of the song.
-
The lyrics and meaning of the song
-
The song is a response to the critics who accused Jay-Z and Eminem of being sell-outs and betraying their roots. The two rappers defend their artistic choices and their success in the rap industry, while also addressing their personal struggles and challenges. The song is divided into three verses, with each rapper taking one verse and then trading bars on the third verse. The chorus is sung by Eminem and repeats the line "I'm a renegade", implying that they are rebels who don't conform to the expectations of others.
-
The reception and impact of the song
-
The song received critical acclaim from music critics and fans alike, who praised the lyrical skills and chemistry of Jay-Z and Eminem. The song also sparked a controversy when rapper Nas used it as a diss track against Jay-Z in their infamous feud. Nas claimed that Eminem outshined Jay-Z on his own song and called him a "fake" rapper. Jay-Z responded by saying that Nas was jealous of his success and that he was not afraid to collaborate with other artists. The song is widely regarded as one of the best rap songs ever made and has influenced many other rappers over the years.
-
Download renegade by jay z and eminem mp3
-Download renegade by jay z curtain call the hits
-Download renegade by jay z soundcloud
-Download renegade by jay z lyrics
-Download renegade by jay z instrumental
-Download renegade by jay z video
-Download renegade by jay z album version
-Download renegade by jay z 320kbps
-Download renegade by jay z feat eminem free
-Download renegade by jay z youtube
-Download renegade by jay z spotify
-Download renegade by jay z genius
-Download renegade by jay z clean
-Download renegade by jay z remix
-Download renegade by jay z live
-Download renegade by jay z karaoke
-Download renegade by jay z ringtone
-Download renegade by jay z reaction
-Download renegade by jay z acapella
-Download renegade by jay z sample
-Download renegade by jay z flac
-Download renegade by jay z itunes
-Download renegade by jay z meaning
-Download renegade by jay z azlyrics
-Download renegade by jay z apple music
-Download renegade by jay z amazon music
-Download renegade by jay z tidal
-Download renegade by jay z deezer
-Download renegade by jay z napster
-Download renegade by jay z pandora
-Download renegade by jay z google play music
-Download renegade by jay z iheartradio
-Download renegade by jay z audiomack
-Download renegade by jay z datpiff
-Download renegade by jay z last.fm
-Download renegade by jay z shazam
-Download renegade by jay z musixmatch
-Download renegade by jay z genius lyrics app
-Download renegade by jay z songfacts
-Download renegade by jay z whosampled
-Download renegade by jay z discogs
-Download renegade by jay z allmusic
-Download renegade by jay z billboard charts history
-
Why should you download Renegade by Jay-Z?
-
Now that you know more about Renegade by Jay-Z, you might be wondering why you should download it and how to do it. Here are some reasons why you should download Renegade by Jay-Z and how to get it on your device.
-
The benefits of downloading music
-
Downloading music has many benefits over streaming or buying CDs. Some of these benefits are:
-
-
You can listen to your favorite songs offline, without worrying about internet connection or data usage.
-
You can create your own playlists and customize your music library according to your preferences.
-
You can enjoy higher sound quality and avoid buffering or skipping issues.
-
You can save money by avoiding subscription fees or ads.
-
You can support your favorite artists by buying their music directly from their official websites or platforms.
-
-
The best music streaming services to download Renegade by Jay-Z
-
There are many music streaming services that offer the option to download Renegade by Jay-Z and other songs for offline listening. Some of the best ones are:
-
-
-
Service
-
Price
-
Features
-
-
-
Spotify
-
$9.99/month for Premium
-
- Unlimited downloads and skips - Ad-free listening - Access to podcasts and videos - Personalized recommendations - Compatible with various devices and platforms
-
-
-
Apple Music
-
$9.99/month for Individual
-
- Unlimited downloads and skips - Ad-free listening - Access to exclusive content and live radio - Integration with iTunes and Siri - Compatible with various devices and platforms
-
-
-
Amazon Music
-
$9.99/month for Unlimited
-
- Unlimited downloads and skips - Ad-free listening - Access to podcasts and videos - Integration with Alexa and Amazon devices - Compatible with various devices and platforms
-
-
-
Tidal
-
$9.99/month for Premium
-
- Unlimited downloads and skips - Ad-free listening - Access to exclusive content and live events - High-fidelity sound quality - Compatible with various devices and platforms
-
-
-
YouTube Music
-
$9.99/month for Premium
-
- Unlimited downloads and skips - Ad-free listening and watching - Access to YouTube Originals and videos - Background play and offline mixtape - Compatible with various devices and platforms
-
-
-
How to download Renegade by Jay-Z from different platforms
-
The process of downloading Renegade by Jay-Z from different platforms may vary slightly, but the general steps are:
-
-
Sign up for a music streaming service that offers the option to download songs.
-
Search for Renegade by Jay-Z on the service's app or website.
-
Select the song and tap on the download icon, which may look like a downward arrow or a plus sign.
-
Wait for the song to be downloaded to your device's storage or cloud.
-
Enjoy listening to Renegade by Jay-Z offline!
-
-
Conclusion
-
In conclusion, Renegade by Jay-Z is a rap classic that you should definitely download and listen to. The song showcases the lyrical prowess and artistic vision of Jay-Z and Eminem, who are two of the most influential rappers of all time. The song also tells a compelling story of their struggles and successes in the rap industry, while also responding to their critics and haters. Downloading Renegade by Jay-Z will allow you to enjoy this rap masterpiece anytime, anywhere, without any interruptions or limitations. You can choose from a variety of music streaming services that offer the option to download songs, such as Spotify, Apple Music, Amazon Music, Tidal, or YouTube Music. All you need to do is sign up for a service, search for the song, and tap on the download icon. It's that easy! So, what are you waiting for? Download Renegade by Jay-Z today and experience the power of rap music!
-
Call to action
-
If you liked this article, please share it with your friends and family who might also be interested in downloading Renegade by Jay-Z. You can also leave a comment below and let us know what you think of the song, the article, or anything else related to rap music. We would love to hear from you!
-
FAQs
-
Here are some frequently asked questions about downloading Renegade by Jay-Z:
-
Q: Can I download Renegade by Jay-Z for free?
-
A: Yes, you can download Renegade by Jay-Z for free if you use a music streaming service that offers a free trial or a free plan. However, you may have to deal with some limitations, such as ads, lower sound quality, or limited downloads. If you want to enjoy the full benefits of downloading music, you may want to consider paying for a premium plan.
-
Q: Can I download Renegade by Jay-Z from other sources?
-
A: Yes, you can download Renegade by Jay-Z from other sources, such as torrent sites, file-sharing platforms, or unofficial websites. However, we do not recommend doing so, as it may be illegal, unsafe, or unethical. You may risk violating the copyright laws of the artists, exposing your device to viruses or malware, or supporting piracy or illegal activities. The best way to download Renegade by Jay-Z is to use a legitimate and reputable music streaming service that respects the rights and safety of the artists and the listeners.
-
Q: Can I download Renegade by Jay-Z in different formats?
-
A: Yes, you can download Renegade by Jay-Z in different formats, depending on the music streaming service and the device you use. Some of the most common formats are MP3, AAC, FLAC, WAV, or OGG. You can choose the format that suits your preferences and needs, such as file size, sound quality, or compatibility. However, some formats may require special software or hardware to play them.
-
Q: Can I download Renegade by Jay-Z in different languages?
-
A: No, you cannot download Renegade by Jay-Z in different languages, as the song is only available in English. However, you can find translations of the lyrics online or use a translation app to understand the meaning of the song in your language. You can also enjoy the song by appreciating the flow, rhythm, and rhyme of the rap verses, even if you don't understand every word.
-
Q: Can I download Renegade by Jay-Z in different versions?
-
A: Yes, you can download Renegade by Jay-Z in different versions, such as remixes, covers, or live performances. You can find these versions on various music streaming services or online platforms, such as YouTube, SoundCloud, or Bandcamp. You can also create your own version of the song by using a music editing software or app. However, you should always give credit to the original artists and respect their work.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Mafia II Definitive Edition - Experience the Golden-era of Crime in HD.md b/spaces/congsaPfin/Manga-OCR/logs/Mafia II Definitive Edition - Experience the Golden-era of Crime in HD.md
deleted file mode 100644
index 62096dc2046d5ff6e2bede9bc77e452c0526e801..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Mafia II Definitive Edition - Experience the Golden-era of Crime in HD.md
+++ /dev/null
@@ -1,119 +0,0 @@
-
-
Mafia 2 Download for PC: How to Live the Life of a Gangster
-
Have you ever wondered what it would be like to be a part of the mafia in the 1940s and 1950s? Do you want to experience the thrill, the drama, and the consequences of being a wise guy in the Golden-era of organized crime? If so, then you should definitely check out Mafia 2, one of the best action-adventure games ever made. In this article, we will tell you everything you need to know about Mafia 2, including what it is, why you should play it, and how to download it for your PC. So, sit back, relax, and get ready to enter the world of Mafia 2.
Mafia 2 is a video game developed by Hangar 13 and D3T, and published by 2K in 2020. It is a remastered version of the original Mafia 2 game that was released in 2010. It is part of the Mafia franchise, which also includes Mafia (2002), Mafia III (2016), and Mafia: Definitive Edition (2020).
-
Mafia 2 is set in Empire Bay, a fictional city based on New York, Chicago, Los Angeles, Boston, and Detroit. It follows the story of Vito Scaletta, a war hero who returns home after World War II and becomes involved with the mob in hopes of paying his father's debts. Along with his friend Joe, Vito works his way up the ranks of the mafia, doing various jobs and missions for different crime families. However, he soon realizes that the life of a gangster is not as glamorous as it seems, and that he has to face the consequences of his actions.
-
Why should you play Mafia 2?
-
Mafia 2 is a game that offers a lot of reasons to play it. Here are some of them:
-
-
It has an engaging and immersive story that will keep you hooked from start to finish. You will feel like you are living in a mafia movie, with twists and turns, betrayals and alliances, and moral dilemmas.
-
It has amazing graphics and sound that will make you feel like you are in the 1940s and 1950s. The game features realistic environments, vehicles, weapons, clothing, music, and voice acting that will transport you to another era.
-
It has a rich and diverse gameplay that will challenge you and entertain you. You will be able to explore the open world of Empire Bay, drive various cars and motorcycles, shoot different guns and melee weapons, stealthily sneak around or fight your way through enemies, interact with various characters and objects, and more.
-
It has a lot of content and replay value that will keep you coming back for more. The game has a main story mode that lasts for about 15 hours, plus several DLCs that add more missions and features. You can also play the game in different ways, choosing different paths and outcomes.
-
-
How to download Mafia 2 for PC
-
Requirements and specifications
-
Before you download Mafia 2 for PC, you need to make sure that your computer meets the minimum or recommended requirements for the game. Here are the specifications according to Steam:
-
mafia 2 definitive edition pc download
-mafia 2 remastered pc download
-mafia 2 free download full version pc
-mafia 2 classic pc download
-mafia 2 pc game download highly compressed
-mafia 2 digital deluxe edition pc download
-mafia 2 director's cut pc download
-mafia 2 steam download pc
-mafia 2 epic games download pc
-mafia 2 full game download for pc
-mafia 2 download for windows 10
-mafia 2 download for windows 7
-mafia 2 download for windows 8
-mafia 2 download for laptop
-mafia 2 download for mac
-mafia 2 download size pc
-mafia 2 download ocean of games pc
-mafia 2 download skidrow pc
-mafia 2 download fitgirl repack pc
-mafia 2 download apunkagames pc
-mafia 2 dlc download pc
-mafia 2 mods download pc
-mafia 2 trainer download pc
-mafia 2 crack download pc
-mafia 2 patch download pc
-mafia 2 save game download pc
-mafia 2 soundtrack download pc
-mafia 2 cheats download pc
-how to download and install mafia 2 on pc
-how to download and play mafia 2 on pc
-how to download and run mafia 2 on pc
-how to download and setup mafia 2 on pc
-how to download and activate mafia 2 on pc
-how to download and update mafia 2 on pc
-how to download and fix mafia 2 on pc
-how to get mafia 2 for free on pc
-how to get mafia 2 definitive edition for free on pc
-how to get mafia 2 remastered for free on pc
-how to get mafia 2 classic for free on pc
-how to get mafia 2 dlc for free on pc
-best site to download mafia 2 for pc
-best way to download mafia 2 for pc
-best place to download mafia 2 for pc
-best link to download mafia 2 for pc
-best torrent to download mafia 2 for pc
-can i download mafia 2 for free on pc
-can i play mafia 2 online on pc
-can i run mafia 2 on my pc
-can i transfer my save from ps4 to pc in mafia ii definitive edition
Once you have checked your PC's compatibility, you can proceed to download and install Mafia 2. Here are the steps to follow:
-
-
Go to Steam and create an account or log in if you already have one.
-
Search for Mafia 2: Definitive Edition and add it to your cart.
-
Proceed to checkout and pay for the game using your preferred method.
-
Once the payment is confirmed, go to your library and click on Mafia 2: Definitive Edition.
-
Click on the install button and choose the destination folder for the game.
-
Wait for the download and installation to complete. This may take some time depending on your internet speed and PC performance.
-
Once the game is installed, click on the play button and enjoy!
-
-
Tips and tricks for playing Mafia 2
-
Mafia 2 is a game that requires some skill and strategy to play. Here are some tips and tricks that will help you get the most out of it:
-
-
Pay attention to the story and the characters. They will give you clues, hints, and information that will help you progress in the game.
-
Explore the city and look for collectibles, side missions, and hidden secrets. They will reward you with money, items, achievements, and more.
-
Be careful with the police and the rival gangs. They will chase you, shoot you, arrest you, or kill you if you break the law or cross them.
-
Use cover and stealth when possible. They will help you avoid unnecessary damage and detection.
-
Upgrade your weapons, cars, and clothes. They will improve your performance, appearance, and reputation.
-
-
Conclusion
-
Summary of the main points
-
Mafia 2 is a game that lets you live the life of a gangster in the 1940s and 1950s. It has a captivating story, stunning graphics, varied gameplay, and plenty of content. It is a game that you should not miss if you are a fan of action-adventure games or mafia movies.
-
Call to action
-
If you are ready to join Vito and Joe in their quest for power, money, and respect, then what are you waiting for? Download Mafia 2 for PC today and start your journey in Empire Bay. You will not regret it!
-
FAQs
-
Here are some frequently asked questions about Mafia 2:
-
-
Is Mafia 2 a remake or a remaster?
-
Mafia 2 is a remaster, not a remake. This means that it has improved graphics, sound, performance, and stability, but it does not have any major changes in gameplay or content from the original game.
-
Is Mafia 2 connected to Mafia and Mafia III?
-
Mafia 2 is connected to Mafia and Mafia III in terms of story and characters. There are references, cameos, and easter eggs that link the three games together. However, each game has its own standalone plot and protagonist, so you do not need to play them in order or at all to enjoy Mafia 2.
-
Can I play Mafia 2 online or with friends?
-
Mafia 2 does not have any online or multiplayer features. It is a single-player game only.
-
How long does it take to finish Mafia 2?
-
Mafia 2 takes about 15 hours to finish the main story mode. However, this may vary depending on your playstyle, difficulty level, and completion rate. If you want to do everything in the game, such as finding all collectibles, doing all side missions, unlocking all achievements, etc., it may take you up to 30 hours or more.
-
Where can I have already written the article for you. Here is the last FAQ question and answer:
Where can I get more information about Mafia 2?
-
If you want to learn more about Mafia 2, you can visit the official website, the Steam page, the Wikipedia page, or the fandom wiki. You can also watch some gameplay videos, reviews, or guides on YouTube or Twitch. Or, you can join some online communities, such as forums, subreddits, or discord servers, where you can chat with other fans and players of Mafia 2.
-
-
I hope you enjoyed reading this article and found it helpful. If you have any questions or feedback, please feel free to leave a comment below. Thank you for your time and attention.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Stumble Guys X Pokemon Go A Fusion of Action Adventure and Comedy.md b/spaces/congsaPfin/Manga-OCR/logs/Stumble Guys X Pokemon Go A Fusion of Action Adventure and Comedy.md
deleted file mode 100644
index 572db3d182acabeec72e295e043fa198239744e4..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Stumble Guys X Pokemon Go A Fusion of Action Adventure and Comedy.md
+++ /dev/null
@@ -1,106 +0,0 @@
-
-
Download Stumble Guys x Pokemon Go: A Fun and Exciting Crossover Game
-
Do you love playing Stumble Guys and Pokemon Go? If so, you might be interested in trying out a new game that combines the best of both worlds. Stumble Guys x Pokemon Go is a modded version of Stumble Guys that features characters, skins, and items from the Pokemon universe. In this game, you can play as your favorite Pokemon, explore different locations, and compete with other players in chaotic obstacle courses. Sounds fun, right?
-
In this article, we will tell you everything you need to know about Stumble Guys x Pokemon Go, including what it is, how to download it, and what are its features and benefits. Read on to find out more!
Stumble Guys x Pokemon Go is a crossover game that merges two popular games: Stumble Guys and Pokemon Go. To understand what this game is all about, let's take a look at each of these games separately.
-
Stumble Guys: A Massive Multiplayer Party Knockout Game
-
Stumble Guys is an online battle royale party game that was released in 2021 by Niantic. In this game, you can race with up to 32 players online through chaotic obstacle courses. You have to run, jump, dash, and slide past opponents and obstacles to reach the finish line before everyone else. Along the way, you can collect items, unlock outfits, and customize your character. The game has 17 unique obstacle courses, each with different themes and challenges. The game is free-to-play and available for iOS and Android devices.
-
Pokemon Go: A Popular Augmented Reality Game
-
Pokemon Go is a 2016 augmented reality game that was developed and published by Niantic in collaboration with Nintendo and The Pokemon Company. In this game, you can explore the real world and search for virtual creatures called Pokemon that appear on your smartphone screen. You can catch, train, evolve, and battle with your Pokemon, as well as join other players in raids, gyms, and events. The game has over 700 species of Pokemon to discover and collect. The game is also free-to-play and available for iOS and Android devices.
-
Stumble Guys x Pokemon Go: A Fusion of Two Amazing Games
-
Stumble Guys x Pokemon Go is a modded version of Stumble Guys that adds elements from Pokemon Go to the game. This means that you can play as different Pokemon characters, such as Pikachu, Charmander, Squirtle, Bulbasaur, Eevee, Snorlax, Mewtwo, and more. You can also dress them up in various outfits and accessories that match their personalities and types. Moreover, you can explore different locations that are inspired by the Pokemon world, such as Kanto, Johto, Hoenn, Sinnoh, Unova, Kalos, Alola, Galar, and more. You can also encounter and catch wild Pokemon along the way.
-
Stumble Guys x Pokemon Go is not an official game by Niantic or Nintendo. It is a fan-made mod that was created by Booboo. It is not available on the App Store or Google Play. You have to download it from a third-party source.
-
How to Download Stumble Guys x Pokemon Go?
-
If you want to try out Stumble Guys x Pokemon Go, you have to follow these steps:
-
Download Stumble Guys from the App Store or Google Play
-
The first step is to download the original Stumble Guys game from the official sources. You can find it on the App Store for iOS devices or Google Play for Android devices. Just search for "Stumble Guys" and install it on your device. You need to have at least 100 MB of free space and a stable internet connection to play the game.
-
How to download stumble guys x pokemon go mod apk
-Stumble guys x pokemon go online game free
-Stumble guys x pokemon go skins and costumes
-Stumble guys x pokemon go gameplay and tips
-Stumble guys x pokemon go collaboration event
-Stumble guys x pokemon go hack and cheats
-Stumble guys x pokemon go review and rating
-Stumble guys x pokemon go best characters and strategies
-Stumble guys x pokemon go latest version and update
-Stumble guys x pokemon go for android and ios devices
-Stumble guys x pokemon go vs fall guys ultimate knockout
-Stumble guys x pokemon go challenges and rewards
-Stumble guys x pokemon go trailer and teaser
-Stumble guys x pokemon go download link and guide
-Stumble guys x pokemon go features and modes
-Stumble guys x pokemon go glitches and bugs
-Stumble guys x pokemon go fan art and memes
-Stumble guys x pokemon go codes and coupons
-Stumble guys x pokemon go news and announcements
-Stumble guys x pokemon go live stream and videos
-Stumble guys x pokemon go wiki and information
-Stumble guys x pokemon go forums and communities
-Stumble guys x pokemon go support and feedback
-Stumble guys x pokemon go wallpapers and stickers
-Stumble guys x pokemon go merchandise and products
-Stumble guys x pokemon go release date and time
-Stumble guys x pokemon go fun facts and trivia
-Stumble guys x pokemon go comparison and analysis
-Stumble guys x pokemon go ranking and statistics
-Stumble guys x pokemon go questions and answers
-Stumble guys x pokemon go theme song and music
-Stumble guys x pokemon go jokes and pranks
-Stumble guys x pokemon go tournaments and competitions
-Stumble guys x pokemon go memes and reactions
-Stumble guys x pokemon go opinions and comments
-Stumble guys x pokemon go secrets and easter eggs
-Stumble guys x pokemon go alternatives and similar games
-Stumble guys x pokemon go history and development
-Stumble guys x pokemon go patch notes and changelog
-Stumble guys x pokemon go rumors and speculations
-Stumble guys x pokemon go beginners guide and tutorial
-Stumble guys x pokemon go pros and cons
-Stumble guys x pokemon go tips tricks and hacks
-Stumble guys x pokemon go best practices
-Download stumble guys mod apk with unlimited money
-Download stumble guys for pc windows 10
-Download stumble guy game for free
-Download stumble guy mod menu
-Download stumble guy latest version
-
Download Stumble Guys x Pokemon Mod APK from a Trusted Source
-
The next step is to download the modded version of Stumble Guys that has the Pokemon features. You can find it on various websites that offer modded APK files, such as APKPure, APKMirror, APKFab, and more. Just search for "Stumble Guys x Pokemon" and download the latest version of the mod. You need to have at least 200 MB of free space and allow unknown sources on your device to install the mod.
-
Install and Launch the Modded Game
-
The final step is to install and launch the modded game on your device. You can do this by tapping on the downloaded APK file and following the instructions on the screen. Once the installation is complete, you can open the game and enjoy playing Stumble Guys x Pokemon Go. You can also update the game regularly to get new features and bug fixes.
-
What are the Features and Benefits of Playing Stumble Guys x Pokemon Go?
-
Stumble Guys x Pokemon Go is a fun and exciting game that offers many features and benefits for players. Here are some of them:
-
Play as Your Favorite Pokemon Characters
-
One of the main attractions of Stumble Guys x Pokemon Go is that you can play as different Pokemon characters, each with their own unique abilities and animations. You can choose from over 50 Pokemon, such as Pikachu, Charmander, Squirtle, Bulbasaur, Eevee, Snorlax, Mewtwo, and more. You can also customize them with various outfits and accessories that match their personalities and types. For example, you can dress up Pikachu in a hat and scarf, or give Snorlax a backpack and sunglasses.
-
Explore and Discover Pokemon in a Whole New World
-
Another feature of Stumble Guys x Pokemon Go is that you can explore different locations that are inspired by the Pokemon world, such as Kanto, Johto, Hoenn, Sinnoh, Unova, Kalos, Alola, Galar, and more. You can see familiar landmarks, such as Pallet Town, Viridian Forest, Mt. Moon, Cerulean City, Pewter Gym, Vermilion City, Saffron City, Lavender Town, Celadon City, Fuchsia City, Cinnabar Island, Indigo Plateau, and more. You can also encounter and catch wild Pokemon along the way. Some of them are rare and legendary, such as Mew, Zapdos, Articuno, Moltres, Lugia, Ho-Oh, Rayquaza, Dialga, Palkia, Giratina, Reshiram, Zekrom, Xerneas, Yveltal, Solgaleo, Lunala, Zacian, Zamazenta, and more. You can also use items, such as Poke Balls, Potions, Berries, and more to help you catch and heal your Pokemon.
-
Race and Compete with Other Players Online
-
A third feature of Stumble Guys x Pokemon Go is that you can race and compete with other players online through chaotic obstacle courses. You can join up to 32 players online and run, jump, dash, and slide past opponents and obstacles to reach the finish line before everyone else. You can also use items, such as Bananas, Bombs, Hammers, and more to sabotage and knock out your rivals. The game has 17 unique obstacle courses, each with different themes and challenges. Some of them are based on the Pokemon regions, such as Kanto Course, Johto Course, Hoenn Course, Sinnoh Course, Unova Course, Kalos Course, Alola Course, Galar Course, and more. You can also unlock new courses by playing the game regularly.
-
Increase Your Physical and Social Activity
-
A fourth feature of Stumble Guys x Pokemon Go is that you can increase your physical and social activity by playing the game. The game encourages you to explore the real world and search for virtual Pokemon that appear on your smartphone screen. You can also join other players in raids, gyms, and events to cooperate and compete with them. The game also rewards you for walking certain distances, hatching eggs, visiting PokeStops, and completing tasks. The game can help you stay fit, healthy, and happy by making exercise fun and social.
-
Conclusion
-
Stumble Guys x Pokemon Go is a fun and exciting crossover game that combines the best of both worlds. You can play as your favorite Pokemon characters, explore different locations inspired by the Pokemon world, race and compete with other players online through chaotic obstacle courses, and increase your physical and social activity by playing the game. The game is not an official game by Niantic or Nintendo. It is a fan-made mod that was created by Booboo. It is not available on the App Store or Google Play. You have to download it from a third-party source.
-
If you are a fan of Stumble Guys and Pokemon Go, you might want to give this game a try. It is a great way to enjoy both games in a new and creative way. However, be careful when downloading and installing the modded game from unknown sources. Make sure you have a backup of your device and a reliable antivirus software to protect your device from malware and viruses. Also, respect the intellectual property rights of Niantic and Nintendo and do not use the modded game for commercial purposes or illegal activities.
-
We hope this article has helped you learn more about Stumble Guys x Pokemon Go. If you have any questions or comments about the game or the article, feel free to leave them below. We would love to hear from you!
-
FAQs
-
Here are some frequently asked questions about Stumble Guys x Pokemon Go:
-
Q: Is Stumble Guys x Pokemon Go safe to download?
-
A: Stumble Guys x Pokemon Go is not an official game by Niantic or Nintendo. It is a fan-made mod that was created by Booboo. It is not available on the App Store or Google Play. You have to download it from a third-party source. Therefore, there is a risk of downloading and installing the modded game from unknown sources. You might encounter malware, viruses, or other harmful software that can damage your device or compromise your personal information. Therefore, you should be careful when downloading and installing the modded game from third-party sources. Make sure you have a backup of your device and a reliable antivirus software to protect your device from any potential threats.
-
Q: Is Stumble Guys x Pokemon Go legal to play?
-
A: Stumble Guys x Pokemon Go is not an official game by Niantic or Nintendo. It is a fan-made mod that was created by Booboo. It uses the assets and intellectual property of Niantic and Nintendo without their permission or authorization. Therefore, it might violate the terms of service and the copyright laws of Niantic and Nintendo. Playing the modded game might result in legal consequences, such as fines, lawsuits, or bans. Therefore, you should respect the intellectual property rights of Niantic and Nintendo and do not use the modded game for commercial purposes or illegal activities.
-
Q: How can I update Stumble Guys x Pokemon Go?
-
A: Stumble Guys x Pokemon Go is not an official game by Niantic or Nintendo. It is a fan-made mod that was created by Booboo. It is not available on the App Store or Google Play. You have to download it from a third-party source. Therefore, you cannot update the modded game from the official sources. You have to download the latest version of the mod from the third-party source where you downloaded it from. You can also check the website or social media accounts of Booboo to get updates and news about the modded game.
-
Q: Can I play Stumble Guys x Pokemon Go with my friends?
-
A: Yes, you can play Stumble Guys x Pokemon Go with your friends online. The modded game has the same multiplayer features as the original Stumble Guys game. You can join up to 32 players online and race and compete with them through chaotic obstacle courses. You can also chat with them and send them emojis and stickers. However, you can only play with other players who have the same modded game as you. You cannot play with players who have the original Stumble Guys game or a different modded version of the game.
-
Q: What are some tips and tricks for playing Stumble Guys x Pokemon Go?
-
A: Here are some tips and tricks for playing Stumble Guys x Pokemon Go:
-
-
Choose a Pokemon character that suits your play style and preference. Some Pokemon have special abilities that can help you in the obstacle courses, such as flying, jumping, dashing, or sliding.
-
Explore different locations and catch wild Pokemon along the way. Some Pokemon are rare and legendary, so you might want to collect them all. You can also use items, such as Poke Balls, Potions, Berries, and more to help you catch and heal your Pokemon.
-
Use items, such as Bananas, Bombs, Hammers, and more to sabotage and knock out your rivals in the obstacle courses. You can also avoid or dodge their attacks by moving around or hiding behind obstacles.
-
Stay alert and watch out for obstacles and hazards in the obstacle courses. Some of them are moving, spinning, bouncing, or exploding, so you might want to time your moves carefully.
-
Have fun and enjoy playing Stumble Guys x Pokemon Go!
-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Zugacoin Merchant App The Best Way to Accept Zuga Coin Payments.md b/spaces/congsaPfin/Manga-OCR/logs/Zugacoin Merchant App The Best Way to Accept Zuga Coin Payments.md
deleted file mode 100644
index 9adadb33090da93e146c28ac198f426dcd1a5931..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Zugacoin Merchant App The Best Way to Accept Zuga Coin Payments.md
+++ /dev/null
@@ -1,184 +0,0 @@
-
-
Zugacoin Merchant App Download: A Guide for Beginners
-
If you are looking for a way to invest in cryptocurrency, trade digital assets, or access loans in Africa, you might want to consider using Zugacoin. Zugacoin is a revolutionary cryptocurrency that aims to rebuild Africa's dying economy by becoming the first coin in equity and investment funding. In this article, we will show you how to download, install, and use the Zugacoin merchant app, which is a secure and convenient platform for buying and selling Zugacoin. We will also review the features and benefits of Zugacoin, as well as its reviews, ratings, pros, cons, and comparison with other cryptocurrencies. By the end of this article, you will have a clear idea of whether Zugacoin is a worthy investment option for you or not.
Zugacoin is a cryptocurrency that is built under the Ethereum blockchain. It is an ERC20 token with the ticker (SZC); it is also tradeable on crypto exchanges. This token launched in late 2020 and is #2672 in the rank of cryptocurrencies in existence. At the time of this writing, Zugacoin is trading at $47.06 (Coinmarketcap).
-
Zugacoin has a maximum supply that is much more limited than the total supply of bitcoin. Bitcoin Max. supply = 21 million BTC, while Zugacoin Max. supply = 1 million SZC. Additionally, this token has proof of stake functionality; this simply means that you can earn rewards by staking or hodling the SZC token.
-
Zugacoin was founded by Archbishop Dr. Sam Zuga, a cleric of House of Joy Church, located at Gboko, Benue State, Nigeria. Sam Zuga wanted a currency that will foster economic development in Africa through decentralized finance. In order to achieve this concept, Zugacoin was conceived.
-
Zugacoin came aboard the crypto space as "Africa's first cryptocurrency for equity and investment funding". It seeks to empower business startups, boost the employment rate, and upgrade the economic growth of Africa through the lending of its crypto asset. Sam Zuga officially launched the Zugacoin on September 2020 but the token's ICO is currently (Feb. 2021) ongoing.
-
Zugacoin aims to be a revolutionary cryptocurrency that will refurbish the Africans economy. It wants to change Africa for good by making use of blockchain technology in the rising economies of Africa and beyond. It aims to unlock the potential by creating, earning, saving, and spending opportunities across Africa.
-
zugacoin merchant app download apk
-zugacoin merchant app download for android
-zugacoin merchant app download free
-zugacoin merchant app download link
-zugacoin merchant app download play store
-zugacoin merchant app download tutorial
-zugacoin merchant app download video
-zugacoin merchant app download youtube
-how to download zugacoin merchant app
-how to install zugacoin merchant app
-how to register for zugacoin merchant app
-how to use zugacoin merchant app
-what is zugacoin merchant app
-why use zugacoin merchant app
-benefits of zugacoin merchant app
-features of zugacoin merchant app
-reviews of zugacoin merchant app
-problems with zugacoin merchant app
-solutions for zugacoin merchant app
-updates for zugacoin merchant app
-alternatives to zugacoin merchant app
-comparison of zugacoin merchant app and other apps
-best practices for zugacoin merchant app
-tips and tricks for zugacoin merchant app
-success stories of zugacoin merchant app users
-challenges of zugacoin merchant app users
-opportunities of zugacoin merchant app users
-risks of zugacoin merchant app users
-rewards of zugacoin merchant app users
-testimonials of zugacoin merchant app users
-faqs of zugacoin merchant app users
-guides of zugacoin merchant app users
-support of zugacoin merchant app users
-community of zugacoin merchant app users
-forum of zugacoin merchant app users
-blog of zugacoin merchant app users
-podcast of zugacoin merchant app users
-webinar of zugacoin merchant app users
-course of zugacoin merchant app users
-ebook of zugacoin merchant app users
-case study of zugacoin merchant app users
-infographic of zugacoin merchant app users
-checklist of zugacoin merchant app users
-template of zugacoin merchant app users
-tool of zugacoin merchant app users
-software of zugacoin merchant app users
-service of zugacoin merchant app users
-
The target users of Zugacoin are underemployed and unemployed people from Africa as well as assisting the African government in developing the economy. Africans are advised to take advantage of this coin for financial freedom especially as it is on Binance network SZCB.
-
Some of the features and benefits of using Zugacoin are:
-
-
It offers fast, secure, and low-cost transactions across borders.
-
It provides access to loans for business startups and personal needs.
It supports multiple payment methods, such as bank transfer, card payment, and mobile money.
-
It allows users to earn passive income by staking or holding Zugacoin in their wallets.
-
It has a limited supply of 1 million SZC, which means it has a high scarcity and demand potential.
-
It is backed by a reputable founder and a team of experts in blockchain, finance, and marketing.
-
It is compatible with the Ethereum network and can be integrated with other decentralized applications.
-
-
How to Download and Install Zugacoin Merchant App
-
If you want to start using Zugacoin, you will need to download and install the Zugacoin merchant app on your smartphone. The app is available for both Android and iOS devices and can be downloaded from the official website or the app stores. Here are the steps to follow:
-
For Android Users
-
-
Go to the Google Play Store and search for "Zugacoin Merchant App".
-
Select the app from the list and tap on "Install".
-
Wait for the app to download and install on your device.
-
Open the app and accept the terms and conditions.
-
-
For iOS Users
-
-
Go to the App Store and search for "Zugacoin Merchant App".
-
Select the app from the list and tap on "Get".
-
Enter your Apple ID password or use Touch ID or Face ID to confirm.
-
Wait for the app to download and install on your device.
-
Open the app and accept the terms and conditions.
-
-
How to Register and Verify Your Account
-
After you have downloaded and installed the Zugacoin merchant app, you will need to register and verify your account before you can start using it. Here are the steps to follow:
-
-
Open the app and tap on "Create Account".
-
Enter your full name, email address, phone number, password, and referral code (if any).
-
Tap on "Register" and check your email for a verification link.
-
Click on the link to verify your email address and activate your account.
-
Login to your account and tap on "Profile".
-
Select "Verification" and upload your identity document (such as passport, driver's license, or national ID card).
Enter your personal details, such as your date of birth, gender, address, and country.
-
Tap on "Submit" and wait for the verification to be completed.
-
You will receive a notification when your account is verified and ready to use.
-
-
How to Buy and Sell Zugacoin on the App
-
Once you have verified your account, you can start buying and selling Zugacoin on the app. There are three main ways to do this: using the scan-to-pay feature, using the P2P exchange, and using the swapping function. Here are the steps to follow for each method:
-
Using the Scan-to-Pay Feature
-
This feature allows you to pay for goods and services with Zugacoin by scanning a QR code. You can also receive payments from other users by generating your own QR code. Here are the steps to follow:
-
-
Open the app and tap on "Scan to Pay".
-
If you want to pay someone, scan their QR code with your camera. If you want to receive payment, tap on "Receive" and show your QR code to the payer.
-
Enter the amount of Zugacoin you want to send or receive and confirm the transaction.
-
You will see a confirmation message and a receipt of the transaction.
-
-
Using the P2P Exchange
-
This feature allows you to buy and sell Zugacoin with other users directly. You can choose from a list of offers or create your own offer. You can also chat with the seller or buyer and rate them after the transaction. Here are the steps to follow:
-
-
Open the app and tap on "P2P Exchange".
-
If you want to buy Zugacoin, tap on "Buy". If you want to sell Zugacoin, tap on "Sell".
-
Browse through the list of offers and select the one that suits your needs. You can filter the offers by payment method, location, price, and rating.
-
Tap on "Trade" and chat with the seller or buyer to agree on the terms of the transaction.
-
Follow the instructions on the screen and complete the payment or transfer of Zugacoin.
-
Tap on "Confirm" and wait for the confirmation from the other party.
-
You will see a confirmation message and a receipt of the transaction.
-
You can also rate and review the seller or buyer after the transaction.
-
-
Using the Swapping Function
-
This feature allows you to swap Zugacoin with other cryptocurrencies, such as Bitcoin, Ethereum, Binance Coin, Tether, etc. You can choose from a list of supported coins or enter a custom amount. Here are the steps to follow:
-
-
Open the app and tap on "Swap".
-
Select the coin you want to swap from and the coin you want to swap to.
Enter the amount of coin you want to swap or use the slider to adjust the amount.
-
Tap on "Swap Now" and confirm the transaction.
-
You will see a confirmation message and a receipt of the transaction.
-
-
Reviews and Ratings of Zugacoin
-
Zugacoin is a relatively new cryptocurrency that has not yet gained much popularity or recognition in the crypto space. However, it has received some reviews and ratings from users and experts who have tried it or analyzed it. Here are some of them:
-
Pros and Cons of Zugacoin
-
Like any other cryptocurrency, Zugacoin has its own pros and cons that you should be aware of before investing in it. Here is a summary of the main advantages and disadvantages of using Zugacoin:
-
-
-
Pros
-
Cons
-
-
-
- It offers fast, secure, and low-cost transactions across borders.
-
- It has a limited supply of 1 million SZC, which may limit its scalability and adoption.
-
-
-
- It provides access to loans for business startups and personal needs.
-
- It is not widely accepted or supported by merchants, exchanges, or wallets.
-
-
-
- It allows users to earn passive income by staking or holding Zugacoin in their wallets.
-
- It is vulnerable to market volatility, regulatory uncertainty, and cyberattacks.
-
-
-
- It is backed by a reputable founder and a team of experts in blockchain, finance, and marketing.
-
- It has a low market capitalization, liquidity, and trading volume.
-
-
-
- It is compatible with the Ethereum network and can be integrated with other decentralized applications.
-
- It has a low awareness, trust, and reputation among the crypto community.
-
-
-
Zugacoin vs Other Cryptocurrencies
-
Zugacoin is not the only cryptocurrency that aims to empower Africa and promote financial inclusion. There are other cryptocurrencies that have similar goals or features, such as Akoin, KubitX, BitSika, etc. How does Zugacoin compare with them? Here are some points of comparison:
-
-
Zugacoin is more focused on providing loans for business startups and personal needs than other cryptocurrencies that offer remittance, payment, or donation services.
Zugacoin has a lower supply, higher price, and higher scarcity than other cryptocurrencies that have higher supply, lower price, and lower scarcity.
-
Zugacoin is more compatible with the Ethereum network and its decentralized applications than other cryptocurrencies that use different blockchains or protocols.
-
Zugacoin has a more limited acceptance, support, and adoption than other cryptocurrencies that have more partnerships, integrations, and exchanges.
-
Zugacoin has a more centralized governance and vision than other cryptocurrencies that have more community involvement and feedback.
-
-
Conclusion
-
Zugacoin is a cryptocurrency that aims to rebuild Africa's economy by providing loans, payments, and investments for business startups and personal needs. It is a fast, secure, and low-cost way to transact across borders and earn passive income by staking or holding Zugacoin in your wallet. It is backed by a reputable founder and a team of experts in blockchain, finance, and marketing. It is compatible with the Ethereum network and can be integrated with other decentralized applications.
-
However, Zugacoin also has some drawbacks that you should consider before investing in it. It has a limited supply of 1 million SZC, which may limit its scalability and adoption. It is not widely accepted or supported by merchants, exchanges, or wallets. It is vulnerable to market volatility, regulatory uncertainty, and cyberattacks. It has a low market capitalization, liquidity, and trading volume. It has a low awareness, trust, and reputation among the crypto community.
-
If you want to try Zugacoin, you will need to download and install the Zugacoin merchant app on your smartphone. The app is a secure and convenient platform for buying and selling Zugacoin. You can use the scan-to-pay feature, the P2P exchange, or the swapping function to trade Zugacoin with other users or cryptocurrencies. You will also need to register and verify your account before you can start using the app.
-
We hope this article has given you a comprehensive guide on how to download, install, and use the Zugacoin merchant app. We also hope you have learned more about the features and benefits of Zugacoin, as well as its reviews, ratings, pros, cons, and comparison with other cryptocurrencies. If you have any questions or feedback about Zugacoin or the app, please feel free to contact us or leave a comment below. Thank you for reading!
-
FAQs
-
Here are some of the frequently asked questions and answers about Zugacoin:
-
-
What is the official website of Zugacoin?
-
The official website of Zugacoin is https://zugacoin.com/. You can find more information about Zugacoin's vision, mission, roadmap, team, partners, news, events, etc. on the website.
-
Where can I buy Zugacoin?
-
You can buy Zugacoin on the Zugacoin merchant app or on some crypto exchanges that support it. Some of the exchanges that list Zugacoin are BitMart, VinDAX, FinexBox, SatoExchange, etc.
-
How can I contact Zugacoin?
You can contact Zugacoin by sending an email to info@zugacoin.com or by calling +234 811 377 7709. You can also follow Zugacoin on social media platforms, such as Facebook, Twitter, Instagram, YouTube, etc.
-
Is Zugacoin a scam?
-
No, Zugacoin is not a scam. It is a legitimate cryptocurrency that is registered and regulated by the Nigerian government. It has a clear vision, mission, roadmap, team, partners, and community. It also has a transparent and auditable blockchain ledger that records all transactions and activities.
-
How can I store Zugacoin?
-
You can store Zugacoin in the Zugacoin merchant app or in any compatible wallet that supports ERC20 tokens. Some of the wallets that you can use are Trust Wallet, MetaMask, MyEtherWallet, etc. You should always keep your private keys and passwords safe and secure.
- 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/CodeVisionAVR V2053 With Crack [PORTABLE] Keyge.md b/spaces/contluForse/HuggingGPT/assets/CodeVisionAVR V2053 With Crack [PORTABLE] Keyge.md
deleted file mode 100644
index 7a46927c3317a5a0b0cb4de4366347b29a242234..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/CodeVisionAVR V2053 With Crack [PORTABLE] Keyge.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-CRACK IRENDER NXT FOR SKETCHUP 8 Free.rar ... stock footage, stock music, and editing tools provider headquartered ... CounterPath EyeBeam 1.5.19.rar. 4d29de3e1b
-
-
-
diff --git a/spaces/diacanFperku/AutoGPT/FULL Let Us C Solutions By Yashwant Kanetkar 9th Edition Ebook Free Download __EXCLUSIVE__.md b/spaces/diacanFperku/AutoGPT/FULL Let Us C Solutions By Yashwant Kanetkar 9th Edition Ebook Free Download __EXCLUSIVE__.md
deleted file mode 100644
index 64d98380d386f6aa968ba877e884aa7ca9038f34..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/FULL Let Us C Solutions By Yashwant Kanetkar 9th Edition Ebook Free Download __EXCLUSIVE__.md
+++ /dev/null
@@ -1,109 +0,0 @@
-
-
FULL Let Us C Solutions By Yashwant Kanetkar 9th Edition Ebook Free Download
-
C programming is one of the most fundamental and widely used programming languages in the world. It is the basis of many other languages and technologies. If you want to learn C programming from scratch, you need a good book that can teach you the basics and beyond. One such book is Let Us C by Yashwant Kanetkar.
-
FULL Let Us C Solutions By Yashwant Kanetkar 9th Edition Ebook Free Download
Let Us C by Yashwant Kanetkar is a classic book for learning C programming. It has been helping students and professionals for over two decades. It covers all the essential topics of C programming with clear and concise explanations and examples. It also provides exercises at the end of each chapter to test your understanding and practice your skills.
-
But how can you find the solutions to these exercises? How can you check your answers and learn from your mistakes? Well, you don't have to search anywhere else. You can get the FULL Let Us C Solutions by Yashwant Kanetkar 9th Edition Ebook for free from this article.
-
What is Let Us C Solutions by Yashwant Kanetkar?
-
Let Us C Solutions by Yashwant Kanetkar is a supplementary book to Let Us C. It contains the solutions to all the exercises given in Let Us C. The solutions are written in a simple and easy-to-understand manner. They help you to learn how to apply the concepts and logic of C programming to solve various problems.
-
The 9th edition of Let Us C Solutions by Yashwant Kanetkar is the latest and updated version of the book. It covers the topics of C99 and C11 standards, as well as some new features of C programming. It also includes some new exercises and solutions to enhance your learning experience.
-
-
Why should you get Let Us C Solutions by Yashwant Kanetkar?
-
There are many advantages of getting Let Us C Solutions by Yashwant Kanetkar. Some of them are:
-
-
You can verify your answers and compare them with the solutions given in the book.
-
You can learn from your mistakes and improve your coding skills.
-
You can gain more confidence and proficiency in C programming.
-
You can prepare for your exams and interviews with the help of the solutions.
-
You can save your time and money by getting the ebook for free.
-
-
How to get Let Us C Solutions by Yashwant Kanetkar?
-
Getting Let Us C Solutions by Yashwant Kanetkar is very easy and simple. You just have to follow these steps:
-
-
Click on the link given below to access the Google Drive file of Let Us C Solutions by Yashwant Kanetkar.
-
Sign in with your Google account or create a new one if you don't have one.
-
Click on the download icon on the top right corner of the screen.
-
Select a location on your device where you want to save the ebook.
-
Wait for the download to complete and enjoy reading the ebook.
Let Us C Solutions by Yashwant Kanetkar is a valuable book for anyone who wants to master C programming. It provides you with all the solutions to the exercises given in Let Us C. It also helps you to learn new concepts and features of C language. You can get the FULL Let Us C Solutions by Yashwant Kanetkar 9th Edition Ebook for free from this article. So, what are you waiting for? Get it now and start learning C programming like a pro!
-
What are the benefits of Let Us C Solutions by Yashwant Kanetkar?
-
Let Us C Solutions by Yashwant Kanetkar is not just a book of solutions. It is also a book of learning. It has many benefits for anyone who wants to learn C programming. Some of them are:
-
-
It helps you to understand the logic and reasoning behind each solution.
-
It helps you to improve your problem-solving and analytical skills.
-
It helps you to learn how to write efficient and optimized code.
-
It helps you to learn how to debug and test your code.
-
It helps you to learn how to use various tools and libraries in C programming.
-
-
Who can use Let Us C Solutions by Yashwant Kanetkar?
-
Let Us C Solutions by Yashwant Kanetkar is a book for everyone who wants to learn C programming. It is suitable for:
-
-
Students who are studying C programming in their courses.
-
Professionals who want to refresh their C skills or learn new features of C language.
-
Hobbyists who want to explore the power and beauty of C programming.
-
Teachers who want to use the book as a reference or a guide for their students.
-
-
How to use Let Us C Solutions by Yashwant Kanetkar?
-
Let Us C Solutions by Yashwant Kanetkar is a book that can be used in various ways. You can use it as:
-
-
A companion book to Let Us C. You can read both the books side by side and practice the exercises and solutions together.
-
A self-study book. You can read the book on your own and learn at your own pace.
-
A revision book. You can use the book to revise the topics and concepts that you have learned before.
-
A reference book. You can use the book to look up the solutions or the topics that you need help with.
-
-
What are the features of Let Us C Solutions by Yashwant Kanetkar?
-
Let Us C Solutions by Yashwant Kanetkar is a book that has many features that make it a useful and enjoyable book for learning C programming. Some of the features are:
-
-
It is written in a simple and lucid language that is easy to understand and follow.
-
It provides clear and concise explanations and examples for each solution.
-
It covers all the topics and concepts of C programming in a systematic and logical manner.
-
It follows the latest and updated standards and features of C language.
-
It provides tips and tricks to write efficient and optimized code.
-
It provides exercises and solutions for different levels of difficulty and complexity.
-
It provides cross-references and links to other topics and resources for further learning.
-
-
Where can you find more resources for learning C programming?
-
If you want to learn more about C programming, you can find many resources online and offline. Some of the resources are:
The books and magazines on C programming: Let Us C by Yashwant Kanetkar, The C Programming Language by Brian Kernighan and Dennis Ritchie, C Programming: A Modern Approach by K. N. King, etc.
-
-
Conclusion
-
Let Us C Solutions by Yashwant Kanetkar is a great book for anyone who wants to learn C programming. It provides you with all the solutions to the exercises given in Let Us C. It also helps you to learn new concepts and features of C language. You can get the FULL Let Us C Solutions by Yashwant Kanetkar 9th Edition Ebook for free from this article. So, what are you waiting for? Get it now and start learning C programming like a pro!
-
What are the reviews of Let Us C Solutions by Yashwant Kanetkar?
-
Let Us C Solutions by Yashwant Kanetkar is a book that has received many positive reviews and feedback from the readers. Some of the reviews are:
-
-
"This book is very helpful for beginners who want to learn C programming. It has clear and simple solutions to all the exercises given in Let Us C. It also explains the concepts and logic behind each solution. I recommend this book to anyone who wants to learn C programming."
-
"This book is a must-have for anyone who is studying or working with C programming. It has solutions to all the problems given in Let Us C. It also covers the latest topics and features of C language. It is very easy to understand and follow. I have learned a lot from this book."
-
"This book is a great companion to Let Us C. It has solutions to all the exercises given in Let Us C. It also helps you to improve your coding skills and problem-solving skills. It is written in a simple and lucid language that is easy to comprehend. I highly recommend this book to anyone who wants to master C programming."
-
-
What are some other books by Yashwant Kanetkar?
-
Yashwant Kanetkar is a renowned author and trainer of programming languages. He has written many books on various topics and languages, such as:
-
-
Let Us C++
-
Let Us Java
-
Understanding Pointers in C
-
Data Structures Through C
-
Exploring C
-
Test Your C Skills
-
Test Your C++ Skills
-
Test Your Java Skills
-
Go Embedded!
-
Let Us Shell
-
-
Conclusion
-
Let Us C Solutions by Yashwant Kanetkar is a wonderful book for anyone who wants to learn C programming. It provides you with all the solutions to the exercises given in Let Us C. It also helps you to learn new concepts and features of C language. You can get the FULL Let Us C Solutions by Yashwant Kanetkar 9th Edition Ebook for free from this article. So, what are you waiting for? Get it now and start learning C programming like a pro!
-
Conclusion
-
C programming is one of the most important and widely used programming languages in the world. It is the foundation of many other languages and technologies. If you want to learn C programming from scratch, you need a good book that can teach you the basics and beyond. One such book is Let Us C by Yashwant Kanetkar.
-
Let Us C by Yashwant Kanetkar is a classic book for learning C programming. It has been helping students and professionals for over two decades. It covers all the essential topics of C programming with clear and concise explanations and examples. It also provides exercises at the end of each chapter to test your understanding and practice your skills.
-
But how can you find the solutions to these exercises? How can you check your answers and learn from your mistakes? Well, you don't have to search anywhere else. You can get the FULL Let Us C Solutions by Yashwant Kanetkar 9th Edition Ebook for free from this article.
-
Let Us C Solutions by Yashwant Kanetkar is a supplementary book to Let Us C. It contains the solutions to all the exercises given in Let Us C. The solutions are written in a simple and easy-to-understand manner. They help you to learn how to apply the concepts and logic of C programming to solve various problems.
-
The 9th edition of Let Us C Solutions by Yashwant Kanetkar is the latest and updated version of the book. It covers the topics of C99 and C11 standards, as well as some new features of C programming. It also includes some new exercises and solutions to enhance your learning experience.
-
Let Us C Solutions by Yashwant Kanetkar is a great book for anyone who wants to master C programming. It provides you with all the solutions to the exercises given in Let Us C. It also helps you to learn new concepts and features of C language. You can get the FULL Let Us C Solutions by Yashwant Kanetkar 9th Edition Ebook for free from this article. So, what are you waiting for? Get it now and start learning C programming like a pro!
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Ghost Rider Spirit Of Vengeance Brrip 1080p Dual Audio ((NEW)).md b/spaces/diacanFperku/AutoGPT/Ghost Rider Spirit Of Vengeance Brrip 1080p Dual Audio ((NEW)).md
deleted file mode 100644
index bfc6c11169e735dad25cca8ec89a5be963a983dc..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Ghost Rider Spirit Of Vengeance Brrip 1080p Dual Audio ((NEW)).md
+++ /dev/null
@@ -1,8 +0,0 @@
-
Ghost Rider Spirit Of Vengeance Brrip 1080p Dual Audio
-
-Download the free 2022 iCloud Bypass Tool iCloud Version 1.5.1 and 2.0 for Windows, Mac or Linux. Unlock iCloud locked iPhone/iPad/iPod Touch. You can also download iCloud Bypass Tool for iOS 2020 Version 1.0 for Windows and Mac. Unlock iCloud locked iPhone/iPad/iPod Touch. Available for download on macOS, the iCloud Bypass Tool for iOS is one of the most popular bypass options. Bypass tool for Mac OS. If you want to select the iCloud for Apple ID option, then go to the Apple website from the main page and click on "Apple ID". 8a78ff9644
-
-
-
diff --git a/spaces/diacanFperku/AutoGPT/InventoryPlus 1.9.5.4 Crack ((BETTER)).md b/spaces/diacanFperku/AutoGPT/InventoryPlus 1.9.5.4 Crack ((BETTER)).md
deleted file mode 100644
index bb7bf59f952818f3209314c8e49ac16639ff0144..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/InventoryPlus 1.9.5.4 Crack ((BETTER)).md
+++ /dev/null
@@ -1,6 +0,0 @@
-
Moviestorm Crack Mac OSX is the final and the better version of Moviestorm Activator. This crack tool supports all other programs versions running in Windows like Moviestorm Pro 2.0.0 and Moviestorm PRO 2.5.7. This is a crack tool because a registration key is used, otherwise the product is a typical keygorn virus.
-
This is a useful Moviestorm 2 crack and keygen created to allow you a Moviestorm 2 trial so you can test if it meets your needs. Moviestorm 2 requires the activation via this Moviestorm 2 serial number after the payment. Moviestorm 2 license key provides you a way to Moviestorm 2 premium then the product is set free.
We invite you to try Moviestorm 1 full crack which has been successfully tested and works great on Windows 8, Windows 7, Windows Vista and Windows XP. Moviestorm 1 serial key renders your Moviestorm 1 trial version free of charge. Furthermore, a registration key is required. You can also check moviestorm 1 crack version 2 or free-vista-version.
-
Moviestorm 2 full crack and Moviestorm 2 patchwork together for the purposes of Moviestorm 2 activation; a keygen will get you it for free. Moviestorm 2 activation key is not on our website, otherwise your Moviestorm 2 trial would be full free for free.
-
The Moviestorm 2.5 license key can activate Moviestorm 2 up to 3 times. For this reason, Moviestorm 2.5 activation key is not on our website. The Moviestorm 2.5 full crack is the most recent Moviestorm 2 crack available. Moviestorm 2.5 crack serial key is offered to Moviestorm 2 subscribers by Moviestorm 2 crack site.
-
If you need Moviestorm 2 trial, Moviestorm 2 serial key is provided by Moviestorm 2 free trial. The Moviestorm 2 activation key is not on our website, otherwise your Moviestorm 2 test version would be full for free. A Moviestorm 2 full crack is required in order to have a valid Moviestorm 2 key in order to copy it to Moviestorm 2.
899543212b
-
-
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Numerologiapitagoricalibropdfdownload ((LINK)).md b/spaces/diacanFperku/AutoGPT/Numerologiapitagoricalibropdfdownload ((LINK)).md
deleted file mode 100644
index ca4296386bd19d1aaf7df21a28e2eb8b84409ad0..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Numerologiapitagoricalibropdfdownload ((LINK)).md
+++ /dev/null
@@ -1,72 +0,0 @@
-
-
Numerologia Pitagorica: Libro PDF Download
-
-
¿Te gustaría conocer el significado oculto de los números y cómo influyen en tu destino? ¿Quieres aprender una técnica milenaria que te permita descubrir tu potencial y tu propósito de vida? Si es así, te invitamos a descargar el libro PDF de Numerologia Pitagorica, una obra del autor y numerólogo italiano Giuseppe Filippi.
-
-
Numerologia Pitagorica es un libro que explora el antiguo sistema de numerología de Pitágoras, el famoso filósofo y matemático griego que afirmó que el universo se rige por proporciones numéricas armoniosas. Según este sistema, cada número tiene un significado simbólico y una vibración energética que se relaciona con los aspectos físicos, mentales, emocionales y espirituales de los seres humanos.
En este libro, Filippi te enseña cómo calcular tu número personal a partir de tu fecha de nacimiento y tu nombre completo, y cómo interpretar su significado en relación con tu personalidad, tus talentos, tus desafíos, tus lecciones y tu misión. Además, te muestra cómo aplicar la numerología pitagórica a otros ámbitos de tu vida, como el amor, el trabajo, la salud, el dinero y la espiritualidad.
-
-
Numerologia Pitagorica es un libro que te ofrece una herramienta práctica y poderosa para conocerte mejor a ti mismo y a los demás, para mejorar tus relaciones, para tomar decisiones acertadas y para alcanzar tus metas. Con este libro, podrás descubrir el plan divino que hay detrás de cada número y cómo alinearte con él para vivir en armonía y felicidad.
-
-
Si quieres descargar el libro PDF de Numerologia Pitagorica, solo tienes que hacer clic en el siguiente enlace y disfrutar de esta obra fascinante y reveladora. No esperes más y empieza hoy mismo a transformar tu vida con el poder de los números.
La numerología pitagórica es una rama de la numerología que se basa en los principios y las enseñanzas de Pitágoras, el célebre filósofo y matemático griego que vivió en el siglo VI a.C. Pitágoras fue el fundador de una escuela esotérica que estudiaba las propiedades y las relaciones de los números, así como su influencia en el orden cósmico y en el destino humano.
-
-
Según la numerología pitagórica, cada número tiene un valor cualitativo y cuantitativo, y representa una cualidad o una fuerza universal que se manifiesta en la naturaleza y en el ser humano. Los números también tienen una correspondencia con las letras del alfabeto, lo que permite calcular el valor numérico de los nombres y las palabras.
-
-
La numerología pitagórica utiliza principalmente los números del 1 al 9, que son los números básicos o raíces, y los números maestros 11, 22 y 33, que son números compuestos con una vibración especial. Cada número tiene un significado simbólico y una interpretación espiritual que revela aspectos ocultos de la realidad y de la personalidad.
-
-
¿Cómo funciona la numerología pitagórica?
-
-
La numerología pitagórica funciona mediante el cálculo y el análisis de los números personales que se obtienen a partir de la fecha de nacimiento y del nombre completo de una persona. Estos números personales son:
-
-
-
-
El número del camino de vida: indica el propósito o la misión que tiene una persona en esta vida, así como sus talentos y sus oportunidades.
-
El número de expresión: indica la forma en que una persona se expresa y se comunica con el mundo, así como sus capacidades y sus potencialidades.
-
El número del alma: indica los deseos más profundos e íntimos de una persona, así como sus motivaciones y sus aspiraciones.
-
El número de personalidad: indica la imagen o la impresión que una persona proyecta a los demás, así como sus rasgos característicos y su forma de actuar.
-
El número del destino: indica el destino o el camino que una persona debe seguir para cumplir su misión y su propósito en esta vida.
-
-
-
Para calcular estos números personales se utiliza una tabla de correspondencia entre las letras y los números, que se basa en el orden alfabético griego. Cada letra tiene un valor numérico del 1 al 9, y se suman los valores de las letras que componen el nombre o la fecha hasta reducirlos a un solo dígito. Por ejemplo:
Una vez obtenidos los números personales, se procede a interpretar su significado según la simbología y la vibración de cada número. De esta manera, se puede obtener información valiosa sobre la personalidad, el carácter, las fortalezas, las debilidades, los desafíos, las lecciones y las oportunidades de cada persona.
-
¿Qué beneficios tiene la numerología pitagórica?
-
-
La numerología pitagórica tiene muchos beneficios para las personas que la practican o la consultan. Algunos de estos beneficios son:
-
-
-
Ayuda a conocerse mejor a sí mismo y a los demás, al revelar los rasgos y las características de cada personalidad.
-
Facilita el desarrollo personal y espiritual, al mostrar las potencialidades y los talentos que cada uno posee.
-
Mejora las relaciones interpersonales, al permitir comprender y aceptar las diferencias y las afinidades entre las personas.
-
Orienta en la toma de decisiones, al indicar el camino o el destino que cada uno debe seguir para cumplir su propósito.
-
Aporta armonía y equilibrio, al sintonizar con la vibración y el significado de cada número.
-
-
-
La numerología pitagórica es una herramienta práctica y poderosa que puede mejorar la calidad de vida de las personas que la utilizan. Con ella, se puede acceder a una sabiduría ancestral que nos conecta con el orden y la belleza del universo.
-
-
¿Cómo descargar el libro PDF de Numerologia Pitagorica?
-
-
Si quieres descargar el libro PDF de Numerologia Pitagorica, solo tienes que seguir estos sencillos pasos:
-
-
-
Haz clic en el enlace que aparece al final de este artículo.
-
Espera unos segundos hasta que se abra la página de descarga.
-
Selecciona el formato PDF y haz clic en el botón de descargar.
-
Guarda el archivo en tu dispositivo o en tu nube personal.
-
Disfruta de la lectura de este libro fascinante y revelador.
-
-
-
Descargar el libro PDF de Numerologia Pitagorica es muy fácil y rápido. No necesitas registrarte ni pagar nada. Solo necesitas una conexión a internet y un dispositivo compatible con el formato PDF. Así podrás leer este libro en cualquier momento y lugar, y aprender todo lo que la numerología pitagórica puede hacer por ti.
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/losses.py b/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- rl = rl.float().detach()
- gl = gl.float()
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- dr = dr.float()
- dg = dg.float()
- r_loss = torch.mean((1-dr)**2)
- g_loss = torch.mean(dg**2)
- loss += (r_loss + g_loss)
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- dg = dg.float()
- l = torch.mean((1-dg)**2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
-
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/start.bat b/spaces/digitalxingtong/Nailv-read-Bert-Vits2/start.bat
deleted file mode 100644
index 418d21233dbf720b0dd09821904d9d6a31b123a2..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/start.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-set PYTHON=venv\python.exe
-start cmd /k "set PYTHON=%PYTHON%"
\ No newline at end of file
diff --git a/spaces/disham993/anime_protagonist_classifier/app.py b/spaces/disham993/anime_protagonist_classifier/app.py
deleted file mode 100644
index c236e3dc358de698c108f3aa0f9b3007861d790d..0000000000000000000000000000000000000000
--- a/spaces/disham993/anime_protagonist_classifier/app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-
-learn = load_learner("model_2.pkl")
-
-categories = learn.dls.vocab
-for index, category in enumerate(categories):
- if category == "Random Anime Photos":
- categories[index] = "Others"
-
-
-def classify_image(img):
- pred, idx, probs = learn.predict(img)
- return dict(zip(categories, map(float, probs)))
-
-
-image = gr.inputs.Image(shape=(360, 360))
-label = gr.outputs.Label()
-examples = [
- "Luffy.jpg",
- "Naruto-Kurama-Mode.png",
- "Goku.jpg",
- "Ichigo.jpeg",
- "Robin.jpeg"
-]
-
-title = "Top 10 Shounen Anime Protagonists Classifier"
-description = "Fine tuned a resnet152 image classifier such that it is able to recognize protagonists of top 10 Shounen Animes. "
-article = """
- Animes and its protagonists this image classifier will recognize:
-
- 1. One Piece - Monkey D. Luffy
- 2. Naruto: Shippuden - Naruto Uzumaki
- 3. My Hero Academia - Izuku Midoriya
- 4. Dragon Ball Z - Son Goku aka Kakarot
- 5. Fairy Tail - Natsu Dragneel
- 6. Yu Yu Hakusho - Yusuke Urameshi
- 7. Bleach - Ichigo Kurosaki
- 8. Hunter X Hunter - Gon Freecss
- 9. Fullmetal Alchemist - Edward Elric
- 10. Attack on Titan - Eren Yeager
-
- Rest all other anime characters will be classified as others.
-"""
-
-
-intf = gr.Interface(
- fn=classify_image,
- inputs=image,
- outputs=label,
- examples=examples,
- title=title,
- description=description,
- article=article,
- theme="huggingface",
-)
-intf.launch(inline=False)
diff --git a/spaces/docs-demos/prophetnet-large-uncased/app.py b/spaces/docs-demos/prophetnet-large-uncased/app.py
deleted file mode 100644
index cd7e5fe8dc4bc21fa100168b18924ec5e016d752..0000000000000000000000000000000000000000
--- a/spaces/docs-demos/prophetnet-large-uncased/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import gradio as gr
-
-title = "ProphetNet"
-
-description = "Gradio Demo for ProphetNet. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-
-article = "
"
-
-examples = [
- ["Paris, city and capital of France, situated in the north-central part of the country. People were living on the site of the present-day city, located along the Seine River some 233 miles (375 km) upstream from the river’s mouth on the English Channel (La Manche), by about 7600 BCE. The modern city has spread from the island (the Île de la Cité) and far beyond both banks of the Seine. Paris occupies a central position in the rich agricultural region known as the Paris Basin, and it constitutes one of eight départements of the Île-de-France administrative region. It is by far the country’s most important centre of commerce and culture. Area city, 41 square miles (105 square km); metropolitan area, 890 square miles (2,300 square km). Pop. (2012) city, 2,265,886; (2015 est.) urban agglomeration, 10,858,000. Character of the city For centuries Paris has been one of the world’s most important and attractive cities. It is appreciated for the opportunities it offers for business and commerce, for study, for culture, and for entertainment; its gastronomy, haute couture, painting, literature, and intellectual community especially enjoy an enviable reputation. Its sobriquet “the City of Light” (“la Ville Lumière”), earned during the Enlightenment, remains appropriate, for Paris has retained its importance as a centre for education and intellectual pursuits. Paris’s site at a crossroads of both water and land routes significant not only to France but also to Europe has had a continuing influence on its growth. Under Roman administration, in the 1st century BCE, the original site on the Île de la Cité was designated the capital of the Parisii tribe and territory. The Frankish king Clovis I had taken Paris from the Gauls by 494 CE and later made his capital there. Under Hugh Capet (ruled 987–996) and the Capetian dynasty the preeminence of Paris was firmly established, and Paris became the political and cultural hub as modern France took shape. France has long been a highly centralized country, and Paris has come to be identified with a powerful central state, drawing to itself much of the talent and vitality of the provinces.","prophetnet-large-uncased"]
-]
-
-io1 = gr.Interface.load("huggingface/microsoft/prophetnet-large-uncased")
-
-io2 = gr.Interface.load("huggingface/microsoft/prophetnet-large-uncased-squad-qg")
-
-io3 = gr.Interface.load("huggingface/microsoft/prophetnet-large-uncased-cnndm")
-
-
-
-def inference(text, model):
- if model == "prophetnet-large-uncased":
- outtext = io1(text)
- elif model == "prophetnet-large-uncased-squad-qg":
- outtext = io2(text)
- else:
- outtext = io3(text)
- return outtext
-
-gr.Interface(
- inference,
- [gr.inputs.Textbox(label="Input", lines=10),gr.inputs.Dropdown(choices=["prophetnet-large-uncased","prophetnet-large-uncased-squad-qg","prophetnet-large-uncased-cnndm"], type="value", default="prophetnet-large-uncased", label="model")
-],
- gr.outputs.Textbox(label="Output"),
- examples=examples,
- article=article,
- title=title,
- description=description).launch(enable_queue=True)
diff --git a/spaces/elina12/asr_arabic/README.md b/spaces/elina12/asr_arabic/README.md
deleted file mode 100644
index 126794de845cc0e2151351b6a225b6e4fe9af529..0000000000000000000000000000000000000000
--- a/spaces/elina12/asr_arabic/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Asr Arabic
-emoji: 👀
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/enesbol/case_dif/util/losses.py b/spaces/enesbol/case_dif/util/losses.py
deleted file mode 100644
index eea6358429895ea25c1c03c15ef5c24ae7365b4d..0000000000000000000000000000000000000000
--- a/spaces/enesbol/case_dif/util/losses.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""
-author: Min Seok Lee and Wooseok Shin
-"""
-import torch
-import torch.nn.functional as F
-
-
-def Optimizer(args, model):
- if args.optimizer == 'Adam':
- optimizer = torch.optim.Adam(params=model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
- elif args.optimizer == 'SGD':
- optimizer = torch.optim.SGD(params=model.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
- return optimizer
-
-
-def Scheduler(args, optimizer):
- if args.scheduler == 'Reduce':
- scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer, mode='min', factor=args.lr_factor, patience=args.patience)
- elif args.scheduler == 'Step':
- scheduler = torch.optim.lr_scheduler.StepLR(
- optimizer, step_size=2, gamma=0.9)
- return scheduler
-
-
-def Criterion(args):
- if args.criterion == 'API':
- criterion = adaptive_pixel_intensity_loss
- elif args.criterion == 'bce':
- criterion = torch.nn.BCELoss()
- return criterion
-
-
-def adaptive_pixel_intensity_loss(pred, mask):
- w1 = torch.abs(F.avg_pool2d(mask, kernel_size=3, stride=1, padding=1) - mask)
- w2 = torch.abs(F.avg_pool2d(mask, kernel_size=15, stride=1, padding=7) - mask)
- w3 = torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)
-
- omega = 1 + 0.5 * (w1 + w2 + w3) * mask
-
- bce = F.binary_cross_entropy(pred, mask, reduce=None)
- abce = (omega * bce).sum(dim=(2, 3)) / (omega + 0.5).sum(dim=(2, 3))
-
- inter = ((pred * mask) * omega).sum(dim=(2, 3))
- union = ((pred + mask) * omega).sum(dim=(2, 3))
- aiou = 1 - (inter + 1) / (union - inter + 1)
-
- mae = F.l1_loss(pred, mask, reduce=None)
- amae = (omega * mae).sum(dim=(2, 3)) / (omega - 1).sum(dim=(2, 3))
-
- return (0.7 * abce + 0.7 * aiou + 0.7 * amae).mean()
\ No newline at end of file
diff --git a/spaces/eson/tokenizer-arena/vocab/README.md b/spaces/eson/tokenizer-arena/vocab/README.md
deleted file mode 100644
index f9c4e49c439c79511b88385c1b64d69f6ff0bf0e..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/README.md
+++ /dev/null
@@ -1,91 +0,0 @@
-
-## 词典构建
-
-bert词典
-gpt词典
-gpt-neox词典
-
-## encode
-
-
-## decode
-
-bert词典有个特殊字符 #
-
-gpt-neox词典呢？
- - _开头表示空格或句首
-
-
-## 关于分词粒度
-
-
-## ss
-
-
-
-bert-chinese vocab_size: 21128
-bert-en
-clue
-glm
-chatglm
-bloom
-
-
-## bert
-
-```
-[PAD]
-...
-[unused99]
-[UNK]
-[CLS]
-[SEP]
-[MASK]
-
-
-!
-...
-
-big
-##ut
-ftp
-carol
-##vi
-```
-
-
-##
-
-https://github.com/pytorch/fairseq/blob/master/tests/test_noising.py#L37
-
-```
-"he@@", "llo", "n@@", "ew", "y@@", "or@@", "k"
-```
-
-跟BERT类似，只不过BERT是词后缀，这里是词前缀。
-
-
-## GPT2
-
-词典见：https://huggingface.co/gpt2/raw/main/vocab.json
-
-
-```
-['What', "'s", 'Ġup', 'Ġwith', 'Ġthe', 'Ġtoken', 'izer', '?']
-```
-跟BERT不同，BERT用特殊符号表示 “连接”，GPT2用特殊符号表示 “空格”。
-
-详见 gpt2/README.md
-
-- 功能符号： `<|endoftext|>` 表示换行。tab？ 空格？
-- 很多数字独立编码，几乎上千个。
-
-- 类似的还有：moss
-
-## 空格、tab、换行
-
-
-
-## reversible and lossless
-
-It's reversible and lossless, so you can convert tokens back into the original text
\ No newline at end of file
diff --git a/spaces/evi0mo/vits-fastapi-server/base_utils/__init__.py b/spaces/evi0mo/vits-fastapi-server/base_utils/__init__.py
deleted file mode 100644
index a996636cf98634059bf3d3d39b1c15fa0aee196e..0000000000000000000000000000000000000000
--- a/spaces/evi0mo/vits-fastapi-server/base_utils/__init__.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import io
-import os
-import subprocess
-
-os.environ["PYGAME_HIDE_SUPPORT_PROMPT"] = "hide"
-
-from pygame import mixer
-
-from scipy.io.wavfile import write
-
-
-def rm_file(file_path):
- try:
- if os.path.exists(file_path):
- os.remove(file_path)
- else:
- print(f"{file_path} not exists")
- except Exception as e:
- print(str(e))
-
-
-def ndarray_to_bytes(audio):
- byte_io = io.BytesIO(bytes())
- write(byte_io, 22050, audio)
- output = byte_io.read()
- return output
-
-
-def save_to_wav(res_path, audio):
- write(res_path, 22050, audio)
- return res_path
-
-
-def wav_to_mp3(wav_file):
- result = wav_file.replace(".wav", ".mp3")
- subprocess.run(args=f"/usr/bin/ffmpeg -i {wav_file} {result}", shell=True)
- return result
-
-
-def playsound_via_pygame(audio: bytes):
- mixer.init()
- # mixer.music.set_volume(0.75) # 音量控制 默认 1.0
- mixer.music.load(io.BytesIO(audio))
- mixer.music.play()
- while mixer.music.get_busy():
- pass
- mixer.music.unload()
- mixer.quit()
diff --git a/spaces/facebook/MusicGen/audiocraft/models/builders.py b/spaces/facebook/MusicGen/audiocraft/models/builders.py
deleted file mode 100644
index b7144874457e569d6e25fe30cafa0cddc1dd59a1..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/models/builders.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-All the functions to build the relevant models and modules
-from the Hydra config.
-"""
-
-import typing as tp
-
-import audiocraft
-import omegaconf
-import torch
-
-from .encodec import CompressionModel, EncodecModel, InterleaveStereoCompressionModel
-from .lm import LMModel
-from ..modules.codebooks_patterns import (
- CodebooksPatternProvider,
- DelayedPatternProvider,
- MusicLMPattern,
- ParallelPatternProvider,
- UnrolledPatternProvider,
- CoarseFirstPattern,
-)
-from ..modules.conditioners import (
- BaseConditioner,
- ChromaStemConditioner,
- CLAPEmbeddingConditioner,
- ConditionFuser,
- ConditioningProvider,
- LUTConditioner,
- T5Conditioner,
-)
-from .unet import DiffusionUnet
-from .. import quantization as qt
-from ..utils.utils import dict_from_config
-from ..modules.diffusion_schedule import MultiBandProcessor, SampleProcessor
-
-
-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', False)
- # deprecated params
- kwargs.pop('renorm', None)
- 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 programmatically
- 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')
- dict_cfg = {} if cfg is None else dict_from_config(cfg)
- conditioners: tp.Dict[str, BaseConditioner] = {}
- condition_provider_args = dict_cfg.pop('args', {})
- condition_provider_args.pop('merge_text_conditions_p', None)
- condition_provider_args.pop('drop_desc_p', None)
-
- for cond, cond_cfg in dict_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':
- conditioners[str(cond)] = ChromaStemConditioner(
- output_dim=output_dim,
- duration=duration,
- device=device,
- **model_args
- )
- elif model_type == 'clap':
- conditioners[str(cond)] = CLAPEmbeddingConditioner(
- output_dim=output_dim,
- 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,
- 'coarse_first': CoarseFirstPattern,
- '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', sample_rate: int = 32000):
- """Instantiate a debug compression model to be used for unit tests."""
- assert sample_rate in [16000, 32000], "unsupported sample rate for debug compression model"
- model_ratios = {
- 16000: [10, 8, 8], # 25 Hz at 16kHz
- 32000: [10, 8, 16] # 25 Hz at 32kHz
- }
- ratios: tp.List[int] = model_ratios[sample_rate]
- frame_rate = 25
- seanet_kwargs: dict = {
- 'n_filters': 4,
- 'n_residual_layers': 1,
- 'dimension': 32,
- 'ratios': ratios,
- }
- 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=frame_rate, sample_rate=sample_rate, channels=1).to(device)
- return compression_model.eval()
-
-
-def get_diffusion_model(cfg: omegaconf.DictConfig):
- # TODO Find a way to infer the channels from dset
- channels = cfg.channels
- num_steps = cfg.schedule.num_steps
- return DiffusionUnet(
- chin=channels, num_steps=num_steps, **cfg.diffusion_unet)
-
-
-def get_processor(cfg, sample_rate: int = 24000):
- sample_processor = SampleProcessor()
- if cfg.use:
- kw = dict(cfg)
- kw.pop('use')
- kw.pop('name')
- if cfg.name == "multi_band_processor":
- sample_processor = MultiBandProcessor(sample_rate=sample_rate, **kw)
- return sample_processor
-
-
-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()
-
-
-def get_wrapped_compression_model(
- compression_model: CompressionModel,
- cfg: omegaconf.DictConfig) -> CompressionModel:
- if hasattr(cfg, 'interleave_stereo_codebooks'):
- if cfg.interleave_stereo_codebooks.use:
- kwargs = dict_from_config(cfg.interleave_stereo_codebooks)
- kwargs.pop('use')
- compression_model = InterleaveStereoCompressionModel(compression_model, **kwargs)
- if hasattr(cfg, 'compression_model_n_q'):
- if cfg.compression_model_n_q is not None:
- compression_model.set_num_codebooks(cfg.compression_model_n_q)
- return compression_model
diff --git a/spaces/fatiXbelha/sd/AXES.io MOD APK Free Shopping Unlimited Coins and More.md b/spaces/fatiXbelha/sd/AXES.io MOD APK Free Shopping Unlimited Coins and More.md
deleted file mode 100644
index e9a047cb9c20d901fe5f2c1f6cf9798e7fb8f332..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/AXES.io MOD APK Free Shopping Unlimited Coins and More.md
+++ /dev/null
@@ -1,71 +0,0 @@
-
-
Axes i.o Mod Apk: A Fun and Exciting Battle Royale Game
-
Are you looking for a new and addictive game to play on your mobile device? Do you love throwing axes at your enemies and watching them explode? If yes, then you should try Axes i.o Mod Apk, a fun and exciting battle royale game that will keep you hooked for hours.
Axes i.o is a multiplayer online game where you have to survive in a chaotic arena full of other players who want to kill you. You have to throw axes at them and dodge their attacks, while collecting coins and gems to upgrade your weapons and skills. The last one standing wins the game.
-
The game features a fast-paced and action-packed gameplay with different modes and maps. You can play solo or team up with your friends in co-op mode. You can also join clans and compete with other clans in clan wars. You can choose from various maps, such as forest, desert, snow, city, etc., each with its own challenges and surprises.
-
The game also has a simple and intuitive control system that makes it easy to learn and play. You just have to swipe on the screen to move your character and tap to throw axes. You can also use special abilities, such as dash, shield, magnet, etc., to gain an edge over your enemies.
-
axes i.o hack apk download
-axes i.o mod apk unlimited money
-axes i.o mod apk latest version
-axes i.o mod apk android 1
-axes i.o mod apk free shopping
-axes i.o mod apk revdl
-axes i.o mod apk no ads
-axes i.o mod apk offline
-axes i.o mod apk all weapons unlocked
-axes i.o mod apk unlimited gems
-axes i.o mod apk 2.8.2
-axes i.o mod apk 2023
-axes i.o mod apk online
-axes i.o mod apk happymod
-axes i.o mod apk rexdl
-axes i.o mod apk pure
-axes i.o mod apk vip
-axes i.o mod apk god mode
-axes i.o mod apk unlimited coins
-axes i.o mod apk unlimited health
-axes i.o mod apk 2.7.1
-axes i.o mod apk 2.6.1
-axes i.o mod apk 2.5.1
-axes i.o mod apk 2.4.1
-axes i.o mod apk 2.3.1
-axes i.o mod menu apk download
-axes i.o hack version download apk
-download game axes io mod apk
-how to download axes io mod apk
-how to install axes io mod apk
-how to play axes io mod apk
-how to update axes io mod apk
-is axes io mod apk safe
-what is axes io mod apk
-where to download axes io mod apk
-
What is Axes i.o Mod Apk?
-
Axes i.o Mod Apk is a modified version of the original game that gives you unlimited coins and gems. With these resources, you can buy anything you want from the shop, such as weapons, skins, abilities, etc., without spending real money. You can also unlock all the weapons and skins that are otherwise locked behind a paywall.
-
The mod apk also ensures a safe and secure download that does not require root or jailbreak. You can install it on any Android device without any hassle. The mod apk does not interfere with the original game or your device performance. It also does not contain any viruses or malware that could harm your device.
-
How to Download and Install Axes i.o Mod Apk?
-
If you want to download and install Axes i.o Mod Apk on your device, you just have to follow these simple steps:
-
Step 1: Click on the download link below to get the apk file
Step 2: Allow unknown sources in your device settings to install the apk file
-
Go to your device settings and look for the security option. Tap on it and enable the unknown sources option. This will allow you to install apps from sources other than the Google Play Store.
-
Step 3: Open the apk file and follow the instructions to complete the installation
-
Locate the apk file in your device storage and tap on it to open it. You will see a pop-up window asking you to confirm the installation. Tap on install and wait for a few seconds until the installation is finished.
-
Step 4: Launch the game and enjoy the mod features
-
Once the installation is done, you can launch the game from your app drawer or home screen. You will see that you have unlimited coins and gems in your account. You can also access all the weapons and skins from the shop. You can now play the game with the mod features and have fun.
-
What are the Features of Axes i.o Mod Apk?
-
Axes i.o Mod Apk has many features that make it more enjoyable and exciting than the original game. Here are some of the features that you can enjoy with the mod apk:
-
Free Shopping: Buy anything you want from the shop without worrying about coins or gems
-
With the mod apk, you can buy anything you want from the shop, such as weapons, skins, abilities, etc., without spending any coins or gems. You can also upgrade your weapons and skills to make them more powerful and effective. You can get the best items in the game without spending real money.
-
Unlimited Coins and Gems: Get unlimited resources to upgrade your weapons and skills
-
The mod apk also gives you unlimited coins and gems in your account. You can use these resources to upgrade your weapons and skills, as well as buy new ones. You can also use them to revive yourself in case you die in the game. You will never run out of coins or gems with the mod apk.
-
All Weapons and Skins Unlocked: Choose from over 40 different weapons and skins to customize your character
-
The mod apk also unlocks all the weapons and skins that are available in the game. You can choose from over 40 different weapons, such as axes, swords, hammers, spears, etc., each with its own stats and effects. You can also choose from over 40 different skins, such as animals, robots, pirates, ninjas, etc., each with its own style and appearance. You can mix and match different weapons and skins to create your own unique character.
-
No Ads: Enjoy the game without any annoying ads or pop-ups
-
The mod apk also removes all the ads and pop-ups that appear in the original game. You can enjoy the game without any interruptions or distractions. You can also save your data and battery by not having to watch or click on any ads.
-
Conclusion
-
Axes i.o Mod Apk is a fun and exciting battle royale game that you can play with your friends or online players. You can throw axes at your enemies and dodge their attacks, while collecting coins and gems to upgrade your weapons and skills. You can also unlock all the weapons and skins with the mod apk, as well as enjoy unlimited coins and gems, free shopping, and no ads.
-
If you are looking for a new and addictive game to play on your mobile device, you should download Axes i.o Mod Apk now and experience the ultimate thrill of throwing axes at your enemies. You will not regret it.
-
Share your feedback and suggestions in the comments section below. We would love to hear from you.
- FAQs - Q: Is Axes i.o Mod Apk safe to download? - A: Yes, Axes i.o Mod Apk is safe to download. It does not contain any viruses or malware that could harm your device. It also does not require root or jailbreak to install. - Q: How do I update Axes i.o Mod Apk? - A: To update Axes i.o Mod Apk, you have to download the latest version of the mod apk from our website and install it on your device. You do not have to uninstall the previous version of the mod apk. - Q: Can I play Axes i.o Mod Apk offline? - A: No, Axes i.o Mod Apk requires an internet connection to play. You have to connect to a stable network to join online matches and access all the features of the game. - Q: Can I play Axes i.o Mod Apk with my friends? - A: Yes, you can play Axes i.o Mod Apk with your friends. - A: You can either invite your friends to join your clan or join their clan. You can also create a private room and share the code with your friends to play together. - Q: What are the best weapons and skins in Axes i.o Mod Apk? - A: The best weapons and skins in Axes i.o Mod Apk depend on your personal preference and play style. However, some of the most popular and powerful weapons are the golden axe, the fire axe, the ice axe, and the lightning axe. Some of the most cool and unique skins are the dragon, the unicorn, the robot, and the ninja. 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Cribbage The Ultimate Offline Card Game Experience.md b/spaces/fatiXbelha/sd/Cribbage The Ultimate Offline Card Game Experience.md
deleted file mode 100644
index ca243d822d951df3bcff43e7051ff5ed5766a847..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Cribbage The Ultimate Offline Card Game Experience.md
+++ /dev/null
@@ -1,117 +0,0 @@
-
- - The Crib: How to form an extra hand for the dealer. - The Play: How to lay down cards and score points. - The Show: How to reveal your hand and the crib and score more points. - Winning the Game: How to reach 121 points and beat your opponent. | | H2: Best Offline Cribbage Games for Mobile Devices | - Cribbage - Offline Card Game by SNG Games: A fun and challenging version of cribbage with customizable options and a custom betting system. - Cribbage Deluxe by Random Salad Games LLC: A free and easy-to-use cribbage game with detailed instructions and achievements. - Cribbage Pro by Fuller Systems Inc.: A classic cribbage game with online multiplayer and leaderboards. | | H2: Conclusion: Why You Should Download an Offline Cribbage Game Today | - Summary of the main points and benefits of playing cribbage offline. - Call to action: Encourage the reader to download one of the recommended games and start playing. | | H2: FAQs: Frequently Asked Questions About Offline Cribbage Games | - Q1: What are the advantages of playing cribbage offline? - Q2: How can I improve my cribbage skills? - Q3: What are some variations of cribbage that I can try? - Q4: How can I play cribbage with friends online? - Q5: Where can I learn more about cribbage? | Table 2: Article with HTML formatting
Free Download Offline Cribbage Game: How to Enjoy the Classic Card Game Anytime, Anywhere
-
Cribbage is a card game that has been enjoyed by players for centuries, and now it is available to play on your mobile device. Whether you are an experienced pro player or just starting out, you will love playing cribbage offline. In this article, you will learn how to play cribbage, what are the best offline cribbage games for mobile devices, and why you should download one of them today.
How to Play Cribbage: Basic Rules, Gameplay, and Strategy
-
Cribbage is a card game where two players try to score points by forming combinations of cards. The game is played over a series of rounds, with the first player to reach 121 points being declared the winner. Here are the basic steps of how to play cribbage:
-
Setup
-
To play cribbage, you will need a cribbage board, pegs, and a deck of cards. The cribbage board has four rows of 30 holes each, divided into two pairs of rows by a central panel. There are usually four additional holes near one end, called "game holes". With the board come four pegs, usually in two contrasting colors.
-
The players cut for first deal, with the player cutting the lowest card (the ace counts as one) dealing first. The dealer shuffles the cards and deals six cards face down to each player. The players look at their cards and discard two of them face down to form the "crib", which is an extra hand for the dealer that is scored at the end of the round.
-
The non-dealer cuts the deck and the dealer turns up the top card of the lower packet and places it face up on top of the deck. This card is called the "starter" or the "cut". If the starter is a jack, it is called "His Heels", and the dealer scores two points immediately.
-
The Play
-
The non-dealer starts by laying down one card face up on the table and announcing its value. The dealer does the same, adding its value to that of the first card. The players take turns playing cards from their hands and adding their values until one of them cannot play without exceeding 31.
-
free offline cribbage card game download
-download cribbage classic offline for free
-free cribbage game offline no internet required
-offline cribbage game free download for pc
-free cribbage app offline mode
-download free cribbage game offline for android
-free offline cribbage board game
-free cribbage game download without internet
-offline cribbage game free for windows 10
-free cribbage offline play against computer
-download cribbage offline card game for free
-free cribbage game app offline
-free offline cribbage game with custom betting
-download free cribbage classic app offline
-free cribbage game offline with discard analyzer
-offline cribbage game free for mac
-free cribbage game download no wifi needed
-offline cribbage card game free for ios
-free cribbage app offline with statistics
-download free ultimate cribbage game offline
-free cribbage game offline with difficulty modes
-offline cribbage game free for chromebook
-free cribbage game download no ads
-offline cribbage board game free for ipad
-free cribbage app offline with customization options
-
If a player cannot play without going over 31, they say "Go" and their opponent scores one point. The opponent then plays any remaining cards they can without exceeding 31 and scores any additional points for pairs, runs, or flush
s. The play then resumes with the player who said "Go" starting a new count from zero.
-
There are several ways to score points during the play:
-
-
If a player makes the total exactly 15, they score two points.
-
If a player makes the total exactly 31, they score two points.
-
If a player plays a card of the same rank as the previous card, they score two points for a pair. If the next player plays a card of the same rank, they score six points for a pair royal. If the fourth card is also of the same rank, they score 12 points for a double pair royal.
-
If a player plays a card that forms a run of three or more consecutive cards (regardless of suit) with the previous cards, they score one point for each card in the run. For example, if the cards played are 5-6-7-8, the player who played the 8 scores four points for a run of four.
-
If a player plays a card of the same suit as the starter, they score one point for a flush. If all four cards in their hand are of the same suit as the starter, they score five points for a flush.
-
-
The Show
-
After the play is over, the players show their hands and score points for combinations of cards. The non-dealer shows their hand first, followed by the dealer. The crib is then shown and scored by the dealer. The starter is used as part of every hand and crib.
-
The scoring combinations are similar to those in the play, with some differences:
-
-
A 15 is worth two points, regardless of how many cards are used to make it.
-
A pair is worth two points, a pair royal is worth six points, and a double pair royal is worth 12 points.
-
A run is worth one point per card, regardless of whether it is in sequence or broken by other cards. For example, if the hand is 7-8-9-J-Q and the starter is 10, the player scores eight points for a run of eight (7-8-9-10-J-Q).
-
A flush is worth four points if all four cards in the hand are of the same suit, and five points if all four cards in the hand and the starter are of the same suit. A flush in the crib must include the starter to count.
-
A jack of the same suit as the starter is worth one point for "His Nobs".
-
-
Winning the Game
-
The players move their pegs along the cribbage board to keep track of their scores. The first player to reach or exceed 121 points wins the game. If both players reach 121 points in the same round, the non-dealer wins as they have scored first.
-
Some variations of cribbage include "skunking" and "double skunking", where a player wins by a large margin. A skunk occurs when a player wins by more than 30 points, and a double skunk occurs when a player wins by more than 60 points. These may count as two or three games respectively in a series.
-
Best Offline Cribbage Games for Mobile Devices
-
If you want to play cribbage on your mobile device without an internet connection, you will need to download an offline cribbage game app. There are many options available on both Android and iOS platforms, but here are some of the best ones that we recommend:
-
Cribbage - Offline Card Game by SNG Games
-
This app offers a fun and challenging version of cribbage that you can play offline against an intelligent computer opponent. You can customize your game settings, such as difficulty level, board design, card style, sound effects, and more. You can also use a custom betting system that allows you to wager coins on each game and unlock new boards and cards. This app is free to download and play, but it contains ads and in-app purchases.
-
Cribbage Deluxe by Random Salad Games LLC
-
This app provides a free and easy-to-use cribbage game that you can play offline at your own pace. You can choose from three difficulty levels and learn how to play with detailed instructions and tips. You can also track your progress and achievements with statistics and leaderboards. This app has simple graphics and smooth gameplay, but it does not have many customization options or features.
-
Cribbage Pro by Fuller Systems Inc.
-
This app delivers a classic cribbage game that you can play offline solo or online with other players around the world. You can select from multiple skill levels and game modes, such as single or best of three, manual or automatic counting, muggins or no muggins, and more. You can also customize your board and cards, view your game history and stats, and compete on global leaderboards. This app is free to download and play, but it contains ads and offers in-app purchases.
-
Conclusion: Why You Should Download an Offline Cribbage Game Today
-
Cribbage is a card game that is fun, challenging, and rewarding to play. It can help you improve your math skills, strategic thinking, and memory. It can also provide you with hours of entertainment and relaxation.
-
By downloading an offline cribbage game app, you can enjoy playing cribbage anytime and anywhere, without worrying about internet connection or data usage. You can also choose from different game settings and features to suit your preferences and skill level.
-
So what are you waiting for? Download one of the best offline cribbage games for mobile devices today and start playing this classic card game. You will not regret it!
-
FAQs: Frequently Asked Questions About Offline Cribbage Games
-
Here are some of the most common questions that people have about offline cribbage games:
-
Q1: What are the advantages of playing cribbage offline?
-
A1: Playing cribbage offline has several benefits, such as:
-
-
You can play cribbage anytime and anywhere, without depending on internet access or data plan.
-
You can save battery life and storage space on your mobile device.
-
You can avoid ads and pop-ups that may interrupt your game or affect your performance.
-
You can play at your own pace and level, without feeling pressured or intimidated by other players.
-
-
Q2: How can I improve my cribbage skills?
-
A2: There are many ways to improve your cribbage skills, such as:
-
-
Practice regularly and learn from your mistakes.
-
Study the rules and scoring system of cribbage and memorize the common combinations and values.
-
Develop a strategy for discarding cards to the crib and playing cards during the play.
-
Use a cribbage calculator or a cribbage trainer app to check your moves and get feedback.
-
Read books, articles, blogs, or forums about cribbage tips and tricks.
-
Watch videos or tutorials of expert cribbage players and observe their techniques.
-
-
Q3: What are some variations of cribbage that I can try?
-
A3: There are many variations of cribbage that you can try, such as:
-
-
Six-card cribbage: The standard version of cribbage that is described in this article.
-
Five-card cribbage: A faster and simpler version of cribbage where each player is dealt five cards instead of six, and there is no starter card.
-
Seven-card cribbage: A more complex and longer version of cribbage where each player is dealt seven cards instead of six, and discards two cards to the crib.
-
Four-handed cribbage: A team version of cribbage where four players play in pairs, with partners sitting opposite each other.
-
Cribbage with jokers: A variation of cribbage where one or two jokers are added to the deck, and they can be used as wild cards to represent any rank or suit.
-
-
Q4: How can I play cribbage with friends online?
-
A4: If you want to play cribbage with friends online, you will need to download an online cribbage game app that allows multiplayer mode. Some of the best online cribbage games for mobile devices are:
-
-
Cribbage With Grandpas by Less Than Three Interactive: A charming and humorous game where you can create your own grandpa character and chat with them while playing cribbage.
-
Cribbage Club by Nickel Buddy LLC: A realistic and social game where you can join clubs, chat with other players, and earn trophies.
-
Cribbage Live by GameColony.com: A competitive and fast-paced game where you can play live cribbage matches with other players around the world.
-
-
Q5: Where can I learn more about cribbage?
-
A5: If you want to learn more about cribbage, you can visit some of the following websites:
-
-
[Cribbage Corner]: A comprehensive website that covers everything you need to know about cribbage, including rules, strategy, history, glossary, variants, software, online games, tournaments, books, and more.[American Cribbage Congress]: The official website of the American Cribbage Congress, the largest cribbage organization in the world, which promotes and sponsors cribbage tournaments and clubs across North America.
-
[Cribbage World]: The online magazine of the American Cribbage Congress, which features news, articles, puzzles, tips, and stories about cribbage.
-
[eCribbage]: A popular website that offers a variety of online cribbage games, such as Kings Cribbage, Cribbage with Jokers, Lowball Cribbage, and more.
-
-
I hope you enjoyed reading this article and learned something new about cribbage. If you have any questions or comments, please feel free to leave them below. Thank you for your time and attention.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/fffiloni/AnimateDiff-Image-Init/scripts/animate.py b/spaces/fffiloni/AnimateDiff-Image-Init/scripts/animate.py
deleted file mode 100644
index 8bb5dd74de1e11eecfd96041758c64edaa02195a..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/AnimateDiff-Image-Init/scripts/animate.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import argparse
-import datetime
-import inspect
-import os
-from omegaconf import OmegaConf
-
-import torch
-
-import diffusers
-from diffusers import AutoencoderKL, DDIMScheduler
-
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from animatediff.models.unet import UNet3DConditionModel
-from animatediff.pipelines.pipeline_animation import AnimationPipeline
-from animatediff.utils.util import save_videos_grid
-from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
-from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora
-from diffusers.utils.import_utils import is_xformers_available
-
-from einops import rearrange, repeat
-
-import csv, pdb, glob
-from safetensors import safe_open
-import math
-from pathlib import Path
-
-
-def main(args):
- *_, func_args = inspect.getargvalues(inspect.currentframe())
- func_args = dict(func_args)
-
- time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
- savedir = f"samples/{Path(args.config).stem}-{time_str}"
- os.makedirs(savedir)
- inference_config = OmegaConf.load(args.inference_config)
-
- config = OmegaConf.load(args.config)
- samples = []
-
- sample_idx = 0
- for model_idx, (config_key, model_config) in enumerate(list(config.items())):
-
- motion_modules = model_config.motion_module
- motion_modules = [motion_modules] if isinstance(motion_modules, str) else list(motion_modules)
- for motion_module in motion_modules:
-
- ### >>> create validation pipeline >>> ###
- tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
- text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder")
- vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae")
- unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
-
- if is_xformers_available(): unet.enable_xformers_memory_efficient_attention()
- else: assert False
-
- pipeline = AnimationPipeline(
- vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
- scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
- ).to("cuda")
-
- # 1. unet ckpt
- # 1.1 motion module
- motion_module_state_dict = torch.load(motion_module, map_location="cpu")
- if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]})
- missing, unexpected = pipeline.unet.load_state_dict(motion_module_state_dict, strict=False)
- assert len(unexpected) == 0
-
- # 1.2 T2I
- if model_config.path != "":
- if model_config.path.endswith(".ckpt"):
- state_dict = torch.load(model_config.path)
- pipeline.unet.load_state_dict(state_dict)
-
- elif model_config.path.endswith(".safetensors"):
- state_dict = {}
- with safe_open(model_config.path, framework="pt", device="cpu") as f:
- for key in f.keys():
- state_dict[key] = f.get_tensor(key)
-
- is_lora = all("lora" in k for k in state_dict.keys())
- if not is_lora:
- base_state_dict = state_dict
- else:
- base_state_dict = {}
- with safe_open(model_config.base, framework="pt", device="cpu") as f:
- for key in f.keys():
- base_state_dict[key] = f.get_tensor(key)
-
- # vae
- converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_state_dict, pipeline.vae.config)
- pipeline.vae.load_state_dict(converted_vae_checkpoint)
- # unet
- converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_state_dict, pipeline.unet.config)
- pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False)
- # text_model
- pipeline.text_encoder = convert_ldm_clip_checkpoint(base_state_dict)
-
- # import pdb
- # pdb.set_trace()
- if is_lora:
- pipeline = convert_lora(pipeline, state_dict, alpha=model_config.lora_alpha)
-
- pipeline.to("cuda")
- ### <<< create validation pipeline <<< ###
-
- prompts = model_config.prompt
- n_prompts = list(model_config.n_prompt) * len(prompts) if len(model_config.n_prompt) == 1 else model_config.n_prompt
-
- random_seeds = model_config.get("seed", [-1])
- random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)
- random_seeds = random_seeds * len(prompts) if len(random_seeds) == 1 else random_seeds
-
- config[config_key].random_seed = []
- for prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, n_prompts, random_seeds)):
-
- # manually set random seed for reproduction
- if random_seed != -1: torch.manual_seed(random_seed)
- else: torch.seed()
- config[config_key].random_seed.append(torch.initial_seed())
-
- print(f"current seed: {torch.initial_seed()}")
- print(f"sampling {prompt} ...")
- sample = pipeline(
- prompt,
- negative_prompt = n_prompt,
- num_inference_steps = model_config.steps,
- guidance_scale = model_config.guidance_scale,
- width = args.W,
- height = args.H,
- video_length = args.L,
- ).videos
- samples.append(sample)
-
- prompt = "-".join((prompt.replace("/", "").split(" ")[:10]))
- save_videos_grid(sample, f"{savedir}/sample/{sample_idx}-{prompt}.gif")
- print(f"save to {savedir}/sample/{prompt}.gif")
-
- sample_idx += 1
-
- samples = torch.concat(samples)
- save_videos_grid(samples, f"{savedir}/sample.gif", n_rows=4)
-
- OmegaConf.save(config, f"{savedir}/config.yaml")
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--pretrained_model_path", type=str, default="models/StableDiffusion/stable-diffusion-v1-5",)
- parser.add_argument("--inference_config", type=str, default="configs/inference/inference.yaml")
- parser.add_argument("--config", type=str, required=True)
-
- parser.add_argument("--L", type=int, default=16 )
- parser.add_argument("--W", type=int, default=512)
- parser.add_argument("--H", type=int, default=512)
-
- args = parser.parse_args()
- main(args)
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/domain.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/domain.d.ts
deleted file mode 100644
index fafe68a5d3fc413012b616cc05bdba2f661ea1af..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/domain.d.ts
+++ /dev/null
@@ -1,170 +0,0 @@
-/**
- * **This module is pending deprecation.** Once a replacement API has been
- * finalized, this module will be fully deprecated. Most developers should
- * **not** have cause to use this module. Users who absolutely must have
- * the functionality that domains provide may rely on it for the time being
- * but should expect to have to migrate to a different solution
- * in the future.
- *
- * Domains provide a way to handle multiple different IO operations as a
- * single group. If any of the event emitters or callbacks registered to a
- * domain emit an `'error'` event, or throw an error, then the domain object
- * will be notified, rather than losing the context of the error in the`process.on('uncaughtException')` handler, or causing the program to
- * exit immediately with an error code.
- * @deprecated Since v1.4.2 - Deprecated
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/domain.js)
- */
-declare module 'domain' {
- import EventEmitter = require('node:events');
- /**
- * The `Domain` class encapsulates the functionality of routing errors and
- * uncaught exceptions to the active `Domain` object.
- *
- * To handle the errors that it catches, listen to its `'error'` event.
- */
- class Domain extends EventEmitter {
- /**
- * An array of timers and event emitters that have been explicitly added
- * to the domain.
- */
- members: Array;
- /**
- * The `enter()` method is plumbing used by the `run()`, `bind()`, and`intercept()` methods to set the active domain. It sets `domain.active` and`process.domain` to the domain, and implicitly
- * pushes the domain onto the domain
- * stack managed by the domain module (see {@link exit} for details on the
- * domain stack). The call to `enter()` delimits the beginning of a chain of
- * asynchronous calls and I/O operations bound to a domain.
- *
- * Calling `enter()` changes only the active domain, and does not alter the domain
- * itself. `enter()` and `exit()` can be called an arbitrary number of times on a
- * single domain.
- */
- enter(): void;
- /**
- * The `exit()` method exits the current domain, popping it off the domain stack.
- * Any time execution is going to switch to the context of a different chain of
- * asynchronous calls, it's important to ensure that the current domain is exited.
- * The call to `exit()` delimits either the end of or an interruption to the chain
- * of asynchronous calls and I/O operations bound to a domain.
- *
- * If there are multiple, nested domains bound to the current execution context,`exit()` will exit any domains nested within this domain.
- *
- * Calling `exit()` changes only the active domain, and does not alter the domain
- * itself. `enter()` and `exit()` can be called an arbitrary number of times on a
- * single domain.
- */
- exit(): void;
- /**
- * Run the supplied function in the context of the domain, implicitly
- * binding all event emitters, timers, and lowlevel requests that are
- * created in that context. Optionally, arguments can be passed to
- * the function.
- *
- * This is the most basic way to use a domain.
- *
- * ```js
- * const domain = require('domain');
- * const fs = require('fs');
- * const d = domain.create();
- * d.on('error', (er) => {
- * console.error('Caught error!', er);
- * });
- * d.run(() => {
- * process.nextTick(() => {
- * setTimeout(() => { // Simulating some various async stuff
- * fs.open('non-existent file', 'r', (er, fd) => {
- * if (er) throw er;
- * // proceed...
- * });
- * }, 100);
- * });
- * });
- * ```
- *
- * In this example, the `d.on('error')` handler will be triggered, rather
- * than crashing the program.
- */
- run(fn: (...args: any[]) => T, ...args: any[]): T;
- /**
- * Explicitly adds an emitter to the domain. If any event handlers called by
- * the emitter throw an error, or if the emitter emits an `'error'` event, it
- * will be routed to the domain's `'error'` event, just like with implicit
- * binding.
- *
- * This also works with timers that are returned from `setInterval()` and `setTimeout()`. If their callback function throws, it will be caught by
- * the domain `'error'` handler.
- *
- * If the Timer or `EventEmitter` was already bound to a domain, it is removed
- * from that one, and bound to this one instead.
- * @param emitter emitter or timer to be added to the domain
- */
- add(emitter: EventEmitter | NodeJS.Timer): void;
- /**
- * The opposite of {@link add}. Removes domain handling from the
- * specified emitter.
- * @param emitter emitter or timer to be removed from the domain
- */
- remove(emitter: EventEmitter | NodeJS.Timer): void;
- /**
- * The returned function will be a wrapper around the supplied callback
- * function. When the returned function is called, any errors that are
- * thrown will be routed to the domain's `'error'` event.
- *
- * ```js
- * const d = domain.create();
- *
- * function readSomeFile(filename, cb) {
- * fs.readFile(filename, 'utf8', d.bind((er, data) => {
- * // If this throws, it will also be passed to the domain.
- * return cb(er, data ? JSON.parse(data) : null);
- * }));
- * }
- *
- * d.on('error', (er) => {
- * // An error occurred somewhere. If we throw it now, it will crash the program
- * // with the normal line number and stack message.
- * });
- * ```
- * @param callback The callback function
- * @return The bound function
- */
- bind(callback: T): T;
- /**
- * This method is almost identical to {@link bind}. However, in
- * addition to catching thrown errors, it will also intercept `Error` objects sent as the first argument to the function.
- *
- * In this way, the common `if (err) return callback(err);` pattern can be replaced
- * with a single error handler in a single place.
- *
- * ```js
- * const d = domain.create();
- *
- * function readSomeFile(filename, cb) {
- * fs.readFile(filename, 'utf8', d.intercept((data) => {
- * // Note, the first argument is never passed to the
- * // callback since it is assumed to be the 'Error' argument
- * // and thus intercepted by the domain.
- *
- * // If this throws, it will also be passed to the domain
- * // so the error-handling logic can be moved to the 'error'
- * // event on the domain instead of being repeated throughout
- * // the program.
- * return cb(null, JSON.parse(data));
- * }));
- * }
- *
- * d.on('error', (er) => {
- * // An error occurred somewhere. If we throw it now, it will crash the program
- * // with the normal line number and stack message.
- * });
- * ```
- * @param callback The callback function
- * @return The intercepted function
- */
- intercept(callback: T): T;
- }
- function create(): Domain;
-}
-declare module 'node:domain' {
- export * from 'domain';
-}
diff --git a/spaces/fightglory/YoloV4-Webcam/models.py b/spaces/fightglory/YoloV4-Webcam/models.py
deleted file mode 100644
index 0f0ae7e84da69f9805bc3a20463e9db488cac889..0000000000000000000000000000000000000000
--- a/spaces/fightglory/YoloV4-Webcam/models.py
+++ /dev/null
@@ -1,530 +0,0 @@
-import numpy as np
-import cv2
-import os
-import json
-from tqdm import tqdm
-from glob import glob
-import matplotlib.pyplot as plt
-import tensorflow as tf
-from tensorflow.keras import layers, models, optimizers
-
-from custom_layers import yolov4_neck, yolov4_head, nms
-from utils import load_weights, get_detection_data, draw_bbox, voc_ap, draw_plot_func, read_txt_to_list
-from config import yolo_config
-from loss import yolo_loss
-
-
-class Yolov4(object):
- def __init__(self,
- weight_path=None,
- class_name_path='coco_classes.txt',
- config=yolo_config,
- ):
- assert config['img_size'][0] == config['img_size'][1], 'not support yet'
- assert config['img_size'][0] % config['strides'][-1] == 0, 'must be a multiple of last stride'
- self.class_names = [line.strip() for line in open(class_name_path).readlines()]
- self.img_size = yolo_config['img_size']
- self.num_classes = len(self.class_names)
- self.weight_path = weight_path
- self.anchors = np.array(yolo_config['anchors']).reshape((3, 3, 2))
- self.xyscale = yolo_config['xyscale']
- self.strides = yolo_config['strides']
- self.output_sizes = [self.img_size[0] // s for s in self.strides]
- self.class_color = {name: list(np.random.random(size=3)*255) for name in self.class_names}
- # Training
- self.max_boxes = yolo_config['max_boxes']
- self.iou_loss_thresh = yolo_config['iou_loss_thresh']
- self.config = yolo_config
- assert self.num_classes > 0, 'no classes detected!'
-
- tf.keras.backend.clear_session()
- if yolo_config['num_gpu'] > 1:
- mirrored_strategy = tf.distribute.MirroredStrategy()
- with mirrored_strategy.scope():
- self.build_model(load_pretrained=True if self.weight_path else False)
- else:
- self.build_model(load_pretrained=True if self.weight_path else False)
-
- def build_model(self, load_pretrained=True):
- # core yolo model
- input_layer = layers.Input(self.img_size)
- yolov4_output = yolov4_neck(input_layer, self.num_classes)
- self.yolo_model = models.Model(input_layer, yolov4_output)
-
- # Build training model
- y_true = [
- layers.Input(name='input_2', shape=(52, 52, 3, (self.num_classes + 5))), # label small boxes
- layers.Input(name='input_3', shape=(26, 26, 3, (self.num_classes + 5))), # label medium boxes
- layers.Input(name='input_4', shape=(13, 13, 3, (self.num_classes + 5))), # label large boxes
- layers.Input(name='input_5', shape=(self.max_boxes, 4)), # true bboxes
- ]
- loss_list = tf.keras.layers.Lambda(yolo_loss, name='yolo_loss',
- arguments={'num_classes': self.num_classes,
- 'iou_loss_thresh': self.iou_loss_thresh,
- 'anchors': self.anchors})([*self.yolo_model.output, *y_true])
- self.training_model = models.Model([self.yolo_model.input, *y_true], loss_list)
-
- # Build inference model
- yolov4_output = yolov4_head(yolov4_output, self.num_classes, self.anchors, self.xyscale)
- # output: [boxes, scores, classes, valid_detections]
- self.inference_model = models.Model(input_layer,
- nms(yolov4_output, self.img_size, self.num_classes,
- iou_threshold=self.config['iou_threshold'],
- score_threshold=self.config['score_threshold']))
-
- if load_pretrained and self.weight_path and self.weight_path.endswith('.weights'):
- if self.weight_path.endswith('.weights'):
- load_weights(self.yolo_model, self.weight_path)
- print(f'load from {self.weight_path}')
- elif self.weight_path.endswith('.h5'):
- self.training_model.load_weights(self.weight_path)
- print(f'load from {self.weight_path}')
-
- self.training_model.compile(optimizer=optimizers.Adam(lr=1e-3),
- loss={'yolo_loss': lambda y_true, y_pred: y_pred})
-
- def load_model(self, path):
- self.yolo_model = models.load_model(path, compile=False)
- yolov4_output = yolov4_head(self.yolo_model.output, self.num_classes, self.anchors, self.xyscale)
- self.inference_model = models.Model(self.yolo_model.input,
- nms(yolov4_output, self.img_size, self.num_classes)) # [boxes, scores, classes, valid_detections]
-
- def save_model(self, path):
- self.yolo_model.save(path)
-
- def preprocess_img(self, img):
- img = cv2.resize(img, self.img_size[:2])
- img = img / 255.
- return img
-
- def fit(self, train_data_gen, epochs, val_data_gen=None, initial_epoch=0, callbacks=None):
- self.training_model.fit(train_data_gen,
- steps_per_epoch=len(train_data_gen),
- validation_data=val_data_gen,
- validation_steps=len(val_data_gen),
- epochs=epochs,
- callbacks=callbacks,
- initial_epoch=initial_epoch)
- # raw_img: RGB
- def predict_img(self, raw_img, random_color=True, plot_img=True, figsize=(10, 10), show_text=True, return_output=True):
- print('img shape: ', raw_img.shape)
- img = self.preprocess_img(raw_img)
- imgs = np.expand_dims(img, axis=0)
- pred_output = self.inference_model.predict(imgs)
- detections = get_detection_data(img=raw_img,
- model_outputs=pred_output,
- class_names=self.class_names)
-
- output_img = draw_bbox(raw_img, detections, cmap=self.class_color, random_color=random_color, figsize=figsize,
- show_text=show_text, show_img=False)
- if return_output:
- return output_img, detections
- else:
- return detections
-
- def predict(self, img_path, random_color=True, plot_img=True, figsize=(10, 10), show_text=True):
- raw_img = img_path
- return self.predict_img(raw_img, random_color, plot_img, figsize, show_text)
-
- def export_gt(self, annotation_path, gt_folder_path):
- with open(annotation_path) as file:
- for line in file:
- line = line.split(' ')
- filename = line[0].split(os.sep)[-1].split('.')[0]
- objs = line[1:]
- # export txt file
- with open(os.path.join(gt_folder_path, filename + '.txt'), 'w') as output_file:
- for obj in objs:
- x_min, y_min, x_max, y_max, class_id = [float(o) for o in obj.strip().split(',')]
- output_file.write(f'{self.class_names[int(class_id)]} {x_min} {y_min} {x_max} {y_max}\n')
-
- def export_prediction(self, annotation_path, pred_folder_path, img_folder_path, bs=2):
- with open(annotation_path) as file:
- img_paths = [os.path.join(img_folder_path, line.split(' ')[0].split(os.sep)[-1]) for line in file]
- # print(img_paths[:20])
- for batch_idx in tqdm(range(0, len(img_paths), bs)):
- # print(len(img_paths), batch_idx, batch_idx*bs, (batch_idx+1)*bs)
- paths = img_paths[batch_idx:batch_idx+bs]
- # print(paths)
- # read and process img
- imgs = np.zeros((len(paths), *self.img_size))
- raw_img_shapes = []
- for j, path in enumerate(paths):
- img = cv2.imread(path)
- raw_img_shapes.append(img.shape)
- img = self.preprocess_img(img)
- imgs[j] = img
-
- # process batch output
- b_boxes, b_scores, b_classes, b_valid_detections = self.inference_model.predict(imgs)
- for k in range(len(paths)):
- num_boxes = b_valid_detections[k]
- raw_img_shape = raw_img_shapes[k]
- boxes = b_boxes[k, :num_boxes]
- classes = b_classes[k, :num_boxes]
- scores = b_scores[k, :num_boxes]
- # print(raw_img_shape)
- boxes[:, [0, 2]] = (boxes[:, [0, 2]] * raw_img_shape[1]) # w
- boxes[:, [1, 3]] = (boxes[:, [1, 3]] * raw_img_shape[0]) # h
- cls_names = [self.class_names[int(c)] for c in classes]
- # print(raw_img_shape, boxes.astype(int), cls_names, scores)
-
- img_path = paths[k]
- filename = img_path.split(os.sep)[-1].split('.')[0]
- # print(filename)
- output_path = os.path.join(pred_folder_path, filename+'.txt')
- with open(output_path, 'w') as pred_file:
- for box_idx in range(num_boxes):
- b = boxes[box_idx]
- pred_file.write(f'{cls_names[box_idx]} {scores[box_idx]} {b[0]} {b[1]} {b[2]} {b[3]}\n')
-
-
- def eval_map(self, gt_folder_path, pred_folder_path, temp_json_folder_path, output_files_path):
- """Process Gt"""
- ground_truth_files_list = glob(gt_folder_path + '/*.txt')
- assert len(ground_truth_files_list) > 0, 'no ground truth file'
- ground_truth_files_list.sort()
- # dictionary with counter per class
- gt_counter_per_class = {}
- counter_images_per_class = {}
-
- gt_files = []
- for txt_file in ground_truth_files_list:
- file_id = txt_file.split(".txt", 1)[0]
- file_id = os.path.basename(os.path.normpath(file_id))
- # check if there is a correspondent detection-results file
- temp_path = os.path.join(pred_folder_path, (file_id + ".txt"))
- assert os.path.exists(temp_path), "Error. File not found: {}\n".format(temp_path)
- lines_list = read_txt_to_list(txt_file)
- # create ground-truth dictionary
- bounding_boxes = []
- is_difficult = False
- already_seen_classes = []
- for line in lines_list:
- class_name, left, top, right, bottom = line.split()
- # check if class is in the ignore list, if yes skip
- bbox = left + " " + top + " " + right + " " + bottom
- bounding_boxes.append({"class_name": class_name, "bbox": bbox, "used": False})
- # count that object
- if class_name in gt_counter_per_class:
- gt_counter_per_class[class_name] += 1
- else:
- # if class didn't exist yet
- gt_counter_per_class[class_name] = 1
-
- if class_name not in already_seen_classes:
- if class_name in counter_images_per_class:
- counter_images_per_class[class_name] += 1
- else:
- # if class didn't exist yet
- counter_images_per_class[class_name] = 1
- already_seen_classes.append(class_name)
-
- # dump bounding_boxes into a ".json" file
- new_temp_file = os.path.join(temp_json_folder_path, file_id+"_ground_truth.json") #TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json"
- gt_files.append(new_temp_file)
- with open(new_temp_file, 'w') as outfile:
- json.dump(bounding_boxes, outfile)
-
- gt_classes = list(gt_counter_per_class.keys())
- # let's sort the classes alphabetically
- gt_classes = sorted(gt_classes)
- n_classes = len(gt_classes)
- print(gt_classes, gt_counter_per_class)
-
- """Process prediction"""
-
- dr_files_list = sorted(glob(os.path.join(pred_folder_path, '*.txt')))
-
- for class_index, class_name in enumerate(gt_classes):
- bounding_boxes = []
- for txt_file in dr_files_list:
- # the first time it checks if all the corresponding ground-truth files exist
- file_id = txt_file.split(".txt", 1)[0]
- file_id = os.path.basename(os.path.normpath(file_id))
- temp_path = os.path.join(gt_folder_path, (file_id + ".txt"))
- if class_index == 0:
- if not os.path.exists(temp_path):
- error_msg = f"Error. File not found: {temp_path}\n"
- print(error_msg)
- lines = read_txt_to_list(txt_file)
- for line in lines:
- try:
- tmp_class_name, confidence, left, top, right, bottom = line.split()
- except ValueError:
- error_msg = f"""Error: File {txt_file} in the wrong format.\n
- Expected: \n
- Received: {line} \n"""
- print(error_msg)
- if tmp_class_name == class_name:
- # print("match")
- bbox = left + " " + top + " " + right + " " + bottom
- bounding_boxes.append({"confidence": confidence, "file_id": file_id, "bbox": bbox})
- # sort detection-results by decreasing confidence
- bounding_boxes.sort(key=lambda x: float(x['confidence']), reverse=True)
- with open(temp_json_folder_path + "/" + class_name + "_dr.json", 'w') as outfile:
- json.dump(bounding_boxes, outfile)
-
- """
- Calculate the AP for each class
- """
- sum_AP = 0.0
- ap_dictionary = {}
- # open file to store the output
- with open(output_files_path + "/output.txt", 'w') as output_file:
- output_file.write("# AP and precision/recall per class\n")
- count_true_positives = {}
- for class_index, class_name in enumerate(gt_classes):
- count_true_positives[class_name] = 0
- """
- Load detection-results of that class
- """
- dr_file = temp_json_folder_path + "/" + class_name + "_dr.json"
- dr_data = json.load(open(dr_file))
-
- """
- Assign detection-results to ground-truth objects
- """
- nd = len(dr_data)
- tp = [0] * nd # creates an array of zeros of size nd
- fp = [0] * nd
- for idx, detection in enumerate(dr_data):
- file_id = detection["file_id"]
- gt_file = temp_json_folder_path + "/" + file_id + "_ground_truth.json"
- ground_truth_data = json.load(open(gt_file))
- ovmax = -1
- gt_match = -1
- # load detected object bounding-box
- bb = [float(x) for x in detection["bbox"].split()]
- for obj in ground_truth_data:
- # look for a class_name match
- if obj["class_name"] == class_name:
- bbgt = [float(x) for x in obj["bbox"].split()]
- bi = [max(bb[0], bbgt[0]), max(bb[1], bbgt[1]), min(bb[2], bbgt[2]), min(bb[3], bbgt[3])]
- iw = bi[2] - bi[0] + 1
- ih = bi[3] - bi[1] + 1
- if iw > 0 and ih > 0:
- # compute overlap (IoU) = area of intersection / area of union
- ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + \
- (bbgt[2] - bbgt[0]+ 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
- ov = iw * ih / ua
- if ov > ovmax:
- ovmax = ov
- gt_match = obj
-
- min_overlap = 0.5
- if ovmax >= min_overlap:
- # if "difficult" not in gt_match:
- if not bool(gt_match["used"]):
- # true positive
- tp[idx] = 1
- gt_match["used"] = True
- count_true_positives[class_name] += 1
- # update the ".json" file
- with open(gt_file, 'w') as f:
- f.write(json.dumps(ground_truth_data))
- else:
- # false positive (multiple detection)
- fp[idx] = 1
- else:
- fp[idx] = 1
-
-
- # compute precision/recall
- cumsum = 0
- for idx, val in enumerate(fp):
- fp[idx] += cumsum
- cumsum += val
- print('fp ', cumsum)
- cumsum = 0
- for idx, val in enumerate(tp):
- tp[idx] += cumsum
- cumsum += val
- print('tp ', cumsum)
- rec = tp[:]
- for idx, val in enumerate(tp):
- rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name]
- print('recall ', cumsum)
- prec = tp[:]
- for idx, val in enumerate(tp):
- prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx])
- print('prec ', cumsum)
-
- ap, mrec, mprec = voc_ap(rec[:], prec[:])
- sum_AP += ap
- text = "{0:.2f}%".format(
- ap * 100) + " = " + class_name + " AP " # class_name + " AP = {0:.2f}%".format(ap*100)
-
- print(text)
- ap_dictionary[class_name] = ap
-
- n_images = counter_images_per_class[class_name]
- # lamr, mr, fppi = log_average_miss_rate(np.array(prec), np.array(rec), n_images)
- # lamr_dictionary[class_name] = lamr
-
- """
- Draw plot
- """
- if True:
- plt.plot(rec, prec, '-o')
- # add a new penultimate point to the list (mrec[-2], 0.0)
- # since the last line segment (and respective area) do not affect the AP value
- area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]]
- area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]]
- plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r')
- # set window title
- fig = plt.gcf() # gcf - get current figure
- fig.canvas.set_window_title('AP ' + class_name)
- # set plot title
- plt.title('class: ' + text)
- # plt.suptitle('This is a somewhat long figure title', fontsize=16)
- # set axis titles
- plt.xlabel('Recall')
- plt.ylabel('Precision')
- # optional - set axes
- axes = plt.gca() # gca - get current axes
- axes.set_xlim([0.0, 1.0])
- axes.set_ylim([0.0, 1.05]) # .05 to give some extra space
- # Alternative option -> wait for button to be pressed
- # while not plt.waitforbuttonpress(): pass # wait for key display
- # Alternative option -> normal display
- plt.show()
- # save the plot
- # fig.savefig(output_files_path + "/classes/" + class_name + ".png")
- # plt.cla() # clear axes for next plot
-
- # if show_animation:
- # cv2.destroyAllWindows()
-
- output_file.write("\n# mAP of all classes\n")
- mAP = sum_AP / n_classes
- text = "mAP = {0:.2f}%".format(mAP * 100)
- output_file.write(text + "\n")
- print(text)
-
- """
- Count total of detection-results
- """
- # iterate through all the files
- det_counter_per_class = {}
- for txt_file in dr_files_list:
- # get lines to list
- lines_list = read_txt_to_list(txt_file)
- for line in lines_list:
- class_name = line.split()[0]
- # check if class is in the ignore list, if yes skip
- # if class_name in args.ignore:
- # continue
- # count that object
- if class_name in det_counter_per_class:
- det_counter_per_class[class_name] += 1
- else:
- # if class didn't exist yet
- det_counter_per_class[class_name] = 1
- # print(det_counter_per_class)
- dr_classes = list(det_counter_per_class.keys())
-
- """
- Plot the total number of occurences of each class in the ground-truth
- """
- if True:
- window_title = "ground-truth-info"
- plot_title = "ground-truth\n"
- plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)"
- x_label = "Number of objects per class"
- output_path = output_files_path + "/ground-truth-info.png"
- to_show = False
- plot_color = 'forestgreen'
- draw_plot_func(
- gt_counter_per_class,
- n_classes,
- window_title,
- plot_title,
- x_label,
- output_path,
- to_show,
- plot_color,
- '',
- )
-
- """
- Finish counting true positives
- """
- for class_name in dr_classes:
- # if class exists in detection-result but not in ground-truth then there are no true positives in that class
- if class_name not in gt_classes:
- count_true_positives[class_name] = 0
- # print(count_true_positives)
-
- """
- Plot the total number of occurences of each class in the "detection-results" folder
- """
- if True:
- window_title = "detection-results-info"
- # Plot title
- plot_title = "detection-results\n"
- plot_title += "(" + str(len(dr_files_list)) + " files and "
- count_non_zero_values_in_dictionary = sum(int(x) > 0 for x in list(det_counter_per_class.values()))
- plot_title += str(count_non_zero_values_in_dictionary) + " detected classes)"
- # end Plot title
- x_label = "Number of objects per class"
- output_path = output_files_path + "/detection-results-info.png"
- to_show = False
- plot_color = 'forestgreen'
- true_p_bar = count_true_positives
- draw_plot_func(
- det_counter_per_class,
- len(det_counter_per_class),
- window_title,
- plot_title,
- x_label,
- output_path,
- to_show,
- plot_color,
- true_p_bar
- )
-
- """
- Draw mAP plot (Show AP's of all classes in decreasing order)
- """
- if True:
- window_title = "mAP"
- plot_title = "mAP = {0:.2f}%".format(mAP * 100)
- x_label = "Average Precision"
- output_path = output_files_path + "/mAP.png"
- to_show = True
- plot_color = 'royalblue'
- draw_plot_func(
- ap_dictionary,
- n_classes,
- window_title,
- plot_title,
- x_label,
- output_path,
- to_show,
- plot_color,
- ""
- )
-
- def predict_raw(self, img_path):
- raw_img = cv2.imread(img_path)
- print('img shape: ', raw_img.shape)
- img = self.preprocess_img(raw_img)
- imgs = np.expand_dims(img, axis=0)
- return self.yolo_model.predict(imgs)
-
- def predict_nonms(self, img_path, iou_threshold=0.413, score_threshold=0.1):
- raw_img = cv2.imread(img_path)
- print('img shape: ', raw_img.shape)
- img = self.preprocess_img(raw_img)
- imgs = np.expand_dims(img, axis=0)
- yolov4_output = self.yolo_model.predict(imgs)
- output = yolov4_head(yolov4_output, self.num_classes, self.anchors, self.xyscale)
- pred_output = nms(output, self.img_size, self.num_classes, iou_threshold, score_threshold)
- pred_output = [p.numpy() for p in pred_output]
- detections = get_detection_data(img=raw_img,
- model_outputs=pred_output,
- class_names=self.class_names)
- draw_bbox(raw_img, detections, cmap=self.class_color, random_color=True)
- return detections
-
diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/blockedunlockpickup.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/blockedunlockpickup.py
deleted file mode 100644
index 4ff8d53faeabed0dd3a00f560f70e2032c3cf440..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/blockedunlockpickup.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from gym_minigrid.minigrid import Ball
-from gym_minigrid.roomgrid import RoomGrid
-from gym_minigrid.register import register
-
-class BlockedUnlockPickup(RoomGrid):
- """
- Unlock a door blocked by a ball, then pick up a box
- in another room
- """
-
- def __init__(self, seed=None):
- room_size = 6
- super().__init__(
- num_rows=1,
- num_cols=2,
- room_size=room_size,
- max_steps=16*room_size**2,
- seed=seed
- )
-
- def _gen_grid(self, width, height):
- super()._gen_grid(width, height)
-
- # Add a box to the room on the right
- obj, _ = self.add_object(1, 0, kind="box")
- # Make sure the two rooms are directly connected by a locked door
- door, pos = self.add_door(0, 0, 0, locked=True)
- # Block the door with a ball
- color = self._rand_color()
- self.grid.set(pos[0]-1, pos[1], Ball(color))
- # Add a key to unlock the door
- self.add_object(0, 0, 'key', door.color)
-
- self.place_agent(0, 0)
-
- self.obj = obj
- self.mission = "pick up the %s %s" % (obj.color, obj.type)
-
- def step(self, action):
- obs, reward, done, info = super().step(action)
-
- if action == self.actions.pickup:
- if self.carrying and self.carrying == self.obj:
- reward = self._reward()
- done = True
-
- return obs, reward, done, info
-
-register(
- id='MiniGrid-BlockedUnlockPickup-v0',
- entry_point='gym_minigrid.envs:BlockedUnlockPickup'
-)
diff --git a/spaces/flowers-team/SocialAISchool/torch-ac/torch_ac/algos/__init__.py b/spaces/flowers-team/SocialAISchool/torch-ac/torch_ac/algos/__init__.py
deleted file mode 100644
index 0d6fa88c9723f478b3a2859e03049ab9fa1a0a68..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/torch-ac/torch_ac/algos/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from torch_ac.algos.a2c import A2CAlgo
-from torch_ac.algos.ppo import PPOAlgo
\ No newline at end of file
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/helpers/gpt4love.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/helpers/gpt4love.py
deleted file mode 100644
index 987fdbf8de5c27f7b827183d9c192dcf48d8ddcf..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/helpers/gpt4love.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import json
-import sys
-from re import findall
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-prompt = config['messages'][-1]['content']
-
-headers = {
- 'authority': 'api.gptplus.one',
- 'accept': 'application/json, text/plain, */*',
- 'accept-language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7,ja;q=0.6,zh-TW;q=0.5,zh;q=0.4',
- 'content-type': 'application/octet-stream',
- 'origin': 'https://ai.gptforlove.com/',
- 'referer': 'https://ai.gptforlove.com/',
- 'sec-ch-ua': '"Google Chrome";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-platform': '"macOS"',
- 'sec-fetch-dest': 'empty',
- 'sec-fetch-mode': 'cors',
- 'sec-fetch-site': 'cross-site',
- 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',
-}
-
-json_data = {
- 'prompt': prompt,
- 'options': {}
-}
-
-def format(chunk):
- try:
- completion_chunk = findall(r'content":"(.*)"},"fin', chunk.decode())[0]
- print(completion_chunk, flush=True, end='')
-
- except Exception as e:
- print(f'[ERROR] an error occured, retrying... | [[{chunk.decode()}]]', flush=True)
- return
-
-while True:
- try:
- response = requests.post('https://api.gptplus.one/api/chat-process',
- headers=headers, json=json_data, content_callback=format, impersonate='chrome110')
-
- exit(0)
-
- except Exception as e:
- print('[ERROR] an error occured, retrying... |', e, flush=True)
- continue
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
deleted file mode 100644
index 722d5d8d71f75486e2db3008907c4eadfca41d63..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .conv_module import ConvModule
-
-
-class DepthwiseSeparableConvModule(nn.Module):
- """Depthwise separable convolution module.
-
- See https://arxiv.org/pdf/1704.04861.pdf for details.
-
- This module can replace a ConvModule with the conv block replaced by two
- conv block: depthwise conv block and pointwise conv block. The depthwise
- conv block contains depthwise-conv/norm/activation layers. The pointwise
- conv block contains pointwise-conv/norm/activation layers. It should be
- noted that there will be norm/activation layer in the depthwise conv block
- if `norm_cfg` and `act_cfg` are specified.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- Same as that in ``nn._ConvNd``.
- out_channels (int): Number of channels produced by the convolution.
- Same as that in ``nn._ConvNd``.
- kernel_size (int | tuple[int]): Size of the convolving kernel.
- Same as that in ``nn._ConvNd``.
- stride (int | tuple[int]): Stride of the convolution.
- Same as that in ``nn._ConvNd``. Default: 1.
- padding (int | tuple[int]): Zero-padding added to both sides of
- the input. Same as that in ``nn._ConvNd``. Default: 0.
- dilation (int | tuple[int]): Spacing between kernel elements.
- Same as that in ``nn._ConvNd``. Default: 1.
- norm_cfg (dict): Default norm config for both depthwise ConvModule and
- pointwise ConvModule. Default: None.
- act_cfg (dict): Default activation config for both depthwise ConvModule
- and pointwise ConvModule. Default: dict(type='ReLU').
- dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- kwargs (optional): Other shared arguments for depthwise and pointwise
- ConvModule. See ConvModule for ref.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- dw_norm_cfg='default',
- dw_act_cfg='default',
- pw_norm_cfg='default',
- pw_act_cfg='default',
- **kwargs):
- super(DepthwiseSeparableConvModule, self).__init__()
- assert 'groups' not in kwargs, 'groups should not be specified'
-
- # if norm/activation config of depthwise/pointwise ConvModule is not
- # specified, use default config.
- dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
- dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
- pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
- pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
-
- # depthwise convolution
- self.depthwise_conv = ConvModule(
- in_channels,
- in_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=in_channels,
- norm_cfg=dw_norm_cfg,
- act_cfg=dw_act_cfg,
- **kwargs)
-
- self.pointwise_conv = ConvModule(
- in_channels,
- out_channels,
- 1,
- norm_cfg=pw_norm_cfg,
- act_cfg=pw_act_cfg,
- **kwargs)
-
- def forward(self, x):
- x = self.depthwise_conv(x)
- x = self.pointwise_conv(x)
- return x
diff --git a/spaces/glyszt/vt/vtoonify/model/raft/core/raft.py b/spaces/glyszt/vt/vtoonify/model/raft/core/raft.py
deleted file mode 100644
index a25c22f78c96470e3dca4c25e81683133ae024e3..0000000000000000000000000000000000000000
--- a/spaces/glyszt/vt/vtoonify/model/raft/core/raft.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from model.raft.core.update import BasicUpdateBlock, SmallUpdateBlock
-from model.raft.core.extractor import BasicEncoder, SmallEncoder
-from model.raft.core.corr import CorrBlock, AlternateCorrBlock
-from model.raft.core.utils.utils import bilinear_sampler, coords_grid, upflow8
-
-try:
- autocast = torch.cuda.amp.autocast
-except:
- # dummy autocast for PyTorch < 1.6
- class autocast:
- def __init__(self, enabled):
- pass
- def __enter__(self):
- pass
- def __exit__(self, *args):
- pass
-
-
-class RAFT(nn.Module):
- def __init__(self, args):
- super(RAFT, self).__init__()
- self.args = args
-
- if args.small:
- self.hidden_dim = hdim = 96
- self.context_dim = cdim = 64
- args.corr_levels = 4
- args.corr_radius = 3
-
- else:
- self.hidden_dim = hdim = 128
- self.context_dim = cdim = 128
- args.corr_levels = 4
- args.corr_radius = 4
-
- if 'dropout' not in self.args:
- self.args.dropout = 0
-
- if 'alternate_corr' not in self.args:
- self.args.alternate_corr = False
-
- # feature network, context network, and update block
- if args.small:
- self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)
- self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
- self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
-
- else:
- self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)
- self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
- self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
-
- def freeze_bn(self):
- for m in self.modules():
- if isinstance(m, nn.BatchNorm2d):
- m.eval()
-
- def initialize_flow(self, img):
- """ Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
- N, C, H, W = img.shape
- coords0 = coords_grid(N, H//8, W//8, device=img.device)
- coords1 = coords_grid(N, H//8, W//8, device=img.device)
-
- # optical flow computed as difference: flow = coords1 - coords0
- return coords0, coords1
-
- def upsample_flow(self, flow, mask):
- """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
- N, _, H, W = flow.shape
- mask = mask.view(N, 1, 9, 8, 8, H, W)
- mask = torch.softmax(mask, dim=2)
-
- up_flow = F.unfold(8 * flow, [3,3], padding=1)
- up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
-
- up_flow = torch.sum(mask * up_flow, dim=2)
- up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
- return up_flow.reshape(N, 2, 8*H, 8*W)
-
-
- def forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
- """ Estimate optical flow between pair of frames """
-
- image1 = 2 * (image1 / 255.0) - 1.0
- image2 = 2 * (image2 / 255.0) - 1.0
-
- image1 = image1.contiguous()
- image2 = image2.contiguous()
-
- hdim = self.hidden_dim
- cdim = self.context_dim
-
- # run the feature network
- with autocast(enabled=self.args.mixed_precision):
- fmap1, fmap2 = self.fnet([image1, image2])
-
- fmap1 = fmap1.float()
- fmap2 = fmap2.float()
- if self.args.alternate_corr:
- corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
- else:
- corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
-
- # run the context network
- with autocast(enabled=self.args.mixed_precision):
- cnet = self.cnet(image1)
- net, inp = torch.split(cnet, [hdim, cdim], dim=1)
- net = torch.tanh(net)
- inp = torch.relu(inp)
-
- coords0, coords1 = self.initialize_flow(image1)
-
- if flow_init is not None:
- coords1 = coords1 + flow_init
-
- flow_predictions = []
- for itr in range(iters):
- coords1 = coords1.detach()
- corr = corr_fn(coords1) # index correlation volume
-
- flow = coords1 - coords0
- with autocast(enabled=self.args.mixed_precision):
- net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
-
- # F(t+1) = F(t) + \Delta(t)
- coords1 = coords1 + delta_flow
-
- # upsample predictions
- if up_mask is None:
- flow_up = upflow8(coords1 - coords0)
- else:
- flow_up = self.upsample_flow(coords1 - coords0, up_mask)
-
- flow_predictions.append(flow_up)
-
- if test_mode:
- return coords1 - coords0, flow_up
-
- return flow_predictions
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Downloadkonamiwinningeleven8fullversionfor51 !FREE!.md b/spaces/gotiQspiryo/whisper-ui/examples/Downloadkonamiwinningeleven8fullversionfor51 !FREE!.md
deleted file mode 100644
index 54b972386b129777be5ebdf1d3ef6411463ef60d..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Downloadkonamiwinningeleven8fullversionfor51 !FREE!.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-
-nk-downloadkonamiwinningeleven8fullversionfor51 nk-downloadkonamiwinningeleven8fullversionfor51 - downloadkonamiwinningeleven8fullversionfor51 - ezdrummer authorization code keygen softwarebfdcm - js support ticket . If you are the author of this content - js support ticket for this content - js support ticket on the - free download - ezdrummer authorization code keygen softwarebfdcm - js support ticket .
-Ezdrummer - keygen - tutsoft-ru
-In general, the security system from the user's point of view should not be visible, because it may lead to an increase in hacking attempts.
-Although, if you plan to use the security system from the user's point of view, it is better to hide it. 8a78ff9644
-
-
-
diff --git a/spaces/gradio/HuBERT/examples/criss/sentence_retrieval/encoder_analysis.py b/spaces/gradio/HuBERT/examples/criss/sentence_retrieval/encoder_analysis.py
deleted file mode 100644
index b41bfbe38789ba14e6a5ea938c75d761424c00ab..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/criss/sentence_retrieval/encoder_analysis.py
+++ /dev/null
@@ -1,92 +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 glob
-
-import numpy as np
-
-
-DIM = 1024
-
-
-def compute_dist(source_embs, target_embs, k=5, return_sim_mat=False):
- target_ids = [tid for tid in target_embs]
- source_mat = np.stack(source_embs.values(), axis=0)
- normalized_source_mat = source_mat / np.linalg.norm(
- source_mat, axis=1, keepdims=True
- )
- target_mat = np.stack(target_embs.values(), axis=0)
- normalized_target_mat = target_mat / np.linalg.norm(
- target_mat, axis=1, keepdims=True
- )
- sim_mat = normalized_source_mat.dot(normalized_target_mat.T)
- if return_sim_mat:
- return sim_mat
- neighbors_map = {}
- for i, sentence_id in enumerate(source_embs):
- idx = np.argsort(sim_mat[i, :])[::-1][:k]
- neighbors_map[sentence_id] = [target_ids[tid] for tid in idx]
- return neighbors_map
-
-
-def load_embeddings(directory, LANGS):
- sentence_embeddings = {}
- sentence_texts = {}
- for lang in LANGS:
- sentence_embeddings[lang] = {}
- sentence_texts[lang] = {}
- lang_dir = f"{directory}/{lang}"
- embedding_files = glob.glob(f"{lang_dir}/all_avg_pool.{lang}.*")
- for embed_file in embedding_files:
- shard_id = embed_file.split(".")[-1]
- embeddings = np.fromfile(embed_file, dtype=np.float32)
- num_rows = embeddings.shape[0] // DIM
- embeddings = embeddings.reshape((num_rows, DIM))
-
- with open(f"{lang_dir}/sentences.{lang}.{shard_id}") as sentence_file:
- for idx, line in enumerate(sentence_file):
- sentence_id, sentence = line.strip().split("\t")
- sentence_texts[lang][sentence_id] = sentence
- sentence_embeddings[lang][sentence_id] = embeddings[idx, :]
-
- return sentence_embeddings, sentence_texts
-
-
-def compute_accuracy(directory, LANGS):
- sentence_embeddings, sentence_texts = load_embeddings(directory, LANGS)
-
- top_1_accuracy = {}
-
- top1_str = " ".join(LANGS) + "\n"
- for source_lang in LANGS:
- top_1_accuracy[source_lang] = {}
- top1_str += f"{source_lang} "
- for target_lang in LANGS:
- top1 = 0
- top5 = 0
- neighbors_map = compute_dist(
- sentence_embeddings[source_lang], sentence_embeddings[target_lang]
- )
- for sentence_id, neighbors in neighbors_map.items():
- if sentence_id == neighbors[0]:
- top1 += 1
- if sentence_id in neighbors[:5]:
- top5 += 1
- n = len(sentence_embeddings[target_lang])
- top1_str += f"{top1/n} "
- top1_str += "\n"
-
- print(top1_str)
- print(top1_str, file=open(f"{directory}/accuracy", "w"))
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(description="Analyze encoder outputs")
- parser.add_argument("directory", help="Source language corpus")
- parser.add_argument("--langs", help="List of langs")
- args = parser.parse_args()
- langs = args.langs.split(",")
- compute_accuracy(args.directory, langs)
diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/data/replabels.py b/spaces/gradio/HuBERT/examples/speech_recognition/data/replabels.py
deleted file mode 100644
index 441f1bd432b95865fc981c6c695cee299b07ed62..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/speech_recognition/data/replabels.py
+++ /dev/null
@@ -1,70 +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.
-
-"""
-Replabel transforms for use with flashlight's ASG criterion.
-"""
-
-
-def replabel_symbol(i):
- """
- Replabel symbols used in flashlight, currently just "1", "2", ...
- This prevents training with numeral tokens, so this might change in the future
- """
- return str(i)
-
-
-def pack_replabels(tokens, dictionary, max_reps):
- """
- Pack a token sequence so that repeated symbols are replaced by replabels
- """
- if len(tokens) == 0 or max_reps <= 0:
- return tokens
-
- replabel_value_to_idx = [0] * (max_reps + 1)
- for i in range(1, max_reps + 1):
- replabel_value_to_idx[i] = dictionary.index(replabel_symbol(i))
-
- result = []
- prev_token = -1
- num_reps = 0
- for token in tokens:
- if token == prev_token and num_reps < max_reps:
- num_reps += 1
- else:
- if num_reps > 0:
- result.append(replabel_value_to_idx[num_reps])
- num_reps = 0
- result.append(token)
- prev_token = token
- if num_reps > 0:
- result.append(replabel_value_to_idx[num_reps])
- return result
-
-
-def unpack_replabels(tokens, dictionary, max_reps):
- """
- Unpack a token sequence so that replabels are replaced by repeated symbols
- """
- if len(tokens) == 0 or max_reps <= 0:
- return tokens
-
- replabel_idx_to_value = {}
- for i in range(1, max_reps + 1):
- replabel_idx_to_value[dictionary.index(replabel_symbol(i))] = i
-
- result = []
- prev_token = -1
- for token in tokens:
- try:
- for _ in range(replabel_idx_to_value[token]):
- result.append(prev_token)
- prev_token = -1
- except KeyError:
- result.append(token)
- prev_token = token
- return result
diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/new/__init__.py b/spaces/gradio/HuBERT/examples/speech_recognition/new/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/scripts/filter_lexicon.py b/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/scripts/filter_lexicon.py
deleted file mode 100644
index 5bf3e51e7a50ac3f07cc41739198cde946dc79aa..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/scripts/filter_lexicon.py
+++ /dev/null
@@ -1,40 +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 sys
-
-from fairseq.data import Dictionary
-
-
-def get_parser():
- parser = argparse.ArgumentParser(
- description="filters a lexicon given a unit dictionary"
- )
- parser.add_argument("-d", "--unit-dict", help="unit dictionary", required=True)
- return parser
-
-
-def main():
- parser = get_parser()
- args = parser.parse_args()
-
- d = Dictionary.load(args.unit_dict)
- symbols = set(d.symbols)
-
- for line in sys.stdin:
- items = line.rstrip().split()
- skip = len(items) < 2
- for x in items[1:]:
- if x not in symbols:
- skip = True
- break
- if not skip:
- print(line, end="")
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/gradio/HuBERT/fairseq/data/encoders/space_tokenizer.py b/spaces/gradio/HuBERT/fairseq/data/encoders/space_tokenizer.py
deleted file mode 100644
index 925ad41b7c1aee6738c63938c36bd3ee16dca812..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/data/encoders/space_tokenizer.py
+++ /dev/null
@@ -1,21 +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 re
-
-from fairseq.data.encoders import register_tokenizer
-from fairseq.dataclass import FairseqDataclass
-
-
-@register_tokenizer("space", dataclass=FairseqDataclass)
-class SpaceTokenizer(object):
- def __init__(self, *unused):
- self.space_tok = re.compile(r"\s+")
-
- def encode(self, x: str) -> str:
- return self.space_tok.sub(" ", x)
-
- def decode(self, x: str) -> str:
- return x
diff --git "a/spaces/halek3550/thaimop/(\340\270\237\340\270\264\340\270\245\340\271\214\340\270\241HD)\342\204\242 \340\270\255\340\270\247\340\270\225\340\270\262\340\270\243: \340\270\247\340\270\264\340\270\226\340\270\265\340\271\201\340\270\253\340\271\210\340\270\207\340\270\252\340\270\262\340\270\242\340\270\231\340\271\211\340\270\263 2023 \340\270\253\340\270\231\340\270\261\340\270\207\340\271\200\340\270\225\340\271\207\340\270\241 | \340\270\224\340\270\271\340\270\255\340\270\255\340\270\231\340\271\204\340\270\245\340\270\231\340\271\214 \340\270\253\340\270\231\340\270\261\340\270\207\340\271\204\340\270\227\340\270\242SUB.md" "b/spaces/halek3550/thaimop/(\340\270\237\340\270\264\340\270\245\340\271\214\340\270\241HD)\342\204\242 \340\270\255\340\270\247\340\270\225\340\270\262\340\270\243: \340\270\247\340\270\264\340\270\226\340\270\265\340\271\201\340\270\253\340\271\210\340\270\207\340\270\252\340\270\262\340\270\242\340\270\231\340\271\211\340\270\263 2023 \340\270\253\340\270\231\340\270\261\340\270\207\340\271\200\340\270\225\340\271\207\340\270\241 | \340\270\224\340\270\271\340\270\255\340\270\255\340\270\231\340\271\204\340\270\245\340\270\231\340\271\214 \340\270\253\340\270\231\340\270\261\340\270\207\340\271\204\340\270\227\340\270\242SUB.md"
deleted file mode 100644
index 7e927e34ce0a66db79089d9a40bac394ff11948b..0000000000000000000000000000000000000000
--- "a/spaces/halek3550/thaimop/(\340\270\237\340\270\264\340\270\245\340\271\214\340\270\241HD)\342\204\242 \340\270\255\340\270\247\340\270\225\340\270\262\340\270\243: \340\270\247\340\270\264\340\270\226\340\270\265\340\271\201\340\270\253\340\271\210\340\270\207\340\270\252\340\270\262\340\270\242\340\270\231\340\271\211\340\270\263 2023 \340\270\253\340\270\231\340\270\261\340\270\207\340\271\200\340\270\225\340\271\207\340\270\241 | \340\270\224\340\270\271\340\270\255\340\270\255\340\270\231\340\271\204\340\270\245\340\270\231\340\271\214 \340\270\253\340\270\231\340\270\261\340\270\207\340\271\204\340\270\227\340\270\242SUB.md"
+++ /dev/null
@@ -1,89 +0,0 @@
-อวตาร: วิถีแห่งสายน้ำ (2023) หนังเต็ม~Thaiเต็มเรื่อง [TH] HD-Full Version
-
-ดูหนัง!! อวตาร: วิถีแห่งสายน้ำ (2023) เต็มเรื่อง - ดูหนังออนไลน์ [HD] Avatar: The Way of Water FullMovie พากย์ไทย! [ดูหนัง] อวตาร: วิถีแห่งสายน้ำ (2023) เต็มเรื่อง - ดูหนังออนไลน์ [HD] Avatar: The Way of Water พากย์ไทย อวตาร: วิถีแห่งสายน้ำ (Avatar: The Way of Water) (2023) หนังเต็ม ซับไทย ฟรี! อวตาร: วิถีแห่งสายน้ำ (Avatar: The Way of Water) ภาษาไทยย่อย ออนไลน์! ดูหนัง อวตาร: วิถีแห่งสายน้ำ (2023) เต็มเรื่อง ออนไลน์ฟรี [HD] พากย์ไทย! ดู-หนัง ▷- อวตาร: วิถีแห่งสายน้ำ (Avatar: The Way of Water) 【2023】 เต็มเรื่อง HD พากย์ไทย THAI!
-
-| 𝟜𝕂 𝕌ℍ𝔻 | 𝟙𝟘𝟠𝟘ℙ 𝔽𝕌𝕃𝕃 ℍ𝔻 | 𝟟𝟚𝟘ℙ ℍ𝔻 | 𝕄𝕂𝕍 | 𝕄ℙ𝟜 | 𝔻𝕍𝔻 | 𝔹𝕝𝕦-ℝ𝕒𝕪 |
-
-⇊⇊⇊⇊⇊⇊⇊⇊⇊⇊
-
-ดูหนังเต็มเรื่องคุณภาพสูง ขอแนะนำ!
-
-กำลังเล่น⇒ ⟹ ➟ https://123mopie.goldandautos.com/th/movie/76600
-
-กำลังเล่น⇒ ⟹ ➟ https://123mopie.goldandautos.com/th/movie/76600
-
-ผ่านไปกว่า 10 ปี จากเหตุการณ์ในภาพยนตร์ภาคแรก “อวตาร: วิถีแห่งสายน้ำ” เริ่มต้นด้วยการเล่าเรื่องราวของครอบครัวซัลลี่ (เจค เนย์ทีรี่ และลูก ๆ ของพวกเขา) และปัญหาที่พวกเขาต้องเผชิญ ทั้งการพยายามอย่างหนักเพื่อปกป้องสมาชิกในครอบครัว การต่อสู้เอาชีวิตรอด และโศกนาฏกรรมที่พวกเขาต้องพบเจอ
-
-เปิดตัว: 2022-12-14
-เวลา: 192 นาที
-ประเภท: นิยายวิทยาศาสตร์, ผจญ, บู๊
-ดาว: Sam Worthington, Zoe Saldaña, Sigourney Weaver, Stephen Lang, Kate Winslet
-ผู้กำกับ: Stephen E. Rivkin, David Valdes, David Valdes, Josh Friedman, Christopher Boyes
-
-เรื่องย่อ
-
-ผ่านไปกว่า 10 ปี จากเหตุการณ์ในภาพยนตร์ภาคแรก “อวตาร: วิถีแห่งสายน้ำ” เริ่มต้นด้วยการเล่าเรื่องราวของครอบครัวซัลลี่ (เจค เนย์ทีรี่ และลูก ๆ ของพวกเขา) และปัญหาที่พวกเขาต้องเผชิญ ทั้งการพยายามอย่างหนักเพื่อปกป้องสมาชิกในครอบครัว การต่อสู้เอาชีวิตรอด และโศกนาฏกรรมที่พวกเขาต้องพบเจอ
-
-กำกับโดยเจมส์ คาเมรอน แสดงโดย แซม เวิร์ธธิงตัน โซอี ซัลดานา ซิกอร์นีย์ วีเวอร์ สตีเฟน แลง คลิฟ เคอร์ติส โจเอล เดวิด มัวร์ ซีซีเอส พาวเดอร์ เอดี้ ฟอลโก เจไมน์ คลีเมนท์ จิโอวานนี่ ริบิซี่ และ เคท วิทสเล็ท บทภาพยนตร์โดยเจมส์ คาเมรอน ริค แจฟฟา และ อแมนด้า ซิลเวอร์ เรื่องโดย เจมส์ คาเมรอน ริค แจฟฟา อแมนด้า ซิลเวอร์ จอร์จ ฟรีดแมน และ เชน ซาเลอร์โน่ อำนวยการสร้างโดย เจมส์ คาเมรอน และ จอห์น แลนเดา ร่วมด้วย เดวิด วัลเดส และ ริชาร์ด เบนแฮม ในฐานะผู้อำนวยการสร้างฝ่ายบริหาร อวตาร: วิถีแห่งสายน้ำเข้าฉายในโรงภาพยนตร์ 14 ธันวาคม 2565
-
-วันนี้!.. อวตาร: วิถีแห่งสายน้ำ/Avatar: The Way of Water หนังเต็มออนไลน์ [THAI-SUB]. อวตาร: วิถีแห่งสายน้ำ (2023) ดูหนังออนไลน์เต็มเรื่องฟรี HD1080 . อวตาร: วิถีแห่งสายน้ำ (2023) | อวตาร: วิถีแห่งสายน้ำ (2023) หนังเต็มออนไลน์. นาฬิกา อวตาร: วิถีแห่งสายน้ำ ภาพยนตร์ (THAI) คำบรรยาย. อวตาร: วิถีแห่งสายน้ำ หนังเต็มเรื่องในโรงภาพยนตร์ THAI. อวตาร: วิถีแห่งสายน้ำ ดูออนไลน์ฟรี (THAI) . อวตาร: วิถีแห่งสายน้ำ เต็ม HD 1080p ฟรีออนไลน์ . Avatar: The Way of Water หนังเต็มเรื่องฟรี HD . รับชมอวตาร: วิถีแห่งสายน้ำออนไลน์ (2023) สตรีมมิ่งประเทศไทยเต็มรูปแบบ HD (1080i) . พร้อมให้ดาวน์โหลด อวตาร: วิถีแห่งสายน้ำ (2023)Avatar: The Way of Water HD,DB,720,1080,4K, MKV, ความละเอียดสูงพิเศษ
-
-ออนไลน์ฟรีAvatar: The Way of Water Full Movie/อวตาร: วิถีแห่งสายน้ำ ฟรี 2023 เต็มเรื่อง พากย์ไทย! Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ ฟรี เต็มเรื่องดูหนังออนไลน์ ออนไลน์ฟรี พากย์ไท! Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ (2023) ดูออนไลน์เต็มเรื่อง (ซับไทย/ซับไทย) ดูหนัง-HD Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ 【2023】ซูม เต็มเรื่อง THAI SUB ดูหนัง Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ 2023 เต็มเรื่อง THAI – พากย์ไทย HD [ดูหนัง] Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ (2023) เต็มเรื่อง - ดูหนังออนไลน์ [HD] พากย์ไทย [หนังไทย-HD] Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ ดูออนไลน์ หนังเต็ม [พากย์ ไทย] Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำHDดูหนังออนไลน์ ดู Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ ภาพยนตร์เต็มเรื่อง 2023 ออนไลน์ Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ Re-หนังเต็ม เรื่องราว ลิงก์ที่รั่วไหล ดู บุAvatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ ออนไลน์สตรีมมิ่ง Avatar: The Way of Water /อวตาร: วิถีแห่งสายน้ำ ภาพยนตร์เต็มเรื่องออนไลน์ฟรี 2023 อวตาร: วิถีแห่งสายน้ำ Re-Releaseภาพยนตร์เต็มเรื่องออนไลน์ HD
-
-[.ดูหนัง-HD] อวตาร: วิถีแห่งสายน้ำ ดูออนไลน์ฟรี [2023] พากย์!
-
-ดูหนัง อวตาร: วิถีแห่งสายน้ำ Avatar: The Way of Water (2023) เต็มเรื่อง HD พากย์ไทย THAI!
-
-ดู Avatar: The Way of Water เต็มเรื่อง (2023)ดูออ!
-
-อวตาร: วิถีแห่งสายน้ำ (2023) Avatar: The Way of Water เต็มเรื่อง - ดูหนังออนไลน์
-
-ดูหนัง Avatar: The Way of Water (2023) อวตาร: วิถีแห่งสายน้ำ ดูหนังออนไลน์ฟรี บรรยายซับไทย HD!
-
-ดูหนัง อวตาร: วิถีแห่งสายน้ำ (2023) เต็มเรื่อง - ดูหนังออนไลน์ THAI!
-
-ดูหนัง อวตาร: วิถีแห่งสายน้ำ (Avatar: The Way of Water 2023) ดูหนังออนไลน์ ซับไทย!
-
-ดูหนัง อวตาร: วิถีแห่งสายน้ำ (2023) เต็มเรื่อง HD พากย์ไทย THAI!
-
-[ดูหนัง] อวตาร: วิถีแห่งสายน้ำ (2023) เต็มเรื่อง - ดูหนังออนไลน์ [HD] พากย์ไทย!
-
-อวตาร: วิถีแห่งสายน้ำ (Avatar: The Way of Water) ภาษาไทยย่อย!
-
-อวตาร: วิถีแห่งสายน้ำ 【2023】 เต็มเรื่อง HD พากย์ไทย หนังออนไลน์เรื่องเต็ม THAI!
-
-ดู อวตาร: วิถีแห่งสายน้ำ 【2023】 เต็มเรื่อง - ดูหนังออนไลน์ภาษาไทยย่อย!
-
-ดูหนัง อวตาร: วิถีแห่งสายน้ำ(2023) THAI HD ซับไทย เต็มเรื่อง!
-
-ดู-หนัง อวตาร: วิถีแห่งสายน้ำ - Avatar: The Way of Water 2023 [HD] เต็มเรื่อง - ซับไทย!
-
-อวตาร: วิถีแห่งสายน้ำ 【2023】 เต็มเรื่อง HD พากย์ไทย THAI
-
-ดูหนัง-HD อวตาร: วิถีแห่งสายน้ำ (2023) ดูหนัง Avatar: The Way of Water เต็มเรื่อง - ดูหนังออนไลน์ THAI!
-
-[ดูหนัง] อวตาร: วิถีแห่งสายน้ำ(2023) เต็มเรื่อง HD พากย์ไทย THAI!
-
-อวตาร: วิถีแห่งสายน้ำ (Avatar: The Way of Water) (2023) หนังเต็ม ซับไทย ฟรี!
-
-❍❍❍ ทีวีฟิล์ม ❍❍❍
-
-การปรากฏตัวทางโทรทัศน์ครั้งแรกของเขาเป็นการทดลองเป็นระยะ ๆ ซึ่งตั้งแต่ทศวรรษที่ 1930 สามารถดูได้ในระยะใกล้ ๆ จากเสากระโดงเรือ รายการโทรทัศน์ เช่น โอลิมปิกฤดูร้อนปี 1936 ในเยอรมนี และพิธีบรมราชาภิเษกของกษัตริย์จอร์จที่ 6 ในสหราชอาณาจักรในปี 19340 และด้วยการเปิดตัวที่มีชื่อเสียงของ David Sarnoff ในงาน New York World's Fair ในปี 1939 ในสหรัฐอเมริกา สื่อยังคงเติบโต แต่สงครามโลกครั้งที่สองทำให้การพัฒนาหลังสงครามหยุดชะงัก ภาพยนตร์โลกในปี 1944 เป็นแรงบันดาลใจให้ชาวอเมริกันจำนวนมากซื้อโทรทัศน์เครื่องแรก ในปี 1948 สถานีวิทยุ Texaco Star Theatre ที่ได้รับความนิยมได้กลายเป็นรายการวาไรตี้ทางโทรทัศน์รายสัปดาห์รายการแรก โดยมี Milton Berle เป็นผู้ดำเนินรายการ และได้รับฉายาว่า "Mr. Television" ซึ่งเป็นการพิสูจน์ว่าสื่อนี้มีความเสถียรและสามารถดึงดูดผู้ลงโฆษณาได้เนื่องจากเป็นรูปแบบความบันเทิงสมัยใหม่ เมื่อวันที่ 4 กันยายน พ.ศ. 2494 การถ่ายทอดสดทางโทรทัศน์ระดับชาติครั้งแรกในสหรัฐอเมริกา เมื่อประธานาธิบดีแฮร์รี ทรูแมนกล่าวสุนทรพจน์เกี่ยวกับระบบถ่ายทอดสัญญาณไมโครเวฟและเคเบิลข้ามทวีปของ AT&T ในการประชุมสันติภาพญี่ปุ่นในซานฟรานซิสโก ซึ่งเป็นการออกอากาศของตลาดท้องถิ่นโดยผู้ประกาศ . ใช่.
-
-เมื่อวันที่ 1 มกราคม พ.ศ. 2497 การแสดงสีระดับชาติครั้งแรก (การแข่งขัน Rose Parade Championships ในปี พ.ศ. 2497) เกิดขึ้นในสหรัฐอเมริกา ในทศวรรษต่อมา วิทยุอินเทอร์เน็ตส่วนใหญ่และวิทยุท้องถิ่นเกือบทั้งหมดยังคงออกอากาศเป็นขาวดำ สวิตช์สีได้รับการประกาศในฤดูใบไม้ร่วงปี 2508 โดยมากกว่าครึ่งหนึ่งของรายการช่วงไพรม์ไทม์ของเครือข่ายออกอากาศด้วยสี หนึ่งปีต่อมา ฤดูแห่งสีสันเต็มรูปแบบครั้งแรกก็มาถึง ในปี 19402 การสนับสนุนครั้งสุดท้ายของการแสดงเครือข่ายในเวลากลางวันถูกแปลงเป็นฤดูกาลเครือข่ายสีเต็มรูปแบบครั้งแรก
-
-❍❍❍ รูปแบบและประเภท ❍❍❍
-
-ดูเพิ่มเติมที่: รายชื่อประเภท § รูปแบบและประเภทภาพยนตร์และทีวี
-
-รายการโทรทัศน์มีความหลากหลายมากกว่าสื่ออื่นๆ ส่วนใหญ่ เนื่องจากรูปแบบและประเภทที่สามารถนำเสนอได้หลากหลาย การแสดงอาจเป็นเรื่องแต่งขึ้น (เช่น ละครตลกและละคร) หรือสารคดี (เช่น สารคดี ข่าว และรายการเรียลลิตี้โชว์) อาจเป็นปัจจุบัน (เช่น รายการข่าวท้องถิ่นและภาพยนตร์โทรทัศน์บางเรื่อง) หรือประวัติศาสตร์ (เช่น สารคดีและภาพยนตร์สมมติ) พวกเขาสามารถให้ความรู้หรือความบันเทิงได้เช่นเดียวกับคอเมดี้และเกมโชว์ [ต้องการอ้างอิง]
-
-รายการละครมักจะประกอบด้วยชุดของนักแสดงที่มีบทบาทในฉากทางประวัติศาสตร์หรือสมัยใหม่ รายการติดตามชีวิตและการผจญภัยของพวกเขา ก่อนทศวรรษที่ 1980 การแสดง (ยกเว้นซีรีส์ละครน้ำเน่า) โดยทั่วไปจะคงที่ ไม่มีโครงเรื่องและมีการเปลี่ยนแปลงเล็กน้อยในตัวละครหลักและเนื้อเรื่อง [ต้องการอ้างอิง] หากมีสิ่งใดเปลี่ยนแปลงในชีวิตของตัวละครในระหว่างตอน ก็มักจะกลับกันในตอนท้าย ดังนั้นจึงสามารถออกอากาศตอนต่างๆ ในลำดับใดก็ได้ [ต้องการอ้างอิง] ตั้งแต่ทศวรรษที่ 1980 ภาพยนตร์หลายเรื่องมีการเปลี่ยนแปลงอย่างค่อยเป็นค่อยไปในเรื่องโครงเรื่อง ตัวละคร หรือทั้งสองอย่าง ตัวอย่างเช่น Hill Street Blues และ St. Elsewhere เป็นภาพยนตร์โทรทัศน์ช่วงไพร์มไทม์เรื่องแรกในสหรัฐอเมริกาที่มีโครงสร้างที่น่าทึ่งนี้[4] [ต้องการแหล่งข้อมูลที่ดีกว่านี้] และภาพยนตร์เรื่องต่อมา Babylon 5 ก็แสดงโครงสร้างนี้เพิ่มเติมด้วยเรื่องราวที่กำหนดไว้ล่วงหน้า เกี่ยวกับห้าฤดูกาลที่วางแผนไว้ [ต้องการอ้างอิง]
-
-ตามรายงาน ภายในปี 2565 ทีวีจะมีสัดส่วนรายได้ของบริษัทสื่อขนาดใหญ่มากกว่าภาพยนตร์ บางคนสังเกตเห็นว่าคุณภาพของรายการทีวีบางรายการเพิ่มขึ้น ในปี 2023 สตีเว่น โซเดอร์เบิร์ก ผู้กำกับภาพยนตร์เจ้าของรางวัลออสการ์ได้ประกาศถึงความคลุมเครือและความซับซ้อนของตัวละครและการเล่าเรื่อง: "ฉันคิดว่าคุณสมบัติเหล่านี้มีอยู่ในโทรทัศน์แล้วในตอนนี้ และผู้ที่ต้องการดูพวกเขากำลังดูทีวี
-
-❍❍❍ขอบคุณสำหรับทุกสิ่งและขอให้สนุกกับการรับชม❍❍❍
-
-ที่นี่ คุณจะพบภาพยนตร์ทั้งหมดที่มีให้สตรีม รวมถึงภาพยนตร์ที่จะเข้าฉายในสัปดาห์นี้ หากคุณสงสัยว่าจะดูอะไรในไซต์นี้ คุณควรทราบว่าไซต์นี้ครอบคลุมประเภทต่างๆ เช่น อาชญากรรม วิทยาศาสตร์ ภาพยนตร์ ภาพยนตร์ รัก ระทึกขวัญ ตลก ดราม่า และภาพยนตร์อนิเมะ
-
-ขอบคุณมาก. เราแจ้งให้ทุกคนที่ต้องการรับข่าวสารหรือข้อมูลเกี่ยวกับโปรแกรมภาพยนตร์ในปีนี้และวิธีรับชมรายการโปรดของคุณ หวังว่าเราจะเป็นพันธมิตรที่ดีที่สุดในการค้นหาคำแนะนำสำหรับภาพยนตร์เรื่องโปรดของคุณ ขอแสดงความนับถือ!
\ No newline at end of file
diff --git a/spaces/hamelcubsfan/AutoGPT/tests/integration/weaviate_memory_tests.py b/spaces/hamelcubsfan/AutoGPT/tests/integration/weaviate_memory_tests.py
deleted file mode 100644
index 015eab05484f485aeb8ee035e92ad7811e9dddd4..0000000000000000000000000000000000000000
--- a/spaces/hamelcubsfan/AutoGPT/tests/integration/weaviate_memory_tests.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os
-import sys
-import unittest
-from unittest import mock
-from uuid import uuid4
-
-from weaviate import Client
-from weaviate.util import get_valid_uuid
-
-from autogpt.config import Config
-from autogpt.memory.base import get_ada_embedding
-from autogpt.memory.weaviate import WeaviateMemory
-
-
-class TestWeaviateMemory(unittest.TestCase):
- cfg = None
- client = None
- index = None
-
- @classmethod
- def setUpClass(cls):
- # only create the connection to weaviate once
- cls.cfg = Config()
-
- if cls.cfg.use_weaviate_embedded:
- from weaviate.embedded import EmbeddedOptions
-
- cls.client = Client(
- embedded_options=EmbeddedOptions(
- hostname=cls.cfg.weaviate_host,
- port=int(cls.cfg.weaviate_port),
- persistence_data_path=cls.cfg.weaviate_embedded_path,
- )
- )
- else:
- cls.client = Client(
- f"{cls.cfg.weaviate_protocol}://{cls.cfg.weaviate_host}:{self.cfg.weaviate_port}"
- )
-
- cls.index = WeaviateMemory.format_classname(cls.cfg.memory_index)
-
- """
- In order to run these tests you will need a local instance of
- Weaviate running. Refer to https://weaviate.io/developers/weaviate/installation/docker-compose
- for creating local instances using docker.
- Alternatively in your .env file set the following environmental variables to run Weaviate embedded (see: https://weaviate.io/developers/weaviate/installation/embedded):
-
- USE_WEAVIATE_EMBEDDED=True
- WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate"
- """
-
- def setUp(self):
- try:
- self.client.schema.delete_class(self.index)
- except:
- pass
-
- self.memory = WeaviateMemory(self.cfg)
-
- def test_add(self):
- doc = "You are a Titan name Thanos and you are looking for the Infinity Stones"
- self.memory.add(doc)
- result = self.client.query.get(self.index, ["raw_text"]).do()
- actual = result["data"]["Get"][self.index]
-
- self.assertEqual(len(actual), 1)
- self.assertEqual(actual[0]["raw_text"], doc)
-
- def test_get(self):
- doc = "You are an Avenger and swore to defend the Galaxy from a menace called Thanos"
-
- with self.client.batch as batch:
- batch.add_data_object(
- uuid=get_valid_uuid(uuid4()),
- data_object={"raw_text": doc},
- class_name=self.index,
- vector=get_ada_embedding(doc),
- )
-
- batch.flush()
-
- actual = self.memory.get(doc)
-
- self.assertEqual(len(actual), 1)
- self.assertEqual(actual[0], doc)
-
- def test_get_stats(self):
- docs = [
- "You are now about to count the number of docs in this index",
- "And then you about to find out if you can count correctly",
- ]
-
- [self.memory.add(doc) for doc in docs]
-
- stats = self.memory.get_stats()
-
- self.assertTrue(stats)
- self.assertTrue("count" in stats)
- self.assertEqual(stats["count"], 2)
-
- def test_clear(self):
- docs = [
- "Shame this is the last test for this class",
- "Testing is fun when someone else is doing it",
- ]
-
- [self.memory.add(doc) for doc in docs]
-
- self.assertEqual(self.memory.get_stats()["count"], 2)
-
- self.memory.clear()
-
- self.assertEqual(self.memory.get_stats()["count"], 0)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/hands012/gpt-academic/config.py b/spaces/hands012/gpt-academic/config.py
deleted file mode 100644
index c3f0192e09f6d4b380a29cba5ff5918a3a8c5c94..0000000000000000000000000000000000000000
--- a/spaces/hands012/gpt-academic/config.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# [step 1]>> 例如： API_KEY = "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" （此key无效）
-API_KEY = "sk-此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
-
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
-USE_PROXY = False
-if USE_PROXY:
- # 填写格式是 [协议]:// [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
- # 例如 "socks5h://localhost:11284"
- # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
- # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
- # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
-
- # 代理网络的地址，打开你的*学*网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
- proxies = {
- # [协议]:// [地址] :[端口]
- "http": "socks5h://localhost:11284", # 再例如 "http": "http://127.0.0.1:7890",
- "https": "socks5h://localhost:11284", # 再例如 "https": "http://127.0.0.1:7890",
- }
-else:
- proxies = None
-
-# [step 3]>> 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
-# 一言以蔽之：免费用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 3
-
-
-# [step 4]>> 以下配置可以优化体验，但大部分场合下并不需要修改
-# 对话窗的高度
-CHATBOT_HEIGHT = 1115
-
-# 代码高亮
-CODE_HIGHLIGHT = True
-
-# 窗口布局
-LAYOUT = "LEFT-RIGHT" # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-DARK_MODE = True # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-
-# 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 30
-
-# 网页的端口, -1代表随机端口
-WEB_PORT = -1
-
-# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 2
-
-# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
-LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
-AVAIL_LLM_MODELS = ["newbing-free", "gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
-
-# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
-LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
-
-# 设置gradio的并行线程数（不需要修改）
-CONCURRENT_COUNT = 100
-
-# 加一个live2d装饰
-ADD_WAIFU = False
-
-# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-# [("username", "password"), ("username2", "password2"), ...]
-AUTHENTICATION = []
-
-# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!!）
-# （高危设置！通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
-# 格式 {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://ai.open.com/api/conversation"}
-API_URL_REDIRECT = {}
-
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
-CUSTOM_PATH = "/"
-
-# 如果需要使用newbing，把newbing的长长的cookie放到这里
-NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
-# 从现在起，如果您调用"newbing-free"模型，则无需填写NEWBING_COOKIES
-NEWBING_COOKIES = """
-your bing cookies here
-"""
-
-# 如果需要使用Slack Claude，使用教程详情见 request_llm/README.md
-SLACK_CLAUDE_BOT_ID = ''
-SLACK_CLAUDE_USER_TOKEN = ''
diff --git a/spaces/hands012/gpt-academic/request_llm/bridge_newbing.py b/spaces/hands012/gpt-academic/request_llm/bridge_newbing.py
deleted file mode 100644
index 2136f01beb3edd25b94dd8048c20b63a14ef905e..0000000000000000000000000000000000000000
--- a/spaces/hands012/gpt-academic/request_llm/bridge_newbing.py
+++ /dev/null
@@ -1,254 +0,0 @@
-"""
-========================================================================
-第一部分：来自EdgeGPT.py
-https://github.com/acheong08/EdgeGPT
-========================================================================
-"""
-from .edge_gpt import NewbingChatbot
-load_message = "等待NewBing响应。"
-
-"""
-========================================================================
-第二部分：子进程Worker（调用主体）
-========================================================================
-"""
-import time
-import json
-import re
-import logging
-import asyncio
-import importlib
-import threading
-from toolbox import update_ui, get_conf, trimmed_format_exc
-from multiprocessing import Process, Pipe
-
-def preprocess_newbing_out(s):
- pattern = r'\^(\d+)\^' # 匹配^数字^
- sub = lambda m: '('+m.group(1)+')' # 将匹配到的数字作为替换值
- result = re.sub(pattern, sub, s) # 替换操作
- if '[1]' in result:
- result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
- return result
-
-def preprocess_newbing_out_simple(result):
- if '[1]' in result:
- result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
- return result
-
-class NewBingHandle(Process):
- def __init__(self):
- super().__init__(daemon=True)
- self.parent, self.child = Pipe()
- self.newbing_model = None
- self.info = ""
- self.success = True
- self.local_history = []
- self.check_dependency()
- self.start()
- self.threadLock = threading.Lock()
-
- def check_dependency(self):
- try:
- self.success = False
- import certifi, httpx, rich
- self.info = "依赖检测通过，等待NewBing响应。注意目前不能多人同时调用NewBing接口（有线程锁），否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时，会自动使用已配置的代理。"
- self.success = True
- except:
- self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
- self.success = False
-
- def ready(self):
- return self.newbing_model is not None
-
- async def async_run(self):
- # 读取配置
- NEWBING_STYLE, = get_conf('NEWBING_STYLE')
- from request_llm.bridge_all import model_info
- endpoint = model_info['newbing']['endpoint']
- while True:
- # 等待
- kwargs = self.child.recv()
- question=kwargs['query']
- history=kwargs['history']
- system_prompt=kwargs['system_prompt']
-
- # 是否重置
- if len(self.local_history) > 0 and len(history)==0:
- await self.newbing_model.reset()
- self.local_history = []
-
- # 开始问问题
- prompt = ""
- if system_prompt not in self.local_history:
- self.local_history.append(system_prompt)
- prompt += system_prompt + '\n'
-
- # 追加历史
- for ab in history:
- a, b = ab
- if a not in self.local_history:
- self.local_history.append(a)
- prompt += a + '\n'
- # if b not in self.local_history:
- # self.local_history.append(b)
- # prompt += b + '\n'
-
- # 问题
- prompt += question
- self.local_history.append(question)
- print('question:', prompt)
- # 提交
- async for final, response in self.newbing_model.ask_stream(
- prompt=question,
- conversation_style=NEWBING_STYLE, # ["creative", "balanced", "precise"]
- wss_link=endpoint, # "wss://sydney.bing.com/sydney/ChatHub"
- ):
- if not final:
- print(response)
- self.child.send(str(response))
- else:
- print('-------- receive final ---------')
- self.child.send('[Finish]')
- # self.local_history.append(response)
-
-
- def run(self):
- """
- 这个函数运行在子进程
- """
- # 第一次运行，加载参数
- self.success = False
- self.local_history = []
- if (self.newbing_model is None) or (not self.success):
- # 代理设置
- proxies, = get_conf('proxies')
- if proxies is None:
- self.proxies_https = None
- else:
- self.proxies_https = proxies['https']
- # cookie
- NEWBING_COOKIES, = get_conf('NEWBING_COOKIES')
- try:
- cookies = json.loads(NEWBING_COOKIES)
- except:
- self.success = False
- tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
- self.child.send(f'[Local Message] 不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
- raise RuntimeError(f"不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。")
-
- try:
- self.newbing_model = NewbingChatbot(proxy=self.proxies_https, cookies=cookies)
- except:
- self.success = False
- tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
- self.child.send(f'[Local Message] 不能加载Newbing组件。{tb_str}')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
- raise RuntimeError(f"不能加载Newbing组件。")
-
- self.success = True
- try:
- # 进入任务等待状态
- asyncio.run(self.async_run())
- except Exception:
- tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
- self.child.send(f'[Local Message] Newbing失败 {tb_str}.')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
-
- def stream_chat(self, **kwargs):
- """
- 这个函数运行在主进程
- """
- self.threadLock.acquire()
- self.parent.send(kwargs) # 发送请求到子进程
- while True:
- res = self.parent.recv() # 等待newbing回复的片段
- if res == '[Finish]':
- break # 结束
- elif res == '[Fail]':
- self.success = False
- break
- else:
- yield res # newbing回复的片段
- self.threadLock.release()
-
-
-"""
-========================================================================
-第三部分：主进程统一调用函数接口
-========================================================================
-"""
-global newbing_handle
-newbing_handle = None
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
- """
- 多线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- global newbing_handle
- if (newbing_handle is None) or (not newbing_handle.success):
- newbing_handle = NewBingHandle()
- observe_window[0] = load_message + "\n\n" + newbing_handle.info
- if not newbing_handle.success:
- error = newbing_handle.info
- newbing_handle = None
- raise RuntimeError(error)
-
- # 没有 sys_prompt 接口，因此把prompt加入 history
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]] )
-
- watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
- response = ""
- observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
- for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
- observe_window[0] = preprocess_newbing_out_simple(response)
- if len(observe_window) >= 2:
- if (time.time()-observe_window[1]) > watch_dog_patience:
- raise RuntimeError("程序终止。")
- return preprocess_newbing_out_simple(response)
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
- """
- 单线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
-
- global newbing_handle
- if (newbing_handle is None) or (not newbing_handle.success):
- newbing_handle = NewBingHandle()
- chatbot[-1] = (inputs, load_message + "\n\n" + newbing_handle.info)
- yield from update_ui(chatbot=chatbot, history=[])
- if not newbing_handle.success:
- newbing_handle = None
- return
-
- if additional_fn is not None:
- import core_functional
- importlib.reload(core_functional) # 热更新prompt
- core_functional = core_functional.get_core_functions()
- if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
- inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]] )
-
- chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
- response = "[Local Message]: 等待NewBing响应中 ..."
- yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
- for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
- chatbot[-1] = (inputs, preprocess_newbing_out(response))
- yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
- if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常，请刷新界面重试 ..."
- history.extend([inputs, response])
- logging.info(f'[raw_input] {inputs}')
- logging.info(f'[response] {response}')
- yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")
-
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/INSTALL.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/INSTALL.md
deleted file mode 100644
index 3985f8ae4f5ecde26b310b4ab01c49b922f742e9..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/INSTALL.md
+++ /dev/null
@@ -1,184 +0,0 @@
-## Installation
-
-Our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-has step-by-step instructions that install detectron2.
-The [Dockerfile](docker)
-also installs detectron2 with a few simple commands.
-
-### Requirements
-- Linux or macOS with Python ≥ 3.6
-- PyTorch ≥ 1.4
-- [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
- You can install them together at [pytorch.org](https://pytorch.org) to make sure of this.
-- OpenCV, optional, needed by demo and visualization
-- pycocotools: `pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'`
-
-
-### Build Detectron2 from Source
-
-gcc & g++ ≥ 5 are required. [ninja](https://ninja-build.org/) is recommended for faster build.
-After having them, run:
-```
-python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
-# (add --user if you don't have permission)
-
-# Or, to install it from a local clone:
-git clone https://github.com/facebookresearch/detectron2.git
-python -m pip install -e detectron2
-
-# Or if you are on macOS
-# CC=clang CXX=clang++ python -m pip install -e .
-```
-
-To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the
-old build first. You often need to rebuild detectron2 after reinstalling PyTorch.
-
-### Install Pre-Built Detectron2 (Linux only)
-```
-# for CUDA 10.1:
-python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/index.html
-```
-You can replace cu101 with "cu{100,92}" or "cpu".
-
-Note that:
-1. Such installation has to be used with certain version of official PyTorch release.
- See [releases](https://github.com/facebookresearch/detectron2/releases) for requirements.
- It will not work with a different version of PyTorch or a non-official build of PyTorch.
-2. Such installation is out-of-date w.r.t. master branch of detectron2. It may not be
- compatible with the master branch of a research project that uses detectron2 (e.g. those in
- [projects](projects) or [meshrcnn](https://github.com/facebookresearch/meshrcnn/)).
-
-### Common Installation Issues
-
-If you met issues using the pre-built detectron2, please uninstall it and try building it from source.
-
-Click each issue for its solutions:
-
-
-
-Undefined torch/aten/caffe2 symbols, or segmentation fault immediately when running the library.
-
-
-
-This usually happens when detectron2 or torchvision is not
-compiled with the version of PyTorch you're running.
-
-Pre-built torchvision or detectron2 has to work with the corresponding official release of pytorch.
-If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them
-following [pytorch.org](http://pytorch.org). So the versions will match.
-
-If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases)
-to see the corresponding pytorch version required for each pre-built detectron2.
-
-If the error comes from detectron2 or torchvision that you built manually from source,
-remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment.
-
-If you cannot resolve this problem, please include the output of `gdb -ex "r" -ex "bt" -ex "quit" --args python -m detectron2.utils.collect_env`
-in your issue.
-
-
-
-
-Undefined C++ symbols (e.g. `GLIBCXX`) or C++ symbols not found.
-
-
-Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
-
-This often happens with old anaconda.
-Try `conda update libgcc`. Then rebuild detectron2.
-
-The fundamental solution is to run the code with proper C++ runtime.
-One way is to use `LD_PRELOAD=/path/to/libstdc++.so`.
-
-
-
-
-
-"Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
-
-
-CUDA is not found when building detectron2.
-You should make sure
-
-```
-python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
-```
-
-print valid outputs at the time you build detectron2.
-
-Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config.
-
-
-
-
-"invalid device function" or "no kernel image is available for execution".
-
-
-Two possibilities:
-
-* You build detectron2 with one version of CUDA but run it with a different version.
-
- To check whether it is the case,
- use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
- In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
- to contain cuda libraries of the same version.
-
- When they are inconsistent,
- you need to either install a different build of PyTorch (or build by yourself)
- to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-
-* Detectron2 or PyTorch/torchvision is not built for the correct GPU architecture (compute compatibility).
-
- The GPU architecture for PyTorch/detectron2/torchvision is available in the "architecture flags" in
- `python -m detectron2.utils.collect_env`.
-
- The GPU architecture flags of detectron2/torchvision by default matches the GPU model detected
- during compilation. This means the compiled code may not work on a different GPU model.
- To overwrite the GPU architecture for detectron2/torchvision, use `TORCH_CUDA_ARCH_LIST` environment variable during compilation.
-
- For example, `export TORCH_CUDA_ARCH_LIST=6.0,7.0` makes it compile for both P100s and V100s.
- Visit [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus) to find out
- the correct compute compatibility number for your device.
-
-
-
-
-
-Undefined CUDA symbols; cannot open libcudart.so; other nvcc failures.
-
-
-The version of NVCC you use to build detectron2 or torchvision does
-not match the version of CUDA you are running with.
-This often happens when using anaconda's CUDA runtime.
-
-Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
-In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
-to contain cuda libraries of the same version.
-
-When they are inconsistent,
-you need to either install a different build of PyTorch (or build by yourself)
-to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-
-
-
-
-
-"ImportError: cannot import name '_C'".
-
-
-Please build and install detectron2 following the instructions above.
-
-If you are running code from detectron2's root directory, `cd` to a different one.
-Otherwise you may not import the code that you installed.
-
-
-
-
-ONNX conversion segfault after some "TraceWarning".
-
-
-The ONNX package is compiled with too old compiler.
-
-Please build and install ONNX from its source code using a compiler
-whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
-
diff --git a/spaces/hayas/rinna-youri-7b-chat/README.md b/spaces/hayas/rinna-youri-7b-chat/README.md
deleted file mode 100644
index 10502498eed35e310c5a62af76357adf4c90a0b4..0000000000000000000000000000000000000000
--- a/spaces/hayas/rinna-youri-7b-chat/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: rinna Youri-7B-chat
-emoji: ⚡
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 4.0.2
-app_file: app.py
-pinned: false
-license: mit
-suggested-hardware: t4-small
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/.github/PULL_REQUEST_TEMPLATE.md b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index d96d5afd283618f483e28c0f29c8d8886c3520ae..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,13 +0,0 @@
-
-
-copilot:all
diff --git a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Title-Thief-Of-Shadows-Maiden-Lane-Author-Elizabeth-Hoyt-rar-HOT.md b/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Title-Thief-Of-Shadows-Maiden-Lane-Author-Elizabeth-Hoyt-rar-HOT.md
deleted file mode 100644
index 0e06fd8deaca76700c049c8929bdaeb77c9b2836..0000000000000000000000000000000000000000
--- a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Title-Thief-Of-Shadows-Maiden-Lane-Author-Elizabeth-Hoyt-rar-HOT.md
+++ /dev/null
@@ -1,55 +0,0 @@
-## Title: Thief Of Shadows (Maiden Lane) Author: Elizabeth Hoyt ....rar
-
-
-
-**LINK ► [https://ditzcosupo.blogspot.com/?d=2twsj5](https://ditzcosupo.blogspot.com/?d=2twsj5)**
-
-
-
-# Thief of Shadows: A Thrilling Romance by Elizabeth Hoyt
-
-
-
-If you are looking for a historical romance that will keep you on the edge of your seat, you might want to check out Thief of Shadows by Elizabeth Hoyt. This is the fourth book in the Maiden Lane series, but it can be read as a standalone novel.
-
-
-
-The story follows Winter Makepeace, the headmaster of a home for orphaned children by day and the masked vigilante known as the Ghost of St. Giles by night. He is determined to protect the poor and innocent from the evils of London's underworld, even if it means risking his own life.
-
-
-
-One night, he is rescued from a brutal attack by Lady Isabel Beckinhall, a wealthy and witty aristocrat who has a penchant for adventure. She is intrigued by the mysterious Ghost and offers to tutor Winter in the art of society and seduction. But as they grow closer, they discover that they have more in common than they thought.
-
-
-
-However, their romance is threatened by a sinister plot that involves kidnapping and murder. Winter must stop the villains before they harm more innocent lives, but he might have to sacrifice everything he holds dear - his home, his identity, and his love.
-
-
-
-Thief of Shadows is a captivating and sensual read that will transport you to the 18th century London. Elizabeth Hoyt is a master of creating complex and compelling characters, vivid settings, and thrilling plots. She also writes with humor and passion, making her books a delight to read.
-
-
-
-If you are a fan of historical romance, you should not miss Thief of Shadows by Elizabeth Hoyt. It is available in Kindle, audiobook, hardcover, paperback, and preloaded digital audio player formats. You can find it on Amazon.com or Goodreads.com.
-
-
-
-Thief of Shadows is part of the Maiden Lane series, which features different couples in each book. The series is set in a fictional area of London called Maiden Lane, where the rich and the poor live side by side. The series also introduces the Ghost of St. Giles, a mysterious hero who fights crime and injustice in the dark alleys of the city.
-
-
-
-The author, Elizabeth Hoyt, is a New York Times bestselling author of historical romance. She has written more than twenty-three books, including the Maiden Lane series, the Legend of the Four Soldiers series, and the Princes trilogy. She also writes contemporary romance under the name Julia Harper. She lives in Minneapolis, Minnesota with her husband and three dogs.
-
-
-
-If you want to learn more about Thief of Shadows and other books by Elizabeth Hoyt, you can visit her website at elizabethhoyt.com. You can also follow her on social media platforms such as Facebook, Twitter, Instagram, and Pinterest. You can also sign up for her newsletter to get updates on her latest releases and exclusive content.
-
-
-
-Thief of Shadows is a book that will appeal to readers who enjoy historical romance with a touch of mystery and suspense. The book has received many positive reviews from critics and readers alike. For example, Publishers Weekly praised the book as "a thrilling and romantic adventure." Romantic Times gave the book 4.5 stars and said that "Hoyt's writing is almost too good to be true." All About Romance called the book "a wonderful addition to the Maiden Lane series."
-
-
-
-If you are looking for a book that will keep you entertained and engaged, you should give Thief of Shadows a try. You will not regret it. You can order your copy today from Amazon.com or Goodreads.com. You can also check out the other books in the Maiden Lane series, such as Wicked Intentions, Notorious Pleasures, Scandalous Desires, Lord of Darkness, Duke of Midnight, Darling Beast, Dearest Rogue, Sweetest Scoundrel, Duke of Sin, Duke of Pleasure, Duke of Desire, and Once Upon a Maiden Lane.
-
- 1b8d091108
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/loss_functions/TopK_loss.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/loss_functions/TopK_loss.py
deleted file mode 100644
index 322305ec76691926d1fed9140ad0a4f5ab1dc21e..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/loss_functions/TopK_loss.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import torch
-from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss
-
-
-class TopKLoss(RobustCrossEntropyLoss):
- """
- Network has to have NO LINEARITY!
- """
- def __init__(self, weight=None, ignore_index=-100, k=10):
- self.k = k
- super(TopKLoss, self).__init__(weight, False, ignore_index, reduce=False)
-
- def forward(self, inp, target):
- target = target[:, 0].long()
- res = super(TopKLoss, self).forward(inp, target)
- num_voxels = np.prod(res.shape, dtype=np.int64)
- res, _ = torch.topk(res.view((-1, )), int(num_voxels * self.k / 100), sorted=False)
- return res.mean()
diff --git a/spaces/hocaionline/ComfyUI_Free/README.md b/spaces/hocaionline/ComfyUI_Free/README.md
deleted file mode 100644
index 6807431bd947cc6b5dd3d63e59af2dfd6c59a958..0000000000000000000000000000000000000000
--- a/spaces/hocaionline/ComfyUI_Free/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Free ComfyUI
-emoji: 🚀
-colorFrom: green
-colorTo: pink
-sdk: static
-license: creativeml-openrail-m
-duplicated_from: diffle/ComfyUI
----
-
-
-
ComfyUI
-
This is UI for everyone! Generate image for FREE!
\ No newline at end of file
diff --git a/spaces/huang4414/anime-aesthetic-predict/README.md b/spaces/huang4414/anime-aesthetic-predict/README.md
deleted file mode 100644
index 03ba62d4e4ec165198c18a657785a23a48f91223..0000000000000000000000000000000000000000
--- a/spaces/huang4414/anime-aesthetic-predict/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anime Aesthetic Predict
-emoji: ❤️🖼️
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hudawang/sydney/README.md b/spaces/hudawang/sydney/README.md
deleted file mode 100644
index 54c3921fa32add7465551a911f0f0d36a26beb19..0000000000000000000000000000000000000000
--- a/spaces/hudawang/sydney/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: sydney
-emoji: 🐢
-colorFrom: yellow
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-duplicated_from: dongsiqie/sydney
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/hzy123/bingo/src/components/chat-header.tsx b/spaces/hzy123/bingo/src/components/chat-header.tsx
deleted file mode 100644
index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000
--- a/spaces/hzy123/bingo/src/components/chat-header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import LogoIcon from '@/assets/images/logo.svg'
-import Image from 'next/image'
-
-export function ChatHeader() {
- return (
-
-
-Barcode Software For Mac More by author All Quizzes | Page 2
-
-Barcode Software For Mac | eBay
-
-Barcode Software For Mac/PC | Unique Software Store
-
-Barcode Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac/Windows. BARCODE Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac. BARCODE Software For Mac Download
-
-Barcode Software For Mac/Windows | File Name: BARCODE
-
-Barcode Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac/Windows. BARCODE Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac. BARCODE Software For Mac Download.
-
-Barcode Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac/Windows. BARCODE Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac.
-
-Barcode Software For Mac. BARCODE Software For Mac Download. BARCODE Software For Mac/Windows. BARCODE Software For Mac. B 4fefd39f24
-
-
-
diff --git a/spaces/inamXcontru/PoeticTTS/DRK Device Root Key For Samsung Galaxy How It Affects Your Device and How To Fix It.md b/spaces/inamXcontru/PoeticTTS/DRK Device Root Key For Samsung Galaxy How It Affects Your Device and How To Fix It.md
deleted file mode 100644
index 3d8e237482c01e9484835295bee3e269d7dae9f0..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/DRK Device Root Key For Samsung Galaxy How It Affects Your Device and How To Fix It.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
How To Fix DRK – Device Root Key For Samsung Galaxy...
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/inamXcontru/PoeticTTS/Dan Brown Inferno Versi Indonesia Download.md b/spaces/inamXcontru/PoeticTTS/Dan Brown Inferno Versi Indonesia Download.md
deleted file mode 100644
index 98aa666953c54ac9838c3042aa598f8a7077ad81..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Dan Brown Inferno Versi Indonesia Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Duplicate files - how to remove them on windows (with. Related Downloads::. DOCS: Crack as of V3.
-
-duplicate files - how to remove them on windows (with windows 7 ultimate x64) - скачать
-
-Duplicate Files Remover is a Windows utility which you can use to delete duplicate files from your hard disk. In previous versions of Duplicate Files Remover the application could only delete duplicate files from your My Documents and Desktop directories. The latest version 3.0 now includes the ability to remove duplicate files from your system or external drive.
-
-Duplicate Files Remover is a Windows utility which you can use to delete duplicate files from your hard disk. Duplicate Files Remover, an excellent utility to remove duplicate files from your hard disk. Features: - remove duplicate files from local disks and external drives - multiple file selection - delete files with specified file properties - a powerful tool to solve the problem of unwanted duplicates - can be used in any situation where you want to remove duplicate files and folders (more than one file). Download Duplicate Files Remover Windows now from Softpedia.
-
-Duplicate Files Remover Crack. Best Duplicate Files Remover Crack which is very helpful in removing duplicate files and folders from your computer without any difficulty. Take a look at the list of software and utilities, you will need in order to remove duplicate files from your computer. Duplicate Files Remover Crack is a software used to remove duplicate files. This software is very easy and simple to use. This software can remove duplicate files from your PC, either if it is on your internal drive or external drive. The software is free and it will remove duplicate files from your computer automatically.
-
-Growl - Growl is a high-performance notification system for the Mac OS X platform that lets users have an unobtrusive display of important information in their system tray area. Growl can display notifications from a variety of sources, such as the current system activity, the Internet, and e-mail messages. It supports events like battery charge levels, disk usage, and URL changes. Growl is designed to work silently in the background, and to update itself as new information is available. It is intended for use by Power Users.
-
-This crack for Duplicate files remover - duplicatenotify will help to remove duplicate files on your computer without overwriting any of your files. You can have the ability to 4fefd39f24
-
-
-
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Motorolacpscommercialseriescpsr0513.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Motorolacpscommercialseriescpsr0513.md
deleted file mode 100644
index 40ad20aef95ef5167a0902819127063d16683080..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Motorolacpscommercialseriescpsr0513.md
+++ /dev/null
@@ -1,26 +0,0 @@
-
-
How to Use Motorola CPS Commercial Series Software
-
The Motorola CPS Commercial Series software is a tool that allows you to program and manage your Motorola business radios. You can use it to customize the features and settings of your radios, such as frequencies, channels, power levels, scan lists, privacy codes, and more. You can also use it to update the firmware of your radios and backup or restore your data.
-
In this article, we will show you how to download, install, and use the Motorola CPS Commercial Series software for your compatible radios. We will also provide some tips and troubleshooting advice for common issues.
The Motorola CPS Commercial Series software is a free download from the Motorola Solutions website. You can find it under the Products > Two-Way Radios > Commercial Business Two-Way Radio Systems > Business Radio Software section. The software is compatible with Windows 7, 8, and 10 operating systems.
-
To download the software, you will need to create a free account on the Motorola Solutions website and accept the terms and conditions. You will also need a USB cable to connect your radio to your computer. The software supports the following radio models: CLS series, CLP Series, Curve, DLR series, DTR series, RDX series and RM series radios.
-
Once you have downloaded the software, you can run the installer and follow the instructions on the screen. The installation process may take a few minutes and may require you to restart your computer. After the installation is complete, you can launch the software from your desktop or start menu.
-
Using the Software
-
To use the software, you will need to connect your radio to your computer using the USB cable. Make sure your radio is turned off before connecting it. Then, turn on your radio and wait for the software to detect it. You should see a message on the screen saying "Radio Detected".
-
Next, you can read or write data from or to your radio. To read data from your radio, click on the "Read" button on the toolbar. This will display the current settings of your radio on the screen. You can then modify any of the settings as you wish. To write data to your radio, click on the "Write" button on the toolbar. This will save your changes to your radio.
-
You can also use the software to update the firmware of your radio or backup or restore your data. To update the firmware of your radio, click on the "Update" button on the toolbar. This will check for any available updates for your radio model and download them if needed. Then, follow the instructions on the screen to complete the update process. To backup or restore your data, click on the "Backup/Restore" button on the toolbar. This will allow you to save or load a file containing all of your radio settings.
-
Tips and Troubleshooting
-
Here are some tips and troubleshooting advice for using the Motorola CPS Commercial Series software:
-
-
-
Make sure you have a compatible radio model and USB cable before using the software.
-
Make sure you have enough battery power on your radio before connecting it to your computer or performing any operations.
-
Make sure you have a stable internet connection before downloading or updating the software or firmware.
-
Do not disconnect or turn off your radio or computer while reading, writing, updating, or backing up data.
-
If you encounter any errors or issues while using the software, try restarting your computer or reinstalling the software.
-
If you need more help or support, you can contact Motorola Solutions customer service or visit their website for more information.
-
d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/inreVtussa/clothingai/Examples/Condor Plane Pack 2 Crack UPD.md b/spaces/inreVtussa/clothingai/Examples/Condor Plane Pack 2 Crack UPD.md
deleted file mode 100644
index 778904417d707d647144e270e67456fe7f69bb01..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Condor Plane Pack 2 Crack UPD.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Condor - The Competition Soaring Simulator all versions serial number and keygen, Condor - The ... Soaring Simulator crack, Condor - The Competition Soaring Simulator activation key, Condor - The ... NHL Simulator 3.1.2 1178 Times. Flight ... 1fdad05405
-
-
-
diff --git a/spaces/ismot/1702t1/README.md b/spaces/ismot/1702t1/README.md
deleted file mode 100644
index 10fb3abf37869b141bd1ef0568e1044b8555afce..0000000000000000000000000000000000000000
--- a/spaces/ismot/1702t1/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: 3D Room Layout Estimation LGT-Net
-emoji: 🕋
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.5
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: zhigangjiang/3D-Room-Layout-Estimation_LGT-Net
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/j43fer/MagicPrompt-Stable-Diffusion/app.py b/spaces/j43fer/MagicPrompt-Stable-Diffusion/app.py
deleted file mode 100644
index 4ef904995708514c1b101049af73529e23c1a3ab..0000000000000000000000000000000000000000
--- a/spaces/j43fer/MagicPrompt-Stable-Diffusion/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from transformers import pipeline, set_seed
-import gradio as grad, random, re
-
-
-gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
-with open("ideas.txt", "r") as f:
- line = f.readlines()
-
-
-def generate(starting_text):
- seed = random.randint(100, 1000000)
- set_seed(seed)
-
- if starting_text == "":
- starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
- starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
-
- response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
- response_list = []
- for x in response:
- resp = x['generated_text'].strip()
- if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
- response_list.append(resp+'\n')
-
- response_end = "\n".join(response_list)
- response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
- response_end = response_end.replace("<", "").replace(">", "")
-
- if response_end != "":
- return response_end
-
-
-txt = grad.Textbox(lines=1, label="Initial Text", placeholder="English Text here")
-out = grad.Textbox(lines=4, label="Generated Prompts")
-
-examples = []
-for x in range(8):
- examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize())
-
-title = "Stable Diffusion Prompt Generator"
-description = 'This is a demo of the model series: "MagicPrompt", in this case, aimed at: "Stable Diffusion". To use it, simply submit your text or click on one of the examples. To learn more about the model, [click here](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion). '
-
-grad.Interface(fn=generate,
- inputs=txt,
- outputs=out,
- examples=examples,
- title=title,
- description=description,
- article='',
- allow_flagging='never',
- cache_examples=False,
- theme="default").launch(enable_queue=True, debug=True)
-
-
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/generation_parameters_copypaste.py b/spaces/jackli888/stable-diffusion-webui/modules/generation_parameters_copypaste.py
deleted file mode 100644
index 3bab793d67baf1fdc598775722f1e44083c94d95..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/generation_parameters_copypaste.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import base64
-import html
-import io
-import math
-import os
-import re
-from pathlib import Path
-
-import gradio as gr
-from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks
-import tempfile
-from PIL import Image
-
-re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
-re_param = re.compile(re_param_code)
-re_imagesize = re.compile(r"^(\d+)x(\d+)$")
-re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
-type_of_gr_update = type(gr.update())
-
-paste_fields = {}
-registered_param_bindings = []
-
-
-class ParamBinding:
- def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None):
- self.paste_button = paste_button
- self.tabname = tabname
- self.source_text_component = source_text_component
- self.source_image_component = source_image_component
- self.source_tabname = source_tabname
- self.override_settings_component = override_settings_component
-
-
-def reset():
- paste_fields.clear()
-
-
-def quote(text):
- if ',' not in str(text):
- return text
-
- text = str(text)
- text = text.replace('\\', '\\\\')
- text = text.replace('"', '\\"')
- return f'"{text}"'
-
-
-def image_from_url_text(filedata):
- if filedata is None:
- return None
-
- if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
- filedata = filedata[0]
-
- if type(filedata) == dict and filedata.get("is_file", False):
- filename = filedata["name"]
- is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
- assert is_in_right_dir, 'trying to open image file outside of allowed directories'
-
- return Image.open(filename)
-
- if type(filedata) == list:
- if len(filedata) == 0:
- return None
-
- filedata = filedata[0]
-
- if filedata.startswith("data:image/png;base64,"):
- filedata = filedata[len("data:image/png;base64,"):]
-
- filedata = base64.decodebytes(filedata.encode('utf-8'))
- image = Image.open(io.BytesIO(filedata))
- return image
-
-
-def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
- paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
-
- # backwards compatibility for existing extensions
- import modules.ui
- if tabname == 'txt2img':
- modules.ui.txt2img_paste_fields = fields
- elif tabname == 'img2img':
- modules.ui.img2img_paste_fields = fields
-
-
-def create_buttons(tabs_list):
- buttons = {}
- for tab in tabs_list:
- buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
- return buttons
-
-
-def bind_buttons(buttons, send_image, send_generate_info):
- """old function for backwards compatibility; do not use this, use register_paste_params_button"""
- for tabname, button in buttons.items():
- source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
- source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
-
- register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname))
-
-
-def register_paste_params_button(binding: ParamBinding):
- registered_param_bindings.append(binding)
-
-
-def connect_paste_params_buttons():
- binding: ParamBinding
- for binding in registered_param_bindings:
- destination_image_component = paste_fields[binding.tabname]["init_img"]
- fields = paste_fields[binding.tabname]["fields"]
- override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
-
- destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
- destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
-
- if binding.source_image_component and destination_image_component:
- if isinstance(binding.source_image_component, gr.Gallery):
- func = send_image_and_dimensions if destination_width_component else image_from_url_text
- jsfunc = "extract_image_from_gallery"
- else:
- func = send_image_and_dimensions if destination_width_component else lambda x: x
- jsfunc = None
-
- binding.paste_button.click(
- fn=func,
- _js=jsfunc,
- inputs=[binding.source_image_component],
- outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
- )
-
- if binding.source_text_component is not None and fields is not None:
- connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
-
- if binding.source_tabname is not None and fields is not None:
- paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
- binding.paste_button.click(
- fn=lambda *x: x,
- inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
- outputs=[field for field, name in fields if name in paste_field_names],
- )
-
- binding.paste_button.click(
- fn=None,
- _js=f"switch_to_{binding.tabname}",
- inputs=None,
- outputs=None,
- )
-
-
-def send_image_and_dimensions(x):
- if isinstance(x, Image.Image):
- img = x
- else:
- img = image_from_url_text(x)
-
- if shared.opts.send_size and isinstance(img, Image.Image):
- w = img.width
- h = img.height
- else:
- w = gr.update()
- h = gr.update()
-
- return img, w, h
-
-
-
-def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
- """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
-
- Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
- parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
-
- If the infotext has no hash, then a hypernet with the same name will be selected instead.
- """
- hypernet_name = hypernet_name.lower()
- if hypernet_hash is not None:
- # Try to match the hash in the name
- for hypernet_key in shared.hypernetworks.keys():
- result = re_hypernet_hash.search(hypernet_key)
- if result is not None and result[1] == hypernet_hash:
- return hypernet_key
- else:
- # Fall back to a hypernet with the same name
- for hypernet_key in shared.hypernetworks.keys():
- if hypernet_key.lower().startswith(hypernet_name):
- return hypernet_key
-
- return None
-
-
-def restore_old_hires_fix_params(res):
- """for infotexts that specify old First pass size parameter, convert it into
- width, height, and hr scale"""
-
- firstpass_width = res.get('First pass size-1', None)
- firstpass_height = res.get('First pass size-2', None)
-
- if shared.opts.use_old_hires_fix_width_height:
- hires_width = int(res.get("Hires resize-1", 0))
- hires_height = int(res.get("Hires resize-2", 0))
-
- if hires_width and hires_height:
- res['Size-1'] = hires_width
- res['Size-2'] = hires_height
- return
-
- if firstpass_width is None or firstpass_height is None:
- return
-
- firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
- width = int(res.get("Size-1", 512))
- height = int(res.get("Size-2", 512))
-
- if firstpass_width == 0 or firstpass_height == 0:
- from modules import processing
- firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)
-
- res['Size-1'] = firstpass_width
- res['Size-2'] = firstpass_height
- res['Hires resize-1'] = width
- res['Hires resize-2'] = height
-
-
-def parse_generation_parameters(x: str):
- """parses generation parameters string, the one you see in text field under the picture in UI:
-```
-girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate
-Negative prompt: ugly, fat, obese, chubby, (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing
-Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hash: 45dee52b
-```
-
- returns a dict with field values
- """
-
- res = {}
-
- prompt = ""
- negative_prompt = ""
-
- done_with_prompt = False
-
- *lines, lastline = x.strip().split("\n")
- if len(re_param.findall(lastline)) < 3:
- lines.append(lastline)
- lastline = ''
-
- for i, line in enumerate(lines):
- line = line.strip()
- if line.startswith("Negative prompt:"):
- done_with_prompt = True
- line = line[16:].strip()
-
- if done_with_prompt:
- negative_prompt += ("" if negative_prompt == "" else "\n") + line
- else:
- prompt += ("" if prompt == "" else "\n") + line
-
- res["Prompt"] = prompt
- res["Negative prompt"] = negative_prompt
-
- for k, v in re_param.findall(lastline):
- v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
- m = re_imagesize.match(v)
- if m is not None:
- res[k+"-1"] = m.group(1)
- res[k+"-2"] = m.group(2)
- else:
- res[k] = v
-
- # Missing CLIP skip means it was set to 1 (the default)
- if "Clip skip" not in res:
- res["Clip skip"] = "1"
-
- hypernet = res.get("Hypernet", None)
- if hypernet is not None:
- res["Prompt"] += f""""""
-
- if "Hires resize-1" not in res:
- res["Hires resize-1"] = 0
- res["Hires resize-2"] = 0
-
- restore_old_hires_fix_params(res)
-
- return res
-
-
-settings_map = {}
-
-infotext_to_setting_name_mapping = [
- ('Clip skip', 'CLIP_stop_at_last_layers', ),
- ('Conditional mask weight', 'inpainting_mask_weight'),
- ('Model hash', 'sd_model_checkpoint'),
- ('ENSD', 'eta_noise_seed_delta'),
- ('Noise multiplier', 'initial_noise_multiplier'),
- ('Eta', 'eta_ancestral'),
- ('Eta DDIM', 'eta_ddim'),
- ('Discard penultimate sigma', 'always_discard_next_to_last_sigma')
-]
-
-
-def create_override_settings_dict(text_pairs):
- """creates processing's override_settings parameters from gradio's multiselect
-
- Example input:
- ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
-
- Example output:
- {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
- """
-
- res = {}
-
- params = {}
- for pair in text_pairs:
- k, v = pair.split(":", maxsplit=1)
-
- params[k] = v.strip()
-
- for param_name, setting_name in infotext_to_setting_name_mapping:
- value = params.get(param_name, None)
-
- if value is None:
- continue
-
- res[setting_name] = shared.opts.cast_value(setting_name, value)
-
- return res
-
-
-def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
- def paste_func(prompt):
- if not prompt and not shared.cmd_opts.hide_ui_dir_config:
- filename = os.path.join(data_path, "params.txt")
- if os.path.exists(filename):
- with open(filename, "r", encoding="utf8") as file:
- prompt = file.read()
-
- params = parse_generation_parameters(prompt)
- script_callbacks.infotext_pasted_callback(prompt, params)
- res = []
-
- for output, key in paste_fields:
- if callable(key):
- v = key(params)
- else:
- v = params.get(key, None)
-
- if v is None:
- res.append(gr.update())
- elif isinstance(v, type_of_gr_update):
- res.append(v)
- else:
- try:
- valtype = type(output.value)
-
- if valtype == bool and v == "False":
- val = False
- else:
- val = valtype(v)
-
- res.append(gr.update(value=val))
- except Exception:
- res.append(gr.update())
-
- return res
-
- if override_settings_component is not None:
- def paste_settings(params):
- vals = {}
-
- for param_name, setting_name in infotext_to_setting_name_mapping:
- v = params.get(param_name, None)
- if v is None:
- continue
-
- if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
- continue
-
- v = shared.opts.cast_value(setting_name, v)
- current_value = getattr(shared.opts, setting_name, None)
-
- if v == current_value:
- continue
-
- vals[param_name] = v
-
- vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
-
- return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
-
- paste_fields = paste_fields + [(override_settings_component, paste_settings)]
-
- button.click(
- fn=paste_func,
- _js=f"recalculate_prompts_{tabname}",
- inputs=[input_comp],
- outputs=[x[0] for x in paste_fields],
- )
-
-
diff --git a/spaces/jason9693/Soongsil-Bot-KoGPT/app.py b/spaces/jason9693/Soongsil-Bot-KoGPT/app.py
deleted file mode 100644
index d9f0676ef4fda9d80789b6535bcf8cd48f217113..0000000000000000000000000000000000000000
--- a/spaces/jason9693/Soongsil-Bot-KoGPT/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import torch
-import streamlit as st
-from transformers import GPT2Tokenizer, GPT2LMHeadModel, PreTrainedTokenizerFast
-import numpy as np
-
-
-model = GPT2LMHeadModel.from_pretrained("jason9693/soongsil-univ-gpt-v1")
-tokenizer = PreTrainedTokenizerFast.from_pretrained("jason9693/soongsil-univ-gpt-v1")
-
-category_map = {
- "숭실대 에타": "",
- "모두의 연애": "",
- "대학생 잡담방": ""
-}
-
-st.markdown("""# University Community KoGPT2 : 숭실대 에브리타임봇
-
-[](https://colab.research.google.com/drive/1p6DIxsesi3eJNPwFwvMw0MeM5LkSGoPW?usp=sharing) [](https://github.com/jason9693/UCK-GPT2/issues) 
-
-## 대학 커뮤니티 게시글 생성기
-
-SKT-AI에서 공개한 [KoGPT2](https://github.com/SKT-AI/KoGPT2) 모델을 파인튜닝하여 대학 커뮤니티 게시글을 생성하는 모델입니다. 총 에브리타임, 캠퍼스픽 데이터 22만개를 이용해서 학습을 진행했으며, 학습에는 대략 **3일**정도 소요되었습니다.
-
-* [GPT 논문 리뷰 링크](https://www.notion.so/Improve-Language-Understanding-by-Generative-Pre-Training-GPT-afb4b5ef6e984961ac022b700c152b6b)
-
-## 시연하기
-""")
-
-
-seed = st.text_input("Seed", "조만식 기념관")
-category = st.selectbox("Category", list(category_map.keys()))
-go = st.button("Generate")
-
-
-st.markdown("## 생성 결과")
-if go:
- input_context = category_map[category] + seed
- input_ids = tokenizer(input_context, return_tensors="pt")
- outputs = model.generate(
- input_ids=input_ids["input_ids"],
- max_length=250,
- num_return_sequences=1,
- no_repeat_ngram_size=3,
- repetition_penalty=2.0,
- do_sample=True,
- use_cache=True,
- eos_token_id=tokenizer.eos_token_id
- )
- st.write(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].replace("", "\n"))
diff --git a/spaces/jbilcke-hf/Panoremix/src/lib/getInitialRenderedScene.ts b/spaces/jbilcke-hf/Panoremix/src/lib/getInitialRenderedScene.ts
deleted file mode 100644
index 7c0739bf8bebdaf16aa4acf610eb6bdad9c15fd2..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/lib/getInitialRenderedScene.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-import { RenderedScene } from "@/types"
-
-export const getInitialRenderedScene = (): RenderedScene => ({
- renderId: "",
- status: "pending",
- assetUrl: "",
- alt: "",
- error: "",
- maskUrl: "",
- segments: []
-})
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/dropdown-menu.tsx b/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/dropdown-menu.tsx
deleted file mode 100644
index 5803489a1d197a9db5018e413e63abe84b2efb8e..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/dropdown-menu.tsx
+++ /dev/null
@@ -1,200 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as DropdownMenuPrimitive from "@radix-ui/react-dropdown-menu"
-import { Check, ChevronRight, Circle } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const DropdownMenu = DropdownMenuPrimitive.Root
-
-const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger
-
-const DropdownMenuGroup = DropdownMenuPrimitive.Group
-
-const DropdownMenuPortal = DropdownMenuPrimitive.Portal
-
-const DropdownMenuSub = DropdownMenuPrimitive.Sub
-
-const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup
-
-const DropdownMenuSubTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef & {
- inset?: boolean
- }
->(({ className, inset, children, ...props }, ref) => (
-
- {children}
-
-
-))
-DropdownMenuSubTrigger.displayName =
- DropdownMenuPrimitive.SubTrigger.displayName
-
-const DropdownMenuSubContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-))
-DropdownMenuSubContent.displayName =
- DropdownMenuPrimitive.SubContent.displayName
-
-const DropdownMenuContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, sideOffset = 4, ...props }, ref) => (
-
-
-
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef & {
- inset?: boolean
- }
->(({ className, inset, ...props }, ref) => (
-
-))
-DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName
-
-const DropdownMenuCheckboxItem = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, checked, ...props }, ref) => (
-
-
-
-
-
-
- {children}
-
-))
-DropdownMenuCheckboxItem.displayName =
- DropdownMenuPrimitive.CheckboxItem.displayName
-
-const DropdownMenuRadioItem = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
-
- {children}
-
-))
-DropdownMenuRadioItem.displayName = DropdownMenuPrimitive.RadioItem.displayName
-
-const DropdownMenuLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef & {
- inset?: boolean
- }
->(({ className, inset, ...props }, ref) => (
-
-))
-DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName
-
-const DropdownMenuSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-))
-DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName
-
-const DropdownMenuShortcut = ({
- className,
- ...props
-}: React.HTMLAttributes) => {
- return (
-
- )
-}
-DropdownMenuShortcut.displayName = "DropdownMenuShortcut"
-
-export {
- DropdownMenu,
- DropdownMenuTrigger,
- DropdownMenuContent,
- DropdownMenuItem,
- DropdownMenuCheckboxItem,
- DropdownMenuRadioItem,
- DropdownMenuLabel,
- DropdownMenuSeparator,
- DropdownMenuShortcut,
- DropdownMenuGroup,
- DropdownMenuPortal,
- DropdownMenuSub,
- DropdownMenuSubContent,
- DropdownMenuSubTrigger,
- DropdownMenuRadioGroup,
-}
diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/lib/useImageDimension.ts b/spaces/jbilcke-hf/ai-comic-factory/src/lib/useImageDimension.ts
deleted file mode 100644
index 9cfd06e473929b1046a5dd9caa9d577ebaf09b7a..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/src/lib/useImageDimension.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-import { useEffect, useState } from "react"
-
-import { ImageDimension, getImageDimension } from "./getImageDimension"
-
-export function useImageDimension(src: string) {
- const [dimension, setDimension] = useState({
- width: 0,
- height: 0,
- })
-
- useEffect(() => {
- const compute = async () => {
- const newDimension = await getImageDimension(src)
- setDimension(newDimension)
- }
- compute()
- }, [src])
-
- return dimension
-}
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/media-server/scripts/init.sh b/spaces/jbilcke-hf/media-server/scripts/init.sh
deleted file mode 100644
index f7e4c71ee394c4d95d5a7abea958d9e8d6a32eec..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/media-server/scripts/init.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/bin/bash
-mkdir -p $WEBTV_AUDIO_STORAGE_PATH_CHANNEL_1
-mkdir -p $WEBTV_VIDEO_STORAGE_PATH_CHANNEL_3
diff --git a/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/README.md b/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/README.md
deleted file mode 100644
index 5d2d20cd9e1cafcdf8bd8dfd83a0a9c47a884a39..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/README.md
+++ /dev/null
@@ -1,193 +0,0 @@
-# CLIP
-
-[[Blog]](https://openai.com/blog/clip/) [[Paper]](https://arxiv.org/abs/2103.00020) [[Model Card]](model-card.md) [[Colab]](https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb)
-
-CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision.
-
-
-
-## Approach
-
-
-
-
-
-## Usage
-
-First, [install PyTorch 1.7.1](https://pytorch.org/get-started/locally/) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick:
-
-```bash
-$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
-$ pip install ftfy regex tqdm
-$ pip install git+https://github.com/openai/CLIP.git
-```
-
-Replace `cudatoolkit=11.0` above with the appropriate CUDA version on your machine or `cpuonly` when installing on a machine without a GPU.
-
-```python
-import torch
-import clip
-from PIL import Image
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = clip.load("ViT-B/32", device=device)
-
-image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
-text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
-
-with torch.no_grad():
- image_features = model.encode_image(image)
- text_features = model.encode_text(text)
-
- logits_per_image, logits_per_text = model(image, text)
- probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-
-print("Label probs:", probs) # prints: [[0.9927937 0.00421068 0.00299572]]
-```
-
-
-## API
-
-The CLIP module `clip` provides the following methods:
-
-#### `clip.available_models()`
-
-Returns the names of the available CLIP models.
-
-#### `clip.load(name, device=..., jit=False)`
-
-Returns the model and the TorchVision transform needed by the model, specified by the model name returned by `clip.available_models()`. It will download the model as necessary. The `name` argument can also be a path to a local checkpoint.
-
-The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. When `jit` is `False`, a non-JIT version of the model will be loaded.
-
-#### `clip.tokenize(text: Union[str, List[str]], context_length=77)`
-
-Returns a LongTensor containing tokenized sequences of given text input(s). This can be used as the input to the model
-
----
-
-The model returned by `clip.load()` supports the following methods:
-
-#### `model.encode_image(image: Tensor)`
-
-Given a batch of images, returns the image features encoded by the vision portion of the CLIP model.
-
-#### `model.encode_text(text: Tensor)`
-
-Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model.
-
-#### `model(image: Tensor, text: Tensor)`
-
-Given a batch of images and a batch of text tokens, returns two Tensors, containing the logit scores corresponding to each image and text input. The values are cosine similarities between the corresponding image and text features, times 100.
-
-
-
-## More Examples
-
-### Zero-Shot Prediction
-
-The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. This example takes an image from the [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), and predicts the most likely labels among the 100 textual labels from the dataset.
-
-```python
-import os
-import clip
-import torch
-from torchvision.datasets import CIFAR100
-
-# Load the model
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = clip.load('ViT-B/32', device)
-
-# Download the dataset
-cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)
-
-# Prepare the inputs
-image, class_id = cifar100[3637]
-image_input = preprocess(image).unsqueeze(0).to(device)
-text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)
-
-# Calculate features
-with torch.no_grad():
- image_features = model.encode_image(image_input)
- text_features = model.encode_text(text_inputs)
-
-# Pick the top 5 most similar labels for the image
-image_features /= image_features.norm(dim=-1, keepdim=True)
-text_features /= text_features.norm(dim=-1, keepdim=True)
-similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
-values, indices = similarity[0].topk(5)
-
-# Print the result
-print("\nTop predictions:\n")
-for value, index in zip(values, indices):
- print(f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%")
-```
-
-The output will look like the following (the exact numbers may be slightly different depending on the compute device):
-
-```
-Top predictions:
-
- snake: 65.31%
- turtle: 12.29%
- sweet_pepper: 3.83%
- lizard: 1.88%
- crocodile: 1.75%
-```
-
-Note that this example uses the `encode_image()` and `encode_text()` methods that return the encoded features of given inputs.
-
-
-### Linear-probe evaluation
-
-The example below uses [scikit-learn](https://scikit-learn.org/) to perform logistic regression on image features.
-
-```python
-import os
-import clip
-import torch
-
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-from torch.utils.data import DataLoader
-from torchvision.datasets import CIFAR100
-from tqdm import tqdm
-
-# Load the model
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = clip.load('ViT-B/32', device)
-
-# Load the dataset
-root = os.path.expanduser("~/.cache")
-train = CIFAR100(root, download=True, train=True, transform=preprocess)
-test = CIFAR100(root, download=True, train=False, transform=preprocess)
-
-
-def get_features(dataset):
- all_features = []
- all_labels = []
-
- with torch.no_grad():
- for images, labels in tqdm(DataLoader(dataset, batch_size=100)):
- features = model.encode_image(images.to(device))
-
- all_features.append(features)
- all_labels.append(labels)
-
- return torch.cat(all_features).cpu().numpy(), torch.cat(all_labels).cpu().numpy()
-
-# Calculate the image features
-train_features, train_labels = get_features(train)
-test_features, test_labels = get_features(test)
-
-# Perform logistic regression
-classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1)
-classifier.fit(train_features, train_labels)
-
-# Evaluate using the logistic regression classifier
-predictions = classifier.predict(test_features)
-accuracy = np.mean((test_labels == predictions).astype(np.float)) * 100.
-print(f"Accuracy = {accuracy:.3f}")
-```
-
-Note that the `C` value should be determined via a hyperparameter sweep using a validation split.
diff --git a/spaces/jhwen/bingo/src/pages/api/blob.ts b/spaces/jhwen/bingo/src/pages/api/blob.ts
deleted file mode 100644
index fecd48031916b2284b8958892196e0a1ad420421..0000000000000000000000000000000000000000
--- a/spaces/jhwen/bingo/src/pages/api/blob.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { Readable } from 'node:stream'
-import { fetch } from '@/lib/isomorphic'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- try {
- const { bcid } = req.query
-
- const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
- {
- method: 'GET',
- headers: {
- "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
- "sec-ch-ua-mobile": "?0",
- "sec-ch-ua-platform": "\"Windows\"",
- "Referrer-Policy": "origin-when-cross-origin",
- },
- },
- )
-
- res.writeHead(200, {
- 'Content-Length': headers.get('content-length')!,
- 'Content-Type': headers.get('content-type')!,
- })
- // @ts-ignore
- return Readable.fromWeb(body!).pipe(res)
- } catch (e) {
- console.log('Error', e)
- return res.json({
- result: {
- value: 'UploadFailed',
- message: `${e}`
- }
- })
- }
-}
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/BdfFontFile.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/BdfFontFile.py
deleted file mode 100644
index 075d462907abcace9610a686052e643582602a8f..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/BdfFontFile.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# bitmap distribution font (bdf) file parser
-#
-# history:
-# 1996-05-16 fl created (as bdf2pil)
-# 1997-08-25 fl converted to FontFile driver
-# 2001-05-25 fl removed bogus __init__ call
-# 2002-11-20 fl robustification (from Kevin Cazabon, Dmitry Vasiliev)
-# 2003-04-22 fl more robustification (from Graham Dumpleton)
-#
-# Copyright (c) 1997-2003 by Secret Labs AB.
-# Copyright (c) 1997-2003 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-Parse X Bitmap Distribution Format (BDF)
-"""
-
-
-from . import FontFile, Image
-
-bdf_slant = {
- "R": "Roman",
- "I": "Italic",
- "O": "Oblique",
- "RI": "Reverse Italic",
- "RO": "Reverse Oblique",
- "OT": "Other",
-}
-
-bdf_spacing = {"P": "Proportional", "M": "Monospaced", "C": "Cell"}
-
-
-def bdf_char(f):
- # skip to STARTCHAR
- while True:
- s = f.readline()
- if not s:
- return None
- if s[:9] == b"STARTCHAR":
- break
- id = s[9:].strip().decode("ascii")
-
- # load symbol properties
- props = {}
- while True:
- s = f.readline()
- if not s or s[:6] == b"BITMAP":
- break
- i = s.find(b" ")
- props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
-
- # load bitmap
- bitmap = []
- while True:
- s = f.readline()
- if not s or s[:7] == b"ENDCHAR":
- break
- bitmap.append(s[:-1])
- bitmap = b"".join(bitmap)
-
- # The word BBX
- # followed by the width in x (BBw), height in y (BBh),
- # and x and y displacement (BBxoff0, BByoff0)
- # of the lower left corner from the origin of the character.
- width, height, x_disp, y_disp = [int(p) for p in props["BBX"].split()]
-
- # The word DWIDTH
- # followed by the width in x and y of the character in device pixels.
- dwx, dwy = [int(p) for p in props["DWIDTH"].split()]
-
- bbox = (
- (dwx, dwy),
- (x_disp, -y_disp - height, width + x_disp, -y_disp),
- (0, 0, width, height),
- )
-
- try:
- im = Image.frombytes("1", (width, height), bitmap, "hex", "1")
- except ValueError:
- # deal with zero-width characters
- im = Image.new("1", (width, height))
-
- return id, int(props["ENCODING"]), bbox, im
-
-
-class BdfFontFile(FontFile.FontFile):
- """Font file plugin for the X11 BDF format."""
-
- def __init__(self, fp):
- super().__init__()
-
- s = fp.readline()
- if s[:13] != b"STARTFONT 2.1":
- msg = "not a valid BDF file"
- raise SyntaxError(msg)
-
- props = {}
- comments = []
-
- while True:
- s = fp.readline()
- if not s or s[:13] == b"ENDPROPERTIES":
- break
- i = s.find(b" ")
- props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
- if s[:i] in [b"COMMENT", b"COPYRIGHT"]:
- if s.find(b"LogicalFontDescription") < 0:
- comments.append(s[i + 1 : -1].decode("ascii"))
-
- while True:
- c = bdf_char(fp)
- if not c:
- break
- id, ch, (xy, dst, src), im = c
- if 0 <= ch < len(self.glyph):
- self.glyph[ch] = xy, dst, src, im
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageFile.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageFile.py
deleted file mode 100644
index 8e4f7dfb2c8854ee3a1f65efd6535732df1764aa..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageFile.py
+++ /dev/null
@@ -1,773 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# base class for image file handlers
-#
-# history:
-# 1995-09-09 fl Created
-# 1996-03-11 fl Fixed load mechanism.
-# 1996-04-15 fl Added pcx/xbm decoders.
-# 1996-04-30 fl Added encoders.
-# 1996-12-14 fl Added load helpers
-# 1997-01-11 fl Use encode_to_file where possible
-# 1997-08-27 fl Flush output in _save
-# 1998-03-05 fl Use memory mapping for some modes
-# 1999-02-04 fl Use memory mapping also for "I;16" and "I;16B"
-# 1999-05-31 fl Added image parser
-# 2000-10-12 fl Set readonly flag on memory-mapped images
-# 2002-03-20 fl Use better messages for common decoder errors
-# 2003-04-21 fl Fall back on mmap/map_buffer if map is not available
-# 2003-10-30 fl Added StubImageFile class
-# 2004-02-25 fl Made incremental parser more robust
-#
-# Copyright (c) 1997-2004 by Secret Labs AB
-# Copyright (c) 1995-2004 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-import itertools
-import struct
-import sys
-
-from . import Image
-from ._util import is_path
-
-MAXBLOCK = 65536
-
-SAFEBLOCK = 1024 * 1024
-
-LOAD_TRUNCATED_IMAGES = False
-"""Whether or not to load truncated image files. User code may change this."""
-
-ERRORS = {
- -1: "image buffer overrun error",
- -2: "decoding error",
- -3: "unknown error",
- -8: "bad configuration",
- -9: "out of memory error",
-}
-"""
-Dict of known error codes returned from :meth:`.PyDecoder.decode`,
-:meth:`.PyEncoder.encode` :meth:`.PyEncoder.encode_to_pyfd` and
-:meth:`.PyEncoder.encode_to_file`.
-"""
-
-
-#
-# --------------------------------------------------------------------
-# Helpers
-
-
-def raise_oserror(error):
- try:
- msg = Image.core.getcodecstatus(error)
- except AttributeError:
- msg = ERRORS.get(error)
- if not msg:
- msg = f"decoder error {error}"
- msg += " when reading image file"
- raise OSError(msg)
-
-
-def _tilesort(t):
- # sort on offset
- return t[2]
-
-
-#
-# --------------------------------------------------------------------
-# ImageFile base class
-
-
-class ImageFile(Image.Image):
- """Base class for image file format handlers."""
-
- def __init__(self, fp=None, filename=None):
- super().__init__()
-
- self._min_frame = 0
-
- self.custom_mimetype = None
-
- self.tile = None
- """ A list of tile descriptors, or ``None`` """
-
- self.readonly = 1 # until we know better
-
- self.decoderconfig = ()
- self.decodermaxblock = MAXBLOCK
-
- if is_path(fp):
- # filename
- self.fp = open(fp, "rb")
- self.filename = fp
- self._exclusive_fp = True
- else:
- # stream
- self.fp = fp
- self.filename = filename
- # can be overridden
- self._exclusive_fp = None
-
- try:
- try:
- self._open()
- except (
- IndexError, # end of data
- TypeError, # end of data (ord)
- KeyError, # unsupported mode
- EOFError, # got header but not the first frame
- struct.error,
- ) as v:
- raise SyntaxError(v) from v
-
- if not self.mode or self.size[0] <= 0 or self.size[1] <= 0:
- msg = "not identified by this driver"
- raise SyntaxError(msg)
- except BaseException:
- # close the file only if we have opened it this constructor
- if self._exclusive_fp:
- self.fp.close()
- raise
-
- def get_format_mimetype(self):
- if self.custom_mimetype:
- return self.custom_mimetype
- if self.format is not None:
- return Image.MIME.get(self.format.upper())
-
- def __setstate__(self, state):
- self.tile = []
- super().__setstate__(state)
-
- def verify(self):
- """Check file integrity"""
-
- # raise exception if something's wrong. must be called
- # directly after open, and closes file when finished.
- if self._exclusive_fp:
- self.fp.close()
- self.fp = None
-
- def load(self):
- """Load image data based on tile list"""
-
- if self.tile is None:
- msg = "cannot load this image"
- raise OSError(msg)
-
- pixel = Image.Image.load(self)
- if not self.tile:
- return pixel
-
- self.map = None
- use_mmap = self.filename and len(self.tile) == 1
- # As of pypy 2.1.0, memory mapping was failing here.
- use_mmap = use_mmap and not hasattr(sys, "pypy_version_info")
-
- readonly = 0
-
- # look for read/seek overrides
- try:
- read = self.load_read
- # don't use mmap if there are custom read/seek functions
- use_mmap = False
- except AttributeError:
- read = self.fp.read
-
- try:
- seek = self.load_seek
- use_mmap = False
- except AttributeError:
- seek = self.fp.seek
-
- if use_mmap:
- # try memory mapping
- decoder_name, extents, offset, args = self.tile[0]
- if (
- decoder_name == "raw"
- and len(args) >= 3
- and args[0] == self.mode
- and args[0] in Image._MAPMODES
- ):
- try:
- # use mmap, if possible
- import mmap
-
- with open(self.filename) as fp:
- self.map = mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ)
- if offset + self.size[1] * args[1] > self.map.size():
- # buffer is not large enough
- raise OSError
- self.im = Image.core.map_buffer(
- self.map, self.size, decoder_name, offset, args
- )
- readonly = 1
- # After trashing self.im,
- # we might need to reload the palette data.
- if self.palette:
- self.palette.dirty = 1
- except (AttributeError, OSError, ImportError):
- self.map = None
-
- self.load_prepare()
- err_code = -3 # initialize to unknown error
- if not self.map:
- # sort tiles in file order
- self.tile.sort(key=_tilesort)
-
- try:
- # FIXME: This is a hack to handle TIFF's JpegTables tag.
- prefix = self.tile_prefix
- except AttributeError:
- prefix = b""
-
- # Remove consecutive duplicates that only differ by their offset
- self.tile = [
- list(tiles)[-1]
- for _, tiles in itertools.groupby(
- self.tile, lambda tile: (tile[0], tile[1], tile[3])
- )
- ]
- for decoder_name, extents, offset, args in self.tile:
- seek(offset)
- decoder = Image._getdecoder(
- self.mode, decoder_name, args, self.decoderconfig
- )
- try:
- decoder.setimage(self.im, extents)
- if decoder.pulls_fd:
- decoder.setfd(self.fp)
- err_code = decoder.decode(b"")[1]
- else:
- b = prefix
- while True:
- try:
- s = read(self.decodermaxblock)
- except (IndexError, struct.error) as e:
- # truncated png/gif
- if LOAD_TRUNCATED_IMAGES:
- break
- else:
- msg = "image file is truncated"
- raise OSError(msg) from e
-
- if not s: # truncated jpeg
- if LOAD_TRUNCATED_IMAGES:
- break
- else:
- msg = (
- "image file is truncated "
- f"({len(b)} bytes not processed)"
- )
- raise OSError(msg)
-
- b = b + s
- n, err_code = decoder.decode(b)
- if n < 0:
- break
- b = b[n:]
- finally:
- # Need to cleanup here to prevent leaks
- decoder.cleanup()
-
- self.tile = []
- self.readonly = readonly
-
- self.load_end()
-
- if self._exclusive_fp and self._close_exclusive_fp_after_loading:
- self.fp.close()
- self.fp = None
-
- if not self.map and not LOAD_TRUNCATED_IMAGES and err_code < 0:
- # still raised if decoder fails to return anything
- raise_oserror(err_code)
-
- return Image.Image.load(self)
-
- def load_prepare(self):
- # create image memory if necessary
- if not self.im or self.im.mode != self.mode or self.im.size != self.size:
- self.im = Image.core.new(self.mode, self.size)
- # create palette (optional)
- if self.mode == "P":
- Image.Image.load(self)
-
- def load_end(self):
- # may be overridden
- pass
-
- # may be defined for contained formats
- # def load_seek(self, pos):
- # pass
-
- # may be defined for blocked formats (e.g. PNG)
- # def load_read(self, bytes):
- # pass
-
- def _seek_check(self, frame):
- if (
- frame < self._min_frame
- # Only check upper limit on frames if additional seek operations
- # are not required to do so
- or (
- not (hasattr(self, "_n_frames") and self._n_frames is None)
- and frame >= self.n_frames + self._min_frame
- )
- ):
- msg = "attempt to seek outside sequence"
- raise EOFError(msg)
-
- return self.tell() != frame
-
-
-class StubImageFile(ImageFile):
- """
- Base class for stub image loaders.
-
- A stub loader is an image loader that can identify files of a
- certain format, but relies on external code to load the file.
- """
-
- def _open(self):
- msg = "StubImageFile subclass must implement _open"
- raise NotImplementedError(msg)
-
- def load(self):
- loader = self._load()
- if loader is None:
- msg = f"cannot find loader for this {self.format} file"
- raise OSError(msg)
- image = loader.load(self)
- assert image is not None
- # become the other object (!)
- self.__class__ = image.__class__
- self.__dict__ = image.__dict__
- return image.load()
-
- def _load(self):
- """(Hook) Find actual image loader."""
- msg = "StubImageFile subclass must implement _load"
- raise NotImplementedError(msg)
-
-
-class Parser:
- """
- Incremental image parser. This class implements the standard
- feed/close consumer interface.
- """
-
- incremental = None
- image = None
- data = None
- decoder = None
- offset = 0
- finished = 0
-
- def reset(self):
- """
- (Consumer) Reset the parser. Note that you can only call this
- method immediately after you've created a parser; parser
- instances cannot be reused.
- """
- assert self.data is None, "cannot reuse parsers"
-
- def feed(self, data):
- """
- (Consumer) Feed data to the parser.
-
- :param data: A string buffer.
- :exception OSError: If the parser failed to parse the image file.
- """
- # collect data
-
- if self.finished:
- return
-
- if self.data is None:
- self.data = data
- else:
- self.data = self.data + data
-
- # parse what we have
- if self.decoder:
- if self.offset > 0:
- # skip header
- skip = min(len(self.data), self.offset)
- self.data = self.data[skip:]
- self.offset = self.offset - skip
- if self.offset > 0 or not self.data:
- return
-
- n, e = self.decoder.decode(self.data)
-
- if n < 0:
- # end of stream
- self.data = None
- self.finished = 1
- if e < 0:
- # decoding error
- self.image = None
- raise_oserror(e)
- else:
- # end of image
- return
- self.data = self.data[n:]
-
- elif self.image:
- # if we end up here with no decoder, this file cannot
- # be incrementally parsed. wait until we've gotten all
- # available data
- pass
-
- else:
- # attempt to open this file
- try:
- with io.BytesIO(self.data) as fp:
- im = Image.open(fp)
- except OSError:
- # traceback.print_exc()
- pass # not enough data
- else:
- flag = hasattr(im, "load_seek") or hasattr(im, "load_read")
- if flag or len(im.tile) != 1:
- # custom load code, or multiple tiles
- self.decode = None
- else:
- # initialize decoder
- im.load_prepare()
- d, e, o, a = im.tile[0]
- im.tile = []
- self.decoder = Image._getdecoder(im.mode, d, a, im.decoderconfig)
- self.decoder.setimage(im.im, e)
-
- # calculate decoder offset
- self.offset = o
- if self.offset <= len(self.data):
- self.data = self.data[self.offset :]
- self.offset = 0
-
- self.image = im
-
- def __enter__(self):
- return self
-
- def __exit__(self, *args):
- self.close()
-
- def close(self):
- """
- (Consumer) Close the stream.
-
- :returns: An image object.
- :exception OSError: If the parser failed to parse the image file either
- because it cannot be identified or cannot be
- decoded.
- """
- # finish decoding
- if self.decoder:
- # get rid of what's left in the buffers
- self.feed(b"")
- self.data = self.decoder = None
- if not self.finished:
- msg = "image was incomplete"
- raise OSError(msg)
- if not self.image:
- msg = "cannot parse this image"
- raise OSError(msg)
- if self.data:
- # incremental parsing not possible; reopen the file
- # not that we have all data
- with io.BytesIO(self.data) as fp:
- try:
- self.image = Image.open(fp)
- finally:
- self.image.load()
- return self.image
-
-
-# --------------------------------------------------------------------
-
-
-def _save(im, fp, tile, bufsize=0):
- """Helper to save image based on tile list
-
- :param im: Image object.
- :param fp: File object.
- :param tile: Tile list.
- :param bufsize: Optional buffer size
- """
-
- im.load()
- if not hasattr(im, "encoderconfig"):
- im.encoderconfig = ()
- tile.sort(key=_tilesort)
- # FIXME: make MAXBLOCK a configuration parameter
- # It would be great if we could have the encoder specify what it needs
- # But, it would need at least the image size in most cases. RawEncode is
- # a tricky case.
- bufsize = max(MAXBLOCK, bufsize, im.size[0] * 4) # see RawEncode.c
- try:
- fh = fp.fileno()
- fp.flush()
- _encode_tile(im, fp, tile, bufsize, fh)
- except (AttributeError, io.UnsupportedOperation) as exc:
- _encode_tile(im, fp, tile, bufsize, None, exc)
- if hasattr(fp, "flush"):
- fp.flush()
-
-
-def _encode_tile(im, fp, tile, bufsize, fh, exc=None):
- for e, b, o, a in tile:
- if o > 0:
- fp.seek(o)
- encoder = Image._getencoder(im.mode, e, a, im.encoderconfig)
- try:
- encoder.setimage(im.im, b)
- if encoder.pushes_fd:
- encoder.setfd(fp)
- errcode = encoder.encode_to_pyfd()[1]
- else:
- if exc:
- # compress to Python file-compatible object
- while True:
- errcode, data = encoder.encode(bufsize)[1:]
- fp.write(data)
- if errcode:
- break
- else:
- # slight speedup: compress to real file object
- errcode = encoder.encode_to_file(fh, bufsize)
- if errcode < 0:
- msg = f"encoder error {errcode} when writing image file"
- raise OSError(msg) from exc
- finally:
- encoder.cleanup()
-
-
-def _safe_read(fp, size):
- """
- Reads large blocks in a safe way. Unlike fp.read(n), this function
- doesn't trust the user. If the requested size is larger than
- SAFEBLOCK, the file is read block by block.
-
- :param fp: File handle. Must implement a read method.
- :param size: Number of bytes to read.
- :returns: A string containing size bytes of data.
-
- Raises an OSError if the file is truncated and the read cannot be completed
-
- """
- if size <= 0:
- return b""
- if size <= SAFEBLOCK:
- data = fp.read(size)
- if len(data) < size:
- msg = "Truncated File Read"
- raise OSError(msg)
- return data
- data = []
- remaining_size = size
- while remaining_size > 0:
- block = fp.read(min(remaining_size, SAFEBLOCK))
- if not block:
- break
- data.append(block)
- remaining_size -= len(block)
- if sum(len(d) for d in data) < size:
- msg = "Truncated File Read"
- raise OSError(msg)
- return b"".join(data)
-
-
-class PyCodecState:
- def __init__(self):
- self.xsize = 0
- self.ysize = 0
- self.xoff = 0
- self.yoff = 0
-
- def extents(self):
- return self.xoff, self.yoff, self.xoff + self.xsize, self.yoff + self.ysize
-
-
-class PyCodec:
- def __init__(self, mode, *args):
- self.im = None
- self.state = PyCodecState()
- self.fd = None
- self.mode = mode
- self.init(args)
-
- def init(self, args):
- """
- Override to perform codec specific initialization
-
- :param args: Array of args items from the tile entry
- :returns: None
- """
- self.args = args
-
- def cleanup(self):
- """
- Override to perform codec specific cleanup
-
- :returns: None
- """
- pass
-
- def setfd(self, fd):
- """
- Called from ImageFile to set the Python file-like object
-
- :param fd: A Python file-like object
- :returns: None
- """
- self.fd = fd
-
- def setimage(self, im, extents=None):
- """
- Called from ImageFile to set the core output image for the codec
-
- :param im: A core image object
- :param extents: a 4 tuple of (x0, y0, x1, y1) defining the rectangle
- for this tile
- :returns: None
- """
-
- # following c code
- self.im = im
-
- if extents:
- (x0, y0, x1, y1) = extents
- else:
- (x0, y0, x1, y1) = (0, 0, 0, 0)
-
- if x0 == 0 and x1 == 0:
- self.state.xsize, self.state.ysize = self.im.size
- else:
- self.state.xoff = x0
- self.state.yoff = y0
- self.state.xsize = x1 - x0
- self.state.ysize = y1 - y0
-
- if self.state.xsize <= 0 or self.state.ysize <= 0:
- msg = "Size cannot be negative"
- raise ValueError(msg)
-
- if (
- self.state.xsize + self.state.xoff > self.im.size[0]
- or self.state.ysize + self.state.yoff > self.im.size[1]
- ):
- msg = "Tile cannot extend outside image"
- raise ValueError(msg)
-
-
-class PyDecoder(PyCodec):
- """
- Python implementation of a format decoder. Override this class and
- add the decoding logic in the :meth:`decode` method.
-
- See :ref:`Writing Your Own File Codec in Python`
- """
-
- _pulls_fd = False
-
- @property
- def pulls_fd(self):
- return self._pulls_fd
-
- def decode(self, buffer):
- """
- Override to perform the decoding process.
-
- :param buffer: A bytes object with the data to be decoded.
- :returns: A tuple of ``(bytes consumed, errcode)``.
- If finished with decoding return -1 for the bytes consumed.
- Err codes are from :data:`.ImageFile.ERRORS`.
- """
- raise NotImplementedError()
-
- def set_as_raw(self, data, rawmode=None):
- """
- Convenience method to set the internal image from a stream of raw data
-
- :param data: Bytes to be set
- :param rawmode: The rawmode to be used for the decoder.
- If not specified, it will default to the mode of the image
- :returns: None
- """
-
- if not rawmode:
- rawmode = self.mode
- d = Image._getdecoder(self.mode, "raw", rawmode)
- d.setimage(self.im, self.state.extents())
- s = d.decode(data)
-
- if s[0] >= 0:
- msg = "not enough image data"
- raise ValueError(msg)
- if s[1] != 0:
- msg = "cannot decode image data"
- raise ValueError(msg)
-
-
-class PyEncoder(PyCodec):
- """
- Python implementation of a format encoder. Override this class and
- add the decoding logic in the :meth:`encode` method.
-
- See :ref:`Writing Your Own File Codec in Python`
- """
-
- _pushes_fd = False
-
- @property
- def pushes_fd(self):
- return self._pushes_fd
-
- def encode(self, bufsize):
- """
- Override to perform the encoding process.
-
- :param bufsize: Buffer size.
- :returns: A tuple of ``(bytes encoded, errcode, bytes)``.
- If finished with encoding return 1 for the error code.
- Err codes are from :data:`.ImageFile.ERRORS`.
- """
- raise NotImplementedError()
-
- def encode_to_pyfd(self):
- """
- If ``pushes_fd`` is ``True``, then this method will be used,
- and ``encode()`` will only be called once.
-
- :returns: A tuple of ``(bytes consumed, errcode)``.
- Err codes are from :data:`.ImageFile.ERRORS`.
- """
- if not self.pushes_fd:
- return 0, -8 # bad configuration
- bytes_consumed, errcode, data = self.encode(0)
- if data:
- self.fd.write(data)
- return bytes_consumed, errcode
-
- def encode_to_file(self, fh, bufsize):
- """
- :param fh: File handle.
- :param bufsize: Buffer size.
-
- :returns: If finished successfully, return 0.
- Otherwise, return an error code. Err codes are from
- :data:`.ImageFile.ERRORS`.
- """
- errcode = 0
- while errcode == 0:
- status, errcode, buf = self.encode(bufsize)
- if status > 0:
- fh.write(buf[status:])
- return errcode
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/cu2qu/errors.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/cu2qu/errors.py
deleted file mode 100644
index fa3dc42937131c5db54890dde8f519b15f5d0ff1..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/cu2qu/errors.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright 2016 Google Inc. 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.
-
-
-class Error(Exception):
- """Base Cu2Qu exception class for all other errors."""
-
-
-class ApproxNotFoundError(Error):
- def __init__(self, curve):
- message = "no approximation found: %s" % curve
- super().__init__(message)
- self.curve = curve
-
-
-class UnequalZipLengthsError(Error):
- pass
-
-
-class IncompatibleGlyphsError(Error):
- def __init__(self, glyphs):
- assert len(glyphs) > 1
- self.glyphs = glyphs
- names = set(repr(g.name) for g in glyphs)
- if len(names) > 1:
- self.combined_name = "{%s}" % ", ".join(sorted(names))
- else:
- self.combined_name = names.pop()
-
- def __repr__(self):
- return "<%s %s>" % (type(self).__name__, self.combined_name)
-
-
-class IncompatibleSegmentNumberError(IncompatibleGlyphsError):
- def __str__(self):
- return "Glyphs named %s have different number of segments" % (
- self.combined_name
- )
-
-
-class IncompatibleSegmentTypesError(IncompatibleGlyphsError):
- def __init__(self, glyphs, segments):
- IncompatibleGlyphsError.__init__(self, glyphs)
- self.segments = segments
-
- def __str__(self):
- lines = []
- ndigits = len(str(max(self.segments)))
- for i, tags in sorted(self.segments.items()):
- lines.append(
- "%s: (%s)" % (str(i).rjust(ndigits), ", ".join(repr(t) for t in tags))
- )
- return "Glyphs named %s have incompatible segment types:\n %s" % (
- self.combined_name,
- "\n ".join(lines),
- )
-
-
-class IncompatibleFontsError(Error):
- def __init__(self, glyph_errors):
- self.glyph_errors = glyph_errors
-
- def __str__(self):
- return "fonts contains incompatible glyphs: %s" % (
- ", ".join(repr(g) for g in sorted(self.glyph_errors.keys()))
- )
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ufoLib/validators.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ufoLib/validators.py
deleted file mode 100644
index 01e3124fd38711fcb471c3b9e0f9ecca8a08c29e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ufoLib/validators.py
+++ /dev/null
@@ -1,1186 +0,0 @@
-"""Various low level data validators."""
-
-import calendar
-from io import open
-import fs.base
-import fs.osfs
-
-from collections.abc import Mapping
-from fontTools.ufoLib.utils import numberTypes
-
-
-# -------
-# Generic
-# -------
-
-
-def isDictEnough(value):
- """
- Some objects will likely come in that aren't
- dicts but are dict-ish enough.
- """
- if isinstance(value, Mapping):
- return True
- for attr in ("keys", "values", "items"):
- if not hasattr(value, attr):
- return False
- return True
-
-
-def genericTypeValidator(value, typ):
- """
- Generic. (Added at version 2.)
- """
- return isinstance(value, typ)
-
-
-def genericIntListValidator(values, validValues):
- """
- Generic. (Added at version 2.)
- """
- if not isinstance(values, (list, tuple)):
- return False
- valuesSet = set(values)
- validValuesSet = set(validValues)
- if valuesSet - validValuesSet:
- return False
- for value in values:
- if not isinstance(value, int):
- return False
- return True
-
-
-def genericNonNegativeIntValidator(value):
- """
- Generic. (Added at version 3.)
- """
- if not isinstance(value, int):
- return False
- if value < 0:
- return False
- return True
-
-
-def genericNonNegativeNumberValidator(value):
- """
- Generic. (Added at version 3.)
- """
- if not isinstance(value, numberTypes):
- return False
- if value < 0:
- return False
- return True
-
-
-def genericDictValidator(value, prototype):
- """
- Generic. (Added at version 3.)
- """
- # not a dict
- if not isinstance(value, Mapping):
- return False
- # missing required keys
- for key, (typ, required) in prototype.items():
- if not required:
- continue
- if key not in value:
- return False
- # unknown keys
- for key in value.keys():
- if key not in prototype:
- return False
- # incorrect types
- for key, v in value.items():
- prototypeType, required = prototype[key]
- if v is None and not required:
- continue
- if not isinstance(v, prototypeType):
- return False
- return True
-
-
-# --------------
-# fontinfo.plist
-# --------------
-
-# Data Validators
-
-
-def fontInfoStyleMapStyleNameValidator(value):
- """
- Version 2+.
- """
- options = ["regular", "italic", "bold", "bold italic"]
- return value in options
-
-
-def fontInfoOpenTypeGaspRangeRecordsValidator(value):
- """
- Version 3+.
- """
- if not isinstance(value, list):
- return False
- if len(value) == 0:
- return True
- validBehaviors = [0, 1, 2, 3]
- dictPrototype = dict(rangeMaxPPEM=(int, True), rangeGaspBehavior=(list, True))
- ppemOrder = []
- for rangeRecord in value:
- if not genericDictValidator(rangeRecord, dictPrototype):
- return False
- ppem = rangeRecord["rangeMaxPPEM"]
- behavior = rangeRecord["rangeGaspBehavior"]
- ppemValidity = genericNonNegativeIntValidator(ppem)
- if not ppemValidity:
- return False
- behaviorValidity = genericIntListValidator(behavior, validBehaviors)
- if not behaviorValidity:
- return False
- ppemOrder.append(ppem)
- if ppemOrder != sorted(ppemOrder):
- return False
- return True
-
-
-def fontInfoOpenTypeHeadCreatedValidator(value):
- """
- Version 2+.
- """
- # format: 0000/00/00 00:00:00
- if not isinstance(value, str):
- return False
- # basic formatting
- if not len(value) == 19:
- return False
- if value.count(" ") != 1:
- return False
- date, time = value.split(" ")
- if date.count("/") != 2:
- return False
- if time.count(":") != 2:
- return False
- # date
- year, month, day = date.split("/")
- if len(year) != 4:
- return False
- if len(month) != 2:
- return False
- if len(day) != 2:
- return False
- try:
- year = int(year)
- month = int(month)
- day = int(day)
- except ValueError:
- return False
- if month < 1 or month > 12:
- return False
- monthMaxDay = calendar.monthrange(year, month)[1]
- if day < 1 or day > monthMaxDay:
- return False
- # time
- hour, minute, second = time.split(":")
- if len(hour) != 2:
- return False
- if len(minute) != 2:
- return False
- if len(second) != 2:
- return False
- try:
- hour = int(hour)
- minute = int(minute)
- second = int(second)
- except ValueError:
- return False
- if hour < 0 or hour > 23:
- return False
- if minute < 0 or minute > 59:
- return False
- if second < 0 or second > 59:
- return False
- # fallback
- return True
-
-
-def fontInfoOpenTypeNameRecordsValidator(value):
- """
- Version 3+.
- """
- if not isinstance(value, list):
- return False
- dictPrototype = dict(
- nameID=(int, True),
- platformID=(int, True),
- encodingID=(int, True),
- languageID=(int, True),
- string=(str, True),
- )
- for nameRecord in value:
- if not genericDictValidator(nameRecord, dictPrototype):
- return False
- return True
-
-
-def fontInfoOpenTypeOS2WeightClassValidator(value):
- """
- Version 2+.
- """
- if not isinstance(value, int):
- return False
- if value < 0:
- return False
- return True
-
-
-def fontInfoOpenTypeOS2WidthClassValidator(value):
- """
- Version 2+.
- """
- if not isinstance(value, int):
- return False
- if value < 1:
- return False
- if value > 9:
- return False
- return True
-
-
-def fontInfoVersion2OpenTypeOS2PanoseValidator(values):
- """
- Version 2.
- """
- if not isinstance(values, (list, tuple)):
- return False
- if len(values) != 10:
- return False
- for value in values:
- if not isinstance(value, int):
- return False
- # XXX further validation?
- return True
-
-
-def fontInfoVersion3OpenTypeOS2PanoseValidator(values):
- """
- Version 3+.
- """
- if not isinstance(values, (list, tuple)):
- return False
- if len(values) != 10:
- return False
- for value in values:
- if not isinstance(value, int):
- return False
- if value < 0:
- return False
- # XXX further validation?
- return True
-
-
-def fontInfoOpenTypeOS2FamilyClassValidator(values):
- """
- Version 2+.
- """
- if not isinstance(values, (list, tuple)):
- return False
- if len(values) != 2:
- return False
- for value in values:
- if not isinstance(value, int):
- return False
- classID, subclassID = values
- if classID < 0 or classID > 14:
- return False
- if subclassID < 0 or subclassID > 15:
- return False
- return True
-
-
-def fontInfoPostscriptBluesValidator(values):
- """
- Version 2+.
- """
- if not isinstance(values, (list, tuple)):
- return False
- if len(values) > 14:
- return False
- if len(values) % 2:
- return False
- for value in values:
- if not isinstance(value, numberTypes):
- return False
- return True
-
-
-def fontInfoPostscriptOtherBluesValidator(values):
- """
- Version 2+.
- """
- if not isinstance(values, (list, tuple)):
- return False
- if len(values) > 10:
- return False
- if len(values) % 2:
- return False
- for value in values:
- if not isinstance(value, numberTypes):
- return False
- return True
-
-
-def fontInfoPostscriptStemsValidator(values):
- """
- Version 2+.
- """
- if not isinstance(values, (list, tuple)):
- return False
- if len(values) > 12:
- return False
- for value in values:
- if not isinstance(value, numberTypes):
- return False
- return True
-
-
-def fontInfoPostscriptWindowsCharacterSetValidator(value):
- """
- Version 2+.
- """
- validValues = list(range(1, 21))
- if value not in validValues:
- return False
- return True
-
-
-def fontInfoWOFFMetadataUniqueIDValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(id=(str, True))
- if not genericDictValidator(value, dictPrototype):
- return False
- return True
-
-
-def fontInfoWOFFMetadataVendorValidator(value):
- """
- Version 3+.
- """
- dictPrototype = {
- "name": (str, True),
- "url": (str, False),
- "dir": (str, False),
- "class": (str, False),
- }
- if not genericDictValidator(value, dictPrototype):
- return False
- if "dir" in value and value.get("dir") not in ("ltr", "rtl"):
- return False
- return True
-
-
-def fontInfoWOFFMetadataCreditsValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(credits=(list, True))
- if not genericDictValidator(value, dictPrototype):
- return False
- if not len(value["credits"]):
- return False
- dictPrototype = {
- "name": (str, True),
- "url": (str, False),
- "role": (str, False),
- "dir": (str, False),
- "class": (str, False),
- }
- for credit in value["credits"]:
- if not genericDictValidator(credit, dictPrototype):
- return False
- if "dir" in credit and credit.get("dir") not in ("ltr", "rtl"):
- return False
- return True
-
-
-def fontInfoWOFFMetadataDescriptionValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(url=(str, False), text=(list, True))
- if not genericDictValidator(value, dictPrototype):
- return False
- for text in value["text"]:
- if not fontInfoWOFFMetadataTextValue(text):
- return False
- return True
-
-
-def fontInfoWOFFMetadataLicenseValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(url=(str, False), text=(list, False), id=(str, False))
- if not genericDictValidator(value, dictPrototype):
- return False
- if "text" in value:
- for text in value["text"]:
- if not fontInfoWOFFMetadataTextValue(text):
- return False
- return True
-
-
-def fontInfoWOFFMetadataTrademarkValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(text=(list, True))
- if not genericDictValidator(value, dictPrototype):
- return False
- for text in value["text"]:
- if not fontInfoWOFFMetadataTextValue(text):
- return False
- return True
-
-
-def fontInfoWOFFMetadataCopyrightValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(text=(list, True))
- if not genericDictValidator(value, dictPrototype):
- return False
- for text in value["text"]:
- if not fontInfoWOFFMetadataTextValue(text):
- return False
- return True
-
-
-def fontInfoWOFFMetadataLicenseeValidator(value):
- """
- Version 3+.
- """
- dictPrototype = {"name": (str, True), "dir": (str, False), "class": (str, False)}
- if not genericDictValidator(value, dictPrototype):
- return False
- if "dir" in value and value.get("dir") not in ("ltr", "rtl"):
- return False
- return True
-
-
-def fontInfoWOFFMetadataTextValue(value):
- """
- Version 3+.
- """
- dictPrototype = {
- "text": (str, True),
- "language": (str, False),
- "dir": (str, False),
- "class": (str, False),
- }
- if not genericDictValidator(value, dictPrototype):
- return False
- if "dir" in value and value.get("dir") not in ("ltr", "rtl"):
- return False
- return True
-
-
-def fontInfoWOFFMetadataExtensionsValidator(value):
- """
- Version 3+.
- """
- if not isinstance(value, list):
- return False
- if not value:
- return False
- for extension in value:
- if not fontInfoWOFFMetadataExtensionValidator(extension):
- return False
- return True
-
-
-def fontInfoWOFFMetadataExtensionValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(names=(list, False), items=(list, True), id=(str, False))
- if not genericDictValidator(value, dictPrototype):
- return False
- if "names" in value:
- for name in value["names"]:
- if not fontInfoWOFFMetadataExtensionNameValidator(name):
- return False
- for item in value["items"]:
- if not fontInfoWOFFMetadataExtensionItemValidator(item):
- return False
- return True
-
-
-def fontInfoWOFFMetadataExtensionItemValidator(value):
- """
- Version 3+.
- """
- dictPrototype = dict(id=(str, False), names=(list, True), values=(list, True))
- if not genericDictValidator(value, dictPrototype):
- return False
- for name in value["names"]:
- if not fontInfoWOFFMetadataExtensionNameValidator(name):
- return False
- for val in value["values"]:
- if not fontInfoWOFFMetadataExtensionValueValidator(val):
- return False
- return True
-
-
-def fontInfoWOFFMetadataExtensionNameValidator(value):
- """
- Version 3+.
- """
- dictPrototype = {
- "text": (str, True),
- "language": (str, False),
- "dir": (str, False),
- "class": (str, False),
- }
- if not genericDictValidator(value, dictPrototype):
- return False
- if "dir" in value and value.get("dir") not in ("ltr", "rtl"):
- return False
- return True
-
-
-def fontInfoWOFFMetadataExtensionValueValidator(value):
- """
- Version 3+.
- """
- dictPrototype = {
- "text": (str, True),
- "language": (str, False),
- "dir": (str, False),
- "class": (str, False),
- }
- if not genericDictValidator(value, dictPrototype):
- return False
- if "dir" in value and value.get("dir") not in ("ltr", "rtl"):
- return False
- return True
-
-
-# ----------
-# Guidelines
-# ----------
-
-
-def guidelinesValidator(value, identifiers=None):
- """
- Version 3+.
- """
- if not isinstance(value, list):
- return False
- if identifiers is None:
- identifiers = set()
- for guide in value:
- if not guidelineValidator(guide):
- return False
- identifier = guide.get("identifier")
- if identifier is not None:
- if identifier in identifiers:
- return False
- identifiers.add(identifier)
- return True
-
-
-_guidelineDictPrototype = dict(
- x=((int, float), False),
- y=((int, float), False),
- angle=((int, float), False),
- name=(str, False),
- color=(str, False),
- identifier=(str, False),
-)
-
-
-def guidelineValidator(value):
- """
- Version 3+.
- """
- if not genericDictValidator(value, _guidelineDictPrototype):
- return False
- x = value.get("x")
- y = value.get("y")
- angle = value.get("angle")
- # x or y must be present
- if x is None and y is None:
- return False
- # if x or y are None, angle must not be present
- if x is None or y is None:
- if angle is not None:
- return False
- # if x and y are defined, angle must be defined
- if x is not None and y is not None and angle is None:
- return False
- # angle must be between 0 and 360
- if angle is not None:
- if angle < 0:
- return False
- if angle > 360:
- return False
- # identifier must be 1 or more characters
- identifier = value.get("identifier")
- if identifier is not None and not identifierValidator(identifier):
- return False
- # color must follow the proper format
- color = value.get("color")
- if color is not None and not colorValidator(color):
- return False
- return True
-
-
-# -------
-# Anchors
-# -------
-
-
-def anchorsValidator(value, identifiers=None):
- """
- Version 3+.
- """
- if not isinstance(value, list):
- return False
- if identifiers is None:
- identifiers = set()
- for anchor in value:
- if not anchorValidator(anchor):
- return False
- identifier = anchor.get("identifier")
- if identifier is not None:
- if identifier in identifiers:
- return False
- identifiers.add(identifier)
- return True
-
-
-_anchorDictPrototype = dict(
- x=((int, float), False),
- y=((int, float), False),
- name=(str, False),
- color=(str, False),
- identifier=(str, False),
-)
-
-
-def anchorValidator(value):
- """
- Version 3+.
- """
- if not genericDictValidator(value, _anchorDictPrototype):
- return False
- x = value.get("x")
- y = value.get("y")
- # x and y must be present
- if x is None or y is None:
- return False
- # identifier must be 1 or more characters
- identifier = value.get("identifier")
- if identifier is not None and not identifierValidator(identifier):
- return False
- # color must follow the proper format
- color = value.get("color")
- if color is not None and not colorValidator(color):
- return False
- return True
-
-
-# ----------
-# Identifier
-# ----------
-
-
-def identifierValidator(value):
- """
- Version 3+.
-
- >>> identifierValidator("a")
- True
- >>> identifierValidator("")
- False
- >>> identifierValidator("a" * 101)
- False
- """
- validCharactersMin = 0x20
- validCharactersMax = 0x7E
- if not isinstance(value, str):
- return False
- if not value:
- return False
- if len(value) > 100:
- return False
- for c in value:
- c = ord(c)
- if c < validCharactersMin or c > validCharactersMax:
- return False
- return True
-
-
-# -----
-# Color
-# -----
-
-
-def colorValidator(value):
- """
- Version 3+.
-
- >>> colorValidator("0,0,0,0")
- True
- >>> colorValidator(".5,.5,.5,.5")
- True
- >>> colorValidator("0.5,0.5,0.5,0.5")
- True
- >>> colorValidator("1,1,1,1")
- True
-
- >>> colorValidator("2,0,0,0")
- False
- >>> colorValidator("0,2,0,0")
- False
- >>> colorValidator("0,0,2,0")
- False
- >>> colorValidator("0,0,0,2")
- False
-
- >>> colorValidator("1r,1,1,1")
- False
- >>> colorValidator("1,1g,1,1")
- False
- >>> colorValidator("1,1,1b,1")
- False
- >>> colorValidator("1,1,1,1a")
- False
-
- >>> colorValidator("1 1 1 1")
- False
- >>> colorValidator("1 1,1,1")
- False
- >>> colorValidator("1,1 1,1")
- False
- >>> colorValidator("1,1,1 1")
- False
-
- >>> colorValidator("1, 1, 1, 1")
- True
- """
- if not isinstance(value, str):
- return False
- parts = value.split(",")
- if len(parts) != 4:
- return False
- for part in parts:
- part = part.strip()
- converted = False
- try:
- part = int(part)
- converted = True
- except ValueError:
- pass
- if not converted:
- try:
- part = float(part)
- converted = True
- except ValueError:
- pass
- if not converted:
- return False
- if part < 0:
- return False
- if part > 1:
- return False
- return True
-
-
-# -----
-# image
-# -----
-
-pngSignature = b"\x89PNG\r\n\x1a\n"
-
-_imageDictPrototype = dict(
- fileName=(str, True),
- xScale=((int, float), False),
- xyScale=((int, float), False),
- yxScale=((int, float), False),
- yScale=((int, float), False),
- xOffset=((int, float), False),
- yOffset=((int, float), False),
- color=(str, False),
-)
-
-
-def imageValidator(value):
- """
- Version 3+.
- """
- if not genericDictValidator(value, _imageDictPrototype):
- return False
- # fileName must be one or more characters
- if not value["fileName"]:
- return False
- # color must follow the proper format
- color = value.get("color")
- if color is not None and not colorValidator(color):
- return False
- return True
-
-
-def pngValidator(path=None, data=None, fileObj=None):
- """
- Version 3+.
-
- This checks the signature of the image data.
- """
- assert path is not None or data is not None or fileObj is not None
- if path is not None:
- with open(path, "rb") as f:
- signature = f.read(8)
- elif data is not None:
- signature = data[:8]
- elif fileObj is not None:
- pos = fileObj.tell()
- signature = fileObj.read(8)
- fileObj.seek(pos)
- if signature != pngSignature:
- return False, "Image does not begin with the PNG signature."
- return True, None
-
-
-# -------------------
-# layercontents.plist
-# -------------------
-
-
-def layerContentsValidator(value, ufoPathOrFileSystem):
- """
- Check the validity of layercontents.plist.
- Version 3+.
- """
- if isinstance(ufoPathOrFileSystem, fs.base.FS):
- fileSystem = ufoPathOrFileSystem
- else:
- fileSystem = fs.osfs.OSFS(ufoPathOrFileSystem)
-
- bogusFileMessage = "layercontents.plist in not in the correct format."
- # file isn't in the right format
- if not isinstance(value, list):
- return False, bogusFileMessage
- # work through each entry
- usedLayerNames = set()
- usedDirectories = set()
- contents = {}
- for entry in value:
- # layer entry in the incorrect format
- if not isinstance(entry, list):
- return False, bogusFileMessage
- if not len(entry) == 2:
- return False, bogusFileMessage
- for i in entry:
- if not isinstance(i, str):
- return False, bogusFileMessage
- layerName, directoryName = entry
- # check directory naming
- if directoryName != "glyphs":
- if not directoryName.startswith("glyphs."):
- return (
- False,
- "Invalid directory name (%s) in layercontents.plist."
- % directoryName,
- )
- if len(layerName) == 0:
- return False, "Empty layer name in layercontents.plist."
- # directory doesn't exist
- if not fileSystem.exists(directoryName):
- return False, "A glyphset does not exist at %s." % directoryName
- # default layer name
- if layerName == "public.default" and directoryName != "glyphs":
- return (
- False,
- "The name public.default is being used by a layer that is not the default.",
- )
- # check usage
- if layerName in usedLayerNames:
- return (
- False,
- "The layer name %s is used by more than one layer." % layerName,
- )
- usedLayerNames.add(layerName)
- if directoryName in usedDirectories:
- return (
- False,
- "The directory %s is used by more than one layer." % directoryName,
- )
- usedDirectories.add(directoryName)
- # store
- contents[layerName] = directoryName
- # missing default layer
- foundDefault = "glyphs" in contents.values()
- if not foundDefault:
- return False, "The required default glyph set is not in the UFO."
- return True, None
-
-
-# ------------
-# groups.plist
-# ------------
-
-
-def groupsValidator(value):
- """
- Check the validity of the groups.
- Version 3+ (though it's backwards compatible with UFO 1 and UFO 2).
-
- >>> groups = {"A" : ["A", "A"], "A2" : ["A"]}
- >>> groupsValidator(groups)
- (True, None)
-
- >>> groups = {"" : ["A"]}
- >>> valid, msg = groupsValidator(groups)
- >>> valid
- False
- >>> print(msg)
- A group has an empty name.
-
- >>> groups = {"public.awesome" : ["A"]}
- >>> groupsValidator(groups)
- (True, None)
-
- >>> groups = {"public.kern1." : ["A"]}
- >>> valid, msg = groupsValidator(groups)
- >>> valid
- False
- >>> print(msg)
- The group data contains a kerning group with an incomplete name.
- >>> groups = {"public.kern2." : ["A"]}
- >>> valid, msg = groupsValidator(groups)
- >>> valid
- False
- >>> print(msg)
- The group data contains a kerning group with an incomplete name.
-
- >>> groups = {"public.kern1.A" : ["A"], "public.kern2.A" : ["A"]}
- >>> groupsValidator(groups)
- (True, None)
-
- >>> groups = {"public.kern1.A1" : ["A"], "public.kern1.A2" : ["A"]}
- >>> valid, msg = groupsValidator(groups)
- >>> valid
- False
- >>> print(msg)
- The glyph "A" occurs in too many kerning groups.
- """
- bogusFormatMessage = "The group data is not in the correct format."
- if not isDictEnough(value):
- return False, bogusFormatMessage
- firstSideMapping = {}
- secondSideMapping = {}
- for groupName, glyphList in value.items():
- if not isinstance(groupName, (str)):
- return False, bogusFormatMessage
- if not isinstance(glyphList, (list, tuple)):
- return False, bogusFormatMessage
- if not groupName:
- return False, "A group has an empty name."
- if groupName.startswith("public."):
- if not groupName.startswith("public.kern1.") and not groupName.startswith(
- "public.kern2."
- ):
- # unknown public.* name. silently skip.
- continue
- else:
- if len("public.kernN.") == len(groupName):
- return (
- False,
- "The group data contains a kerning group with an incomplete name.",
- )
- if groupName.startswith("public.kern1."):
- d = firstSideMapping
- else:
- d = secondSideMapping
- for glyphName in glyphList:
- if not isinstance(glyphName, str):
- return (
- False,
- "The group data %s contains an invalid member." % groupName,
- )
- if glyphName in d:
- return (
- False,
- 'The glyph "%s" occurs in too many kerning groups.' % glyphName,
- )
- d[glyphName] = groupName
- return True, None
-
-
-# -------------
-# kerning.plist
-# -------------
-
-
-def kerningValidator(data):
- """
- Check the validity of the kerning data structure.
- Version 3+ (though it's backwards compatible with UFO 1 and UFO 2).
-
- >>> kerning = {"A" : {"B" : 100}}
- >>> kerningValidator(kerning)
- (True, None)
-
- >>> kerning = {"A" : ["B"]}
- >>> valid, msg = kerningValidator(kerning)
- >>> valid
- False
- >>> print(msg)
- The kerning data is not in the correct format.
-
- >>> kerning = {"A" : {"B" : "100"}}
- >>> valid, msg = kerningValidator(kerning)
- >>> valid
- False
- >>> print(msg)
- The kerning data is not in the correct format.
- """
- bogusFormatMessage = "The kerning data is not in the correct format."
- if not isinstance(data, Mapping):
- return False, bogusFormatMessage
- for first, secondDict in data.items():
- if not isinstance(first, str):
- return False, bogusFormatMessage
- elif not isinstance(secondDict, Mapping):
- return False, bogusFormatMessage
- for second, value in secondDict.items():
- if not isinstance(second, str):
- return False, bogusFormatMessage
- elif not isinstance(value, numberTypes):
- return False, bogusFormatMessage
- return True, None
-
-
-# -------------
-# lib.plist/lib
-# -------------
-
-_bogusLibFormatMessage = "The lib data is not in the correct format: %s"
-
-
-def fontLibValidator(value):
- """
- Check the validity of the lib.
- Version 3+ (though it's backwards compatible with UFO 1 and UFO 2).
-
- >>> lib = {"foo" : "bar"}
- >>> fontLibValidator(lib)
- (True, None)
-
- >>> lib = {"public.awesome" : "hello"}
- >>> fontLibValidator(lib)
- (True, None)
-
- >>> lib = {"public.glyphOrder" : ["A", "C", "B"]}
- >>> fontLibValidator(lib)
- (True, None)
-
- >>> lib = "hello"
- >>> valid, msg = fontLibValidator(lib)
- >>> valid
- False
- >>> print(msg) # doctest: +ELLIPSIS
- The lib data is not in the correct format: expected a dictionary, ...
-
- >>> lib = {1: "hello"}
- >>> valid, msg = fontLibValidator(lib)
- >>> valid
- False
- >>> print(msg)
- The lib key is not properly formatted: expected str, found int: 1
-
- >>> lib = {"public.glyphOrder" : "hello"}
- >>> valid, msg = fontLibValidator(lib)
- >>> valid
- False
- >>> print(msg) # doctest: +ELLIPSIS
- public.glyphOrder is not properly formatted: expected list or tuple,...
-
- >>> lib = {"public.glyphOrder" : ["A", 1, "B"]}
- >>> valid, msg = fontLibValidator(lib)
- >>> valid
- False
- >>> print(msg) # doctest: +ELLIPSIS
- public.glyphOrder is not properly formatted: expected str,...
- """
- if not isDictEnough(value):
- reason = "expected a dictionary, found %s" % type(value).__name__
- return False, _bogusLibFormatMessage % reason
- for key, value in value.items():
- if not isinstance(key, str):
- return False, (
- "The lib key is not properly formatted: expected str, found %s: %r"
- % (type(key).__name__, key)
- )
- # public.glyphOrder
- if key == "public.glyphOrder":
- bogusGlyphOrderMessage = "public.glyphOrder is not properly formatted: %s"
- if not isinstance(value, (list, tuple)):
- reason = "expected list or tuple, found %s" % type(value).__name__
- return False, bogusGlyphOrderMessage % reason
- for glyphName in value:
- if not isinstance(glyphName, str):
- reason = "expected str, found %s" % type(glyphName).__name__
- return False, bogusGlyphOrderMessage % reason
- return True, None
-
-
-# --------
-# GLIF lib
-# --------
-
-
-def glyphLibValidator(value):
- """
- Check the validity of the lib.
- Version 3+ (though it's backwards compatible with UFO 1 and UFO 2).
-
- >>> lib = {"foo" : "bar"}
- >>> glyphLibValidator(lib)
- (True, None)
-
- >>> lib = {"public.awesome" : "hello"}
- >>> glyphLibValidator(lib)
- (True, None)
-
- >>> lib = {"public.markColor" : "1,0,0,0.5"}
- >>> glyphLibValidator(lib)
- (True, None)
-
- >>> lib = {"public.markColor" : 1}
- >>> valid, msg = glyphLibValidator(lib)
- >>> valid
- False
- >>> print(msg)
- public.markColor is not properly formatted.
- """
- if not isDictEnough(value):
- reason = "expected a dictionary, found %s" % type(value).__name__
- return False, _bogusLibFormatMessage % reason
- for key, value in value.items():
- if not isinstance(key, str):
- reason = "key (%s) should be a string" % key
- return False, _bogusLibFormatMessage % reason
- # public.markColor
- if key == "public.markColor":
- if not colorValidator(value):
- return False, "public.markColor is not properly formatted."
- return True, None
-
-
-if __name__ == "__main__":
- import doctest
-
- doctest.testmod()
diff --git a/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/inpaint_gradio.py b/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/inpaint_gradio.py
deleted file mode 100644
index f547c0f9789bb29c1b016edb28426b18f78f259b..0000000000000000000000000000000000000000
--- a/spaces/johnslegers/stable-diffusion-gui-test/optimizedSD/inpaint_gradio.py
+++ /dev/null
@@ -1,328 +0,0 @@
-import argparse
-import os
-import re
-import time
-from contextlib import nullcontext
-from itertools import islice
-from random import randint
-
-import gradio as gr
-import numpy as np
-import torch
-from PIL import Image
-from einops import rearrange, repeat
-from omegaconf import OmegaConf
-from pytorch_lightning import seed_everything
-from torch import autocast
-from torchvision.utils import make_grid
-from tqdm import tqdm, trange
-from transformers import logging
-
-from ldmlib.util import instantiate_from_config
-from optimUtils import split_weighted_subprompts, logger
-
-logging.set_verbosity_error()
-import mimetypes
-
-mimetypes.init()
-mimetypes.add_type("application/javascript", ".js")
-
-
-def chunk(it, size):
- it = iter(it)
- return iter(lambda: tuple(islice(it, size)), ())
-
-
-def load_model_from_config(ckpt, verbose=False):
- print(f"Loading model from {ckpt}")
- pl_sd = torch.load(ckpt, map_location="cpu")
- if "global_step" in pl_sd:
- print(f"Global Step: {pl_sd['global_step']}")
- sd = pl_sd["state_dict"]
- return sd
-
-
-def load_img(image, h0, w0):
- image = image.convert("RGB")
- w, h = image.size
- print(f"loaded input image of size ({w}, {h})")
- if h0 is not None and w0 is not None:
- h, w = h0, w0
-
- w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32
-
- print(f"New image size ({w}, {h})")
- image = image.resize((w, h), resample=Image.LANCZOS)
- image = np.array(image).astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return 2.0 * image - 1.0
-
-
-def load_mask(mask, h0, w0, newH, newW, invert=False):
- image = mask.convert("RGB")
- w, h = image.size
- print(f"loaded input mask of size ({w}, {h})")
- if h0 is not None and w0 is not None:
- h, w = h0, w0
-
- w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32
-
- print(f"New mask size ({w}, {h})")
- image = image.resize((newW, newH), resample=Image.LANCZOS)
- # image = image.resize((64, 64), resample=Image.LANCZOS)
- image = np.array(image)
-
- if invert:
- print("inverted")
- where_0, where_1 = np.where(image == 0), np.where(image == 255)
- image[where_0], image[where_1] = 255, 0
- image = image.astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return image
-
-
-def generate(
- image,
- mask_image,
- prompt,
- strength,
- ddim_steps,
- n_iter,
- batch_size,
- Height,
- Width,
- scale,
- ddim_eta,
- unet_bs,
- device,
- seed,
- outdir,
- img_format,
- turbo,
- full_precision,
-):
- if seed == "":
- seed = randint(0, 1000000)
- seed = int(seed)
- seed_everything(seed)
- sampler = "ddim"
-
- # Logging
- logger(locals(), log_csv="logs/inpaint_gradio_logs.csv")
-
- init_image = load_img(image['image'], Height, Width).to(device)
-
- model.unet_bs = unet_bs
- model.turbo = turbo
- model.cdevice = device
- modelCS.cond_stage_model.device = device
-
- if device != "cpu" and full_precision == False:
- model.half()
- modelCS.half()
- modelFS.half()
- init_image = init_image.half()
- # mask.half()
-
- tic = time.time()
- os.makedirs(outdir, exist_ok=True)
- outpath = outdir
- sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompt)))[:150]
- os.makedirs(sample_path, exist_ok=True)
- base_count = len(os.listdir(sample_path))
-
- # n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
- assert prompt is not None
- data = [batch_size * [prompt]]
-
- modelFS.to(device)
-
- init_latent = modelFS.get_first_stage_encoding(modelFS.encode_first_stage(init_image)) # move to latent space
- init_latent = repeat(init_latent, "1 ... -> b ...", b=batch_size)
- if mask_image is None:
- mask = load_mask(image['mask'], Height, Width, init_latent.shape[2], init_latent.shape[3], True).to(device)
- else:
- image['mask']=mask_image
- mask = load_mask(mask_image, Height, Width, init_latent.shape[2], init_latent.shape[3], True).to(device)
-
- mask = mask[0][0].unsqueeze(0).repeat(4, 1, 1).unsqueeze(0)
- mask = repeat(mask, '1 ... -> b ...', b=batch_size)
-
- if device != "cpu":
- mem = torch.cuda.memory_allocated() / 1e6
- modelFS.to("cpu")
- while torch.cuda.memory_allocated() / 1e6 >= mem:
- time.sleep(1)
-
- if strength == 1:
- print("strength should be less than 1, setting it to 0.999")
- strength = 0.999
- assert 0.0 <= strength < 1.0, "can only work with strength in [0.0, 1.0]"
- t_enc = int(strength * ddim_steps)
- print(f"target t_enc is {t_enc} steps")
-
- if full_precision == False and device != "cpu":
- precision_scope = autocast
- else:
- precision_scope = nullcontext
-
- all_samples = []
- seeds = ""
- with torch.no_grad():
- all_samples = list()
- for _ in trange(n_iter, desc="Sampling"):
- for prompts in tqdm(data, desc="data"):
- with precision_scope("cuda"):
- modelCS.to(device)
- uc = None
- if scale != 1.0:
- uc = modelCS.get_learned_conditioning(batch_size * [""])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
-
- subprompts, weights = split_weighted_subprompts(prompts[0])
- if len(subprompts) > 1:
- c = torch.zeros_like(uc)
- totalWeight = sum(weights)
- # normalize each "sub prompt" and add it
- for i in range(len(subprompts)):
- weight = weights[i]
- # if not skip_normalize:
- weight = weight / totalWeight
- c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
- else:
- c = modelCS.get_learned_conditioning(prompts)
-
- if device != "cpu":
- mem = torch.cuda.memory_allocated() / 1e6
- modelCS.to("cpu")
- while torch.cuda.memory_allocated() / 1e6 >= mem:
- time.sleep(1)
-
- # encode (scaled latent)
- z_enc = model.stochastic_encode(
- init_latent, torch.tensor([t_enc] * batch_size).to(device),
- seed, ddim_eta, ddim_steps)
-
- # decode it
- samples_ddim = model.sample(
- t_enc,
- c,
- z_enc,
- unconditional_guidance_scale=scale,
- unconditional_conditioning=uc,
- mask=mask,
- x_T=init_latent,
- sampler=sampler,
- )
-
- modelFS.to(device)
- print("saving images")
- for i in range(batch_size):
- x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))
- x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
- all_samples.append(x_sample.to("cpu"))
- x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
- Image.fromarray(x_sample.astype(np.uint8)).save(
- os.path.join(sample_path, "seed_" + str(seed) + "_" + f"{base_count:05}.{img_format}")
- )
- seeds += str(seed) + ","
- seed += 1
- base_count += 1
-
- if device != "cpu":
- mem = torch.cuda.memory_allocated() / 1e6
- modelFS.to("cpu")
- while torch.cuda.memory_allocated() / 1e6 >= mem:
- time.sleep(1)
-
- del samples_ddim
- del x_sample
- del x_samples_ddim
- print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
-
- toc = time.time()
-
- time_taken = (toc - tic) / 60.0
- grid = torch.cat(all_samples, 0)
- grid = make_grid(grid, nrow=n_iter)
- grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
-
- txt = (
- "Samples finished in "
- + str(round(time_taken, 3))
- + " minutes and exported to \n"
- + sample_path
- + "\nSeeds used = "
- + seeds[:-1]
- )
- return Image.fromarray(grid.astype(np.uint8)), image['mask'], txt
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='txt2img using gradio')
- parser.add_argument('--config_path', default="optimizedSD/v1-inference.yaml", type=str, help='config path')
- parser.add_argument('--ckpt_path', default="models/ldm/stable-diffusion-v1/model.ckpt", type=str, help='ckpt path')
- args = parser.parse_args()
- config = args.config_path
- ckpt = args.ckpt_path
- sd = load_model_from_config(f"{ckpt}")
- li, lo = [], []
- for key, v_ in sd.items():
- sp = key.split(".")
- if (sp[0]) == "model":
- if "input_blocks" in sp:
- li.append(key)
- elif "middle_block" in sp:
- li.append(key)
- elif "time_embed" in sp:
- li.append(key)
- else:
- lo.append(key)
- for key in li:
- sd["model1." + key[6:]] = sd.pop(key)
- for key in lo:
- sd["model2." + key[6:]] = sd.pop(key)
-
- config = OmegaConf.load(f"{config}")
-
- model = instantiate_from_config(config.modelUNet)
- _, _ = model.load_state_dict(sd, strict=False)
- model.eval()
-
- modelCS = instantiate_from_config(config.modelCondStage)
- _, _ = modelCS.load_state_dict(sd, strict=False)
- modelCS.eval()
-
- modelFS = instantiate_from_config(config.modelFirstStage)
- _, _ = modelFS.load_state_dict(sd, strict=False)
- modelFS.eval()
- del sd
-
- demo = gr.Interface(
- fn=generate,
- inputs=[
- gr.Image(tool="sketch", type="pil"),
- gr.Image(tool="editor", type="pil"),
- "text",
- gr.Slider(0, 0.99, value=0.99, step=0.01),
- gr.Slider(1, 1000, value=50),
- gr.Slider(1, 100, step=1),
- gr.Slider(1, 100, step=1),
- gr.Slider(64, 4096, value=512, step=64),
- gr.Slider(64, 4096, value=512, step=64),
- gr.Slider(0, 50, value=7.5, step=0.1),
- gr.Slider(0, 1, step=0.01),
- gr.Slider(1, 2, value=1, step=1),
- gr.Text(value="cuda"),
- "text",
- gr.Text(value="outputs/inpaint-samples"),
- gr.Radio(["png", "jpg"], value='png'),
- "checkbox",
- "checkbox",
- ],
- outputs=["image", "image", "text"],
- )
- demo.launch()
diff --git a/spaces/julien-c/sveltekit-demo/README.md b/spaces/julien-c/sveltekit-demo/README.md
deleted file mode 100644
index 28e93f5b318098fcbc35b470d400a9b77aeab0de..0000000000000000000000000000000000000000
--- a/spaces/julien-c/sveltekit-demo/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Sveltekit Demo
-emoji: ⚡️
-colorFrom: yellow
-colorTo: red
-sdk: static
-pinned: false
-app_file: build/index.html
----
-
-# static sveltekit-demo
-
-`adapter-static` for static hosts under a relative folder, like GitHub Pages:
-read https://github.com/sveltejs/kit/tree/master/packages/adapter-static#github-pages
-
diff --git a/spaces/k1ngtai/MMS/uroman/lib/NLP/stringDistance.pm b/spaces/k1ngtai/MMS/uroman/lib/NLP/stringDistance.pm
deleted file mode 100644
index 17279d4a064c65fd6683a1b31d04831d93c5b416..0000000000000000000000000000000000000000
--- a/spaces/k1ngtai/MMS/uroman/lib/NLP/stringDistance.pm
+++ /dev/null
@@ -1,724 +0,0 @@
-################################################################
-# #
-# stringDistance #
-# #
-################################################################
-
-package NLP::stringDistance;
-
-use List::Util qw(min max);
-$utf8 = NLP::UTF8;
-$util = NLP::utilities;
-$romanizer = NLP::Romanizer;
-
-%dummy_ht = ();
-
-sub rule_string_expansion {
- local($this, *ht, $s, $lang_code) = @_;
-
- my @characters = $utf8->split_into_utf8_characters($s, "return only chars, return trailing whitespaces", *dummy_ht);
- foreach $sub_len ((0 .. ($#characters-1))) {
- my $sub = join("", @characters[0 .. $sub_len]);
- foreach $super_len ((($sub_len + 1) .. $#characters)) {
- my $super = join("", @characters[0 .. $super_len]);
- # print STDERR " $sub -> $super\n" unless $ht{RULE_STRING_EXPANSION}->{$lang_code}->{$sub}->{$super};
- $ht{RULE_STRING_EXPANSION}->{$lang_code}->{$sub}->{$super} = 1;
- $ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$sub} = 1;
- # print STDERR " RULE_STRING_HAS_EXPANSION $lang_code $sub\n";
- }
- }
-}
-
-sub load_string_distance_data {
- local($this, $filename, *ht, $verbose) = @_;
-
- $verbose = 0 unless defined($verbose);
- open(IN,$filename) || die "Could not open $filename";
- my $line_number = 0;
- my $n_cost_rules = 0;
- while () {
- $line_number++;
- my $line = $_;
- $line =~ s/^\xEF\xBB\xBF//;
- $line =~ s/\s*$//;
- next if $line =~ /^\s*(\#.*)?$/;
- print STDERR "** Warning: line $line_number contains suspicious control character: $line\n" if $line =~ /[\x00-\x1F]/;
- my $s1 = $util->slot_value_in_double_colon_del_list($line, "s1");
- my $s2 = $util->slot_value_in_double_colon_del_list($line, "s2");
- $s1 = $util->dequote_string($s1); # 'can\'t' => can't
- $s2 = $util->dequote_string($s2);
- my $cost = $util->slot_value_in_double_colon_del_list($line, "cost");
- if (($s1 eq "") && ($s2 eq "")) {
- print STDERR "Ignoring bad line $line_number in $filename, because both s1 and s2 are empty strings\n";
- next;
- }
- unless ($cost =~ /^\d+(\.\d+)?$/) {
- if ($cost eq "") {
- print STDERR "Ignoring bad line $line_number in $filename, because of missing cost\n";
- } else {
- print STDERR "Ignoring bad line $line_number in $filename, because of ill-formed cost $cost\n";
- }
- next;
- }
- my $lang_code1_s = $util->slot_value_in_double_colon_del_list($line, "lc1");
- my $lang_code2_s = $util->slot_value_in_double_colon_del_list($line, "lc2");
- my @lang_codes_1 = ($lang_code1_s eq "") ? ("") : split(/,\s*/, $lang_code1_s);
- my @lang_codes_2 = ($lang_code2_s eq "") ? ("") : split(/,\s*/, $lang_code2_s);
- my $left_context1 = $util->slot_value_in_double_colon_del_list($line, "left1");
- my $left_context2 = $util->slot_value_in_double_colon_del_list($line, "left2");
- my $right_context1 = $util->slot_value_in_double_colon_del_list($line, "right1");
- my $right_context2 = $util->slot_value_in_double_colon_del_list($line, "right2");
- my $bad_left = $util->slot_value_in_double_colon_del_list($line, "left");
- if ($bad_left) {
- print STDERR "** Warning: slot '::left $bad_left' in line $line_number\n";
- next;
- }
- my $bad_right = $util->slot_value_in_double_colon_del_list($line, "right");
- if ($bad_right) {
- print STDERR "** Warning: slot '::right $bad_right' in line $line_number\n";
- next;
- }
- my $in_lang_codes1 = $util->slot_value_in_double_colon_del_list($line, "in-lc1");
- my $in_lang_codes2 = $util->slot_value_in_double_colon_del_list($line, "in-lc2");
- my $out_lang_codes1 = $util->slot_value_in_double_colon_del_list($line, "out-lc1");
- my $out_lang_codes2 = $util->slot_value_in_double_colon_del_list($line, "out-lc2");
- if ($left_context1) {
- if ($left_context1 =~ /^\/.*\/$/) {
- $left_context1 =~ s/^\///;
- $left_context1 =~ s/\/$//;
- } else {
- print STDERR "Ignoring unrecognized non-regular-express ::left1 $left_context1 in $line_number of $filename\n";
- $left_context1 = "";
- }
- }
- if ($left_context2) {
- if ($left_context2 =~ /^\/.*\/$/) {
- $left_context2 =~ s/^\///;
- $left_context2 =~ s/\/$//;
- } else {
- $left_context2 = "";
- print STDERR "Ignoring unrecognized non-regular-express ::left2 $left_context2 in $line_number of $filename\n";
- }
- }
- if ($right_context1) {
- unless ($right_context1 =~ /^(\[[^\[\]]*\])+$/) {
- $right_context1 = "";
- print STDERR "Ignoring unrecognized right-context ::right1 $right_context1 in $line_number of $filename\n";
- }
- }
- if ($right_context2) {
- unless ($right_context2 =~ /^(\[[^\[\]]*\])+$/) {
- $right_context2 = "";
- print STDERR "Ignoring unrecognized right-context ::right2 $right_context2 in $line_number of $filename\n";
- }
- }
- foreach $lang_code1 (@lang_codes_1) {
- foreach $lang_code2 (@lang_codes_2) {
- $n_cost_rules++;
- my $cost_rule_id = $n_cost_rules;
- $ht{COST}->{$lang_code1}->{$lang_code2}->{$s1}->{$s2}->{$cost_rule_id} = $cost;
- $ht{RULE_STRING}->{$lang_code1}->{$s1} = 1;
- $ht{RULE_STRING}->{$lang_code2}->{$s2} = 1;
- $ht{LEFT1}->{$cost_rule_id} = $left_context1;
- $ht{LEFT2}->{$cost_rule_id} = $left_context2;
- $ht{RIGHT1}->{$cost_rule_id} = $right_context1;
- $ht{RIGHT2}->{$cost_rule_id} = $right_context2;
- $ht{INLC1}->{$cost_rule_id} = $in_lang_codes1;
- $ht{INLC2}->{$cost_rule_id} = $in_lang_codes2;
- $ht{OUTLC1}->{$cost_rule_id} = $out_lang_codes1;
- $ht{OUTLC2}->{$cost_rule_id} = $out_lang_codes2;
- unless (($s1 eq $s2)
- && ($lang_code1 eq $lang_code2)
- && ($left_context1 eq $left_context2)
- && ($right_context1 eq $right_context2)
- && ($in_lang_codes1 eq $in_lang_codes2)
- && ($out_lang_codes1 eq $out_lang_codes2)) {
- $n_cost_rules++;
- $cost_rule_id = $n_cost_rules;
- $ht{COST}->{$lang_code2}->{$lang_code1}->{$s2}->{$s1}->{$cost_rule_id} = $cost;
- $ht{LEFT1}->{$cost_rule_id} = $left_context2;
- $ht{LEFT2}->{$cost_rule_id} = $left_context1;
- $ht{RIGHT1}->{$cost_rule_id} = $right_context2;
- $ht{RIGHT2}->{$cost_rule_id} = $right_context1;
- $ht{INLC1}->{$cost_rule_id} = $in_lang_codes2;
- $ht{INLC2}->{$cost_rule_id} = $in_lang_codes1;
- $ht{OUTLC1}->{$cost_rule_id} = $out_lang_codes2;
- $ht{OUTLC2}->{$cost_rule_id} = $out_lang_codes1;
- # print STDERR " Flip rule in line $line: $line\n";
- }
- $this->rule_string_expansion(*ht, $s1, $lang_code1);
- $this->rule_string_expansion(*ht, $s2, $lang_code2);
- }
- }
- }
- close(IN);
- print STDERR "Read in $n_cost_rules rules from $line_number lines in $filename\n" if $verbose;
-}
-
-sub romanized_string_to_simple_chart {
- local($this, $s, *chart_ht) = @_;
-
- my @characters = $utf8->split_into_utf8_characters($s, "return only chars, return trailing whitespaces", *dummy_ht);
- $chart_ht{N_CHARS} = $#characters + 1;
- $chart_ht{N_NODES} = 0;
- foreach $i ((0 .. $#characters)) {
- $romanizer->add_node($characters[$i], $i, ($i+1), *chart_ht, "", "");
- }
-}
-
-sub linearize_chart_points {
- local($this, *chart_ht, $chart_id, *sd_ht, $verbose) = @_;
-
- $verbose = 0 unless defined($verbose);
- print STDERR "Linearize $chart_id\n" if $verbose;
- my $current_chart_pos = 0;
- my $current_linear_chart_pos = 0;
- $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos} = $current_linear_chart_pos;
- $sd_ht{LINPOS2POS}->{$chart_id}->{$current_linear_chart_pos} = $current_chart_pos;
- print STDERR " LINPOS2POS.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos\n" if $verbose;
- my @end_chart_positions = keys %{$chart_ht{NODES_ENDING_AT}};
- my $end_chart_pos = (@end_chart_positions) ? max(@end_chart_positions) : 0;
- $sd_ht{MAXPOS}->{$chart_id} = $end_chart_pos;
- print STDERR " Chart span: $current_chart_pos-$end_chart_pos\n" if $verbose;
- while ($current_chart_pos < $end_chart_pos) {
- my @node_ids = keys %{$chart_ht{NODES_STARTING_AT}->{$current_chart_pos}};
- foreach $node_id (@node_ids) {
- my $roman_s = $chart_ht{NODE_ROMAN}->{$node_id};
- my @roman_chars = $utf8->split_into_utf8_characters($roman_s, "return only chars, return trailing whitespaces", *dummy_ht);
- print STDERR " $current_chart_pos/$current_linear_chart_pos node: $node_id $roman_s (@roman_chars)\n" if $verbose;
- if ($#roman_chars >= 1) {
- foreach $i ((1 .. $#roman_chars)) {
- $current_linear_chart_pos++;
- $sd_ht{SPLITPOS2LINPOS}->{$chart_id}->{$current_chart_pos}->{$node_id}->{$i} = $current_linear_chart_pos;
- $sd_ht{LINPOS2SPLITPOS}->{$chart_id}->{$current_linear_chart_pos}->{$current_chart_pos}->{$node_id}->{$i} = 1;
- print STDERR " LINPOS2SPLITPOS.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos NODE: $node_id I: $i\n" if $verbose;
- }
- }
- }
- $current_chart_pos++;
- if ($util->member($current_chart_pos, @end_chart_positions)) {
- $current_linear_chart_pos++;
- $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos} = $current_linear_chart_pos;
- $sd_ht{LINPOS2POS}->{$chart_id}->{$current_linear_chart_pos} = $current_chart_pos;
- print STDERR " LINPOS2POS.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos\n" if $verbose;
- }
- }
- $current_chart_pos = 0;
- while ($current_chart_pos <= $end_chart_pos) {
- my $current_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos};
- $current_linear_chart_pos = "?" unless defined($current_linear_chart_pos);
- my @node_ids = keys %{$chart_ht{NODES_STARTING_AT}->{$current_chart_pos}};
- # print STDERR " LINROM.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos NODES: @node_ids\n" if $verbose;
- foreach $node_id (@node_ids) {
- my $end_pos = $chart_ht{NODE_END}->{$node_id};
- my $end_linpos = $sd_ht{POS2LINPOS}->{$chart_id}->{$end_pos};
- my $roman_s = $chart_ht{NODE_ROMAN}->{$node_id};
- my @roman_chars = $utf8->split_into_utf8_characters($roman_s, "return only chars, return trailing whitespaces", *dummy_ht);
- print STDERR " LINROM.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos NODE: $node_id CHARS: @roman_chars\n" if $verbose;
- if (@roman_chars) {
- foreach $i ((0 .. $#roman_chars)) {
- my $from_linear_chart_pos
- = (($i == 0)
- ? $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos}
- : $sd_ht{SPLITPOS2LINPOS}->{$chart_id}->{$current_chart_pos}->{$node_id}->{$i});
- print STDERR " FROM.$chart_id I: $i POS: $current_chart_pos NODE: $node_id FROM: $from_linear_chart_pos\n" if $verbose;
- my $to_linear_chart_pos
- = (($i == $#roman_chars)
- ? $end_linpos
- : $sd_ht{SPLITPOS2LINPOS}->{$chart_id}->{$current_chart_pos}->{$node_id}->{($i+1)});
- print STDERR " TO.$chart_id I: $i POS: $current_chart_pos NODE: $node_id FROM: $to_linear_chart_pos\n" if $verbose;
- my $roman_char = $roman_chars[$i];
- $sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}->{$to_linear_chart_pos}->{$roman_char} = 1;
- }
- } else {
- my $from_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos};
- my $to_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{($current_chart_pos+1)};
- # HHERE check this out
- my $i = 1;
- while (! (defined($to_linear_chart_pos))) {
- $i++;
- $to_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{($current_chart_pos+$i)};
- }
- if (defined($from_linear_chart_pos) && defined($to_linear_chart_pos)) {
- $sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}->{$to_linear_chart_pos}->{""} = 1
- } else {
- print STDERR " UNDEF.$chart_id from: "
- . ((defined($from_linear_chart_pos)) ? $from_linear_chart_pos : "?")
- . " to: "
- . ((defined($to_linear_chart_pos)) ? $to_linear_chart_pos : "?")
- . "\n";
- }
- }
- }
- $current_chart_pos++;
- }
- $sd_ht{MAXLINPOS}->{$chart_id} = $sd_ht{POS2LINPOS}->{$chart_id}->{$end_chart_pos};
-}
-
-sub expand_lin_ij_roman {
- local($this, *sd_ht, $chart_id, $lang_code, *ht) = @_;
-
- foreach $start (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}}) {
- foreach $end (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start}}) {
- foreach $roman (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start}->{$end}}) {
- if ($ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$roman}
- || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$roman}) {
- $this->expand_lin_ij_roman_rec(*sd_ht, $chart_id, $start, $end, $roman, $lang_code, *ht);
- }
- }
- }
- }
-}
-
-sub expand_lin_ij_roman_rec {
- local($this, *sd_ht, $chart_id, $start, $end, $roman, $lang_code, *ht) = @_;
-
- # print STDERR " expand_lin_ij_roman_rec.$chart_id $start-$end $lang_code $roman\n";
- return unless $ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$roman}
- || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$roman};
- foreach $new_end (keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$end}}) {
- foreach $next_roman (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$end}->{$new_end}}) {
- my $exp_roman = join("", $roman, $next_roman);
- if ($ht{RULE_STRING}->{$lang_code}->{$exp_roman}
- || $ht{RULE_STRING}->{""}->{$exp_roman}) {
- $sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start}->{$new_end}->{$exp_roman} = 1;
- # print STDERR " Expansion ($start-$new_end) $exp_roman\n";
- }
- if ($ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$exp_roman}
- || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$exp_roman}) {
- $this->expand_lin_ij_roman_rec(*sd_ht, $chart_id, $start, $new_end, $exp_roman, $lang_code, *ht);
- }
- }
- }
-}
-
-sub trace_string_distance {
- local($this, *sd_ht, $chart1_id, $chart2_id, $control, $line_number, $cost) = @_;
-
- my $chart_comb_id = join("/", $chart1_id, $chart2_id);
- return "mismatch" if $sd_ht{MISMATCH}->{$chart_comb_id};
- my $chart1_end = $sd_ht{MAXLINPOS}->{$chart1_id};
- my $chart2_end = $sd_ht{MAXLINPOS}->{$chart2_id};
- my $verbose = ($control =~ /verbose/);
- my $chunks_p = ($control =~ /chunks/);
- my @traces = ();
- my @s1_s = ();
- my @s2_s = ();
- my @e1_s = ();
- my @e2_s = ();
- my @r1_s = ();
- my @r2_s = ();
- my @ic_s = ();
-
- # print STDERR "trace_string_distance $chart1_id $chart2_id $line_number\n";
- while ($chart1_end || $chart2_end) {
- my $incr_cost = $sd_ht{INCR_COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- my $prec_i = $sd_ht{PREC_I}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- my $prec_j = $sd_ht{PREC_J}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- if ($incr_cost || $verbose || $chunks_p) {
- my $roman1 = $sd_ht{ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- my $roman2 = $sd_ht{ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- if ($verbose) {
- push(@traces, "$prec_i-$chart1_end/$prec_j-$chart2_end:$roman1/$roman2:$incr_cost");
- } else {
- if (defined($roman1)) {
- push(@traces, "$roman1/$roman2:$incr_cost");
- } else {
- $print_prec_i = (defined($prec_i)) ? $prec_i : "?";
- $print_prec_j = (defined($prec_j)) ? $prec_j : "?";
- print STDERR " $prec_i-$chart1_end, $prec_j-$chart2_end\n";
- }
- }
- if ($chunks_p) {
- push(@s1_s, $prec_i);
- push(@s2_s, $prec_j);
- push(@e1_s, $chart1_end);
- push(@e2_s, $chart2_end);
- push(@r1_s, $roman1);
- push(@r2_s, $roman2);
- push(@ic_s, $incr_cost);
- }
- }
- $chart1_end = $prec_i;
- $chart2_end = $prec_j;
- }
- if ($chunks_p) {
- my $r1 = "";
- my $r2 = "";
- my $tc = 0;
- my $in_chunk = 0;
- foreach $i ((0 .. $#ic_s)) {
- if ($ic_s[$i]) {
- $r1 = $r1_s[$i] . $r1;
- $r2 = $r2_s[$i] . $r2;
- $tc += $ic_s[$i];
- $in_chunk = 1;
- } elsif ($in_chunk) {
- $chunk = "$r1/$r2/$tc";
- $chunk .= "*" if $cost > 5;
- $sd_ht{N_COST_CHUNK}->{$chunk} = ($sd_ht{N_COST_CHUNK}->{$chunk} || 0) + 1;
- $sd_ht{EX_COST_CHUNK}->{$chunk}->{$line_number} = 1;
- $r1 = "";
- $r2 = "";
- $tc = 0;
- $in_chunk = 0;
- }
- }
- if ($in_chunk) {
- $chunk = "$r1/$r2/$tc";
- $chunk .= "*" if $cost > 5;
- $sd_ht{N_COST_CHUNK}->{$chunk} = ($sd_ht{N_COST_CHUNK}->{$chunk} || 0) + 1;
- $sd_ht{EX_COST_CHUNK}->{$chunk}->{$line_number} = 1;
- }
- } else {
- return join(" ", reverse @traces);
- }
-}
-
-sub right_context_match {
- local($this, $right_context_rule, *sd_ht, $chart_id, $start_pos) = @_;
-
- return 1 if $right_context_rule eq "";
- if (($right_context_item, $right_context_rest) = ($right_context_rule =~ /^\[([^\[\]]*)\]*(.*)$/)) {
- my $guarded_right_context_item = $right_context_item;
- $guarded_right_context_item =~ s/\$/\\\$/g;
- my @end_positions = keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start_pos}};
- return 1 if ($#end_positions == -1)
- && (($right_context_item eq "")
- || ($right_context_item =~ /\$/));
- foreach $end_pos (@end_positions) {
- my @romans = keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start_pos}->{$end_pos}};
- foreach $roman (@romans) {
- if ($roman =~ /^[$guarded_right_context_item]/) {
- return $this->right_context_match($right_context_rest, *sd_ht, $chart_id, $end_pos);
- }
- }
- }
- }
- return 0;
-}
-
-sub string_distance {
- local($this, *sd_ht, $chart1_id, $chart2_id, $lang_code1, $lang_code2, *ht, $control) = @_;
-
- my $verbose = ($control =~ /verbose/i);
- my $chart_comb_id = join("/", $chart1_id, $chart2_id);
-
- my $chart1_end_pos = $sd_ht{MAXLINPOS}->{$chart1_id};
- my $chart2_end_pos = $sd_ht{MAXLINPOS}->{$chart2_id};
- print STDERR "string_distance.$chart_comb_id $chart1_end_pos/$chart2_end_pos\n" if $verbose;
- $sd_ht{COST_IJ}->{$chart_comb_id}->{0}->{0} = 0;
- $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{0}->{0} = "";
- $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{0}->{0} = "";
- # HHERE
- foreach $chart1_start ((0 .. $chart1_end_pos)) {
- # print STDERR " C1 $chart1_start- ($chart1_start .. $chart1_end_pos)\n";
- my $prev_further_expansion_possible = 0;
- my @chart1_ends = sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart1_id}->{$chart1_start}};
- my $max_chart1_ends = (@chart1_ends) ? $chart1_ends[$#chart1_ends] : -1;
- foreach $chart1_end (($chart1_start .. $chart1_end_pos)) {
- my $further_expansion_possible = ($chart1_start == $chart1_end)
- || defined($sd_ht{LINPOS2SPLITPOS}->{$chart1_id}->{$chart1_start})
- || ($chart1_end < $max_chart1_ends);
- my @romans1 = (($chart1_start == $chart1_end)
- ? ("")
- : (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart1_id}->{$chart1_start}->{$chart1_end}}));
- if ($#romans1 == -1) {
- $further_expansion_possible = 1 if $prev_further_expansion_possible;
- } else {
- $prev_further_expansion_possible = 0;
- }
- # print STDERR " C1 $chart1_start-$chart1_end romans1: @romans1 {$further_expansion_possible} *l*\n";
- foreach $roman1 (@romans1) {
- # print STDERR " C1 $chart1_start-$chart1_end $roman1 {$further_expansion_possible} *?*\n";
- next unless $ht{RULE_STRING}->{$lang_code1}->{$roman1}
- || $ht{RULE_STRING}->{""}->{$roman1};
- # print STDERR " C1 $chart1_start-$chart1_end $roman1 {$further_expansion_possible} ***\n";
- foreach $lang_code1o (($lang_code1, "")) {
- foreach $lang_code2o (($lang_code2, "")) {
- my @chart2_starts = (sort { $a <=> $b } keys %{$sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_start}});
- foreach $chart2_start (@chart2_starts) {
- # print STDERR " C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start- (@chart2_starts)\n";
- foreach $chart2_end (($chart2_start .. $chart2_end_pos)) {
- print STDERR " C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start-$chart2_end\n";
- my @romans2 = (($chart2_start == $chart2_end)
- ? ("")
- : (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart2_id}->{$chart2_start}->{$chart2_end}}));
- foreach $roman2 (@romans2) {
- if ($roman1 eq $roman2) {
- print STDERR " C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start-$chart2_end $roman2 (IDENTITY)\n";
- my $cost = 0;
- my $preceding_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
- my $combined_cost = $preceding_cost + $cost;
- my $old_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
- $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $combined_cost;
- push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
- $sd_ht{PREC_I}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart1_start;
- $sd_ht{PREC_J}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart2_start;
- $sd_ht{ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman1;
- $sd_ht{ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman2;
- $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
- = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman1;
- $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
- = $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman2;
- $comb_left_roman1 = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- $sd_ht{INCR_COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $cost;
- $sd_ht{COST_RULE}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = "IDENTITY";
- print STDERR " New cost $chart1_end/$chart2_end: $combined_cost (+$cost from $chart1_start/$chart2_start $roman1/$roman2)\n" if $verbose;
- }
- } else {
- next unless $ht{RULE_STRING}->{$lang_code2o}->{$roman2};
- print STDERR " C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start-$chart2_end $roman2\n";
- next unless defined($ht{COST}->{$lang_code1o}->{$lang_code2o}->{$roman1}->{$roman2});
- my @cost_rule_ids = keys %{$ht{COST}->{$lang_code1o}->{$lang_code2o}->{$roman1}->{$roman2}};
- foreach $cost_rule_id (@cost_rule_ids) {
- ## check whether any context requirements are satisfied
- # left context rules are regular expressions
- my $left_context_rule1 = $ht{LEFT1}->{$cost_rule_id};
- if ($left_context_rule1) {
- my $comb_left_roman1 = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
- if (defined($comb_left_roman1)) {
- next unless $comb_left_roman1 =~ /$left_context_rule1/;
- } else {
- print STDERR " No comb_left_roman1 value for $chart_comb_id $chart1_start,$chart2_start\n";
- }
- }
- my $left_context_rule2 = $ht{LEFT2}->{$cost_rule_id};
- if ($left_context_rule2) {
- my $comb_left_roman2 = $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
- if (defined($comb_left_roman2)) {
- next unless $comb_left_roman2 =~ /$left_context_rule2/;
- } else {
- print STDERR " No comb_left_roman2 value for $chart_comb_id $chart1_start,$chart2_start\n";
- }
- }
- my $right_context_rule1 = $ht{RIGHT1}->{$cost_rule_id};
- if ($right_context_rule1) {
- my $match_p = $this->right_context_match($right_context_rule1, *sd_ht, $chart1_id, $chart1_end);
- # print STDERR " Match?($right_context_rule1, 1, $chart1_end) = $match_p\n";
- next unless $match_p;
- }
- my $right_context_rule2 = $ht{RIGHT2}->{$cost_rule_id};
- if ($right_context_rule2) {
- my $match_p = $this->right_context_match($right_context_rule2, *sd_ht, $chart2_id, $chart2_end);
- # print STDERR " Match?($right_context_rule2, 2, $chart2_end) = $match_p\n";
- next unless $match_p;
- }
- my $cost = $ht{COST}->{$lang_code1o}->{$lang_code2o}->{$roman1}->{$roman2}->{$cost_rule_id};
- my $preceding_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
- my $combined_cost = $preceding_cost + $cost;
- my $old_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
- $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $combined_cost;
- push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
- $sd_ht{PREC_I}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart1_start;
- $sd_ht{PREC_J}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart2_start;
- $sd_ht{ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman1;
- $sd_ht{ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman2;
- $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
- = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman1;
- $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
- = $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman2;
- $comb_left_roman1 = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
- # print STDERR " Comb-left-roman1($chart_comb_id,$chart1_end,$chart2_end) = $comb_left_roman1\n";
- $sd_ht{INCR_COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $cost;
- $sd_ht{COST_RULE}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $cost_rule_id;
- print STDERR " New cost $chart1_end/$chart2_end: $combined_cost (+$cost from $chart1_start/$chart2_start $roman1/$roman2)\n" if $verbose;
- }
- }
- }
- }
- }
- }
- }
- }
- $further_expansion_possible = 1
- if $ht{RULE_STRING_HAS_EXPANSION}->{$lang_code1}->{$roman1}
- || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$roman1};
- # print STDERR " further_expansion_possible: $further_expansion_possible (lc: $lang_code1 r1: $roman1) ***\n";
- }
- # print STDERR " last C1 $chart1_start-$chart1_end (@romans1)\n" unless $further_expansion_possible;
- last unless $further_expansion_possible;
- $prev_further_expansion_possible = 1 if $further_expansion_possible;
- }
- }
- my $total_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end_pos}->{$chart2_end_pos};
- unless (defined($total_cost)) {
- $total_cost = 99.9999;
- $sd_ht{MISMATCH}->{$chart_comb_id} = 1;
- }
- return $total_cost;
-}
-
-sub print_sd_ht {
- local($this, *sd_ht, $chart1_id, $chart2_id, *OUT) = @_;
-
- print OUT "string-distance chart:\n";
- foreach $chart_id (($chart1_id, $chart2_id)) {
- print OUT "SD chart $chart_id:\n";
- foreach $from_linear_chart_pos (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}}) {
- foreach $to_linear_chart_pos (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}}) {
- foreach $roman_char (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}->{$to_linear_chart_pos}}) {
- print OUT " Lnode($from_linear_chart_pos-$to_linear_chart_pos): $roman_char\n";
- }
- }
- }
- }
-}
-
-sub print_chart_ht {
- local($this, *chart_ht, *OUT) = @_;
-
- print OUT "uroman chart:\n";
- foreach $start (sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AT}}) {
- foreach $end (sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}}) {
- foreach $node_id (keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}}) {
- $roman_s = $chart_ht{NODE_ROMAN}->{$node_id};
- print OUT " Node $node_id ($start-$end): $roman_s\n";
- }
- }
- }
-}
-
-sub normalize_string {
- local($this, $s) = @_;
-
-# $s =~ s/(\xE2\x80\x8C)//g; # delete zero width non-joiner
- $s =~ s/(\xE2\x80[\x93-\x94])/-/g; # en-dash, em-dash
- $s =~ s/([\x00-\x7F\xC0-\xFE][\x80-\xBF]*)\1+/$1$1/g; # shorten 3 or more occurrences of same character in a row to 2
- $s =~ s/[ \t]+/ /g;
-
- return $s;
-}
-
-my $string_distance_chart_id = 0;
-sub string_distance_by_chart {
- local($this, $s1, $s2, $lang_code1, $lang_code2, *ht, *pinyin_ht, $control) = @_;
-
- $control = "" unless defined($control);
- %sd_ht = ();
-
- $s1 = $this->normalize_string($s1);
- my $lc_s1 = $utf8->extended_lower_case($s1);
- $string_distance_chart_id++;
- my $chart1_id = $string_distance_chart_id;
- *chart_ht = $romanizer->romanize($lc_s1, $lang_code1, "", *ht, *pinyin_ht, 0, "return chart", $chart1_id);
- $this->linearize_chart_points(*chart_ht, $chart1_id, *sd_ht);
- $this->expand_lin_ij_roman(*sd_ht, $chart1_id, $lang_code1, *ht);
-
- $s2 = $this->normalize_string($s2);
- my $lc_s2 = $utf8->extended_lower_case($s2);
- $string_distance_chart_id++;
- my $chart2_id = $string_distance_chart_id;
- *chart_ht = $romanizer->romanize($lc_s2, $lang_code2, "", *ht, *pinyin_ht, 0, "return chart", $chart2_id);
- $this->linearize_chart_points(*chart_ht, $chart2_id, *sd_ht);
- $this->expand_lin_ij_roman(*sd_ht, $chart2_id, $lang_code2, *ht);
-
- my $cost = $this->string_distance(*sd_ht, $chart1_id, $chart2_id, $lang_code1, $lang_code2, *ht, $control);
- return $cost;
-}
-
-my $n_quick_romanized_string_distance = 0;
-sub quick_romanized_string_distance_by_chart {
- local($this, $s1, $s2, *ht, $control, $lang_code1, $lang_code2) = @_;
-
- # my $verbose = ($s1 eq "apit") && ($s2 eq "apet");
- # print STDERR "Start quick_romanized_string_distance_by_chart\n";
- $s1 = lc $s1;
- $s2 = lc $s2;
- $control = "" unless defined($control);
- $lang_code1 = "" unless defined($lang_code1);
- $lang_code2 = "" unless defined($lang_code2);
- my $cache_p = ($control =~ /cache/);
- my $total_cost;
- if ($cache_p) {
- $total_cost = $ht{CACHED_QRSD}->{$s1}->{$s2};
- if (defined($total_cost)) {
- return $total_cost;
- }
- }
- my @lang_codes1 = ($lang_code1 eq "") ? ("") : ($lang_code1, "");
- my @lang_codes2 = ($lang_code2 eq "") ? ("") : ($lang_code2, "");
- my $chart1_end_pos = length($s1);
- my $chart2_end_pos = length($s2);
- my %sd_ht = ();
- $sd_ht{COST_IJ}->{0}->{0} = 0;
- foreach $chart1_start ((0 .. $chart1_end_pos)) {
- foreach $chart1_end (($chart1_start .. $chart1_end_pos)) {
- my $substr1 = substr($s1, $chart1_start, ($chart1_end-$chart1_start));
- foreach $lang_code1o (@lang_codes1) {
- foreach $lang_code2o (@lang_codes2) {
- # next unless defined($ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1});
- }
- }
- my @chart2_starts = (sort { $a <=> $b } keys %{$sd_ht{COST_IJ}->{$chart1_start}});
- foreach $chart2_start (@chart2_starts) {
- foreach $chart2_end (($chart2_start .. $chart2_end_pos)) {
- my $substr2 = substr($s2, $chart2_start, ($chart2_end-$chart2_start));
- foreach $lang_code1o (@lang_codes1) {
- foreach $lang_code2o (@lang_codes2) {
- if ($substr1 eq $substr2) {
- my $cost = 0;
- my $preceding_cost = $sd_ht{COST_IJ}->{$chart1_start}->{$chart2_start};
- if (defined($preceding_cost)) {
- my $combined_cost = $preceding_cost + $cost;
- my $old_cost = $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end};
- if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
- $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end} = $combined_cost;
- push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
- }
- }
- } else {
- next unless defined($ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1}->{$substr2});
- my @cost_rule_ids = keys %{$ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1}->{$substr2}};
- my $best_cost = 99.99;
- foreach $cost_rule_id (@cost_rule_ids) {
- my $cost = $ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1}->{$substr2}->{$cost_rule_id};
- my $left_context_rule1 = $ht{LEFT1}->{$cost_rule_id};
- next if $left_context_rule1
- && (! (substr($s1, 0, $chart1_start) =~ /$left_context_rule1/));
- my $left_context_rule2 = $ht{LEFT2}->{$cost_rule_id};
- next if $left_context_rule2
- && (! (substr($s2, 0, $chart2_start) =~ /$left_context_rule2/));
- my $right_context_rule1 = $ht{RIGHT1}->{$cost_rule_id};
- my $right_context1 = substr($s1, $chart1_end);
- next if $right_context_rule1
- && (! (($right_context1 =~ /^$right_context_rule1/)
- || (($right_context_rule1 =~ /^\[[^\[\]]*\$/)
- && ($right_context1 eq ""))));
- my $right_context_rule2 = $ht{RIGHT2}->{$cost_rule_id};
- my $right_context2 = substr($s2, $chart2_end);
- next if $right_context_rule2
- && (! (($right_context2 =~ /^$right_context_rule2/)
- || (($right_context_rule2 =~ /^\[[^\[\]]*\$/)
- && ($right_context2 eq ""))));
- $best_cost = $cost if $cost < $best_cost;
- my $preceding_cost = $sd_ht{COST_IJ}->{$chart1_start}->{$chart2_start};
- my $combined_cost = $preceding_cost + $cost;
- my $old_cost = $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end};
- if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
- $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end} = $combined_cost;
- push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
- }
- }
- }
- }
- }
- }
- }
- }
- }
- $total_cost = $sd_ht{COST_IJ}->{$chart1_end_pos}->{$chart2_end_pos};
- $total_cost = 99.99 unless defined($total_cost);
- $ht{CACHED_QRSD}->{$s1}->{$s2} = $total_cost if $cache_p;
- $n_quick_romanized_string_distance++;
- return $total_cost;
-}
-
-sub get_n_quick_romanized_string_distance {
- return $n_quick_romanized_string_distance;
-}
-
-1;
-
diff --git a/spaces/katanaml-org/sparrow-ml/routers/training.py b/spaces/katanaml-org/sparrow-ml/routers/training.py
deleted file mode 100644
index 2e38c207cb916d7787a510b809affbbd94c975fe..0000000000000000000000000000000000000000
--- a/spaces/katanaml-org/sparrow-ml/routers/training.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from fastapi import APIRouter, Form, BackgroundTasks
-from config import settings
-import os
-import json
-from routers.donut_evaluate import run_evaluate_donut
-from routers.donut_training import run_training_donut
-import utils
-
-
-router = APIRouter()
-
-
-def invoke_training(max_epochs, val_check_interval, warmup_steps, model_in_use, sparrow_key):
- if sparrow_key != settings.sparrow_key:
- return {"error": "Invalid Sparrow key."}
-
- if model_in_use == 'donut':
- processing_time = run_training_donut(max_epochs, val_check_interval, warmup_steps)
- utils.log_stats(settings.training_stats_file, [processing_time, settings.model])
- print(f"Processing time training: {processing_time:.2f} seconds")
-
-
-@router.post("/training")
-async def run_training(background_tasks: BackgroundTasks,
- max_epochs: int = Form(30),
- val_check_interval: float = Form(0.4),
- warmup_steps: int = Form(81),
- model_in_use: str = Form('donut'),
- sparrow_key: str = Form(None)):
-
- background_tasks.add_task(invoke_training, max_epochs, val_check_interval, warmup_steps, model_in_use, sparrow_key)
-
- return {"message": "Sparrow ML training started in the background"}
-
-
-def invoke_evaluate(model_in_use, sparrow_key):
- if sparrow_key != settings.sparrow_key:
- return {"error": "Invalid Sparrow key."}
-
- if model_in_use == 'donut':
- scores, accuracy, processing_time = run_evaluate_donut()
- utils.log_stats(settings.evaluate_stats_file, [processing_time, scores, accuracy, settings.model])
- print(f"Processing time evaluate: {processing_time:.2f} seconds")
-
-
-@router.post("/evaluate")
-async def run_evaluate(background_tasks: BackgroundTasks,
- model_in_use: str = Form('donut'),
- sparrow_key: str = Form(None)):
-
- background_tasks.add_task(invoke_evaluate, model_in_use, sparrow_key)
-
- return {"message": "Sparrow ML model evaluation started in the background"}
-
-
-@router.get("/statistics/training")
-async def get_statistics_training():
- file_path = settings.training_stats_file
-
- # Check if the file exists, and read its content
- if os.path.exists(file_path):
- with open(file_path, 'r') as file:
- try:
- content = json.load(file)
- except json.JSONDecodeError:
- content = []
- else:
- content = []
-
- return content
-
-
-@router.get("/statistics/evaluate")
-async def get_statistics_evaluate():
- file_path = settings.evaluate_stats_file
-
- # Check if the file exists, and read its content
- if os.path.exists(file_path):
- with open(file_path, 'r') as file:
- try:
- content = json.load(file)
- except json.JSONDecodeError:
- content = []
- else:
- content = []
-
- return content
\ No newline at end of file
diff --git a/spaces/keras-dreambooth/dreambooth_hogwarts_legacy/README.md b/spaces/keras-dreambooth/dreambooth_hogwarts_legacy/README.md
deleted file mode 100644
index ae476c2f95819cde354603f20a09df351f489033..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/dreambooth_hogwarts_legacy/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Dreambooth Hogwarts Legacy
-emoji: 🌖
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
-tags:
-- keras-dreambooth
-- scifi
----
-Demo space using the [dreambooth_hogwarts_legacy](https://huggingface.co/keras-dreambooth/dreambooth_hogwarts_legacy) model to draw a game character.
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/mel_processing.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/backbones/iresnet2060.py b/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/backbones/iresnet2060.py
deleted file mode 100644
index 21d1122144d207637d2444cba1f68fe630c89f31..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/backbones/iresnet2060.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import torch
-from torch import nn
-
-assert torch.__version__ >= "1.8.1"
-from torch.utils.checkpoint import checkpoint_sequential
-
-__all__ = ['iresnet2060']
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes,
- out_planes,
- kernel_size=3,
- stride=stride,
- padding=dilation,
- groups=groups,
- bias=False,
- dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
- """1x1 convolution"""
- return nn.Conv2d(in_planes,
- out_planes,
- kernel_size=1,
- stride=stride,
- bias=False)
-
-
-class IBasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None,
- groups=1, base_width=64, dilation=1):
- super(IBasicBlock, self).__init__()
- if groups != 1 or base_width != 64:
- raise ValueError('BasicBlock only supports groups=1 and base_width=64')
- if dilation > 1:
- raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
- self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, )
- self.conv1 = conv3x3(inplanes, planes)
- self.bn2 = nn.BatchNorm2d(planes, eps=1e-05, )
- self.prelu = nn.PReLU(planes)
- self.conv2 = conv3x3(planes, planes, stride)
- self.bn3 = nn.BatchNorm2d(planes, eps=1e-05, )
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- identity = x
- out = self.bn1(x)
- out = self.conv1(out)
- out = self.bn2(out)
- out = self.prelu(out)
- out = self.conv2(out)
- out = self.bn3(out)
- if self.downsample is not None:
- identity = self.downsample(x)
- out += identity
- return out
-
-
-class IResNet(nn.Module):
- fc_scale = 7 * 7
-
- def __init__(self,
- block, layers, dropout=0, num_features=512, zero_init_residual=False,
- groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
- super(IResNet, self).__init__()
- self.fp16 = fp16
- self.inplanes = 64
- self.dilation = 1
- if replace_stride_with_dilation is None:
- replace_stride_with_dilation = [False, False, False]
- if len(replace_stride_with_dilation) != 3:
- raise ValueError("replace_stride_with_dilation should be None "
- "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
- self.groups = groups
- self.base_width = width_per_group
- self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
- self.prelu = nn.PReLU(self.inplanes)
- self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
- self.layer2 = self._make_layer(block,
- 128,
- layers[1],
- stride=2,
- dilate=replace_stride_with_dilation[0])
- self.layer3 = self._make_layer(block,
- 256,
- layers[2],
- stride=2,
- dilate=replace_stride_with_dilation[1])
- self.layer4 = self._make_layer(block,
- 512,
- layers[3],
- stride=2,
- dilate=replace_stride_with_dilation[2])
- self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05, )
- self.dropout = nn.Dropout(p=dropout, inplace=True)
- self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
- self.features = nn.BatchNorm1d(num_features, eps=1e-05)
- nn.init.constant_(self.features.weight, 1.0)
- self.features.weight.requires_grad = False
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.normal_(m.weight, 0, 0.1)
- elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
-
- if zero_init_residual:
- for m in self.modules():
- if isinstance(m, IBasicBlock):
- nn.init.constant_(m.bn2.weight, 0)
-
- def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
- downsample = None
- previous_dilation = self.dilation
- if dilate:
- self.dilation *= stride
- stride = 1
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- conv1x1(self.inplanes, planes * block.expansion, stride),
- nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
- )
- layers = []
- layers.append(
- block(self.inplanes, planes, stride, downsample, self.groups,
- self.base_width, previous_dilation))
- self.inplanes = planes * block.expansion
- for _ in range(1, blocks):
- layers.append(
- block(self.inplanes,
- planes,
- groups=self.groups,
- base_width=self.base_width,
- dilation=self.dilation))
-
- return nn.Sequential(*layers)
-
- def checkpoint(self, func, num_seg, x):
- if self.training:
- return checkpoint_sequential(func, num_seg, x)
- else:
- return func(x)
-
- def forward(self, x):
- with torch.cuda.amp.autocast(self.fp16):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.prelu(x)
- x = self.layer1(x)
- x = self.checkpoint(self.layer2, 20, x)
- x = self.checkpoint(self.layer3, 100, x)
- x = self.layer4(x)
- x = self.bn2(x)
- x = torch.flatten(x, 1)
- x = self.dropout(x)
- x = self.fc(x.float() if self.fp16 else x)
- x = self.features(x)
- return x
-
-
-def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
- model = IResNet(block, layers, **kwargs)
- if pretrained:
- raise ValueError()
- return model
-
-
-def iresnet2060(pretrained=False, progress=True, **kwargs):
- return _iresnet('iresnet2060', IBasicBlock, [3, 128, 1024 - 128, 3], pretrained, progress, **kwargs)
diff --git a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/app.py b/spaces/king007/Stable-Diffusion-ControlNet-WebUI/app.py
deleted file mode 100644
index 4b260dbe1104d2b40485cbffd45dcd74cca0a8d6..0000000000000000000000000000000000000000
--- a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import gradio as gr
-
-from diffusion_webui.helpers import (
- CodeformerUpscalerGenerator,
- StableDiffusionControlInpaintNetDepthGenerator,
- StableDiffusionControlNetCannyGenerator,
- StableDiffusionControlNetDepthGenerator,
- StableDiffusionControlNetHEDGenerator,
- StableDiffusionControlNetInpaintCannyGenerator,
- StableDiffusionControlNetInpaintHedGenerator,
- StableDiffusionControlNetInpaintMlsdGenerator,
- StableDiffusionControlNetInpaintPoseGenerator,
- StableDiffusionControlNetInpaintScribbleGenerator,
- StableDiffusionControlNetInpaintSegGenerator,
- StableDiffusionControlNetMLSDGenerator,
- StableDiffusionControlNetPoseGenerator,
- StableDiffusionControlNetScribbleGenerator,
- StableDiffusionControlNetSegGenerator,
- StableDiffusionImage2ImageGenerator,
- StableDiffusionInpaintGenerator,
- StableDiffusionText2ImageGenerator,
-)
-
-
-def main():
- app = gr.Blocks()
- with app:
- with gr.Row():
- with gr.Column():
- with gr.Tab("Text2Img"):
- StableDiffusionText2ImageGenerator.app()
- with gr.Tab("Img2Img"):
- StableDiffusionImage2ImageGenerator.app()
- with gr.Tab("Inpaint"):
- StableDiffusionInpaintGenerator.app()
- with gr.Tab("ControlNet"):
- with gr.Tab("Canny"):
- StableDiffusionControlNetCannyGenerator.app()
- with gr.Tab("Depth"):
- StableDiffusionControlNetDepthGenerator.app()
- with gr.Tab("HED"):
- StableDiffusionControlNetHEDGenerator.app()
- with gr.Tab("MLSD"):
- StableDiffusionControlNetMLSDGenerator.app()
- with gr.Tab("Pose"):
- StableDiffusionControlNetPoseGenerator.app()
- with gr.Tab("Scribble"):
- StableDiffusionControlNetScribbleGenerator.app()
- with gr.Tab("Seg"):
- StableDiffusionControlNetSegGenerator.app()
- with gr.Tab("ControlNet Inpaint"):
- with gr.Tab("Canny"):
- StableDiffusionControlNetInpaintCannyGenerator.app()
- with gr.Tab("Depth"):
- StableDiffusionControlInpaintNetDepthGenerator.app()
- with gr.Tab("HED"):
- StableDiffusionControlNetInpaintHedGenerator.app()
- with gr.Tab("MLSD"):
- StableDiffusionControlNetInpaintMlsdGenerator.app()
- with gr.Tab("Pose"):
- StableDiffusionControlNetInpaintPoseGenerator.app()
- with gr.Tab("Scribble"):
- StableDiffusionControlNetInpaintScribbleGenerator.app()
- with gr.Tab("Seg"):
- StableDiffusionControlNetInpaintSegGenerator.app()
- with gr.Tab("Upscaler"):
- CodeformerUpscalerGenerator.app()
-
- app.queue(concurrency_count=2)
- app.launch(debug=True, enable_queue=True)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/ann_r50-d8.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/ann_r50-d8.py
deleted file mode 100644
index a2cb653827e44e6015b3b83bc578003e614a6aa1..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/ann_r50-d8.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='EncoderDecoder',
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=dict(
- type='ANNHead',
- in_channels=[1024, 2048],
- in_index=[2, 3],
- channels=512,
- project_channels=256,
- query_scales=(1, ),
- key_pool_scales=(1, 3, 6, 8),
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
- auxiliary_head=dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/correlation.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/correlation.py
deleted file mode 100644
index 3d0b79c301b29915dfaf4d2b1846c59be73127d3..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/correlation.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import Tensor, nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['correlation_forward', 'correlation_backward'])
-
-
-class CorrelationFunction(Function):
-
- @staticmethod
- def forward(ctx,
- input1,
- input2,
- kernel_size=1,
- max_displacement=1,
- stride=1,
- padding=1,
- dilation=1,
- dilation_patch=1):
-
- ctx.save_for_backward(input1, input2)
-
- kH, kW = ctx.kernel_size = _pair(kernel_size)
- patch_size = max_displacement * 2 + 1
- ctx.patch_size = patch_size
- dH, dW = ctx.stride = _pair(stride)
- padH, padW = ctx.padding = _pair(padding)
- dilationH, dilationW = ctx.dilation = _pair(dilation)
- dilation_patchH, dilation_patchW = ctx.dilation_patch = _pair(
- dilation_patch)
-
- output_size = CorrelationFunction._output_size(ctx, input1)
-
- output = input1.new_zeros(output_size)
-
- ext_module.correlation_forward(
- input1,
- input2,
- output,
- kH=kH,
- kW=kW,
- patchH=patch_size,
- patchW=patch_size,
- padH=padH,
- padW=padW,
- dilationH=dilationH,
- dilationW=dilationW,
- dilation_patchH=dilation_patchH,
- dilation_patchW=dilation_patchW,
- dH=dH,
- dW=dW)
-
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input1, input2 = ctx.saved_tensors
-
- kH, kW = ctx.kernel_size
- patch_size = ctx.patch_size
- padH, padW = ctx.padding
- dilationH, dilationW = ctx.dilation
- dilation_patchH, dilation_patchW = ctx.dilation_patch
- dH, dW = ctx.stride
- grad_input1 = torch.zeros_like(input1)
- grad_input2 = torch.zeros_like(input2)
-
- ext_module.correlation_backward(
- grad_output,
- input1,
- input2,
- grad_input1,
- grad_input2,
- kH=kH,
- kW=kW,
- patchH=patch_size,
- patchW=patch_size,
- padH=padH,
- padW=padW,
- dilationH=dilationH,
- dilationW=dilationW,
- dilation_patchH=dilation_patchH,
- dilation_patchW=dilation_patchW,
- dH=dH,
- dW=dW)
- return grad_input1, grad_input2, None, None, None, None, None, None
-
- @staticmethod
- def _output_size(ctx, input1):
- iH, iW = input1.size(2), input1.size(3)
- batch_size = input1.size(0)
- kH, kW = ctx.kernel_size
- patch_size = ctx.patch_size
- dH, dW = ctx.stride
- padH, padW = ctx.padding
- dilationH, dilationW = ctx.dilation
- dilatedKH = (kH - 1) * dilationH + 1
- dilatedKW = (kW - 1) * dilationW + 1
-
- oH = int((iH + 2 * padH - dilatedKH) / dH + 1)
- oW = int((iW + 2 * padW - dilatedKW) / dW + 1)
-
- output_size = (batch_size, patch_size, patch_size, oH, oW)
- return output_size
-
-
-class Correlation(nn.Module):
- r"""Correlation operator
-
- This correlation operator works for optical flow correlation computation.
-
- There are two batched tensors with shape :math:`(N, C, H, W)`,
- and the correlation output's shape is :math:`(N, max\_displacement \times
- 2 + 1, max\_displacement * 2 + 1, H_{out}, W_{out})`
-
- where
-
- .. math::
- H_{out} = \left\lfloor\frac{H_{in} + 2 \times padding -
- dilation \times (kernel\_size - 1) - 1}
- {stride} + 1\right\rfloor
-
- .. math::
- W_{out} = \left\lfloor\frac{W_{in} + 2 \times padding - dilation
- \times (kernel\_size - 1) - 1}
- {stride} + 1\right\rfloor
-
- the correlation item :math:`(N_i, dy, dx)` is formed by taking the sliding
- window convolution between input1 and shifted input2,
-
- .. math::
- Corr(N_i, dx, dy) =
- \sum_{c=0}^{C-1}
- input1(N_i, c) \star
- \mathcal{S}(input2(N_i, c), dy, dx)
-
- where :math:`\star` is the valid 2d sliding window convolution operator,
- and :math:`\mathcal{S}` means shifting the input features (auto-complete
- zero marginal), and :math:`dx, dy` are shifting distance, :math:`dx, dy \in
- [-max\_displacement \times dilation\_patch, max\_displacement \times
- dilation\_patch]`.
-
- Args:
- kernel_size (int): The size of sliding window i.e. local neighborhood
- representing the center points and involved in correlation
- computation. Defaults to 1.
- max_displacement (int): The radius for computing correlation volume,
- but the actual working space can be dilated by dilation_patch.
- Defaults to 1.
- stride (int): The stride of the sliding blocks in the input spatial
- dimensions. Defaults to 1.
- padding (int): Zero padding added to all four sides of the input1.
- Defaults to 0.
- dilation (int): The spacing of local neighborhood that will involved
- in correlation. Defaults to 1.
- dilation_patch (int): The spacing between position need to compute
- correlation. Defaults to 1.
- """
-
- def __init__(self,
- kernel_size: int = 1,
- max_displacement: int = 1,
- stride: int = 1,
- padding: int = 0,
- dilation: int = 1,
- dilation_patch: int = 1) -> None:
- super().__init__()
- self.kernel_size = kernel_size
- self.max_displacement = max_displacement
- self.stride = stride
- self.padding = padding
- self.dilation = dilation
- self.dilation_patch = dilation_patch
-
- def forward(self, input1: Tensor, input2: Tensor) -> Tensor:
- return CorrelationFunction.apply(input1, input2, self.kernel_size,
- self.max_displacement, self.stride,
- self.padding, self.dilation,
- self.dilation_patch)
-
- def __repr__(self) -> str:
- s = self.__class__.__name__
- s += f'(kernel_size={self.kernel_size}, '
- s += f'max_displacement={self.max_displacement}, '
- s += f'stride={self.stride}, '
- s += f'padding={self.padding}, '
- s += f'dilation={self.dilation}, '
- s += f'dilation_patch={self.dilation_patch})'
- return s
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh b/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh
deleted file mode 100644
index 9e9297f08947027685ff508bfa91ff26b0d8ea0c..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/usr/bin/env bash
-# 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.
-
-# Prepare librispeech dataset
-
-base_url=www.openslr.org/resources/12
-train_dir=train_960
-
-if [ "$#" -ne 2 ]; then
- echo "Usage: $0 "
- echo "e.g.: $0 /tmp/librispeech_raw/ ~/data/librispeech_final"
- exit 1
-fi
-
-download_dir=${1%/}
-out_dir=${2%/}
-
-fairseq_root=~/fairseq-py/
-mkdir -p ${out_dir}
-cd ${out_dir} || exit
-
-nbpe=5000
-bpemode=unigram
-
-if [ ! -d "$fairseq_root" ]; then
- echo "$0: Please set correct fairseq_root"
- exit 1
-fi
-
-echo "Data Download"
-for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do
- url=$base_url/$part.tar.gz
- if ! wget -P $download_dir $url; then
- echo "$0: wget failed for $url"
- exit 1
- fi
- if ! tar -C $download_dir -xvzf $download_dir/$part.tar.gz; then
- echo "$0: error un-tarring archive $download_dir/$part.tar.gz"
- exit 1
- fi
-done
-
-echo "Merge all train packs into one"
-mkdir -p ${download_dir}/LibriSpeech/${train_dir}/
-for part in train-clean-100 train-clean-360 train-other-500; do
- mv ${download_dir}/LibriSpeech/${part}/* $download_dir/LibriSpeech/${train_dir}/
-done
-echo "Merge train text"
-find ${download_dir}/LibriSpeech/${train_dir}/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/${train_dir}/text
-
-# Use combined dev-clean and dev-other as validation set
-find ${download_dir}/LibriSpeech/dev-clean/ ${download_dir}/LibriSpeech/dev-other/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/valid_text
-find ${download_dir}/LibriSpeech/test-clean/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/test-clean/text
-find ${download_dir}/LibriSpeech/test-other/ -name '*.txt' -exec cat {} \; >> ${download_dir}/LibriSpeech/test-other/text
-
-
-dict=data/lang_char/${train_dir}_${bpemode}${nbpe}_units.txt
-encoded=data/lang_char/${train_dir}_${bpemode}${nbpe}_encoded.txt
-fairseq_dict=data/lang_char/${train_dir}_${bpemode}${nbpe}_fairseq_dict.txt
-bpemodel=data/lang_char/${train_dir}_${bpemode}${nbpe}
-echo "dictionary: ${dict}"
-echo "Dictionary preparation"
-mkdir -p data/lang_char/
-echo " 3" > ${dict}
-echo " 2" >> ${dict}
-echo " 1" >> ${dict}
-cut -f 2- -d" " ${download_dir}/LibriSpeech/${train_dir}/text > data/lang_char/input.txt
-spm_train --input=data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000 --unk_id=3 --eos_id=2 --pad_id=1 --bos_id=-1 --character_coverage=1
-spm_encode --model=${bpemodel}.model --output_format=piece < data/lang_char/input.txt > ${encoded}
-cat ${encoded} | tr ' ' '\n' | sort | uniq | awk '{print $0 " " NR+3}' >> ${dict}
-cat ${encoded} | tr ' ' '\n' | sort | uniq -c | awk '{print $2 " " $1}' > ${fairseq_dict}
-wc -l ${dict}
-
-echo "Prepare train and test jsons"
-for part in train_960 test-other test-clean; do
- python ${fairseq_root}/examples/speech_recognition/datasets/asr_prep_json.py --audio-dirs ${download_dir}/LibriSpeech/${part} --labels ${download_dir}/LibriSpeech/${part}/text --spm-model ${bpemodel}.model --audio-format flac --dictionary ${fairseq_dict} --output ${part}.json
-done
-# fairseq expects to find train.json and valid.json during training
-mv train_960.json train.json
-
-echo "Prepare valid json"
-python ${fairseq_root}/examples/speech_recognition/datasets/asr_prep_json.py --audio-dirs ${download_dir}/LibriSpeech/dev-clean ${download_dir}/LibriSpeech/dev-other --labels ${download_dir}/LibriSpeech/valid_text --spm-model ${bpemodel}.model --audio-format flac --dictionary ${fairseq_dict} --output valid.json
-
-cp ${fairseq_dict} ./dict.txt
-cp ${bpemodel}.model ./spm.model
diff --git a/spaces/kokofixcomputers/chat-ui/src/lib/utils/models.ts b/spaces/kokofixcomputers/chat-ui/src/lib/utils/models.ts
deleted file mode 100644
index 181288d8d6a2cc1a852c9087dcbb8a3392e70051..0000000000000000000000000000000000000000
--- a/spaces/kokofixcomputers/chat-ui/src/lib/utils/models.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import type { Model } from "$lib/types/Model";
-
-export const findCurrentModel = (models: Model[], id?: string) =>
- models.find((m) => m.id === id) ?? models[0];
diff --git a/spaces/kukuhtw/AutoGPT/tests/browse_tests.py b/spaces/kukuhtw/AutoGPT/tests/browse_tests.py
deleted file mode 100644
index f896e7dd751b1b661d5e989909448b7e182eab69..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/tests/browse_tests.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import sys
-import unittest
-
-from bs4 import BeautifulSoup
-
-sys.path.append(os.path.abspath("../scripts"))
-
-from browse import extract_hyperlinks
-
-
-class TestBrowseLinks(unittest.TestCase):
- def test_extract_hyperlinks(self):
- body = """
-
- Google
- Foo
-
Some other crap
-
- """
- soup = BeautifulSoup(body, "html.parser")
- links = extract_hyperlinks(soup, "http://example.com")
- self.assertEqual(
- links,
- [("Google", "https://google.com"), ("Foo", "http://example.com/foo.html")],
- )
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-0422ddb0.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-0422ddb0.js
deleted file mode 100644
index dad6e4018fa5ab0652ecbb8d4e2dfe1fc10cc14f..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-0422ddb0.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as Z,i as $,s as x,e as E,m as q,p as I,t as y,n as C,r as ee,x as le,$ as te,H as S,G as M,C as d,g as k,h as se,j as ne,l as oe,o as ae,q as v,f as ie,L as D,M as A,E as L,F,N as Q,I as re,D as G,J as B,K as ue,a0 as fe}from"./index-8c3da1d9.js";import{B as _e}from"./Button-62634b34.js";import{B as me}from"./BlockLabel-98ef75ee.js";import{E as ce}from"./Empty-5d52e655.js";/* empty css */import{I as R}from"./Image-4b4cd6af.js";import{n as H}from"./ModifyUpload.svelte_svelte_type_style_lang-ba6baa96.js";function J(t,e,s){const l=t.slice();return l[22]=e[s][0],l[7]=e[s][1],l[24]=s,l}function K(t,e,s){const l=t.slice();return l[25]=e[s][0],l[7]=e[s][1],l[24]=s,l}function ge(t){let e,s,l,n,i,o,_=t[8]?t[8][1]:[],m=[];for(let a=0;a<_.length;a+=1)m[a]=T(K(t,_,a));let c=t[4]&&t[8]&&O(t);return{c(){e=M("div"),s=M("img"),n=S();for(let a=0;a{f[p]=null}),ae(),_=f[o],_?_.p(r,h):(_=f[o]=g[o](r),_.c()),I(_,1),_.m(i,null))},i(r){m||(I(e.$$.fragment,r),I(l.$$.fragment,r),I(_),m=!0)},o(r){y(e.$$.fragment,r),y(l.$$.fragment,r),y(_),m=!1},d(r){C(e,r),r&&v(s),C(l,r),r&&v(n),r&&v(i),f[o].d()}}}function ke(t){let e,s;return e=new _e({props:{visible:t[2],elem_id:t[0],elem_classes:t[1],padding:!1,style:{height:t[5].height,width:t[5].width},allow_overflow:!1,$$slots:{default:[be]},$$scope:{ctx:t}}}),{c(){E(e.$$.fragment)},m(l,n){q(e,l,n),s=!0},p(l,[n]){const i={};n&4&&(i.visible=l[2]),n&1&&(i.elem_id=l[0]),n&2&&(i.elem_classes=l[1]),n&32&&(i.style={height:l[5].height,width:l[5].width}),n&134218744&&(i.$$scope={dirty:n,ctx:l}),e.$set(i)},i(l){s||(I(e.$$.fragment,l),s=!0)},o(l){y(e.$$.fragment,l),s=!1},d(l){C(e,l)}}}function ve(t,e,s){let{elem_id:l=""}=e,{elem_classes:n=[]}=e,{visible:i=!0}=e,{value:o}=e,_,m,{label:c="Annotated Image"}=e,{show_label:a=!0}=e,{show_legend:g=!0}=e,{style:f={}}=e,{root:b}=e,{root_url:r}=e,h=null,{loading_status:j}=e;const w=ee();function p(u){s(9,h=u)}function N(){s(9,h=null)}const U=u=>p(u),V=u=>p(u),W=()=>N(),X=()=>N(),Y=(u,z)=>w("select",{index:u,value:z});return t.$$set=u=>{"elem_id"in u&&s(0,l=u.elem_id),"elem_classes"in u&&s(1,n=u.elem_classes),"visible"in u&&s(2,i=u.visible),"value"in u&&s(13,o=u.value),"label"in u&&s(7,c=u.label),"show_label"in u&&s(3,a=u.show_label),"show_legend"in u&&s(4,g=u.show_legend),"style"in u&&s(5,f=u.style),"root"in u&&s(14,b=u.root),"root_url"in u&&s(15,r=u.root_url),"loading_status"in u&&s(6,j=u.loading_status)},t.$$.update=()=>{t.$$.dirty&122880&&(o!==_&&(s(16,_=o),w("change")),o?s(8,m=[H(o[0],b,r),o[1].map(([u,z])=>[H(u,b,r),z])]):s(8,m=null))},[l,n,i,a,g,f,j,c,m,h,w,p,N,o,b,r,_,U,V,W,X,Y]}class we extends Z{constructor(e){super(),$(this,e,ve,ke,x,{elem_id:0,elem_classes:1,visible:2,value:13,label:7,show_label:3,show_legend:4,style:5,root:14,root_url:15,loading_status:6})}}const qe=we,Ce=["static"],Se=t=>({type:{payload:"[string, Array<[string, string]>]"},description:{payload:"path to base image, followed by a list of tuples [mask image path, label]"}});export{qe as Component,Se as document,Ce as modes};
-//# sourceMappingURL=index-0422ddb0.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/tests/test_util.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/tests/test_util.py
deleted file mode 100644
index 79bc095185c79313b238fb034ef746c7f67b9d93..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/tests/test_util.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import re
-import sys
-import traceback
-from typing import NoReturn
-
-import pytest
-
-from .._util import (
- bytesify,
- LocalProtocolError,
- ProtocolError,
- RemoteProtocolError,
- Sentinel,
- validate,
-)
-
-
-def test_ProtocolError() -> None:
- with pytest.raises(TypeError):
- ProtocolError("abstract base class")
-
-
-def test_LocalProtocolError() -> None:
- try:
- raise LocalProtocolError("foo")
- except LocalProtocolError as e:
- assert str(e) == "foo"
- assert e.error_status_hint == 400
-
- try:
- raise LocalProtocolError("foo", error_status_hint=418)
- except LocalProtocolError as e:
- assert str(e) == "foo"
- assert e.error_status_hint == 418
-
- def thunk() -> NoReturn:
- raise LocalProtocolError("a", error_status_hint=420)
-
- try:
- try:
- thunk()
- except LocalProtocolError as exc1:
- orig_traceback = "".join(traceback.format_tb(sys.exc_info()[2]))
- exc1._reraise_as_remote_protocol_error()
- except RemoteProtocolError as exc2:
- assert type(exc2) is RemoteProtocolError
- assert exc2.args == ("a",)
- assert exc2.error_status_hint == 420
- new_traceback = "".join(traceback.format_tb(sys.exc_info()[2]))
- assert new_traceback.endswith(orig_traceback)
-
-
-def test_validate() -> None:
- my_re = re.compile(rb"(?P[0-9]+)\.(?P[0-9]+)")
- with pytest.raises(LocalProtocolError):
- validate(my_re, b"0.")
-
- groups = validate(my_re, b"0.1")
- assert groups == {"group1": b"0", "group2": b"1"}
-
- # successful partial matches are an error - must match whole string
- with pytest.raises(LocalProtocolError):
- validate(my_re, b"0.1xx")
- with pytest.raises(LocalProtocolError):
- validate(my_re, b"0.1\n")
-
-
-def test_validate_formatting() -> None:
- my_re = re.compile(rb"foo")
-
- with pytest.raises(LocalProtocolError) as excinfo:
- validate(my_re, b"", "oops")
- assert "oops" in str(excinfo.value)
-
- with pytest.raises(LocalProtocolError) as excinfo:
- validate(my_re, b"", "oops {}")
- assert "oops {}" in str(excinfo.value)
-
- with pytest.raises(LocalProtocolError) as excinfo:
- validate(my_re, b"", "oops {} xx", 10)
- assert "oops 10 xx" in str(excinfo.value)
-
-
-def test_make_sentinel() -> None:
- class S(Sentinel, metaclass=Sentinel):
- pass
-
- assert repr(S) == "S"
- assert S == S
- assert type(S).__name__ == "S"
- assert S in {S}
- assert type(S) is S
-
- class S2(Sentinel, metaclass=Sentinel):
- pass
-
- assert repr(S2) == "S2"
- assert S != S2
- assert S not in {S2}
- assert type(S) is not type(S2)
-
-
-def test_bytesify() -> None:
- assert bytesify(b"123") == b"123"
- assert bytesify(bytearray(b"123")) == b"123"
- assert bytesify("123") == b"123"
-
- with pytest.raises(UnicodeEncodeError):
- bytesify("\u1234")
-
- with pytest.raises(TypeError):
- bytesify(10)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/__init__.py
deleted file mode 100644
index e32392679edd18917d0f218bfb316d5256203042..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/__init__.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""Jinja is a template engine written in pure Python. It provides a
-non-XML syntax that supports inline expressions and an optional
-sandboxed environment.
-"""
-from .bccache import BytecodeCache as BytecodeCache
-from .bccache import FileSystemBytecodeCache as FileSystemBytecodeCache
-from .bccache import MemcachedBytecodeCache as MemcachedBytecodeCache
-from .environment import Environment as Environment
-from .environment import Template as Template
-from .exceptions import TemplateAssertionError as TemplateAssertionError
-from .exceptions import TemplateError as TemplateError
-from .exceptions import TemplateNotFound as TemplateNotFound
-from .exceptions import TemplateRuntimeError as TemplateRuntimeError
-from .exceptions import TemplatesNotFound as TemplatesNotFound
-from .exceptions import TemplateSyntaxError as TemplateSyntaxError
-from .exceptions import UndefinedError as UndefinedError
-from .loaders import BaseLoader as BaseLoader
-from .loaders import ChoiceLoader as ChoiceLoader
-from .loaders import DictLoader as DictLoader
-from .loaders import FileSystemLoader as FileSystemLoader
-from .loaders import FunctionLoader as FunctionLoader
-from .loaders import ModuleLoader as ModuleLoader
-from .loaders import PackageLoader as PackageLoader
-from .loaders import PrefixLoader as PrefixLoader
-from .runtime import ChainableUndefined as ChainableUndefined
-from .runtime import DebugUndefined as DebugUndefined
-from .runtime import make_logging_undefined as make_logging_undefined
-from .runtime import StrictUndefined as StrictUndefined
-from .runtime import Undefined as Undefined
-from .utils import clear_caches as clear_caches
-from .utils import is_undefined as is_undefined
-from .utils import pass_context as pass_context
-from .utils import pass_environment as pass_environment
-from .utils import pass_eval_context as pass_eval_context
-from .utils import select_autoescape as select_autoescape
-
-__version__ = "3.1.2"
diff --git a/spaces/lazyboy450/RVCv2-Genshin/app.py b/spaces/lazyboy450/RVCv2-Genshin/app.py
deleted file mode 100644
index 99d51d11594f3fc1630a6e3b78a81a8d5fc0d6f2..0000000000000000000000000000000000000000
--- a/spaces/lazyboy450/RVCv2-Genshin/app.py
+++ /dev/null
@@ -1,517 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"
-
-audio_mode = []
-f0method_mode = []
-f0method_info = ""
-if limitation is True:
- audio_mode = ["Upload audio", "TTS Audio"]
- f0method_mode = ["pm", "harvest"]
- f0method_info = "PM is fast, Harvest is good but extremely slow. (Default: PM)"
-else:
- audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
- f0method_mode = ["pm", "harvest", "crepe"]
- f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"
-
-def create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, file_index):
- def vc_fn(
- vc_audio_mode,
- vc_input,
- vc_upload,
- tts_text,
- tts_voice,
- f0_up_key,
- f0_method,
- index_rate,
- filter_radius,
- resample_sr,
- rms_mix_rate,
- protect,
- ):
- try:
- if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
- audio, sr = librosa.load(vc_input, sr=16000, mono=True)
- elif vc_audio_mode == "Upload audio":
- if vc_upload is None:
- return "You need to upload an audio", None
- sampling_rate, audio = vc_upload
- 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)
- elif vc_audio_mode == "TTS Audio":
- 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)
- vc_input = "tts.mp3"
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- vc_input,
- times,
- f0_up_key,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- f0_file=None,
- )
- info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- print(f"{model_title} | {info}")
- return info, (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, None
- return vc_fn
-
-def load_model():
- categories = []
- with open("weights/folder_info.json", "r", encoding="utf-8") as f:
- folder_info = json.load(f)
- for category_name, category_info in folder_info.items():
- if not category_info['enable']:
- continue
- category_title = category_info['title']
- category_folder = category_info['folder_path']
- description = category_info['description']
- models = []
- with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for character_name, info in models_info.items():
- if not info['enable']:
- continue
- model_title = info['title']
- model_name = info['model_path']
- model_author = info.get("author", None)
- model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
- model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
- cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", 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)
- version = cpt.get("version", "v1")
- if version == "v1":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- model_version = "V1"
- elif version == "v2":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- model_version = "V2"
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False))
- net_g.eval().to(config.device)
- if config.is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, config)
- print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
- models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, model_index)))
- categories.append([category_title, category_folder, description, models])
- return categories
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
- if url != "":
- if not os.path.exists("dl_audio"):
- os.mkdir("dl_audio")
- if audio_provider == "Youtube":
- ydl_opts = {
- 'noplaylist': True,
- 'format': 'bestaudio/best',
- 'postprocessors': [{
- 'key': 'FFmpegExtractAudio',
- 'preferredcodec': 'wav',
- }],
- "outtmpl": 'dl_audio/youtube_audio',
- }
- with yt_dlp.YoutubeDL(ydl_opts) as ydl:
- ydl.download([url])
- audio_path = "dl_audio/youtube_audio.wav"
- if split_model == "htdemucs":
- command = f"demucs --two-stems=vocals {audio_path} -o output"
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
- else:
- command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
- else:
- raise gr.Error("URL Required!")
- return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_volume, split_model):
- if not os.path.exists("output/result"):
- os.mkdir("output/result")
- vocal_path = "output/result/output.wav"
- output_path = "output/result/combine.mp3"
- if split_model == "htdemucs":
- inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
- else:
- inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
- with wave.open(vocal_path, "w") as wave_file:
- wave_file.setnchannels(1)
- wave_file.setsampwidth(2)
- wave_file.setframerate(audio_data[0])
- wave_file.writeframes(audio_data[1].tobytes())
- command = f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return output_path
-
-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(config.device)
- if config.is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-def change_audio_mode(vc_audio_mode):
- if vc_audio_mode == "Input path":
- return (
- # Input & Upload
- gr.Textbox.update(visible=True),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "Upload audio":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=True),
- gr.Audio.update(visible=True),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "Youtube":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=True),
- gr.Textbox.update(visible=True),
- gr.Dropdown.update(visible=True),
- gr.Button.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Slider.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Button.update(visible=True),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "TTS Audio":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=True),
- gr.Dropdown.update(visible=True)
- )
- else:
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=True),
- gr.Audio.update(visible=True),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
-
-def use_microphone(microphone):
- if microphone == True:
- return gr.Audio.update(source="microphone")
- else:
- return gr.Audio.update(source="upload")
-
-if __name__ == '__main__':
- load_hubert()
- categories = load_model()
- 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 gr.Blocks() as app:
- gr.Markdown(
- "
\n\n"+
- "# RVC V2 GENSHIN IMPACT\n\n"+
- "### Recommended to use Google Colab to use other character and feature.\n\n"+
- "[](https://colab.research.google.com/drive/1Ra0qbsgg6E-R-e1uyr6w6eEimFdAyApc)\n\n"+
- "
\n\n"+
- "[](https://github.com/ArkanDash/Multi-Model-RVC-Inference)"
- )
- for (folder_title, folder, description, models) in categories:
- with gr.TabItem(folder_title):
- if description:
- gr.Markdown(f"###
{description}")
- with gr.Tabs():
- if not models:
- gr.Markdown("#
No Model Loaded.")
- gr.Markdown("##
Please add model or fix your model path.")
- continue
- for (name, title, author, cover, model_version, vc_fn) in models:
- with gr.TabItem(name):
- with gr.Row():
- gr.Markdown(
- '
'
- f'
{title}
\n'+
- f'
RVC {model_version} Model
\n'+
- (f'
Model author: {author}
' if author else "")+
- (f'' if cover else "")+
- '
'
- )
- with gr.Row():
- with gr.Column():
- vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
- # Input
- vc_input = gr.Textbox(label="Input audio path", visible=False)
- # Upload
- vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
- vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
- # Youtube
- vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
- vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
- vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
- vc_split = gr.Button("Split Audio", variant="primary", visible=False)
- vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
- vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
- vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
- # TTS
- tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- with gr.Column():
- vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
- f0method0 = gr.Radio(
- label="Pitch extraction algorithm",
- info=f0method_info,
- choices=f0method_mode,
- value="pm",
- interactive=True
- )
- index_rate1 = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- info="(Default: 0.7)",
- value=0.7,
- interactive=True,
- )
- filter_radius0 = gr.Slider(
- minimum=0,
- maximum=7,
- label="Apply Median Filtering",
- info="The value represents the filter radius and can reduce breathiness.",
- value=3,
- step=1,
- interactive=True,
- )
- resample_sr0 = gr.Slider(
- minimum=0,
- maximum=48000,
- label="Resample the output audio",
- info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
- value=0,
- step=1,
- interactive=True,
- )
- rms_mix_rate0 = gr.Slider(
- minimum=0,
- maximum=1,
- label="Volume Envelope",
- info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
- value=1,
- interactive=True,
- )
- protect0 = gr.Slider(
- minimum=0,
- maximum=0.5,
- label="Voice Protection",
- info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
- value=0.5,
- step=0.01,
- interactive=True,
- )
- with gr.Column():
- vc_log = gr.Textbox(label="Output Information", interactive=False)
- vc_output = gr.Audio(label="Output Audio", interactive=False)
- vc_convert = gr.Button("Convert", variant="primary")
- vc_volume = gr.Slider(
- minimum=0,
- maximum=10,
- label="Vocal volume",
- value=4,
- interactive=True,
- step=1,
- info="Adjust vocal volume (Default: 4}",
- visible=False
- )
- vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
- vc_combine = gr.Button("Combine",variant="primary", visible=False)
- vc_convert.click(
- fn=vc_fn,
- inputs=[
- vc_audio_mode,
- vc_input,
- vc_upload,
- tts_text,
- tts_voice,
- vc_transform0,
- f0method0,
- index_rate1,
- filter_radius0,
- resample_sr0,
- rms_mix_rate0,
- protect0,
- ],
- outputs=[vc_log ,vc_output]
- )
- vc_split.click(
- fn=cut_vocal_and_inst,
- inputs=[vc_link, vc_download_audio, vc_split_model],
- outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
- )
- vc_combine.click(
- fn=combine_vocal_and_inst,
- inputs=[vc_output, vc_volume, vc_split_model],
- outputs=[vc_combined_output]
- )
- vc_microphone_mode.change(
- fn=use_microphone,
- inputs=vc_microphone_mode,
- outputs=vc_upload
- )
- vc_audio_mode.change(
- fn=change_audio_mode,
- inputs=[vc_audio_mode],
- outputs=[
- vc_input,
- vc_microphone_mode,
- vc_upload,
- vc_download_audio,
- vc_link,
- vc_split_model,
- vc_split,
- vc_vocal_preview,
- vc_inst_preview,
- vc_audio_preview,
- vc_volume,
- vc_combined_output,
- vc_combine,
- tts_text,
- tts_voice
- ]
- )
- app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/leo-step/imagenet-demo/README.md b/spaces/leo-step/imagenet-demo/README.md
deleted file mode 100644
index 748d5578c19c7cf097d8b7d7cbc005de9b3c4221..0000000000000000000000000000000000000000
--- a/spaces/leo-step/imagenet-demo/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Imagenet Demo
-emoji: 📈
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.1.6
-app_file: demo.py
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/CRACK SolidWorks 2016 SP2 X64 --- With SN And Activator [REPACK].md b/spaces/lincquiQcaudo/Top-20-Diffusion/CRACK SolidWorks 2016 SP2 X64 --- With SN And Activator [REPACK].md
deleted file mode 100644
index 7f549dcaa0d7447fcf8cf4e9681d42e73601816c..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/CRACK SolidWorks 2016 SP2 X64 --- With SN And Activator [REPACK].md
+++ /dev/null
@@ -1,6 +0,0 @@
-
CRACK SolidWorks 2016 SP2 x64 --- With SN and activator
-
-Download solidworks 2015 64 bit full crack crackeado portugues 32 ... SolidWorks ... "SolidWorks 2016 SP2 x64 --- With SN and activator" . 1fdad05405
-
-
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Diagbox 6.01 !FREE! Keygen Causa Generation Efi.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Diagbox 6.01 !FREE! Keygen Causa Generation Efi.md
deleted file mode 100644
index b0c1a9bab91dc2539d2dba658dde51a4d688b574..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Diagbox 6.01 !FREE! Keygen Causa Generation Efi.md
+++ /dev/null
@@ -1,142 +0,0 @@
-
-
Diagbox 6.01 Keygen Causa Generation Efi: The Ultimate Tool for Car Diagnostics and Repairs
-
-
Do you own a car that has electronic systems, such as engine, transmission, ABS, airbag, climate control, navigation, multimedia, and more? If so, you might have encountered some problems or errors with these systems at some point. Maybe your car's performance is not optimal, or maybe you see some warning lights on your dashboard.
-
-
How do you fix these problems or errors? You could take your car to a garage or a dealership, but that would cost you time and money. Or you could use Diagbox 6.01 Keygen Causa Generation Efi, a software tool that allows you to diagnose and repair your car's electronic systems by yourself.
In this article, we will tell you everything you need to know about Diagbox 6.01 Keygen Causa Generation Efi, including what it is, how it works, what are its features and benefits, how to install and use it, and some tips and tricks for using it effectively.
-
-
What Is Diagbox 6.01 Keygen Causa Generation Efi?
-
-
Diagbox 6.01 Keygen Causa Generation Efi is a software tool that generates activation codes for Diagbox 6.01, a program that allows you to diagnose and repair your car's electronic systems. Diagbox 6.01 is compatible with various car models and brands, such as Peugeot, Citroen, Renault, Dacia, and more.
-
-
Diagbox 6.01 Keygen Causa Generation Efi works by creating unique codes based on your computer's hardware ID and serial number. These codes are then used to activate Diagbox 6.01 on your computer, allowing you to access its full features and functions.
-
-
How Does Diagbox 6.01 Keygen Causa Generation Efi Work?
-
-
Diagbox 6.01 Keygen Causa Generation Efi works by connecting your computer to your car's OBD port using a USB cable or interface. OBD stands for On-Board Diagnostics, which is a system that monitors your car's electronic systems and reports any problems or errors.
-
-
Once your computer is connected to your car's OBD port, you can run Diagbox 6.01 software on your computer and select your car model and brand from the list of options. Then you can select the function or system that you want to diagnose or repair from the menu.
-
-
-
Diagbox 6.01 software will then scan your car's electronic systems and provide you with detailed information and data about their performance and condition. You can also view live data, read fault codes, clear fault codes, reset service indicators, program new keys, update firmware, and more.
-
-
What Are the Features and Benefits of Diagbox 6.01 Keygen Causa Generation Efi?
-
-
Diagbox 6.01 Keygen Causa Generation Efi has many features and benefits that make it a valuable tool for car owners and mechanics. Some of them are:
-
-
-
It is easy to use and does not require any technical skills or knowledge.
-
It generates activation codes quickly and efficiently.
-
It supports multiple languages and regions.
-
It works with various car models and brands.
-
It allows you to diagnose and repair your car's electronic systems, such as engine, transmission, ABS, airbag, climate control, navigation, multimedia, and more.
-
It provides you with detailed information and data about your car's performance and condition.
-
It helps you to save time and money by avoiding unnecessary visits to the garage or dealership.
-
-
-
How to Install and Use Diagbox 6.01 Keygen Causa Generation Efi?
-
-
To install and use Diagbox 6.01 Keygen Causa Generation Efi, you will need the following:
-
-
-
A computer with Windows XP or higher operating system.
-
A USB cable or interface that connects your computer to your car's OBD port.
-
A copy of Diagbox 6.01 software that matches your car model and brand.
-
A copy of Diagbox 6.01 Keygen Causa Generation Efi software that matches your computer's hardware ID and serial number.
-
-
-
Once you have everything ready, follow these steps:
-
-
-
Install Diagbox 6.01 software on your computer by following the instructions on the screen.
-
Run Diagbox 6.01 Keygen Causa Generation Efi software on your computer by double-clicking on its icon.
-
Enter your computer's hardware ID and serial number in the corresponding fields.
-
Click on the "Generate" button to create an activation code for Diagbox 6.01.
-
Copy the activation code and paste it in the activation window of Diagbox 6.01 software.
-
Click on the "Activate" button to complete the activation process.
-
Connect your computer to your car's OBD port using the USB cable or interface.
-
Run Diagbox 6.01 software on your computer by double-clicking on its icon.
-
Select your car model and brand from the list of options.
-
Select the function or system that you want to diagnose or repair from the menu.
-
Follow the instructions on the screen to perform the diagnosis or repair.
-
-
-
Tips and Tricks for Using Diagbox 6.01 Keygen Causa Generation Efi
-
-
To make the most out of Diagbox 6.01 Keygen Causa Generation Efi, here are some tips and tricks that you can follow:
-
-
-
Make sure that your computer meets the minimum system requirements for running Diagbox 6.01 software.
-
Make sure that your USB cable or interface is compatible with your car's OBD port and supports all the protocols that Diagbox 6.01 software uses.
-
Make sure that your copy of Diagbox 6.01 software is updated to the latest version and matches your car model and brand.
-
Make sure that your copy of Diagbox 6.01 Keygen Causa Generation Efi software is updated to the latest version and matches your computer's hardware ID and serial number.
-
Make sure that you enter your computer's hardware ID correctly when generating an activation code for Diagbox 6.01 software.
-
Make sure that you copy correctly when activating Diagbox 6.01 software.
-
Make sure that you select the correct car model when using Diagbox 6.01 software.
-
Make sure that you select the correct function when using Diagbox 6.01 software.
-
Make sure that you follow carefully when using Diagbox 6.01 software.
-
-
-
Conclusion
-
-
Diagbox 6.01 Keygen Causa Generation Efi is a software tool that generates activation codes for Diagbox 6.01 software, a program that allows you to diagnose and repair your car's electronic systems.
-
-It is easy to use supports multiple languages works with various car models provides detailed information helps you save time.
-
-To install use Diagbox 6.01 Keygen Causa Generation Efi need a computer USB cable copy of Diagbox 6.01 software copy of Diagbox 6.01 Keygen Causa Generation Efi software.
-
-To make most out of Diagbox 6.01 Keygen Causa Generation Efi follow these tips: make sure computer meets system requirements make sure USB cable compatible make sure copy of Diagbox updated make sure copy of keygen updated make sure enter hardware ID correctly make sure copy activation code correctly make sure select correct car model make sure select correct function make sure follow instructions carefully.
-
-We hope this article has helped you understand what Diagbox 6.01 Keygen Causa Generation Efi is
-and how to use it effectively.
-
FAQs About Diagbox 6.01 Keygen Causa Generation Efi
-
-
In this section, we will answer some of the most frequently asked questions about Diagbox 6.01 Keygen Causa Generation Efi. If you have any other questions, feel free to leave a comment below or contact us directly.
-
-
Is Diagbox 6.01 Keygen Causa Generation Efi Safe to Use?
-
-
Diagbox 6.01 Keygen Causa Generation Efi is safe to use as long as you download it from a trusted source and scan it with an antivirus program before using it. However, you should always be careful when using any software that modifies your car's electronic systems, as it may cause damage or malfunction if used incorrectly or incompatibly.
-
-
Is Diagbox 6.01 Keygen Causa Generation Efi Legal to Use?
-
-
Diagbox 6.01 Keygen Causa Generation Efi is legal to use for personal and educational purposes only. You should not use it for commercial or illegal purposes, such as selling or distributing activation codes, hacking or stealing cars, or violating any laws or regulations.
-
-
Does Diagbox 6.01 Keygen Causa Generation Efi Work with All Car Models and Brands?
-
-
Diagbox 6.01 Keygen Causa Generation Efi works with most car models and brands that are compatible with Diagbox 6.01 software. However, some car models and brands may require specific versions or updates of Diagbox 6.01 software, which may not be available or supported by Diagbox 6.01 Keygen Causa Generation Efi. Therefore, you should always check the compatibility of your car model and brand before using Diagbox 6.01 Keygen Causa Generation Efi.
-
-
Can I Use Diagbox 6.01 Keygen Causa Generation Efi on Multiple Computers?
-
-
No, you cannot use Diagbox 6.01 Keygen Causa Generation Efi on multiple computers. Diagbox 6.01 Keygen Causa Generation Efi generates activation codes based on your computer's hardware ID and serial number, which are unique for each computer. Therefore, you can only use Diagbox 6.01 Keygen Causa Generation Efi on the computer that matches your hardware ID and serial number.
-
-
Can I Use Diagbox 6.01 Keygen Causa Generation Efi Offline?
-
-
Yes, you can use Diagbox 6.01 Keygen Causa Generation Efi offline once you have activated Diagbox 6.01 software on your computer. However, you will need an internet connection to download and install Diagbox 6.01 software and Diagbox 6.01 Keygen Causa Generation Efi software on your computer.
-
Reviews and Testimonials About Diagbox 6.01 Keygen Causa Generation Efi
-
-
In this section, we will share some of the reviews and testimonials that we have received from our customers and users about Diagbox 6.01 Keygen Causa Generation Efi. These reviews and testimonials are genuine and authentic, and they reflect the opinions and experiences of real people who have used Diagbox 6.01 Keygen Causa Generation Efi.
-
-
Review by John Smith
-
-
"I have been using Diagbox 6.01 Keygen Causa Generation Efi for a few months now, and I must say that it is a great tool for car diagnostics and repairs. I have a Peugeot 307 that had some problems with the engine and the ABS system. I used Diagbox 6.01 Keygen Causa Generation Efi to generate an activation code for Diagbox 6.01 software, and then I used Diagbox 6.01 software to scan and fix my car's electronic systems. It was very easy to use, and it provided me with detailed information and data about my car's performance and condition. I was able to clear the fault codes, reset the service indicators, and update the firmware. My car is now running smoothly and efficiently, and I have not encountered any problems or errors since then. I highly recommend Diagbox 6.01 Keygen Causa Generation Efi to anyone who owns a car that has electronic systems."
-
-
Testimonial by Jane Doe
-
-
"I am a mechanic who works with various car models and brands, such as Peugeot, Citroen, Renault, Dacia, and more. I use Diagbox 6.01 Keygen Causa Generation Efi as part of my daily work, and I can say that it is a very useful tool for car diagnostics and repairs. It allows me to generate activation codes for Diagbox 6.01 software quickly and efficiently, and then I can use Diagbox 6.01 software to diagnose and repair my clients' cars' electronic systems. It supports multiple languages and regions, which is very convenient for me as I work with clients from different countries and backgrounds. It also works with various car models and brands, which is very helpful for me as I work with different types of cars. It provides me with detailed information and data about the cars' performance and condition, which helps me to identify and solve the problems or errors effectively. It also helps me to save time and money by avoiding unnecessary visits to the garage or dealership. My clients are very satisfied with my work, and they often refer me to their friends and family members who need car diagnostics or repairs."
-
Conclusion
-
-
Diagbox 6.01 Keygen Causa Generation Efi is a software tool that generates activation codes for Diagbox 6.01 software, a program that allows you to diagnose and repair your car's electronic systems.
-
-It is easy to use, supports multiple languages and regions, works with various car models and brands, provides detailed information and data about your car's performance and condition, and helps you save time and money by avoiding unnecessary visits to the garage or dealership.
-
-To install and use Diagbox 6.01 Keygen Causa Generation Efi, you will need a computer with Windows XP or higher operating system, a USB cable or interface that connects your computer to your car's OBD port, a copy of Diagbox 6.01 software that matches your car model and brand, a copy of Diagbox 6.01 Keygen Causa Generation Efi software that matches your computer's hardware ID and serial number.
-
-To make the most out of Diagbox 6.01 Keygen Causa Generation Efi, follow these tips: make sure that your computer meets the minimum system requirements for running Diagbox 6.01 software; make sure that your USB cable or interface is compatible with your car's OBD port; make sure that your copy of Diagbox 6.01 software is updated; make sure that your copy of Diagbox 6.01 Keygen Causa Generation Efi software is updated; make sure that you enter your computer's hardware ID correctly; make sure that you copy correctly when activating Diagbox 6.01 software; make sure that you select the correct car model; make sure that you select the correct function; make sure that you follow carefully when using Diagbox 6.01 software.
-
-We hope this article has helped you understand what Diagbox 6.01 Keygen Causa Generation Efi is
-and how to use it effectively. If you have any questions or feedback, please leave a comment below or contact us directly. Thank you for reading and happy diagnosing and repairing!
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 [NEW].md b/spaces/lincquiQcaudo/Top-20-Diffusion/Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 [NEW].md
deleted file mode 100644
index 752977d43f4ef623c746290f687d7c15a7113fce..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 [NEW].md
+++ /dev/null
@@ -1,71 +0,0 @@
-
-
What is Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592?
-
Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 is a powerful software solution that protects your Windows servers from any unwanted changes, malware, or user errors. It does this by creating a virtual snapshot of your server configuration and restoring it to the original state after every reboot.
-
Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592
With Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592, you can ensure that your servers are always running smoothly and securely, without any downtime or data loss. You can also manage your servers remotely from a central console, schedule maintenance tasks, and apply updates without affecting the Frozen state.
-
Why do you need Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592?
-
Servers are essential for any business that relies on IT infrastructure, but they are also vulnerable to various threats and challenges, such as:
-
-
Malware infections that can compromise your data and network security.
-
User errors that can cause configuration issues or accidental deletions.
-
Software conflicts that can result in system crashes or performance degradation.
-
Hardware failures that can lead to data corruption or loss.
-
Compliance requirements that demand regular audits and reports.
-
-
All these scenarios can cause serious problems for your business, such as lost productivity, customer dissatisfaction, reputation damage, or legal penalties.
-
That's why you need Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 to protect your servers from any potential risks and ensure that they are always available and functional.
-
How does Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 work?
-
Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 works by creating a virtual layer between your server's operating system and its physical storage device. This layer preserves the original configuration of your server and redirects any changes to a temporary location.
-
When you reboot your server, Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 discards all the changes made to the virtual layer and restores your server to its original state.
-
-
This way, you can keep your server in a pristine condition, free from any unwanted modifications or malware infections.
-
What are the benefits of Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592?
-
Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 offers many benefits for your business, such as:
-
-
Enhanced security: You can prevent any unauthorized changes or malware attacks on your servers, and easily recover from any incidents.
-
Reduced maintenance: You can eliminate the need for time-consuming troubleshooting or re-imaging of your servers, and save on IT resources and costs.
-
Simplified management: You can control all your servers from a single console, and apply updates or patches without affecting the Frozen state.
-
Improved compliance: You can maintain a consistent server configuration across your network, and generate detailed reports on server activity and status.
-
Increased productivity: You can ensure that your servers are always up and running, and provide optimal performance and reliability for your business operations.
-
-
How to get started with Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592?
-
If you want to try Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 for yourself, you can download a free 30-day trial from the Faronics website.
-
You will need to install the Configuration Administrator and the Enterprise Console on a Windows workstation, and then create an installation file for the Deep Freeze client on your servers.
-
You can then deploy the installation file to your servers using the Enterprise Console or any third-party tool of your choice.
-
Once installed, you can configure the settings for each server, such as the ThawSpaces, the Thawed Periods, the Maintenance Schedule, and the Passwords.
-
You can also monitor and manage your servers remotely from the Enterprise Console, and perform actions such as Thawing, Freezing, Restarting, or Shutting Down.
-
To learn more about Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592, you can refer to the User Guide or contact the Technical Support team for assistance.
-
What are the features of Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592?
-
Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 has many features that make it a versatile and reliable solution for server protection, such as:
-
-
ThawSpaces: You can create virtual partitions on your servers that are exempt from freezing, where you can store important data or applications that need to be updated regularly.
-
Thawed Periods: You can schedule your servers to enter a Thawed state at specific times or intervals, where you can perform maintenance tasks or apply updates without rebooting.
-
Maintenance Schedule: You can automate the process of updating your servers by configuring them to download and install Windows updates or custom batch files during the Thawed Periods.
-
Passwords: You can secure your servers with one-time passwords or workstation-specific passwords that prevent unauthorized access or changes to the Deep Freeze configuration.
-
Command Line Control: You can use command line options to perform various actions on your servers, such as Freezing, Thawing, Restarting, or Shutting Down.
-
-What are the system requirements for Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592?
-
Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 has the following system requirements:
-
-
Operating System: Windows Server 2008 R2 (32 and 64-bit), Windows Server 2012, 2016, 2019, 2022 (64-bit)
-
Hard Disk Space: 10% of the partition size
-
RAM: Minimum 2 GB
-
CPU: Intel Pentium III / AMD Athlon or higher
-
Network: TCP/IP protocol installed
-
-How to uninstall Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592?
-
If you want to uninstall Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 from your servers, you need to follow these steps:
-
-
Thaw your servers using the Enterprise Console or the command line options.
-
Run the installation file that you used to install Deep Freeze on your servers.
-
Select Uninstall and follow the instructions on the screen.
-
Reboot your servers when prompted.
-
-
You can also use the Configuration Administrator to create an uninstall file that you can deploy to your servers remotely.
-Conclusion
-
Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 is a powerful and reliable software solution that protects your Windows servers from any unwanted changes, malware, or user errors. It does this by creating a virtual snapshot of your server configuration and restoring it to the original state after every reboot.
-
With Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592, you can enhance your server security, reduce your maintenance costs, simplify your management tasks, improve your compliance standards, and increase your productivity. You can also manage your servers remotely from a central console, schedule maintenance tasks, and apply updates without affecting the Frozen state.
-
If you want to try Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592 for yourself, you can download a free 30-day trial from the Faronics website. You will need to install the Configuration Administrator and the Enterprise Console on a Windows workstation, and then create an installation file for the Deep Freeze client on your servers. You can then deploy the installation file to your servers using the Enterprise Console or any third-party tool of your choice.
-
To learn more about Faronics Deep Freeze Standard Enterprise Server 8.60.020.5592, you can refer to the User Guide or contact the Technical Support team for assistance.
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/llamaindex/llama_index_sql_sandbox/utils.py b/spaces/llamaindex/llama_index_sql_sandbox/utils.py
deleted file mode 100644
index 0a0e1b0261ac65f9590f10428c904d421f8924ad..0000000000000000000000000000000000000000
--- a/spaces/llamaindex/llama_index_sql_sandbox/utils.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-from langchain import OpenAI
-from langchain.chat_models import ChatOpenAI
-
-
-def get_sql_index_tool(sql_index, table_context_dict):
- table_context_str = "\n".join(table_context_dict.values())
-
- def run_sql_index_query(query_text):
- try:
- response = sql_index.as_query_engine(synthesize_response=False).query(query_text)
- except Exception as e:
- return f"Error running SQL {e}.\nNot able to retrieve answer."
- text = str(response)
- sql = response.extra_info["sql_query"]
- return f"Here are the details on the SQL table: {table_context_str}\nSQL Query Used: {sql}\nSQL Result: {text}\n"
- # return f"SQL Query Used: {sql}\nSQL Result: {text}\n"
-
- return run_sql_index_query
-
-
-def get_llm(llm_name, model_temperature, api_key):
- os.environ["OPENAI_API_KEY"] = api_key
- if llm_name == "text-davinci-003":
- return OpenAI(temperature=model_temperature, model_name=llm_name)
- else:
- return ChatOpenAI(temperature=model_temperature, model_name=llm_name)
diff --git a/spaces/ltgoslo/ssa-perin/mtool/ucca/convert.py b/spaces/ltgoslo/ssa-perin/mtool/ucca/convert.py
deleted file mode 100644
index 76087f55eece1cb704174e208d1a4c96ac28a6bd..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/mtool/ucca/convert.py
+++ /dev/null
@@ -1,1358 +0,0 @@
-"""Converter module between different UCCA annotation formats.
-
-This module contains utilities to convert between UCCA annotation in different
-forms, to/from the :class:`core`.Passage form, acts as a pivot for all
-conversions.
-
-The possible other formats are:
- site XML
- standard XML
- conll (CoNLL-X dependency parsing shared task)
- sdp (SemEval 2015 semantic dependency parsing shared task)
-"""
-
-import os
-import pickle
-import re
-import sys
-import xml.etree.ElementTree as ET
-import xml.sax.saxutils
-from collections import defaultdict
-from itertools import repeat, groupby
-from operator import attrgetter, itemgetter
-
-from ucca import textutil, core, layer0, layer1
-from ucca.layer1 import EdgeTags
-from ucca.normalization import attach_punct, COORDINATED_MAIN_REL
-
-try:
- # noinspection PyPackageRequirements
- import simplejson as json
- from simplejson.scanner import JSONDecodeError
-except ImportError:
- import json
- from json.decoder import JSONDecodeError
-
-
-class SiteXMLUnknownElement(core.UCCAError):
- pass
-
-
-class SiteCfg:
- """Contains static configuration for conversion to/from the site XML."""
-
- """
- XML Elements' tags in the site XML format of different annotation
- components - FNodes (Unit), Terminals, remote and implicit Units
- and linkages.
- """
- class _Tags:
- Unit = 'unit'
- Terminal = 'word'
- Remote = 'remoteUnit'
- Implicit = 'implicitUnit'
- Linkage = 'linkage'
-
- class _Paths:
- """Paths (from the XML root) to different parts of the annotation -
- the main units part, the discontiguous units, the paragraph
- elements and the annotation units.
- """
- Main = 'units'
- Attrib = 'attrib'
- Paragraphs = 'units/unit/*'
- Annotation = 'units/unit/*/*'
- Discontiguous = 'unitGroups'
-
- class _Types:
- """Possible types for the Type attribute, which is roughly equivalent
- to Edge/Node tag. Only specially-handled types are here, which is
- the punctuation type.
- """
- Punct = 'Punctuation'
-
- class _Attr:
- """Attribute names in the XML elements (not all exist in all elements)
- - passage and site ID, discontiguous unit ID, UCCA tag, uncertain
- flag, user remarks and linkage arguments. NodeID is special
- because we set it for every unit that was already converted, and
- it's not present in the original XML.
- """
- PassageID = 'passageID'
- SiteID = 'id'
- NodeID = 'internal_id'
- ElemTag = 'type'
- Uncertain = 'uncertain'
- Unanalyzable = 'unanalyzable'
- Remarks = 'remarks'
- GroupID = 'unitGroupID'
- LinkageArgs = 'args'
- Suggestion = 'suggestion'
- CoordinatedMainRel = 'cmr'
-
- __init__ = None
- Tags = _Tags
- Paths = _Paths
- Types = _Types
- Attr = _Attr
-
- """ XML tag used for wrapping words (non-punctuation) and unit groups """
- TBD = 'To Be Defined'
-
- """ values for True/False in the site XML (strings) """
- TRUE = 'true'
- FALSE = 'false'
-
- """ version of site XML scheme which self adheres to """
- SchemeVersion = '1.0.4'
- """ mapping of site XML tag attribute to layer1 edge tags. """
- TagConversion = {'Linked U': EdgeTags.ParallelScene,
- 'Parallel Scene': EdgeTags.ParallelScene,
- 'Function': EdgeTags.Function,
- 'Participant': EdgeTags.Participant,
- 'Process': EdgeTags.Process,
- 'State': EdgeTags.State,
- 'aDverbial': EdgeTags.Adverbial,
- 'Center': EdgeTags.Center,
- 'Elaborator': EdgeTags.Elaborator,
- 'Linker': EdgeTags.Linker,
- 'Ground': EdgeTags.Ground,
- 'Connector': EdgeTags.Connector,
- 'Role Marker': EdgeTags.Relator,
- 'Relator': EdgeTags.Relator,
- 'Time': EdgeTags.Time,
- 'Quantifier': EdgeTags.Quantifier,
- }
-
- """ mapping of layer1.EdgeTags to site XML tag attributes. """
- EdgeConversion = {EdgeTags.ParallelScene: 'Parallel Scene',
- EdgeTags.Function: 'Function',
- EdgeTags.Participant: 'Participant',
- EdgeTags.Process: 'Process',
- EdgeTags.State: 'State',
- EdgeTags.Adverbial: 'aDverbial',
- EdgeTags.Center: 'Center',
- EdgeTags.Elaborator: 'Elaborator',
- EdgeTags.Linker: 'Linker',
- EdgeTags.Ground: 'Ground',
- EdgeTags.Connector: 'Connector',
- EdgeTags.Relator: 'Relator',
- EdgeTags.Time: 'Time',
- EdgeTags.Quantifier: 'Quantifier',
- }
-
-
-class SiteUtil:
- """Contains utility functions for converting to/from the site XML.
-
- Functions:
- unescape: converts escaped characters to their original form.
- set_id: sets the Node ID (internal) attribute in the XML element.
- get_node: gets the node corresponding to the element given from
- the mapping. If not found, returns None
- set_node: writes the element site ID + node pair to the mapping
-
- """
- __init__ = None
-
- @staticmethod
- def unescape(x):
- return xml.sax.saxutils.unescape(x, {'"': '"', r"\u2019": "'"})
-
- @staticmethod
- def set_id(e, i):
- e.set(SiteCfg.Attr.NodeID, i)
-
- @staticmethod
- def get_node(e, mapp):
- return mapp.get(e.get(SiteCfg.Attr.SiteID))
-
- @staticmethod
- def set_node(e, n, mapp):
- mapp.update({e.get(SiteCfg.Attr.SiteID): n})
-
-
-def _from_site_terminals(elem, passage, elem2node):
- """Extract the Terminals from the site XML format.
-
- Some of the terminals metadata (remarks, type) is saved in a wrapper unit
- which encapsulates each terminal, so we use both for creating our
- :class:`layer0`.Terminal objects.
-
- :param elem: root element of the XML hierarchy
- :param passage: passage to add the Terminals to, already with Layer0 object
- :param elem2node: dictionary whose keys are site IDs and values are the
- created UCCA Nodes which are equivalent. This function updates the
- dictionary by mapping each word wrapper to a UCCA Terminal.
- """
- layer0.Layer0(passage)
- for para_num, paragraph in enumerate(elem.iterfind(
- SiteCfg.Paths.Paragraphs)):
- words = list(paragraph.iter(SiteCfg.Tags.Terminal))
- wrappers = []
- for word in words:
- # the list added has only one element, because XML is hierarchical
- wrappers += [x for x in paragraph.iter(SiteCfg.Tags.Unit)
- if word in list(x)]
- for word, wrapper in zip(words, wrappers):
- punct = (wrapper.get(SiteCfg.Attr.ElemTag) == SiteCfg.Types.Punct)
- text = SiteUtil.unescape(word.text)
- # Paragraphs start at 1 and enumeration at 0, so add +1 to para_num
- t = passage.layer(layer0.LAYER_ID).add_terminal(text, punct,
- para_num + 1)
- SiteUtil.set_id(word, t.ID)
- SiteUtil.set_node(wrapper, t, elem2node)
-
-
-def _parse_site_units(elem, parent, passage, groups, elem2node):
- """Parses the given element in the site annotation.
-
- The parser works recursively by determining how to parse the current XML
- element, then adding it with a core.Edge object to the parent given.
- After creating (or retrieving) the current node, which corresponds to the
- XML element given, we iterate its subelements and parse them recursively.
-
- :param elem: the XML element to parse
- :param parent: layer1.FoundationalNode parent of the current XML element
- :param passage: the core.Passage we are converting to
- :param groups: the main XML element of the discontiguous units (unitGroups)
- :param elem2node: mapping between site IDs and Nodes, updated here
-
- :return: a list of (parent, elem) pairs which weren't process, as they should
- be process last (usually because they contain references to not-yet
- created Nodes).
- """
-
- def _get_node(node_elem):
- """Given an XML element, returns its node if it was already created.
-
- If not created, returns None. If the element is a part of discontiguous
- unit, returns the discontiguous unit corresponding Node (if exists).
-
- """
- gid = node_elem.get(SiteCfg.Attr.GroupID)
- return SiteUtil.get_node(node_elem, elem2node) if gid is None else elem2node.get(gid)
-
- def _get_work_elem(node_elem):
- """Given XML element, return either itself or its discontiguous unit."""
- gid = node_elem.get(SiteCfg.Attr.GroupID)
- return (node_elem if gid is None
- else [group_elem for group_elem in groups
- if group_elem.get(SiteCfg.Attr.SiteID) == gid][0])
-
- def _fill_attributes(node_elem, target_node):
- """Fills in node the remarks and uncertain attributes from XML elem."""
- if node_elem.get(SiteCfg.Attr.Uncertain) == 'true':
- target_node.attrib['uncertain'] = True
- if node_elem.get(SiteCfg.Attr.Remarks) is not None:
- target_node.extra['remarks'] = SiteUtil.unescape(
- node_elem.get(SiteCfg.Attr.Remarks))
-
- l1 = passage.layer(layer1.LAYER_ID)
- tbd = []
-
- # Unit tag means its a regular, hierarchically built unit
- if elem.tag == SiteCfg.Tags.Unit:
- node = _get_node(elem)
-
- # Only nodes created by now are the terminals, or discontiguous units
- if node is not None:
-
- if node.tag == layer0.NodeTags.Word:
- parent.add(EdgeTags.Terminal, node)
- elif node.tag == layer0.NodeTags.Punct:
- SiteUtil.set_node(elem, l1.add_punct(
- parent, node), elem2node)
- else:
- # if we got here, we are the second (or later) chunk of a
- # discontiguous unit, whose node was already created.
- # So, we don't need to create the node, just keep processing
- # our subelements (as subelements of the discontiguous unit)
-
- # Added by Omri to address cases where remote units direct at the chunks of discontiguous units
- SiteUtil.set_node(elem, node, elem2node)
-
- for subelem in elem:
- tbd += _parse_site_units(subelem, node, passage,
- groups, elem2node)
- else:
- # Creating a new node, either regular or discontiguous.
- # Note that for discontiguous units we have a different work_elem,
- # because all the data on them are stored outside the hierarchy
- work_elem = _get_work_elem(elem)
- edge_tags = [(SiteCfg.TagConversion[tag],)
- for tag in work_elem.get(SiteCfg.Attr.ElemTag, "").split("|") or None]
- attrib = {}
- if work_elem.get(SiteCfg.Attr.CoordinatedMainRel) == SiteCfg.TRUE:
- attrib[COORDINATED_MAIN_REL] = True
- node = l1.add_fnode_multiple(parent, edge_tags, edge_attrib=attrib)
- SiteUtil.set_node(work_elem, node, elem2node)
-
- # Added by Omri to address cases where remote units direct at the chunks of discontiguous units
- SiteUtil.set_node(elem, node, elem2node)
-
- _fill_attributes(work_elem, node)
- # For iterating the subelements, we don't use work_elem, as it may
- # out of the current XML hierarchy we are processing (discont...)
- for parent_elem in [elem] if elem is work_elem else [elem, work_elem]:
- for subelem in parent_elem:
- tbd += _parse_site_units(subelem, node, passage,
- groups, elem2node)
- # Implicit units have their own tag, and aren't recursive, but nonetheless
- # are treated the same as regular units
- elif elem.tag == SiteCfg.Tags.Implicit:
- edge_tags = [(SiteCfg.TagConversion[tag],)
- for tag in elem.get(SiteCfg.Attr.ElemTag, "").split("|") or None]
- node = l1.add_fnode_multiple(parent, edge_tags, implicit=True)
- SiteUtil.set_node(elem, node, elem2node)
- _fill_attributes(elem, node)
- # non-unit, probably remote or linkage, which should be created in the end
- else:
- tbd.append((parent, elem))
-
- return tbd
-
-
-def _from_site_annotation(elem, passage, elem2node):
- """Parses site XML annotation.
-
- Parses the whole annotation, given that the terminals are already processed
- and converted and appear in elem2node.
-
- :param elem: root XML element
- :param passage: the passage to create, with layer0, w/o layer1
- :param elem2node: mapping from site ID to Nodes, should contain the Terminals
-
- :raise SiteXMLUnknownElement: if an unknown, unhandled element is found
-
- """
- tbd = []
- l1 = layer1.Layer1(passage)
- l1head = l1.heads[0]
- groups_root = elem.find(SiteCfg.Paths.Discontiguous)
-
- # this takes care of the hierarchical annotation
- for subelem in elem.iterfind(SiteCfg.Paths.Annotation):
- tbd += _parse_site_units(subelem, l1head, passage, groups_root,
- elem2node)
-
- # Handling remotes and linkages, which usually contain IDs from all over
- # the annotation, hence must be taken care of after all elements are
- # converted
- for parent, elem in tbd:
- if elem.tag == SiteCfg.Tags.Remote:
- edge_tags = [(SiteCfg.TagConversion[tag],)
- for tag in elem.get(SiteCfg.Attr.ElemTag, "").split("|") or None]
- child = SiteUtil.get_node(elem, elem2node)
- if child is None: # bug in XML, points to an invalid ID
- print("Warning: remoteUnit with ID {} is invalid - skipping".
- format(elem.get(SiteCfg.Attr.SiteID)), file=sys.stderr)
- continue
- l1.add_remote_multiple(parent, edge_tags, child)
- elif elem.tag == SiteCfg.Tags.Linkage:
- args = [elem2node[x] for x in
- elem.get(SiteCfg.Attr.LinkageArgs).split(',')]
- l1.add_linkage(parent, *args)
- else:
- raise SiteXMLUnknownElement(elem.tag)
-
-
-def from_site(elem):
- """Converts site XML structure to :class:`core`.Passage object.
-
- :param elem: root element of the XML structure
-
- :return: The converted core.Passage object
- """
- pid = elem.find(SiteCfg.Paths.Main).get(SiteCfg.Attr.PassageID)
- attrib = elem.find(SiteCfg.Paths.Attrib)
- passage = core.Passage(pid, attrib=None if attrib is None else attrib.attrib)
- elem2node = {}
- _from_site_terminals(elem, passage, elem2node)
- _from_site_annotation(elem, passage, elem2node)
- return passage
-
-
-def to_site(passage):
- """Converts a passage to the site XML format.
-
- :param passage: the passage to convert
-
- :return: the root element of the standard XML structure
- """
-
- class _State:
- def __init__(self):
- self.ID = 1
- self.mapping = {}
- self.elems = {}
-
- def get_id(self):
- ret = str(self.ID)
- self.ID += 1
- return ret
-
- def update(self, node_elem, node):
- self.mapping[node.ID] = node_elem.get(SiteCfg.Attr.SiteID)
- self.elems[node.ID] = node_elem
-
- state = _State()
-
- def _word(terminal):
- tag = SiteCfg.Types.Punct if terminal.punct else SiteCfg.TBD
- word = ET.Element(SiteCfg.Tags.Terminal,
- {SiteCfg.Attr.SiteID: state.get_id()})
- word.text = terminal.text
- word_elem = ET.Element(SiteCfg.Tags.Unit,
- {SiteCfg.Attr.ElemTag: tag,
- SiteCfg.Attr.SiteID: state.get_id(),
- SiteCfg.Attr.Unanalyzable: SiteCfg.FALSE,
- SiteCfg.Attr.Uncertain: SiteCfg.FALSE})
- word_elem.append(word)
- state.update(word_elem, terminal)
- return word_elem
-
- def _cunit(node, cunit_subelem):
- uncertain = (SiteCfg.TRUE if node.attrib.get('uncertain')
- else SiteCfg.FALSE)
- suggestion = (SiteCfg.TRUE if node.attrib.get('suggest')
- else SiteCfg.FALSE)
- unanalyzable = (
- SiteCfg.TRUE if len(node) > 1 and all(
- e.tag in (EdgeTags.Terminal,
- EdgeTags.Punctuation)
- for e in node)
- else SiteCfg.FALSE)
- elem_tag = "|".join(SiteCfg.EdgeConversion[tag] for tag in node.ftags)
- attrib = {SiteCfg.Attr.ElemTag: elem_tag,
- SiteCfg.Attr.SiteID: state.get_id(),
- SiteCfg.Attr.Unanalyzable: unanalyzable,
- SiteCfg.Attr.Uncertain: uncertain,
- SiteCfg.Attr.Suggestion: suggestion}
- remarks = node.attrib.get("remarks")
- if remarks:
- attrib[SiteCfg.Attr.Remarks] = remarks
- if any(edge.attrib.get(COORDINATED_MAIN_REL) for edge in node.incoming):
- attrib[SiteCfg.Attr.CoordinatedMainRel] = SiteCfg.TRUE
- cunit_elem = ET.Element(SiteCfg.Tags.Unit, attrib)
- if cunit_subelem is not None:
- cunit_elem.append(cunit_subelem)
- # When we add chunks of discontiguous units, we don't want them to
- # overwrite the original mapping (leave it to the unitGroupId)
- if node.ID not in state.mapping:
- state.update(cunit_elem, node)
- return cunit_elem
-
- def _remote(edge):
- uncertain = (SiteCfg.TRUE if edge.child.attrib.get('uncertain')
- else SiteCfg.FALSE)
- suggestion = (SiteCfg.TRUE if edge.child.attrib.get('suggest')
- else SiteCfg.FALSE)
- remote_elem = ET.Element(SiteCfg.Tags.Remote,
- {SiteCfg.Attr.ElemTag:
- "|".join(SiteCfg.EdgeConversion[tag] for tag in edge.tags),
- SiteCfg.Attr.SiteID: state.mapping[edge.child.ID],
- SiteCfg.Attr.Unanalyzable: SiteCfg.FALSE,
- SiteCfg.Attr.Uncertain: uncertain,
- SiteCfg.Attr.Suggestion: suggestion})
- state.elems[edge.parent.ID].insert(0, remote_elem)
-
- def _implicit(node):
- uncertain = (SiteCfg.TRUE if node.incoming[0].attrib.get('uncertain')
- else SiteCfg.FALSE)
- suggestion = (SiteCfg.TRUE if node.attrib.get('suggest')
- else SiteCfg.FALSE)
- implicit_elem = ET.Element(SiteCfg.Tags.Implicit,
- {SiteCfg.Attr.ElemTag:
- "|".join(SiteCfg.EdgeConversion[tag] for tag in node.ftags),
- SiteCfg.Attr.SiteID: state.get_id(),
- SiteCfg.Attr.Unanalyzable: SiteCfg.FALSE,
- SiteCfg.Attr.Uncertain: uncertain,
- SiteCfg.Attr.Suggestion: suggestion})
- state.elems[node.fparent.ID].insert(0, implicit_elem)
-
- def _linkage(link):
- args = [str(state.mapping[x.ID]) for x in link.arguments]
- linker_elem = state.elems[link.relation.ID]
- linkage_elem = ET.Element(SiteCfg.Tags.Linkage, {'args': ','.join(args)})
- linker_elem.insert(0, linkage_elem)
-
- def _fparent(node):
- primary, remotes = [[e.parent for e in node.incoming if e.attrib.get("remote", False) is v]
- for v in (False, True)]
- for parents in primary, remotes:
- try:
- return parents[0]
- except IndexError:
- pass
- return None
-
- def _get_parent(node):
- ret = _fparent(node)
- if ret and ret.tag == layer1.NodeTags.Punctuation:
- ret = _fparent(ret)
- if ret and ret in passage.layer(layer1.LAYER_ID).heads:
- ret = None # the parent is the fake FNodes head
- return ret
-
- para_elems = []
-
- # The IDs are used to check whether a parent should be real or a chunk
- # of a larger unit -- in the latter case we need the new ID
- split_ids = [ID for ID, node in passage.nodes.items()
- if node.tag == layer1.NodeTags.Foundational and
- node.discontiguous]
- unit_groups = [_cunit(passage.by_id(ID), None) for ID in split_ids]
- state.elems.update((ID, elem) for ID, elem in zip(split_ids, unit_groups))
-
- for term in sorted(list(passage.layer(layer0.LAYER_ID).all),
- key=lambda x: x.position):
- unit = _word(term)
- parent = _get_parent(term)
- while parent is not None:
- if parent.ID in state.mapping and parent.ID not in split_ids:
- state.elems[parent.ID].append(unit)
- break
- elem = _cunit(parent, unit)
- if parent.ID in split_ids:
- elem.set(SiteCfg.Attr.ElemTag, SiteCfg.TBD)
- elem.set(SiteCfg.Attr.GroupID, state.mapping[parent.ID])
- unit = elem
- parent = _get_parent(parent)
- # The uppermost unit (w.o parents) should be the subelement of a
- # paragraph element, if it exists
- if parent is None:
- if term.para_pos == 1: # need to add paragraph element
- para_elems.append(ET.Element(
- SiteCfg.Tags.Unit,
- {SiteCfg.Attr.ElemTag: SiteCfg.TBD,
- SiteCfg.Attr.SiteID: state.get_id()}))
- para_elems[-1].append(unit)
-
- # Because we identify a partial discontiguous unit (marked as TBD) only
- # after we create the elements, we may end with something like:
- # ... ...
- # which we would like to merge under one element.
- # Because we keep changing the tree, we must break and re-iterate each time
- while True:
- for elems_root in para_elems:
- changed = False
- for parent in elems_root.iter():
- changed = False
- if any(x.get(SiteCfg.Attr.GroupID) for x in parent):
- # Must use list() as we change parent members
- for i, elem in enumerate(list(parent)):
- if (i > 0 and elem.get(SiteCfg.Attr.GroupID) and
- elem.get(SiteCfg.Attr.GroupID) ==
- parent[i - 1].get(SiteCfg.Attr.GroupID)):
- parent.remove(elem)
- for subelem in list(elem): # merging
- elem.remove(subelem)
- parent[i - 1].append(subelem)
- changed = True
- break
- if changed:
- break
- if changed:
- break
- else:
- break
-
- # Handling remotes, implicits and linkages
- for remote in [e for n in passage.layer(layer1.LAYER_ID).all
- for e in n if e.attrib.get('remote')]:
- _remote(remote)
- for implicit in [n for n in passage.layer(layer1.LAYER_ID).all
- if n.attrib.get('implicit')]:
- _implicit(implicit)
- for linkage in filter(lambda x: x.tag == layer1.NodeTags.Linkage,
- passage.layer(layer1.LAYER_ID).heads):
- _linkage(linkage)
-
- # Creating the XML tree
- root = ET.Element('root', {'schemeVersion': SiteCfg.SchemeVersion})
- groups = ET.SubElement(root, 'unitGroups')
- groups.extend(unit_groups)
- units = ET.SubElement(root, SiteCfg.Paths.Main, {SiteCfg.Attr.PassageID: passage.ID})
- ET.SubElement(root, SiteCfg.Paths.Attrib, passage.attrib.copy())
- units0 = ET.SubElement(units, SiteCfg.Tags.Unit,
- {SiteCfg.Attr.ElemTag: SiteCfg.TBD,
- SiteCfg.Attr.SiteID: '0',
- SiteCfg.Attr.Unanalyzable: SiteCfg.FALSE,
- SiteCfg.Attr.Uncertain: SiteCfg.FALSE})
- units0.extend(para_elems)
- ET.SubElement(root, 'LRUunits')
- ET.SubElement(root, 'hiddenUnits')
-
- return root
-
-
-def to_standard(passage):
- """Converts a Passage object to a standard XML root element.
-
- The standard XML specification is not contained here, but it uses a very
- shallow structure with attributes to create hierarchy.
-
- :param passage: the passage to convert
-
- :return: the root element of the standard XML structure
- """
-
- # This utility stringifies the Unit's attributes for proper XML
- # we don't need to escape the character - the serializer of the XML element
- # will do it (e.g. tostring())
- def _dumps(dic):
- return {str(k): str(v) if type(v) in (str, bool) else json.dumps(v) for k, v in dic.items()}
-
- # Utility to add an extra element if exists in the object
- def _add_extra(obj, elem):
- return obj.extra and ET.SubElement(elem, 'extra', _dumps(obj.extra))
-
- # Adds attributes element (even if empty)
- def _add_attrib(obj, elem):
- return ET.SubElement(elem, 'attributes', _dumps(obj.attrib))
-
- root = ET.Element('root', passageID=str(passage.ID), annotationID='0')
- _add_attrib(passage, root)
- _add_extra(passage, root)
-
- for layer in sorted(passage.layers, key=attrgetter('ID')):
- layer_elem = ET.SubElement(root, 'layer', layerID=layer.ID)
- _add_attrib(layer, layer_elem)
- _add_extra(layer, layer_elem)
- for node in layer.all:
- node_elem = ET.SubElement(layer_elem, 'node',
- ID=node.ID, type=node.tag)
- _add_attrib(node, node_elem)
- _add_extra(node, node_elem)
- for edge in node:
- edge_elem = ET.SubElement(node_elem, 'edge',
- toID=edge.child.ID, type=edge.tag)
- _add_attrib(edge, edge_elem)
- _add_extra(edge, edge_elem)
- for category in edge:
- attrs = {}
- if category.tag:
- attrs["tag"] = category.tag
- if category.slot:
- attrs["slot"] = str(category.slot)
- if category.layer:
- attrs["layer_name"] = category.layer
- if category.parent:
- attrs["parent_name"] = category.parent
- category_elem = ET.SubElement(edge_elem, "category", **attrs)
- _add_extra(category, category_elem)
- return root
-
-
-def from_standard(root, extra_funcs=None):
- def _str2bool(x):
- return x == "True"
-
- attribute_converters = {
- 'paragraph': int,
- 'paragraph_position': int,
- 'remote': _str2bool,
- 'implicit': _str2bool,
- 'uncertain': _str2bool,
- 'suggest': _str2bool,
- None: str,
- }
-
- def _loads(x):
- try:
- return False if x == "False" else x == "True" or json.loads(x)
- except JSONDecodeError:
- return x
-
- layer_objs = {layer0.LAYER_ID: layer0.Layer0,
- layer1.LAYER_ID: layer1.Layer1}
-
- node_objs = {layer0.NodeTags.Word: layer0.Terminal,
- layer0.NodeTags.Punct: layer0.Terminal,
- layer1.NodeTags.Foundational: layer1.FoundationalNode,
- layer1.NodeTags.Linkage: layer1.Linkage,
- layer1.NodeTags.Punctuation: layer1.PunctNode}
-
- def _get_attrib(elem):
- try:
- return {k: attribute_converters.get(k, str)(v)
- for k, v in elem.find('attributes').items()}
- except AttributeError as e:
- raise core.UCCAError("Element %s has no attributes" % elem.get("ID")) from e
-
- def _add_extra(obj, elem):
- if elem.find('extra') is not None:
- for k, v in elem.find('extra').items():
- obj.extra[k] = (extra_funcs or {}).get(k, _loads)(v)
-
- passage = core.Passage(root.get('passageID'), attrib=_get_attrib(root))
- _add_extra(passage, root)
- edge_elems = []
- for layer_elem in root.findall('layer'):
- layer_id = layer_elem.get('layerID')
- layer = layer_objs[layer_id](passage, attrib=_get_attrib(layer_elem))
- _add_extra(layer, layer_elem)
- # some nodes are created automatically, skip creating them when found
- # in the XML (they should have 'constant' IDs) but take their edges
- # and attributes/extra from the XML (may have changed from the default)
- created_nodes = {x.ID: x for x in layer.all}
- for node_elem in layer_elem.findall('node'):
- node_id = node_elem.get('ID')
- tag = node_elem.get('type')
- node = created_nodes.get(node_id)
- if node is None:
- node = node_objs[tag](root=passage, ID=node_id, tag=tag, attrib=_get_attrib(node_elem))
- else:
- for key, value in _get_attrib(node_elem).items():
- node.attrib[key] = value
- _add_extra(node, node_elem)
- edge_elems += [(node, x) for x in node_elem.findall('edge')]
-
- # Adding edges (must have all nodes before doing so)
- for from_node, edge_elem in edge_elems:
- to_node = passage.nodes[edge_elem.get('toID')]
- categories_elems = edge_elem.findall('category')
- categories = []
- for c in categories_elems:
- tag = c.get('tag')
- slot = c.get('slot')
- layer = c.get('layer_name')
- parent = c.get('parent_name')
- categories.append((tag, slot, layer, parent))
- if not categories: # an old xml format
- tag = edge_elem.get('type')
- categories.append((tag, "", "", ""))
- edge = from_node.add_multiple(categories, to_node, edge_attrib=_get_attrib(edge_elem))
- _add_extra(edge, edge_elem)
-
- return passage
-
-
-def from_text(text, passage_id="1", tokenized=False, one_per_line=False, extra_format=None, lang="en", *args, **kwargs):
- """Converts from tokenized strings to a Passage object.
-
- :param text: a multi-line string or a sequence of strings:
- each line will be a new paragraph, and blank lines separate passages
- :param passage_id: prefix of ID to set for returned passages
- :param tokenized: whether the text is already given as a list of tokens
- :param one_per_line: each line will be a new passage rather than just a new paragraph
- :param extra_format: value to set in passage.extra["format"]
- :param lang: language to use for tokenization model
-
- :return: generator of Passage object with only Terminal units
- """
- del args, kwargs
- if isinstance(text, str):
- text = text.splitlines()
- if tokenized:
- text = (text,) # text is a list of tokens, not list of lines
- p = l0 = paragraph = None
- i = 0
- for line in text:
- if not tokenized:
- line = line.strip()
- if line or one_per_line:
- if p is None:
- p = core.Passage("%s_%d" % (passage_id, i), attrib=dict(lang=lang))
- if extra_format is not None:
- p.extra["format"] = extra_format
- l0 = layer0.Layer0(p)
- layer1.Layer1(p)
- paragraph = 1
- for lex in textutil.get_tokenizer(tokenized, lang=lang)(line):
- l0.add_terminal(text=lex.orth_, punct=lex.is_punct, paragraph=paragraph)
- paragraph += 1
- if p and (not line or one_per_line):
- yield p
- p = None
- i += 1
- if p:
- yield p
-
-
-def to_text(passage, sentences=True, lang="en", *args, **kwargs):
- """Converts from a Passage object to tokenized strings.
-
- :param passage: the Passage object to convert
- :param sentences: whether to break the Passage to sentences (one for string)
- or leave as one string. Defaults to True
- :param lang: language to use for sentence splitting model
-
- :return: a list of strings - 1 if sentences=False, # of sentences otherwise
- """
- del args, kwargs
- tokens = [x.text for x in sorted(passage.layer(layer0.LAYER_ID).all,
- key=attrgetter('position'))]
- # break2sentences return the positions of the end tokens, which is
- # always the index into tokens incremented by ones (tokens index starts
- # with 0, positions with 1). So in essence, it returns the index to start
- # the next sentence from, and we should add index 0 for the first sentence
- if sentences:
- starts = [0] + textutil.break2sentences(passage, lang=lang)
- else:
- starts = [0, len(tokens)]
- return [' '.join(tokens[starts[i]:starts[i + 1]])
- for i in range(len(starts) - 1)]
-
-
-def to_sequence(passage):
- """Converts from a Passage object to linearized text sequence.
-
- :param passage: the Passage object to convert
-
- :return: a list of strings - 1 if sentences=False, # of sentences otherwise
- """
- def _position(edge):
- while edge.child.layer.ID != layer0.LAYER_ID:
- edge = edge.child.outgoing[0]
- return tuple(map(edge.child.attrib.get, ('paragraph', 'paragraph_position')))
-
- seq = ''
- stacks = []
- edges = [e for u in passage.layer(layer1.LAYER_ID).all
- if not u.incoming for e in u.outgoing]
- # should avoid printing the same node more than once, refer to it by ID
- # convert back to passage
- # use Node.__str__ as it already does this...
- while True:
- if edges:
- stacks.append(sorted(edges, key=_position, reverse=True))
- else:
- stacks[-1].pop()
- while not stacks[-1]:
- stacks.pop()
- if not stacks:
- return seq.rstrip()
- seq += ']_'
- seq += stacks[-1][-1].tag
- seq += ' '
- stacks[-1].pop()
- e = stacks[-1][-1]
- edges = e.child.outgoing
- if edges:
- seq += '['
- seq += e.child.attrib.get('text') or e.tag
- seq += ' '
-
-
-UNANALYZABLE = "Unanalyzable"
-UNCERTAIN = "Uncertain"
-IGNORED_CATEGORIES = {UNANALYZABLE, UNCERTAIN, COORDINATED_MAIN_REL}
-IGNORED_ABBREVIATIONS = {EdgeTags.Unanalyzable, EdgeTags.Uncertain, COORDINATED_MAIN_REL}
-
-
-def get_json_attrib(d):
- attrib = {}
- user = d.get("user")
- if user:
- user_id = user.get("id")
- if user_id:
- attrib["userID"] = user_id
- remarks = d.get("user_comment")
- if remarks:
- attrib["remarks"] = remarks
- annotation_id = d.get("id")
- if annotation_id:
- attrib["annotationID"] = annotation_id
- return attrib or None
-
-
-def get_categories_details(d):
- # mapping from a category id to the category's parent, layer
- curr_layer = d['project']['layer']
- categories = {}
- base_layer = None
- while curr_layer:
- base_layer = curr_layer['name']
- for c in curr_layer['categories']:
- categories[c['id']] = {'name': c["name"], 'parent': c.get("parent"), 'layer': base_layer}
- curr_layer = curr_layer['parent']
- return base_layer, categories
-
-
-def from_json(lines, *args, skip_category_mapping=False, by_external_id=False, **kwargs):
- """Convert text (or dict) in UCCA-App JSON format to a Passage object.
- According to the API, annotation units are organized in a tree, where the full unit is included as a child of
- its parent: https://github.com/omriabnd/UCCA-App/blob/master/UCCAApp_REST_API_Reference.pdf
- Just token children are included in the simple form ("id" only), in the "children_tokens" field.
- Note: children_tokens contains all tokens that are descendants of the unit, not just immediate children.
- tree_id: encodes the path leading to the node, e.g., 3-5-2.
- 1-based, and in reverse order to the children's appearance, so that 1 is last, 2 is before last, etc.
- The exception is the first level, where there is just 0, and the next level starts from 1 (not 0-1).
- parent_tree_id: the tree_id of the node's parent, where 0 is the root
- :param lines: iterable of lines in JSON format, describing a single passage.
- :param skip_category_mapping: if False, translate category names to edge tag abbreviations; if True, don't
- :param by_external_id: set passage ID to be the external ID of the source passage rather than its ID
- :return: generator of Passage objects
- """
- del args, kwargs
- d = lines if isinstance(lines, dict) else json.loads("".join(lines))
- passage_id = d["passage"]["id"]
- attrib = get_json_attrib(d)
- base_layer, categories = get_categories_details(d)
- base_slot = ""
- if by_external_id:
- attrib["passageID"] = passage_id
- external_id = d["passage"]["external_id"]
- assert external_id, "No external ID found for passage %s (task %s)" % (passage_id, d.get("id", "unknown"))
- passage_id = external_id
- passage = core.Passage(str(passage_id), attrib=attrib)
-
- # Create terminals
- l0 = layer0.Layer0(passage)
- token_id_to_terminal = {token["id"]: l0.add_terminal(
- text=token["text"], punct=not token["require_annotation"], paragraph=1)
- for token in sorted(d["tokens"], key=itemgetter("index_in_task"))}
-
- # Create non-terminals
- l1 = layer1.Layer1(passage)
- tree_id_to_node = {}
- token_id_to_preterminal = {}
- category_name_to_edge_tag = {} if skip_category_mapping else EdgeTags.__dict__
- # Assuming topological sort: parents always appear before children
- for unit in sorted(d["annotation_units"], key=itemgetter("is_remote_copy")): # Get non-remotes first
- tree_id = unit["tree_id"]
- remote = unit["is_remote_copy"]
- cloned_from_tree_id = None
- if remote:
- cloned_from_tree_id = unit.get("cloned_from_tree_id")
- if cloned_from_tree_id is None:
- raise ValueError("Remote unit %s without cloned_from_tree_id" % tree_id)
- elif tree_id in tree_id_to_node:
- raise ValueError("Unit %s is repeated" % tree_id)
- parent_tree_id = unit["parent_tree_id"]
- if parent_tree_id is None: # Root node: no need to create
- tree_id_to_node[tree_id] = None
- continue
- try:
- parent_node = tree_id_to_node[parent_tree_id]
- except KeyError as e:
- raise ValueError("Unit %s appears before its parent, %s" % (tree_id, parent_tree_id)) from e
-
- unit_categories = []
- for category in unit.get("categories", ()):
- try:
- category_name = category.get("name") or categories[category["id"]]['name']
- except KeyError as e:
- raise ValueError("Category missing from layer: " + category["id"]) from e
- c_tag = category_name_to_edge_tag.get(category_name.replace(" ", ""), category_name.replace(" ", "_"))
- c_slot = category.get("slot", "")
- c_data = categories[category["id"]]
- c_layer = c_data['layer']
- if c_layer == base_layer:
- base_slot = c_slot
- c_parent = c_data['parent']
- if c_parent: # make sure it is not empty
- c_parent = category_name_to_edge_tag.get(c_parent['name'].replace(" ", ""),
- c_parent['name'].replace(" ", "_"))
- unit_categories.append((c_tag, c_slot, c_layer, c_parent))
-
- if not unit_categories:
- raise ValueError("Unit %s has no categories" % tree_id)
-
- edge_attrib = {}
- for unit_category, *_ in unit_categories:
- if unit_category == EdgeTags.Uncertain:
- edge_attrib["uncertain"] = True
- elif unit_category == COORDINATED_MAIN_REL:
- edge_attrib[COORDINATED_MAIN_REL] = True
- if not edge_attrib:
- edge_attrib = None
- unit_categories = [uc for uc in unit_categories if uc[0] not in IGNORED_ABBREVIATIONS]
- children_tokens = [] if unit["type"] == "IMPLICIT" else unit["children_tokens"]
- try:
- terminal = token_id_to_terminal[children_tokens[0]["id"]] if len(children_tokens) == 1 else None
- except (IndexError, KeyError):
- terminal = None
- if remote:
- try:
- node = tree_id_to_node[cloned_from_tree_id]
- except KeyError as e:
- raise ValueError("Remote copy %s refers to nonexistent unit: %s" %
- (tree_id, cloned_from_tree_id)) from e
- l1.add_remote_multiple(parent_node, unit_categories, node, edge_attrib=edge_attrib)
- elif not skip_category_mapping and terminal and layer0.is_punct(terminal):
- tree_id_to_node[tree_id] = l1.add_punct(None, terminal, base_layer, base_slot, edge_attrib=edge_attrib)
- elif tree_id not in tree_id_to_node:
- node = tree_id_to_node[tree_id] = l1.add_fnode_multiple(parent_node, unit_categories,
- implicit=unit["type"] == "IMPLICIT",
- edge_attrib=edge_attrib)
- node.extra['tree_id'] = tree_id
- comment = unit.get("comment")
- if comment:
- node.extra['remarks'] = comment
- for token in children_tokens:
- token_id_to_preterminal[token["id"]] = node
-
- # Attach terminals to non-terminals
- for token_id, node in token_id_to_preterminal.items():
- terminal = token_id_to_terminal[token_id]
- if skip_category_mapping or not layer0.is_punct(terminal):
- node.add(EdgeTags.Terminal, terminal)
-
- return passage
-
-
-IGNORED_EDGE_TAGS = {EdgeTags.Punctuation, EdgeTags.Terminal}
-
-
-def to_json(passage, *args, return_dict=False, tok_task=None, all_categories=None, skip_category_mapping=False,
- **kwargs):
- """Convert a Passage object to text (or dict) in UCCA-App JSON
- :param passage: the Passage object to convert
- :param return_dict: whether to return dict rather than list of lines
- :param tok_task: either None (to do tokenization too), or a completed tokenization task dict with token IDs,
- or True, to indicate that the function should do only tokenization and not annotation
- :param all_categories: list of category dicts so that IDs can be added, if available - otherwise names are used
- :param skip_category_mapping: if False, translate edge tag abbreviations to category names; if True, don't
- :return: list of lines in JSON format if return_dict=False, or task dict if True
- """
- del args, kwargs
- # Create tokens
- terminal_id_to_token_id = {}
- terminals = sorted(passage.layer(layer0.LAYER_ID).all, key=attrgetter("position"))
- if tok_task is True or tok_task is None: # Necessary because bool(tok_task) == True also if a task dict is given
- tokens = []
- start_index = 0
- for terminal in terminals:
- end_index = start_index + len(terminal.text)
- token = dict(text=terminal.text, start_index=start_index, end_index=end_index,
- index_in_task=terminal.position - 1,
- require_annotation=not layer0.is_punct(terminal))
- if tok_task is None: # When doing tokenization as a task, no need to fill the IDs (done by the server)
- token["id"] = terminal_id_to_token_id[terminal.ID] = terminal.position
- tokens.append(token)
- start_index = end_index + 1
- else:
- tokens = sorted(tok_task["tokens"], key=itemgetter("start_index"))
- if len(tokens) != len(terminals):
- raise ValueError("Number of tokens in tokenization task != number of terminals in passage: %d != %d" %
- (len(tokens), len(terminals)))
- for token, terminal in zip(tokens, terminals):
- terminal_id_to_token_id[terminal.ID] = token["id"]
- # Create annotation units
- category_name_to_id = {c["name"]: c["id"] for c in all_categories} if all_categories else None
- annotation_units = []
- if tok_task is not True: # Annotation required, not just tokenization; tok_task might be None or a full task dict
-
- def _create_unit(elements, n, ts, cs, is_remote_copy=False, parent_tree_id=None):
- implicit = n.attrib.get("implicit")
- assert implicit or ts, "Only implicit units may not have a children_tokens field: " + n.ID
- return dict(tree_id="-".join(map(str, elements)),
- type="IMPLICIT" if implicit else "REGULAR", is_remote_copy=is_remote_copy,
- categories=cs, comment=n.extra.get("remarks", ""), cluster="", cloned_from_tree_id=None,
- parent_tree_id=parent_tree_id, gui_status="OPEN",
- children_tokens=[dict(id=terminal_id_to_token_id[t.ID]) for t in ts])
-
- root_node = passage.layer(layer1.LAYER_ID).heads[0] # Ignoring Linkage: taking only the first head
- root_unit = _create_unit([0], root_node, terminals, [])
- annotation_units.append(root_unit)
- node_id_to_primary_annotation_unit = {root_node.ID: root_unit}
- node_id_to_remote_annotation_units = defaultdict(list)
- edge_tag_to_category_name = {} if skip_category_mapping else \
- {v: re.sub(r"(?<=[a-z])(?=[A-Z])", " ", k) for k, v in EdgeTags.__dict__.items()}
-
- def _outgoing(elements, n): # (ID element, outgoing edges sharing parent & child) for all n's children
- return [(elements + [i], list(es)) for i, (_, es) in enumerate(
- groupby(sorted([e for e in n if e.tag not in IGNORED_EDGE_TAGS],
- key=attrgetter("child.start_position", "child.ID")),
- key=attrgetter("child.ID")), start=1)]
-
- # (tree id elements, edges per child) for each edge
- queue = _outgoing([], root_node)
- while queue: # breadth-first search
- tree_id_elements, edges = queue.pop(0) # edges all have the same child but may differ by category
- edge = edges[0]
- node = edge.child
- remote = edge.attrib.get("remote", False)
- parent_annotation_unit = node_id_to_primary_annotation_unit[edge.parent.ID]
- # This can be used for additional tags written in the remarks -- no agreed format but some workaround:
- # list(filter(None, (_extra_tag(e) for e in edges if not e.attrib.get("remote"))))
- categories = [dict(name=edge_tag_to_category_name.get(c.tag, c.tag), slot=int(c.slot) if c.slot else 1)
- for c in edge]
- terminals = node.get_terminals()
- outgoing = _outgoing(tree_id_elements, node)
- if not outgoing and len(terminals) > 1:
- categories.insert(0, dict(name=UNANALYZABLE, slot=1))
- if node.attrib.get("uncertain"):
- categories.append(dict(name=UNCERTAIN, slot=1))
- if all_categories:
- for category in categories:
- try:
- category["id"] = category_name_to_id[category["name"]]
- del category["name"]
- except KeyError as exception:
- raise ValueError("Category missing from layer: " + category["name"]) from exception
- assert categories, "Non-root unit without categories: %s" % node.ID
- unit = _create_unit(tree_id_elements, node, terminals, categories, is_remote_copy=remote,
- parent_tree_id=parent_annotation_unit["tree_id"])
- if remote:
- node_id_to_remote_annotation_units[node.ID].append(unit)
- else:
- queue += outgoing
- node_id_to_primary_annotation_unit[node.ID] = unit
- annotation_units.append(unit)
- # Update cloned_from_tree_id of remote copies to be the tree_id of their non-remote units
- for node_id, remote_annotation_units in node_id_to_remote_annotation_units.items():
- for unit in remote_annotation_units:
- unit["cloned_from_tree_id"] = node_id_to_primary_annotation_unit[node_id]["tree_id"]
-
- def _tree_id_key(u):
- return tuple(map(int, u["tree_id"].split("-")))
-
- annotation_units = sorted(annotation_units, key=_tree_id_key)
- if tokens and annotation_units:
- for _, units in groupby(annotation_units[1:], key=lambda u: _tree_id_key(u)[:-1]):
- units = list(units)
- start_indices = [min([t["start_index"] for t in tokens
- if any(s["id"] == t["id"] for s in u["children_tokens"])] or [-1]) for u in units]
- assert all(i == -1 or i < j for i, j in zip(start_indices[:-1], start_indices[1:])), \
- "Siblings are not correctly ordered by their minimal start_index: " +\
- ", ".join(u["comment"] for u in units)
-
- d = dict(tokens=tokens, annotation_units=annotation_units, manager_comment=passage.ID)
- return d if return_dict else json.dumps(d).splitlines()
-
-
-def file2passage(filename):
- """Opens a file and returns its parsed Passage object
- Tries to read both as a standard XML file and as a binary pickle
- :param filename: file name to write to
- """
- methods = [pickle2passage, xml2passage]
- _, ext = os.path.splitext(filename)
- if ext == ".xml":
- del methods[0]
- elif ext == ".pickle":
- del methods[1]
- exception = None
- for method in methods:
- try:
- return method(filename)
- except Exception as e:
- exception = e
- if exception:
- raise IOError("Failed reading '%s'" % filename) from exception
-
-
-def xml2passage(filename):
- with open(filename, encoding="utf-8") as f:
- return from_standard(ET.ElementTree().parse(f))
-
-
-def pickle2passage(filename):
- with open(filename, "rb") as h:
- return pickle.load(h)
-
-
-def passage2file(passage, filename, indent=True, binary=False):
- """Writes a UCCA passage as a standard XML file or a binary pickle
- :param passage: passage object to write
- :param filename: file name to write to
- :param indent: whether to indent each line
- :param binary: whether to write pickle format (or XML)
- """
- if binary:
- with open(filename, "wb") as h:
- pickle.dump(passage, h)
- else: # xml
- root = to_standard(passage)
- xml_string = ET.tostring(root).decode()
- output = textutil.indent_xml(xml_string) if indent else xml_string
- with open(filename, "w", encoding="utf-8") as h:
- h.write(output)
-
-
-def split2sentences(passage, remarks=False, lang="en", ids=None):
- return split2segments(passage, is_sentences=True, remarks=remarks, lang=lang, ids=ids)
-
-
-def split2paragraphs(passage, remarks=False, lang="en", ids=None):
- return split2segments(passage, is_sentences=False, remarks=remarks, lang=lang, ids=ids)
-
-
-def split2segments(passage, is_sentences, remarks=False, lang="en", ids=None):
- """
- Split passage to sub-passages
- :param passage: Passage object
- :param is_sentences: if True, split to sentences; otherwise, paragraphs
- :param remarks: Whether to add remarks with original node IDs
- :param lang: language to use for sentence splitting model
- :param ids: optional iterable of ids to set passage IDs for each split
- :return: sequence of passages
- """
- ends = (textutil.break2sentences if is_sentences else textutil.break2paragraphs)(passage, lang=lang)
- return split_passage(passage, ends, remarks=remarks, ids=ids)
-
-
-def split_passage(passage, ends, remarks=False, ids=None, suffix_format="%03d", suffix_start=0):
- """
- Split the passage on the given terminal positions
- :param passage: passage to split
- :param ends: sequence of positions at which the split passages will end
- :param remarks: add original node ID as remarks to the new nodes
- :param ids: optional iterable of ids, the same length as ends, to set passage IDs for each split
- :param suffix_format: in case ids is None, use this format for the running index suffix
- :param suffix_start: in case ids is None, use this starting index for the running index suffix
- :return: sequence of passages
- """
- passages = []
- for i, (start, end, index) in enumerate(zip([0] + ends[:-1], ends, ids or repeat(None)), start=suffix_start):
- if start == end:
- continue
- other = core.Passage(ID=index or ("%s" + suffix_format) % (passage.ID, i), attrib=passage.attrib.copy())
- other.extra = passage.extra.copy()
- # Create terminals and find layer 1 nodes to be included
- l0 = passage.layer(layer0.LAYER_ID)
- other_l0 = layer0.Layer0(root=other, attrib=l0.attrib.copy())
- other_l0.extra = l0.extra.copy()
- level = set()
- nodes = set()
- id_to_other = {}
- paragraphs = []
- for terminal in l0.all[start:end]:
- other_terminal = other_l0.add_terminal(terminal.text, terminal.punct, 1)
- _copy_extra(terminal, other_terminal, remarks)
- other_terminal.extra["orig_paragraph"] = terminal.paragraph
- if terminal.paragraph not in paragraphs:
- paragraphs.append(terminal.paragraph)
- id_to_other[terminal.ID] = other_terminal
- level.update(terminal.parents)
- nodes.add(terminal)
- while level:
- nodes.update(level)
- level = set(e.parent for n in level for e in n.incoming if not e.attrib.get("remote") and
- e.tag != layer1.EdgeTags.Punctuation and e.parent not in nodes)
-
- other_l1 = layer1.Layer1(root=other, attrib=passage.layer(layer1.LAYER_ID).attrib.copy())
- _copy_l1_nodes(passage, other, id_to_other, set(nodes), remarks=remarks)
- attach_punct(other_l0, other_l1)
- for j, paragraph in enumerate(paragraphs, start=1):
- other_l0.doc(j)[:] = l0.doc(paragraph)
- other.frozen = passage.frozen
- passages.append(other)
- return passages
-
-
-def join_passages(passages, passage_id=None, remarks=False):
- """
- Join passages to one passage with all the nodes in order
- :param passages: sequence of passages to join
- :param passage_id: ID of newly created passage (otherwise, ID of first passage)
- :param remarks: add original node ID as remarks to the new nodes
- :return: joined passage
- """
- if not passages:
- raise ValueError("Cannot join empty list of passages")
- other = core.Passage(ID=passage_id or passages[0].ID, attrib=passages[0].attrib.copy())
- other.extra = passages[0].extra.copy()
- l0 = passages[0].layer(layer0.LAYER_ID)
- l1 = passages[0].layer(layer1.LAYER_ID)
- other_l0 = layer0.Layer0(root=other, attrib=l0.attrib.copy())
- layer1.Layer1(root=other, attrib=l1.attrib.copy())
- id_to_other = {}
- paragraph = 0
- for passage in passages:
- l0 = passage.layer(layer0.LAYER_ID)
- paragraphs = set()
- for terminal in l0.all:
- if terminal.para_pos == 1:
- paragraph += 1
- orig_paragraph = terminal.extra.get("orig_paragraph")
- if orig_paragraph is not None:
- paragraph = orig_paragraph
- paragraphs.add(paragraph)
- other_terminal = other_l0.add_terminal(terminal.text, terminal.punct, paragraph)
- _copy_extra(terminal, other_terminal, remarks)
- id_to_other[terminal.ID] = other_terminal
- for paragraph in paragraphs:
- other_l0.doc(paragraph).extend(l0.doc(1))
- _copy_l1_nodes(passage, other, id_to_other, remarks=remarks)
- return other
-
-
-def _copy_l1_nodes(passage, other, id_to_other, include=None, remarks=False):
- """
- Copy all layer 1 nodes from one passage to another
- :param passage: source passage
- :param other: target passage
- :param id_to_other: dictionary mapping IDs from passage to existing nodes from other
- :param include: if given, only the nodes from this set will be copied
- :param remarks: add original node ID as remarks to the new nodes
- """
- l1 = passage.layer(layer1.LAYER_ID)
- other_l1 = other.layer(layer1.LAYER_ID)
- queue = [(n, None) for n in l1.heads]
- linkages = []
- remotes = []
- heads = []
- while queue:
- node, other_node = queue.pop()
- if node.tag == layer1.NodeTags.Linkage:
- if include is None or include.issuperset(node.children):
- linkages.append(node)
- continue
- if other_node is None:
- heads.append(node)
- other_node = other_l1.heads[0]
- for edge in node:
- is_remote = edge.attrib.get("remote", False)
- if include is None or edge.child in include or _unanchored(edge.child):
- if is_remote:
- remotes.append((edge, other_node))
- continue
- if edge.child.layer.ID == layer0.LAYER_ID:
- edge_categories = [(c.tag, c.slot, c.layer, c.parent) for c in edge.categories]
- other_node.add_multiple(edge_categories, id_to_other[edge.child.ID])
- continue
- if edge.child.tag == layer1.NodeTags.Punctuation:
- grandchild = edge.child.children[0]
- other_child = other_l1.add_punct(other_node, id_to_other[grandchild.ID])
- other_child.incoming[0].categories = edge.categories
- else:
- edge_categories = [(c.tag, c.slot, c.layer, c.parent) for c in edge.categories]
- other_child = other_l1.add_fnode_multiple(other_node, edge_categories,
- implicit=edge.child.attrib.get("implicit"))
- queue.append((edge.child, other_child))
- id_to_other[edge.child.ID] = other_child
- _copy_extra(edge.child, other_child, remarks) # Add remotes
- elif is_remote: # Cross-paragraph remote edge -> create implicit child instead
- edge_categories = [(c.tag, c.slot, c.layer, c.parent) for c in edge.categories]
- other_l1.add_fnode_multiple(other_node, edge_categories, implicit=True)
- for edge, parent in remotes:
- other_child = id_to_other.get(edge.child.ID)
- edge_categories = [(c.tag, c.slot, c.layer, c.parent) for c in edge.categories]
- if other_child is None: # Promote remote edge to primary if the original primary parent is gone due to split
- id_to_other[edge.child.ID] = other_child = \
- other_l1.add_fnode_multiple(parent, edge_categories, implicit=edge.child.attrib.get("implicit"))
- _copy_extra(edge.child, other_child, remarks)
- else:
- other_l1.add_remote_multiple(parent, edge_categories, other_child)
- # Add linkages
- for linkage in linkages:
- try:
- arguments = [id_to_other[argument.ID] for argument in linkage.arguments]
- other_linkage = other_l1.add_linkage(id_to_other[linkage.relation.ID], *arguments)
- _copy_extra(linkage, other_linkage, remarks)
- except layer1.MissingRelationError:
- pass
- for head, other_head in zip(heads, other_l1.heads):
- _copy_extra(head, other_head, remarks)
-
-
-def _copy_extra(node, other, remarks=False):
- other.extra.update(node.extra)
- if remarks:
- other.extra["remarks"] = node.ID
-
-
-def _unanchored(n):
- unanchored_children = False
- for e in n:
- if not e.attrib.get("remote"):
- if _unanchored(e.child):
- unanchored_children = True
- else:
- return False
- return n.attrib.get("implicit") or unanchored_children
diff --git a/spaces/ludusc/latent-space-theories/check_images.py b/spaces/ludusc/latent-space-theories/check_images.py
deleted file mode 100644
index 6be11b136596e9852a30a786e18903b3e22881cb..0000000000000000000000000000000000000000
--- a/spaces/ludusc/latent-space-theories/check_images.py
+++ /dev/null
@@ -1,256 +0,0 @@
-#!/usr/bin/env python
-
-import numpy as np
-import pandas as pd
-
-from sklearn.svm import SVC
-from sklearn.decomposition import PCA
-from sklearn.linear_model import LogisticRegression, LinearRegression
-from sklearn.model_selection import train_test_split
-
-from tqdm import tqdm
-import random
-from os.path import join
-import os
-import pickle
-
-import torch
-
-import matplotlib.pyplot as plt
-import PIL
-from PIL import Image, ImageColor
-
-import sys
-sys.path.append('backend')
-from color_annotations import extract_color
-from networks_stylegan3 import *
-sys.path.append('.')
-
-import dnnlib
-import legacy
-
-def hex2rgb(hex_value):
- h = hex_value.strip("#")
- rgb = tuple(int(h[i:i+2], 16) for i in (0, 2, 4))
- return rgb
-
-def rgb2hsv(r, g, b):
- # Normalize R, G, B values
- r, g, b = r / 255.0, g / 255.0, b / 255.0
-
- # h, s, v = hue, saturation, value
- max_rgb = max(r, g, b)
- min_rgb = min(r, g, b)
- difference = max_rgb-min_rgb
-
- # if max_rgb and max_rgb are equal then h = 0
- if max_rgb == min_rgb:
- h = 0
-
- # if max_rgb==r then h is computed as follows
- elif max_rgb == r:
- h = (60 * ((g - b) / difference) + 360) % 360
-
- # if max_rgb==g then compute h as follows
- elif max_rgb == g:
- h = (60 * ((b - r) / difference) + 120) % 360
-
- # if max_rgb=b then compute h
- elif max_rgb == b:
- h = (60 * ((r - g) / difference) + 240) % 360
-
- # if max_rgb==zero then s=0
- if max_rgb == 0:
- s = 0
- else:
- s = (difference / max_rgb) * 100
-
- # compute v
- v = max_rgb * 100
- # return rounded values of H, S and V
- return tuple(map(round, (h, s, v)))
-
-
-class DisentanglementBase:
- def __init__(self, repo_folder, model, annotations, df, space, colors_list, compute_s=False, variable='H1', categorical=True):
- self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
- print('Using device', self.device)
- self.repo_folder = repo_folder
- self.model = model.to(self.device)
- self.annotations = annotations
- self.df = df
- self.space = space
- self.categorical = categorical
- self.variable = variable
-
- self.layers = ['input', 'L0_36_512', 'L1_36_512', 'L2_36_512', 'L3_52_512',
- 'L4_52_512', 'L5_84_512', 'L6_84_512', 'L7_148_512', 'L8_148_512',
- 'L9_148_362', 'L10_276_256', 'L11_276_181', 'L12_276_128',
- 'L13_256_128', 'L14_256_3']
- self.layers_shapes = [4, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 362, 256, 181, 128, 128]
- self.decoding_layers = 16
- self.colors_list = colors_list
-
- self.to_hsv()
- if compute_s:
- self.get_s_space()
-
-
- def to_hsv(self):
- """
- The tohsv function takes the top 3 colors of each image and converts them to HSV values.
- It then adds these values as new columns in the dataframe.
-
- :param self: Allow the function to access the dataframe
- :return: The dataframe with the new columns added
- :doc-author: Trelent
- """
- print('Adding HSV encoding')
- self.df['H1'] = self.df['top1col'].map(lambda x: rgb2hsv(*hex2rgb(x))[0])
- self.df['H2'] = self.df['top2col'].map(lambda x: rgb2hsv(*hex2rgb(x))[0])
- self.df['H3'] = self.df['top3col'].map(lambda x: rgb2hsv(*hex2rgb(x))[0])
-
- self.df['S1'] = self.df['top1col'].map(lambda x: rgb2hsv(*hex2rgb(x))[1])
- self.df['S2'] = self.df['top2col'].map(lambda x: rgb2hsv(*hex2rgb(x))[1])
- self.df['S3'] = self.df['top3col'].map(lambda x: rgb2hsv(*hex2rgb(x))[1])
-
- self.df['V1'] = self.df['top1col'].map(lambda x: rgb2hsv(*hex2rgb(x))[2])
- self.df['V2'] = self.df['top2col'].map(lambda x: rgb2hsv(*hex2rgb(x))[2])
- self.df['V3'] = self.df['top3col'].map(lambda x: rgb2hsv(*hex2rgb(x))[2])
-
- print('Adding RGB encoding')
- self.df['R1'] = self.df['top1col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[0])
- self.df['R2'] = self.df['top2col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[0])
- self.df['R3'] = self.df['top3col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[0])
-
- self.df['G1'] = self.df['top1col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[1])
- self.df['G2'] = self.df['top2col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[1])
- self.df['G3'] = self.df['top3col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[1])
-
- self.df['B1'] = self.df['top1col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[2])
- self.df['B2'] = self.df['top2col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[2])
- self.df['B3'] = self.df['top3col'].map(lambda x: ImageColor.getcolor(x, 'RGB')[2])
- return self.df
-
- def get_encoded_latent(self):
- # ... (existing code for getX)
- if self.space.lower() == 'w':
- X = np.array(self.annotations['w_vectors']).reshape((len(self.annotations['w_vectors']), 512))
- elif self.space.lower() == 'z':
- X = np.array(self.annotations['z_vectors']).reshape((len(self.annotations['z_vectors']), 512))
- elif self.space.lower() == 's':
- concat_v = []
- for i in range(len(self.annotations['w_vectors'])):
- concat_v.append(np.concatenate(self.annotations['s_vectors'][i], axis=1))
- X = np.array(concat_v)
- X = X[:, 0, :]
- else:
- Exception("Sorry, option not available, select among Z, W, S")
-
- print('Shape embedding:', X.shape)
- return X
-
- def get_train_val(self, extremes=False):
- X = self.get_encoded_latent()
- y = np.array(self.df[self.variable].values)
- if self.categorical:
- y_cat = pd.cut(y,
- bins=[x * 360 / len(self.colors_list) if x < len(self.colors_list)
- else 360 for x in range(len(self.colors_list) + 1)],
- labels=self.colors_list
- ).fillna(self.colors_list[0])
- x_train, x_val, y_train, y_val = train_test_split(X, y_cat, test_size=0.2)
- else:
- if extremes:
- # Calculate the number of elements to consider (10% of array size)
- num_elements = int(0.2 * len(y))
- # Get indices of the top num_elements maximum values
- top_indices = np.argpartition(array, -num_elements)[-num_elements:]
- bottom_indices = np.argpartition(array, -num_elements)[:num_elements]
- y_ext = y[top_indices + bottom_indices, :]
- X_ext = X[top_indices + bottom_indices, :]
- x_train, x_val, y_train, y_val = train_test_split(X_ext, y_ext, test_size=0.2)
- else:
- x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
- return x_train, x_val, y_train, y_val
-
- def generate_orig_image(self, vec, seed=False):
- """
- The generate_original_image function takes in a latent vector and the model,
- and returns an image generated from that latent vector.
-
-
- :param z: Generate the image
- :param model: Generate the image
- :return: A pil image
- :doc-author: Trelent
- """
- G = self.model.to(self.device) # type: ignore
- # Labels.
- label = torch.zeros([1, G.c_dim], device=self.device)
- if seed:
- seed = vec
- vec = self.annotations['z_vectors'][seed]
-
- Z = torch.from_numpy(vec.copy()).to(self.device)
- img = G(Z, label, truncation_psi=1, noise_mode='const')
- img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
- img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
- return img
-
-def main():
- repo_folder = '.'
- annotations_file = join(repo_folder, 'data/textile_annotated_files/seeds0000-100000_S.pkl')
- with open(annotations_file, 'rb') as f:
- annotations = pickle.load(f)
-
- df_file = join(repo_folder, 'data/textile_annotated_files/top_three_colours.csv')
- df = pd.read_csv(df_file).fillna('#000000')
-
- model_file = join(repo_folder, 'data/textile_model_files/network-snapshot-005000.pkl')
- with dnnlib.util.open_url(model_file) as f:
- model = legacy.load_network_pkl(f)['G_ema'] # type: ignore
-
- colors_list = ['Red', 'Orange', 'Yellow', 'Yellow Green', 'Chartreuse Green',
- 'Kelly Green', 'Green Blue Seafoam', 'Cyan Blue',
- 'Warm Blue', 'Indigo', 'Purple Magenta', 'Magenta Pink']
- colors_list = ['Red Orange', 'Yellow', 'Green', 'Light Blue',
- 'Blue', 'Purple', 'Pink']
-
-
- disentanglemnet_exp = DisentanglementBase(repo_folder, model, annotations, df, space='w', colors_list=colors_list)
- # x_train, x_val, y_train, y_val = disentanglemnet_exp.get_train_val()
- # print(colors_list)
- # print(np.unique(y_train, return_counts=True))
-
-
- # for i, color in enumerate(colors_list):
- # idxs = np.where(y_train == color)
- # x_color = x_train[idxs][:30, :]
- # print(x_color.shape)
- # print('Generating images of color ' + color)
- # for j, vec in enumerate(x_color):
- # vec = np.expand_dims(vec, axis=0)
- # img = disentanglemnet_exp.generate_orig_image(vec)
- # img.save(f'{repo_folder}/colors_test/color_{color}_{j}.png')
-
- df = disentanglemnet_exp.to_hsv()
- df['color'] = pd.cut(df['H1'],
- bins=[x * 360 / len(colors_list) if x < len(colors_list)
- else 360 for x in range(len(colors_list) + 1)],
- labels=colors_list
- ).fillna(colors_list[0])
-
- print(df['color'].value_counts())
- df['seed'] = df['fname'].str.split('/').apply(lambda x: x[-1]).str.replace('seed', '').str.replace('.png','').astype(int)
- print(df[df['seed'] == 3][['H1', 'S1', 'V1', 'R1', 'B1', 'G1']])
- for i, color in enumerate(colors_list):
- idxs = df['color'] == color
- x_color = df['seed'][idxs][:30]
- print('Generating images of color ' + color)
- for j, vec in enumerate(x_color):
- img = disentanglemnet_exp.generate_orig_image(int(vec), seed=True)
- img.save(f'{repo_folder}/colors_test/color_{color}_{j}corrected.png')
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/spaces/lunarring/latentblending/ldm/modules/midas/midas/transforms.py b/spaces/lunarring/latentblending/ldm/modules/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/lunarring/latentblending/ldm/modules/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
- """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
- Args:
- sample (dict): sample
- size (tuple): image size
-
- Returns:
- tuple: new size
- """
- shape = list(sample["disparity"].shape)
-
- if shape[0] >= size[0] and shape[1] >= size[1]:
- return sample
-
- scale = [0, 0]
- scale[0] = size[0] / shape[0]
- scale[1] = size[1] / shape[1]
-
- scale = max(scale)
-
- shape[0] = math.ceil(scale * shape[0])
- shape[1] = math.ceil(scale * shape[1])
-
- # resize
- sample["image"] = cv2.resize(
- sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
- )
-
- sample["disparity"] = cv2.resize(
- sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
- )
- sample["mask"] = cv2.resize(
- sample["mask"].astype(np.float32),
- tuple(shape[::-1]),
- interpolation=cv2.INTER_NEAREST,
- )
- sample["mask"] = sample["mask"].astype(bool)
-
- return tuple(shape)
-
-
-class Resize(object):
- """Resize sample to given size (width, height).
- """
-
- def __init__(
- self,
- width,
- height,
- resize_target=True,
- keep_aspect_ratio=False,
- ensure_multiple_of=1,
- resize_method="lower_bound",
- image_interpolation_method=cv2.INTER_AREA,
- ):
- """Init.
-
- Args:
- width (int): desired output width
- height (int): desired output height
- resize_target (bool, optional):
- True: Resize the full sample (image, mask, target).
- False: Resize image only.
- Defaults to True.
- keep_aspect_ratio (bool, optional):
- True: Keep the aspect ratio of the input sample.
- Output sample might not have the given width and height, and
- resize behaviour depends on the parameter 'resize_method'.
- Defaults to False.
- ensure_multiple_of (int, optional):
- Output width and height is constrained to be multiple of this parameter.
- Defaults to 1.
- resize_method (str, optional):
- "lower_bound": Output will be at least as large as the given size.
- "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
- "minimal": Scale as least as possible. (Output size might be smaller than given size.)
- Defaults to "lower_bound".
- """
- self.__width = width
- self.__height = height
-
- self.__resize_target = resize_target
- self.__keep_aspect_ratio = keep_aspect_ratio
- self.__multiple_of = ensure_multiple_of
- self.__resize_method = resize_method
- self.__image_interpolation_method = image_interpolation_method
-
- def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
- y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
- if max_val is not None and y > max_val:
- y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
- if y < min_val:
- y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
- return y
-
- def get_size(self, width, height):
- # determine new height and width
- scale_height = self.__height / height
- scale_width = self.__width / width
-
- if self.__keep_aspect_ratio:
- if self.__resize_method == "lower_bound":
- # scale such that output size is lower bound
- if scale_width > scale_height:
- # fit width
- scale_height = scale_width
- else:
- # fit height
- scale_width = scale_height
- elif self.__resize_method == "upper_bound":
- # scale such that output size is upper bound
- if scale_width < scale_height:
- # fit width
- scale_height = scale_width
- else:
- # fit height
- scale_width = scale_height
- elif self.__resize_method == "minimal":
- # scale as least as possbile
- if abs(1 - scale_width) < abs(1 - scale_height):
- # fit width
- scale_height = scale_width
- else:
- # fit height
- scale_width = scale_height
- else:
- raise ValueError(
- f"resize_method {self.__resize_method} not implemented"
- )
-
- if self.__resize_method == "lower_bound":
- new_height = self.constrain_to_multiple_of(
- scale_height * height, min_val=self.__height
- )
- new_width = self.constrain_to_multiple_of(
- scale_width * width, min_val=self.__width
- )
- elif self.__resize_method == "upper_bound":
- new_height = self.constrain_to_multiple_of(
- scale_height * height, max_val=self.__height
- )
- new_width = self.constrain_to_multiple_of(
- scale_width * width, max_val=self.__width
- )
- elif self.__resize_method == "minimal":
- new_height = self.constrain_to_multiple_of(scale_height * height)
- new_width = self.constrain_to_multiple_of(scale_width * width)
- else:
- raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
- return (new_width, new_height)
-
- def __call__(self, sample):
- width, height = self.get_size(
- sample["image"].shape[1], sample["image"].shape[0]
- )
-
- # resize sample
- sample["image"] = cv2.resize(
- sample["image"],
- (width, height),
- interpolation=self.__image_interpolation_method,
- )
-
- if self.__resize_target:
- if "disparity" in sample:
- sample["disparity"] = cv2.resize(
- sample["disparity"],
- (width, height),
- interpolation=cv2.INTER_NEAREST,
- )
-
- if "depth" in sample:
- sample["depth"] = cv2.resize(
- sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
- )
-
- sample["mask"] = cv2.resize(
- sample["mask"].astype(np.float32),
- (width, height),
- interpolation=cv2.INTER_NEAREST,
- )
- sample["mask"] = sample["mask"].astype(bool)
-
- return sample
-
-
-class NormalizeImage(object):
- """Normlize image by given mean and std.
- """
-
- def __init__(self, mean, std):
- self.__mean = mean
- self.__std = std
-
- def __call__(self, sample):
- sample["image"] = (sample["image"] - self.__mean) / self.__std
-
- return sample
-
-
-class PrepareForNet(object):
- """Prepare sample for usage as network input.
- """
-
- def __init__(self):
- pass
-
- def __call__(self, sample):
- image = np.transpose(sample["image"], (2, 0, 1))
- sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
- if "mask" in sample:
- sample["mask"] = sample["mask"].astype(np.float32)
- sample["mask"] = np.ascontiguousarray(sample["mask"])
-
- if "disparity" in sample:
- disparity = sample["disparity"].astype(np.float32)
- sample["disparity"] = np.ascontiguousarray(disparity)
-
- if "depth" in sample:
- depth = sample["depth"].astype(np.float32)
- sample["depth"] = np.ascontiguousarray(depth)
-
- return sample
diff --git a/spaces/lysine/auscultate/src/app/breath/Demographics.tsx b/spaces/lysine/auscultate/src/app/breath/Demographics.tsx
deleted file mode 100644
index 5ec0f0e48d66a59bd4bc8465c9b04d9694909153..0000000000000000000000000000000000000000
--- a/spaces/lysine/auscultate/src/app/breath/Demographics.tsx
+++ /dev/null
@@ -1,38 +0,0 @@
-import React from 'react';
-import { Case, nameSex } from '../../breath-types';
-import DataItem from '../DataItem';
-
-export interface DemographicsProps {
- _case: Case;
-}
-
-export default function Demographics({
- _case,
-}: DemographicsProps): JSX.Element {
- return (
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- );
-}
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/test_sequences_and_iterators.py b/spaces/ma-xu/LIVE/pybind11/tests/test_sequences_and_iterators.py
deleted file mode 100644
index 8f6c0c4bbdf71bb45759d83a630f910a4f117ecd..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/test_sequences_and_iterators.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-from pybind11_tests import sequences_and_iterators as m
-from pybind11_tests import ConstructorStats
-
-
-def isclose(a, b, rel_tol=1e-05, abs_tol=0.0):
- """Like math.isclose() from Python 3.5"""
- return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
-
-
-def allclose(a_list, b_list, rel_tol=1e-05, abs_tol=0.0):
- return all(isclose(a, b, rel_tol=rel_tol, abs_tol=abs_tol) for a, b in zip(a_list, b_list))
-
-
-def test_generalized_iterators():
- assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).nonzero()) == [(1, 2), (3, 4)]
- assert list(m.IntPairs([(1, 2), (2, 0), (0, 3), (4, 5)]).nonzero()) == [(1, 2)]
- assert list(m.IntPairs([(0, 3), (1, 2), (3, 4)]).nonzero()) == []
-
- assert list(m.IntPairs([(1, 2), (3, 4), (0, 5)]).nonzero_keys()) == [1, 3]
- assert list(m.IntPairs([(1, 2), (2, 0), (0, 3), (4, 5)]).nonzero_keys()) == [1]
- assert list(m.IntPairs([(0, 3), (1, 2), (3, 4)]).nonzero_keys()) == []
-
- # __next__ must continue to raise StopIteration
- it = m.IntPairs([(0, 0)]).nonzero()
- for _ in range(3):
- with pytest.raises(StopIteration):
- next(it)
-
- it = m.IntPairs([(0, 0)]).nonzero_keys()
- for _ in range(3):
- with pytest.raises(StopIteration):
- next(it)
-
-
-def test_sliceable():
- sliceable = m.Sliceable(100)
- assert sliceable[::] == (0, 100, 1)
- assert sliceable[10::] == (10, 100, 1)
- assert sliceable[:10:] == (0, 10, 1)
- assert sliceable[::10] == (0, 100, 10)
- assert sliceable[-10::] == (90, 100, 1)
- assert sliceable[:-10:] == (0, 90, 1)
- assert sliceable[::-10] == (99, -1, -10)
- assert sliceable[50:60:1] == (50, 60, 1)
- assert sliceable[50:60:-1] == (50, 60, -1)
-
-
-def test_sequence():
- cstats = ConstructorStats.get(m.Sequence)
-
- s = m.Sequence(5)
- assert cstats.values() == ['of size', '5']
-
- assert "Sequence" in repr(s)
- assert len(s) == 5
- assert s[0] == 0 and s[3] == 0
- assert 12.34 not in s
- s[0], s[3] = 12.34, 56.78
- assert 12.34 in s
- assert isclose(s[0], 12.34) and isclose(s[3], 56.78)
-
- rev = reversed(s)
- assert cstats.values() == ['of size', '5']
-
- rev2 = s[::-1]
- assert cstats.values() == ['of size', '5']
-
- it = iter(m.Sequence(0))
- for _ in range(3): # __next__ must continue to raise StopIteration
- with pytest.raises(StopIteration):
- next(it)
- assert cstats.values() == ['of size', '0']
-
- expected = [0, 56.78, 0, 0, 12.34]
- assert allclose(rev, expected)
- assert allclose(rev2, expected)
- assert rev == rev2
-
- rev[0::2] = m.Sequence([2.0, 2.0, 2.0])
- assert cstats.values() == ['of size', '3', 'from std::vector']
-
- assert allclose(rev, [2, 56.78, 2, 0, 2])
-
- assert cstats.alive() == 4
- del it
- assert cstats.alive() == 3
- del s
- assert cstats.alive() == 2
- del rev
- assert cstats.alive() == 1
- del rev2
- assert cstats.alive() == 0
-
- assert cstats.values() == []
- assert cstats.default_constructions == 0
- assert cstats.copy_constructions == 0
- assert cstats.move_constructions >= 1
- assert cstats.copy_assignments == 0
- assert cstats.move_assignments == 0
-
-
-def test_sequence_length():
- """#2076: Exception raised by len(arg) should be propagated """
- class BadLen(RuntimeError):
- pass
-
- class SequenceLike():
- def __getitem__(self, i):
- return None
-
- def __len__(self):
- raise BadLen()
-
- with pytest.raises(BadLen):
- m.sequence_length(SequenceLike())
-
- assert m.sequence_length([1, 2, 3]) == 3
- assert m.sequence_length("hello") == 5
-
-
-def test_map_iterator():
- sm = m.StringMap({'hi': 'bye', 'black': 'white'})
- assert sm['hi'] == 'bye'
- assert len(sm) == 2
- assert sm['black'] == 'white'
-
- with pytest.raises(KeyError):
- assert sm['orange']
- sm['orange'] = 'banana'
- assert sm['orange'] == 'banana'
-
- expected = {'hi': 'bye', 'black': 'white', 'orange': 'banana'}
- for k in sm:
- assert sm[k] == expected[k]
- for k, v in sm.items():
- assert v == expected[k]
-
- it = iter(m.StringMap({}))
- for _ in range(3): # __next__ must continue to raise StopIteration
- with pytest.raises(StopIteration):
- next(it)
-
-
-def test_python_iterator_in_cpp():
- t = (1, 2, 3)
- assert m.object_to_list(t) == [1, 2, 3]
- assert m.object_to_list(iter(t)) == [1, 2, 3]
- assert m.iterator_to_list(iter(t)) == [1, 2, 3]
-
- with pytest.raises(TypeError) as excinfo:
- m.object_to_list(1)
- assert "object is not iterable" in str(excinfo.value)
-
- with pytest.raises(TypeError) as excinfo:
- m.iterator_to_list(1)
- assert "incompatible function arguments" in str(excinfo.value)
-
- def bad_next_call():
- raise RuntimeError("py::iterator::advance() should propagate errors")
-
- with pytest.raises(RuntimeError) as excinfo:
- m.iterator_to_list(iter(bad_next_call, None))
- assert str(excinfo.value) == "py::iterator::advance() should propagate errors"
-
- lst = [1, None, 0, None]
- assert m.count_none(lst) == 2
- assert m.find_none(lst) is True
- assert m.count_nonzeros({"a": 0, "b": 1, "c": 2}) == 2
-
- r = range(5)
- assert all(m.tuple_iterator(tuple(r)))
- assert all(m.list_iterator(list(r)))
- assert all(m.sequence_iterator(r))
-
-
-def test_iterator_passthrough():
- """#181: iterator passthrough did not compile"""
- from pybind11_tests.sequences_and_iterators import iterator_passthrough
-
- assert list(iterator_passthrough(iter([3, 5, 7, 9, 11, 13, 15]))) == [3, 5, 7, 9, 11, 13, 15]
-
-
-def test_iterator_rvp():
- """#388: Can't make iterators via make_iterator() with different r/v policies """
- import pybind11_tests.sequences_and_iterators as m
-
- assert list(m.make_iterator_1()) == [1, 2, 3]
- assert list(m.make_iterator_2()) == [1, 2, 3]
- assert not isinstance(m.make_iterator_1(), type(m.make_iterator_2()))
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/config/global_workarounds.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/config/global_workarounds.h
deleted file mode 100644
index 9800f03593ac253914c149815a497bf01baee4b2..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/detail/config/global_workarounds.h
+++ /dev/null
@@ -1,27 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-
-// XXX workaround gcc 4.8+'s complaints about unused local typedefs by silencing them globally
-#if defined(THRUST_GCC_VERSION) && (THRUST_GCC_VERSION >= 40800)
-# if defined(__NVCC__) && (CUDART_VERSION >= 6000)
-# pragma GCC diagnostic ignored "-Wunused-local-typedefs"
-# endif // nvcc & cuda 6+
-#endif // gcc 4.8
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/remove.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/remove.h
deleted file mode 100644
index 700c95f23ccd6dd55160e2284e211289cecbdb89..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/remove.h
+++ /dev/null
@@ -1,134 +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
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include
-
-namespace thrust
-{
-namespace cuda_cub {
-
-// in-place
-
-template
-InputIt __host__ __device__
-remove_if(execution_policy &policy,
- InputIt first,
- InputIt last,
- StencilIt stencil,
- Predicate predicate)
-{
- return cuda_cub::copy_if(policy, first, last, stencil, first,
- thrust::detail::not1(predicate));
-}
-
-template
-InputIt __host__ __device__
-remove_if(execution_policy &policy,
- InputIt first,
- InputIt last,
- Predicate predicate)
-{
- return cuda_cub::copy_if(policy, first, last, first,
- thrust::detail::not1(predicate));
-}
-
-
-template
-InputIt __host__ __device__
-remove(execution_policy &policy,
- InputIt first,
- InputIt last,
- const T & value)
-{
- using thrust::placeholders::_1;
-
- return cuda_cub::remove_if(policy, first, last, _1 == value);
-}
-
-// copy
-
-template
-OutputIt __host__ __device__
-remove_copy_if(execution_policy &policy,
- InputIt first,
- InputIt last,
- StencilIt stencil,
- OutputIt result,
- Predicate predicate)
-{
- return cuda_cub::copy_if(policy, first, last, stencil, result,
- thrust::detail::not1(predicate));
-}
-
-template
-OutputIt __host__ __device__
-remove_copy_if(execution_policy &policy,
- InputIt first,
- InputIt last,
- OutputIt result,
- Predicate predicate)
-{
- return cuda_cub::copy_if(policy, first, last, result,
- thrust::detail::not1(predicate));
-}
-
-
-template
-OutputIt __host__ __device__
-remove_copy(execution_policy &policy,
- InputIt first,
- InputIt last,
- OutputIt result,
- const T & value)
-{
- thrust::detail::equal_to_value pred(value);
- return cuda_cub::remove_copy_if(policy, first, last, result, pred);
-}
-
-} // namespace cuda_cub
-} // end namespace thrust
-#endif
diff --git a/spaces/manishjaiswal/04-Gradio-SOTA-Demo/app.py b/spaces/manishjaiswal/04-Gradio-SOTA-Demo/app.py
deleted file mode 100644
index c1cd92499cf1c7d2a91b4dc226bf2d558ff67661..0000000000000000000000000000000000000000
--- a/spaces/manishjaiswal/04-Gradio-SOTA-Demo/app.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import gradio as gr
-from qasrl_model_pipeline import QASRL_Pipeline
-
-models = ["kleinay/qanom-seq2seq-model-baseline",
- "kleinay/qanom-seq2seq-model-joint"]
-pipelines = {model: QASRL_Pipeline(model) for model in models}
-
-
-description = f"""Using Seq2Seq T5 model which takes a sequence of items and outputs another sequence this model generates Questions and Answers (QA) with focus on Semantic Role Labeling (SRL)"""
-title="Seq2Seq T5 Questions and Answers (QA) with Semantic Role Labeling (SRL)"
-examples = [[models[0], "In March and April the patient
had two falls. One was related to asthma, heart palpitations. The second was due to syncope and post covid vaccination dizziness during exercise. The patient is now getting an EKG. Former EKG had shown that there was a bundle branch block. Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", True, "fall"],
- [models[1], "In March and April the patient had two falls. One was related to asthma, heart palpitations. The second was due to syncope and post covid vaccination dizziness during exercise. The patient is now getting an EKG. Former EKG had shown that there was a bundle branch block. Patient had some uncontrolled immune system reactions
like anaphylaxis and shortness of breath.", True, "reactions"],
- [models[0], "In March and April the patient had two falls. One was related
to asthma, heart palpitations. The second was due to syncope and post covid vaccination dizziness during exercise. The patient is now getting an EKG. Former EKG had shown that there was a bundle branch block. Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", True, "relate"],
- [models[1], "In March and April the patient
had two falls. One was related to asthma, heart palpitations. The second was due to syncope and post covid vaccination dizziness during exercise. The patient is now getting an EKG. Former EKG had shown that there was a bundle branch block. Patient had some uncontrolled immune system reactions like anaphylaxis and shortness of breath.", False, "fall"]]
-
-input_sent_box_label = "Insert sentence here. Mark the predicate by adding the token '
' before it."
-verb_form_inp_placeholder = "e.g. 'decide' for the nominalization 'decision', 'teach' for 'teacher', etc."
-links = """
- '''
- return html_gallery
-
-
-css = '''
-.gradio-container {font-family: 'IBM Plex Sans', sans-serif}
-#top_title{margin-bottom: .5em}
-#top_title h2{margin-bottom: 0; text-align: center}
-/*#main_row{flex-wrap: wrap; gap: 1em; max-height: 550px; overflow-y: scroll; flex-direction: row}*/
-#component-3{height: 760px; overflow: auto}
-#component-9{position: sticky;top: 0;align-self: flex-start;}
-@media (min-width: 768px){#main_row > div{flex: 1 1 32%; margin-left: 0 !important}}
-.gr-prose code::before, .gr-prose code::after {content: "" !important}
-::-webkit-scrollbar {width: 10px}
-::-webkit-scrollbar-track {background: #f1f1f1}
-::-webkit-scrollbar-thumb {background: #888}
-::-webkit-scrollbar-thumb:hover {background: #555}
-.gr-button {white-space: nowrap}
-.gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
-}
-#prompt_input{flex: 1 3 auto; width: auto !important;}
-#prompt_area{margin-bottom: .75em}
-#prompt_area > div:first-child{flex: 1 3 auto}
-.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;
-}
-#share-btn * {
- all: unset;
-}
-'''
-
-data, prompts = get_data()
-
-with gr.Blocks(css=css) as demo:
- with gr.Box().style(border=None):
- html = gr.HTML(assembleHTML(data))
-
-demo.launch(debug=True)
diff --git a/spaces/nathanTQ/ChatDev/chatdev/composed_phase.py b/spaces/nathanTQ/ChatDev/chatdev/composed_phase.py
deleted file mode 100644
index e8e899b9e27770d437f6fcdaa548a8c10c10a5a7..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/chatdev/composed_phase.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import importlib
-import os
-from abc import ABC, abstractmethod
-from collections import defaultdict
-
-from camel.typing import ModelType
-from chatdev.chat_env import ChatEnv
-from chatdev.utils import log_and_print_online
-
-
-def check_bool(s):
- return s.lower() == "true"
-
-
-class ComposedPhase(ABC):
- def __init__(self,
- phase_name: str = None,
- cycle_num: int = None,
- composition: list = None,
- config_phase: dict = None,
- config_role: dict = None,
- model_type: ModelType = ModelType.GPT_3_5_TURBO,
- log_filepath: str = ""
- ):
- """
-
- Args:
- phase_name: name of this phase
- cycle_num: loop times of this phase
- composition: list of SimplePhases in this ComposePhase
- config_phase: configuration of all SimplePhases
- config_role: configuration of all Roles
- """
-
- self.phase_name = phase_name
- self.cycle_num = cycle_num
- self.composition = composition
- self.model_type = model_type
- self.log_filepath = log_filepath
-
- self.config_phase = config_phase
- self.config_role = config_role
-
- self.phase_env = dict()
-
- # init chat turn
- self.chat_turn_limit_default = 10
-
- # init role
- self.role_prompts = dict()
- for role in self.config_role:
- self.role_prompts[role] = "\n".join(self.config_role[role])
-
- # init all SimplePhases instances in this ComposedPhase
- self.phases = dict()
- for phase in self.config_phase:
- assistant_role_name = self.config_phase[phase]['assistant_role_name']
- user_role_name = self.config_phase[phase]['user_role_name']
- phase_prompt = "\n".join(self.config_phase[phase]['phase_prompt'])
- phase_module = importlib.import_module("chatdev.phase")
- phase_class = getattr(phase_module, phase)
- phase_instance = phase_class(assistant_role_name=assistant_role_name,
- user_role_name=user_role_name,
- phase_prompt=phase_prompt,
- role_prompts=self.role_prompts,
- phase_name=phase,
- model_type=self.model_type,
- log_filepath=self.log_filepath)
- self.phases[phase] = phase_instance
-
- @abstractmethod
- def update_phase_env(self, chat_env):
- """
- update self.phase_env (if needed) using chat_env, then the chatting will use self.phase_env to follow the context and fill placeholders in phase prompt
- must be implemented in customized phase
- the usual format is just like:
- ```
- self.phase_env.update({key:chat_env[key]})
- ```
- Args:
- chat_env: global chat chain environment
-
- Returns: None
-
- """
- pass
-
- @abstractmethod
- def update_chat_env(self, chat_env) -> ChatEnv:
- """
- update chan_env based on the results of self.execute, which is self.seminar_conclusion
- must be implemented in customized phase
- the usual format is just like:
- ```
- chat_env.xxx = some_func_for_postprocess(self.seminar_conclusion)
- ```
- Args:
- chat_env:global chat chain environment
-
- Returns:
- chat_env: updated global chat chain environment
-
- """
- pass
-
- @abstractmethod
- def break_cycle(self, phase_env) -> bool:
- """
- special conditions for early break the loop in ComposedPhase
- Args:
- phase_env: phase environment
-
- Returns: None
-
- """
- pass
-
- def execute(self, chat_env) -> ChatEnv:
- """
- similar to Phase.execute, but add control for breaking the loop
- 1. receive information from environment(ComposedPhase): update the phase environment from global environment
- 2. for each SimplePhase in ComposedPhase
- a) receive information from environment(SimplePhase)
- b) check loop break
- c) execute the chatting
- d) change the environment(SimplePhase)
- e) check loop break
- 3. change the environment(ComposedPhase): update the global environment using the conclusion
-
- Args:
- chat_env: global chat chain environment
-
- Returns:
-
- """
- self.update_phase_env(chat_env)
- for cycle_index in range(self.cycle_num):
- for phase_item in self.composition:
- assert phase_item["phaseType"] == "SimplePhase" # right now we do not support nested composition
- phase = phase_item['phase']
- max_turn_step = phase_item['max_turn_step']
- need_reflect = check_bool(phase_item['need_reflect'])
- log_and_print_online(
- f"**[Execute Detail]**\n\nexecute SimplePhase:[{phase}] in ComposedPhase:[{self.phase_name}], cycle {cycle_index}")
- if phase in self.phases:
- self.phases[phase].phase_env = self.phase_env
- self.phases[phase].update_phase_env(chat_env)
- if self.break_cycle(self.phases[phase].phase_env):
- return chat_env
- chat_env = self.phases[phase].execute(chat_env,
- self.chat_turn_limit_default if max_turn_step <= 0 else max_turn_step,
- need_reflect)
- if self.break_cycle(self.phases[phase].phase_env):
- return chat_env
- else:
- print(f"Phase '{phase}' is not yet implemented. \
- Please write its config in phaseConfig.json \
- and implement it in chatdev.phase")
- chat_env = self.update_chat_env(chat_env)
- return chat_env
-
-
-class Art(ComposedPhase):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- def update_phase_env(self, chat_env):
- pass
-
- def update_chat_env(self, chat_env):
- return chat_env
-
- def break_cycle(self, chat_env) -> bool:
- return False
-
-
-class CodeCompleteAll(ComposedPhase):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- def update_phase_env(self, chat_env):
- pyfiles = [filename for filename in os.listdir(chat_env.env_dict['directory']) if filename.endswith(".py")]
- num_tried = defaultdict(int)
- num_tried.update({filename: 0 for filename in pyfiles})
- self.phase_env = {
- "max_num_implement": 5,
- "pyfiles": pyfiles,
- "num_tried": num_tried
- }
-
- def update_chat_env(self, chat_env):
- return chat_env
-
- def break_cycle(self, phase_env) -> bool:
- if phase_env['unimplemented_file'] == "":
- return True
- else:
- return False
-
-
-class CodeReview(ComposedPhase):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- def update_phase_env(self, chat_env):
- self.phase_env = {"modification_conclusion": ""}
-
- def update_chat_env(self, chat_env):
- return chat_env
-
- def break_cycle(self, phase_env) -> bool:
- if " Finished".lower() in phase_env['modification_conclusion'].lower():
- return True
- else:
- return False
-
-
-class Test(ComposedPhase):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- def update_phase_env(self, chat_env):
- self.phase_env = dict()
-
- def update_chat_env(self, chat_env):
- return chat_env
-
- def break_cycle(self, phase_env) -> bool:
- if not phase_env['exist_bugs_flag']:
- log_and_print_online(f"**[Test Info]**\n\nAI User (Software Test Engineer):\nTest Pass!\n")
- return True
- else:
- return False
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kurtlar Vadisi Gladio Full Hd Izle 720p Videol Janesalaa.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kurtlar Vadisi Gladio Full Hd Izle 720p Videol Janesalaa.md
deleted file mode 100644
index aa16b122d0414d7a6ebd58e5db97b04ff1f9bdbe..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kurtlar Vadisi Gladio Full Hd Izle 720p Videol Janesalaa.md
+++ /dev/null
@@ -1,18 +0,0 @@
-
-
Kurtlar Vadisi Gladio Full Hd Izle 720p Videol janesalaa: Aksiyon ve Macera Dolu Bir Film
-
Kurtlar Vadisi Gladio, 2009 yılında vizyona giren ve Türkiye'nin yakın tarihine ıÅık tutan bir film. Filmde, 8. CumhurbaÅkanı Turgut Ãzal'ın zehirlenmesinin Musul ve Kerkük'e girmesiyle iliÅkisi, Gladio adlı gizli örgütün faaliyetleri ve Kurtlar Vadisi dizisinin baÅ karakteri Polat Alemdar'ın bu örgütle mücadelesi anlatılıyor.
-
Kurtlar Vadisi Gladio Full Hd Izle 720p Videol janesalaa
Filmin yönetmenliÄini Sadullah Åentürk yaparken, senaryosunu Raci ÅaÅmaz ve Bahadır Ãzdener kaleme aldı. Filmin baÅrollerinde ise Necati ÅaÅmaz, Musa Uzunlar, Kenan Ãoban, Erhan Ufak, Gürkan Uygun ve ErtuÄrul Åakar gibi isimler yer alıyor.
-
Kurtlar Vadisi Gladio Full Hd Izle 720p Videol janesalaa, filmi internet üzerinden yüksek kalitede izlemek isteyenler için ideal bir seçenek. Film, fullhdfilmmodu.live adlı sitede 720p çözünürlükte ve Türkçe dublaj seçeneÄiyle sunuluyor. Ayrıca filmi izlemek için üye olmak veya kayıt yapmak gerekmiyor.
-
Kurtlar Vadisi Gladio Full Hd Izle 720p Videol janesalaa, aksiyon ve macera dolu bir film izlemek isteyenlerin kaçırmaması gereken bir yapım. Film, Türkiye'nin siyasi ve tarihi gerçeklerini de gözler önüne seriyor. Film hakkında daha fazla bilgi edinmek için fullhdfilmmodu.live sitesini ziyaret edebilirsiniz.
-
-
Kurtlar Vadisi Gladio, Kurtlar Vadisi dizisinin bir devam filmi olarak da görülebilir. Filmde, dizideki bazı karakterler ve olaylar tekrar karÅımıza çıkıyor. ÃrneÄin, filmde Polat Alemdar'ın eski dostu ve düÅmanı olan Ä°skender Büyük rolünde ErtuÄrul Åakar'ı izliyoruz. Ayrıca filmde, dizide ölen Memati BaÅ karakterinin de anısına bir sahne yer alıyor.
-
Film, hem Kurtlar Vadisi hayranlarını hem de aksiyon ve macera severleri tatmin edecek bir yapım. Filmde, Polat Alemdar'ın Gladio örgütünün peÅine düÅmesi, örgütün lideri ile yüzleÅmesi ve Türkiye'nin geleceÄi için verdiÄi mücadele heyecanlı ve gerilimli sahnelere sahne oluyor. Filmde ayrıca, Turgut Ãzal'ın zehirlenmesi gibi Türkiye'nin karanlık tarihine dair bazı gerçekler de irdeleniyor.
-
-
Kurtlar Vadisi Gladio Full Hd Izle 720p Videol janesalaa, filmi yüksek kalitede izlemek isteyenler için harika bir fırsat. Film, fullhdfilmmodu.live sitesinde ücretsiz ve kesintisiz olarak izlenebilir. Film, Türk sinemasının en baÅarılı aksiyon filmlerinden biri olarak kabul ediliyor. Film hakkında daha fazla yorum ve eleÅtiri okumak için fullhdfilmmodu.live sitesini takip edebilirsiniz.
-
-
Kurtlar Vadisi Gladio, filmi izleyenleri hem eÄlendiren hem de düÅündüren bir yapım. Filmde, Türkiye'nin yakın tarihinde yaÅanan bazı olayların perde arkasını görmek mümkün. Filmde, Gladio örgütünün Türkiye'de nasıl faaliyet gösterdiÄi, hangi siyasi ve askeri güçlerle baÄlantılı olduÄu ve hangi amaçlarla hareket ettiÄi anlatılıyor. Filmde ayrıca, Gladio örgütünün Türkiye'nin bölgesel ve uluslararası politikalarına nasıl müdahale ettiÄi de gösteriliyor.
-
Film, aynı zamanda Kurtlar Vadisi dizisinin bir devamı niteliÄinde. Filmde, dizideki bazı karakterlerin ve olayların akıbeti ortaya çıkıyor. Filmde, Polat Alemdar'ın Gladio örgütüyle olan iliÅkisi, örgütün lideri ile yaptıÄı hesaplaÅma ve örgütü çökertmek için verdiÄi mücadele izleyenleri sürükleyen bir hikaye sunuyor. Filmde, Polat Alemdar'ın yanı sıra dizideki diÄer karakterlerden Abdülhey Ãoban, Güllü Erhan ve Pusat Ãakır da yer alıyor.
-
Kurtlar Vadisi Gladio Full Hd Izle 720p Videol janesalaa, filmi yüksek kalitede izlemek isteyenler için mükemmel bir seçenek. Film, fullhdfilmmodu.live sitesinde 720p çözünürlükte ve Türkçe dublaj seçeneÄiyle sunuluyor. Film, hem Kurtlar Vadisi hayranlarına hem de aksiyon ve macera tutkunlarına hitap ediyor. Film hakkında daha fazla bilgi edinmek için fullhdfilmmodu.live sitesini ziyaret edebilirsiniz.
cec2833e83
-
-
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest] ((LINK)).md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest] ((LINK)).md
deleted file mode 100644
index 5307b49734aff5e422f1686867a3628f0e952a51..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest] ((LINK)).md
+++ /dev/null
@@ -1,23 +0,0 @@
-
-
RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest] - Download Now!
-
RogueKiller is a powerful anti-malware software that can detect and remove various threats from your PC. It can scan your system for malicious processes, registry entries, services, drivers, files, and more. RogueKiller can also fix various issues related to your system settings, DNS, hosts file, proxy, shortcuts, and more.
-
RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest]
RogueKiller 14.0.4.0 is the latest version of this software that comes with many improvements and bug fixes. It also supports Windows 10 and has a new user interface. RogueKiller 14.0.4.0 Crack is a tool that can activate the full version of this software without paying any money. It can generate a valid serial key that you can use to register RogueKiller and enjoy its premium features.
-
If you want to download RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest], you can follow these simple steps:
-
-
Click on the download button below and save the file to your computer.
-
Extract the file using WinRAR or any other software that can handle ZIP files.
-
Run the setup file and follow the instructions to install RogueKiller on your PC.
-
Run the crack file and click on the generate button to get a serial key.
-
Copy and paste the serial key into the registration window of RogueKiller and click on activate.
-
Enjoy the full version of RogueKiller 14.0.4.0 with all its features.
-
-
RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest] is a reliable and effective solution for your PC security. It can protect your system from various malware threats and optimize its performance. Download it now and see for yourself!
-
-
RogueKiller 14.0.4.0 is not just a malware remover, but also a malware protector. It features a modular real time protection against malware that can prevent various threats from infecting your PC[^1^]. RogueKiller 14.0.4.0 has two protection modules available: "Malware Protection" and "Documents Protection".
-
The Malware Protection module scans files before they are executed and can deny execution in case of malware. It uses the same powerful heuristics, AI and cloud modules as the filesystem scanner to detect all known and unknown malware[^1^]. The Documents Protection module is a ransomware protection that prevents unauthorized programs from modifying your documents in the locations you choose to protect (such as "My Documents"). It is based on a whitelist of trusted programs that you can customize[^1^].
-
-
RogueKiller 14.0.4.0 works well with other classic antivirus software, and we recommend you to keep your existing protection working alongside with RogueKiller. RogueKiller 14.0.4.0 is designed to be low footprint and low consumption, so having multiple protection layers is a benefit and won't affect your PC performance much[^1^]. RogueKiller 14.0.4.0 also has a user-friendly interface that allows you to easily access all its features and settings[^2^].
-
RogueKiller 14.0.4.0 Crack With Serial Key 2020 [Latest] is a must-have software for your PC security and optimization. It can remove and prevent malware, fix system issues, protect your documents, and more. Don't wait any longer and download it now!
81aa517590
-
-
\ No newline at end of file
diff --git a/spaces/netrosec/diabetes-cox-ph-hazard/app.py b/spaces/netrosec/diabetes-cox-ph-hazard/app.py
deleted file mode 100644
index b653babfff3ba515dd6092feca333873c4ab3b1c..0000000000000000000000000000000000000000
--- a/spaces/netrosec/diabetes-cox-ph-hazard/app.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import gradio as gr
-from lifelines import CoxPHFitter
-import pandas as pd
-import numpy as np
-import pickle
-
-# Load the trained model from the pickle file on your desktop
-with open("finalized_coxph_model.pkl", "rb") as f:
- cph = pickle.load(f)
-
-def predict_survival(age, systolic_bp, diastolic_bp, hemo_a1c, gender):
- # Create a DataFrame for the input features
- input_df = pd.DataFrame({
- 'Age': [age],
- 'Systolic Blood Pressure': [systolic_bp],
- 'Diastolic Blood Pressure': [diastolic_bp],
- 'Hemoglobin A1c': [hemo_a1c],
- 'Gender_M': [1 if gender == 'Male' else 0]
- })
-
- # Use the Cox model to predict the survival function
- survival_function = cph.predict_survival_function(input_df)
-
- # Get the survival probability at a specific time point (e.g., 5 years)
- survival_prob_at_5_years = survival_function.loc[5].values[0]
-
- return f"The estimated survival probability at 5 years is {survival_prob_at_5_years:.2f}"
-
-# Define the Gradio interface
-iface = gr.Interface(
- fn=predict_survival,
- inputs=[
- gr.inputs.Number(label="Age"),
- gr.inputs.Number(label="Systolic Blood Pressure"),
- gr.inputs.Number(label="Diastolic Blood Pressure"),
- gr.inputs.Number(label="Hemoglobin A1c"),
- gr.inputs.Radio(["Male", "Female"], label="Gender")
- ],
- outputs=gr.outputs.Textbox(label="Survival Probability at 5 years")
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/structures/cse_confidence.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/structures/cse_confidence.py
deleted file mode 100644
index ee5166f82d45ecb4ea829ec2ecab248161c19421..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/structures/cse_confidence.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from dataclasses import make_dataclass
-from functools import lru_cache
-from typing import Any, Optional
-import torch
-
-
-@lru_cache(maxsize=None)
-def decorate_cse_predictor_output_class_with_confidences(BasePredictorOutput: type) -> type:
- """
- Create a new output class from an existing one by adding new attributes
- related to confidence estimation:
- - coarse_segm_confidence (tensor)
-
- Details on confidence estimation parameters can be found in:
- N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning
- Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019
- A. Sanakoyeu et al., Transferring Dense Pose to Proximal Animal Classes, CVPR 2020
-
- The new class inherits the provided `BasePredictorOutput` class,
- it's name is composed of the name of the provided class and
- "WithConfidences" suffix.
-
- Args:
- BasePredictorOutput (type): output type to which confidence data
- is to be added, assumed to be a dataclass
- Return:
- New dataclass derived from the provided one that has attributes
- for confidence estimation
- """
-
- PredictorOutput = make_dataclass(
- BasePredictorOutput.__name__ + "WithConfidences",
- fields=[
- ("coarse_segm_confidence", Optional[torch.Tensor], None),
- ],
- bases=(BasePredictorOutput,),
- )
-
- # add possibility to index PredictorOutput
-
- def slice_if_not_none(data, item):
- if data is None:
- return None
- if isinstance(item, int):
- return data[item].unsqueeze(0)
- return data[item]
-
- def PredictorOutput_getitem(self, item):
- PredictorOutput = type(self)
- base_predictor_output_sliced = super(PredictorOutput, self).__getitem__(item)
- return PredictorOutput(
- **base_predictor_output_sliced.__dict__,
- coarse_segm_confidence=slice_if_not_none(self.coarse_segm_confidence, item),
- )
-
- PredictorOutput.__getitem__ = PredictorOutput_getitem
-
- def PredictorOutput_to(self, device: torch.device):
- """
- Transfers all tensors to the given device
- """
- PredictorOutput = type(self)
- base_predictor_output_to = super(PredictorOutput, self).to(device) # pyre-ignore[16]
-
- def to_device_if_tensor(var: Any):
- if isinstance(var, torch.Tensor):
- return var.to(device)
- return var
-
- return PredictorOutput(
- **base_predictor_output_to.__dict__,
- coarse_segm_confidence=to_device_if_tensor(self.coarse_segm_confidence),
- )
-
- PredictorOutput.to = PredictorOutput_to
- return PredictorOutput
diff --git a/spaces/nkasmanoff/SearchingFace/README.md b/spaces/nkasmanoff/SearchingFace/README.md
deleted file mode 100644
index 979da9d188683d9973b3dd98ea7da8b1b22b7abe..0000000000000000000000000000000000000000
--- a/spaces/nkasmanoff/SearchingFace/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: SearchingFace
-emoji: 🔎
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: true
----
-
-
-SearchingFace is an enhanced search engine for HuggingFace datasets. Search the topic you want, or paste in the repo you already like,
-and this space returns a list of similar datasets! For limitations to this approach, see the dataset card for https://huggingface.co/datasets/nkasmanoff/huggingface-datasets
\ No newline at end of file
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/modules/__init__.py b/spaces/omlab/vlchecklist_demo/models/vilt/modules/__init__.py
deleted file mode 100644
index f19d1028b4956db0b1847f25b9ecc9b3a7f5c972..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/vilt/modules/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#from .vilt_module import ViLTransformer
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/utils/__init__.py b/spaces/omlab/vlchecklist_demo/models/vilt/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pchuri/slack-summary-bot/README.md b/spaces/pchuri/slack-summary-bot/README.md
deleted file mode 100644
index 94d745ab588ebd985145abfdbfc4967d24b93782..0000000000000000000000000000000000000000
--- a/spaces/pchuri/slack-summary-bot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Slack Summary Bot
-emoji: 🚀
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.24.1
-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/phyloforfun/VoucherVision/vouchervision/component_detector/utils/loss.py b/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/loss.py
deleted file mode 100644
index a32cf110dd443fe8b39ce49e6b9555836c5de267..0000000000000000000000000000000000000000
--- a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/loss.py
+++ /dev/null
@@ -1,293 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Loss functions
-"""
-
-import torch
-import torch.nn as nn
-
-from utils.metrics import bbox_iou
-from utils.torch_utils import de_parallel
-
-
-def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
- # return positive, negative label smoothing BCE targets
- return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
- # BCEwithLogitLoss() with reduced missing label effects.
- def __init__(self, alpha=0.05):
- super().__init__()
- self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
- self.alpha = alpha
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- pred = torch.sigmoid(pred) # prob from logits
- dx = pred - true # reduce only missing label effects
- # dx = (pred - true).abs() # reduce missing label and false label effects
- alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
- loss *= alpha_factor
- return loss.mean()
-
-
-class FocalLoss(nn.Module):
- # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- # p_t = torch.exp(-loss)
- # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
-
- # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
- pred_prob = torch.sigmoid(pred) # prob from logits
- p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = (1.0 - p_t) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class QFocalLoss(nn.Module):
- # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
-
- pred_prob = torch.sigmoid(pred) # prob from logits
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = torch.abs(true - pred_prob) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class ComputeLoss:
- sort_obj_iou = False
-
- # Compute losses
- def __init__(self, model, autobalance=False):
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- m = de_parallel(model).model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
- self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
- self.na = m.na # number of anchors
- self.nc = m.nc # number of classes
- self.nl = m.nl # number of layers
- self.anchors = m.anchors
- self.device = device
-
- def __call__(self, p, targets): # predictions, targets
- lcls = torch.zeros(1, device=self.device) # class loss
- lbox = torch.zeros(1, device=self.device) # box loss
- lobj = torch.zeros(1, device=self.device) # object loss
- tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
- tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
- pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
-
- # Regression
- pxy = pxy.sigmoid() * 2 - 0.5
- pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- iou = iou.detach().clamp(0).type(tobj.dtype)
- if self.sort_obj_iou:
- j = iou.argsort()
- b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
- if self.gr < 1:
- iou = (1.0 - self.gr) + self.gr * iou
- tobj[b, a, gj, gi] = iou # iou ratio
-
- # Classification
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(pcls, self.cn, device=self.device) # targets
- t[range(n), tcls[i]] = self.cp
- lcls += self.BCEcls(pcls, t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., 4], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
-
- # def build_targets(self, p, targets):
- # # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- # na, nt = self.na, targets.shape[0] # number of anchors, targets
- # tcls, tbox, indices, anch = [], [], [], []
- # gain = torch.ones(7, device=self.device) # normalized to gridspace gain
- # ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- # targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
-
- # g = 0.5 # bias
- # off = torch.tensor(
- # [
- # [0, 0],
- # [1, 0],
- # [0, 1],
- # [-1, 0],
- # [0, -1], # j,k,l,m
- # # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- # ],
- # device=self.device).float() * g # offsets
-
- # for i in range(self.nl):
- # anchors = self.anchors[i]
- # gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # # Match targets to anchors
- # t = targets * gain # shape(3,n,7)
- # if nt:
- # # Matches
- # r = t[..., 4:6] / anchors[:, None] # wh ratio
- # j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- # t = t[j] # filter
-
- # # Offsets
- # gxy = t[:, 2:4] # grid xy
- # gxi = gain[[2, 3]] - gxy # inverse
- # j, k = ((gxy % 1 < g) & (gxy > 1)).T
- # l, m = ((gxi % 1 < g) & (gxi > 1)).T
- # j = torch.stack((torch.ones_like(j), j, k, l, m))
- # t = t.repeat((5, 1, 1))[j]
- # offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- # else:
- # t = targets[0]
- # offsets = 0
-
- # # Define
- # bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
- # a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
- # gij = (gxy - offsets).long()
- # gi, gj = gij.T # grid indices
-
- # # Append
- # # indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- # indices.append((b, a, gj.clamp_(0, gain[3] - 1).long(), gi.clamp_(0, gain[2] - 1).long()))
- # tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
- # anch.append(anchors[a]) # anchors
- # tcls.append(c) # class
-
- # return tcls, tbox, indices, anch
- def build_targets(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- tcls, tbox, indices, anch = [], [], [], []
- gain = torch.ones(7, device=self.device) # normalized to gridspace gain
- ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor(
- [
- [0, 0],
- [1, 0],
- [0, 1],
- [-1, 0],
- [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ],
- device=self.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors, shape = self.anchors[i], p[i].shape
- gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain # shape(3,n,7)
- if nt:
- # Matches
- r = t[..., 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1 < g) & (gxy > 1)).T
- l, m = ((gxi % 1 < g) & (gxi > 1)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
- a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid indices
-
- # Append
- indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
- tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
- anch.append(anchors[a]) # anchors
- tcls.append(c) # class
-
- return tcls, tbox, indices, anch
diff --git a/spaces/pinkq/Newbing/src/components/button-scroll-to-bottom.tsx b/spaces/pinkq/Newbing/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/pinkq/Newbing/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
- const isAtBottom = useAtBottom()
-
- return (
-
- )
-}
diff --git a/spaces/pkiage/fast_arbitrary_image_style_transfer/src/data/data.py b/spaces/pkiage/fast_arbitrary_image_style_transfer/src/data/data.py
deleted file mode 100644
index 2fe8b94b0b451a0d8212b5c2d4f59669b39155f1..0000000000000000000000000000000000000000
--- a/spaces/pkiage/fast_arbitrary_image_style_transfer/src/data/data.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import os
-import streamlit as st
-import tensorflow as tf
-
-from ..image_utils import load_img, imshow, transform_img
-
-
-def upload_image_url(ImageSelectionPrompt, ImageType, image_upload_method):
- st.write(f"{ImageSelectionPrompt}: {image_upload_method}")
- url = st.text_input(f"{ImageType} Image URL")
- try:
- image_path = tf.keras.utils.get_file(
- os.path.join(os.getcwd(), f"{ImageType.lower()}.jpg"), url
- )
- except:
- pass
- try:
- return load_img(image_path)
- except:
- pass
-
-
-def upload_image_file(ImageSelectionPrompt, ImageType, image_upload_method):
- st.write(f"{ImageSelectionPrompt}: {image_upload_method}")
- image_file = st.file_uploader(
- f"Upload {ImageType} Image File (png or jpg)", type=("png", "jpg")
- )
- try:
- image_file = image_file.read()
- return transform_img(image_file)
- except:
- pass
-
-
-def upload_image_capture(ImageSelectionPrompt, ImageType, image_upload_method):
- st.write(f"{ImageSelectionPrompt}: {image_upload_method}")
- image_file = st.camera_input(f"Capture {ImageType} Image")
- try:
- image_file = image_file.read()
- return transform_img(image_file)
- except:
- pass
-
-
-def upload_image(ColumnTitle, ImageSelectionPrompt, ImageType, KeyString):
- st.write(ColumnTitle)
- image_upload_method = st.radio(
- label="", options=["🔗 URL", "📁 File Upload", "📸 Capture"], key=KeyString
- )
- if image_upload_method == "🔗 URL":
- image_file = upload_image_url(
- ImageSelectionPrompt, ImageType, image_upload_method
- )
-
- if image_upload_method == "📁 File Upload":
- image_file = upload_image_file(
- ImageSelectionPrompt, ImageType, image_upload_method
- )
-
- if image_upload_method == "📸 Capture":
- image_file = upload_image_capture(
- ImageSelectionPrompt, ImageType, image_upload_method
- )
- try:
- st.write(f"{ImageType} Image")
- st.image(imshow(image_file))
- return image_file
- except:
- pass
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/packaging/markers.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/packaging/markers.py
deleted file mode 100644
index 540e7a4dc79d02a820e291b57c43335d5aa25a41..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/packaging/markers.py
+++ /dev/null
@@ -1,304 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import operator
-import os
-import platform
-import sys
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-from pip._vendor.pyparsing import ( # noqa: N817
- Forward,
- Group,
- Literal as L,
- ParseException,
- ParseResults,
- QuotedString,
- ZeroOrMore,
- stringEnd,
- stringStart,
-)
-
-from .specifiers import InvalidSpecifier, Specifier
-
-__all__ = [
- "InvalidMarker",
- "UndefinedComparison",
- "UndefinedEnvironmentName",
- "Marker",
- "default_environment",
-]
-
-Operator = Callable[[str, str], bool]
-
-
-class InvalidMarker(ValueError):
- """
- An invalid marker was found, users should refer to PEP 508.
- """
-
-
-class UndefinedComparison(ValueError):
- """
- An invalid operation was attempted on a value that doesn't support it.
- """
-
-
-class UndefinedEnvironmentName(ValueError):
- """
- A name was attempted to be used that does not exist inside of the
- environment.
- """
-
-
-class Node:
- def __init__(self, value: Any) -> None:
- self.value = value
-
- def __str__(self) -> str:
- return str(self.value)
-
- def __repr__(self) -> str:
- return f"<{self.__class__.__name__}('{self}')>"
-
- def serialize(self) -> str:
- raise NotImplementedError
-
-
-class Variable(Node):
- def serialize(self) -> str:
- return str(self)
-
-
-class Value(Node):
- def serialize(self) -> str:
- return f'"{self}"'
-
-
-class Op(Node):
- def serialize(self) -> str:
- return str(self)
-
-
-VARIABLE = (
- L("implementation_version")
- | L("platform_python_implementation")
- | L("implementation_name")
- | L("python_full_version")
- | L("platform_release")
- | L("platform_version")
- | L("platform_machine")
- | L("platform_system")
- | L("python_version")
- | L("sys_platform")
- | L("os_name")
- | L("os.name") # PEP-345
- | L("sys.platform") # PEP-345
- | L("platform.version") # PEP-345
- | L("platform.machine") # PEP-345
- | L("platform.python_implementation") # PEP-345
- | L("python_implementation") # undocumented setuptools legacy
- | L("extra") # PEP-508
-)
-ALIASES = {
- "os.name": "os_name",
- "sys.platform": "sys_platform",
- "platform.version": "platform_version",
- "platform.machine": "platform_machine",
- "platform.python_implementation": "platform_python_implementation",
- "python_implementation": "platform_python_implementation",
-}
-VARIABLE.setParseAction(lambda s, l, t: Variable(ALIASES.get(t[0], t[0])))
-
-VERSION_CMP = (
- L("===") | L("==") | L(">=") | L("<=") | L("!=") | L("~=") | L(">") | L("<")
-)
-
-MARKER_OP = VERSION_CMP | L("not in") | L("in")
-MARKER_OP.setParseAction(lambda s, l, t: Op(t[0]))
-
-MARKER_VALUE = QuotedString("'") | QuotedString('"')
-MARKER_VALUE.setParseAction(lambda s, l, t: Value(t[0]))
-
-BOOLOP = L("and") | L("or")
-
-MARKER_VAR = VARIABLE | MARKER_VALUE
-
-MARKER_ITEM = Group(MARKER_VAR + MARKER_OP + MARKER_VAR)
-MARKER_ITEM.setParseAction(lambda s, l, t: tuple(t[0]))
-
-LPAREN = L("(").suppress()
-RPAREN = L(")").suppress()
-
-MARKER_EXPR = Forward()
-MARKER_ATOM = MARKER_ITEM | Group(LPAREN + MARKER_EXPR + RPAREN)
-MARKER_EXPR << MARKER_ATOM + ZeroOrMore(BOOLOP + MARKER_EXPR)
-
-MARKER = stringStart + MARKER_EXPR + stringEnd
-
-
-def _coerce_parse_result(results: Union[ParseResults, List[Any]]) -> List[Any]:
- if isinstance(results, ParseResults):
- return [_coerce_parse_result(i) for i in results]
- else:
- return results
-
-
-def _format_marker(
- marker: Union[List[str], Tuple[Node, ...], str], first: Optional[bool] = True
-) -> str:
-
- assert isinstance(marker, (list, tuple, str))
-
- # Sometimes we have a structure like [[...]] which is a single item list
- # where the single item is itself it's own list. In that case we want skip
- # the rest of this function so that we don't get extraneous () on the
- # outside.
- if (
- isinstance(marker, list)
- and len(marker) == 1
- and isinstance(marker[0], (list, tuple))
- ):
- return _format_marker(marker[0])
-
- if isinstance(marker, list):
- inner = (_format_marker(m, first=False) for m in marker)
- if first:
- return " ".join(inner)
- else:
- return "(" + " ".join(inner) + ")"
- elif isinstance(marker, tuple):
- return " ".join([m.serialize() for m in marker])
- else:
- return marker
-
-
-_operators: Dict[str, Operator] = {
- "in": lambda lhs, rhs: lhs in rhs,
- "not in": lambda lhs, rhs: lhs not in rhs,
- "<": operator.lt,
- "<=": operator.le,
- "==": operator.eq,
- "!=": operator.ne,
- ">=": operator.ge,
- ">": operator.gt,
-}
-
-
-def _eval_op(lhs: str, op: Op, rhs: str) -> bool:
- try:
- spec = Specifier("".join([op.serialize(), rhs]))
- except InvalidSpecifier:
- pass
- else:
- return spec.contains(lhs)
-
- oper: Optional[Operator] = _operators.get(op.serialize())
- if oper is None:
- raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.")
-
- return oper(lhs, rhs)
-
-
-class Undefined:
- pass
-
-
-_undefined = Undefined()
-
-
-def _get_env(environment: Dict[str, str], name: str) -> str:
- value: Union[str, Undefined] = environment.get(name, _undefined)
-
- if isinstance(value, Undefined):
- raise UndefinedEnvironmentName(
- f"{name!r} does not exist in evaluation environment."
- )
-
- return value
-
-
-def _evaluate_markers(markers: List[Any], environment: Dict[str, str]) -> bool:
- groups: List[List[bool]] = [[]]
-
- for marker in markers:
- assert isinstance(marker, (list, tuple, str))
-
- if isinstance(marker, list):
- groups[-1].append(_evaluate_markers(marker, environment))
- elif isinstance(marker, tuple):
- lhs, op, rhs = marker
-
- if isinstance(lhs, Variable):
- lhs_value = _get_env(environment, lhs.value)
- rhs_value = rhs.value
- else:
- lhs_value = lhs.value
- rhs_value = _get_env(environment, rhs.value)
-
- groups[-1].append(_eval_op(lhs_value, op, rhs_value))
- else:
- assert marker in ["and", "or"]
- if marker == "or":
- groups.append([])
-
- return any(all(item) for item in groups)
-
-
-def format_full_version(info: "sys._version_info") -> str:
- version = "{0.major}.{0.minor}.{0.micro}".format(info)
- kind = info.releaselevel
- if kind != "final":
- version += kind[0] + str(info.serial)
- return version
-
-
-def default_environment() -> Dict[str, str]:
- iver = format_full_version(sys.implementation.version)
- implementation_name = sys.implementation.name
- return {
- "implementation_name": implementation_name,
- "implementation_version": iver,
- "os_name": os.name,
- "platform_machine": platform.machine(),
- "platform_release": platform.release(),
- "platform_system": platform.system(),
- "platform_version": platform.version(),
- "python_full_version": platform.python_version(),
- "platform_python_implementation": platform.python_implementation(),
- "python_version": ".".join(platform.python_version_tuple()[:2]),
- "sys_platform": sys.platform,
- }
-
-
-class Marker:
- def __init__(self, marker: str) -> None:
- try:
- self._markers = _coerce_parse_result(MARKER.parseString(marker))
- except ParseException as e:
- raise InvalidMarker(
- f"Invalid marker: {marker!r}, parse error at "
- f"{marker[e.loc : e.loc + 8]!r}"
- )
-
- def __str__(self) -> str:
- return _format_marker(self._markers)
-
- def __repr__(self) -> str:
- return f""
-
- def evaluate(self, environment: Optional[Dict[str, str]] = None) -> bool:
- """Evaluate a marker.
-
- Return the boolean from evaluating the given marker against the
- environment. environment is an optional argument to override all or
- part of the determined environment.
-
- The environment is determined from the current Python process.
- """
- current_environment = default_environment()
- if environment is not None:
- current_environment.update(environment)
-
- return _evaluate_markers(self._markers, current_environment)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/egg_info.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/egg_info.py
deleted file mode 100644
index 66228f9beee62955ad077f0dfb60f21dee457f64..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/egg_info.py
+++ /dev/null
@@ -1,761 +0,0 @@
-"""setuptools.command.egg_info
-
-Create a distribution's .egg-info directory and contents"""
-
-from distutils.filelist import FileList as _FileList
-from distutils.errors import DistutilsInternalError
-from distutils.util import convert_path
-from distutils import log
-import distutils.errors
-import distutils.filelist
-import functools
-import os
-import re
-import sys
-import io
-import time
-import collections
-
-from .._importlib import metadata
-from .. import _entry_points, _normalization
-
-from setuptools import Command
-from setuptools.command.sdist import sdist
-from setuptools.command.sdist import walk_revctrl
-from setuptools.command.setopt import edit_config
-from setuptools.command import bdist_egg
-import setuptools.unicode_utils as unicode_utils
-from setuptools.glob import glob
-
-from setuptools.extern import packaging
-from setuptools.extern.jaraco.text import yield_lines
-from ..warnings import SetuptoolsDeprecationWarning
-
-
-PY_MAJOR = '{}.{}'.format(*sys.version_info)
-
-
-def translate_pattern(glob): # noqa: C901 # is too complex (14) # FIXME
- """
- Translate a file path glob like '*.txt' in to a regular expression.
- This differs from fnmatch.translate which allows wildcards to match
- directory separators. It also knows about '**/' which matches any number of
- directories.
- """
- pat = ''
-
- # This will split on '/' within [character classes]. This is deliberate.
- chunks = glob.split(os.path.sep)
-
- sep = re.escape(os.sep)
- valid_char = '[^%s]' % (sep,)
-
- for c, chunk in enumerate(chunks):
- last_chunk = c == len(chunks) - 1
-
- # Chunks that are a literal ** are globstars. They match anything.
- if chunk == '**':
- if last_chunk:
- # Match anything if this is the last component
- pat += '.*'
- else:
- # Match '(name/)*'
- pat += '(?:%s+%s)*' % (valid_char, sep)
- continue # Break here as the whole path component has been handled
-
- # Find any special characters in the remainder
- i = 0
- chunk_len = len(chunk)
- while i < chunk_len:
- char = chunk[i]
- if char == '*':
- # Match any number of name characters
- pat += valid_char + '*'
- elif char == '?':
- # Match a name character
- pat += valid_char
- elif char == '[':
- # Character class
- inner_i = i + 1
- # Skip initial !/] chars
- if inner_i < chunk_len and chunk[inner_i] == '!':
- inner_i = inner_i + 1
- if inner_i < chunk_len and chunk[inner_i] == ']':
- inner_i = inner_i + 1
-
- # Loop till the closing ] is found
- while inner_i < chunk_len and chunk[inner_i] != ']':
- inner_i = inner_i + 1
-
- if inner_i >= chunk_len:
- # Got to the end of the string without finding a closing ]
- # Do not treat this as a matching group, but as a literal [
- pat += re.escape(char)
- else:
- # Grab the insides of the [brackets]
- inner = chunk[i + 1:inner_i]
- char_class = ''
-
- # Class negation
- if inner[0] == '!':
- char_class = '^'
- inner = inner[1:]
-
- char_class += re.escape(inner)
- pat += '[%s]' % (char_class,)
-
- # Skip to the end ]
- i = inner_i
- else:
- pat += re.escape(char)
- i += 1
-
- # Join each chunk with the dir separator
- if not last_chunk:
- pat += sep
-
- pat += r'\Z'
- return re.compile(pat, flags=re.MULTILINE | re.DOTALL)
-
-
-class InfoCommon:
- tag_build = None
- tag_date = None
-
- @property
- def name(self):
- return _normalization.safe_name(self.distribution.get_name())
-
- def tagged_version(self):
- tagged = self._maybe_tag(self.distribution.get_version())
- return _normalization.best_effort_version(tagged)
-
- def _maybe_tag(self, version):
- """
- egg_info may be called more than once for a distribution,
- in which case the version string already contains all tags.
- """
- return (
- version if self.vtags and self._already_tagged(version)
- else version + self.vtags
- )
-
- def _already_tagged(self, version: str) -> bool:
- # Depending on their format, tags may change with version normalization.
- # So in addition the regular tags, we have to search for the normalized ones.
- return version.endswith(self.vtags) or version.endswith(self._safe_tags())
-
- def _safe_tags(self) -> str:
- # To implement this we can rely on `safe_version` pretending to be version 0
- # followed by tags. Then we simply discard the starting 0 (fake version number)
- return _normalization.best_effort_version(f"0{self.vtags}")[1:]
-
- def tags(self) -> str:
- version = ''
- if self.tag_build:
- version += self.tag_build
- if self.tag_date:
- version += time.strftime("%Y%m%d")
- return version
- vtags = property(tags)
-
-
-class egg_info(InfoCommon, Command):
- description = "create a distribution's .egg-info directory"
-
- user_options = [
- ('egg-base=', 'e', "directory containing .egg-info directories"
- " (default: top of the source tree)"),
- ('tag-date', 'd', "Add date stamp (e.g. 20050528) to version number"),
- ('tag-build=', 'b', "Specify explicit tag to add to version number"),
- ('no-date', 'D', "Don't include date stamp [default]"),
- ]
-
- boolean_options = ['tag-date']
- negative_opt = {
- 'no-date': 'tag-date',
- }
-
- def initialize_options(self):
- self.egg_base = None
- self.egg_name = None
- self.egg_info = None
- self.egg_version = None
- self.ignore_egg_info_in_manifest = False
-
- ####################################
- # allow the 'tag_svn_revision' to be detected and
- # set, supporting sdists built on older Setuptools.
- @property
- def tag_svn_revision(self):
- pass
-
- @tag_svn_revision.setter
- def tag_svn_revision(self, value):
- pass
- ####################################
-
- def save_version_info(self, filename):
- """
- Materialize the value of date into the
- build tag. Install build keys in a deterministic order
- to avoid arbitrary reordering on subsequent builds.
- """
- egg_info = collections.OrderedDict()
- # follow the order these keys would have been added
- # when PYTHONHASHSEED=0
- egg_info['tag_build'] = self.tags()
- egg_info['tag_date'] = 0
- edit_config(filename, dict(egg_info=egg_info))
-
- def finalize_options(self):
- # Note: we need to capture the current value returned
- # by `self.tagged_version()`, so we can later update
- # `self.distribution.metadata.version` without
- # repercussions.
- self.egg_name = self.name
- self.egg_version = self.tagged_version()
- parsed_version = packaging.version.Version(self.egg_version)
-
- try:
- is_version = isinstance(parsed_version, packaging.version.Version)
- spec = "%s==%s" if is_version else "%s===%s"
- packaging.requirements.Requirement(spec % (self.egg_name, self.egg_version))
- except ValueError as e:
- raise distutils.errors.DistutilsOptionError(
- "Invalid distribution name or version syntax: %s-%s" %
- (self.egg_name, self.egg_version)
- ) from e
-
- if self.egg_base is None:
- dirs = self.distribution.package_dir
- self.egg_base = (dirs or {}).get('', os.curdir)
-
- self.ensure_dirname('egg_base')
- self.egg_info = _normalization.filename_component(self.egg_name) + '.egg-info'
- if self.egg_base != os.curdir:
- self.egg_info = os.path.join(self.egg_base, self.egg_info)
-
- # Set package version for the benefit of dumber commands
- # (e.g. sdist, bdist_wininst, etc.)
- #
- self.distribution.metadata.version = self.egg_version
-
- # If we bootstrapped around the lack of a PKG-INFO, as might be the
- # case in a fresh checkout, make sure that any special tags get added
- # to the version info
- #
- pd = self.distribution._patched_dist
- key = getattr(pd, "key", None) or getattr(pd, "name", None)
- if pd is not None and key == self.egg_name.lower():
- pd._version = self.egg_version
- pd._parsed_version = packaging.version.Version(self.egg_version)
- self.distribution._patched_dist = None
-
- def _get_egg_basename(self, py_version=PY_MAJOR, platform=None):
- """Compute filename of the output egg. Private API."""
- return _egg_basename(self.egg_name, self.egg_version, py_version, platform)
-
- def write_or_delete_file(self, what, filename, data, force=False):
- """Write `data` to `filename` or delete if empty
-
- If `data` is non-empty, this routine is the same as ``write_file()``.
- If `data` is empty but not ``None``, this is the same as calling
- ``delete_file(filename)`. If `data` is ``None``, then this is a no-op
- unless `filename` exists, in which case a warning is issued about the
- orphaned file (if `force` is false), or deleted (if `force` is true).
- """
- if data:
- self.write_file(what, filename, data)
- elif os.path.exists(filename):
- if data is None and not force:
- log.warn(
- "%s not set in setup(), but %s exists", what, filename
- )
- return
- else:
- self.delete_file(filename)
-
- def write_file(self, what, filename, data):
- """Write `data` to `filename` (if not a dry run) after announcing it
-
- `what` is used in a log message to identify what is being written
- to the file.
- """
- log.info("writing %s to %s", what, filename)
- data = data.encode("utf-8")
- if not self.dry_run:
- f = open(filename, 'wb')
- f.write(data)
- f.close()
-
- def delete_file(self, filename):
- """Delete `filename` (if not a dry run) after announcing it"""
- log.info("deleting %s", filename)
- if not self.dry_run:
- os.unlink(filename)
-
- def run(self):
- self.mkpath(self.egg_info)
- try:
- os.utime(self.egg_info, None)
- except OSError as e:
- msg = f"Cannot update time stamp of directory '{self.egg_info}'"
- raise distutils.errors.DistutilsFileError(msg) from e
- for ep in metadata.entry_points(group='egg_info.writers'):
- writer = ep.load()
- writer(self, ep.name, os.path.join(self.egg_info, ep.name))
-
- # Get rid of native_libs.txt if it was put there by older bdist_egg
- nl = os.path.join(self.egg_info, "native_libs.txt")
- if os.path.exists(nl):
- self.delete_file(nl)
-
- self.find_sources()
-
- def find_sources(self):
- """Generate SOURCES.txt manifest file"""
- manifest_filename = os.path.join(self.egg_info, "SOURCES.txt")
- mm = manifest_maker(self.distribution)
- mm.ignore_egg_info_dir = self.ignore_egg_info_in_manifest
- mm.manifest = manifest_filename
- mm.run()
- self.filelist = mm.filelist
-
-
-class FileList(_FileList):
- # Implementations of the various MANIFEST.in commands
-
- def __init__(self, warn=None, debug_print=None, ignore_egg_info_dir=False):
- super().__init__(warn, debug_print)
- self.ignore_egg_info_dir = ignore_egg_info_dir
-
- def process_template_line(self, line):
- # Parse the line: split it up, make sure the right number of words
- # is there, and return the relevant words. 'action' is always
- # defined: it's the first word of the line. Which of the other
- # three are defined depends on the action; it'll be either
- # patterns, (dir and patterns), or (dir_pattern).
- (action, patterns, dir, dir_pattern) = self._parse_template_line(line)
-
- action_map = {
- 'include': self.include,
- 'exclude': self.exclude,
- 'global-include': self.global_include,
- 'global-exclude': self.global_exclude,
- 'recursive-include': functools.partial(
- self.recursive_include, dir,
- ),
- 'recursive-exclude': functools.partial(
- self.recursive_exclude, dir,
- ),
- 'graft': self.graft,
- 'prune': self.prune,
- }
- log_map = {
- 'include': "warning: no files found matching '%s'",
- 'exclude': (
- "warning: no previously-included files found "
- "matching '%s'"
- ),
- 'global-include': (
- "warning: no files found matching '%s' "
- "anywhere in distribution"
- ),
- 'global-exclude': (
- "warning: no previously-included files matching "
- "'%s' found anywhere in distribution"
- ),
- 'recursive-include': (
- "warning: no files found matching '%s' "
- "under directory '%s'"
- ),
- 'recursive-exclude': (
- "warning: no previously-included files matching "
- "'%s' found under directory '%s'"
- ),
- 'graft': "warning: no directories found matching '%s'",
- 'prune': "no previously-included directories found matching '%s'",
- }
-
- try:
- process_action = action_map[action]
- except KeyError:
- raise DistutilsInternalError(
- "this cannot happen: invalid action '{action!s}'".
- format(action=action),
- )
-
- # OK, now we know that the action is valid and we have the
- # right number of words on the line for that action -- so we
- # can proceed with minimal error-checking.
-
- action_is_recursive = action.startswith('recursive-')
- if action in {'graft', 'prune'}:
- patterns = [dir_pattern]
- extra_log_args = (dir, ) if action_is_recursive else ()
- log_tmpl = log_map[action]
-
- self.debug_print(
- ' '.join(
- [action] +
- ([dir] if action_is_recursive else []) +
- patterns,
- )
- )
- for pattern in patterns:
- if not process_action(pattern):
- log.warn(log_tmpl, pattern, *extra_log_args)
-
- def _remove_files(self, predicate):
- """
- Remove all files from the file list that match the predicate.
- Return True if any matching files were removed
- """
- found = False
- for i in range(len(self.files) - 1, -1, -1):
- if predicate(self.files[i]):
- self.debug_print(" removing " + self.files[i])
- del self.files[i]
- found = True
- return found
-
- def include(self, pattern):
- """Include files that match 'pattern'."""
- found = [f for f in glob(pattern) if not os.path.isdir(f)]
- self.extend(found)
- return bool(found)
-
- def exclude(self, pattern):
- """Exclude files that match 'pattern'."""
- match = translate_pattern(pattern)
- return self._remove_files(match.match)
-
- def recursive_include(self, dir, pattern):
- """
- Include all files anywhere in 'dir/' that match the pattern.
- """
- full_pattern = os.path.join(dir, '**', pattern)
- found = [f for f in glob(full_pattern, recursive=True)
- if not os.path.isdir(f)]
- self.extend(found)
- return bool(found)
-
- def recursive_exclude(self, dir, pattern):
- """
- Exclude any file anywhere in 'dir/' that match the pattern.
- """
- match = translate_pattern(os.path.join(dir, '**', pattern))
- return self._remove_files(match.match)
-
- def graft(self, dir):
- """Include all files from 'dir/'."""
- found = [
- item
- for match_dir in glob(dir)
- for item in distutils.filelist.findall(match_dir)
- ]
- self.extend(found)
- return bool(found)
-
- def prune(self, dir):
- """Filter out files from 'dir/'."""
- match = translate_pattern(os.path.join(dir, '**'))
- return self._remove_files(match.match)
-
- def global_include(self, pattern):
- """
- Include all files anywhere in the current directory that match the
- pattern. This is very inefficient on large file trees.
- """
- if self.allfiles is None:
- self.findall()
- match = translate_pattern(os.path.join('**', pattern))
- found = [f for f in self.allfiles if match.match(f)]
- self.extend(found)
- return bool(found)
-
- def global_exclude(self, pattern):
- """
- Exclude all files anywhere that match the pattern.
- """
- match = translate_pattern(os.path.join('**', pattern))
- return self._remove_files(match.match)
-
- def append(self, item):
- if item.endswith('\r'): # Fix older sdists built on Windows
- item = item[:-1]
- path = convert_path(item)
-
- if self._safe_path(path):
- self.files.append(path)
-
- def extend(self, paths):
- self.files.extend(filter(self._safe_path, paths))
-
- def _repair(self):
- """
- Replace self.files with only safe paths
-
- Because some owners of FileList manipulate the underlying
- ``files`` attribute directly, this method must be called to
- repair those paths.
- """
- self.files = list(filter(self._safe_path, self.files))
-
- def _safe_path(self, path):
- enc_warn = "'%s' not %s encodable -- skipping"
-
- # To avoid accidental trans-codings errors, first to unicode
- u_path = unicode_utils.filesys_decode(path)
- if u_path is None:
- log.warn("'%s' in unexpected encoding -- skipping" % path)
- return False
-
- # Must ensure utf-8 encodability
- utf8_path = unicode_utils.try_encode(u_path, "utf-8")
- if utf8_path is None:
- log.warn(enc_warn, path, 'utf-8')
- return False
-
- try:
- # ignore egg-info paths
- is_egg_info = ".egg-info" in u_path or b".egg-info" in utf8_path
- if self.ignore_egg_info_dir and is_egg_info:
- return False
- # accept is either way checks out
- if os.path.exists(u_path) or os.path.exists(utf8_path):
- return True
- # this will catch any encode errors decoding u_path
- except UnicodeEncodeError:
- log.warn(enc_warn, path, sys.getfilesystemencoding())
-
-
-class manifest_maker(sdist):
- template = "MANIFEST.in"
-
- def initialize_options(self):
- self.use_defaults = 1
- self.prune = 1
- self.manifest_only = 1
- self.force_manifest = 1
- self.ignore_egg_info_dir = False
-
- def finalize_options(self):
- pass
-
- def run(self):
- self.filelist = FileList(ignore_egg_info_dir=self.ignore_egg_info_dir)
- if not os.path.exists(self.manifest):
- self.write_manifest() # it must exist so it'll get in the list
- self.add_defaults()
- if os.path.exists(self.template):
- self.read_template()
- self.add_license_files()
- self._add_referenced_files()
- self.prune_file_list()
- self.filelist.sort()
- self.filelist.remove_duplicates()
- self.write_manifest()
-
- def _manifest_normalize(self, path):
- path = unicode_utils.filesys_decode(path)
- return path.replace(os.sep, '/')
-
- def write_manifest(self):
- """
- Write the file list in 'self.filelist' to the manifest file
- named by 'self.manifest'.
- """
- self.filelist._repair()
-
- # Now _repairs should encodability, but not unicode
- files = [self._manifest_normalize(f) for f in self.filelist.files]
- msg = "writing manifest file '%s'" % self.manifest
- self.execute(write_file, (self.manifest, files), msg)
-
- def warn(self, msg):
- if not self._should_suppress_warning(msg):
- sdist.warn(self, msg)
-
- @staticmethod
- def _should_suppress_warning(msg):
- """
- suppress missing-file warnings from sdist
- """
- return re.match(r"standard file .*not found", msg)
-
- def add_defaults(self):
- sdist.add_defaults(self)
- self.filelist.append(self.template)
- self.filelist.append(self.manifest)
- rcfiles = list(walk_revctrl())
- if rcfiles:
- self.filelist.extend(rcfiles)
- elif os.path.exists(self.manifest):
- self.read_manifest()
-
- if os.path.exists("setup.py"):
- # setup.py should be included by default, even if it's not
- # the script called to create the sdist
- self.filelist.append("setup.py")
-
- ei_cmd = self.get_finalized_command('egg_info')
- self.filelist.graft(ei_cmd.egg_info)
-
- def add_license_files(self):
- license_files = self.distribution.metadata.license_files or []
- for lf in license_files:
- log.info("adding license file '%s'", lf)
- self.filelist.extend(license_files)
-
- def _add_referenced_files(self):
- """Add files referenced by the config (e.g. `file:` directive) to filelist"""
- referenced = getattr(self.distribution, '_referenced_files', [])
- # ^-- fallback if dist comes from distutils or is a custom class
- for rf in referenced:
- log.debug("adding file referenced by config '%s'", rf)
- self.filelist.extend(referenced)
-
- def prune_file_list(self):
- build = self.get_finalized_command('build')
- base_dir = self.distribution.get_fullname()
- self.filelist.prune(build.build_base)
- self.filelist.prune(base_dir)
- sep = re.escape(os.sep)
- self.filelist.exclude_pattern(r'(^|' + sep + r')(RCS|CVS|\.svn)' + sep,
- is_regex=1)
-
- def _safe_data_files(self, build_py):
- """
- The parent class implementation of this method
- (``sdist``) will try to include data files, which
- might cause recursion problems when
- ``include_package_data=True``.
-
- Therefore, avoid triggering any attempt of
- analyzing/building the manifest again.
- """
- if hasattr(build_py, 'get_data_files_without_manifest'):
- return build_py.get_data_files_without_manifest()
-
- SetuptoolsDeprecationWarning.emit(
- "`build_py` command does not inherit from setuptools' `build_py`.",
- """
- Custom 'build_py' does not implement 'get_data_files_without_manifest'.
- Please extend command classes from setuptools instead of distutils.
- """,
- see_url="https://peps.python.org/pep-0632/",
- # due_date not defined yet, old projects might still do it?
- )
- return build_py.get_data_files()
-
-
-def write_file(filename, contents):
- """Create a file with the specified name and write 'contents' (a
- sequence of strings without line terminators) to it.
- """
- contents = "\n".join(contents)
-
- # assuming the contents has been vetted for utf-8 encoding
- contents = contents.encode("utf-8")
-
- with open(filename, "wb") as f: # always write POSIX-style manifest
- f.write(contents)
-
-
-def write_pkg_info(cmd, basename, filename):
- log.info("writing %s", filename)
- if not cmd.dry_run:
- metadata = cmd.distribution.metadata
- metadata.version, oldver = cmd.egg_version, metadata.version
- metadata.name, oldname = cmd.egg_name, metadata.name
-
- try:
- # write unescaped data to PKG-INFO, so older pkg_resources
- # can still parse it
- metadata.write_pkg_info(cmd.egg_info)
- finally:
- metadata.name, metadata.version = oldname, oldver
-
- safe = getattr(cmd.distribution, 'zip_safe', None)
-
- bdist_egg.write_safety_flag(cmd.egg_info, safe)
-
-
-def warn_depends_obsolete(cmd, basename, filename):
- """
- Unused: left to avoid errors when updating (from source) from <= 67.8.
- Old installations have a .dist-info directory with the entry-point
- ``depends.txt = setuptools.command.egg_info:warn_depends_obsolete``.
- This may trigger errors when running the first egg_info in build_meta.
- TODO: Remove this function in a version sufficiently > 68.
- """
-
-
-def _write_requirements(stream, reqs):
- lines = yield_lines(reqs or ())
-
- def append_cr(line):
- return line + '\n'
- lines = map(append_cr, lines)
- stream.writelines(lines)
-
-
-def write_requirements(cmd, basename, filename):
- dist = cmd.distribution
- data = io.StringIO()
- _write_requirements(data, dist.install_requires)
- extras_require = dist.extras_require or {}
- for extra in sorted(extras_require):
- data.write('\n[{extra}]\n'.format(**vars()))
- _write_requirements(data, extras_require[extra])
- cmd.write_or_delete_file("requirements", filename, data.getvalue())
-
-
-def write_setup_requirements(cmd, basename, filename):
- data = io.StringIO()
- _write_requirements(data, cmd.distribution.setup_requires)
- cmd.write_or_delete_file("setup-requirements", filename, data.getvalue())
-
-
-def write_toplevel_names(cmd, basename, filename):
- pkgs = dict.fromkeys(
- [
- k.split('.', 1)[0]
- for k in cmd.distribution.iter_distribution_names()
- ]
- )
- cmd.write_file("top-level names", filename, '\n'.join(sorted(pkgs)) + '\n')
-
-
-def overwrite_arg(cmd, basename, filename):
- write_arg(cmd, basename, filename, True)
-
-
-def write_arg(cmd, basename, filename, force=False):
- argname = os.path.splitext(basename)[0]
- value = getattr(cmd.distribution, argname, None)
- if value is not None:
- value = '\n'.join(value) + '\n'
- cmd.write_or_delete_file(argname, filename, value, force)
-
-
-def write_entries(cmd, basename, filename):
- eps = _entry_points.load(cmd.distribution.entry_points)
- defn = _entry_points.render(eps)
- cmd.write_or_delete_file('entry points', filename, defn, True)
-
-
-def _egg_basename(egg_name, egg_version, py_version=None, platform=None):
- """Compute filename of the output egg. Private API."""
- name = _normalization.filename_component(egg_name)
- version = _normalization.filename_component(egg_version)
- egg = f"{name}-{version}-py{py_version or PY_MAJOR}"
- if platform:
- egg += f"-{platform}"
- return egg
-
-
-class EggInfoDeprecationWarning(SetuptoolsDeprecationWarning):
- """Deprecated behavior warning for EggInfo, bypassing suppression."""
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/t1Lib/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/t1Lib/__init__.py
deleted file mode 100644
index a64f78097c25ec79908b548f1fb58cdbdd66c628..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/t1Lib/__init__.py
+++ /dev/null
@@ -1,648 +0,0 @@
-"""fontTools.t1Lib.py -- Tools for PostScript Type 1 fonts.
-
-Functions for reading and writing raw Type 1 data:
-
-read(path)
- reads any Type 1 font file, returns the raw data and a type indicator:
- 'LWFN', 'PFB' or 'OTHER', depending on the format of the file pointed
- to by 'path'.
- Raises an error when the file does not contain valid Type 1 data.
-
-write(path, data, kind='OTHER', dohex=False)
- writes raw Type 1 data to the file pointed to by 'path'.
- 'kind' can be one of 'LWFN', 'PFB' or 'OTHER'; it defaults to 'OTHER'.
- 'dohex' is a flag which determines whether the eexec encrypted
- part should be written as hexadecimal or binary, but only if kind
- is 'OTHER'.
-"""
-import fontTools
-from fontTools.misc import eexec
-from fontTools.misc.macCreatorType import getMacCreatorAndType
-from fontTools.misc.textTools import bytechr, byteord, bytesjoin, tobytes
-from fontTools.misc.psOperators import (
- _type1_pre_eexec_order,
- _type1_fontinfo_order,
- _type1_post_eexec_order,
-)
-from fontTools.encodings.StandardEncoding import StandardEncoding
-import os
-import re
-
-__author__ = "jvr"
-__version__ = "1.0b3"
-DEBUG = 0
-
-
-try:
- try:
- from Carbon import Res
- except ImportError:
- import Res # MacPython < 2.2
-except ImportError:
- haveMacSupport = 0
-else:
- haveMacSupport = 1
-
-
-class T1Error(Exception):
- pass
-
-
-class T1Font(object):
-
- """Type 1 font class.
-
- Uses a minimal interpeter that supports just about enough PS to parse
- Type 1 fonts.
- """
-
- def __init__(self, path, encoding="ascii", kind=None):
- if kind is None:
- self.data, _ = read(path)
- elif kind == "LWFN":
- self.data = readLWFN(path)
- elif kind == "PFB":
- self.data = readPFB(path)
- elif kind == "OTHER":
- self.data = readOther(path)
- else:
- raise ValueError(kind)
- self.encoding = encoding
-
- def saveAs(self, path, type, dohex=False):
- write(path, self.getData(), type, dohex)
-
- def getData(self):
- if not hasattr(self, "data"):
- self.data = self.createData()
- return self.data
-
- def getGlyphSet(self):
- """Return a generic GlyphSet, which is a dict-like object
- mapping glyph names to glyph objects. The returned glyph objects
- have a .draw() method that supports the Pen protocol, and will
- have an attribute named 'width', but only *after* the .draw() method
- has been called.
-
- In the case of Type 1, the GlyphSet is simply the CharStrings dict.
- """
- return self["CharStrings"]
-
- def __getitem__(self, key):
- if not hasattr(self, "font"):
- self.parse()
- return self.font[key]
-
- def parse(self):
- from fontTools.misc import psLib
- from fontTools.misc import psCharStrings
-
- self.font = psLib.suckfont(self.data, self.encoding)
- charStrings = self.font["CharStrings"]
- lenIV = self.font["Private"].get("lenIV", 4)
- assert lenIV >= 0
- subrs = self.font["Private"]["Subrs"]
- for glyphName, charString in charStrings.items():
- charString, R = eexec.decrypt(charString, 4330)
- charStrings[glyphName] = psCharStrings.T1CharString(
- charString[lenIV:], subrs=subrs
- )
- for i in range(len(subrs)):
- charString, R = eexec.decrypt(subrs[i], 4330)
- subrs[i] = psCharStrings.T1CharString(charString[lenIV:], subrs=subrs)
- del self.data
-
- def createData(self):
- sf = self.font
-
- eexec_began = False
- eexec_dict = {}
- lines = []
- lines.extend(
- [
- self._tobytes(f"%!FontType1-1.1: {sf['FontName']}"),
- self._tobytes(f"%t1Font: ({fontTools.version})"),
- self._tobytes(f"%%BeginResource: font {sf['FontName']}"),
- ]
- )
- # follow t1write.c:writeRegNameKeyedFont
- size = 3 # Headroom for new key addition
- size += 1 # FontMatrix is always counted
- size += 1 + 1 # Private, CharStings
- for key in font_dictionary_keys:
- size += int(key in sf)
- lines.append(self._tobytes(f"{size} dict dup begin"))
-
- for key, value in sf.items():
- if eexec_began:
- eexec_dict[key] = value
- continue
-
- if key == "FontInfo":
- fi = sf["FontInfo"]
- # follow t1write.c:writeFontInfoDict
- size = 3 # Headroom for new key addition
- for subkey in FontInfo_dictionary_keys:
- size += int(subkey in fi)
- lines.append(self._tobytes(f"/FontInfo {size} dict dup begin"))
-
- for subkey, subvalue in fi.items():
- lines.extend(self._make_lines(subkey, subvalue))
- lines.append(b"end def")
- elif key in _type1_post_eexec_order: # usually 'Private'
- eexec_dict[key] = value
- eexec_began = True
- else:
- lines.extend(self._make_lines(key, value))
- lines.append(b"end")
- eexec_portion = self.encode_eexec(eexec_dict)
- lines.append(bytesjoin([b"currentfile eexec ", eexec_portion]))
-
- for _ in range(8):
- lines.append(self._tobytes("0" * 64))
- lines.extend([b"cleartomark", b"%%EndResource", b"%%EOF"])
-
- data = bytesjoin(lines, "\n")
- return data
-
- def encode_eexec(self, eexec_dict):
- lines = []
-
- # '-|', '|-', '|'
- RD_key, ND_key, NP_key = None, None, None
- lenIV = 4
- subrs = std_subrs
-
- # Ensure we look at Private first, because we need RD_key, ND_key, NP_key and lenIV
- sortedItems = sorted(eexec_dict.items(), key=lambda item: item[0] != "Private")
-
- for key, value in sortedItems:
- if key == "Private":
- pr = eexec_dict["Private"]
- # follow t1write.c:writePrivateDict
- size = 3 # for RD, ND, NP
- for subkey in Private_dictionary_keys:
- size += int(subkey in pr)
- lines.append(b"dup /Private")
- lines.append(self._tobytes(f"{size} dict dup begin"))
- for subkey, subvalue in pr.items():
- if not RD_key and subvalue == RD_value:
- RD_key = subkey
- elif not ND_key and subvalue in ND_values:
- ND_key = subkey
- elif not NP_key and subvalue in PD_values:
- NP_key = subkey
-
- if subkey == "lenIV":
- lenIV = subvalue
-
- if subkey == "OtherSubrs":
- # XXX: assert that no flex hint is used
- lines.append(self._tobytes(hintothers))
- elif subkey == "Subrs":
- for subr_bin in subvalue:
- subr_bin.compile()
- subrs = [subr_bin.bytecode for subr_bin in subvalue]
- lines.append(f"/Subrs {len(subrs)} array".encode("ascii"))
- for i, subr_bin in enumerate(subrs):
- encrypted_subr, R = eexec.encrypt(
- bytesjoin([char_IV[:lenIV], subr_bin]), 4330
- )
- lines.append(
- bytesjoin(
- [
- self._tobytes(
- f"dup {i} {len(encrypted_subr)} {RD_key} "
- ),
- encrypted_subr,
- self._tobytes(f" {NP_key}"),
- ]
- )
- )
- lines.append(b"def")
-
- lines.append(b"put")
- else:
- lines.extend(self._make_lines(subkey, subvalue))
- elif key == "CharStrings":
- lines.append(b"dup /CharStrings")
- lines.append(
- self._tobytes(f"{len(eexec_dict['CharStrings'])} dict dup begin")
- )
- for glyph_name, char_bin in eexec_dict["CharStrings"].items():
- char_bin.compile()
- encrypted_char, R = eexec.encrypt(
- bytesjoin([char_IV[:lenIV], char_bin.bytecode]), 4330
- )
- lines.append(
- bytesjoin(
- [
- self._tobytes(
- f"/{glyph_name} {len(encrypted_char)} {RD_key} "
- ),
- encrypted_char,
- self._tobytes(f" {ND_key}"),
- ]
- )
- )
- lines.append(b"end put")
- else:
- lines.extend(self._make_lines(key, value))
-
- lines.extend(
- [
- b"end",
- b"dup /FontName get exch definefont pop",
- b"mark",
- b"currentfile closefile\n",
- ]
- )
-
- eexec_portion = bytesjoin(lines, "\n")
- encrypted_eexec, R = eexec.encrypt(bytesjoin([eexec_IV, eexec_portion]), 55665)
-
- return encrypted_eexec
-
- def _make_lines(self, key, value):
- if key == "FontName":
- return [self._tobytes(f"/{key} /{value} def")]
- if key in ["isFixedPitch", "ForceBold", "RndStemUp"]:
- return [self._tobytes(f"/{key} {'true' if value else 'false'} def")]
- elif key == "Encoding":
- if value == StandardEncoding:
- return [self._tobytes(f"/{key} StandardEncoding def")]
- else:
- # follow fontTools.misc.psOperators._type1_Encoding_repr
- lines = []
- lines.append(b"/Encoding 256 array")
- lines.append(b"0 1 255 {1 index exch /.notdef put} for")
- for i in range(256):
- name = value[i]
- if name != ".notdef":
- lines.append(self._tobytes(f"dup {i} /{name} put"))
- lines.append(b"def")
- return lines
- if isinstance(value, str):
- return [self._tobytes(f"/{key} ({value}) def")]
- elif isinstance(value, bool):
- return [self._tobytes(f"/{key} {'true' if value else 'false'} def")]
- elif isinstance(value, list):
- return [self._tobytes(f"/{key} [{' '.join(str(v) for v in value)}] def")]
- elif isinstance(value, tuple):
- return [self._tobytes(f"/{key} {{{' '.join(str(v) for v in value)}}} def")]
- else:
- return [self._tobytes(f"/{key} {value} def")]
-
- def _tobytes(self, s, errors="strict"):
- return tobytes(s, self.encoding, errors)
-
-
-# low level T1 data read and write functions
-
-
-def read(path, onlyHeader=False):
- """reads any Type 1 font file, returns raw data"""
- _, ext = os.path.splitext(path)
- ext = ext.lower()
- creator, typ = getMacCreatorAndType(path)
- if typ == "LWFN":
- return readLWFN(path, onlyHeader), "LWFN"
- if ext == ".pfb":
- return readPFB(path, onlyHeader), "PFB"
- else:
- return readOther(path), "OTHER"
-
-
-def write(path, data, kind="OTHER", dohex=False):
- assertType1(data)
- kind = kind.upper()
- try:
- os.remove(path)
- except os.error:
- pass
- err = 1
- try:
- if kind == "LWFN":
- writeLWFN(path, data)
- elif kind == "PFB":
- writePFB(path, data)
- else:
- writeOther(path, data, dohex)
- err = 0
- finally:
- if err and not DEBUG:
- try:
- os.remove(path)
- except os.error:
- pass
-
-
-# -- internal --
-
-LWFNCHUNKSIZE = 2000
-HEXLINELENGTH = 80
-
-
-def readLWFN(path, onlyHeader=False):
- """reads an LWFN font file, returns raw data"""
- from fontTools.misc.macRes import ResourceReader
-
- reader = ResourceReader(path)
- try:
- data = []
- for res in reader.get("POST", []):
- code = byteord(res.data[0])
- if byteord(res.data[1]) != 0:
- raise T1Error("corrupt LWFN file")
- if code in [1, 2]:
- if onlyHeader and code == 2:
- break
- data.append(res.data[2:])
- elif code in [3, 5]:
- break
- elif code == 4:
- with open(path, "rb") as f:
- data.append(f.read())
- elif code == 0:
- pass # comment, ignore
- else:
- raise T1Error("bad chunk code: " + repr(code))
- finally:
- reader.close()
- data = bytesjoin(data)
- assertType1(data)
- return data
-
-
-def readPFB(path, onlyHeader=False):
- """reads a PFB font file, returns raw data"""
- data = []
- with open(path, "rb") as f:
- while True:
- if f.read(1) != bytechr(128):
- raise T1Error("corrupt PFB file")
- code = byteord(f.read(1))
- if code in [1, 2]:
- chunklen = stringToLong(f.read(4))
- chunk = f.read(chunklen)
- assert len(chunk) == chunklen
- data.append(chunk)
- elif code == 3:
- break
- else:
- raise T1Error("bad chunk code: " + repr(code))
- if onlyHeader:
- break
- data = bytesjoin(data)
- assertType1(data)
- return data
-
-
-def readOther(path):
- """reads any (font) file, returns raw data"""
- with open(path, "rb") as f:
- data = f.read()
- assertType1(data)
- chunks = findEncryptedChunks(data)
- data = []
- for isEncrypted, chunk in chunks:
- if isEncrypted and isHex(chunk[:4]):
- data.append(deHexString(chunk))
- else:
- data.append(chunk)
- return bytesjoin(data)
-
-
-# file writing tools
-
-
-def writeLWFN(path, data):
- # Res.FSpCreateResFile was deprecated in OS X 10.5
- Res.FSpCreateResFile(path, "just", "LWFN", 0)
- resRef = Res.FSOpenResFile(path, 2) # write-only
- try:
- Res.UseResFile(resRef)
- resID = 501
- chunks = findEncryptedChunks(data)
- for isEncrypted, chunk in chunks:
- if isEncrypted:
- code = 2
- else:
- code = 1
- while chunk:
- res = Res.Resource(bytechr(code) + "\0" + chunk[: LWFNCHUNKSIZE - 2])
- res.AddResource("POST", resID, "")
- chunk = chunk[LWFNCHUNKSIZE - 2 :]
- resID = resID + 1
- res = Res.Resource(bytechr(5) + "\0")
- res.AddResource("POST", resID, "")
- finally:
- Res.CloseResFile(resRef)
-
-
-def writePFB(path, data):
- chunks = findEncryptedChunks(data)
- with open(path, "wb") as f:
- for isEncrypted, chunk in chunks:
- if isEncrypted:
- code = 2
- else:
- code = 1
- f.write(bytechr(128) + bytechr(code))
- f.write(longToString(len(chunk)))
- f.write(chunk)
- f.write(bytechr(128) + bytechr(3))
-
-
-def writeOther(path, data, dohex=False):
- chunks = findEncryptedChunks(data)
- with open(path, "wb") as f:
- hexlinelen = HEXLINELENGTH // 2
- for isEncrypted, chunk in chunks:
- if isEncrypted:
- code = 2
- else:
- code = 1
- if code == 2 and dohex:
- while chunk:
- f.write(eexec.hexString(chunk[:hexlinelen]))
- f.write(b"\r")
- chunk = chunk[hexlinelen:]
- else:
- f.write(chunk)
-
-
-# decryption tools
-
-EEXECBEGIN = b"currentfile eexec"
-# The spec allows for 512 ASCII zeros interrupted by arbitrary whitespace to
-# follow eexec
-EEXECEND = re.compile(b"(0[ \t\r\n]*){512}", flags=re.M)
-EEXECINTERNALEND = b"currentfile closefile"
-EEXECBEGINMARKER = b"%-- eexec start\r"
-EEXECENDMARKER = b"%-- eexec end\r"
-
-_ishexRE = re.compile(b"[0-9A-Fa-f]*$")
-
-
-def isHex(text):
- return _ishexRE.match(text) is not None
-
-
-def decryptType1(data):
- chunks = findEncryptedChunks(data)
- data = []
- for isEncrypted, chunk in chunks:
- if isEncrypted:
- if isHex(chunk[:4]):
- chunk = deHexString(chunk)
- decrypted, R = eexec.decrypt(chunk, 55665)
- decrypted = decrypted[4:]
- if (
- decrypted[-len(EEXECINTERNALEND) - 1 : -1] != EEXECINTERNALEND
- and decrypted[-len(EEXECINTERNALEND) - 2 : -2] != EEXECINTERNALEND
- ):
- raise T1Error("invalid end of eexec part")
- decrypted = decrypted[: -len(EEXECINTERNALEND) - 2] + b"\r"
- data.append(EEXECBEGINMARKER + decrypted + EEXECENDMARKER)
- else:
- if chunk[-len(EEXECBEGIN) - 1 : -1] == EEXECBEGIN:
- data.append(chunk[: -len(EEXECBEGIN) - 1])
- else:
- data.append(chunk)
- return bytesjoin(data)
-
-
-def findEncryptedChunks(data):
- chunks = []
- while True:
- eBegin = data.find(EEXECBEGIN)
- if eBegin < 0:
- break
- eBegin = eBegin + len(EEXECBEGIN) + 1
- endMatch = EEXECEND.search(data, eBegin)
- if endMatch is None:
- raise T1Error("can't find end of eexec part")
- eEnd = endMatch.start()
- cypherText = data[eBegin : eEnd + 2]
- if isHex(cypherText[:4]):
- cypherText = deHexString(cypherText)
- plainText, R = eexec.decrypt(cypherText, 55665)
- eEndLocal = plainText.find(EEXECINTERNALEND)
- if eEndLocal < 0:
- raise T1Error("can't find end of eexec part")
- chunks.append((0, data[:eBegin]))
- chunks.append((1, cypherText[: eEndLocal + len(EEXECINTERNALEND) + 1]))
- data = data[eEnd:]
- chunks.append((0, data))
- return chunks
-
-
-def deHexString(hexstring):
- return eexec.deHexString(bytesjoin(hexstring.split()))
-
-
-# Type 1 assertion
-
-_fontType1RE = re.compile(rb"/FontType\s+1\s+def")
-
-
-def assertType1(data):
- for head in [b"%!PS-AdobeFont", b"%!FontType1"]:
- if data[: len(head)] == head:
- break
- else:
- raise T1Error("not a PostScript font")
- if not _fontType1RE.search(data):
- raise T1Error("not a Type 1 font")
- if data.find(b"currentfile eexec") < 0:
- raise T1Error("not an encrypted Type 1 font")
- # XXX what else?
- return data
-
-
-# pfb helpers
-
-
-def longToString(long):
- s = b""
- for i in range(4):
- s += bytechr((long & (0xFF << (i * 8))) >> i * 8)
- return s
-
-
-def stringToLong(s):
- if len(s) != 4:
- raise ValueError("string must be 4 bytes long")
- l = 0
- for i in range(4):
- l += byteord(s[i]) << (i * 8)
- return l
-
-
-# PS stream helpers
-
-font_dictionary_keys = list(_type1_pre_eexec_order)
-# t1write.c:writeRegNameKeyedFont
-# always counts following keys
-font_dictionary_keys.remove("FontMatrix")
-
-FontInfo_dictionary_keys = list(_type1_fontinfo_order)
-# extend because AFDKO tx may use following keys
-FontInfo_dictionary_keys.extend(
- [
- "FSType",
- "Copyright",
- ]
-)
-
-Private_dictionary_keys = [
- # We don't know what names will be actually used.
- # "RD",
- # "ND",
- # "NP",
- "Subrs",
- "OtherSubrs",
- "UniqueID",
- "BlueValues",
- "OtherBlues",
- "FamilyBlues",
- "FamilyOtherBlues",
- "BlueScale",
- "BlueShift",
- "BlueFuzz",
- "StdHW",
- "StdVW",
- "StemSnapH",
- "StemSnapV",
- "ForceBold",
- "LanguageGroup",
- "password",
- "lenIV",
- "MinFeature",
- "RndStemUp",
-]
-
-# t1write_hintothers.h
-hintothers = """/OtherSubrs[{}{}{}{systemdict/internaldict known not{pop 3}{1183615869
-systemdict/internaldict get exec dup/startlock known{/startlock get exec}{dup
-/strtlck known{/strtlck get exec}{pop 3}ifelse}ifelse}ifelse}executeonly]def"""
-# t1write.c:saveStdSubrs
-std_subrs = [
- # 3 0 callother pop pop setcurrentpoint return
- b"\x8e\x8b\x0c\x10\x0c\x11\x0c\x11\x0c\x21\x0b",
- # 0 1 callother return
- b"\x8b\x8c\x0c\x10\x0b",
- # 0 2 callother return
- b"\x8b\x8d\x0c\x10\x0b",
- # return
- b"\x0b",
- # 3 1 3 callother pop callsubr return
- b"\x8e\x8c\x8e\x0c\x10\x0c\x11\x0a\x0b",
-]
-# follow t1write.c:writeRegNameKeyedFont
-eexec_IV = b"cccc"
-char_IV = b"\x0c\x0c\x0c\x0c"
-RD_value = ("string", "currentfile", "exch", "readstring", "pop")
-ND_values = [("def",), ("noaccess", "def")]
-PD_values = [("put",), ("noaccess", "put")]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/__init__.py
deleted file mode 100644
index 2d59b89e6b8a0ef1addde24ffafc476b98f92d89..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/__init__.py
+++ /dev/null
@@ -1,180 +0,0 @@
-"""
-Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
-
-Please note that this module is private. All functions and objects
-are available in the main ``numpy`` namespace - use that instead.
-
-"""
-
-import os
-import warnings
-
-from numpy.version import version as __version__
-
-
-# disables OpenBLAS affinity setting of the main thread that limits
-# python threads or processes to one core
-env_added = []
-for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
- if envkey not in os.environ:
- os.environ[envkey] = '1'
- env_added.append(envkey)
-
-try:
- from . import multiarray
-except ImportError as exc:
- import sys
- msg = """
-
-IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
-
-Importing the numpy C-extensions failed. This error can happen for
-many reasons, often due to issues with your setup or how NumPy was
-installed.
-
-We have compiled some common reasons and troubleshooting tips at:
-
- https://numpy.org/devdocs/user/troubleshooting-importerror.html
-
-Please note and check the following:
-
- * The Python version is: Python%d.%d from "%s"
- * The NumPy version is: "%s"
-
-and make sure that they are the versions you expect.
-Please carefully study the documentation linked above for further help.
-
-Original error was: %s
-""" % (sys.version_info[0], sys.version_info[1], sys.executable,
- __version__, exc)
- raise ImportError(msg)
-finally:
- for envkey in env_added:
- del os.environ[envkey]
-del envkey
-del env_added
-del os
-
-from . import umath
-
-# Check that multiarray,umath are pure python modules wrapping
-# _multiarray_umath and not either of the old c-extension modules
-if not (hasattr(multiarray, '_multiarray_umath') and
- hasattr(umath, '_multiarray_umath')):
- import sys
- path = sys.modules['numpy'].__path__
- msg = ("Something is wrong with the numpy installation. "
- "While importing we detected an older version of "
- "numpy in {}. One method of fixing this is to repeatedly uninstall "
- "numpy until none is found, then reinstall this version.")
- raise ImportError(msg.format(path))
-
-from . import numerictypes as nt
-multiarray.set_typeDict(nt.sctypeDict)
-from . import numeric
-from .numeric import *
-from . import fromnumeric
-from .fromnumeric import *
-from . import defchararray as char
-from . import records
-from . import records as rec
-from .records import record, recarray, format_parser
-# Note: module name memmap is overwritten by a class with same name
-from .memmap import *
-from .defchararray import chararray
-from . import function_base
-from .function_base import *
-from . import _machar
-from . import getlimits
-from .getlimits import *
-from . import shape_base
-from .shape_base import *
-from . import einsumfunc
-from .einsumfunc import *
-del nt
-
-from .numeric import absolute as abs
-
-# do this after everything else, to minimize the chance of this misleadingly
-# appearing in an import-time traceback
-from . import _add_newdocs
-from . import _add_newdocs_scalars
-# add these for module-freeze analysis (like PyInstaller)
-from . import _dtype_ctypes
-from . import _internal
-from . import _dtype
-from . import _methods
-
-__all__ = ['char', 'rec', 'memmap']
-__all__ += numeric.__all__
-__all__ += ['record', 'recarray', 'format_parser']
-__all__ += ['chararray']
-__all__ += function_base.__all__
-__all__ += getlimits.__all__
-__all__ += shape_base.__all__
-__all__ += einsumfunc.__all__
-
-# We used to use `np.core._ufunc_reconstruct` to unpickle. This is unnecessary,
-# but old pickles saved before 1.20 will be using it, and there is no reason
-# to break loading them.
-def _ufunc_reconstruct(module, name):
- # The `fromlist` kwarg is required to ensure that `mod` points to the
- # inner-most module rather than the parent package when module name is
- # nested. This makes it possible to pickle non-toplevel ufuncs such as
- # scipy.special.expit for instance.
- mod = __import__(module, fromlist=[name])
- return getattr(mod, name)
-
-
-def _ufunc_reduce(func):
- # Report the `__name__`. pickle will try to find the module. Note that
- # pickle supports for this `__name__` to be a `__qualname__`. It may
- # make sense to add a `__qualname__` to ufuncs, to allow this more
- # explicitly (Numba has ufuncs as attributes).
- # See also: https://github.com/dask/distributed/issues/3450
- return func.__name__
-
-
-def _DType_reconstruct(scalar_type):
- # This is a work-around to pickle type(np.dtype(np.float64)), etc.
- # and it should eventually be replaced with a better solution, e.g. when
- # DTypes become HeapTypes.
- return type(dtype(scalar_type))
-
-
-def _DType_reduce(DType):
- # As types/classes, most DTypes can simply be pickled by their name:
- if not DType._legacy or DType.__module__ == "numpy.dtypes":
- return DType.__name__
-
- # However, user defined legacy dtypes (like rational) do not end up in
- # `numpy.dtypes` as module and do not have a public class at all.
- # For these, we pickle them by reconstructing them from the scalar type:
- scalar_type = DType.type
- return _DType_reconstruct, (scalar_type,)
-
-
-def __getattr__(name):
- # Deprecated 2022-11-22, NumPy 1.25.
- if name == "MachAr":
- warnings.warn(
- "The `np.core.MachAr` is considered private API (NumPy 1.24)",
- DeprecationWarning, stacklevel=2,
- )
- return _machar.MachAr
- raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
-
-
-import copyreg
-
-copyreg.pickle(ufunc, _ufunc_reduce)
-copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
-
-# Unclutter namespace (must keep _*_reconstruct for unpickling)
-del copyreg
-del _ufunc_reduce
-del _DType_reduce
-
-from numpy._pytesttester import PytestTester
-test = PytestTester(__name__)
-del PytestTester
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/fcompiler/compaq.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/fcompiler/compaq.py
deleted file mode 100644
index 01314c136acff7171298dc2819db4b50d7eec091..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/fcompiler/compaq.py
+++ /dev/null
@@ -1,120 +0,0 @@
-
-#http://www.compaq.com/fortran/docs/
-import os
-import sys
-
-from numpy.distutils.fcompiler import FCompiler
-from distutils.errors import DistutilsPlatformError
-
-compilers = ['CompaqFCompiler']
-if os.name != 'posix' or sys.platform[:6] == 'cygwin' :
- # Otherwise we'd get a false positive on posix systems with
- # case-insensitive filesystems (like darwin), because we'll pick
- # up /bin/df
- compilers.append('CompaqVisualFCompiler')
-
-class CompaqFCompiler(FCompiler):
-
- compiler_type = 'compaq'
- description = 'Compaq Fortran Compiler'
- version_pattern = r'Compaq Fortran (?P[^\s]*).*'
-
- if sys.platform[:5]=='linux':
- fc_exe = 'fort'
- else:
- fc_exe = 'f90'
-
- executables = {
- 'version_cmd' : ['', "-version"],
- 'compiler_f77' : [fc_exe, "-f77rtl", "-fixed"],
- 'compiler_fix' : [fc_exe, "-fixed"],
- 'compiler_f90' : [fc_exe],
- 'linker_so' : [''],
- 'archiver' : ["ar", "-cr"],
- 'ranlib' : ["ranlib"]
- }
-
- module_dir_switch = '-module ' # not tested
- module_include_switch = '-I'
-
- def get_flags(self):
- return ['-assume no2underscore', '-nomixed_str_len_arg']
- def get_flags_debug(self):
- return ['-g', '-check bounds']
- def get_flags_opt(self):
- return ['-O4', '-align dcommons', '-assume bigarrays',
- '-assume nozsize', '-math_library fast']
- def get_flags_arch(self):
- return ['-arch host', '-tune host']
- def get_flags_linker_so(self):
- if sys.platform[:5]=='linux':
- return ['-shared']
- return ['-shared', '-Wl,-expect_unresolved,*']
-
-class CompaqVisualFCompiler(FCompiler):
-
- compiler_type = 'compaqv'
- description = 'DIGITAL or Compaq Visual Fortran Compiler'
- version_pattern = (r'(DIGITAL|Compaq) Visual Fortran Optimizing Compiler'
- r' Version (?P[^\s]*).*')
-
- compile_switch = '/compile_only'
- object_switch = '/object:'
- library_switch = '/OUT:' #No space after /OUT:!
-
- static_lib_extension = ".lib"
- static_lib_format = "%s%s"
- module_dir_switch = '/module:'
- module_include_switch = '/I'
-
- ar_exe = 'lib.exe'
- fc_exe = 'DF'
-
- if sys.platform=='win32':
- from numpy.distutils.msvccompiler import MSVCCompiler
-
- try:
- m = MSVCCompiler()
- m.initialize()
- ar_exe = m.lib
- except DistutilsPlatformError:
- pass
- except AttributeError as e:
- if '_MSVCCompiler__root' in str(e):
- print('Ignoring "%s" (I think it is msvccompiler.py bug)' % (e))
- else:
- raise
- except OSError as e:
- if not "vcvarsall.bat" in str(e):
- print("Unexpected OSError in", __file__)
- raise
- except ValueError as e:
- if not "'path'" in str(e):
- print("Unexpected ValueError in", __file__)
- raise
-
- executables = {
- 'version_cmd' : ['', "/what"],
- 'compiler_f77' : [fc_exe, "/f77rtl", "/fixed"],
- 'compiler_fix' : [fc_exe, "/fixed"],
- 'compiler_f90' : [fc_exe],
- 'linker_so' : [''],
- 'archiver' : [ar_exe, "/OUT:"],
- 'ranlib' : None
- }
-
- def get_flags(self):
- return ['/nologo', '/MD', '/WX', '/iface=(cref,nomixed_str_len_arg)',
- '/names:lowercase', '/assume:underscore']
- def get_flags_opt(self):
- return ['/Ox', '/fast', '/optimize:5', '/unroll:0', '/math_library:fast']
- def get_flags_arch(self):
- return ['/threads']
- def get_flags_debug(self):
- return ['/debug']
-
-if __name__ == '__main__':
- from distutils import log
- log.set_verbosity(2)
- from numpy.distutils import customized_fcompiler
- print(customized_fcompiler(compiler='compaq').get_version())
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/openai/tests/test_url_composition.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/openai/tests/test_url_composition.py
deleted file mode 100644
index 5034354a05787aa3e47f7875ab89ed1e81d7cca6..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/openai/tests/test_url_composition.py
+++ /dev/null
@@ -1,209 +0,0 @@
-from sys import api_version
-
-import pytest
-
-from openai import Completion, Engine
-from openai.util import ApiType
-
-
-@pytest.mark.url
-def test_completions_url_composition_azure() -> None:
- url = Completion.class_url("test_engine", "azure", "2021-11-01-preview")
- assert (
- url
- == "/openai/deployments/test_engine/completions?api-version=2021-11-01-preview"
- )
-
-
-@pytest.mark.url
-def test_completions_url_composition_azure_ad() -> None:
- url = Completion.class_url("test_engine", "azure_ad", "2021-11-01-preview")
- assert (
- url
- == "/openai/deployments/test_engine/completions?api-version=2021-11-01-preview"
- )
-
-
-@pytest.mark.url
-def test_completions_url_composition_default() -> None:
- url = Completion.class_url("test_engine")
- assert url == "/engines/test_engine/completions"
-
-
-@pytest.mark.url
-def test_completions_url_composition_open_ai() -> None:
- url = Completion.class_url("test_engine", "open_ai")
- assert url == "/engines/test_engine/completions"
-
-
-@pytest.mark.url
-def test_completions_url_composition_invalid_type() -> None:
- with pytest.raises(Exception):
- url = Completion.class_url("test_engine", "invalid")
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_azure() -> None:
- completion = Completion(
- id="test_id",
- engine="test_engine",
- api_type="azure",
- api_version="2021-11-01-preview",
- )
- url = completion.instance_url()
- assert (
- url
- == "/openai/deployments/test_engine/completions/test_id?api-version=2021-11-01-preview"
- )
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_azure_ad() -> None:
- completion = Completion(
- id="test_id",
- engine="test_engine",
- api_type="azure_ad",
- api_version="2021-11-01-preview",
- )
- url = completion.instance_url()
- assert (
- url
- == "/openai/deployments/test_engine/completions/test_id?api-version=2021-11-01-preview"
- )
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_azure_no_version() -> None:
- completion = Completion(
- id="test_id", engine="test_engine", api_type="azure", api_version=None
- )
- with pytest.raises(Exception):
- completion.instance_url()
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_default() -> None:
- completion = Completion(id="test_id", engine="test_engine")
- url = completion.instance_url()
- assert url == "/engines/test_engine/completions/test_id"
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_open_ai() -> None:
- completion = Completion(
- id="test_id",
- engine="test_engine",
- api_type="open_ai",
- api_version="2021-11-01-preview",
- )
- url = completion.instance_url()
- assert url == "/engines/test_engine/completions/test_id"
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_invalid() -> None:
- completion = Completion(id="test_id", engine="test_engine", api_type="invalid")
- with pytest.raises(Exception):
- url = completion.instance_url()
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_timeout_azure() -> None:
- completion = Completion(
- id="test_id",
- engine="test_engine",
- api_type="azure",
- api_version="2021-11-01-preview",
- )
- completion["timeout"] = 12
- url = completion.instance_url()
- assert (
- url
- == "/openai/deployments/test_engine/completions/test_id?api-version=2021-11-01-preview&timeout=12"
- )
-
-
-@pytest.mark.url
-def test_completions_url_composition_instance_url_timeout_openai() -> None:
- completion = Completion(id="test_id", engine="test_engine", api_type="open_ai")
- completion["timeout"] = 12
- url = completion.instance_url()
- assert url == "/engines/test_engine/completions/test_id?timeout=12"
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_azure() -> None:
- engine = Engine(id="test_id", api_type="azure", api_version="2021-11-01-preview")
- assert engine.api_type == "azure"
- assert engine.typed_api_type == ApiType.AZURE
- url = engine.instance_url("test_operation")
- assert (
- url
- == "/openai/deployments/test_id/test_operation?api-version=2021-11-01-preview"
- )
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_azure_ad() -> None:
- engine = Engine(id="test_id", api_type="azure_ad", api_version="2021-11-01-preview")
- assert engine.api_type == "azure_ad"
- assert engine.typed_api_type == ApiType.AZURE_AD
- url = engine.instance_url("test_operation")
- assert (
- url
- == "/openai/deployments/test_id/test_operation?api-version=2021-11-01-preview"
- )
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_azure_no_version() -> None:
- engine = Engine(id="test_id", api_type="azure", api_version=None)
- assert engine.api_type == "azure"
- assert engine.typed_api_type == ApiType.AZURE
- with pytest.raises(Exception):
- engine.instance_url("test_operation")
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_azure_no_operation() -> None:
- engine = Engine(id="test_id", api_type="azure", api_version="2021-11-01-preview")
- assert engine.api_type == "azure"
- assert engine.typed_api_type == ApiType.AZURE
- assert (
- engine.instance_url()
- == "/openai/engines/test_id?api-version=2021-11-01-preview"
- )
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_default() -> None:
- engine = Engine(id="test_id")
- assert engine.api_type == None
- assert engine.typed_api_type == ApiType.OPEN_AI
- url = engine.instance_url()
- assert url == "/engines/test_id"
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_open_ai() -> None:
- engine = Engine(id="test_id", api_type="open_ai")
- assert engine.api_type == "open_ai"
- assert engine.typed_api_type == ApiType.OPEN_AI
- url = engine.instance_url()
- assert url == "/engines/test_id"
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_invalid_type() -> None:
- engine = Engine(id="test_id", api_type="invalid")
- assert engine.api_type == "invalid"
- with pytest.raises(Exception):
- assert engine.typed_api_type == ApiType.OPEN_AI
-
-
-@pytest.mark.url
-def test_engine_search_url_composition_invalid_search() -> None:
- engine = Engine(id="test_id", api_type="invalid")
- assert engine.api_type == "invalid"
- with pytest.raises(Exception):
- engine.search()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/sparse/test_astype.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/sparse/test_astype.py
deleted file mode 100644
index 83a507e679d460d687b27d09a4bed8321e1199e2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/sparse/test_astype.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas._libs.sparse import IntIndex
-
-from pandas import (
- SparseDtype,
- Timestamp,
-)
-import pandas._testing as tm
-from pandas.core.arrays.sparse import SparseArray
-
-
-class TestAstype:
- def test_astype(self):
- # float -> float
- arr = SparseArray([None, None, 0, 2])
- result = arr.astype("Sparse[float32]")
- expected = SparseArray([None, None, 0, 2], dtype=np.dtype("float32"))
- tm.assert_sp_array_equal(result, expected)
-
- dtype = SparseDtype("float64", fill_value=0)
- result = arr.astype(dtype)
- expected = SparseArray._simple_new(
- np.array([0.0, 2.0], dtype=dtype.subtype), IntIndex(4, [2, 3]), dtype
- )
- tm.assert_sp_array_equal(result, expected)
-
- dtype = SparseDtype("int64", 0)
- result = arr.astype(dtype)
- expected = SparseArray._simple_new(
- np.array([0, 2], dtype=np.int64), IntIndex(4, [2, 3]), dtype
- )
- tm.assert_sp_array_equal(result, expected)
-
- arr = SparseArray([0, np.nan, 0, 1], fill_value=0)
- with pytest.raises(ValueError, match="NA"):
- arr.astype("Sparse[i8]")
-
- def test_astype_bool(self):
- a = SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0))
- result = a.astype(bool)
- expected = np.array([1, 0, 0, 1], dtype=bool)
- tm.assert_numpy_array_equal(result, expected)
-
- # update fill value
- result = a.astype(SparseDtype(bool, False))
- expected = SparseArray(
- [True, False, False, True], dtype=SparseDtype(bool, False)
- )
- tm.assert_sp_array_equal(result, expected)
-
- def test_astype_all(self, any_real_numpy_dtype):
- vals = np.array([1, 2, 3])
- arr = SparseArray(vals, fill_value=1)
- typ = np.dtype(any_real_numpy_dtype)
- res = arr.astype(typ)
- tm.assert_numpy_array_equal(res, vals.astype(any_real_numpy_dtype))
-
- @pytest.mark.parametrize(
- "arr, dtype, expected",
- [
- (
- SparseArray([0, 1]),
- "float",
- SparseArray([0.0, 1.0], dtype=SparseDtype(float, 0.0)),
- ),
- (SparseArray([0, 1]), bool, SparseArray([False, True])),
- (
- SparseArray([0, 1], fill_value=1),
- bool,
- SparseArray([False, True], dtype=SparseDtype(bool, True)),
- ),
- pytest.param(
- SparseArray([0, 1]),
- "datetime64[ns]",
- SparseArray(
- np.array([0, 1], dtype="datetime64[ns]"),
- dtype=SparseDtype("datetime64[ns]", Timestamp("1970")),
- ),
- ),
- (
- SparseArray([0, 1, 10]),
- str,
- SparseArray(["0", "1", "10"], dtype=SparseDtype(str, "0")),
- ),
- (SparseArray(["10", "20"]), float, SparseArray([10.0, 20.0])),
- (
- SparseArray([0, 1, 0]),
- object,
- SparseArray([0, 1, 0], dtype=SparseDtype(object, 0)),
- ),
- ],
- )
- def test_astype_more(self, arr, dtype, expected):
- result = arr.astype(arr.dtype.update_dtype(dtype))
- tm.assert_sp_array_equal(result, expected)
-
- def test_astype_nan_raises(self):
- arr = SparseArray([1.0, np.nan])
- with pytest.raises(ValueError, match="Cannot convert non-finite"):
- arr.astype(int)
-
- def test_astype_copy_false(self):
- # GH#34456 bug caused by using .view instead of .astype in astype_nansafe
- arr = SparseArray([1, 2, 3])
-
- dtype = SparseDtype(float, 0)
-
- result = arr.astype(dtype, copy=False)
- expected = SparseArray([1.0, 2.0, 3.0], fill_value=0.0)
- tm.assert_sp_array_equal(result, expected)
-
- def test_astype_dt64_to_int64(self):
- # GH#49631 match non-sparse behavior
- values = np.array(["NaT", "2016-01-02", "2016-01-03"], dtype="M8[ns]")
-
- arr = SparseArray(values)
- result = arr.astype("int64")
- expected = values.astype("int64")
- tm.assert_numpy_array_equal(result, expected)
-
- # we should also be able to cast to equivalent Sparse[int64]
- dtype_int64 = SparseDtype("int64", np.iinfo(np.int64).min)
- result2 = arr.astype(dtype_int64)
- tm.assert_numpy_array_equal(result2.to_numpy(), expected)
-
- # GH#50087 we should match the non-sparse behavior regardless of
- # if we have a fill_value other than NaT
- dtype = SparseDtype("datetime64[ns]", values[1])
- arr3 = SparseArray(values, dtype=dtype)
- result3 = arr3.astype("int64")
- tm.assert_numpy_array_equal(result3, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_equivalence.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_equivalence.py
deleted file mode 100644
index 9babbd5b8d56d64d704978758efb81f8d730274f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_equivalence.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas.core.dtypes.common import is_any_real_numeric_dtype
-
-import pandas as pd
-from pandas import (
- Index,
- MultiIndex,
- Series,
-)
-import pandas._testing as tm
-
-
-def test_equals(idx):
- assert idx.equals(idx)
- assert idx.equals(idx.copy())
- assert idx.equals(idx.astype(object))
- assert idx.equals(idx.to_flat_index())
- assert idx.equals(idx.to_flat_index().astype("category"))
-
- assert not idx.equals(list(idx))
- assert not idx.equals(np.array(idx))
-
- same_values = Index(idx, dtype=object)
- assert idx.equals(same_values)
- assert same_values.equals(idx)
-
- if idx.nlevels == 1:
- # do not test MultiIndex
- assert not idx.equals(Series(idx))
-
-
-def test_equals_op(idx):
- # GH9947, GH10637
- index_a = idx
-
- n = len(index_a)
- index_b = index_a[0:-1]
- index_c = index_a[0:-1].append(index_a[-2:-1])
- index_d = index_a[0:1]
- with pytest.raises(ValueError, match="Lengths must match"):
- index_a == index_b
- expected1 = np.array([True] * n)
- expected2 = np.array([True] * (n - 1) + [False])
- tm.assert_numpy_array_equal(index_a == index_a, expected1)
- tm.assert_numpy_array_equal(index_a == index_c, expected2)
-
- # test comparisons with numpy arrays
- array_a = np.array(index_a)
- array_b = np.array(index_a[0:-1])
- array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
- array_d = np.array(index_a[0:1])
- with pytest.raises(ValueError, match="Lengths must match"):
- index_a == array_b
- tm.assert_numpy_array_equal(index_a == array_a, expected1)
- tm.assert_numpy_array_equal(index_a == array_c, expected2)
-
- # test comparisons with Series
- series_a = Series(array_a)
- series_b = Series(array_b)
- series_c = Series(array_c)
- series_d = Series(array_d)
- with pytest.raises(ValueError, match="Lengths must match"):
- index_a == series_b
-
- tm.assert_numpy_array_equal(index_a == series_a, expected1)
- tm.assert_numpy_array_equal(index_a == series_c, expected2)
-
- # cases where length is 1 for one of them
- with pytest.raises(ValueError, match="Lengths must match"):
- index_a == index_d
- with pytest.raises(ValueError, match="Lengths must match"):
- index_a == series_d
- with pytest.raises(ValueError, match="Lengths must match"):
- index_a == array_d
- msg = "Can only compare identically-labeled Series objects"
- with pytest.raises(ValueError, match=msg):
- series_a == series_d
- with pytest.raises(ValueError, match="Lengths must match"):
- series_a == array_d
-
- # comparing with a scalar should broadcast; note that we are excluding
- # MultiIndex because in this case each item in the index is a tuple of
- # length 2, and therefore is considered an array of length 2 in the
- # comparison instead of a scalar
- if not isinstance(index_a, MultiIndex):
- expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
- # assuming the 2nd to last item is unique in the data
- item = index_a[-2]
- tm.assert_numpy_array_equal(index_a == item, expected3)
- tm.assert_series_equal(series_a == item, Series(expected3))
-
-
-def test_compare_tuple():
- # GH#21517
- mi = MultiIndex.from_product([[1, 2]] * 2)
-
- all_false = np.array([False, False, False, False])
-
- result = mi == mi[0]
- expected = np.array([True, False, False, False])
- tm.assert_numpy_array_equal(result, expected)
-
- result = mi != mi[0]
- tm.assert_numpy_array_equal(result, ~expected)
-
- result = mi < mi[0]
- tm.assert_numpy_array_equal(result, all_false)
-
- result = mi <= mi[0]
- tm.assert_numpy_array_equal(result, expected)
-
- result = mi > mi[0]
- tm.assert_numpy_array_equal(result, ~expected)
-
- result = mi >= mi[0]
- tm.assert_numpy_array_equal(result, ~all_false)
-
-
-def test_compare_tuple_strs():
- # GH#34180
-
- mi = MultiIndex.from_tuples([("a", "b"), ("b", "c"), ("c", "a")])
-
- result = mi == ("c", "a")
- expected = np.array([False, False, True])
- tm.assert_numpy_array_equal(result, expected)
-
- result = mi == ("c",)
- expected = np.array([False, False, False])
- tm.assert_numpy_array_equal(result, expected)
-
-
-def test_equals_multi(idx):
- assert idx.equals(idx)
- assert not idx.equals(idx.values)
- assert idx.equals(Index(idx.values))
-
- assert idx.equal_levels(idx)
- assert not idx.equals(idx[:-1])
- assert not idx.equals(idx[-1])
-
- # different number of levels
- index = MultiIndex(
- levels=[Index(list(range(4))), Index(list(range(4))), Index(list(range(4)))],
- codes=[
- np.array([0, 0, 1, 2, 2, 2, 3, 3]),
- np.array([0, 1, 0, 0, 0, 1, 0, 1]),
- np.array([1, 0, 1, 1, 0, 0, 1, 0]),
- ],
- )
-
- index2 = MultiIndex(levels=index.levels[:-1], codes=index.codes[:-1])
- assert not index.equals(index2)
- assert not index.equal_levels(index2)
-
- # levels are different
- major_axis = Index(list(range(4)))
- minor_axis = Index(list(range(2)))
-
- major_codes = np.array([0, 0, 1, 2, 2, 3])
- minor_codes = np.array([0, 1, 0, 0, 1, 0])
-
- index = MultiIndex(
- levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
- )
- assert not idx.equals(index)
- assert not idx.equal_levels(index)
-
- # some of the labels are different
- major_axis = Index(["foo", "bar", "baz", "qux"])
- minor_axis = Index(["one", "two"])
-
- major_codes = np.array([0, 0, 2, 2, 3, 3])
- minor_codes = np.array([0, 1, 0, 1, 0, 1])
-
- index = MultiIndex(
- levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
- )
- assert not idx.equals(index)
-
-
-def test_identical(idx):
- mi = idx.copy()
- mi2 = idx.copy()
- assert mi.identical(mi2)
-
- mi = mi.set_names(["new1", "new2"])
- assert mi.equals(mi2)
- assert not mi.identical(mi2)
-
- mi2 = mi2.set_names(["new1", "new2"])
- assert mi.identical(mi2)
-
- mi4 = Index(mi.tolist(), tupleize_cols=False)
- assert not mi.identical(mi4)
- assert mi.equals(mi4)
-
-
-def test_equals_operator(idx):
- # GH9785
- assert (idx == idx).all()
-
-
-def test_equals_missing_values():
- # make sure take is not using -1
- i = MultiIndex.from_tuples([(0, pd.NaT), (0, pd.Timestamp("20130101"))])
- result = i[0:1].equals(i[0])
- assert not result
- result = i[1:2].equals(i[1])
- assert not result
-
-
-def test_equals_missing_values_differently_sorted():
- # GH#38439
- mi1 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)])
- mi2 = MultiIndex.from_tuples([(np.nan, np.nan), (81.0, np.nan)])
- assert not mi1.equals(mi2)
-
- mi2 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)])
- assert mi1.equals(mi2)
-
-
-def test_is_():
- mi = MultiIndex.from_tuples(zip(range(10), range(10)))
- assert mi.is_(mi)
- assert mi.is_(mi.view())
- assert mi.is_(mi.view().view().view().view())
- mi2 = mi.view()
- # names are metadata, they don't change id
- mi2.names = ["A", "B"]
- assert mi2.is_(mi)
- assert mi.is_(mi2)
-
- assert not mi.is_(mi.set_names(["C", "D"]))
- # levels are inherent properties, they change identity
- mi3 = mi2.set_levels([list(range(10)), list(range(10))])
- assert not mi3.is_(mi2)
- # shouldn't change
- assert mi2.is_(mi)
- mi4 = mi3.view()
-
- # GH 17464 - Remove duplicate MultiIndex levels
- mi4 = mi4.set_levels([list(range(10)), list(range(10))])
- assert not mi4.is_(mi3)
- mi5 = mi.view()
- mi5 = mi5.set_levels(mi5.levels)
- assert not mi5.is_(mi)
-
-
-def test_is_all_dates(idx):
- assert not idx._is_all_dates
-
-
-def test_is_numeric(idx):
- # MultiIndex is never numeric
- assert not is_any_real_numeric_dtype(idx)
-
-
-def test_multiindex_compare():
- # GH 21149
- # Ensure comparison operations for MultiIndex with nlevels == 1
- # behave consistently with those for MultiIndex with nlevels > 1
-
- midx = MultiIndex.from_product([[0, 1]])
-
- # Equality self-test: MultiIndex object vs self
- expected = Series([True, True])
- result = Series(midx == midx)
- tm.assert_series_equal(result, expected)
-
- # Greater than comparison: MultiIndex object vs self
- expected = Series([False, False])
- result = Series(midx > midx)
- tm.assert_series_equal(result, expected)
-
-
-def test_equals_ea_int_regular_int():
- # GH#46026
- mi1 = MultiIndex.from_arrays([Index([1, 2], dtype="Int64"), [3, 4]])
- mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]])
- assert not mi1.equals(mi2)
- assert not mi2.equals(mi1)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/cli/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/cli/__init__.py
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/cli/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/text_file.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/text_file.py
deleted file mode 100644
index 93abad38f434d7ecf8d208cdefc310bd73a5a673..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/text_file.py
+++ /dev/null
@@ -1,286 +0,0 @@
-"""text_file
-
-provides the TextFile class, which gives an interface to text files
-that (optionally) takes care of stripping comments, ignoring blank
-lines, and joining lines with backslashes."""
-
-import sys, io
-
-
-class TextFile:
- """Provides a file-like object that takes care of all the things you
- commonly want to do when processing a text file that has some
- line-by-line syntax: strip comments (as long as "#" is your
- comment character), skip blank lines, join adjacent lines by
- escaping the newline (ie. backslash at end of line), strip
- leading and/or trailing whitespace. All of these are optional
- and independently controllable.
-
- Provides a 'warn()' method so you can generate warning messages that
- report physical line number, even if the logical line in question
- spans multiple physical lines. Also provides 'unreadline()' for
- implementing line-at-a-time lookahead.
-
- Constructor is called as:
-
- TextFile (filename=None, file=None, **options)
-
- It bombs (RuntimeError) if both 'filename' and 'file' are None;
- 'filename' should be a string, and 'file' a file object (or
- something that provides 'readline()' and 'close()' methods). It is
- recommended that you supply at least 'filename', so that TextFile
- can include it in warning messages. If 'file' is not supplied,
- TextFile creates its own using 'io.open()'.
-
- The options are all boolean, and affect the value returned by
- 'readline()':
- strip_comments [default: true]
- strip from "#" to end-of-line, as well as any whitespace
- leading up to the "#" -- unless it is escaped by a backslash
- lstrip_ws [default: false]
- strip leading whitespace from each line before returning it
- rstrip_ws [default: true]
- strip trailing whitespace (including line terminator!) from
- each line before returning it
- skip_blanks [default: true}
- skip lines that are empty *after* stripping comments and
- whitespace. (If both lstrip_ws and rstrip_ws are false,
- then some lines may consist of solely whitespace: these will
- *not* be skipped, even if 'skip_blanks' is true.)
- join_lines [default: false]
- if a backslash is the last non-newline character on a line
- after stripping comments and whitespace, join the following line
- to it to form one "logical line"; if N consecutive lines end
- with a backslash, then N+1 physical lines will be joined to
- form one logical line.
- collapse_join [default: false]
- strip leading whitespace from lines that are joined to their
- predecessor; only matters if (join_lines and not lstrip_ws)
- errors [default: 'strict']
- error handler used to decode the file content
-
- Note that since 'rstrip_ws' can strip the trailing newline, the
- semantics of 'readline()' must differ from those of the builtin file
- object's 'readline()' method! In particular, 'readline()' returns
- None for end-of-file: an empty string might just be a blank line (or
- an all-whitespace line), if 'rstrip_ws' is true but 'skip_blanks' is
- not."""
-
- default_options = { 'strip_comments': 1,
- 'skip_blanks': 1,
- 'lstrip_ws': 0,
- 'rstrip_ws': 1,
- 'join_lines': 0,
- 'collapse_join': 0,
- 'errors': 'strict',
- }
-
- def __init__(self, filename=None, file=None, **options):
- """Construct a new TextFile object. At least one of 'filename'
- (a string) and 'file' (a file-like object) must be supplied.
- They keyword argument options are described above and affect
- the values returned by 'readline()'."""
- if filename is None and file is None:
- raise RuntimeError("you must supply either or both of 'filename' and 'file'")
-
- # set values for all options -- either from client option hash
- # or fallback to default_options
- for opt in self.default_options.keys():
- if opt in options:
- setattr(self, opt, options[opt])
- else:
- setattr(self, opt, self.default_options[opt])
-
- # sanity check client option hash
- for opt in options.keys():
- if opt not in self.default_options:
- raise KeyError("invalid TextFile option '%s'" % opt)
-
- if file is None:
- self.open(filename)
- else:
- self.filename = filename
- self.file = file
- self.current_line = 0 # assuming that file is at BOF!
-
- # 'linebuf' is a stack of lines that will be emptied before we
- # actually read from the file; it's only populated by an
- # 'unreadline()' operation
- self.linebuf = []
-
- def open(self, filename):
- """Open a new file named 'filename'. This overrides both the
- 'filename' and 'file' arguments to the constructor."""
- self.filename = filename
- self.file = io.open(self.filename, 'r', errors=self.errors)
- self.current_line = 0
-
- def close(self):
- """Close the current file and forget everything we know about it
- (filename, current line number)."""
- file = self.file
- self.file = None
- self.filename = None
- self.current_line = None
- file.close()
-
- def gen_error(self, msg, line=None):
- outmsg = []
- if line is None:
- line = self.current_line
- outmsg.append(self.filename + ", ")
- if isinstance(line, (list, tuple)):
- outmsg.append("lines %d-%d: " % tuple(line))
- else:
- outmsg.append("line %d: " % line)
- outmsg.append(str(msg))
- return "".join(outmsg)
-
- def error(self, msg, line=None):
- raise ValueError("error: " + self.gen_error(msg, line))
-
- def warn(self, msg, line=None):
- """Print (to stderr) a warning message tied to the current logical
- line in the current file. If the current logical line in the
- file spans multiple physical lines, the warning refers to the
- whole range, eg. "lines 3-5". If 'line' supplied, it overrides
- the current line number; it may be a list or tuple to indicate a
- range of physical lines, or an integer for a single physical
- line."""
- sys.stderr.write("warning: " + self.gen_error(msg, line) + "\n")
-
- def readline(self):
- """Read and return a single logical line from the current file (or
- from an internal buffer if lines have previously been "unread"
- with 'unreadline()'). If the 'join_lines' option is true, this
- may involve reading multiple physical lines concatenated into a
- single string. Updates the current line number, so calling
- 'warn()' after 'readline()' emits a warning about the physical
- line(s) just read. Returns None on end-of-file, since the empty
- string can occur if 'rstrip_ws' is true but 'strip_blanks' is
- not."""
- # If any "unread" lines waiting in 'linebuf', return the top
- # one. (We don't actually buffer read-ahead data -- lines only
- # get put in 'linebuf' if the client explicitly does an
- # 'unreadline()'.
- if self.linebuf:
- line = self.linebuf[-1]
- del self.linebuf[-1]
- return line
-
- buildup_line = ''
-
- while True:
- # read the line, make it None if EOF
- line = self.file.readline()
- if line == '':
- line = None
-
- if self.strip_comments and line:
-
- # Look for the first "#" in the line. If none, never
- # mind. If we find one and it's the first character, or
- # is not preceded by "\", then it starts a comment --
- # strip the comment, strip whitespace before it, and
- # carry on. Otherwise, it's just an escaped "#", so
- # unescape it (and any other escaped "#"'s that might be
- # lurking in there) and otherwise leave the line alone.
-
- pos = line.find("#")
- if pos == -1: # no "#" -- no comments
- pass
-
- # It's definitely a comment -- either "#" is the first
- # character, or it's elsewhere and unescaped.
- elif pos == 0 or line[pos-1] != "\\":
- # Have to preserve the trailing newline, because it's
- # the job of a later step (rstrip_ws) to remove it --
- # and if rstrip_ws is false, we'd better preserve it!
- # (NB. this means that if the final line is all comment
- # and has no trailing newline, we will think that it's
- # EOF; I think that's OK.)
- eol = (line[-1] == '\n') and '\n' or ''
- line = line[0:pos] + eol
-
- # If all that's left is whitespace, then skip line
- # *now*, before we try to join it to 'buildup_line' --
- # that way constructs like
- # hello \\
- # # comment that should be ignored
- # there
- # result in "hello there".
- if line.strip() == "":
- continue
- else: # it's an escaped "#"
- line = line.replace("\\#", "#")
-
- # did previous line end with a backslash? then accumulate
- if self.join_lines and buildup_line:
- # oops: end of file
- if line is None:
- self.warn("continuation line immediately precedes "
- "end-of-file")
- return buildup_line
-
- if self.collapse_join:
- line = line.lstrip()
- line = buildup_line + line
-
- # careful: pay attention to line number when incrementing it
- if isinstance(self.current_line, list):
- self.current_line[1] = self.current_line[1] + 1
- else:
- self.current_line = [self.current_line,
- self.current_line + 1]
- # just an ordinary line, read it as usual
- else:
- if line is None: # eof
- return None
-
- # still have to be careful about incrementing the line number!
- if isinstance(self.current_line, list):
- self.current_line = self.current_line[1] + 1
- else:
- self.current_line = self.current_line + 1
-
- # strip whitespace however the client wants (leading and
- # trailing, or one or the other, or neither)
- if self.lstrip_ws and self.rstrip_ws:
- line = line.strip()
- elif self.lstrip_ws:
- line = line.lstrip()
- elif self.rstrip_ws:
- line = line.rstrip()
-
- # blank line (whether we rstrip'ed or not)? skip to next line
- # if appropriate
- if (line == '' or line == '\n') and self.skip_blanks:
- continue
-
- if self.join_lines:
- if line[-1] == '\\':
- buildup_line = line[:-1]
- continue
-
- if line[-2:] == '\\\n':
- buildup_line = line[0:-2] + '\n'
- continue
-
- # well, I guess there's some actual content there: return it
- return line
-
- def readlines(self):
- """Read and return the list of all logical lines remaining in the
- current file."""
- lines = []
- while True:
- line = self.readline()
- if line is None:
- return lines
- lines.append(line)
-
- def unreadline(self, line):
- """Push 'line' (a string) onto an internal buffer that will be
- checked by future 'readline()' calls. Handy for implementing
- a parser with line-at-a-time lookahead."""
- self.linebuf.append(line)
diff --git a/spaces/projecte-aina/aguila-7b/sagemaker_endpoint.py b/spaces/projecte-aina/aguila-7b/sagemaker_endpoint.py
deleted file mode 100644
index 3590e059b6e01357ee23762306d8b49156989949..0000000000000000000000000000000000000000
--- a/spaces/projecte-aina/aguila-7b/sagemaker_endpoint.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-from traceback import print_exc
-import boto3
-from handler import ContentHandler
-from dotenv import load_dotenv
-
-load_dotenv()
-
-endpoint_name = os.environ.get("AWS_ENDPOINT_NAME")
-aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID")
-aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY")
-aws_region_name = os.environ.get("AWS_REGION_NAME")
-
-boto_client = boto3.client(
- service_name='sagemaker-runtime',
- aws_access_key_id=aws_access_key_id,
- aws_secret_access_key=aws_secret_access_key,
- region_name=aws_region_name)
-
-content_handler = ContentHandler()
-
-def invoke_endpoint(
- input_,
- model_parameters,
- ):
- try:
- response = boto_client.invoke_endpoint(
- EndpointName=endpoint_name,
- ContentType='application/json',
- Body=content_handler.transform_input(prompt=input_, model_kwargs=model_parameters)
- )
- return content_handler.transform_output(response['Body'])
- except:
- print_exc()
- return None
\ No newline at end of file
diff --git a/spaces/pyodide-demo/self-hosted/logbook.js b/spaces/pyodide-demo/self-hosted/logbook.js
deleted file mode 100644
index 28aec6bc0a09f4eb50033798e46fd958c58a8469..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/logbook.js
+++ /dev/null
@@ -1 +0,0 @@
-var Module=typeof globalThis.__pyodide_module!=="undefined"?globalThis.__pyodide_module:{};if(!Module.expectedDataFileDownloads){Module.expectedDataFileDownloads=0}Module.expectedDataFileDownloads++;(function(){var loadPackage=function(metadata){var PACKAGE_PATH="";if(typeof window==="object"){PACKAGE_PATH=window["encodeURIComponent"](window.location.pathname.toString().substring(0,window.location.pathname.toString().lastIndexOf("/"))+"/")}else if(typeof process==="undefined"&&typeof location!=="undefined"){PACKAGE_PATH=encodeURIComponent(location.pathname.toString().substring(0,location.pathname.toString().lastIndexOf("/"))+"/")}var PACKAGE_NAME="logbook.data";var REMOTE_PACKAGE_BASE="logbook.data";if(typeof Module["locateFilePackage"]==="function"&&!Module["locateFile"]){Module["locateFile"]=Module["locateFilePackage"];err("warning: you defined Module.locateFilePackage, that has been renamed to Module.locateFile (using your locateFilePackage for now)")}var REMOTE_PACKAGE_NAME=Module["locateFile"]?Module["locateFile"](REMOTE_PACKAGE_BASE,""):REMOTE_PACKAGE_BASE;var REMOTE_PACKAGE_SIZE=metadata["remote_package_size"];var PACKAGE_UUID=metadata["package_uuid"];function fetchRemotePackage(packageName,packageSize,callback,errback){if(typeof process==="object"){require("fs").readFile(packageName,(function(err,contents){if(err){errback(err)}else{callback(contents.buffer)}}));return}var xhr=new XMLHttpRequest;xhr.open("GET",packageName,true);xhr.responseType="arraybuffer";xhr.onprogress=function(event){var url=packageName;var size=packageSize;if(event.total)size=event.total;if(event.loaded){if(!xhr.addedTotal){xhr.addedTotal=true;if(!Module.dataFileDownloads)Module.dataFileDownloads={};Module.dataFileDownloads[url]={loaded:event.loaded,total:size}}else{Module.dataFileDownloads[url].loaded=event.loaded}var total=0;var loaded=0;var num=0;for(var download in Module.dataFileDownloads){var data=Module.dataFileDownloads[download];total+=data.total;loaded+=data.loaded;num++}total=Math.ceil(total*Module.expectedDataFileDownloads/num);if(Module["setStatus"])Module["setStatus"]("Downloading data... ("+loaded+"/"+total+")")}else if(!Module.dataFileDownloads){if(Module["setStatus"])Module["setStatus"]("Downloading data...")}};xhr.onerror=function(event){throw new Error("NetworkError for: "+packageName)};xhr.onload=function(event){if(xhr.status==200||xhr.status==304||xhr.status==206||xhr.status==0&&xhr.response){var packageData=xhr.response;callback(packageData)}else{throw new Error(xhr.statusText+" : "+xhr.responseURL)}};xhr.send(null)}function handleError(error){console.error("package error:",error)}var fetchedCallback=null;var fetched=Module["getPreloadedPackage"]?Module["getPreloadedPackage"](REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE):null;if(!fetched)fetchRemotePackage(REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE,(function(data){if(fetchedCallback){fetchedCallback(data);fetchedCallback=null}else{fetched=data}}),handleError);function runWithFS(){function assert(check,msg){if(!check)throw msg+(new Error).stack}Module["FS_createPath"]("/","lib",true,true);Module["FS_createPath"]("/lib","python3.9",true,true);Module["FS_createPath"]("/lib/python3.9","site-packages",true,true);Module["FS_createPath"]("/lib/python3.9/site-packages","logbook",true,true);Module["FS_createPath"]("/lib/python3.9/site-packages","Logbook-1.5.2-py3.9.egg-info",true,true);function processPackageData(arrayBuffer){assert(arrayBuffer,"Loading data file failed.");assert(arrayBuffer instanceof ArrayBuffer,"bad input to processPackageData");var byteArray=new Uint8Array(arrayBuffer);var curr;var compressedData={data:null,cachedOffset:197945,cachedIndexes:[-1,-1],cachedChunks:[null,null],offsets:[0,1278,2380,3145,4106,5058,6386,7618,8897,9911,10930,12255,13528,14897,16194,17442,18726,19469,20595,21437,22506,23591,24847,26091,27460,28619,30043,31173,32302,33520,34677,35779,36942,37916,39085,40174,41585,42861,44194,45528,46817,47837,49087,50159,51471,52639,53630,54752,56095,57303,58464,59518,60768,61347,62178,63691,64819,66069,67295,68287,69429,70636,71704,72706,73780,74819,76249,77527,78751,79917,81160,82179,83389,84691,85998,87309,88531,89707,90988,92123,93356,94375,95500,96753,97892,99018,100231,101353,102452,103825,105030,106322,107551,108785,109924,111158,112371,113542,114661,115800,116919,118236,119280,120444,121336,122263,123435,124469,125425,126766,128041,129219,130175,131594,132853,134139,135269,136506,137811,138709,139545,140587,141823,142832,143981,144835,145645,146861,148292,149692,151155,152570,154103,155476,156721,158095,159281,160599,161630,162744,164307,165785,167023,168426,169611,171121,172325,173816,175280,176619,178010,179280,180743,182073,183270,184169,185567,187023,188386,189774,191293,192498,193237,194300,195094,195768,196394,197605],sizes:[1278,1102,765,961,952,1328,1232,1279,1014,1019,1325,1273,1369,1297,1248,1284,743,1126,842,1069,1085,1256,1244,1369,1159,1424,1130,1129,1218,1157,1102,1163,974,1169,1089,1411,1276,1333,1334,1289,1020,1250,1072,1312,1168,991,1122,1343,1208,1161,1054,1250,579,831,1513,1128,1250,1226,992,1142,1207,1068,1002,1074,1039,1430,1278,1224,1166,1243,1019,1210,1302,1307,1311,1222,1176,1281,1135,1233,1019,1125,1253,1139,1126,1213,1122,1099,1373,1205,1292,1229,1234,1139,1234,1213,1171,1119,1139,1119,1317,1044,1164,892,927,1172,1034,956,1341,1275,1178,956,1419,1259,1286,1130,1237,1305,898,836,1042,1236,1009,1149,854,810,1216,1431,1400,1463,1415,1533,1373,1245,1374,1186,1318,1031,1114,1563,1478,1238,1403,1185,1510,1204,1491,1464,1339,1391,1270,1463,1330,1197,899,1398,1456,1363,1388,1519,1205,739,1063,794,674,626,1211,340],successes:[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]};compressedData["data"]=byteArray;assert(typeof Module.LZ4==="object","LZ4 not present - was your app build with -s LZ4=1 ?");Module.LZ4.loadPackage({metadata:metadata,compressedData:compressedData},true);Module["removeRunDependency"]("datafile_logbook.data")}Module["addRunDependency"]("datafile_logbook.data");if(!Module.preloadResults)Module.preloadResults={};Module.preloadResults[PACKAGE_NAME]={fromCache:false};if(fetched){processPackageData(fetched);fetched=null}else{fetchedCallback=processPackageData}}if(Module["calledRun"]){runWithFS()}else{if(!Module["preRun"])Module["preRun"]=[];Module["preRun"].push(runWithFS)}};loadPackage({files:[{filename:"/lib/python3.9/site-packages/logbook/__init__.py",start:0,end:1772,audio:0},{filename:"/lib/python3.9/site-packages/logbook/__version__.py",start:1772,end:1794,audio:0},{filename:"/lib/python3.9/site-packages/logbook/_fallback.py",start:1794,end:9906,audio:0},{filename:"/lib/python3.9/site-packages/logbook/_termcolors.py",start:9906,end:11044,audio:0},{filename:"/lib/python3.9/site-packages/logbook/base.py",start:11044,end:52388,audio:0},{filename:"/lib/python3.9/site-packages/logbook/compat.py",start:52388,end:62744,audio:0},{filename:"/lib/python3.9/site-packages/logbook/concurrency.py",start:62744,end:69001,audio:0},{filename:"/lib/python3.9/site-packages/logbook/handlers.py",start:69001,end:140506,audio:0},{filename:"/lib/python3.9/site-packages/logbook/helpers.py",start:140506,end:148802,audio:0},{filename:"/lib/python3.9/site-packages/logbook/more.py",start:148802,end:168578,audio:0},{filename:"/lib/python3.9/site-packages/logbook/notifiers.py",start:168578,end:180613,audio:0},{filename:"/lib/python3.9/site-packages/logbook/queues.py",start:180613,end:205276,audio:0},{filename:"/lib/python3.9/site-packages/logbook/ticketing.py",start:205276,end:224507,audio:0},{filename:"/lib/python3.9/site-packages/logbook/utils.py",start:224507,end:230290,audio:0},{filename:"/lib/python3.9/site-packages/logbook/_speedups.so",start:230290,end:339038,audio:0},{filename:"/lib/python3.9/site-packages/Logbook-1.5.2-py3.9.egg-info/PKG-INFO",start:339038,end:341157,audio:0},{filename:"/lib/python3.9/site-packages/Logbook-1.5.2-py3.9.egg-info/not-zip-safe",start:341157,end:341158,audio:0},{filename:"/lib/python3.9/site-packages/Logbook-1.5.2-py3.9.egg-info/dependency_links.txt",start:341158,end:341159,audio:0},{filename:"/lib/python3.9/site-packages/Logbook-1.5.2-py3.9.egg-info/requires.txt",start:341159,end:341445,audio:0},{filename:"/lib/python3.9/site-packages/Logbook-1.5.2-py3.9.egg-info/top_level.txt",start:341445,end:341453,audio:0},{filename:"/lib/python3.9/site-packages/Logbook-1.5.2-py3.9.egg-info/SOURCES.txt",start:341453,end:342776,audio:0}],remote_package_size:202041,package_uuid:"f6638221-9013-40b2-8a9c-2369924edc5f"})})();
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Corel Draw X7 Update 6 Crack [CRACKED].md b/spaces/quidiaMuxgu/Expedit-SAM/Corel Draw X7 Update 6 Crack [CRACKED].md
deleted file mode 100644
index ce885dc5d68bcc5d5d5fa82a26318e30aa585f46..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Corel Draw X7 Update 6 Crack [CRACKED].md
+++ /dev/null
@@ -1,14 +0,0 @@
-
-
-x files .x new release. Consequently, the user who did not download the update to .x to .x software may not be able to access these features. The update is available for download for all CorelDRAW Graphics Suite X7.x users. The full release notes for CorelDRAW Graphics Suite X7.6 are contained in the .pdf file included with this release.
-
-## New Features and Enhancements in CorelDRAW Graphics Suite X7.6
-
-The .x software update, CorelDRAW Graphics Suite X7.6, contains a number of significant enhancements, as well as some changes to the user interface.
-
-To simplify the overall user experience, CorelDRAW has been reorganized into .x and .x sub-packages. Within the .x package, you can find the majority of the graphics and illustration tools as well as any new functionality. With this new organizational structure, the .x package can be managed entirely within the CorelDRAW Graphics Suite .x control panel.
-
-The most recent .x software update also improves the overall performance of the CorelDRAW Graphics Suite .x software. Additionally, new features are introduced in this release of the software. 4fefd39f24
-
-
-
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/HDPsyChart FULL Version Download [VERIFIED].md b/spaces/quidiaMuxgu/Expedit-SAM/HDPsyChart FULL Version Download [VERIFIED].md
deleted file mode 100644
index 1410d4af8e1f9c6c41adb201b3fb670880d816c7..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/HDPsyChart FULL Version Download [VERIFIED].md
+++ /dev/null
@@ -1,18 +0,0 @@
-
-
-Click on “Download HDPsyChart” link available on the “Demo” section of this page. Save the file to any folder of your choice. Now open the zip file and extract the folder content in any folder. Finally, rename the extracted folder as “HDPsyChart”. Please note that the program cannot be installed on computers that use the Mac operating system.
-
-Introduction #intro
-
-============
-
-The human brain relies on specific patterns of electrical activity in order to function properly. The brain wave frequency bands or electroencephalograph (EEG) refer to the pattern of brain activity as reflected in the brain’s electrical activity. The frequency band of brain waves are categorized as delta, theta, alpha, beta, gamma. The brain wave ranges are similar to the frequency range of the radio or television. The brain wave patterns can be used to measure and determine changes in our brain activity. The changes in brain activity can be utilized to diagnose medical conditions.
-
-The traditional, clinical methods used for diagnosis are based on visual inspection of the patient’s EEG data by a qualified healthcare professional. However, the traditional methods are subjective in nature and it is prone to human error. The traditional methods are also tedious and expensive. The recent developments of wearable, cost effective EEG devices have allowed for a faster and more accurate diagnosis. The current article will focus on the use of HDPSyChart, an open source, wearable and low-cost EEG recording device.
-
-HDPSyChart is a Windows application developed by the HDSS Neurological Team at the University of Manchester and University of Surrey. HDSS is the University’s research unit for neuroscience and stroke and includes Neurophysiologists, biomedical engineers and medical scientists. The application has been developed using the psychophysiological Signal Processing Toolkit (PSPTK) library. PSPTK is an open source library for development of mobile EEG applications. PSPTK allows for easy implementation of the various EEG analysis algorithms in a graphical user interface.
-
-HDPSyChart, allows users to create a screen capturing custom application for diagnosis of their patients using EEG analysis. HDPSyChart comes with the option of recording the patient’s brain activity while using the application. The analysis options provided include: visual inspection, which can be done to assess the frequency ranges of the patient’s brain activity; neuro-imaging analysis, which can be done to examine the 4fefd39f24
-
-
-
diff --git a/spaces/r0seyyyd33p/sdui-custom/app.py b/spaces/r0seyyyd33p/sdui-custom/app.py
deleted file mode 100644
index d79e4caf7cbc8aa2dc188124cf07fba4a3b691d3..0000000000000000000000000000000000000000
--- a/spaces/r0seyyyd33p/sdui-custom/app.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""
-Stable Diffusion Webui Version 1.6
-https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.6.0
-
-"""
-commit_id=r"5ef669de080814067961f28357256e8fe27544f4" #Version 1.3.0
-import os
-from sys import executable
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:pathlib.Path ) -> int :
- if pathlib.Path.exists(ClonePath):
- return 0
- for z in range(10):
- i=subprocess.run([r"git",r"clone",str(URI),str(ClonePath)])
- if(i.returncode == 0 ):
- del i
- return 0
- else :
- del i
- raise Exception(str.format("clone \'{0}\' failed",URI))
-
-
-def DownLoad(URI:str,DownloadPath:pathlib.Path,DownLoadFileName:str ) -> int:
- if (DownloadPath / DownLoadFileName).is_file(): return 0
- for z in range(10):
- i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",str(DownloadPath),r"-o",DownLoadFileName,URI]);
- if(i.returncode == 0 ):
- del i
- gc.collect()
- return 0
- else :
- del i
- raise Exception(str.format("download \'{0}\' failed",URI))
-
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",user_home / r"stable-diffusion-webui")
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard "+commit_id)
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",user_home / r"stable-diffusion-webui" / r"embeddings" / r"negative")
-Gitclone(r"https://huggingface.co/embed/lora",user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive")
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN" ,r"4x-UltraSharp.pth")
-while (True):
- i=subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")])
- if(i.returncode == 0 ):
- del i
- gc.collect()
- break
- else :
- del i
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" )
-Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser")
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface")
-Gitclone(r"https://github.com/BlafKing/sd-civitai-browser-plus",user_home / r"stable-diffusion-webui" / r"extensions" / r"civitai-browser")
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks")
-Gitclone(r"https://github.com/Bing-su/adetailer",user_home / r"stable-diffusion-webui" / r"extensions" / r"adetailer")
-Gitclone(r"https://github.com/nolanaatama/microsoftexcel-controlnet",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet")
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib")
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete")
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels")
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui")
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin")
-Gitclone(r"https://tinyurl.com/aspect-ratio-v",user_home / r"stable-diffusion-webui" / r"extensions" / r"aspect-ratio")
-os.chdir(user_home / r"stable-diffusion-webui")
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
- r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors"]
-for i in range(0,len(dList)): DownLoad(dList[i],user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet" / r"models",pathlib.Path(dList[i]).name)
-del dList
-#download model
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-#Stable Diffusion Checkpoint Model
-#MeinaPastelV5
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Baked%20VAE.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"MeinaPastelV5_BakedVAE.safetensors")
-#Photon
-DownLoad(r"https://huggingface.co/nolanaatama/phtn/resolve/main/phtn.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"phtn.safetensors")
-#RV
-DownLoad(r"https://huggingface.co/SG161222/Realistic_Vision_V5.1_noVAE/resolve/main/Realistic_Vision_V5.1_fp16-no-ema.safetensors",user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion",r"Realistic_Vision_latest.safetensors")
-#Lora Model
-#Better Light
-DownLoad(r"https://civitai.com/api/download/models/39885",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"Better_light.safetensors")
-#Backlighting
-DownLoad(r"https://civitai.com/api/download/models/39164",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"backlighting.safetensors")
-#Add details
-DownLoad(r"https://civitai.com/api/download/models/62833",user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora",r"add_detail.safetensors")
-#GFPGAN Model
-#detection Resnet50
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"detection_Resnet50_Final.pth")
-#parsing_parsenet
-DownLoad(r"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"parsing_parsenet.pth")
-#GFPGANv1.4
-DownLoad(r"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",user_home / r"stable-diffusion-webui"/r"models"/r"GFPGAN",r"GFPGANv1.4.pth")
-#strt Stable Diffusion Webui
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-gc.collect()
-while True:
- ret=subprocess.run([executable ,user_home / r"stable-diffusion-webui" / r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
- if(ret.returncode == 0 ):
- del ret
- gc.collect()
- else :
- del ret
-del os ,user_home ,pyexecutable ,subprocess
\ No newline at end of file
diff --git a/spaces/r3gm/RVC_HF/go-applio-manager-recode.bat b/spaces/r3gm/RVC_HF/go-applio-manager-recode.bat
deleted file mode 100644
index 91b8acfc0c69a356fd5b1d77650b2cd728b1072b..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/go-applio-manager-recode.bat
+++ /dev/null
@@ -1,322 +0,0 @@
-@echo off
-title Applio Installer
-
-::: _ _ _____ _
-::: /\ | (_) | __ \ | |
-::: / \ _ __ _ __ | |_ ___ | |__) |___ ___ ___ __| | ___
-::: / /\ \ | '_ \| '_ \| | |/ _ \ | _ // _ \/ __/ _ \ / _` |/ _ \
-::: / ____ \| |_) | |_) | | | (_) | | | \ \ __/ (_| (_) | (_| | __/
-::: /_/ \_\ .__/| .__/|_|_|\___/ |_| \_\___|\___\___/ \__,_|\___|
-::: | | | |
-::: |_| |_|
-:::
-:::
-
-setlocal
-set "branch=applio-recode"
-set "runtime=runtime-recode"
-set "repoUrl=https://github.com/IAHispano/Applio-RVC-Fork/archive/refs/heads/%branch%.zip"
-set "fixesFolder=fixes"
-set "localFixesPy=local_fixes.py"
-set "principal=%cd%"
-set "URL_BASE=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main"
-set "URL_EXTRA=https://huggingface.co/IAHispano/applio/resolve/main"
-
-:menu
-for /f "delims=: tokens=*" %%A in ('findstr /b ":::" "%~f0"') do @echo(%%A
-
-echo [1] Reinstall Applio
-echo [2] Update Applio
-echo [3] Update Applio + Runtime
-echo.
-
-set /p choice=Select an option:
-set choice=%choice: =%
-
-if "%choice%"=="1" (
- cls
- echo Starting Applio Reinstaller...
- echo.
- goto reinstaller
- pause
- cls
- goto menu
-
-)
-
-if "%choice%"=="2" (
- cls
- echo Starting Applio Updater...
- echo.
- goto updater
- pause
- cls
- goto menu
-)
-
-if "%choice%"=="3" (
- cls
- echo Updating Applio + Runtime...
- echo.
- goto updaterRuntime
- pause
- cls
- goto menu
-
-)
-
-cls
-echo Invalid option. Please enter a number from 1 to 3.
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
-
-:reinstaller
-
-echo WARNING: Remember to install Microsoft C++ Build Tools, Redistributable, Python, and Git before continuing.
-echo.
-echo Step-by-step guide: https://rentry.org/appliolocal
-echo Build Tools: https://aka.ms/vs/17/release/vs_BuildTools.exe
-echo Redistributable: https://aka.ms/vs/17/release/vc_redist.x64.exe
-echo Git: https://github.com/git-for-windows/git/releases/download/v2.42.0.windows.2/Git-2.42.0.2-64-bit.exe
-echo Python: Add this route to the windows enviroment variables the user path variable: %principal%\runtime\Scripts
-echo.
-pause
-cls
-
-echo Downloading ZIP file...
-powershell -command "& { Invoke-WebRequest -Uri '%repoUrl%' -OutFile '%principal%\repo.zip' }"
-echo.
-
-echo Extracting ZIP file...
-powershell -command "& { Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory('%principal%\repo.zip', '%principal%') }"
-echo.
-
-echo Copying folder and file structure from subdirectory to main directory...
-robocopy "%principal%\Applio-RVC-Fork-%branch%" "%principal%" /E
-echo.
-
-echo Deleting contents of subdirectory (files and folders)...
-rmdir "%principal%\Applio-RVC-Fork-%branch%" /S /Q
-echo.
-
-echo Cleaning up...
-del "%principal%\repo.zip"
-echo.
-cls
-
-echo Proceeding to download the models...
-echo.
-
-echo WARNING: At this point, it's recommended to disable antivirus or firewall, as errors might occur when downloading pretrained models.
-pause
-cls
-
-echo Downloading models in the assets folder...
-cd "assets"
-echo.
-echo Downloading the "pretrained" folder...
-cd "pretrained"
-curl -LJO "%URL_BASE%/pretrained/D32k.pth"
-curl -LJO "%URL_BASE%/pretrained/D40k.pth"
-curl -LJO "%URL_BASE%/pretrained/D48k.pth"
-curl -LJO "%URL_BASE%/pretrained/G32k.pth"
-curl -LJO "%URL_BASE%/pretrained/G40k.pth"
-curl -LJO "%URL_BASE%/pretrained/G48k.pth"
-curl -LJO "%URL_BASE%/pretrained/f0D32k.pth"
-curl -LJO "%URL_BASE%/pretrained/f0D40k.pth"
-curl -LJO "%URL_BASE%/pretrained/f0D48k.pth"
-curl -LJO "%URL_BASE%/pretrained/f0G32k.pth"
-curl -LJO "%URL_BASE%/pretrained/f0G40k.pth"
-curl -LJO "%URL_BASE%/pretrained/f0G48k.pth"
-cd ".."
-echo.
-cls
-
-echo Downloading the "pretrained_v2" folder...
-cd "pretrained_v2"
-curl -LJO "%URL_BASE%/pretrained_v2/D32k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/D40k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/D48k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/G32k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/G40k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/G48k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/f0D32k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/f0D40k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/f0D48k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/f0G32k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/f0G40k.pth"
-curl -LJO "%URL_BASE%/pretrained_v2/f0G48k.pth"
-cd ".."
-echo.
-cls
-
-echo Downloading the hubert_base.pt file...
-cd "hubert"
-curl -LJO "%URL_BASE%/hubert_base.pt"
-cd ".."
-echo.
-cls
-
-
-echo Downloading the rmvpe.pt file...
-cd "rmvpe"
-curl -LJO "%URL_BASE%/rmvpe.pt"
-echo.
-cls
-
-echo Downloading the rmvpe.onnx file...
-curl -LJO "%URL_BASE%/rmvpe.onnx"
-cd ".."
-cd ".."
-echo.
-cls
-
-echo Downloading the rest of the large files
-
-echo Downloading the "uvr5_weights" folder...
-cd "uvr5_weights"
-curl -LJO "%URL_BASE%/uvr5_weights/HP2_all_vocals.pth"
-curl -LJO "%URL_BASE%/uvr5_weights/HP3_all_vocals.pth"
-curl -LJO "%URL_BASE%/uvr5_weights/HP5_only_main_vocal.pth"
-curl -LJO "%URL_BASE%/uvr5_weights/VR-DeEchoAggressive.pth"
-curl -LJO "%URL_BASE%/uvr5_weights/VR-DeEchoDeReverb.pth"
-curl -LJO "%URL_BASE%/uvr5_weights/VR-DeEchoNormal.pth"
-cd ".."
-echo.
-cls
-
-echo Downloading the ffmpeg.exe file...
-curl -LJO "%URL_BASE%/ffmpeg.exe"
-echo.
-cls
-
-echo Downloading the ffprobe.exe file...
-curl -LJO "%URL_BASE%/ffprobe.exe"
-echo.
-cls
-
-echo Downloading the runtime.zip file...
-curl -LJO "%URL_EXTRA%/%runtime%.zip"
-echo.
-cls
-
-echo Extracting the runtime.zip file, this might take a while...
-powershell -Command "Expand-Archive -Path '%runtime%.zip' -DestinationPath '.'"
-del %runtime%.zip
-echo.
-cls
-
-echo Downloads completed!
-echo.
-
-echo Checking if the local_fixes.py file exists in the Fixes folder...
-if exist "%fixesFolder%\%localFixesPy%" (
- echo Running the file...
- runtime\python.exe "%fixesFolder%\%localFixesPy%"
-) else (
- echo The "%localFixesPy%" file was not found in the "Fixes" folder.
-)
-echo.
-
-echo Fixes Applied!
-echo.
-
-echo Applio has been reinstalled!
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
-
-
-:updater
-
-echo Downloading the ZIP file...
-powershell -command "& { Invoke-WebRequest -Uri '%repoUrl%' -OutFile '%principal%\repo.zip' }"
-echo.
-
-echo Extracting ZIP file...
-powershell -command "& { Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory('%principal%\repo.zip', '%principal%') }"
-echo.
-
-echo Copying folder and file structure from subdirectory to main directory...
-robocopy "%principal%\Applio-RVC-Fork-%branch%" "%principal%" /E
-echo.
-
-echo Deleting contents of the subdirectory (files and folders)...
-rmdir "%principal%\Applio-RVC-Fork-%branch%" /S /Q
-echo.
-
-echo Cleaning up...
-del "%principal%\repo.zip"
-echo.
-cls
-
-echo Verifying if the local_fixes.py file exists in the Fixes folder...
-if exist "%fixesFolder%\%localFixesPy%" (
- echo Running the file...
- runtime\python.exe "%fixesFolder%\%localFixesPy%"
-) else (
- echo The file "%localFixesPy%" was not found in the "Fixes" folder.
-)
-echo.
-
-echo Applio has been updated!
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
-
-
-:updaterRuntime
-
-echo Downloading the ZIP file...
-powershell -command "& { Invoke-WebRequest -Uri '%repoUrl%' -OutFile '%principal%\repo.zip' }"
-echo.
-
-echo Extracting ZIP file...
-powershell -command "& { Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory('%principal%\repo.zip', '%principal%') }"
-echo.
-
-echo Copying folder and file structure from subdirectory to main directory...
-robocopy "%principal%\Applio-RVC-Fork-%branch%" "%principal%" /E
-echo.
-
-echo Deleting contents of the subdirectory (files and folders)...
-rmdir "%principal%\Applio-RVC-Fork-%branch%" /S /Q
-echo.
-
-echo Cleaning up...
-del "%principal%\repo.zip"
-echo.
-cls
-
-echo Downloading the runtime.zip file...
-curl -LJO "%URL_EXTRA%/%runtime%.zip"
-echo.
-cls
-echo Extracting the runtime.zip file, this might take a while...
-powershell -Command "Expand-Archive -Path '%runtime%.zip' -DestinationPath '.'"
-del runtime.zip
-echo.
-cls
-
-echo Verifying if the local_fixes.py file exists in the Fixes folder...
-if exist "%fixesFolder%\%localFixesPy%" (
- echo Running the file...
- runtime\python.exe "%fixesFolder%\%localFixesPy%"
-) else (
- echo The file "%localFixesPy%" was not found in the "Fixes" folder.
-)
-echo.
-
-echo Applio has been updated!
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
diff --git a/spaces/r3gm/RVC_HF/infer/modules/ipex/__init__.py.py b/spaces/r3gm/RVC_HF/infer/modules/ipex/__init__.py.py
deleted file mode 100644
index 9f53b2d3f7025b2d71369dababa4e6f2a4affc48..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/infer/modules/ipex/__init__.py.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import os
-import sys
-import contextlib
-import torch
-import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-from .hijacks import ipex_hijacks
-from .attention import attention_init
-
-# pylint: disable=protected-access, missing-function-docstring, line-too-long
-
-def ipex_init(): # pylint: disable=too-many-statements
- try:
- #Replace cuda with xpu:
- torch.cuda.current_device = torch.xpu.current_device
- torch.cuda.current_stream = torch.xpu.current_stream
- torch.cuda.device = torch.xpu.device
- torch.cuda.device_count = torch.xpu.device_count
- torch.cuda.device_of = torch.xpu.device_of
- torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
- torch.cuda.get_device_name = torch.xpu.get_device_name
- torch.cuda.get_device_properties = torch.xpu.get_device_properties
- torch.cuda.init = torch.xpu.init
- torch.cuda.is_available = torch.xpu.is_available
- torch.cuda.is_initialized = torch.xpu.is_initialized
- torch.cuda.is_current_stream_capturing = lambda: False
- torch.cuda.set_device = torch.xpu.set_device
- torch.cuda.stream = torch.xpu.stream
- torch.cuda.synchronize = torch.xpu.synchronize
- torch.cuda.Event = torch.xpu.Event
- torch.cuda.Stream = torch.xpu.Stream
- torch.cuda.FloatTensor = torch.xpu.FloatTensor
- torch.Tensor.cuda = torch.Tensor.xpu
- torch.Tensor.is_cuda = torch.Tensor.is_xpu
- torch.cuda._initialization_lock = torch.xpu.lazy_init._initialization_lock
- torch.cuda._initialized = torch.xpu.lazy_init._initialized
- torch.cuda._lazy_seed_tracker = torch.xpu.lazy_init._lazy_seed_tracker
- torch.cuda._queued_calls = torch.xpu.lazy_init._queued_calls
- torch.cuda._tls = torch.xpu.lazy_init._tls
- torch.cuda.threading = torch.xpu.lazy_init.threading
- torch.cuda.traceback = torch.xpu.lazy_init.traceback
- torch.cuda.Optional = torch.xpu.Optional
- torch.cuda.__cached__ = torch.xpu.__cached__
- torch.cuda.__loader__ = torch.xpu.__loader__
- torch.cuda.ComplexFloatStorage = torch.xpu.ComplexFloatStorage
- torch.cuda.Tuple = torch.xpu.Tuple
- torch.cuda.streams = torch.xpu.streams
- torch.cuda._lazy_new = torch.xpu._lazy_new
- torch.cuda.FloatStorage = torch.xpu.FloatStorage
- torch.cuda.Any = torch.xpu.Any
- torch.cuda.__doc__ = torch.xpu.__doc__
- torch.cuda.default_generators = torch.xpu.default_generators
- torch.cuda.HalfTensor = torch.xpu.HalfTensor
- torch.cuda._get_device_index = torch.xpu._get_device_index
- torch.cuda.__path__ = torch.xpu.__path__
- torch.cuda.Device = torch.xpu.Device
- torch.cuda.IntTensor = torch.xpu.IntTensor
- torch.cuda.ByteStorage = torch.xpu.ByteStorage
- torch.cuda.set_stream = torch.xpu.set_stream
- torch.cuda.BoolStorage = torch.xpu.BoolStorage
- torch.cuda.os = torch.xpu.os
- torch.cuda.torch = torch.xpu.torch
- torch.cuda.BFloat16Storage = torch.xpu.BFloat16Storage
- torch.cuda.Union = torch.xpu.Union
- torch.cuda.DoubleTensor = torch.xpu.DoubleTensor
- torch.cuda.ShortTensor = torch.xpu.ShortTensor
- torch.cuda.LongTensor = torch.xpu.LongTensor
- torch.cuda.IntStorage = torch.xpu.IntStorage
- torch.cuda.LongStorage = torch.xpu.LongStorage
- torch.cuda.__annotations__ = torch.xpu.__annotations__
- torch.cuda.__package__ = torch.xpu.__package__
- torch.cuda.__builtins__ = torch.xpu.__builtins__
- torch.cuda.CharTensor = torch.xpu.CharTensor
- torch.cuda.List = torch.xpu.List
- torch.cuda._lazy_init = torch.xpu._lazy_init
- torch.cuda.BFloat16Tensor = torch.xpu.BFloat16Tensor
- torch.cuda.DoubleStorage = torch.xpu.DoubleStorage
- torch.cuda.ByteTensor = torch.xpu.ByteTensor
- torch.cuda.StreamContext = torch.xpu.StreamContext
- torch.cuda.ComplexDoubleStorage = torch.xpu.ComplexDoubleStorage
- torch.cuda.ShortStorage = torch.xpu.ShortStorage
- torch.cuda._lazy_call = torch.xpu._lazy_call
- torch.cuda.HalfStorage = torch.xpu.HalfStorage
- torch.cuda.random = torch.xpu.random
- torch.cuda._device = torch.xpu._device
- torch.cuda.classproperty = torch.xpu.classproperty
- torch.cuda.__name__ = torch.xpu.__name__
- torch.cuda._device_t = torch.xpu._device_t
- torch.cuda.warnings = torch.xpu.warnings
- torch.cuda.__spec__ = torch.xpu.__spec__
- torch.cuda.BoolTensor = torch.xpu.BoolTensor
- torch.cuda.CharStorage = torch.xpu.CharStorage
- torch.cuda.__file__ = torch.xpu.__file__
- torch.cuda._is_in_bad_fork = torch.xpu.lazy_init._is_in_bad_fork
- #torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing
-
- #Memory:
- torch.cuda.memory = torch.xpu.memory
- if 'linux' in sys.platform and "WSL2" in os.popen("uname -a").read():
- torch.xpu.empty_cache = lambda: None
- torch.cuda.empty_cache = torch.xpu.empty_cache
- torch.cuda.memory_stats = torch.xpu.memory_stats
- torch.cuda.memory_summary = torch.xpu.memory_summary
- torch.cuda.memory_snapshot = torch.xpu.memory_snapshot
- torch.cuda.memory_allocated = torch.xpu.memory_allocated
- torch.cuda.max_memory_allocated = torch.xpu.max_memory_allocated
- torch.cuda.memory_reserved = torch.xpu.memory_reserved
- torch.cuda.memory_cached = torch.xpu.memory_reserved
- torch.cuda.max_memory_reserved = torch.xpu.max_memory_reserved
- torch.cuda.max_memory_cached = torch.xpu.max_memory_reserved
- torch.cuda.reset_peak_memory_stats = torch.xpu.reset_peak_memory_stats
- torch.cuda.reset_max_memory_cached = torch.xpu.reset_peak_memory_stats
- torch.cuda.reset_max_memory_allocated = torch.xpu.reset_peak_memory_stats
- torch.cuda.memory_stats_as_nested_dict = torch.xpu.memory_stats_as_nested_dict
- torch.cuda.reset_accumulated_memory_stats = torch.xpu.reset_accumulated_memory_stats
-
- #RNG:
- torch.cuda.get_rng_state = torch.xpu.get_rng_state
- torch.cuda.get_rng_state_all = torch.xpu.get_rng_state_all
- torch.cuda.set_rng_state = torch.xpu.set_rng_state
- torch.cuda.set_rng_state_all = torch.xpu.set_rng_state_all
- torch.cuda.manual_seed = torch.xpu.manual_seed
- torch.cuda.manual_seed_all = torch.xpu.manual_seed_all
- torch.cuda.seed = torch.xpu.seed
- torch.cuda.seed_all = torch.xpu.seed_all
- torch.cuda.initial_seed = torch.xpu.initial_seed
-
- #AMP:
- torch.cuda.amp = torch.xpu.amp
- if not hasattr(torch.cuda.amp, "common"):
- torch.cuda.amp.common = contextlib.nullcontext()
- torch.cuda.amp.common.amp_definitely_not_available = lambda: False
- try:
- torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
- except Exception: # pylint: disable=broad-exception-caught
- try:
- from .gradscaler import gradscaler_init # pylint: disable=import-outside-toplevel, import-error
- gradscaler_init()
- torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
- except Exception: # pylint: disable=broad-exception-caught
- torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
-
- #C
- torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream
- ipex._C._DeviceProperties.major = 2023
- ipex._C._DeviceProperties.minor = 2
-
- #Fix functions with ipex:
- torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_allocated(device)), torch.xpu.get_device_properties(device).total_memory]
- torch._utils._get_available_device_type = lambda: "xpu"
- torch.has_cuda = True
- torch.cuda.has_half = True
- torch.cuda.is_bf16_supported = lambda *args, **kwargs: True
- torch.cuda.is_fp16_supported = lambda *args, **kwargs: True
- torch.version.cuda = "11.7"
- torch.cuda.get_device_capability = lambda *args, **kwargs: [11,7]
- torch.cuda.get_device_properties.major = 11
- torch.cuda.get_device_properties.minor = 7
- torch.cuda.ipc_collect = lambda *args, **kwargs: None
- torch.cuda.utilization = lambda *args, **kwargs: 0
-
- ipex_hijacks()
- attention_init()
- except Exception as e:
- return False, e
- return True, None
\ No newline at end of file
diff --git a/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/app.py b/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/app.py
deleted file mode 100644
index 0b6fa99b00fa7f475dca27c70896c3c83220cb02..0000000000000000000000000000000000000000
--- a/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import sys
-from pathlib import Path
-import torch
-from PIL import Image
-import torchvision.transforms as transforms
-
-from networks.drn_seg import DRNSub, DRNSeg
-from utils.tools import *
-from utils.visualize import *
-
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-
-def load_classifier(type, model_path, device=torch.device("cpu")):
- if type == 'global':
- model = DRNSub(1)
- elif type == 'local':
- model = DRNSeg(2)
- state_dict = torch.load(model_path, map_location=device)
- model.load_state_dict(state_dict['model'])
- model.to(device)
- model.device = device
- model.eval()
- return model
-
-
-local_model_file = hf_hub_download(
- repo_id="radames/FALdetector", filename="local.pth", token=True)
-global_model_file = hf_hub_download(
- repo_id="radames/FALdetector", filename="global.pth", token=True)
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-global_model = load_classifier("global", global_model_file, device)
-local_model = load_classifier("local", local_model_file, device)
-faces_model_file = 'utils/dlib_face_detector/mmod_human_face_detector.dat'
-
-tf = transforms.Compose([transforms.ToTensor(),
- transforms.Normalize(mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225])])
-
-
-def predict(img_path):
- im_w, im_h = Image.open(img_path).size
- faces = face_detection(img_path, verbose=False,
- model_file=faces_model_file)
- if len(faces) == 0:
- raise gr.Error("No face detected by dlib")
-
- face, box = faces[0]
- face = resize_shorter_side(face, 400)[0]
- face_tens = tf(face).to(device)
-
- with torch.no_grad():
- prob = global_model(face_tens.unsqueeze(0))[0].sigmoid().cpu().item()
- flow = local_model(face_tens.unsqueeze(0))[0].cpu().numpy()
- flow = np.transpose(flow, (1, 2, 0))
- h, w, _ = flow.shape
-
- # Undoing the warps
- modified = face.resize((w, h), Image.BICUBIC)
- modified_np = np.asarray(modified)
- reverse_np = warp(modified_np, flow)
- reverse = Image.fromarray(reverse_np)
- # heat map
- flow_magn = np.sqrt(flow[:, :, 0]**2 + flow[:, :, 1]**2)
- cv_out = get_heatmap_cv(modified_np, flow_magn, max_flow_mag=7)
- heat_map = Image.fromarray(cv_out)
-
- print(prob)
- return {"Probability FAL": prob}, modified, heat_map, reverse
-
-
-with gr.Blocks() as blocks:
- gr.Markdown("""
-## Unofficial Demo
-### Detecting Photoshopped Faces by Scripting Photoshop
-#### FAL Detector Live Demo
-
-* https://arxiv.org/abs/1906.05856
-* https://peterwang512.github.io/FALdetector/
- """)
- with gr.Row():
- with gr.Column():
- in_image = gr.Image(label="Input Image", type="filepath")
- run_btn = gr.Button(label="Run")
- with gr.Column():
- label = gr.Label(
- label="Probability being modified by Photoshop FAL")
- with gr.Row():
- cropped = gr.Image(label="Cropped Input Image")
- heatmap = gr.Image(label="Heatmap")
- warped = gr.Image(label="Suggested Undo")
- run_btn.click(fn=predict, inputs=[in_image], outputs=[
- label, cropped, heatmap, warped])
- gr.Examples(fn=predict,
- examples=list(Path("./examples").glob("*.png")),
- inputs=[in_image],
- outputs=[label, cropped, heatmap, warped],
- cache_examples=True)
-blocks.launch()
diff --git a/spaces/radames/dpt-depth-estimation-3d-obj/app.py b/spaces/radames/dpt-depth-estimation-3d-obj/app.py
deleted file mode 100644
index e03e734dc952b388f89c99dda1b7106a4f886079..0000000000000000000000000000000000000000
--- a/spaces/radames/dpt-depth-estimation-3d-obj/app.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import gradio as gr
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
-import torch
-import numpy as np
-from PIL import Image
-import open3d as o3d
-from pathlib import Path
-import os
-
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
-model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
-
-
-def process_image(image_path):
- image_path = Path(image_path)
- image_raw = Image.open(image_path)
- image = image_raw.resize(
- (800, int(800 * image_raw.size[1] / image_raw.size[0])),
- Image.Resampling.LANCZOS)
-
- # prepare image for the model
- encoding = feature_extractor(image, return_tensors="pt")
-
- # forward pass
- with torch.no_grad():
- outputs = model(**encoding)
- predicted_depth = outputs.predicted_depth
-
- # interpolate to original size
- prediction = torch.nn.functional.interpolate(
- predicted_depth.unsqueeze(1),
- size=image.size[::-1],
- mode="bicubic",
- align_corners=False,
- ).squeeze()
- output = prediction.cpu().numpy()
- depth_image = (output * 255 / np.max(output)).astype('uint8')
- try:
- gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
- img = Image.fromarray(depth_image)
- return [img, gltf_path, gltf_path]
- except Exception as e:
- gltf_path = create_3d_obj(
- np.array(image), depth_image, image_path, depth=8)
- img = Image.fromarray(depth_image)
- return [img, gltf_path, gltf_path]
- except:
- print("Error reconstructing 3D model")
- raise Exception("Error reconstructing 3D model")
-
-
-def create_3d_obj(rgb_image, depth_image, image_path, depth=10):
- depth_o3d = o3d.geometry.Image(depth_image)
- image_o3d = o3d.geometry.Image(rgb_image)
- rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
- image_o3d, depth_o3d, convert_rgb_to_intensity=False)
- w = int(depth_image.shape[1])
- h = int(depth_image.shape[0])
-
- camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
- camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)
-
- pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
- rgbd_image, camera_intrinsic)
-
- print('normals')
- pcd.normals = o3d.utility.Vector3dVector(
- np.zeros((1, 3))) # invalidate existing normals
- pcd.estimate_normals(
- search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))
- pcd.orient_normals_towards_camera_location(
- camera_location=np.array([0., 0., 1000.]))
- pcd.transform([[1, 0, 0, 0],
- [0, -1, 0, 0],
- [0, 0, -1, 0],
- [0, 0, 0, 1]])
- pcd.transform([[-1, 0, 0, 0],
- [0, 1, 0, 0],
- [0, 0, 1, 0],
- [0, 0, 0, 1]])
-
- print('run Poisson surface reconstruction')
- with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
- mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
- pcd, depth=depth, width=0, scale=1.1, linear_fit=True)
-
- voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256
- print(f'voxel_size = {voxel_size:e}')
- mesh = mesh_raw.simplify_vertex_clustering(
- voxel_size=voxel_size,
- contraction=o3d.geometry.SimplificationContraction.Average)
-
- # vertices_to_remove = densities < np.quantile(densities, 0.001)
- # mesh.remove_vertices_by_mask(vertices_to_remove)
- bbox = pcd.get_axis_aligned_bounding_box()
- mesh_crop = mesh.crop(bbox)
- gltf_path = f'./{image_path.stem}.gltf'
- o3d.io.write_triangle_mesh(
- gltf_path, mesh_crop, write_triangle_uvs=True)
- return gltf_path
-
-
-title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"
-description = "This demo is a variation from the original DPT Demo. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
-examples = [["examples/" + img] for img in os.listdir("examples/")]
-
-iface = gr.Interface(fn=process_image,
- inputs=[gr.Image(
- type="filepath", label="Input Image")],
- outputs=[gr.Image(label="predicted depth", type="pil"),
- gr.Model3D(label="3d mesh reconstruction", clear_color=[
- 1.0, 1.0, 1.0, 1.0]),
- gr.File(label="3d gLTF")],
- title=title,
- description=description,
- examples=examples,
- allow_flagging="never",
- cache_examples=False)
-iface.launch(debug=True, enable_queue=False)
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Artlantis 5 x64 with crack keygen How to activate the full features of the powerful design tool.md b/spaces/raedeXanto/academic-chatgpt-beta/Artlantis 5 x64 with crack keygen How to activate the full features of the powerful design tool.md
deleted file mode 100644
index 7e554403f04e9d5a2b47be83b4db8592952dcbdf..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Artlantis 5 x64 with crack keygen How to activate the full features of the powerful design tool.md
+++ /dev/null
@@ -1,104 +0,0 @@
-
-
Artlantis 5 x64 with crack keygen: A Comprehensive Guide
-
Are you looking for a powerful and easy-to-use software to create stunning 3D designs, animations and panoramas? Do you want to impress your clients, colleagues or friends with your professional and realistic renderings? If yes, then you should definitely check out Artlantis 5 x64 with crack keygen.
Artlantis 5 is a stand-alone 3D rendering application developed specifically for architects and designers. It allows you to create high-quality images, Quicktime VR objects and complex animations from your CAD models. It has a rich user interface, advanced rendering technology and integration with other popular CAD software. With Artlantis 5 x64 with crack keygen, you can enjoy all the features of this amazing software without paying a dime.
-
In this article, we will show you how to download and install Artlantis 5 x64 with crack keygen, how to use it to create stunning 3D designs, and answer some common questions about it. Let's get started!
-
Features of Artlantis 5: How it can help you create stunning 3D designs
-
Artlantis 5 has many features that make it one of the best 3D rendering software in the market. Here are some of them:
-
Rich user interface for better workflow
-
The design interface of Artlantis is very clear and ergonomic, easy to learn and use. All the tools you need are located near the real-time preview window, so you can see the results of each change instantly. You can also customize the interface according to your preferences and needs.
-
Artlantis also allows you to save as many views as you want, and adjust them according to different scenarios. You can change the lighting, environment, perspective or shaders whenever you want. You can also save each view as a postcard in the library, and reuse any element from it by dragging them to the scene.
-
Artlantis 5 64-bit full version with serial number
-How to download and install Artlantis 5 x64 cracked
-Artlantis 5 x64 keygen generator free download
-Artlantis 5 x64 patch activation code
-Artlantis 5 x64 license key crack
-Artlantis 5 x64 torrent with crack
-Artlantis 5 x64 for windows with crack
-Artlantis 5 x64 for mac with crack
-Artlantis 5 x64 software with crack download
-Artlantis 5 x64 crack only download
-Artlantis 5 x64 latest version with crack
-Artlantis 5 x64 update with crack
-Artlantis 5 x64 portable with crack
-Artlantis 5 x64 offline installer with crack
-Artlantis 5 x64 setup with crack
-Artlantis 5 x64 system requirements with crack
-Artlantis 5 x64 features with crack
-Artlantis 5 x64 tutorial with crack
-Artlantis 5 x64 review with crack
-Artlantis 5 x64 comparison with crack
-Artlantis 5 x64 alternatives with crack
-Artlantis 5 x64 tips and tricks with crack
-Artlantis 5 x64 problems and solutions with crack
-Artlantis 5 x64 support and help with crack
-Artlantis 5 x64 forum and community with crack
-Artlantis 5 x64 discount and coupon with crack
-Artlantis 5 x64 free trial and demo with crack
-Artlantis 5 x64 testimonials and feedback with crack
-Artlantis 5 x64 benefits and advantages with crack
-Artlantis 5 x64 drawbacks and disadvantages with crack
-Artlantis 5 x64 pros and cons with crack
-Artlantis 5 x64 best practices and recommendations with crack
-Artlantis 5 x64 case studies and examples with crack
-Artlantis 5 x64 FAQs and answers with crack
-Artlantis 5 x64 guides and manuals with crack
-Artlantis 5 x64 videos and screenshots with crack
-Artlantis 5 x64 blogs and articles with crack
-Artlantis 5 x64 news and updates with crack
-Artlantis 5 x64 awards and recognition with crack
-Artlantis 5 x64 history and development with crack
-Artlantis Studio vs Render vs Shaders vs Media vs Objects vs Animation vs Batch vs Site Insertion vs iVisit360 vs Twinlinker - which one to choose?
-How to use Artlantis Studio for architectural visualization?
-How to create realistic materials in Artlantis Render?
-How to add objects and media to your scene in Artlantis Media?
-How to animate your scene in Artlantis Animation?
-How to batch render multiple views in Artlantis Batch?
-How to insert your model into a site photo in Artlantis Site Insertion?
-How to create interactive panoramas in iVisit360?
-How to share your projects online in Twinlinker?
-
Advanced rendering technology for realistic results
-
Artlantis has a powerful rendering engine that can produce realistic images in a short time. It supports global illumination, ambient occlusion, depth of field, motion blur, lens flare and more. It also has a wide range of shaders and materials that you can apply to your objects to enhance their appearance.
-
Artlantis also has a new feature called Neon shader, which allows you to create glowing effects on your objects. It is very useful for creating night scenes or highlighting certain elements. The Neon shader has been greatly improved both in terms of quality and rendering speed.
-
Integration with other CAD software for seamless workflow
-
Artlantis can import files from many popular CAD software such as ArchiCAD, Revit, SketchUp Pro, VectorWorks and more. It supports formats such as DWF, OBJ, FBX, DXF, DWG and SKP. This way, you can easily transfer your models from your CAD software to Artlantis without losing any information.
-
Artlantis also has plugins for some CAD software that allow you to export your models directly to Artlantis with one click. This saves you time and ensures compatibility between your files.
-
How to download and install Artlantis 5 x64 with crack keygen
-
If you want to download and install Artlantis 5 x64 with crack keygen on your Windows device, here are the steps you need to follow:
-
Requirements and precautions
-
-
Make sure your device meets the minimum system requirements for running Artlantis 5. You need at least Windows Vista/7/8/10 (64-bit), Intel Core i3 or equivalent processor, 4 GB of RAM and OpenGL graphic card.
-
Make sure you have enough disk space for installing Artlantis 5. You need at least 500 MB of free space.
-
Make sure you have a stable internet connection for downloading Artlantis 5 x64 with crack keygen.
-
Make sure you have an antivirus software installed on your device and disable it temporarily before installing Artlantis 5 x64 with crack keygen. Some antivirus software may detect the crack file as a virus or malware and delete it.
-
Make sure you have a backup of your important files before installing Artlantis 5 x64 with crack keygen. Installing cracked software may cause some problems or errors on your device.
-
-
Steps to download and install Artlantis 5 x64 with crack keygen
-
-
Download Artlantis Studio Full Version from this link. Choose either Artlantis Studio (x64) or (x86) depending on your device architecture.
-
Extract the downloaded file using WinRAR or any other file extractor software.
-
Run the setup file and follow the instructions to install Artlantis Studio on your device.
-
Copy the files from the crack folder or use the keymaker provided in the downloaded file to activate Artlantis Studio.
-
Run the software and enjoy using it!
-
-
How to use Artlantis Studio
-
Now that you have installed Artlantis Studio on your device, let's see how to use it to create stunning 3D designs. Here are some basic steps:
-
How to create a new project in Artlantis Studio
-
-
To create a new project in Artlantis Studio, click on File > New Project or press Ctrl + N on your keyboard.
-
A dialog box will appear where you can choose the type of project you want to create. You can choose between Image (for still images), Animation (for videos), Panorama (for spherical images) or VR Object (for interactive objects).
-
You can also choose the resolution, quality and format of your project according to your needs.
-
Click OK to create your project.
-
-
How to import and edit objects in Artlantis Studio
-
-
To import objects from other CAD software into your project, click on File > Open or press Ctrl + O on your keyboard.
-
A dialog box will appear where you can browse your files and select the file you want to import. You can also drag and drop files from your folders into the preview window.
-
You can import multiple files at once by holding down Ctrl or Shift while selecting them.
-
You can also use plugins for some CAD software that allow you to export your models directly into Artlantis Studio without opening them first.
-
To edit objects in your project, use the tools on the left side of the interface. You can move, rotate, scale, duplicate or delete objects using these tools.
-
You can also use the inspector window on the right side of the interface to change the properties of objects such as name, layer, visibility or position.
-
You can also use keyboard shortcuts for some common actions such as Ctrl + Z (undo), Ctrl + Y (redo), Ctrl + C (copy), Ctrl + V (paste), Ctrl + X (cut) or Delete (delete
0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download Alias Surface 2018 Crack How to Avoid Viruses and Malware.md b/spaces/raedeXanto/academic-chatgpt-beta/Download Alias Surface 2018 Crack How to Avoid Viruses and Malware.md
deleted file mode 100644
index dd56e01f97546d0f462536ced150c9a33bb0f49f..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download Alias Surface 2018 Crack How to Avoid Viruses and Malware.md
+++ /dev/null
@@ -1,179 +0,0 @@
-
-
Download Alias Surface 2018 Crack: A Guide for Beginners
-
If you are looking for a software that can help you create stunning 3D models and surfaces, you might have heard of Alias Surface 2018. This software is one of the best tools for industrial design, automotive design, product design, and more. However, it is also one of the most expensive software in the market, costing thousands of dollars for a single license. That's why many people are looking for a way to download Alias Surface 2018 crack, which is a modified version of the software that can bypass the activation process and let you use it for free. But how do you download and use Alias Surface 2018 crack safely and effectively? In this article, we will show you everything you need to know about this software and how to get it without paying a dime.
-
What is Alias Surface 2018?
-
Alias Surface 2018 is a software that allows you to create and edit complex 3D models and surfaces with ease. It is designed for professionals who need to create high-quality designs for various industries and purposes. With Alias Surface 2018, you can:
Create smooth and organic shapes with NURBS (Non-Uniform Rational B-Splines) modeling
-
Use dynamic shape modeling to manipulate curves, surfaces, and meshes in real time
-
Apply textures, materials, colors, and lighting effects to your models
-
Export your models to other software such as Maya, AutoCAD, or Inventor
-
Collaborate with other designers using cloud-based services
-
-
Features and benefits of Alias Surface 2018
-
Some of the features and benefits that make Alias Surface 2018 stand out from other software are:
-
-
It has a user-friendly interface that is easy to navigate and customize
-
It has a powerful toolset that can handle complex geometries and surfaces
-
It has a fast and accurate rendering engine that can produce realistic images and animations
-
It has a flexible workflow that can adapt to different design stages and scenarios
-
It has a comprehensive documentation and support system that can help you learn and troubleshoot the software
-
-
System requirements and compatibility of Alias Surface 2018
-
To run Alias Surface 2018 smoothly on your computer, you need to meet the following system requirements:
-
-
Operating system
Windows 10 (64-bit) or Windows 7 SP1 (64-bit)
-
CPU
Intel Core i5 or higher, or AMD equivalent
-
RAM
8 GB or more
-
Disk space
10 GB or more
-
Graphics card
NVIDIA Quadro or GeForce with at least 2 GB VRAM, or AMD Radeon Pro or FirePro with at least 4 GB VRAM
-
Display resolution
1920 x 1080 or higher
-
Internet connection
Required for installation, activation, updates, and cloud services
-
-
In addition, you need to have Microsoft .NET Framework 4.6 or later installed on your computer. You also need to have an Autodesk account to access some of the features and services of the software.
-
Why do you need a crack for Alias Surface 2018?
-
If you are wondering why you would need a crack for Alias Surface 2018, here are some reasons why:
-
The disadvantages of using the official version of Alias Surface 2018
-
The official version of Alias Surface 2018 has some disadvantages that might make you think twice before buying it. Some of these are:
-
Autodesk Alias Surface 2018 x64 full crack[^1^]
-Autodesk Alias Surface 2018 activation crack[^1^]
-Download Autodesk Alias Surface 2018 with keygen[^1^]
-How to crack Autodesk Alias Surface 2018[^1^]
-Autodesk Alias Surface 2018 free download full version[^1^]
-Autodesk Alias Surface 2018 torrent download[^1^]
-Autodesk Alias Surface 2018 serial number generator[^1^]
-Autodesk Alias Surface 2018 patch download[^1^]
-Autodesk Alias Surface 2018 license key crack[^1^]
-Autodesk Alias Surface 2018 product key crack[^1^]
-Download Autodesk Alias Surface 2018 x64 + Crack - jyvsoft[^1^]
-Stardock Windowblinds 10.5 full crack + Autodesk Alias Surface 2018[^1^]
-Movavi 360 Video Editor v1.0.1 + Activation Crack + Autodesk Alias Surface 2018[^1^]
-Autodesk Point Layout 2019 crack + Autodesk Alias Surface 2018[^1^]
-Autodesk Alias Surface 2018 crack complet[^2^]
-Data Engineer Setup OEA and build, schedule and maintain pipelines for data ingestion and preparation + Autodesk Alias Surface 2018 crack[^2^]
-Personalized Learning Pathways Teahouse (ebook) by Emirain + Autodesk Alias Surface 2018 crack[^2^]
-Data Project Manager Oversee use case definition and operationalize Responsible AI and data governance + Autodesk Alias Surface 2018 crack[^2^]
-Innocent Heart Return to Jorgaldur: the battle mage + Autodesk Alias Surface 2018 crack[^2^]
-Open Education High-quality educational opportunities should be available to all learners + Autodesk Alias Surface 2018 crack[^2^]
-Alexander Hislop + Autodesk Alias Surface 2018 crack[^2^]
-Download Autodesk Alias Surface 2018 for Windows 10
-Download Autodesk Alias Surface 2018 for Mac OS X
-Download Autodesk Alias Surface 2018 for Linux
-Download Autodesk Alias Surface 2018 portable version
-Download Autodesk Alias Surface 2018 offline installer
-Download Autodesk Alias Surface 2018 ISO file
-Download Autodesk Alias Surface 2018 from official website
-Download Autodesk Alias Surface 2018 from Google Drive
-Download Autodesk Alias Surface 2018 from Mega.nz
-Download Autodesk Alias Surface 2018 from Mediafire.com
-Download Autodesk Alias Surface 2018 from Rapidgator.net
-Download Autodesk Alias Surface 2018 from Zippyshare.com
-Download Autodesk Alias Surface 2018 from Uptobox.com
-Download Autodesk Alias Surface 2018 from Uploaded.net
-Download Autodesk Alias Surface 2018 from Turbobit.net
-Download Autodesk Alias Surface 2018 from Nitroflare.com
-Download Autodesk Alias Surface 2018 cracked by Xforce team
-Download Autodesk Alias Surface 2018 cracked by Codex team
-Download Autodesk Alias Surface 2018 cracked by Skidrow team
-Download Autodesk Alias Surface 2018 cracked by Reloaded team
-Download Autodesk Alias Surface 2018 cracked by CPY team
-Download Autodesk Alias Surface 2018 cracked by Fitgirl repack
-Download Autodesk Alias Surface 2018 cracked by Corepack repack
-Download Autodesk Alias Surface 2018 cracked by Blackbox repack
-
-
It is very expensive. The official price of Alias Surface 2018 is $6,250 per year for a subscription license, or $16,500 for a perpetual license. That's a lot of money for a single software that you might not use all the time.
-
It requires constant internet connection. To use the official version of Alias Surface 2018, you need to have an active internet connection at all times. This means that you cannot use it offline or in places where the internet is slow or unreliable.
-
It has limited compatibility. The official version of Alias Surface 2018 only works on Windows operating systems. If you have a Mac or Linux computer, you cannot use it unless you install a virtual machine or dual boot your system.
-
It has strict activation rules. The official version of Alias Surface 2018 requires you to activate it online using your Autodesk account. You can only activate it on one computer at a time, and you need to deactivate it before moving it to another computer. If you forget to do so, or if your computer crashes or gets stolen, you might lose access to your license.
-
It has frequent updates. The official version of Alias Surface 2018 gets updated regularly by Autodesk. While this might seem like a good thing, it also means that you have to download and install these updates every time they are released. This can be annoying and time-consuming, especially if you have a slow internet connection or limited disk space.
-
-
The advantages of using a cracked version of Alias Surface 2018
-
The cracked version of Alias Surface 2018 is a modified version of the software that can bypass the activation process and let you use it for free. Some of the advantages of using a cracked version are:
-
-
It is free. The cracked version of Alias Surface 2018 does not cost anything. You can download it from various sources online without paying a dime.
-2018 does not need an internet connection to work. You can use it offline or in places where the internet is not available.
-
It has more compatibility. The cracked version of Alias Surface 2018 can work on any operating system that can run the software. You can use it on Mac or Linux computers without any problems.
-
It has no activation rules. The cracked version of Alias Surface 2018 does not require you to activate it online or offline. You can use it on any computer you want, and you can move it to another computer without deactivating it.
-
It has no updates. The cracked version of Alias Surface 2018 does not get updated by Autodesk. This means that you do not have to worry about downloading and installing new versions of the software. You can use the same version for as long as you want.
-
-
How to download and install Alias Surface 2018 crack?
-
If you are convinced that you want to download and install Alias Surface 2018 crack, here are the steps that you need to follow:
-
Step 1: Find a reliable source for the crack file
-
The first step is to find a reliable source for the crack file. The crack file is a small program that can modify the original software and make it work without activation. There are many websites that offer crack files for various software, but not all of them are trustworthy. Some of them might contain viruses, malware, or spyware that can harm your computer or steal your personal information. Therefore, you need to be careful when choosing a source for the crack file.
-
One way to find a reliable source is to use a search engine such as Google or Bing and type in keywords such as "Alias Surface 2018 crack", "Alias Surface 2018 keygen", or "Alias Surface 2018 patch". You will get many results, but you need to check the reviews and ratings of each website before clicking on them. You can also use forums, blogs, or social media platforms where other users share their experiences and recommendations for crack files. You can also ask your friends or colleagues who have used crack files before for their suggestions.
-
Another way to find a reliable source is to use a torrent website such as The Pirate Bay, Kickass Torrents, or 1337x. These websites allow users to share files with each other using peer-to-peer networks. You can download crack files from these websites using a torrent client such as BitTorrent, uTorrent, or qBittorrent. However, you need to be careful when using torrent websites as well, as some of them might contain fake or malicious files that can damage your computer or expose you to legal risks. Therefore, you need to check the comments and feedback of other users before downloading any file from these websites.
-
Step 2: Download the crack file and the setup file
-
The next step is to download the crack file and the setup file. The setup file is the original installation file of Alias Surface 2018 that you can get from Autodesk's website or from other sources online. You need both files to install and run Alias Surface 2018 crack on your computer.
-
To download the crack file, you need to click on the link or button that says "Download", "Get", "Download Now", or something similar on the website that you have chosen. You might have to complete some surveys, captcha tests, or offers before you can access the download link. You might also have to wait for some time before the download starts. Once the download starts, you need to save the file in a folder on your computer where you can easily find it later.
-
To download the setup file, you need to go to Autodesk's website and sign up for a free trial of Alias Surface 2018. You will have to provide some personal information such as your name, email address, country, and industry. You will also have to agree to their terms and conditions and privacy policy. After that, you will receive an email with a link to download the setup file. You need to click on that link and save the file in the same folder where you saved the crack file.
-
Step 3: Disable your antivirus and internet connection
-
The third step is to disable your antivirus and internet connection. This is because your antivirus might detect the crack file as a threat and delete it or block it from running. Your internet connection might also interfere with the installation process and prevent it from completing successfully. Therefore, you need to turn off both before proceeding with the next step.
-
To disable your antivirus, you need to open it and go to its settings or options menu. There, you will find an option that says "Disable", "Turn Off", "Pause", or something similar. You need to click on that option and choose how long you want to disable your antivirus for. You can choose from options such as "Until I Restart My Computer", "For 15 Minutes", "For 1 Hour", etc. You need to choose an option that covers the duration of the installation process.
-
To disable your internet connection, you need to go to your network settings on your computer and turn off your Wi-Fi or Ethernet connection. You can also unplug your modem or router from the power source if you have one.
- to the installation folder
-
The fourth step is to install the setup file and copy the crack file to the installation folder. The installation folder is the location on your computer where Alias Surface 2018 will be installed and stored. You need to copy the crack file to this folder so that it can replace the original file and make the software work without activation.
-
To install the setup file, you need to double-click on it and follow the instructions on the screen. You will have to accept the license agreement, choose the installation type, select the components and features that you want to install, and specify the installation folder. You can use the default folder or choose a different one if you want. After that, you need to click on "Install" and wait for the installation process to finish.
-
To copy the crack file to the installation folder, you need to open the folder where you saved the crack file and right-click on it. You will see an option that says "Copy", "Copy To", or something similar. You need to click on that option and then go to the installation folder. You can find it by going to "C:\Program Files\Autodesk\Alias Surface 2018" or by searching for it on your computer. Once you are in the installation folder, you need to right-click on an empty space and choose "Paste", "Paste Here", or something similar. You will see a message that asks you if you want to replace the existing file with the new one. You need to click on "Yes", "Replace", or something similar.
-
Step 5: Run the crack file and activate the software
-
The fifth and final step is to run the crack file and activate the software. The crack file is a program that can generate a serial number or a license key that can activate Alias Surface 2018 without requiring an internet connection or an Autodesk account. You need to run this program and enter the serial number or license key into the software to make it work.
-
To run the crack file, you need to double-click on it and wait for it to open. You will see a window that shows some information and instructions about the software and how to use it. You will also see a button that says "Generate", "Create", "Crack", or something similar. You need to click on that button and wait for a few seconds. You will see a serial number or a license key appear on the screen. You need to copy this number or key by selecting it and pressing Ctrl+C on your keyboard.
-
To activate the software, you need to open Alias Surface 2018 and go to its activation menu. You can find it by clicking on "Help", "About", "Activate", or something similar on the software's interface. You will see a window that asks you to enter your serial number or license key. You need to paste it by clicking on the input field and pressing Ctrl+V on your keyboard. After that, you need to click on "Activate", "OK", or something similar and wait for a few seconds. You will see a message that confirms that your software has been activated successfully.
-
How to use Alias Surface 2018 crack?
-
Now that you have downloaded and installed Alias Surface 2018 crack, you might be wondering how to use it effectively. Here are some tips and tricks that can help you get started:
-
Tips and tricks for using Alias Surface 2018 crack
-
-
Learn how to use Alias Surface 2018 by watching tutorials, reading manuals, or taking courses online. There are many resources available online that can teach you how to use this software for various purposes and projects.
-
Practice your skills by creating different models and surfaces using Alias Surface 2018 crack. You can use your own imagination or follow some examples from other designers online.
-
Save your work frequently by clicking on "File", "Save", or something similar on the software's interface. This will prevent you from losing your progress in case of a power outage, a computer crash, or a software error.
-
Backup your work by copying your files to an external hard drive, a USB flash drive, or a cloud storage service such as Google Drive, Dropbox, or OneDrive. This will protect your files from being corrupted, deleted, or stolen.
-
Share your work with others by exporting your models and surfaces to other formats such as OBJ, STL, IGES, STEP, or DWG. You can then upload them to online platforms such as Sketchfab, Thingiverse, or GrabCAD where other users can view, download, or comment on them.
-
-
Common errors and solutions for using Alias Surface 2018 crack
-
-
The crack file does not work or is missing. This might happen if you downloaded the crack file from an unreliable source, or if your antivirus deleted or blocked it. To fix this, you need to download the crack file again from a reliable source, and disable your antivirus before running it.
-
The software does not open or crashes. This might happen if you installed the software incorrectly, or if your computer does not meet the system requirements. To fix this, you need to reinstall the software properly, and make sure your computer has enough RAM, disk space, and graphics card power.
-
The software asks for activation or shows a trial message. This might happen if you did not copy the crack file to the installation folder, or if you entered the wrong serial number or license key. To fix this, you need to copy the crack file again to the installation folder, and enter the correct serial number or license key.
-
The software does not work properly or has missing features. This might happen if you used an outdated or incompatible version of the crack file or the software. To fix this, you need to update both the crack file and the software to the latest version available online.
-
The software has viruses, malware, or spyware. This might happen if you downloaded the crack file or the software from an unsafe source, or if you visited a malicious website while using them. To fix this, you need to scan your computer with a reputable antivirus program and remove any threats that are detected.
-
-
Conclusion
-
In conclusion, Alias Surface 2018 is a powerful software that can help you create and edit complex 3D models and surfaces for various purposes and industries. However, it is also very expensive and requires constant internet connection and activation. That's why many people prefer to download and use Alias Surface 2018 crack, which is a modified version of the software that can work without activation and internet connection. To download and use Alias Surface 2018 crack safely and effectively, you need to follow these steps:
-
-
Find a reliable source for the crack file
-
Download the crack file and the setup file
-
Disable your antivirus and internet connection
-
Install the setup file and copy the crack file to the installation folder
-
Run the crack file and activate the software
-
-
You also need to learn some tips and tricks for using Alias Surface 2018 crack, such as watching tutorials, practicing your skills, saving your work, backing up your work, and sharing your work. You also need to be aware of some common errors and solutions for using Alias Surface 2018 crack, such as downloading from reliable sources, installing properly, copying correctly, updating regularly, and scanning for viruses.
-
By following this guide, you can enjoy using Alias Surface 2018 crack for free and create amazing 3D models and surfaces that will impress your clients, colleagues, or friends.
-
FAQs
-
Here are some frequently asked questions about Alias Surface 2018 crack:
-
Is it legal to use Alias Surface 2018 crack?
-
No, it is not legal to use Alias Surface 2018 crack. It is a violation of Autodesk's terms of service and intellectual property rights. It is also a form of piracy that can result in legal consequences such as fines or lawsuits. Therefore, we do not recommend using Alias Surface 2018 crack for any purpose.
-
Is it safe to use Alias Surface 2018 crack?
- malware, spyware, or hackers. It can also damage your software or hardware, or cause errors or crashes. It can also compromise your personal information or data, or expose you to legal risks. Therefore, we do not recommend using Alias Surface 2018 crack for any purpose.
-
Is it ethical to use Alias Surface 2018 crack?
-
No, it is not ethical to use Alias Surface 2018 crack. It is a form of stealing that deprives Autodesk of their rightful income and recognition. It is also a form of cheating that gives you an unfair advantage over other users who pay for the software. It is also a form of disrespecting the hard work and creativity of the developers and designers who created the software. Therefore, we do not recommend using Alias Surface 2018 crack for any purpose.
-
Is there an alternative to using Alias Surface 2018 crack?
-
Yes, there is an alternative to using Alias Surface 2018 crack. You can use other software that are similar to Alias Surface 2018 but are free or cheaper. Some of these software are:
-
-
Blender: A free and open-source software that can create and edit 3D models and surfaces, as well as animations, games, and visual effects.
-
Rhino: A software that can create and edit NURBS models and surfaces, as well as curves, meshes, and solids. It costs $995 for a perpetual license.
-
SketchUp: A software that can create and edit 3D models and surfaces, as well as architectural designs, landscapes, and interiors. It costs $299 per year for a subscription license.
-
Fusion 360: A software that can create and edit 3D models and surfaces, as well as mechanical engineering, electrical engineering, and manufacturing designs. It costs $495 per year for a subscription license.
-
-
You can also use Alias Surface 2018 legally by buying a license from Autodesk or from an authorized reseller. You can choose from different options such as subscription license, perpetual license, student license, or trial license. You can also use Alias Surface 2018 for free for a limited time by signing up for a free trial on Autodesk's website.
-
How can I contact Autodesk for support or feedback?
-
If you have any questions, issues, or suggestions regarding Alias Surface 2018 or any other Autodesk product, you can contact Autodesk through various channels such as:
-
-
Email: You can send an email to support@autodesk.com or feedback@autodesk.com and get a reply within 24 hours.
-
Phone: You can call the toll-free number 1-800-964-6432 or the local number +1-415-507-5000 and talk to a customer service representative.
-
Chat: You can chat with a customer service representative online by visiting https://www.autodesk.com/support/contact-chat and clicking on "Start Chat".
-
Forum: You can post your question or feedback on the official Autodesk forum by visiting https://forums.autodesk.com/ and choosing the appropriate category.
-
Help Center: You can find answers to common questions or problems by visiting https://knowledge.autodesk.com/support/alias-products and browsing the topics or searching the keywords.
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download ebook berpikir dan berjiwa besar pdf Teknik-teknik sederhana untuk mengatasi ketakutan dan hambatan Anda.md b/spaces/raedeXanto/academic-chatgpt-beta/Download ebook berpikir dan berjiwa besar pdf Teknik-teknik sederhana untuk mengatasi ketakutan dan hambatan Anda.md
deleted file mode 100644
index c84d370cbab2bbbc1df48a96c8bf914809f437ce..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download ebook berpikir dan berjiwa besar pdf Teknik-teknik sederhana untuk mengatasi ketakutan dan hambatan Anda.md
+++ /dev/null
@@ -1,85 +0,0 @@
-
-
Xfer LFO Tool Crack With Free Download Full Latest Version 2020 [New]
-
If you are looking for a plugin that can help you create dynamic and expressive effects for your music production, you might want to check out Xfer LFO Tool. This plugin is a powerful and versatile tool that allows you to manipulate the sound of any audio source with customizable LFO curves and shapes. You can use it to create tremolo, auto-pan, trance-gate, sidechain compression simulation, dubstep wobble, and more.
-
However, Xfer LFO Tool is not a free plugin. It costs $49.95 on the official website. If you don't want to pay for it, you might be tempted to look for a cracked version online. But is it worth it? In this article, we will explore the features, benefits, drawbacks, and alternatives of Xfer LFO Tool Crack. We will also show you how to install and use it on your Windows or Mac computer.
-
Xfer LFO Tool Crack With Free Download Full Latest Version 2020 [New]
Xfer LFO Tool is a plugin that can process incoming audio as well as generate a MIDI message stream for controlling other instruments. It has a simple and intuitive interface that lets you create your own LFO curves and shapes with a point-and-tension curve editor. You can also choose from dozens of presets that cover various effects and genres.
-
Some of the key features of Xfer LFO Tool are:
-
-
Customizable LFO curves and shapes: You can draw your own LFO curves and shapes with up to 12 graphs per preset. You can also switch between them via MIDI notes or automation. You can adjust the rate, swing, phase, smoothness, delay, attack, release, and shape parameters of each graph.
-
MIDI compatibility: You can use Xfer LFO Tool to send MIDI CC messages to control other software or hardware synthesizers, effects, or devices that respond to MIDI CC messages. You can also sync the LFO rate to the BPM or Hz of your host or external MIDI clock.
-
Low CPU usage: Xfer LFO Tool is coded entirely in C++ to minimize the CPU load during operation. It supports 32-bit and 64-bit VST, AudioUnit, and AAX formats on both Windows and Mac platforms.
-
Multiple graphs simultaneously: You can use up to four graphs simultaneously to modulate different parameters such as cutoff, resonance, pan, volume, etc. You can also assign different filters to each graph for more sonic possibilities.
-
Preset shape graph: You can access a drop-down menu with preset shape graphs that range from basic sine waves to complex patterns. You can also save your own custom shapes for future use.
-
-
How to install Xfer LFO Tool Crack
-
If you want to try Xfer LFO Tool Crack for free, you will need to download it from an unofficial source online. However, this comes with some risks and disadvantages that we will discuss later. For now, here are the steps to install Xfer LFO Tool Crack on your Windows or Mac computer:
-
-
Download the Xfer LFO Tool Crack file from a reliable website. For example, you can use this link from Google Drive.
-
Extract the file using a program like WinRAR or 7-Zip. You should see two folders: one for Windows and one for Mac.
-
For Windows users: Copy the file "XFER_LFOTOOL_VST_V1_0_0_X86_X64-AUDIO_L3GION.dll" from the Windows folder and paste it into your VST plugins folder (usually located in C:\Program Files\VstPlugins or C:\Program Files (x86)\VstPlugins).
-
For Mac users: Open the file "XFER_LFOTOOL_VST_V1_0_0_X86_X64-AUDIO_L3GION.dmg" from the Mac folder and run the installer "XFER_LFOTOOL_VST_V1_0_0_X86_X64-AUDIO_L3GION.pkg". Follow the instructions on the screen.
-
Restart your DAW (digital audio workstation) and scan for new plugins. You should see Xfer LFO Tool in your plugin list.
-
-
How to use Xfer LFO Tool Crack
-
Once you have installed Xfer LFO Tool Crack on your computer, you can start using it to create various effects for your audio tracks. Here are some examples of how to use Xfer LFO Tool Crack:
-
-
Tremolo: To create a tremolo effect (a periodic variation in volume), load Xfer LFO Tool on an audio track and select a sine wave shape graph from the drop-down menu. Adjust the rate parameter to set the speed of the tremolo. You can also adjust the swing parameter to add some groove to the tremolo.
-
Auto-pan: To create an auto-pan effect (a periodic variation in stereo position), load Xfer LFO Tool on an audio track and select a triangle wave shape graph from the drop-down menu. Adjust the rate parameter to set the speed of the auto-pan. You can also adjust the phase parameter to offset the left and right channels.
-
Trance-gate: To create a trance-gate effect (a rhythmic gating effect), load Xfer LFO Tool on an audio track and select a square wave shape graph from the drop-down menu. Adjust the rate parameter to set the tempo of the trance-gate. You can also adjust the smoothness parameter to soften or sharpen the edges of the gate.
-
Sidechain compression simulation: To create a sidechain compression simulation effect (a ducking effect that reduces the volume of one track when another track plays), load Xfer LFO Tool on an audio track that you want to duck (e.g., a bass track) and select a custom shape graph from the drop-down menu that resembles a downward slope. Adjust the rate parameter to match the tempo of another track that you want to trigger the ducking (e.g., a kick drum track). You can also adjust the attack and release parameters to control how fast or slow the ducking occurs.
-
Dubstep wobble: To create a dubstep wobble effect (a modulation effect that changes the pitch or filter cutoff of a sound), load Xfer LFO Tool on an audio track that contains a bass sound and select a custom shape graph from the drop-down menu that resembles a zigzag pattern. Adjust the rate parameter to set the speed of the wobble. You can also assign different filters to each graph and modulate their cutoff frequencies with different shapes.
-
-
Benefits of Xfer LFO Tool Crack
-
Xfer LFO Tool Crack offers some benefits for music producers who want to experiment with different effects without spending money on buying the original plugin. Some of these benefits are:
-
-
Drawbacks of Xfer LFO Tool Crack
-
While Xfer LFO Tool Crack might seem like a good deal, it also comes with some serious drawbacks that you should be aware of before downloading it. Some of these drawbacks are:
-
-
Legal and ethical issues: Downloading and using Xfer LFO Tool Crack is illegal and unethical. You are violating the intellectual property rights of the original developers and depriving them of their deserved income. You are also exposing yourself to potential lawsuits and fines if you get caught.
-
Potential malware: Downloading Xfer LFO Tool Crack from an unofficial source online is risky and dangerous. You might end up with a corrupted file that does not work properly or worse, a virus or malware that can harm your computer or steal your personal information.
-
Lack of updates: Using Xfer LFO Tool Crack means that you will not be able to access any updates or bug fixes that the original developers release. You will be stuck with an outdated and possibly unstable version of the plugin that might not work well with your DAW or operating system.
-
-
Alternatives to Xfer LFO Tool Crack
-
If you are looking for a legal and safe way to create dynamic and expressive effects for your music production, you might want to consider some alternatives to Xfer LFO Tool Crack. There are other plugins or software that can achieve similar results without breaking the law or risking your computer's security. Here are some of them:
-
Cableguys ShaperBox 2
-
Cableguys ShaperBox 2 is a bundle of six effects plugins that allow you to shape your sound with flexible modulation options. You can use it to create volume, filter, pan, width, time, and crush effects with up to five LFOs per plugin. You can also draw your own LFO curves and shapes with a point-and-click editor. Cableguys ShaperBox 2 costs $99 on the official website.
-
Xfer Records LFOTool v1.768 Full version[^1^]
-LFOTool v1.762 VST/AAX/AU for Windows and Mac[^2^]
-LFOTool v1.7.6.2 VST-AU WiN-MAC[^3^]
-Xfer Records LFOTool v1.762 VST/AAX/AU [WiN-MacOS] x32 x64 [17.03.2021][^4^]
-Xfer LFOTool - VST Crackz[^5^]
-LFOTool v1.6.9.2 for Windows - Free Download[^5^]
-LFOTool v1.7.0.0 for Mac - Free Download[^5^]
-How to install Xfer Records LFOTool on your computer[^1^] [^2^] [^3^] [^4^]
-Xfer Records LFOTool review and tutorial[^1^] [^2^] [^4^]
-Xfer Records LFOTool presets and tips[^1^] [^2^] [^4^]
-Xfer Records LFOTool vs other LFO plugins[^1^] [^2^] [^4^]
-Xfer Records LFOTool features and specifications[^1^] [^2^] [^3^] [^4^]
-Xfer Records LFOTool discount and coupon code[^1^] [^2^] [^4^]
-Xfer Records LFOTool alternative and similar plugins[^1^] [^2^] [^4^]
-Xfer Records LFOTool compatibility and system requirements[^1^] [^2^] [^3^] [^4^]
-Xfer Records LFOTool license and activation[^1^] [^2^] [^3^] [^4^]
-Xfer Records LFOTool update and changelog[^1^] [^2^] [^3^] [^4^]
-Xfer Records LFOTool support and customer service[^1
-
MeldaProduction MMultiAnalyzer
-
MeldaProduction MMultiAnalyzer is a unique multitrack analyzer and sonogram plugin that lets you see and compare multiple audio tracks on the same screen. You can use it to analyze the frequency, phase, loudness, stereo field, and dynamics of your tracks. You can also use it to modulate any parameter with any of the 40+ modulators, including LFOs, envelopes, randomizers, etc. MeldaProduction MMultiAnalyzer costs €59 on the official website.
-
Glitchmachines Fracture XT
-
Glitchmachines Fracture XT is a granular glitch processor plugin that lets you create complex rhythmic and textural effects with a modular patchbay. You can use it to process any audio source with granular buffers, ring modulation, distortion, filtering, delay, and more. You can also modulate any parameter with four flexible LFOs and envelopes. Glitchmachines Fracture XT costs $59 on the official website.
-
Conclusion
-
Xfer LFO Tool is a powerful and versatile plugin that allows you to create dynamic and expressive effects for your music production with customizable LFO curves and shapes. However, downloading and using Xfer LFO Tool Crack is illegal and unethical, as well as risky and unreliable. You might end up with legal problems, malware infections, or outdated versions of the plugin.
-
If you want to create similar effects without breaking the law or risking your computer's security, you might want to consider some alternatives to Xfer LFO Tool Crack. There are other plugins or software that can offer comparable features and results at reasonable prices or even for free.
-
We hope this article has helped you understand the pros and cons of Xfer LFO Tool Crack and its alternatives. Remember to always respect the intellectual property rights of the original developers and support their work by buying their products.
-
FAQs
-
Here are some common questions and answers about Xfer LFO Tool Crack:
-
-
What is an LFO? An LFO (low-frequency oscillator) is a device that generates a periodic signal that can modulate various parameters of an audio signal, such as volume, pitch, filter cutoff, etc.
-
What is a crack? A crack is a modified version of a software or plugin that bypasses its copy protection or activation mechanism.
-
Is Xfer LFO Tool Crack safe? No, Xfer LFO Tool Crack is not safe. It is illegal and unethical to download and use it. It might also contain malware that can harm your computer or steal your personal information.
-
How do I get Xfer LFO Tool for free? You cannot get Xfer LFO Tool for free legally. The only way to get it for free is to download a cracked version online, which is illegal and unethical.
-
What are some alternatives to Xfer LFO Tool Crack? Some alternatives to Xfer LFO Tool Crack are Cableguys ShaperBox 2, MeldaProduction MMultiAnalyzer, Glitchmachines Fracture XT, etc.
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Experience the Magic of Gabriel Garcia Marquezs Love in the Time of Cholera in Sinhala with Kolara Kale Ale PDF Free.md b/spaces/raedeXanto/academic-chatgpt-beta/Experience the Magic of Gabriel Garcia Marquezs Love in the Time of Cholera in Sinhala with Kolara Kale Ale PDF Free.md
deleted file mode 100644
index 28bc4f02238777143feca7f13303ac953babd9bb..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Experience the Magic of Gabriel Garcia Marquezs Love in the Time of Cholera in Sinhala with Kolara Kale Ale PDF Free.md
+++ /dev/null
@@ -1,79 +0,0 @@
-
-
Kolara Kale Ale: A Sinhala Translation of Love in the Time of Cholera by Gabriel Garcia Marquez
-
Introduction
-
Kolara Kale Ale is a Sinhala translation of Love in the Time of Cholera, one of the most celebrated novels by Nobel laureate Gabriel Garcia Marquez. The novel tells the story of a passionate and enduring love that spans over half a century, against the backdrop of a turbulent and colorful Colombia. It is a masterpiece of magical realism, a literary genre that blends realistic and fantastical elements to create a vivid and enchanting world.
Gabriel Garcia Marquez was a Colombian writer who is widely regarded as one of the greatest authors of the 20th century. He was awarded the Nobel Prize in Literature in 1982 for his novels and short stories that explore the themes of love, power, violence, history, and identity in Latin America. Some of his other famous works include One Hundred Years of Solitude, Chronicle of a Death Foretold, The Autumn of the Patriarch, and The General in His Labyrinth.
-
The translator of Kolara Kale Ale is Gamini Viyangoda, a prominent Sinhala writer, journalist, and activist. He has translated several other works by Gabriel Garcia Marquez, such as Leaf Storm, No One Writes to the Colonel, The Story of a Shipwrecked Sailor, and News of a Kidnapping. He has also translated works by other renowned authors such as Franz Kafka, Albert Camus, Milan Kundera, and Salman Rushdie. He has received several awards for his translations, such as the State Literary Award and the Godage Literary Award.
-
Summary of the plot
-
The novel begins in an unnamed Caribbean city in the late 19th century, where Dr. Juvenal Urbino, a respected physician and social reformer, dies after falling from a ladder while trying to catch his escaped parrot. His widow, Fermina Daza, is visited by Florentino Ariza, an old friend who declares his undying love for her. He reveals that he has been secretly waiting for her husband's death for over fifty years, ever since she rejected his marriage proposal when they were young.
-
The novel then flashes back to their youth, when Florentino first saw Fermina at her father's market stall and fell madly in love with her. He wooed her with letters and poems, but they could only communicate through secret messages and brief encounters. Fermina's father, Lorenzo Daza, disapproved of their relationship and took her away on a long trip to separate them. During their absence, Fermina realized that her feelings for Florentino were based on illusion and decided to break off their engagement.
-
Upon her return, she met Dr. Juvenal Urbino, who was impressed by her beauty and intelligence. He proposed to her and she accepted, hoping to find stability and respectability with him. They married and had two children, but their marriage was not very happy or passionate. Dr. Urbino was often unfaithful to her and she felt lonely and bored. Florentino, meanwhile, never gave up on his love for Fermina. He became a successful businessman and a notorious womanizer, but he always considered Fermina as his true love.
-
The novel follows their lives over several decades, as they experience various events such as wars, epidemics, revolutions, political upheavals, social changes, personal tragedies, and joys. They occasionally cross paths but never rekindle their romance. Florentino keeps track of Fermina's whereabouts through his network of spies and informers. He also writes letters to her every day but never sends them.
-
The novel ends with Florentino's visit to Fermina after Dr. Urbino's death. At first she is outraged by his audacity and rejects him again. But gradually she softens towards him and agrees to give him another chance. They embark on a river cruise together on Florentino's company boat. They rediscover their love for each other and make love for the first time. They decide to live together until they die. When they reach their destination, they raise a yellow flag on their boat to indicate that they have cholera on board. This prevents anyone from boarding or disembarking their boat. They sail away into eternity together.
-
Analysis of themes
-
The novel explores several themes that are characteristic of Gabriel Garcia Marquez's work. Some of these themes are:
-
-
Love: The novel portrays different types of love: romantic love, platonic love, familial love, sexual love, unrequited love, obsessive love, and eternal love. It shows how love can be both beautiful and destructive, both liberating and enslaving, both joyful and painful. It also shows how love can transcend time, space, death, and memory.
-
Aging: The novel depicts how aging affects people's lives, bodies, minds, and relationships. It shows how aging can bring wisdom, serenity, and acceptance, but also regret, loneliness, and nostalgia. It also shows how aging can challenge social norms, expectations, and prejudices.
-
Death: The novel confronts death as an inevitable reality that shapes human existence. It shows how death can be sudden, violent, or peaceful, but also mysterious, ironic, or humorous. It also shows how death can inspire fear, grief, or hope.
-
Memory: The novel illustrates how memory plays a crucial role in preserving or distorting people's identities, histories, and emotions. It shows how memory can be selective, subjective, or unreliable, but also vivid, powerful, or healing. It also shows how memory can connect or disconnect people from their pasts, presents, and futures.
-
History: The novel reflects how history influences people's lives in personal and collective ways. It shows how history can be chaotic, violent, or oppressive, but also dynamic, creative, or transformative. It also shows how history can be forgotten or remembered through stories or silences.
-
Culture: The novel represents how culture shapes people's identities and values in diverse and complex ways. It shows how culture can be rich and colorful but also oppressive and violent. It also shows how culture can be hybridized or homogenized through contact or isolation.
-
-
Evaluation of translation
-
The translation of Kolara Kale Ale by Gamini Viyangoda is an admirable attempt to render one of the most challenging and rewarding novels by Gabriel Garcia Marquez into Sinhala. The translation is faithful to both the content and style of the original text but also adapts it to suit the linguistic and cultural context of Sinhala readers.
-
kolara kale ale sinhala novel pdf free download
-kolara kale ale book review in sinhala
-kolara kale ale sarasavi publishers
-kolara kale ale gamini viyangoda translation
-kolara kale ale love in the time of cholera sinhala
-kolara kale ale gabriel garcia marquez sinhala
-kolara kale ale online reading sinhala
-kolara kale ale kbooks lk
-kolara kale ale slbooks lk
-kolara kale ale pdf free download fgdll com
-kolara kale ale sinhala novel summary
-kolara kale ale sinhala novel online
-kolara kale ale sinhala novel buy online
-kolara kale ale sinhala novel price
-kolara kale ale sinhala novel pages
-kolara kale ale sinhala novel language
-kolara kale ale sinhala novel binding
-kolara kale ale sinhala novel isbn
-kolara kale ale sinhala novel availability
-kolara kale ale sinhala novel shipping methods
-kolara kale ale readers also buy books
-kolara kale ale related books in sinhala
-kolara kale ale similar books in sinhala
-kolara kale ale best books in sinhala
-kolara kale ale new books in sinhala
-kolara kale ale 2019 books in sinhala
-kolara kale ale translations books in sinhala
-kolara kale ale gamini viyangoda books in sinhala
-kolara kale ale gabriel garcia marquez books in sinhala
-kolara kale ale love in the time of cholera books in sinhala
-kolara kale ale pdf free read online
-kolara kale ale pdf free download link
-kolara kale ale pdf free download site
-kolara kale ale pdf free download forum
-kolara kale ale pdf free download without registration
-kolara kale ale pdf free download no password
-kolara kale ale pdf free download high quality
-kolara kale ale pdf free download full version
-kolara kale ale pdf free download latest edition
-kolara kale ale pdf free download updated version
-
The translation captures well the tone humor and symbolism of the novel. The tone is lyrical # Article with HTML formatting (continued)
Evaluation of translation
-
The translation of Kolara Kale Ale by Gamini Viyangoda is an admirable attempt to render one of the most challenging and rewarding novels by Gabriel Garcia Marquez into Sinhala. The translation is faithful to both the content and style of the original text but also adapts it to suit the linguistic and cultural context of Sinhala readers.
-
The translation captures well the tone humor and symbolism of the novel. The tone is lyrical poetic and nostalgic reflecting the romanticism and melancholy of the story. The humor is subtle ironic and witty highlighting the absurdity and paradoxes of life. The symbolism is rich evocative and meaningful conveying the themes and messages of the novel.
-
The translation also preserves well the style structure and language of the novel. The style is elegant expressive and imaginative using various literary devices such as metaphors, similes, alliterations, repetitions, and contrasts. The structure is complex, nonlinear, and circular, alternating between different time periods, perspectives, and narratives. The language is diverse, colorful, and authentic, incorporating different registers, dialects, and idioms.
-
The translation also adapts well to the Sinhala language and culture. The translator has made some changes and additions to make the text more accessible and appealing to Sinhala readers. For example, he has used Sinhala names for some of the characters and places, such as පියුමිලා for Fermina Daza and මහනුවර for Cartagena. He has also added some explanations and footnotes for some of the unfamiliar or obscure references and terms, such as චොලේරා for cholera and පිටක් පිටක් වැඩ for petit point work. He has also used some Sinhala proverbs and sayings to convey some of the wisdom and humor of the novel, such as අන්දම වැඩ කරන අය දෙන්න කොල ද? for Do you think God will reward those who love him?
-
The translation of Kolara Kale Ale by Gamini Viyangoda is a remarkable achievement that deserves praise and appreciation. It is a valuable contribution to Sinhala literature and culture that introduces a classic novel by a world-renowned author to a new audience. It is also a enjoyable read that offers a captivating story of love, life, and death that transcends time and space.
-
Conclusion
-
In this article, we have discussed Kolara Kale Ale, a Sinhala translation of Love in the Time of Cholera by Gabriel Garcia Marquez. We have summarized the plot of the novel, analyzed its themes, and evaluated its translation. We have seen how the novel is a masterpiece of magical realism that explores various aspects of human existence through a epic love story. We have also seen how the translation is a faithful and creative rendition of the novel that adapts it to the Sinhala language and culture.
-
Kolara Kale Ale is a novel that can appeal to anyone who loves literature, romance, history, or culture. It is a novel that can inspire, entertain, educate, and move its readers. It is a novel that can be read again and again, discovering new meanings and insights each time. It is a novel that can be cherished as a treasure of world literature.
-
If you are interested in reading Kolara Kale Ale or learning more about Love in the Time of Cholera and its author Gabriel Garcia Marquez, you can find some useful resources below:
- - You can get a free PDF download of Kolara Kale Ale from this link: - You can buy a hard copy of Kolara Kale Ale from this website: - You can read an English summary and analysis of Love in the Time of Cholera from this website: - You can watch a video interview with Gabriel Garcia Marquez about Love in the Time of Cholera from this link: - You can read an article about Gabriel Garcia Marquez's life and works from this website:
FAQs
- - Q: Where can I get a free PDF download of Kolara Kale Ale? - A: You can get a free PDF download of Kolara Kale Ale from this link: - Q: When was Love in the Time of Cholera published and how long did it take to write? - A: Love in the Time of Cholera was published in 1985 and it took Gabriel Garcia Marquez about ten years to write it. - Q: Is Love in the Time of Cholera based on a true story or inspired by real people? - A: Love in the Time of Cholera is partly based on a true story and partly inspired by real people. The story of Florentino's love for Fermina was inspired by Gabriel Garcia Marquez's parents' courtship. The story of Dr. Urbino's death was based on an incident that happened to one of Gabriel Garcia Marquez's friends. - Q: What are some of the literary awards and honors that Love in the Time of Cholera has received? - A: Love in the Time of Cholera has received many literary awards and honors, such as: - The Los Angeles Times Book Prize for Fiction in 1988. - The Premio Grinzane Cavour for Foreign Fiction in 1988. - The XVII Premio Internazionale Mondello for Foreign Fiction in 1989. - The Nobel Prize in Literature for Gabriel Garcia Marquez in 1982 (for his entire body of work). - Q: What are some of the other famous novels by Gabriel Garcia Marquez? - A: Some of the other famous novels by Gabriel Garcia Marquez are: - One Hundred Years of Solitude (1967), which tells the saga of seven generations of a family in a mythical town called Macondo. - Chronicle of a Death Foretold (1981), which recounts the murder of a young man by two brothers who believe he dishonored their sister. - The Autumn of the Patriarch (1975), which depicts the life and death of a tyrannical dictator in an unnamed Caribbean country. - The General in His Labyrinth (1989), which narrates the final days of Simon Bolivar, the liberator of South America. 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/raghung/Play-Canvas-Sim/index.html b/spaces/raghung/Play-Canvas-Sim/index.html
deleted file mode 100644
index 514d8ec32ab769c75928229a2785ee99a95f5e14..0000000000000000000000000000000000000000
--- a/spaces/raghung/Play-Canvas-Sim/index.html
+++ /dev/null
@@ -1,10 +0,0 @@
-
-
SimPhysics
-
User input: WASD
-
This WebGL demo demonstrates PlayCanvas and a physics vehicle simulation that is web based and playable anywhere your browser goes🤗 Inference API.
-
-">shotachan In the wake of the WikiLeaks disclosure, Obama ordered new . ... HD Online Player (Agneepath 2015 hindi 720p downloadgolkes) 4d29de3e1b
-
-
-
diff --git a/spaces/renumics/cifar10-outlier/run.py b/spaces/renumics/cifar10-outlier/run.py
deleted file mode 100644
index 9187b885aed48a35bb462931027895b09e433984..0000000000000000000000000000000000000000
--- a/spaces/renumics/cifar10-outlier/run.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import pickle
-from renumics import spotlight
-import os
-
-if __name__ == "__main__":
- cache_file = "dataset_cache.pkl"
- if os.path.exists(cache_file):
- # Load dataset from cache
- with open(cache_file, "rb") as file:
- df = pickle.load(file)
- print("Dataset loaded from cache.")
-
-
- while True:
- dtypes = {
- "nn_image": spotlight.Image,
- "image": spotlight.Image,
- "embedding_ft": spotlight.Embedding,
- "embedding_foundation": spotlight.Embedding,
- "embedding_ft_precalc": spotlight.Embedding,
- "embedding_foundation_precalc": spotlight.Embedding,
- }
- view = spotlight.show(
- df,
- dtype=dtypes,
- layout="https://spotlight.renumics.com/resources/layout_pre_post_ft_hf.json",
- port=7860,
- host="0.0.0.0",
- allow_filebrowsing=False
- )
-
- view.close()
-
- else:
- print(f"Dataset {cache_file} not found. Please run prepare.py first.")
-
-
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ga_rpn_head.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ga_rpn_head.py
deleted file mode 100644
index 4123c8b3f56f29f94668920d77b7db75ae78d8a2..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/ga_rpn_head.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv import ConfigDict
-from mmcv.ops import nms
-
-from ..builder import HEADS
-from .guided_anchor_head import GuidedAnchorHead
-
-
-@HEADS.register_module()
-class GARPNHead(GuidedAnchorHead):
- """Guided-Anchor-based RPN head."""
-
- def __init__(self,
- in_channels,
- init_cfg=dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='conv_loc',
- std=0.01,
- bias_prob=0.01)),
- **kwargs):
- super(GARPNHead, self).__init__(
- 1, in_channels, init_cfg=init_cfg, **kwargs)
-
- def _init_layers(self):
- """Initialize layers of the head."""
- self.rpn_conv = nn.Conv2d(
- self.in_channels, self.feat_channels, 3, padding=1)
- super(GARPNHead, self)._init_layers()
-
- def forward_single(self, x):
- """Forward feature of a single scale level."""
-
- x = self.rpn_conv(x)
- x = F.relu(x, inplace=True)
- (cls_score, bbox_pred, shape_pred,
- loc_pred) = super(GARPNHead, self).forward_single(x)
- return cls_score, bbox_pred, shape_pred, loc_pred
-
- def loss(self,
- cls_scores,
- bbox_preds,
- shape_preds,
- loc_preds,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore=None):
- losses = super(GARPNHead, self).loss(
- cls_scores,
- bbox_preds,
- shape_preds,
- loc_preds,
- gt_bboxes,
- None,
- img_metas,
- gt_bboxes_ignore=gt_bboxes_ignore)
- return dict(
- loss_rpn_cls=losses['loss_cls'],
- loss_rpn_bbox=losses['loss_bbox'],
- loss_anchor_shape=losses['loss_shape'],
- loss_anchor_loc=losses['loss_loc'])
-
- def _get_bboxes_single(self,
- cls_scores,
- bbox_preds,
- mlvl_anchors,
- mlvl_masks,
- img_shape,
- scale_factor,
- cfg,
- rescale=False):
- cfg = self.test_cfg if cfg is None else cfg
-
- cfg = copy.deepcopy(cfg)
-
- # deprecate arguments warning
- if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
- warnings.warn(
- 'In rpn_proposal or test_cfg, '
- 'nms_thr has been moved to a dict named nms as '
- 'iou_threshold, max_num has been renamed as max_per_img, '
- 'name of original arguments and the way to specify '
- 'iou_threshold of NMS will be deprecated.')
- if 'nms' not in cfg:
- cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
- if 'max_num' in cfg:
- if 'max_per_img' in cfg:
- assert cfg.max_num == cfg.max_per_img, f'You ' \
- f'set max_num and max_per_img at the same time, ' \
- f'but get {cfg.max_num} ' \
- f'and {cfg.max_per_img} respectively' \
- 'Please delete max_num which will be deprecated.'
- else:
- cfg.max_per_img = cfg.max_num
- if 'nms_thr' in cfg:
- assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \
- f'iou_threshold in nms and ' \
- f'nms_thr at the same time, but get ' \
- f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \
- f' respectively. Please delete the ' \
- f'nms_thr which will be deprecated.'
-
- assert cfg.nms.get('type', 'nms') == 'nms', 'GARPNHead only support ' \
- 'naive nms.'
-
- mlvl_proposals = []
- for idx in range(len(cls_scores)):
- rpn_cls_score = cls_scores[idx]
- rpn_bbox_pred = bbox_preds[idx]
- anchors = mlvl_anchors[idx]
- mask = mlvl_masks[idx]
- assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
- # if no location is kept, end.
- if mask.sum() == 0:
- continue
- rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
- if self.use_sigmoid_cls:
- rpn_cls_score = rpn_cls_score.reshape(-1)
- scores = rpn_cls_score.sigmoid()
- else:
- rpn_cls_score = rpn_cls_score.reshape(-1, 2)
- # remind that we set FG labels to [0, num_class-1]
- # since mmdet v2.0
- # BG cat_id: num_class
- scores = rpn_cls_score.softmax(dim=1)[:, :-1]
- # filter scores, bbox_pred w.r.t. mask.
- # anchors are filtered in get_anchors() beforehand.
- scores = scores[mask]
- rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1,
- 4)[mask, :]
- if scores.dim() == 0:
- rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0)
- anchors = anchors.unsqueeze(0)
- scores = scores.unsqueeze(0)
- # filter anchors, bbox_pred, scores w.r.t. scores
- if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
- _, topk_inds = scores.topk(cfg.nms_pre)
- rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
- anchors = anchors[topk_inds, :]
- scores = scores[topk_inds]
- # get proposals w.r.t. anchors and rpn_bbox_pred
- proposals = self.bbox_coder.decode(
- anchors, rpn_bbox_pred, max_shape=img_shape)
- # filter out too small bboxes
- if cfg.min_bbox_size >= 0:
- w = proposals[:, 2] - proposals[:, 0]
- h = proposals[:, 3] - proposals[:, 1]
- valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
- if not valid_mask.all():
- proposals = proposals[valid_mask]
- scores = scores[valid_mask]
-
- # NMS in current level
- proposals, _ = nms(proposals, scores, cfg.nms.iou_threshold)
- proposals = proposals[:cfg.nms_post, :]
- mlvl_proposals.append(proposals)
- proposals = torch.cat(mlvl_proposals, 0)
- if cfg.get('nms_across_levels', False):
- # NMS across multi levels
- proposals, _ = nms(proposals[:, :4], proposals[:, -1],
- cfg.nms.iou_threshold)
- proposals = proposals[:cfg.max_per_img, :]
- else:
- scores = proposals[:, 4]
- num = min(cfg.max_per_img, proposals.shape[0])
- _, topk_inds = scores.topk(num)
- proposals = proposals[topk_inds, :]
- return proposals
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/gfl.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/gfl.py
deleted file mode 100644
index 4628e2e7c929bb7195ef51f741da9ca66bf9c3d8..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/gfl.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class GFL(SingleStageDetector):
-
- def __init__(self,
- backbone,
- neck,
- bbox_head,
- train_cfg=None,
- test_cfg=None,
- pretrained=None,
- init_cfg=None):
- super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg,
- test_cfg, pretrained, init_cfg)
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/utils/replace_cfg_vals.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/utils/replace_cfg_vals.py
deleted file mode 100644
index 6ca301dc937bb9c3fe376d7a047b8c0430e8ec73..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/utils/replace_cfg_vals.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import re
-
-from mmcv.utils import Config
-
-
-def replace_cfg_vals(ori_cfg):
- """Replace the string "${key}" with the corresponding value.
-
- Replace the "${key}" with the value of ori_cfg.key in the config. And
- support replacing the chained ${key}. Such as, replace "${key0.key1}"
- with the value of cfg.key0.key1. Code is modified from `vars.py
- < https://github.com/microsoft/SoftTeacher/blob/main/ssod/utils/vars.py>`_ # noqa: E501
-
- Args:
- ori_cfg (mmcv.utils.config.Config):
- The origin config with "${key}" generated from a file.
-
- Returns:
- updated_cfg [mmcv.utils.config.Config]:
- The config with "${key}" replaced by the corresponding value.
- """
-
- def get_value(cfg, key):
- for k in key.split('.'):
- cfg = cfg[k]
- return cfg
-
- def replace_value(cfg):
- if isinstance(cfg, dict):
- return {key: replace_value(value) for key, value in cfg.items()}
- elif isinstance(cfg, list):
- return [replace_value(item) for item in cfg]
- elif isinstance(cfg, tuple):
- return tuple([replace_value(item) for item in cfg])
- elif isinstance(cfg, str):
- # the format of string cfg may be:
- # 1) "${key}", which will be replaced with cfg.key directly
- # 2) "xxx${key}xxx" or "xxx${key1}xxx${key2}xxx",
- # which will be replaced with the string of the cfg.key
- keys = pattern_key.findall(cfg)
- values = [get_value(ori_cfg, key[2:-1]) for key in keys]
- if len(keys) == 1 and keys[0] == cfg:
- # the format of string cfg is "${key}"
- cfg = values[0]
- else:
- for key, value in zip(keys, values):
- # the format of string cfg is
- # "xxx${key}xxx" or "xxx${key1}xxx${key2}xxx"
- assert not isinstance(value, (dict, list, tuple)), \
- f'for the format of string cfg is ' \
- f"'xxxxx${key}xxxxx' or 'xxx${key}xxx${key}xxx', " \
- f"the type of the value of '${key}' " \
- f'can not be dict, list, or tuple' \
- f'but you input {type(value)} in {cfg}'
- cfg = cfg.replace(key, str(value))
- return cfg
- else:
- return cfg
-
- # the pattern of string "${key}"
- pattern_key = re.compile(r'\$\{[a-zA-Z\d_.]*\}')
- # the type of ori_cfg._cfg_dict is mmcv.utils.config.ConfigDict
- updated_cfg = Config(
- replace_value(ori_cfg._cfg_dict), filename=ori_cfg.filename)
- # replace the model with model_wrapper
- if updated_cfg.get('model_wrapper', None) is not None:
- updated_cfg.model = updated_cfg.model_wrapper
- updated_cfg.pop('model_wrapper')
- return updated_cfg
diff --git a/spaces/romero61/hendata/pages/05-AWS.py b/spaces/romero61/hendata/pages/05-AWS.py
deleted file mode 100644
index 7ebed92b413fca2ee4182bba186dd58eaa517dc9..0000000000000000000000000000000000000000
--- a/spaces/romero61/hendata/pages/05-AWS.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import os
-import solara
-import datetime
-import psycopg2
-import json
-import pandas as pd
-import ipyleaflet
-from psycopg2 import pool
-
-# Database connection parameters
-# Create a connection pool
-key_content = os.environ.get('DATABASE_SECRETS')
-key_dict = json.loads(key_content)
-
-connection_pool = pool.SimpleConnectionPool(1, 10, # min and max connections in the pool
- **key_dict)
-
-# Fetch column names from the 'clientes_test' table
-# Fetch column names from the 'clientes_test' table
-def get_columns():
- conn = connection_pool.getconn()
- with conn:
- with conn.cursor() as cur:
- cur.execute("SELECT column_name FROM information_schema.columns WHERE table_name = 'clientes_test';")
- columns = [col[0] for col in cur.fetchall()]
- connection_pool.putconn(conn)
- return columns
-
-columns = get_columns()
-
-zoom_default = 7
-center_default = -25.272019833438247, -57.62672424316407
-maps = {
- "OpenStreetMap.Mapnik": ipyleaflet.basemaps.OpenStreetMap.Mapnik,
- "OpenTopoMap": ipyleaflet.basemaps.OpenTopoMap,
- "Esri.WorldTopoMap": ipyleaflet.basemaps.Esri.WorldTopoMap,
- "Stamen.Watercolor": ipyleaflet.basemaps.Stamen.Watercolor,
-}
-
-zoom = solara.reactive(zoom_default)
-center = solara.reactive(center_default)
-marker_location = solara.reactive(center_default)
-map_name = solara.reactive(list(maps)[0])
-
-@solara.component
-def Page():
- # Map Component
- def location_changed(location):
- marker_location.set(location)
-
- with solara.Column(style={"min-width": "500px", "height": "500px"}):
- solara.Markdown(f"Market set to: {marker_location.value}", style={"color": "#6e6e6e"})
-
- map = maps[map_name.value]
- url = map.build_url()
-
- def goto_marker():
- center.value = marker_location.value
- zoom.value = 13
-
- def reset_view():
- center.value = center_default
- zoom.value = zoom_default
-
- solara.Select(label="Map", value=map_name, values=list(maps), style={"z-index": "10000"})
- solara.SliderInt(label="Zoom level", value=zoom, min=1, max=20)
- with solara.Row():
- solara.Button(label="Zoom to marker", on_click=goto_marker)
- solara.Button(label="Reset view", on_click=reset_view)
-
- ipyleaflet.Map.element(
- zoom=zoom.value,
- on_zoom=zoom.set,
- center=center.value,
- on_center=center.set,
- scroll_wheel_zoom=True,
- layers=[
- ipyleaflet.TileLayer.element(url=url),
- ipyleaflet.Marker.element(location=marker_location.value, draggable=True, on_location=location_changed),
- ],
- )
-
-
-
- # Input fields
- inputs = {col: solara.use_state('') for col in columns}
-
- # Selected row for updating
- selected_row, set_selected_row = solara.use_state(None)
-
- def fetch_data():
- conn = connection_pool.getconn()
- with conn:
- with conn.cursor() as cur:
- cur.execute("SELECT * FROM clientes_test;")
- data = cur.fetchall()
- connection_pool.putconn(conn)
- # Convert the data to a DataFrame
- df = pd.DataFrame(data, columns=columns)
- return df
-
- def handle_submit():
- conn = connection_pool.getconn()
- with conn:
- with conn.cursor() as cur:
- columns_str = ", ".join(columns[1:]) # Exclude 'id' since it's auto-incremented
- values_str = ", ".join(["%s"] * len(columns[1:]))
- cur.execute(f"INSERT INTO clientes_test ({columns_str}) VALUES ({values_str});", [inputs[col][0] for col in columns[1:]])
- conn.commit()
- connection_pool.putconn(conn)
-
- def handle_update():
- conn = connection_pool.getconn()
- with conn:
- with conn.cursor() as cur:
- if selected_row is not None:
- set_str = ", ".join([f"{col} = %s" for col in columns[1:]])
- cur.execute(f"UPDATE clientes_test SET {set_str} WHERE id = {selected_row};", [inputs[col][0] for col in columns[1:]])
- conn.commit()
- connection_pool.putconn(conn)
-
- def handle_delete():
- conn = connection_pool.getconn()
- with conn:
- with conn.cursor() as cur:
- if selected_row is not None:
- cur.execute(f"DELETE FROM clientes_test WHERE id = {selected_row};")
- conn.commit()
- connection_pool.putconn(conn)
-
- def handle_row_selection(row_index):
- conn = connection_pool.getconn()
- with conn:
- with conn.cursor() as cur:
- cur.execute("SELECT * FROM clientes_test WHERE id = %s;", (row_index,))
- row_data = cur.fetchone()
- for idx, col in enumerate(columns):
- inputs[col][1](str(row_data[idx]))
- connection_pool.putconn(conn)
-
- # Render components
- for col in columns:
- solara.InputText(col, value=inputs[col][0], on_value=inputs[col][1])
-
- # Dropdown for selecting a row to update
- data = fetch_data()
- if not data.empty:
- solara.Select(label="Select a row to update", values=[row[0] for row in data], value=selected_row, on_value=handle_row_selection)
-
- solara.Button("Submit", on_click=handle_submit)
- solara.Button("Update", on_click=handle_update)
- solara.Button("Delete", on_click=handle_delete)
- solara.Button("Clear", on_click=lambda: [inputs[col][1]('') for col in columns])
- solara.DataFrame(data, items_per_page=50)
-
-
diff --git a/spaces/rorallitri/biomedical-language-models/logs/F1 2010 Crack Only Razor 1911 267 LINK _TOP_.md b/spaces/rorallitri/biomedical-language-models/logs/F1 2010 Crack Only Razor 1911 267 LINK _TOP_.md
deleted file mode 100644
index 8c54bba0bf3cc250abd3b1e0915cda351db6b4d6..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/F1 2010 Crack Only Razor 1911 267 LINK _TOP_.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
HCV is primarily transmitted parenterally, usually through shared drug-injecting needles and paraphernalia. HCV also can be transmitted through exposures in health care settings as a consequence of inadequate infection control practices (1314). Transmission after receipt of blood from donors and from transplantation of tissues and organs with HCV infection has occurred only rarely since 1992, when routine screening of these donated products was mandated in the United States (1367,1369). Tattoos applied in regulated settings have not been associated with HCV transmission, although those obtained in certain settings have been linked to such transmission (1336). Occupational and perinatal exposures also can result in transmission of HCV; however, such transmission is uncommon.
Windows.Post.Install.[WPI][2011][www.mundoteam.es].rar utorrent SILKYPIX Developer Studio Pro 9E v9.0.6.0 Crack Mac Osx technology strategy for managers and entrepreneurs pdf free download download traducao sniper elite 3 driver-blue-link-bl-u90nbfdcm nulldc 1.0.6 easy duplicate finder 3.2.0.18 crack.rar flobo hard disk repair full crack 11 easyworship 6 license file YouTube Video Downloader PRO v15.13.4.6 FINAL Serial Key keygen
-
Eurosystems EuroCut Pro 6 .1.0.9 Multilingual.rar RAR 31.00M-adds 1 29 HACK Corel DRAW Graphics Suite X7 new update version 2018 movie hd 1080p blu-ray full Thanks Maa Intel D865gsa Drivers For Windows 7 32-bit Download HD Online Player (X: Past is Present download utorrent) The AntMan English Dual Audio Hindi Torrent Aaaina Movie Free Download In Hd Nu sinh Phan Ngoc Hien Can Tho 2010.rar Tumko Na Bhool Paayenge 4 movie in hindi download mp4 Download configurar router linksys como repetidor wrt120n 8
-
-09-Aug-2020 07:51 - Chinnomul Bangla Full HD Movie 2016 By Maruf -(. ... 07:52 - Dobir Saheber Songsar 2015 Bangla Movie HD 720P. ... 09-Aug-2020 07:57 - Pora Mon (2014) Bangla Full Movie Orginal DVDRI. 4d29de3e1b
-
-
-
diff --git a/spaces/sdhsdhk/bingo111/src/components/chat-attachments.tsx b/spaces/sdhsdhk/bingo111/src/components/chat-attachments.tsx
deleted file mode 100644
index ef43d4e262935d263b6099138c56f7daade5299d..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/src/components/chat-attachments.tsx
+++ /dev/null
@@ -1,37 +0,0 @@
-import Image from 'next/image'
-import ClearIcon from '@/assets/images/clear.svg'
-import RefreshIcon from '@/assets/images/refresh.svg'
-import { FileItem } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-import { useBing } from '@/lib/hooks/use-bing'
-
-type ChatAttachmentsProps = Pick, 'attachmentList' | 'setAttachmentList' | 'uploadImage'>
-
-export function ChatAttachments({ attachmentList = [], setAttachmentList, uploadImage }: ChatAttachmentsProps) {
- return attachmentList.length ? (
-
- {attachmentList.map(file => (
-
- {file.status === 'loading' && (
-
-
-
)
- }
- {file.status !== 'error' && (
-
-
-
)
- }
- {file.status === 'error' && (
-
- uploadImage(file.url)} />
-
- )}
-
-
- ))}
-
- ) : null
-}
diff --git a/spaces/segments-tobias/conex/espnet2/lm/transformer_lm.py b/spaces/segments-tobias/conex/espnet2/lm/transformer_lm.py
deleted file mode 100644
index 57df87bb11c49fba65b2fdf31157a20109f1ee17..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/lm/transformer_lm.py
+++ /dev/null
@@ -1,131 +0,0 @@
-from typing import Any
-from typing import List
-from typing import Tuple
-
-import torch
-import torch.nn as nn
-
-from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
-from espnet.nets.pytorch_backend.transformer.encoder import Encoder
-from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
-from espnet2.lm.abs_model import AbsLM
-
-
-class TransformerLM(AbsLM):
- def __init__(
- self,
- vocab_size: int,
- pos_enc: str = None,
- embed_unit: int = 128,
- att_unit: int = 256,
- head: int = 2,
- unit: int = 1024,
- layer: int = 4,
- dropout_rate: float = 0.5,
- ):
- super().__init__()
- if pos_enc == "sinusoidal":
- pos_enc_class = PositionalEncoding
- elif pos_enc is None:
-
- def pos_enc_class(*args, **kwargs):
- return nn.Sequential() # indentity
-
- else:
- raise ValueError(f"unknown pos-enc option: {pos_enc}")
-
- self.embed = nn.Embedding(vocab_size, embed_unit)
- self.encoder = Encoder(
- idim=embed_unit,
- attention_dim=att_unit,
- attention_heads=head,
- linear_units=unit,
- num_blocks=layer,
- dropout_rate=dropout_rate,
- input_layer="linear",
- pos_enc_class=pos_enc_class,
- )
- self.decoder = nn.Linear(att_unit, vocab_size)
-
- def _target_mask(self, ys_in_pad):
- ys_mask = ys_in_pad != 0
- m = subsequent_mask(ys_mask.size(-1), device=ys_mask.device).unsqueeze(0)
- return ys_mask.unsqueeze(-2) & m
-
- def forward(self, input: torch.Tensor, hidden: None) -> Tuple[torch.Tensor, None]:
- """Compute LM loss value from buffer sequences.
-
- Args:
- input (torch.Tensor): Input ids. (batch, len)
- hidden (torch.Tensor): Target ids. (batch, len)
-
- """
- x = self.embed(input)
- mask = self._target_mask(input)
- h, _ = self.encoder(x, mask)
- y = self.decoder(h)
- return y, None
-
- def score(
- self, y: torch.Tensor, state: Any, x: torch.Tensor
- ) -> Tuple[torch.Tensor, Any]:
- """Score new token.
-
- Args:
- y (torch.Tensor): 1D torch.int64 prefix tokens.
- state: Scorer state for prefix tokens
- x (torch.Tensor): encoder feature that generates ys.
-
- Returns:
- tuple[torch.Tensor, Any]: Tuple of
- torch.float32 scores for next token (vocab_size)
- and next state for ys
-
- """
- y = y.unsqueeze(0)
- h, _, cache = self.encoder.forward_one_step(
- self.embed(y), self._target_mask(y), cache=state
- )
- h = self.decoder(h[:, -1])
- logp = h.log_softmax(dim=-1).squeeze(0)
- return logp, cache
-
- def batch_score(
- self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
- ) -> Tuple[torch.Tensor, List[Any]]:
- """Score new token batch.
-
- Args:
- ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
- states (List[Any]): Scorer states for prefix tokens.
- xs (torch.Tensor):
- The encoder feature that generates ys (n_batch, xlen, n_feat).
-
- Returns:
- tuple[torch.Tensor, List[Any]]: Tuple of
- batchfied scores for next token with shape of `(n_batch, vocab_size)`
- and next state list for ys.
-
- """
- # merge states
- n_batch = len(ys)
- n_layers = len(self.encoder.encoders)
- if states[0] is None:
- batch_state = None
- else:
- # transpose state of [batch, layer] into [layer, batch]
- batch_state = [
- torch.stack([states[b][i] for b in range(n_batch)])
- for i in range(n_layers)
- ]
-
- # batch decoding
- h, _, states = self.encoder.forward_one_step(
- self.embed(ys), self._target_mask(ys), cache=batch_state
- )
- h = self.decoder(h[:, -1])
- logp = h.log_softmax(dim=-1)
-
- # transpose state of [layer, batch] into [batch, layer]
- state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
- return logp, state_list
diff --git a/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py
deleted file mode 100644
index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000
--- a/spaces/segments/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py
+++ /dev/null
@@ -1,221 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Backbone modules.
-"""
-
-from typing import Dict, List
-
-import torch
-import torch.nn.functional as F
-import torchvision
-from torch import nn
-from torchvision.models._utils import IntermediateLayerGetter
-
-from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
-
-from .position_encoding import build_position_encoding
-from .swin_transformer import build_swin_transformer
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
- """
- BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
- Copy-paste from torchvision.misc.ops with added eps before rqsrt,
- without which any other models than torchvision.models.resnet[18,34,50,101]
- produce nans.
- """
-
- def __init__(self, n):
- super(FrozenBatchNorm2d, self).__init__()
- self.register_buffer("weight", torch.ones(n))
- self.register_buffer("bias", torch.zeros(n))
- self.register_buffer("running_mean", torch.zeros(n))
- self.register_buffer("running_var", torch.ones(n))
-
- def _load_from_state_dict(
- self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
- ):
- num_batches_tracked_key = prefix + "num_batches_tracked"
- if num_batches_tracked_key in state_dict:
- del state_dict[num_batches_tracked_key]
-
- super(FrozenBatchNorm2d, self)._load_from_state_dict(
- state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
- )
-
- def forward(self, x):
- # move reshapes to the beginning
- # to make it fuser-friendly
- w = self.weight.reshape(1, -1, 1, 1)
- b = self.bias.reshape(1, -1, 1, 1)
- rv = self.running_var.reshape(1, -1, 1, 1)
- rm = self.running_mean.reshape(1, -1, 1, 1)
- eps = 1e-5
- scale = w * (rv + eps).rsqrt()
- bias = b - rm * scale
- return x * scale + bias
-
-
-class BackboneBase(nn.Module):
- def __init__(
- self,
- backbone: nn.Module,
- train_backbone: bool,
- num_channels: int,
- return_interm_indices: list,
- ):
- super().__init__()
- for name, parameter in backbone.named_parameters():
- if (
- not train_backbone
- or "layer2" not in name
- and "layer3" not in name
- and "layer4" not in name
- ):
- parameter.requires_grad_(False)
-
- return_layers = {}
- for idx, layer_index in enumerate(return_interm_indices):
- return_layers.update(
- {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
- )
-
- # if len:
- # if use_stage1_feature:
- # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
- # else:
- # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
- # else:
- # return_layers = {'layer4': "0"}
- self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
- self.num_channels = num_channels
-
- def forward(self, tensor_list: NestedTensor):
- xs = self.body(tensor_list.tensors)
- out: Dict[str, NestedTensor] = {}
- for name, x in xs.items():
- m = tensor_list.mask
- assert m is not None
- mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
- out[name] = NestedTensor(x, mask)
- # import ipdb; ipdb.set_trace()
- return out
-
-
-class Backbone(BackboneBase):
- """ResNet backbone with frozen BatchNorm."""
-
- def __init__(
- self,
- name: str,
- train_backbone: bool,
- dilation: bool,
- return_interm_indices: list,
- batch_norm=FrozenBatchNorm2d,
- ):
- if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
- backbone = getattr(torchvision.models, name)(
- replace_stride_with_dilation=[False, False, dilation],
- pretrained=is_main_process(),
- norm_layer=batch_norm,
- )
- else:
- raise NotImplementedError("Why you can get here with name {}".format(name))
- # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
- assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
- assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
- num_channels_all = [256, 512, 1024, 2048]
- num_channels = num_channels_all[4 - len(return_interm_indices) :]
- super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
-
-
-class Joiner(nn.Sequential):
- def __init__(self, backbone, position_embedding):
- super().__init__(backbone, position_embedding)
-
- def forward(self, tensor_list: NestedTensor):
- xs = self[0](tensor_list)
- out: List[NestedTensor] = []
- pos = []
- for name, x in xs.items():
- out.append(x)
- # position encoding
- pos.append(self[1](x).to(x.tensors.dtype))
-
- return out, pos
-
-
-def build_backbone(args):
- """
- Useful args:
- - backbone: backbone name
- - lr_backbone:
- - dilation
- - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
- - backbone_freeze_keywords:
- - use_checkpoint: for swin only for now
-
- """
- position_embedding = build_position_encoding(args)
- train_backbone = True
- if not train_backbone:
- raise ValueError("Please set lr_backbone > 0")
- return_interm_indices = args.return_interm_indices
- assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
- args.backbone_freeze_keywords
- use_checkpoint = getattr(args, "use_checkpoint", False)
-
- if args.backbone in ["resnet50", "resnet101"]:
- backbone = Backbone(
- args.backbone,
- train_backbone,
- args.dilation,
- return_interm_indices,
- batch_norm=FrozenBatchNorm2d,
- )
- bb_num_channels = backbone.num_channels
- elif args.backbone in [
- "swin_T_224_1k",
- "swin_B_224_22k",
- "swin_B_384_22k",
- "swin_L_224_22k",
- "swin_L_384_22k",
- ]:
- pretrain_img_size = int(args.backbone.split("_")[-2])
- backbone = build_swin_transformer(
- args.backbone,
- pretrain_img_size=pretrain_img_size,
- out_indices=tuple(return_interm_indices),
- dilation=False,
- use_checkpoint=use_checkpoint,
- )
-
- bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
- else:
- raise NotImplementedError("Unknown backbone {}".format(args.backbone))
-
- assert len(bb_num_channels) == len(
- return_interm_indices
- ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
-
- model = Joiner(backbone, position_embedding)
- model.num_channels = bb_num_channels
- assert isinstance(
- bb_num_channels, List
- ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
- # import ipdb; ipdb.set_trace()
- return model
diff --git a/spaces/select-case/Can_You_Hug_the_Bear/app.py b/spaces/select-case/Can_You_Hug_the_Bear/app.py
deleted file mode 100644
index f0c116448d0a8f3fa0fc82a20e20f9cda153c0e1..0000000000000000000000000000000000000000
--- a/spaces/select-case/Can_You_Hug_the_Bear/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-
-def is_grizzly(x): return x[0].isupper()
-
-learn = load_learner('model.pkl')
-
-categories = ("Black","Grizzly","Polar","Teddy")
-
-def classify_image(img):
- pred,idx,probs = learn.predict(img)
- if max(probs) == probs[3]:
- return "Go Ahead, that's a Teddy Bear"
- elif max(probs) == probs[0]:
- return "Hold Back"
- elif max(probs) == probs[1]:
- return "Get Help and there are better ways to attempt suicide"
- else:
- return "Enjoy being bear meat"
-
-
-
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-
-iface = gr.Interface(fn=classify_image, inputs="image", outputs="text")
-iface.launch(inline = False)
\ No newline at end of file
diff --git a/spaces/semomos3/Movie_Recommender/app.py b/spaces/semomos3/Movie_Recommender/app.py
deleted file mode 100644
index d0cdb95f25e38525f944c7028b7745f97d01bd46..0000000000000000000000000000000000000000
--- a/spaces/semomos3/Movie_Recommender/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import streamlit as st
-import pandas as pd
-import os
-import nbformat
-from nbconvert.preprocessors import ExecutePreprocessor
-from nbformat import read, NO_CONVERT
-import pickle
-import requests
-
-movies_dict = pickle.load(open('movie_dict.pkl', 'rb'))
-similarity = pickle.load(open('similarity.pkl', 'rb'))
-movies = pd.DataFrame(movies_dict)
-
-@st.cache_data
-def load_movie_data(file_path):
- movie_data = pd.read_csv('movies_metadata.csv')
- movie_data = movie_data.sort_values('title')
- return movie_data['title']
-
-def fetch_poster(movie_id):
- #Using leaked key
- response = requests.get('https://api.themoviedb.org/3/movie/{0}?api_key=d61204e42321cddd34e0f954c0bf649f&language=en-US'.format(movie_id))
- data = response.json()
- return "https://image.tmdb.org/t/p/w500/"+ data['poster_path']
-
-# Recommender function
-def recomend(movie):
- # st.text(movies['title'])
- count = -1
- for moviei in movies['title']:
- count = count + 1
- # st.text(moviei)
- # st.text(movie)
- if (moviei == movie):
- movie_index = count
- # st.text(movie_index)
- break
- # movie_index = movies[movies['title'] == movie].index[0]
- distances = similarity[movie_index]
- movie_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
- recomendedMovies = []
- recomended_movies_posters = []
- for i in movie_list:
- movie_id = movies.iloc[i[0]].movie_id
- recomendedMovies.append(movies.iloc[i[0]].title)
- recomended_movies_posters.append(fetch_poster(movie_id))
- return recomendedMovies, recomended_movies_posters
-
-
-
-
-def main():
-
-
- with st.sidebar:
- st.title("Movie Recommender Pro")
- st.subheader("Developer:")
- st.markdown("Sepehr mousaviyan", unsafe_allow_html=True)
- st.markdown("Checkout my colab to see full version of many methods:")
- st.markdown("Note book link", unsafe_allow_html=True)
-
- st.title("Movie Recommender System")
-
-
- selected = st.selectbox("Select your favorite movies(at least 3 movies)", movies['title'].values)
-
- if st.button("Get Recommendations"):
- recomend_movies, poster = recomend(selected)
- st.write("Recommended Movies:[Just made for you:)]")
- col1, col2, col3, col4, col5 = st.columns(5)
- with col1:
- st.text(recomend_movies[0])
- st.image(poster[0])
- with col2:
- st.text(recomend_movies[1])
- st.image(poster[1])
- with col3:
- st.text(recomend_movies[2])
- st.image(poster[2])
- with col4:
- st.text(recomend_movies[3])
- st.image(poster[3])
- with col5:
- st.text(recomend_movies[4])
- st.image(poster[4])
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/shatrunjai/FutureMeMotivator/docs/header.md b/spaces/shatrunjai/FutureMeMotivator/docs/header.md
deleted file mode 100644
index b769a567592faae5ba9db1d9bc563418339d28d8..0000000000000000000000000000000000000000
--- a/spaces/shatrunjai/FutureMeMotivator/docs/header.md
+++ /dev/null
@@ -1,2 +0,0 @@
-
-
\ No newline at end of file
diff --git a/spaces/shivammehta25/Diff-TTSG/pymo/parsers.py b/spaces/shivammehta25/Diff-TTSG/pymo/parsers.py
deleted file mode 100644
index 092bba2cdc2a6d313f26b48887247ca2313d092c..0000000000000000000000000000000000000000
--- a/spaces/shivammehta25/Diff-TTSG/pymo/parsers.py
+++ /dev/null
@@ -1,268 +0,0 @@
-"""
-BVH Parser Class
-
-By Omid Alemi
-Created: June 12, 2017
-
-Based on: https://gist.github.com/johnfredcee/2007503
-
-"""
-import os
-import re
-
-import numpy as np
-
-from pymo.data import Joint, MocapData
-
-
-class BVHScanner:
- """
- A wrapper class for re.Scanner
- """
-
- def __init__(self):
- def identifier(scanner, token):
- return "IDENT", token
-
- def operator(scanner, token):
- return "OPERATOR", token
-
- def digit(scanner, token):
- return "DIGIT", token
-
- def open_brace(scanner, token):
- return "OPEN_BRACE", token
-
- def close_brace(scanner, token):
- return "CLOSE_BRACE", token
-
- self.scanner = re.Scanner(
- [
- (r"[a-zA-Z_]\w*", identifier),
- # (r'-*[0-9]+(\.[0-9]+)?', digit), # won't work for .34
- # (r'[-+]?[0-9]*\.?[0-9]+', digit), # won't work for 4.56e-2
- # (r'[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?', digit),
- (r"-*[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?", digit),
- (r"}", close_brace),
- (r"}", close_brace),
- (r"{", open_brace),
- (r":", None),
- (r"\s+", None),
- ]
- )
-
- def scan(self, stuff):
- return self.scanner.scan(stuff)
-
-
-class BVHParser:
- """
- A class to parse a BVH file.
-
- Extracts the skeleton and channel values
- """
-
- def __init__(self, filename=None):
- self.reset()
-
- def reset(self):
- self._skeleton = {}
- self.bone_context = []
- self._motion_channels = []
- self._motions = []
- self.current_token = 0
- self.framerate = 0.0
- self.root_name = ""
-
- self.scanner = BVHScanner()
-
- self.data = MocapData()
-
- def parse(self, filename, start=0, stop=-1):
- self.reset()
-
- with open(filename) as bvh_file:
- raw_contents = bvh_file.read()
- tokens, remainder = self.scanner.scan(raw_contents)
- self._parse_hierarchy(tokens)
- self.current_token = self.current_token + 1
- self._parse_motion(tokens, start, stop)
-
- self.data.skeleton = self._skeleton
- self.data.channel_names = self._motion_channels
- self.data.values = self._to_DataFrame()
- self.data.root_name = self.root_name
- self.data.framerate = self.framerate
- self.data.take_name = os.path.basename(os.path.splitext(filename)[0])
-
- return self.data
-
- def _to_DataFrame(self):
- """Returns all of the channels parsed from the file as a pandas DataFrame"""
-
- import pandas as pd
-
- time_index = pd.to_timedelta([f[0] for f in self._motions], unit="s")
- frames = [f[1] for f in self._motions]
- channels = np.asarray([[channel[2] for channel in frame] for frame in frames])
- column_names = ["%s_%s" % (c[0], c[1]) for c in self._motion_channels]
-
- return pd.DataFrame(data=channels, index=time_index, columns=column_names)
-
- def _new_bone(self, parent, name):
- bone = {"parent": parent, "channels": [], "offsets": [], "order": "", "children": []}
- return bone
-
- def _push_bone_context(self, name):
- self.bone_context.append(name)
-
- def _get_bone_context(self):
- return self.bone_context[len(self.bone_context) - 1]
-
- def _pop_bone_context(self):
- self.bone_context = self.bone_context[:-1]
- return self.bone_context[len(self.bone_context) - 1]
-
- def _read_offset(self, bvh, token_index):
- if bvh[token_index] != ("IDENT", "OFFSET"):
- return None, None
- token_index = token_index + 1
- offsets = [0.0] * 3
- for i in range(3):
- offsets[i] = float(bvh[token_index][1])
- token_index = token_index + 1
- return offsets, token_index
-
- def _read_channels(self, bvh, token_index):
- if bvh[token_index] != ("IDENT", "CHANNELS"):
- return None, None
- token_index = token_index + 1
- channel_count = int(bvh[token_index][1])
- token_index = token_index + 1
- channels = [""] * channel_count
- order = ""
- for i in range(channel_count):
- channels[i] = bvh[token_index][1]
- token_index = token_index + 1
- if channels[i] == "Xrotation" or channels[i] == "Yrotation" or channels[i] == "Zrotation":
- order += channels[i][0]
- else:
- order = ""
- return channels, token_index, order
-
- def _parse_joint(self, bvh, token_index):
- end_site = False
- joint_id = bvh[token_index][1]
- token_index = token_index + 1
- joint_name = bvh[token_index][1]
- token_index = token_index + 1
-
- parent_name = self._get_bone_context()
-
- if joint_id == "End":
- joint_name = parent_name + "_Nub"
- end_site = True
- joint = self._new_bone(parent_name, joint_name)
- if bvh[token_index][0] != "OPEN_BRACE":
- print("Was expecting brance, got ", bvh[token_index])
- return None
- token_index = token_index + 1
- offsets, token_index = self._read_offset(bvh, token_index)
- joint["offsets"] = offsets
- if not end_site:
- channels, token_index, order = self._read_channels(bvh, token_index)
- joint["channels"] = channels
- joint["order"] = order
- for channel in channels:
- self._motion_channels.append((joint_name, channel))
-
- self._skeleton[joint_name] = joint
- self._skeleton[parent_name]["children"].append(joint_name)
-
- while (bvh[token_index][0] == "IDENT" and bvh[token_index][1] == "JOINT") or (
- bvh[token_index][0] == "IDENT" and bvh[token_index][1] == "End"
- ):
- self._push_bone_context(joint_name)
- token_index = self._parse_joint(bvh, token_index)
- self._pop_bone_context()
-
- if bvh[token_index][0] == "CLOSE_BRACE":
- return token_index + 1
-
- print("Unexpected token ", bvh[token_index])
-
- def _parse_hierarchy(self, bvh):
- self.current_token = 0
- if bvh[self.current_token] != ("IDENT", "HIERARCHY"):
- return None
- self.current_token = self.current_token + 1
- if bvh[self.current_token] != ("IDENT", "ROOT"):
- return None
- self.current_token = self.current_token + 1
- if bvh[self.current_token][0] != "IDENT":
- return None
-
- root_name = bvh[self.current_token][1]
- root_bone = self._new_bone(None, root_name)
- self.current_token = self.current_token + 2 # skipping open brace
- offsets, self.current_token = self._read_offset(bvh, self.current_token)
- channels, self.current_token, order = self._read_channels(bvh, self.current_token)
- root_bone["offsets"] = offsets
- root_bone["channels"] = channels
- root_bone["order"] = order
- self._skeleton[root_name] = root_bone
- self._push_bone_context(root_name)
-
- for channel in channels:
- self._motion_channels.append((root_name, channel))
-
- while bvh[self.current_token][1] == "JOINT":
- self.current_token = self._parse_joint(bvh, self.current_token)
-
- self.root_name = root_name
-
- def _parse_motion(self, bvh, start, stop):
- if bvh[self.current_token][0] != "IDENT":
- print("Unexpected text")
- return None
- if bvh[self.current_token][1] != "MOTION":
- print("No motion section")
- return None
- self.current_token = self.current_token + 1
- if bvh[self.current_token][1] != "Frames":
- return None
- self.current_token = self.current_token + 1
- frame_count = int(bvh[self.current_token][1])
-
- if stop < 0 or stop > frame_count:
- stop = frame_count
-
- assert start >= 0
- assert start < stop
-
- self.current_token = self.current_token + 1
- if bvh[self.current_token][1] != "Frame":
- return None
- self.current_token = self.current_token + 1
- if bvh[self.current_token][1] != "Time":
- return None
- self.current_token = self.current_token + 1
- frame_rate = float(bvh[self.current_token][1])
-
- self.framerate = frame_rate
-
- self.current_token = self.current_token + 1
-
- frame_time = 0.0
- self._motions = [()] * (stop - start)
- idx = 0
- for i in range(stop):
- channel_values = []
- for channel in self._motion_channels:
- channel_values.append((channel[0], channel[1], float(bvh[self.current_token][1])))
- self.current_token = self.current_token + 1
-
- if i >= start:
- self._motions[idx] = (frame_time, channel_values)
- frame_time = frame_time + frame_rate
- idx += 1
diff --git a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/dcn/src/deform_conv_ext.cpp b/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/dcn/src/deform_conv_ext.cpp
deleted file mode 100644
index 41c6df6f721bd95a525fd6a03dd9882e863de042..0000000000000000000000000000000000000000
--- a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/dcn/src/deform_conv_ext.cpp
+++ /dev/null
@@ -1,164 +0,0 @@
-// modify from
-// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
-
-#include
-#include
-
-#include
-#include
-
-#define WITH_CUDA // always use cuda
-#ifdef WITH_CUDA
-int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight,
- at::Tensor offset, at::Tensor output,
- at::Tensor columns, at::Tensor ones, int kW,
- int kH, int dW, int dH, int padW, int padH,
- int dilationW, int dilationH, int group,
- int deformable_group, int im2col_step);
-
-int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset,
- at::Tensor gradOutput, at::Tensor gradInput,
- at::Tensor gradOffset, at::Tensor weight,
- at::Tensor columns, int kW, int kH, int dW,
- int dH, int padW, int padH, int dilationW,
- int dilationH, int group,
- int deformable_group, int im2col_step);
-
-int deform_conv_backward_parameters_cuda(
- at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
- at::Tensor gradWeight, // at::Tensor gradBias,
- at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
- int padW, int padH, int dilationW, int dilationH, int group,
- int deformable_group, float scale, int im2col_step);
-
-void modulated_deform_conv_cuda_forward(
- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
- at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
- int kernel_h, int kernel_w, const int stride_h, const int stride_w,
- const int pad_h, const int pad_w, const int dilation_h,
- const int dilation_w, const int group, const int deformable_group,
- const bool with_bias);
-
-void modulated_deform_conv_cuda_backward(
- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
- at::Tensor offset, at::Tensor mask, at::Tensor columns,
- at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
- at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
- int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
- int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
- const bool with_bias);
-#endif
-
-int deform_conv_forward(at::Tensor input, at::Tensor weight,
- at::Tensor offset, at::Tensor output,
- at::Tensor columns, at::Tensor ones, int kW,
- int kH, int dW, int dH, int padW, int padH,
- int dilationW, int dilationH, int group,
- int deformable_group, int im2col_step) {
- if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
- return deform_conv_forward_cuda(input, weight, offset, output, columns,
- ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group,
- deformable_group, im2col_step);
-#else
- AT_ERROR("deform conv is not compiled with GPU support");
-#endif
- }
- AT_ERROR("deform conv is not implemented on CPU");
-}
-
-int deform_conv_backward_input(at::Tensor input, at::Tensor offset,
- at::Tensor gradOutput, at::Tensor gradInput,
- at::Tensor gradOffset, at::Tensor weight,
- at::Tensor columns, int kW, int kH, int dW,
- int dH, int padW, int padH, int dilationW,
- int dilationH, int group,
- int deformable_group, int im2col_step) {
- if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
- return deform_conv_backward_input_cuda(input, offset, gradOutput,
- gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH,
- dilationW, dilationH, group, deformable_group, im2col_step);
-#else
- AT_ERROR("deform conv is not compiled with GPU support");
-#endif
- }
- AT_ERROR("deform conv is not implemented on CPU");
-}
-
-int deform_conv_backward_parameters(
- at::Tensor input, at::Tensor offset, at::Tensor gradOutput,
- at::Tensor gradWeight, // at::Tensor gradBias,
- at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH,
- int padW, int padH, int dilationW, int dilationH, int group,
- int deformable_group, float scale, int im2col_step) {
- if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
- return deform_conv_backward_parameters_cuda(input, offset, gradOutput,
- gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW,
- dilationH, group, deformable_group, scale, im2col_step);
-#else
- AT_ERROR("deform conv is not compiled with GPU support");
-#endif
- }
- AT_ERROR("deform conv is not implemented on CPU");
-}
-
-void modulated_deform_conv_forward(
- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
- at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns,
- int kernel_h, int kernel_w, const int stride_h, const int stride_w,
- const int pad_h, const int pad_w, const int dilation_h,
- const int dilation_w, const int group, const int deformable_group,
- const bool with_bias) {
- if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
- return modulated_deform_conv_cuda_forward(input, weight, bias, ones,
- offset, mask, output, columns, kernel_h, kernel_w, stride_h,
- stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
- deformable_group, with_bias);
-#else
- AT_ERROR("modulated deform conv is not compiled with GPU support");
-#endif
- }
- AT_ERROR("modulated deform conv is not implemented on CPU");
-}
-
-void modulated_deform_conv_backward(
- at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones,
- at::Tensor offset, at::Tensor mask, at::Tensor columns,
- at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias,
- at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output,
- int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
- int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
- const bool with_bias) {
- if (input.device().is_cuda()) {
-#ifdef WITH_CUDA
- return modulated_deform_conv_cuda_backward(input, weight, bias, ones,
- offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset,
- grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w,
- pad_h, pad_w, dilation_h, dilation_w, group, deformable_group,
- with_bias);
-#else
- AT_ERROR("modulated deform conv is not compiled with GPU support");
-#endif
- }
- AT_ERROR("modulated deform conv is not implemented on CPU");
-}
-
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("deform_conv_forward", &deform_conv_forward,
- "deform forward");
- m.def("deform_conv_backward_input", &deform_conv_backward_input,
- "deform_conv_backward_input");
- m.def("deform_conv_backward_parameters",
- &deform_conv_backward_parameters,
- "deform_conv_backward_parameters");
- m.def("modulated_deform_conv_forward",
- &modulated_deform_conv_forward,
- "modulated deform conv forward");
- m.def("modulated_deform_conv_backward",
- &modulated_deform_conv_backward,
- "modulated deform conv backward");
-}
diff --git a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp b/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp
deleted file mode 100644
index 43d0b6783a5b512b55815a291fcac2bebeea31e0..0000000000000000000000000000000000000000
--- a/spaces/shiwan10000/CodeFormer/CodeFormer/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp
+++ /dev/null
@@ -1,24 +0,0 @@
-// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.cpp
-#include
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#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)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
- CHECK_CUDA(input);
- CHECK_CUDA(kernel);
-
- return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Descarga Stickman Warriors Super Dragon Shadow Fight Mod APK y disfruta de dinero gemas y personajes ilimitados - Todo Desbloqueado.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Descarga Stickman Warriors Super Dragon Shadow Fight Mod APK y disfruta de dinero gemas y personajes ilimitados - Todo Desbloqueado.md
deleted file mode 100644
index f1f511e34ca24285217adccef4e5b57299452d4e..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Descarga Stickman Warriors Super Dragon Shadow Fight Mod APK y disfruta de dinero gemas y personajes ilimitados - Todo Desbloqueado.md
+++ /dev/null
@@ -1,83 +0,0 @@
-
-
-
-
-
Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado
-
If you are looking for a thrilling action game with amazing graphics and sound effects, then you should try Stickman Warriors Super Dragon Shadow Fight. This game is inspired by the popular anime series Dragon Ball Z, where you can control your own stickman warrior and fight against powerful enemies using various skills and transformations. And if you want to make your gaming experience even more exciting, then you should download Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado. This mod apk will give you access to unlimited money, gems, unlocked characters, skins, and no ads. Sounds awesome, right? In this article, we will tell you everything you need to know about this game and this mod apk, including how to download and install it on your device. So, let's get started!
-
stickman warriors super dragon shadow fight mod apk todo desbloqueado
What is Stickman Warriors Super Dragon Shadow Fight?
-
Stickman Warriors Super Dragon Shadow Fight is a 2D action game developed by SkySoft Studio. It was released in 2021 and has received over 10 million downloads on Google Play Store. The game is based on the famous Dragon Ball Z anime series, where you can play as your favorite characters such as Goku, Vegeta, Gohan, Piccolo, Frieza, Cell, Buu, and more. You can also create your own stickman warrior with different skins and skills. The game has two modes: solo mode and multiplayer mode. In solo mode, you can fight against various enemies and bosses in different stages and levels. You can also collect coins and gems to buy items and upgrade your skills. In multiplayer mode, you can challenge your friends or other players online in real-time battles. You can also join clans and chat with other players. The game has stunning graphics and sound effects that will make you feel like you are in the Dragon Ball Z world. The game also has simple and intuitive controls that are easy to learn and master.
-
Why play Stickman Warriors Super Dragon Shadow Fight?
-
There are many reasons why you should play Stickman Warriors Super Dragon Shadow Fight. Here are some of them:
-
-
It is fun and addictive. If you love action games and Dragon Ball Z, then you will enjoy this game. You can unleash your inner warrior and fight against powerful enemies using various skills and transformations. You can also customize your stickman warrior with different skins and skills to suit your style.
-
It is challenging and rewarding. The game has different levels of difficulty and challenges that will test your skills and strategy. You can also earn coins and gems to buy items and upgrade your skills. You can also unlock new characters and skins as you progress in the game.
-
It is social and interactive. The game has a multiplayer mode where you can challenge your friends or other players online in real-time battles. You can also join clans and chat with other players. You can also share your achievements and screenshots with your friends on social media.
-
-
Fight against powerful enemies in solo mode
-
In solo mode, you can fight against various enemies and bosses in different stages and levels. Each stage has a different theme and background, such as desert, forest, city, space, etc. Each level has a different number of enemies and a boss at the end. You have to defeat all the enemies and the boss to clear the level. You can use the joystick on the left side of the screen to move your stickman warrior, and the buttons on the right side of the screen to attack, defend, jump, dash, transform, etc. You can also use special skills such as kamehameha, final flash, spirit bomb, etc., by tapping on the icons at the bottom of the screen. You have to be careful of your health bar and energy bar at the top of the screen. If your health bar reaches zero, you will lose the level. If your energy bar reaches zero, you will not be able to use special skills or transform. You can refill your health bar by using items or collecting health orbs from defeated enemies. You can refill your energy bar by waiting or collecting energy orbs from defeated enemies.
-
Challenge your friends in multiplayer mode
-
In multiplayer mode, you can challenge your friends or other players online in real-time battles. You can choose from three modes: 1v1, 2v2, or 3v3. You can also choose from three arenas: arena 1, arena 2, or arena 3. Each arena has a different size and layout. You can invite your friends by tapping on the invite button at the bottom of the screen, or join a random match by tapping on the quick match button at the bottom of the screen. You can also join a clan by tapping on the clan button at the top of the screen, or create your own clan by tapping on the create clan button at the top of the screen. By joining a clan, you can chat with other clan members, request items or gems from them, donate items or gems to them, etc. You can also participate in clan wars by tapping on the clan war button at the top of the screen. In clan wars, you can fight against other clans for glory and rewards.
-
Customize your stickman warrior with different skins and skills
-
You can customize your stickman warrior with different skins and skills to suit your style. You can access the customization menu
Steps for downloading and installing Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado
-
After you have checked the requirements, you can follow these steps to download and install Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado:
Wait for the download to finish and locate the file in your device's file manager.
-
Tap on the file and select Install.
-
Wait for the installation to complete and tap on Open.
-
Enjoy the game with unlimited money, gems, unlocked characters, skins, and no ads.
-
-
What are the features of Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado?
-
Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado has many features that will enhance your gaming experience. Here are some of them:
-
stickman warriors super dragon shadow fight unlimited money gems unlocked apk
-stickman warriors super dragon shadow fight mod apk download latest version
-stickman warriors super dragon shadow fight hack apk todo desbloqueado
-stickman warriors super dragon shadow fight cheats codes tips tricks
-stickman warriors super dragon shadow fight gameplay review guide
-stickman warriors super dragon shadow fight best characters skills combos
-stickman warriors super dragon shadow fight online multiplayer mode
-stickman warriors super dragon shadow fight offline mod apk todo desbloqueado
-stickman warriors super dragon shadow fight android ios free download
-stickman warriors super dragon shadow fight 2d action game mod apk
-stickman warriors super dragon shadow fight mod menu apk todo desbloqueado
-stickman warriors super dragon shadow fight how to install mod apk
-stickman warriors super dragon shadow fight new update features mod apk
-stickman warriors super dragon shadow fight legendary heroes mod apk todo desbloqueado
-stickman warriors super dragon shadow fight epic boss battles mod apk
-stickman warriors super dragon shadow fight custom skins mod apk todo desbloqueado
-stickman warriors super dragon shadow fight fan made characters mod apk
-stickman warriors super dragon shadow fight secrets hidden easter eggs mod apk
-stickman warriors super dragon shadow fight ranking system mod apk todo desbloqueado
-stickman warriors super dragon shadow fight tournaments events mod apk
-stickman warriors super dragon shadow fight vs other stickman games mod apk
-stickman warriors super dragon shadow fight inspired by famous universe mod apk todo desbloqueado
-stickman warriors super dragon shadow fight no ads no root mod apk
-stickman warriors super dragon shadow fight unlimited energy stamina mod apk todo desbloqueado
-stickman warriors super dragon shadow fight realistic physics ragdoll effects mod apk
-
Unlimited money and gems
-
With this mod apk, you will have unlimited money and gems in your account. You can use them to buy any items or upgrade any skills you want. You can also use them to unlock new characters and skins. You don't have to worry about running out of money or gems ever again.
-
Unlocked characters and skins
-
With this mod apk, you will have access to all the characters and skins in the game without spending money or gems. You can choose from different categories of characters and skins, such as basic, super, ultra, legend, etc. You can also switch between them anytime you want. You can play as your favorite characters such as Goku, Vegeta, Gohan, Piccolo, Frieza, Cell, Buu, and more. You can also customize your stickman warrior with different skins and skills to suit your style.
-
No ads
-
With this mod apk, you will not see any ads in the game. You can enjoy the game without any interruptions or distractions from ads that may ruin your mood or gameplay. You can focus on the action and the fun without any annoyance.
-
Conclusion
-
Stickman Warriors Super Dragon Shadow Fight is a great game for fans of action and Dragon Ball Z. You can control your own stickman warrior and fight against powerful enemies using various skills and transformations. You can also customize your stickman warrior with different skins and skills. You can also challenge your friends or other players online in real-time battles. You can also join clans and chat with other players. The game has amazing graphics and sound effects that will make you feel like you are in the Dragon Ball Z world. The game also has simple and intuitive controls that are easy to learn and master.
-
If you want to make your gaming experience even more exciting, then you should download Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado. This mod apk will give you access to unlimited money, gems, unlocked characters, skins, and no ads. This means that you can enjoy the game without any limitations or restrictions. You can buy any items or upgrade any skills you want without worrying about running out of money or gems. You can also access all the characters and skins in the game without spending money or gems. You can also enjoy the game without any interruptions from ads that may ruin your mood or gameplay.
-
So, what are you waiting for? Download Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado now and unleash your inner warrior!
-
FAQs
-
Here are some frequently asked questions about the game or the mod apk with short answers:
-
-
Q: Is Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado safe to use?
-
A: Yes, it is safe to use. It does not contain any viruses or malware that may harm your device or data. It is also compatible with most Android devices.
-
Q: Do I need to root my device to use Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado?
-
A: No, you do not need to root your device to use Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado. It works fine on both rooted and non-rooted devices.
-
Q: How do I update Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado?
-
A: To update Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado, you need to download the latest version of the mod apk from the same link that you used to download the previous version. You can also check for updates by tapping on the update button at the bottom of the screen in the game. You need to uninstall the previous version of the mod apk before installing the new one.
-
Q: Can I play Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado offline?
-
A: Yes, you can play Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado offline. However, you will not be able to access some features such as multiplayer mode, clan wars, events, etc. You will also not be able to save your progress online or sync it with other devices.
-
Q: Can I play Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado with other players who have the original game?
-
A: No, you cannot play Stickman Warriors Super Dragon Shadow Fight Mod APK Todo Desbloqueado with other players who have the original game. The mod apk has a different version and server than the original game. You can only play with other players who have the same mod apk as you.
-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download and Play with Extra Roles MOD for Among Us - 4 New Roles to Spice Up Your Games.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download and Play with Extra Roles MOD for Among Us - 4 New Roles to Spice Up Your Games.md
deleted file mode 100644
index a0cca0f26c4aa82589660f7dca461aa055bf9dec..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download and Play with Extra Roles MOD for Among Us - 4 New Roles to Spice Up Your Games.md
+++ /dev/null
@@ -1,53 +0,0 @@
-
-
Among Us New Roles Mod APK: How to Download and Play
| | H2: What is Among Us New Roles Mod APK? |
What is Among Us New Roles Mod APK?
Among Us is a popular online multiplayer game where you have to work together with your crewmates to complete tasks and find the impostor who is trying to sabotage and kill everyone. But what if you want to spice up your gameplay with some new roles and abilities? That's where Among Us New Roles Mod APK comes in.
Among Us New Roles Mod APK is a mod pack for Among Us that adds more than 25 new roles to the game, along with new settings, colors, hats, and features. Some of the new roles are inspired by other famous mods, such as Town of Us and The Other Roles, but many others are unique and original to this mod pack. You can play as a Mayor, a Sheriff, a Jester, a Jackal, an Arsonist, and many more.
The best part is that this mod pack is compatible with the latest version of Among Us, so you can play with up to 15 players in online or local mode. However, you need to make sure that all players have the same version of Among Us and the same version of the mod pack installed. Otherwise, you won't be able to join or host a game.
| | H2: How to Download and Install Among Us New Roles Mod APK? |
How to Download and Install Among Us New Roles Mod APK?
If you want to try out this mod pack, you need to follow these steps:
Download Among Us New Roles Mod APK from this link: [Download](^1^)
Uninstall your original Among Us app from your device.
Enable unknown sources on your device settings.
Install the downloaded mod apk file on your device.
Launch the game and enjoy!
Note: This mod pack is only for Android devices. If you want to play it on PC, you need to use an Android emulator or download another mod pack that is compatible with Steam or Epic Games versions.
| | H2: How to Play Among Us New Roles Mod APK? |
How to Play Among Us New Roles Mod APK?
Playing Among Us New Roles Mod APK is similar to playing the original game, but with some differences:
You can choose from different game modes, such as Classic, Bloody, Guesser Modifier, Hide 'n' Seek, Vanilla Hide 'n' Seek, and Tie Breaker.
You can customize your character with new colors and hats.
You can adjust the settings for each role in the lobby menu.
You can use chat commands to kick players or change roles as the host.
The main difference is that each player will have a different role assigned to them at the start of each game. Each role has its own abilities and objectives that can affect the gameplay. Here are some examples of the new roles:
Role
Type
Description
Mayor
Crewmate
The Mayor has two votes instead of one in meetings.
Sheriff
Crewmate
The Sheriff can kill one person per round. If they kill an impostor or a neutral role, they win. If they kill a crewmate, they die.
Jester
Neutral
The Jester wants to get voted out. If they succeed, they win. If they die or the game ends, they lose.
Jackal
Impostor
The Jackal can kill and vent like an impostor, but they have their own team and win condition. They can also recruit one crewmate as their sidekick.
Arsonist
Impostor
The Arsonist can douse players with gasoline and ignite them all at once. They win if they kill everyone except themselves.
There are many more roles to discover and play with, so you can have a lot of fun and variety in your games. You can also mix and match different roles and settings to create your own custom game modes.
| | H2: Why Should You Play Among Us New Roles Mod APK? |
Why Should You Play Among Us New Roles Mod APK?
Among Us New Roles Mod APK is a great way to enjoy Among Us with a new twist. Here are some of the benefits of playing this mod pack:
You can experience new gameplay mechanics and strategies with the new roles and abilities.
You can challenge yourself and your friends with different game modes and settings.
You can customize your character and game with new colors, hats, and features.
You can have more fun and excitement with the added unpredictability and chaos.
If you are a fan of Among Us and want to try something new, you should definitely give this mod pack a try. You will not regret it!
-
among us mod pack with new roles
-among us the other roles mod download
-among us extra roles mod apk
-among us town of us mod apk
-among us mod apk with new impostor roles
-among us mod apk with vampire role
-among us mod apk with jester role
-among us mod apk with sheriff role
-among us mod apk with medic role
-among us mod apk with engineer role
-among us mod apk with spy role
-among us mod apk with hacker role
-among us mod apk with arsonist role
-among us mod apk with sidekick role
-among us mod apk with jackal role
-among us mod apk with trickster role
-among us mod apk with morphling role
-among us mod apk with janitor role
-among us mod apk with godfather role
-among us mod apk with warlock role
-among us mod apk with evil guesser role
-among us mod apk with nice guesser role
-among us mod apk with detective role
-among us mod apk with seer role
-among us mod apk with security guard role
-among us mod apk with lover role
-among us mod apk with major role
-among us mod apk with time master role
-among us mod apk with tracker role
-among us mod apk with lighter role
-among us mod apk with snitch role
-among us mod apk with swapper role
-among us mod apk with shifter role
-among us mod apk with implorer role
-among us mod apk with camouflager role
-among us mod apk with eraser role
-among us mod apk with cleaner role
-among us mod apk with evil mini role
-among us mod apk with nice mini role
-how to install new roles in among us android
-how to play new roles in among us mobile
-how to get new roles in among us for free
-how to download new roles in among us on pc
-how to update new roles in among us
-how to create new roles in among us
-how to enable new roles in among us
-how to customize new roles in among us
-how to join new roles in among us
| | H2: Conclusion |
Conclusion
Among Us New Roles Mod APK is a mod pack for Among Us that adds more than 25 new roles to the game, along with new settings, colors, hats, and features. You can download and install it on your Android device easily and play it online or offline with up to 15 players. You can choose from different game modes, such as Classic, Bloody, Guesser Modifier, Hide 'n' Seek, Vanilla Hide 'n' Seek, and Tie Breaker. You can also adjust the settings for each role in the lobby menu. Each role has its own abilities and objectives that can affect the gameplay. You can have a lot of fun and variety in your games with this mod pack. It is compatible with the latest version of Among Us, so you don't have to worry about missing out on any updates or features. If you are looking for a new way to enjoy Among Us, you should definitely check out Among Us New Roles Mod APK!
| | H2: FAQs |
FAQs
Here are some of the frequently asked questions about Among Us New Roles Mod APK:
Q: Is this mod pack safe to use? A: Yes, this mod pack is safe to use as long as you download it from a trusted source and follow the installation instructions carefully. However, you should always be careful when downloading any mod apk files from the internet, as some of them may contain viruses or malware that can harm your device or steal your data.
Q: Can I play this mod pack with other players who don't have it? A: No, you cannot play this mod pack with other players who don't have it. You need to make sure that all players have the same version of Among Us and the same version of the mod pack installed. Otherwise, you won't be able to join or host a game.
Q: Can I play this mod pack on PC? A: No, this mod pack is only for Android devices. If you want to play it on PC, you need to use an Android emulator or download another mod pack that is compatible with Steam or Epic Games versions.
Q: How do I update this mod pack? A: To update this mod pack, you need to uninstall the old version and install the new version from the same link. You should also check for updates regularly to make sure that you have the latest features and bug fixes.
Q: Where can I find more information about this mod pack? A: You can find more information about this mod pack on its official website: [Website]. You can also join its Discord server: [Discord] to chat with other players, report bugs, suggest ideas, and get support.
| | Custom message | | [^ 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Experience the Adventure of Minecraft in Survival Mode - Free Trial Download.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Experience the Adventure of Minecraft in Survival Mode - Free Trial Download.md
deleted file mode 100644
index b843a6d24d18e2365416548e9bcba22addb26860..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Experience the Adventure of Minecraft in Survival Mode - Free Trial Download.md
+++ /dev/null
@@ -1,133 +0,0 @@
-
-
How to Download and Play Minecraft Trial for Free
-
Minecraft is one of the most popular and creative games in the world, where you can build, explore and survive in infinite worlds. But what if you want to try it out before buying it? Well, you can do that with Minecraft Trial, a free version of the game that lets you play for a limited time. In this article, we will show you how to download and play Minecraft Trial for free on different devices, and give you some tips and tricks to make the most of your experience.
Minecraft Trial is a time-limited version of Minecraft that allows you to play in survival mode, where you have to craft weapons and armor, gather resources, build shelters and farms, and fight or avoid dangerous mobs. You can also explore infinite worlds, from deserts to jungles, from mountains to oceans, and from caves to dungeons. You can play solo or with friends online.
-
Features of Minecraft Trial
-
Minecraft Trial has many features that make it fun and engaging, such as:
-
-
Randomly generated worlds that are different every time you play.
-
A variety of biomes, structures, animals, plants, and items to discover.
-
A day-night cycle that affects the gameplay and the difficulty.
-
A creative mode that lets you build anything you can imagine without any limits or dangers.
-
A multiplayer mode that lets you join servers and play with other players around the world.
-
-
Limitations of Minecraft Trial
-
However, Minecraft Trial also has some limitations that make it different from the full version of the game, such as:
-
-
A time limit of 90 minutes per session, after which you can't place or break blocks, open doors, or interact with anything.
-
A lack of updates and new features that are available in the full version.
-
A limited number of worlds that you can create or join.
-
A lack of customization options for your character, your world, or your game settings.
-
A lack of support for mods, texture packs, skins, or other add-ons that enhance the game.
-
-
How to Download Minecraft Trial for Different Devices
-
If you want to download and play Minecraft Trial for free, you need to follow different steps depending on your device. Here are the instructions for Windows, Mac, Linux, Android, and iOS devices.
-
For Windows, Mac and Linux
-
Step 1: Go to the official website
-
The first step is to go to the official website of Minecraft at [Minecraft.net](^1^). There you will find information about the game, its features, its updates, its community, and more. You will also see a button that says "Get Minecraft". Click on it to proceed.
-
minecraft trial download free for windows
-minecraft trial download free for mac
-minecraft trial download free for linux
-minecraft trial download free for android
-minecraft trial download free for ios
-minecraft trial download free for xbox
-minecraft trial download free for playstation
-minecraft trial download free for switch
-minecraft trial download free for pc
-minecraft trial download free for laptop
-minecraft trial download free no virus
-minecraft trial download free no account
-minecraft trial download free no sign up
-minecraft trial download free no credit card
-minecraft trial download free no survey
-minecraft trial download free online
-minecraft trial download free offline
-minecraft trial download free full version
-minecraft trial download free unlimited time
-minecraft trial download free latest version
-minecraft trial download free apk
-minecraft trial download free exe
-minecraft trial download free zip
-minecraft trial download free rar
-minecraft trial download free java edition
-minecraft trial download free bedrock edition
-minecraft trial download free windows 10 edition
-minecraft trial download free education edition
-minecraft trial download free dungeons edition
-minecraft trial download free legends edition
-minecraft trial download free server software
-minecraft trial download free mods
-minecraft trial download free texture packs
-minecraft trial download free skins
-minecraft trial download free maps
-minecraft trial download free seeds
-minecraft trial download free shaders
-minecraft trial download free resource packs
-minecraft trial download free data packs
-minecraft trial download free commands
-minecraft trial download free cheats
-minecraft trial download free hacks
-minecraft trial download free tips and tricks
-minecraft trial download free guide and tutorial
-minecraft trial download free gameplay and review
-how to play minecraft without downloading it for free
-
Step 2: Choose your platform
-
The next step is to choose your platform from the list of options. You will see Windows, Mac, Linux, Windows 10, PlayStation, Xbox, Nintendo Switch, iOS , Android, and more. Select the one that matches your device. For this example, we will choose Windows.
-
Step 3: Download and install the launcher
-
The next step is to download and install the launcher for your platform. The launcher is a program that allows you to access and play the game. You will see a button that says "Download for Windows". Click on it and save the file to your computer. Then, open the file and follow the instructions to install the launcher.
-
Step 4: Create or log in to your account
-
The next step is to create or log in to your account. You need an account to play Minecraft Trial or the full version of the game. If you already have an account, you can log in with your email and password. If you don't have an account, you can create one for free by clicking on "Register" and filling out the form.
-
Step 5: Start the trial mode
-
The final step is to start the trial mode. After you log in to your account, you will see a button that says "Play Demo". Click on it and wait for the game to load. You will see a screen that shows you how much time you have left in your trial session. You can also choose between singleplayer or multiplayer mode, and between creative or survival mode. Click on "Play" and enjoy!
-
For Android and iOS
-
Step 1: Go to the app store
-
The first step is to go to the app store of your device. For Android devices, you need to go to Google Play Store. For iOS devices, you need to go to Apple App Store. There you will find thousands of apps and games that you can download and install on your device.
-
Step 2: Search for Minecraft Trial
-
The next step is to search for Minecraft Trial in the app store. You can use the search bar or browse through the categories. You will see a list of results that match your query. Look for the one that says "Minecraft Trial" and has the official logo of the game. Tap on it to open its page.
-
Step 3: Download and install the app
-
The next step is to download and install the app on your device. You will see a button that says "Install" or "Get". Tap on it and accept the permissions and terms of service. The app will start downloading and installing automatically. You may need to enter your password or use your fingerprint or face ID to confirm the installation.
-
Step 4: Open the app and start the trial mode
-
The final step is to open the app and start the trial mode. After the installation is complete, you will see an icon of the game on your home screen or app drawer. Tap on it to launch the game. You will see a screen that shows you how much time you have left in your trial session. You can also choose between singleplayer or multiplayer mode, and between creative or survival mode. Tap on "Play" and enjoy!
-
Tips and Tricks for Playing Minecraft Trial
-
Minecraft Trial is a fun and exciting game, but it can also be challenging and confusing at times. Here are some tips and tricks that can help you play better and have more fun:
-
Explore the world and collect resources
-
One of the main aspects of Minecraft Trial is exploring the world and collecting resources. You can find different types of blocks, items, plants, animals, and structures in different biomes. You can use these resources to craft tools, weapons, armor, food, potions, and more. You can also use them to build anything you want, from simple shelters to complex machines.
-
To explore the world, you need to move around using the keyboard or the touchscreen controls. You can also jump, swim, fly, or ride animals or vehicles. To collect resources, you need to break blocks using your hand or a tool. You can also pick up items by walking over them or tapping on them.
-
Here is a table that shows some of the common resources you can find in Minecraft Trial:
- | Resource | Description | Use | | --- | --- | --- | | Wood | A brown block that comes from trees | Can be used to craft planks, sticks, tools, weapons, doors, fences, boats, etc | | Stone | A gray block that comes from underground | Can be used to craft cobblestone, tools, weapons, furnaces, etc | | Coal | A black mineral that comes from underground | Can be used as fuel for furnaces or torches | | Iron | A gray mineral that comes from underground | Can be smelted into iron ingots, which can be used to craft tools, weapons, armor, rails, etc | | Gold | A yellow mineral that comes from underground | Can be smelted into gold ingots, which can be used to craft tools, weapons, armor, clocks, etc | | Diamond | A blue mineral that comes from underground | Can be used to craft the most durable and powerful tools, weapons, and armor | | Redstone | A red mineral that comes from underground | Can be used to craft redstone circuits, switches, levers, buttons, lamps, etc | | Wheat | A yellow crop that comes from farms | Can be used to craft bread, cake, cookies, etc | | Wool | A white block that comes from sheep | Can be used to craft beds, carpets, banners, etc | | Leather | A brown item that comes from cows | Can be used to craft armor or books | | Egg | A white item that comes from chickens | Can be used to craft cake or spawn baby chickens | | Feather | A white item that comes from chickens | Can be used to craft arrows or books | | Sugar cane | A green plant that grows near water | Can be used to craft sugar or paper |
Craft tools and weapons
-
Another important aspect of Minecraft Trial is crafting tools and weapons. You need tools to break blocks faster and more efficiently. You need weapons to defend yourself and attack enemies. You can craft different types of tools and weapons using different materials and recipes.
-
To craft tools and weapons, you need a crafting table. You can make a crafting table by placing four wooden planks in a 2x2 square in your inventory. Then you can place the crafting table on the ground and use it to access a 3x3 crafting grid. There you can place the materials in specific patterns to create the items you want.
-
Here is a table that shows some of the common tools and weapons you can craft in Minecraft Trial:
- | Tool/Weapon | Description | Recipe | | --- | --- | --- | | Wooden sword | A basic weapon that deals 4 damage per hit | Two wooden planks on top of a stick | | Stone sword | A slightly better weapon that deals 5 damage per hit | Two cobblestone on top of a stick | | Iron sword | A decent weapon that deals 6 damage per hit | Two iron ingots on top of a stick | | Gold sword | A weak weapon that deals 4 damage per hit but has high enchantability | Two gold ingots on top of a stick | | Diamond sword | The best weapon that deals 7 damage per hit and has high durability | Two diamonds on top of a stick | | Wooden pickaxe | A basic tool that can mine stone and coal | Three wooden planks on top of two sticks | | Stone pickaxe | A slightly better tool that can mine iron and redstone | Three cobblestone on top of two sticks | | Iron pickaxe | A decent tool that can mine gold and diamond | Three iron ingots on top of two sticks | | Gold pickaxe | A weak tool that can mine stone and coal but has high enchantability | Three gold ingots on top of two sticks | | Diamond pickaxe | The best tool that can mine any block and has high durability | Three diamonds on top of two sticks | | Wooden shovel | A basic tool that can dig dirt, sand, gravel, and snow | One wooden plank on top of a stick | | Stone shovel | A slightly better tool that can dig faster than a wooden shovel | One cobblestone on top of a stick | | Iron shovel | A decent tool that can dig faster than a stone shovel | One iron ingot on top of a stick | | Gold shovel | A weak tool that can dig faster than an iron shovel but has low durability and high enchantability | One gold ingot on top of a stick | | Diamond shovel | The best tool that can dig the fastest and has high durability | One diamond on top of a stick | | Wooden axe | A basic tool that can chop wood and wooden items | Three wooden planks in an L shape on top of two sticks | | Stone axe | A slightly better tool that can chop faster than a wooden axe | Three cobblestone in an L shape on top of two sticks | | Iron axe | A decent tool that can chop faster than a stone axe | Three iron ingots in an L shape on top of two sticks | | Gold axe | A weak tool that can chop faster than an iron axe but has low durability and high enchantability | Three gold ingots in an L shape on top of two sticks | | Diamond axe | The best tool that can chop the fastest and has high durability | Three diamonds in an L shape on top of two sticks | | Wooden hoe | A basic tool that can till dirt and grass into farmland | Two wooden planks in a horizontal line on top of a stick | | Stone hoe | A slightly better tool that can till faster than a wooden hoe | Two cobblestone in a horizontal line on top of a stick | | Iron hoe | A decent tool that can till faster than a stone hoe | Two iron ingots in a horizontal line on top of a stick | | Gold hoe | A weak tool that can till faster than an iron hoe but has low durability and high enchantability | Two gold ingots in a horizontal line on top of a stick | | Diamond hoe | The best tool that can till the fastest and has high durability | Two diamonds in a horizontal line on top of a stick | | Bow | A ranged weapon that can shoot arrows | Three sticks and three strings in a diagonal shape | | Arrow | A projectile that can be shot by a bow | One flint, one stick, and one feather in a vertical line | | Flint and steel | A tool that can light fires or activate TNT | One flint and one iron ingot in a diagonal shape | | Fishing rod | A tool that can catch fish or other items from water | Three sticks and two strings in a diagonal shape | | Shears | A tool that can shear sheep or cut wool, leaves, vines, etc | Two iron ingots in a diagonal shape |
Build shelters and farms
-
Another important aspect of Minecraft Trial is building shelters and farms. You need shelters to protect yourself from the weather, the darkness, and the mobs. You need farms to grow crops, breed animals, and produce food. You can build different types of shelters and farms using different materials and designs.
-
To build shelters and farms, you need to place blocks on the ground or on other blocks. You can also use doors, windows, ladders, stairs, fences, gates, chests, beds, furnaces, crafting tables, and other items to decorate and furnish your buildings. You can also use water, lava, redstone, pistons, hoppers, dispensers, droppers, and other items to create mechanisms and contraptions.
-
Here is a table that shows some of the common shelters and farms you can build in Minecraft Trial:
- | Shelter/Farm | Description | Materials | | --- | --- | --- | | Basic hut | A simple shelter that has four walls, a roof, a door, and a window | Wood, cobblestone, glass | | Underground base | A hidden shelter that is dug out under the ground or inside a mountain | Stone, dirt, torches | | Treehouse | A cozy shelter that is built on top of a tree or among the leaves | Wood, leaves, ladders | | Castle | A majestic shelter that has towers, walls, gates, bridges, and flags | Stone bricks, stone slabs, iron bars | | Wheat farm | A farm that grows wheat using water and seeds | Dirt, water bucket, seeds | | Animal farm | A farm that breeds animals using fences and food | Fences , wheat, carrots, potatoes, animals | | Sugarcane farm | A farm that grows sugarcane using water and sand | Sand, water bucket, sugarcane | | Cactus farm | A farm that grows cactus using sand and fences | Sand, fences, cactus | | Mushroom farm | A farm that grows mushrooms using dark and moist conditions | Dirt, torches, mushrooms | | Flower farm | A farm that grows flowers using grass and bonemeal | Grass, bonemeal, flowers |
Fight or avoid mobs
-
Another important aspect of Minecraft Trial is fighting or avoiding mobs. Mobs are living creatures that can be friendly, neutral, or hostile. You can interact with them in different ways, such as feeding them, taming them, trading with them, or killing them. You can also loot them for items or experience points.
-
To fight or avoid mobs, you need to be aware of your surroundings and your equipment. You can use weapons, armor, shields, potions, and other items to increase your damage and defense. You can also use traps, walls, doors, torches, and other items to prevent or deter mobs from attacking you. You can also use stealth, speed, or flight to escape from mobs.
-
Here is a table that shows some of the common mobs you can encounter in Minecraft Trial:
- | Mob | Description | Behavior | | --- | --- | --- | | Zombie | A green undead creature that wears ragged clothes | Hostile: Attacks players and villagers on sight | | Skeleton | A white undead creature that shoots arrows with a bow | Hostile: Attacks players and wolves on sight | | Spider | A black arachnid creature that has eight legs and red eyes | Neutral: Attacks players only at night or in dark places | | Creeper | A green creature that explodes when near players or other mobs | Hostile: Sneaks up on players and blows up | | Enderman | A tall black creature that can teleport and pick up blocks | Neutral: Attacks players only if provoked or looked at | | Villager | A human-like creature that wears different outfits and trades with players | Friendly: Offers various items for emeralds | | Wolf | A gray canine creature that can be tamed with bones | Neutral: Attacks players only if provoked or attacked by their owner | | Sheep | A white wooly creature that can be sheared for wool or killed for mutton | Friendly: Eats grass and follows players who hold wheat | | Cow | A brown bovine creature that can be milked with a bucket or killed for leather or beef | Friendly: Moo and follow players who hold wheat | | Chicken | A white feathered creature that lays eggs or can be killed for feathers or chicken | Friendly: Cluck and follow players who hold seeds | | Pig | A pink porcine creature that can be ridden with a saddle or killed for porkchops | Friendly: Oink and follow players who hold carrots or potatoes |
Save your progress before the time runs out
-
The final and most important aspect of Minecraft Trial is saving your progress before the time runs out. As mentioned before, you only have 90 minutes to play in each session, after which you can't interact with anything in the game. However, you can still save your progress and resume it later, as long as you don't delete or overwrite your world.
-
To save your progress, you need to pause the game and click on "Save and Quit". This will take you back to the main menu, where you can see your world and its name, date, and time. You can also rename or delete your world by clicking on the buttons below it. To resume your progress, you need to click on "Play Demo" and select your world from the list. You will start from where you left off, but with a new timer.
-
Here is a table that shows some of the benefits and drawbacks of saving your progress in Minecraft Trial:
- | Benefit | Drawback | | --- | --- | | You can continue playing your world in multiple sessions | You can't play more than 90 minutes in each session | | You can keep your items, buildings, and achievements | You can't access new updates or features | | You can explore different parts of the world | You can't create or join more than five worlds | | You can try different modes and settings | You can't customize your character or game |
Conclusion
-
Minecraft Trial is a great way to experience the game for free and see if you like it. You can download and play it on different devices, and enjoy its features, limitations, tips, and tricks. However, if you want to unlock the full potential of the game, you will need to buy the full version, which costs $26.95 for Windows, Mac, and Linux, $6.99 for Android and iOS, and varies for other platforms. You can buy it from the official website or the app store of your device.
-
We hope this article helped you learn how to download and play Minecraft Trial for free. If you have any questions or feedback, please leave them in the comments below. Happy gaming!
-
FAQs
-
Here are some of the frequently asked questions about Minecraft Trial:
-
Q: How long can I play Minecraft Trial?
-
A: You can play Minecraft Trial for 90 minutes per session. After that, you can't place or break blocks, open doors, or interact with anything. However, you can save your progress and resume it later.
-
Q: How many worlds can I create or join in Minecraft Trial?
-
A: You can create or join up to five worlds in Minecraft Trial. Each world has a different seed, which determines how it is generated. You can also choose between singleplayer or multiplayer mode, and between creative or survival mode.
-
Q: How do I switch between creative and survival mode in Minecraft Trial?
-
A: You can switch between creative and survival mode in Minecraft Trial by pausing the game and clicking on "Options". There you will see a button that says "Game Mode". Click on it and choose between "Creative" or "Survival". Creative mode lets you build anything without any limits or dangers. Survival mode lets you craft weapons and armor, gather resources , build shelters and farms, and fight or avoid mobs.
-
Q: How do I play with friends online in Minecraft Trial?
-
A: You can play with friends online in Minecraft Trial by joining servers that host the game. You can find servers by searching online or by asking your friends for their IP addresses. To join a server, you need to pause the game and click on "Multiplayer". There you will see a button that says "Add Server". Click on it and enter the server name and address. Then click on "Done" and select the server from the list. You will need an internet connection and a valid account to play online.
-
Q: How do I update Minecraft Trial to the latest version?
-
A: You can't update Minecraft Trial to the latest version, as it is a time-limited version of the game that doesn't receive any updates or new features. If you want to play the latest version of the game, you will need to buy the full version, which costs $26.95 for Windows, Mac, and Linux, $6.99 for Android and iOS, and varies for other platforms. You can buy it from the official website or the app store of your device.
-
Q: How do I buy the full version of Minecraft?
-
A: You can buy the full version of Minecraft from the official website or the app store of your device. To buy it from the website, you need to go to [Minecraft.net] and click on "Get Minecraft". There you will see a list of platforms and prices. Choose the one that matches your device and click on "Buy Now". You will need to create or log in to your account and enter your payment details. To buy it from the app store, you need to go to Google Play Store or Apple App Store and search for Minecraft. There you will see a button that says "Buy" or "Purchase". Tap on it and enter your password or use your fingerprint or face ID to confirm the purchase.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get Naruto Senki Mod APK with Unlimited Skill and All Character Unlock - Download Now.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get Naruto Senki Mod APK with Unlimited Skill and All Character Unlock - Download Now.md
deleted file mode 100644
index 7b009816a3b94715e05eb5070d81dd96ad11d173..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Get Naruto Senki Mod APK with Unlimited Skill and All Character Unlock - Download Now.md
+++ /dev/null
@@ -1,102 +0,0 @@
-
-
Download Naruto Senki Mod Apk Unlimited Skill and Unlock All Character
-
If you are a fan of Naruto anime and manga series, you must have heard of Naruto Senki, a popular game based on the Naruto universe. Naruto Senki is an action-adventure game that lets you play as your favorite Naruto characters and fight against various enemies and bosses. You can also customize your character with different outfits, weapons, and skills.
-
download naruto senki mod apk unlimited skill and unlock all character
However, if you want to enjoy the game to the fullest, you need to download Naruto Senki Mod Apk, which is a modified version of the original game that gives you unlimited skill and unlock all character. With this mod apk, you can access all the features and content of the game without spending any money or time. You can also play online with other players and show off your skills and combos.
-
In this article, we will tell you what is Naruto Senki Mod Apk, what are its features, how to download and install it, and some tips and tricks for playing it. Read on to find out more.
-
What is Naruto Senki Mod Apk?
-
Naruto Senki Mod Apk is a modified version of the original Naruto Senki game that gives you unlimited skill and unlock all character. This means that you can use any skill you want without any cooldown or limit, and you can play as any character you want without unlocking them first. You can also enjoy other features such as high-quality graphics and sound, easy and smooth controls, and online multiplayer mode.
-
Naruto Senki Mod Apk is not available on the official app stores, so you need to download it from a third-party source. However, you need to be careful when downloading mod apk files, as some of them may contain viruses or malware that can harm your device. Therefore, we recommend you to download Naruto Senki Mod Apk from a trusted and reliable source, such as . This website provides safe and secure downloads of various mod apk files for free.
-
naruto senki mod apk full character and skill unlocked
-how to download naruto senki mod apk with unlimited skill
-naruto senki mod apk latest version unlock all character
-naruto senki mod apk free download for android unlimited skill
-best naruto senki mod apk with all character and skill
-naruto senki mod apk unlimited skill and money
-download naruto senki mod apk full character no cooldown
-naruto senki mod apk unlock all character and jutsu
-naruto senki mod apk unlimited skill and health
-download naruto senki mod apk full character offline
-naruto senki mod apk all character and skill unlocked 2023
-download naruto senki mod apk unlimited skill and coins
-naruto senki mod apk unlock all character and mode
-naruto senki mod apk unlimited skill and chakra
-download naruto senki mod apk full character online
-naruto senki mod apk all character and skill unlocked 2022
-download naruto senki mod apk unlimited skill and gems
-naruto senki mod apk unlock all character and map
-naruto senki mod apk unlimited skill and stamina
-download naruto senki mod apk full character update
-naruto senki mod apk all character and skill unlocked 2021
-download naruto senki mod apk unlimited skill and gold
-naruto senki mod apk unlock all character and item
-naruto senki mod apk unlimited skill and power
-download naruto senki mod apk full character terbaru
-naruto senki mod apk all character and skill unlocked 2020
-download naruto senki mod apk unlimited skill and diamond
-naruto senki mod apk unlock all character and feature
-naruto senki mod apk unlimited skill and level
-download naruto senki mod apk full character gratis
-naruto senki mod apk all character and skill unlocked 2019
-download naruto senki mod apk unlimited skill and energy
-naruto senki mod apk unlock all character and costume
-naruto senki mod apk unlimited skill and weapon
-download naruto senki mod apk full character original
-naruto senki mod apk all character and skill unlocked 2018
-download naruto senki mod apk unlimited skill and ninja point
-naruto senki mod apk unlock all character and skin
-naruto senki mod apk unlimited skill and combo
-download naruto senki mod apk full character boruto
-naruto senki mod apk all character and skill unlocked 2017
-download naruto senki mod apk unlimited skill and cheat menu
-naruto senki mod apk unlock all character and stage
-naruto senki mod apk unlimited skill and damage
-download naruto senki mod apk full character shippuden
-naruto senki mod apk all character and skill unlocked 2016
-download naruto senki mod apk unlimited skill and hack tool
-naruto senki mod apk unlock all character and difficulty
-
Features of Naruto Senki Mod Apk
-
Unlimited Skill
-
One of the best features of Naruto Senki Mod Apk is unlimited skill. This means that you can use any skill you want without any cooldown or limit. You can unleash powerful attacks and combos on your enemies and defeat them easily. You can also switch between different skills according to the situation and your preference. Unlimited skill will make your gameplay more fun and exciting.
-
Unlock All Character
-
Another great feature of Naruto Senki Mod Apk is unlock all character. This means that you can play as any character you want without unlocking them first. You can choose from over 100 characters from the Naruto series, each with their own unique abilities and styles. You can also customize your character with different outfits, weapons, and skills. Unlock all character will give you more options and variety in your gameplay.
-
High-Quality Graphics and Sound
-
Naruto Senki Mod Apk also has high-quality graphics and sound that will enhance your gaming experience. The game has anime-style graphics that are colorful and detailed, with smooth animations and effects. The game also has realistic sound effects that match the actions and events in the game, as well as original voice-overs from the anime actors. The game will make you feel like you are in the Naruto world.
-
Easy and Smooth Controls
-
Naruto Sen ki Mod Apk has easy and smooth controls that will make your gameplay more comfortable and convenient. The game has a simple and intuitive interface that shows you the buttons for moving, attacking, and using skills. You can also adjust the sensitivity and layout of the controls according to your preference. The game also supports touch screen and joystick controls, so you can choose the one that suits you best.
-
How to Download and Install Naruto Senki Mod Apk?
-
If you want to download and install Naruto Senki Mod Apk, you need to follow these simple steps:
-
Step 1: Enable Unknown Sources
-
Since Naruto Senki Mod Apk is not available on the official app stores, you need to enable unknown sources on your device. This will allow you to install apps from third-party sources. To do this, go to your device settings, then security, then unknown sources, and turn it on.
-
Step 2: Download the Apk File
-
Next, you need to download the apk file of Naruto Senki Mod Apk from a trusted and reliable source, such as . This website provides safe and secure downloads of various mod apk files for free. To download the apk file, click on the download button on the website, and wait for a few seconds until the download is complete.
-
Step 3: Install the Apk File
-
After downloading the apk file, you need to install it on your device. To do this, locate the apk file in your device storage, and tap on it. You will see a pop-up window asking for your permission to install the app. Click on install, and wait for a few seconds until the installation is complete.
-
Step 4: Enjoy the Game
-
Finally, you can enjoy playing Naruto Senki Mod Apk on your device. You can launch the game from your app drawer or home screen, and start playing as your favorite Naruto character with unlimited skill and unlock all character.
-
Tips and Tricks for Playing Naruto Senki Mod Apk
-
If you want to improve your gameplay and have more fun with Naruto Senki Mod Apk, here are some tips and tricks that you can follow:
-
Choose Your Favorite Character
-
Naruto Senki Mod Apk gives you access to over 100 characters from the Naruto series, each with their own unique abilities and styles. You can choose any character you want without unlocking them first. However, you should choose a character that suits your preference and playstyle. For example, if you like fast and agile characters, you can choose Naruto or Sasuke. If you like strong and durable characters, you can choose Gaara or Kisame. If you like balanced and versatile characters, you can choose Kakashi or Itachi.
-
Master Your Skills and Combos
-
Naruto Senki Mod Apk also gives you unlimited skill, which means that you can use any skill you want without any cooldown or limit. You can unleash powerful attacks and combos on your enemies and defeat them easily. However, you should master your skills and combos, as they have different effects and advantages. For example, some skills are good for dealing damage, some are good for stunning or knocking back enemies, some are good for healing or buffing yourself or your allies, and some are good for escaping or dodging enemy attacks. You should also learn how to chain your skills and combos together to create more damage and effects.
-
Upgrade Your Character and Equipment
-
Naruto Senki Mod Apk also allows you to upgrade your character and equipment with coins that you earn from playing the game. You can use coins to increase your character's level, stats, and skills. You can also use coins to buy new outfits, weapons, and accessories for your character. Upgrading your character and equipment will make them stronger and more effective in battle.
-
Play with Friends Online
-
Naruto Senki Mod Apk also supports online multiplayer mode, which means that you can play with other players online. You can join or create a room with up to four players, and choose between cooperative or competitive modes. You can also chat with other players using text or voice messages. Playing with friends online will make your gameplay more fun and challenging.
-
Conclusion
-
Naruto Senki Mod Apk is a modified version of the original Naruto Senki game that gives you unlimited skill and unlock all character. It also has other features such as high-quality graphics and sound, easy and smooth controls, and online multiplayer mode. You can download Naruto Senki Mod Apk from a trusted source such as , which provides safe and secure downloads of various mod apk files for free. If you are a fan of Naruto anime and manga series, you should definitely try Naruto Senki Mod Apk and enjoy playing as your favorite Naruto character with unlimited skill and unlock all character.
-
Here are some FAQs that you may have about Naruto Senki Mod Apk:
-
FAQs
-
Q: Is Naruto Senki Mod Apk safe to download and install?
-
A: Yes, Naruto Senki Mod Apk is safe to download and install, as long as you download it from a trusted and reliable source, such as . This website provides safe and secure downloads of various mod apk files for free. However, you should always scan the apk file with an antivirus software before installing it, just to be on the safe side.
-
Q: Do I need to root my device to use Naruto Senki Mod Apk?
-
A: No, you do not need to root your device to use Naruto Senki Mod Apk. You can use the mod apk on any Android device without rooting it. However, you need to enable unknown sources on your device settings, as explained in the previous section.
-
Q: Can I play Naruto Senki Mod Apk offline?
-
A: Yes, you can play Naruto Senki Mod Apk offline, as the game does not require an internet connection to run. However, if you want to play online with other players, you need to have a stable internet connection.
-
Q: Can I update Naruto Senki Mod Apk?
-
A: Yes, you can update Naruto Senki Mod Apk whenever there is a new version available. However, you need to download and install the new version from the same source that you downloaded the previous version from. You should also backup your game data before updating, as some updates may erase your progress.
-
Q: Can I use Naruto Senki Mod Apk with other mods or cheats?
-
A: No, you cannot use Naruto Senki Mod Apk with other mods or cheats, as they may cause conflicts or errors in the game. You should only use Naruto Senki Mod Apk as it is, without modifying or altering it in any way.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/classification/finetune_classification_zen1-base_afqmc.sh b/spaces/skf15963/summary/fengshen/examples/classification/finetune_classification_zen1-base_afqmc.sh
deleted file mode 100644
index 845e93093cc6390db2c332c22e860ff88688a657..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/classification/finetune_classification_zen1-base_afqmc.sh
+++ /dev/null
@@ -1,151 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=afqmc-bart-base # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=2 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:2 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc.
-#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id)
-
-
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
-
-MODEL_NAME=fengshen-zen1
-
-TASK=afqmc
-TEXTA_NAME=sentence1
-TEXTB_NAME=sentence2
-LABEL_NAME=label
-ID_NAME=id
-
-
-BATCH_SIZE=8
-VAL_BATCH_SIZE=32
-ZERO_STAGE=1
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-ROOT_DIR=/cognitive_comp/ganruyi/experiments/classification_finetune/${MODEL_NAME}_${TASK}
-if [ ! -d ${ROOT_DIR} ];then
- mkdir -p ${ROOT_DIR}
- echo ${ROOT_DIR} created!!!!!!!!!!!!!!
-else
- echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
-fi
-
-DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/
-PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/ZEN_pretrain_base_v0.1.0
-
-CHECKPOINT_PATH=${ROOT_DIR}/ckpt/
-OUTPUT_PATH=${ROOT_DIR}/predict.json
-
-
-config_json="${ROOT_DIR}/ds_config.json"
-# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
-# reduce_bucket_size: hidden_size*hidden_size
-# stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size
-# stage3_param_persistence_threshold: 10 * hidden_size
-
-cat < $config_json
-{
- "train_micro_batch_size_per_gpu": $BATCH_SIZE,
- "steps_per_print": 100,
- "gradient_clipping": 0.1,
- "zero_optimization": {
- "stage": ${ZERO_STAGE}
- },
- "optimizer": {
- "type": "Adam",
- "params": {
- "lr": 1e-7,
- "eps": 1e-12,
- "weight_decay": 1e-2
- }
- },
- "scheduler": {
- "type": "WarmupLR",
- "params":{
- "warmup_min_lr": 1e-5,
- "warmup_max_lr": 1e-4,
- "warmup_num_steps": 400,
- "warmup_type": "linear"
- }
- },
- "zero_allow_untested_optimizer": false,
- "fp16": {
- "enabled": false,
- "loss_scale": 0,
- "loss_scale_window": 1000,
- "hysteresis": 2,
- "min_loss_scale": 1
- },
- "activation_checkpointing": {
- "partition_activations": false,
- "contiguous_memory_optimization": false
- },
- "wall_clock_breakdown": false
-}
-EOT
-
-export PL_DEEPSPEED_CONFIG_PATH=$config_json
-
-
-DATA_ARGS="\
- --data_dir $DATA_DIR \
- --train_data train.json \
- --valid_data dev.json \
- --test_data test.json \
- --train_batchsize $BATCH_SIZE \
- --valid_batchsize $VAL_BATCH_SIZE \
- --max_length 64 \
- --texta_name $TEXTA_NAME \
- --textb_name $TEXTB_NAME \
- --label_name $LABEL_NAME \
- --id_name $ID_NAME \
- "
-
-MODEL_ARGS="\
- --learning_rate 1e-5 \
- --weight_decay 1e-2 \
- --warmup 0.01 \
- --num_labels 2 \
- "
-
-MODEL_CHECKPOINT_ARGS="\
- --monitor val_acc \
- --save_top_k 3 \
- --mode max \
- --every_n_train_steps 200 \
- --save_weights_only True \
- --dirpath $CHECKPOINT_PATH \
- --filename model-{epoch:02d}-{val_acc:.4f} \
- "
-
-
-TRAINER_ARGS="\
- --max_epochs 10 \
- --gpus 1 \
- --num_nodes 1 \
- --strategy $STRATEGY \
- --gradient_clip_val 1.0 \
- --check_val_every_n_epoch 1 \
- --val_check_interval 1.0 \
- --default_root_dir $ROOT_DIR \
- "
-
-options=" \
- --pretrained_model_path $PRETRAINED_MODEL_PATH \
- --output_save_path $OUTPUT_PATH \
- $DATA_ARGS \
- $MODEL_ARGS \
- $MODEL_CHECKPOINT_ARGS \
- $TRAINER_ARGS \
- "
-
-SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
-SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/classification/finetune_classification.py
-
-# python3 $SCRIPT_PATH $options
-source activate base
-# srun singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/losses/loss_util.py b/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/losses/loss_util.py
deleted file mode 100644
index 744eeb46d1f3b5a7b4553ca23237ddd9c899a698..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/losses/loss_util.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import functools
-from torch.nn import functional as F
-
-
-def reduce_loss(loss, reduction):
- """Reduce loss as specified.
-
- Args:
- loss (Tensor): Elementwise loss tensor.
- reduction (str): Options are 'none', 'mean' and 'sum'.
-
- Returns:
- Tensor: Reduced loss tensor.
- """
- reduction_enum = F._Reduction.get_enum(reduction)
- # none: 0, elementwise_mean:1, sum: 2
- if reduction_enum == 0:
- return loss
- elif reduction_enum == 1:
- return loss.mean()
- else:
- return loss.sum()
-
-
-def weight_reduce_loss(loss, weight=None, reduction='mean'):
- """Apply element-wise weight and reduce loss.
-
- Args:
- loss (Tensor): Element-wise loss.
- weight (Tensor): Element-wise weights. Default: None.
- reduction (str): Same as built-in losses of PyTorch. Options are
- 'none', 'mean' and 'sum'. Default: 'mean'.
-
- Returns:
- Tensor: Loss values.
- """
- # if weight is specified, apply element-wise weight
- if weight is not None:
- assert weight.dim() == loss.dim()
- assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
- loss = loss * weight
-
- # if weight is not specified or reduction is sum, just reduce the loss
- if weight is None or reduction == 'sum':
- loss = reduce_loss(loss, reduction)
- # if reduction is mean, then compute mean over weight region
- elif reduction == 'mean':
- if weight.size(1) > 1:
- weight = weight.sum()
- else:
- weight = weight.sum() * loss.size(1)
- loss = loss.sum() / weight
-
- return loss
-
-
-def weighted_loss(loss_func):
- """Create a weighted version of a given loss function.
-
- To use this decorator, the loss function must have the signature like
- `loss_func(pred, target, **kwargs)`. The function only needs to compute
- element-wise loss without any reduction. This decorator will add weight
- and reduction arguments to the function. The decorated function will have
- the signature like `loss_func(pred, target, weight=None, reduction='mean',
- **kwargs)`.
-
- :Example:
-
- >>> import torch
- >>> @weighted_loss
- >>> def l1_loss(pred, target):
- >>> return (pred - target).abs()
-
- >>> pred = torch.Tensor([0, 2, 3])
- >>> target = torch.Tensor([1, 1, 1])
- >>> weight = torch.Tensor([1, 0, 1])
-
- >>> l1_loss(pred, target)
- tensor(1.3333)
- >>> l1_loss(pred, target, weight)
- tensor(1.5000)
- >>> l1_loss(pred, target, reduction='none')
- tensor([1., 1., 2.])
- >>> l1_loss(pred, target, weight, reduction='sum')
- tensor(3.)
- """
-
- @functools.wraps(loss_func)
- def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
- # get element-wise loss
- loss = loss_func(pred, target, **kwargs)
- loss = weight_reduce_loss(loss, weight, reduction)
- return loss
-
- return wrapper
diff --git a/spaces/slush0/petals-playground/app.py b/spaces/slush0/petals-playground/app.py
deleted file mode 100644
index 5926fc8f4848b33eb56d62b767b0ff633b5db327..0000000000000000000000000000000000000000
--- a/spaces/slush0/petals-playground/app.py
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/usr/bin/env python
-# or gradio app.py
-
-import gradio as gr
-
-from chat import iface_chat
-from prompt import iface_prompt
-
-with gr.Blocks() as iface:
- gr.Markdown(
- """# Petals playground
- **Let's play with prompts and inference settings for various big LLM models running on Petals!**
-
- This space uses websocket API of [chat.petals.dev](http://chat.petals.dev). Health status of Petals network [lives here](http://health.petals.dev)."""
- )
-
- gr.TabbedInterface([iface_prompt, iface_chat], ["Prompt mode", "Chat mode"])
-
-# Queues are required to enable generators
-iface.queue(concurrency_count=5, max_size=50)
-iface.launch(show_error=True)
diff --git a/spaces/society-ethics/ethical-charters/charters/multimodal-project.md b/spaces/society-ethics/ethical-charters/charters/multimodal-project.md
deleted file mode 100644
index 63c98461c5bf140fd43b17e742499dd010024d11..0000000000000000000000000000000000000000
--- a/spaces/society-ethics/ethical-charters/charters/multimodal-project.md
+++ /dev/null
@@ -1,46 +0,0 @@
-## Purpose of the ethical charter
-
-It has been well documented that machine learning research and applications can potentially lead to "data privacy issues, algorithmic biases, automation risks and malicious uses" (NeurIPS 2021 ethics guidelines). The purpose of this short document is to formalize the ethical principles that we (the multimodal learning group at Hugging Face) adopt for the project we are pursuing. By defining these ethical principles at the beginning of the project, we make them core to our machine learning lifecycle.
-
-By being transparent about the decisions we're making in the project, who is working on which aspects of the system, and how the team can be contacted, we hope to receive feedback early enough in the process to make meaningful changes, and ground discussions about choices in an awareness of the goals we aim to achieve and the values we hope to incorporate.
-
-This document is the result of discussions led by the multimodal learning group at Hugging Face (composed of machine learning researchers and engineers), with the contributions of multiple experts in ethics operationalization, data governance, and personal privacy.
-
-## Limitations of this ethical charter
-
-This document is a work in progress and reflects a state of reflection as of May 2022. There is no consensus nor official definition of "ethical AI" and our considerations are very likely to change over time. In case of updates, we will reflect changes directly in this document while providing the rationale for changes and tracking the history of updates through GitHub. This document is not intended to be a source of truth about best practices for ethical AI. We believe that even though it is imperfect, thinking about the impact of our research, the potential harms we foresee, and strategies we can take to mitigate these harms is going in the right direction for the machine learning community. Throughout the project, we will document how we operationalize the values described in this document, along with the advantages and limitations we observe in the context of the project.
-
-
-## Content policy
-
-Studying the current state-of-the-art multimodal systems, we foresee several misuses of the technologies we aim at as part of this project. We provide guidelines on some of the use cases we ultimately want to prevent:
-
-- Promotion of content and activities which are detrimental in nature, such as violence, harassment, bullying, harm, hate, and all forms of discrimination. Prejudice targeted at specific identity subpopulations based on gender, race, age, ability status, LGBTQA+ orientation, religion, education, socioeconomic status, and other sensitive categories (such as sexism/misogyny, casteism, racism, ableism, transphobia, homophobia).
-- Violation of regulations, privacy, copyrights, human rights, cultural rights, fundamental rights, laws, and any other form of binding documents.
-- Generating personally identifiable information.
-- Generating false information without any accountability and/or with the purpose of harming and triggering others.
-- Incautious usage of the model in high-risk domains - such as medical, legal, finance, and immigration - that can fundamentally damage people’s lives.
-
-## Values for the project
-
-We note that some of these values can sometimes be in conflict (for instance being fair and sharing open and reproducible work, or respecting individuals’ privacy and sharing datasets), and emphasize the need to consider risks and benefits of our decisions on a case by case basis.
-
-### Be transparent
-
-We are transparent and open about the intent, sources of data, tools, and decisions. By being transparent, we expose the weak points of our work to the community and thus are responsible and can be held accountable.
-
-### Share open and reproducible work
-
-Openness touches on two aspects: the processes and the results. We believe it is good research practice to share precise descriptions of the data, tools, and experimental conditions. Research artifacts, including tools and model checkpoints, must be accessible - for use within the intended scope - to all without discrimination (e.g., religion, ethnicity, sexual orientation, gender, political orientation, age, ability). We define accessibility as ensuring that our research can be easily explained to an audience beyond the machine learning research community.
-
-### Be fair
-
-We define fairness as the equal treatment of all human beings. Being fair implies monitoring and mitigating unwanted biases that are based on characteristics such as race, gender, disabilities, and sexual orientation. To limit as much as possible negative outcomes, especially outcomes that impact marginalized and vulnerable groups, reviews of unfair biases - such as racism for predictive policing algorithms - should be conducted on both the data and the model outputs.
-
-### Be self-critical
-
-We are aware of our imperfections and we should constantly lookout for ways to better operationalize ethical values and other responsible AI decisions. For instance, this includes better strategies for curating and filtering training data. We should not overclaim or entertain spurious discourses and hype.
-
-### Give credit
-
-We should respect and acknowledge people's work through proper licensing and credit attribution.
\ No newline at end of file
diff --git a/spaces/songweig/rich-text-to-image/models/attention_processor.py b/spaces/songweig/rich-text-to-image/models/attention_processor.py
deleted file mode 100644
index 6ded2d700179b5f889816be42c580a3424141b77..0000000000000000000000000000000000000000
--- a/spaces/songweig/rich-text-to-image/models/attention_processor.py
+++ /dev/null
@@ -1,1687 +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.
-from typing import Callable, Optional, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.utils import deprecate, logging, maybe_allow_in_graph
-from diffusers.utils.import_utils import is_xformers_available
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-if is_xformers_available():
- import xformers
- import xformers.ops
-else:
- xformers = None
-
-
-@maybe_allow_in_graph
-class Attention(nn.Module):
- r"""
- A cross attention layer.
-
- Parameters:
- query_dim (`int`): The number of channels in the query.
- cross_attention_dim (`int`, *optional*):
- The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
- heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
- dim_head (`int`, *optional*, defaults to 64): The number of channels in each head.
- dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
- bias (`bool`, *optional*, defaults to False):
- Set to `True` for the query, key, and value linear layers to contain a bias parameter.
- """
-
- def __init__(
- self,
- query_dim: int,
- cross_attention_dim: Optional[int] = None,
- heads: int = 8,
- dim_head: int = 64,
- dropout: float = 0.0,
- bias=False,
- upcast_attention: bool = False,
- upcast_softmax: bool = False,
- cross_attention_norm: Optional[str] = None,
- cross_attention_norm_num_groups: int = 32,
- added_kv_proj_dim: Optional[int] = None,
- norm_num_groups: Optional[int] = None,
- spatial_norm_dim: Optional[int] = None,
- out_bias: bool = True,
- scale_qk: bool = True,
- only_cross_attention: bool = False,
- eps: float = 1e-5,
- rescale_output_factor: float = 1.0,
- residual_connection: bool = False,
- _from_deprecated_attn_block=False,
- processor: Optional["AttnProcessor"] = None,
- ):
- super().__init__()
- inner_dim = dim_head * heads
- cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
- self.upcast_attention = upcast_attention
- self.upcast_softmax = upcast_softmax
- self.rescale_output_factor = rescale_output_factor
- self.residual_connection = residual_connection
- self.dropout = dropout
-
- # we make use of this private variable to know whether this class is loaded
- # with an deprecated state dict so that we can convert it on the fly
- self._from_deprecated_attn_block = _from_deprecated_attn_block
-
- self.scale_qk = scale_qk
- self.scale = dim_head**-0.5 if self.scale_qk else 1.0
-
- self.heads = heads
- # for slice_size > 0 the attention score computation
- # is split across the batch axis to save memory
- # You can set slice_size with `set_attention_slice`
- self.sliceable_head_dim = heads
-
- self.added_kv_proj_dim = added_kv_proj_dim
- self.only_cross_attention = only_cross_attention
-
- if self.added_kv_proj_dim is None and self.only_cross_attention:
- raise ValueError(
- "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
- )
-
- if norm_num_groups is not None:
- self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
- else:
- self.group_norm = None
-
- if spatial_norm_dim is not None:
- self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)
- else:
- self.spatial_norm = None
-
- if cross_attention_norm is None:
- self.norm_cross = None
- elif cross_attention_norm == "layer_norm":
- self.norm_cross = nn.LayerNorm(cross_attention_dim)
- elif cross_attention_norm == "group_norm":
- if self.added_kv_proj_dim is not None:
- # The given `encoder_hidden_states` are initially of shape
- # (batch_size, seq_len, added_kv_proj_dim) before being projected
- # to (batch_size, seq_len, cross_attention_dim). The norm is applied
- # before the projection, so we need to use `added_kv_proj_dim` as
- # the number of channels for the group norm.
- norm_cross_num_channels = added_kv_proj_dim
- else:
- norm_cross_num_channels = cross_attention_dim
-
- self.norm_cross = nn.GroupNorm(
- num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
- )
- else:
- raise ValueError(
- f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
- )
-
- self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
-
- if not self.only_cross_attention:
- # only relevant for the `AddedKVProcessor` classes
- self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
- self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
- else:
- self.to_k = None
- self.to_v = None
-
- if self.added_kv_proj_dim is not None:
- self.add_k_proj = nn.Linear(added_kv_proj_dim, inner_dim)
- self.add_v_proj = nn.Linear(added_kv_proj_dim, inner_dim)
-
- self.to_out = nn.ModuleList([])
- self.to_out.append(nn.Linear(inner_dim, query_dim, bias=out_bias))
- self.to_out.append(nn.Dropout(dropout))
-
- # set attention processor
- # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
- # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
- # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
- if processor is None:
- processor = (
- AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
- )
- self.set_processor(processor)
-
- # Rich-Text: util function for averaging over attention heads
- def reshape_batch_dim_to_heads_and_average(self, tensor):
- batch_size, seq_len, seq_len2 = tensor.shape
- head_size = self.heads
- tensor = tensor.reshape(batch_size // head_size,
- head_size, seq_len, seq_len2)
- return tensor.mean(1)
-
- def set_use_memory_efficient_attention_xformers(
- self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
- ):
- is_lora = hasattr(self, "processor") and isinstance(
- self.processor,
- (LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, LoRAAttnAddedKVProcessor),
- )
- is_custom_diffusion = hasattr(self, "processor") and isinstance(
- self.processor, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor)
- )
- is_added_kv_processor = hasattr(self, "processor") and isinstance(
- self.processor,
- (
- AttnAddedKVProcessor,
- AttnAddedKVProcessor2_0,
- SlicedAttnAddedKVProcessor,
- XFormersAttnAddedKVProcessor,
- LoRAAttnAddedKVProcessor,
- ),
- )
-
- if use_memory_efficient_attention_xformers:
- if is_added_kv_processor and (is_lora or is_custom_diffusion):
- raise NotImplementedError(
- f"Memory efficient attention is currently not supported for LoRA or custom diffuson for attention processor type {self.processor}"
- )
- if not is_xformers_available():
- raise ModuleNotFoundError(
- (
- "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
- " xformers"
- ),
- name="xformers",
- )
- elif not torch.cuda.is_available():
- raise ValueError(
- "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
- " only available for GPU "
- )
- else:
- try:
- # Make sure we can run the memory efficient attention
- _ = xformers.ops.memory_efficient_attention(
- torch.randn((1, 2, 40), device="cuda"),
- torch.randn((1, 2, 40), device="cuda"),
- torch.randn((1, 2, 40), device="cuda"),
- )
- except Exception as e:
- raise e
-
- if is_lora:
- # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers
- # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?
- processor = LoRAXFormersAttnProcessor(
- hidden_size=self.processor.hidden_size,
- cross_attention_dim=self.processor.cross_attention_dim,
- rank=self.processor.rank,
- attention_op=attention_op,
- )
- processor.load_state_dict(self.processor.state_dict())
- processor.to(self.processor.to_q_lora.up.weight.device)
- elif is_custom_diffusion:
- processor = CustomDiffusionXFormersAttnProcessor(
- train_kv=self.processor.train_kv,
- train_q_out=self.processor.train_q_out,
- hidden_size=self.processor.hidden_size,
- cross_attention_dim=self.processor.cross_attention_dim,
- attention_op=attention_op,
- )
- processor.load_state_dict(self.processor.state_dict())
- if hasattr(self.processor, "to_k_custom_diffusion"):
- processor.to(self.processor.to_k_custom_diffusion.weight.device)
- elif is_added_kv_processor:
- # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
- # which uses this type of cross attention ONLY because the attention mask of format
- # [0, ..., -10.000, ..., 0, ...,] is not supported
- # throw warning
- logger.info(
- "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
- )
- processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
- else:
- processor = XFormersAttnProcessor(attention_op=attention_op)
- else:
- if is_lora:
- attn_processor_class = (
- LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
- )
- processor = attn_processor_class(
- hidden_size=self.processor.hidden_size,
- cross_attention_dim=self.processor.cross_attention_dim,
- rank=self.processor.rank,
- )
- processor.load_state_dict(self.processor.state_dict())
- processor.to(self.processor.to_q_lora.up.weight.device)
- elif is_custom_diffusion:
- processor = CustomDiffusionAttnProcessor(
- train_kv=self.processor.train_kv,
- train_q_out=self.processor.train_q_out,
- hidden_size=self.processor.hidden_size,
- cross_attention_dim=self.processor.cross_attention_dim,
- )
- processor.load_state_dict(self.processor.state_dict())
- if hasattr(self.processor, "to_k_custom_diffusion"):
- processor.to(self.processor.to_k_custom_diffusion.weight.device)
- else:
- # set attention processor
- # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
- # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
- # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
- processor = (
- AttnProcessor2_0()
- if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
- else AttnProcessor()
- )
-
- self.set_processor(processor)
-
- def set_attention_slice(self, slice_size):
- if slice_size is not None and slice_size > self.sliceable_head_dim:
- raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
-
- if slice_size is not None and self.added_kv_proj_dim is not None:
- processor = SlicedAttnAddedKVProcessor(slice_size)
- elif slice_size is not None:
- processor = SlicedAttnProcessor(slice_size)
- elif self.added_kv_proj_dim is not None:
- processor = AttnAddedKVProcessor()
- else:
- # set attention processor
- # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
- # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
- # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
- processor = (
- AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
- )
-
- self.set_processor(processor)
-
- def set_processor(self, processor: "AttnProcessor"):
- # if current processor is in `self._modules` and if passed `processor` is not, we need to
- # pop `processor` from `self._modules`
- if (
- hasattr(self, "processor")
- and isinstance(self.processor, torch.nn.Module)
- and not isinstance(processor, torch.nn.Module)
- ):
- logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
- self._modules.pop("processor")
-
- self.processor = processor
-
- # Rich-Text: inject self-attention maps
- def forward(self, hidden_states, real_attn_probs=None, attn_weights=None, encoder_hidden_states=None, attention_mask=None, **cross_attention_kwargs):
- # The `Attention` class can call different attention processors / attention functions
- # here we simply pass along all tensors to the selected processor class
- # For standard processors that are defined here, `**cross_attention_kwargs` is empty
- return self.processor(
- self,
- hidden_states,
- real_attn_probs=real_attn_probs,
- attn_weights=attn_weights,
- encoder_hidden_states=encoder_hidden_states,
- attention_mask=attention_mask,
- **cross_attention_kwargs,
- )
-
- def batch_to_head_dim(self, tensor):
- head_size = self.heads
- batch_size, seq_len, dim = tensor.shape
- tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
- tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
- return tensor
-
- def head_to_batch_dim(self, tensor, out_dim=3):
- head_size = self.heads
- batch_size, seq_len, dim = tensor.shape
- tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
- tensor = tensor.permute(0, 2, 1, 3)
-
- if out_dim == 3:
- tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
-
- return tensor
-
- # Rich-Text: return attention scores
- def get_attention_scores(self, query, key, attention_mask=None, attn_weights=False):
- dtype = query.dtype
- if self.upcast_attention:
- query = query.float()
- key = key.float()
-
- if attention_mask is None:
- baddbmm_input = torch.empty(
- query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
- )
- beta = 0
- else:
- baddbmm_input = attention_mask
- beta = 1
-
- attention_scores = torch.baddbmm(
- baddbmm_input,
- query,
- key.transpose(-1, -2),
- beta=beta,
- alpha=self.scale,
- )
- del baddbmm_input
-
- if self.upcast_softmax:
- attention_scores = attention_scores.float()
-
- # Rich-Text: font size
- if attn_weights is not None:
- assert key.shape[1] == 77
- attention_scores_stable = attention_scores - attention_scores.max(-1, True)[0]
- attention_score_exp = attention_scores_stable.float().exp()
- # attention_score_exp = attention_scores.float().exp()
- font_size_abs, font_size_sign = attn_weights['font_size'].abs(), attn_weights['font_size'].sign()
- attention_score_exp[:, :, attn_weights['word_pos']] = attention_score_exp[:, :, attn_weights['word_pos']].clone(
- )*font_size_abs
- attention_probs = attention_score_exp / attention_score_exp.sum(-1, True)
- attention_probs[:, :, attn_weights['word_pos']] *= font_size_sign
- # import ipdb; ipdb.set_trace()
- if attention_probs.isnan().any():
- import ipdb; ipdb.set_trace()
- else:
- attention_probs = attention_scores.softmax(dim=-1)
-
- del attention_scores
-
- attention_probs = attention_probs.to(dtype)
-
- return attention_probs
-
- def prepare_attention_mask(self, attention_mask, target_length, batch_size=None, out_dim=3):
- if batch_size is None:
- deprecate(
- "batch_size=None",
- "0.0.15",
- (
- "Not passing the `batch_size` parameter to `prepare_attention_mask` can lead to incorrect"
- " attention mask preparation and is deprecated behavior. Please make sure to pass `batch_size` to"
- " `prepare_attention_mask` when preparing the attention_mask."
- ),
- )
- batch_size = 1
-
- head_size = self.heads
- if attention_mask is None:
- return attention_mask
-
- current_length: int = attention_mask.shape[-1]
- if current_length != target_length:
- if attention_mask.device.type == "mps":
- # HACK: MPS: Does not support padding by greater than dimension of input tensor.
- # Instead, we can manually construct the padding tensor.
- padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
- padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
- attention_mask = torch.cat([attention_mask, padding], dim=2)
- else:
- # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
- # we want to instead pad by (0, remaining_length), where remaining_length is:
- # remaining_length: int = target_length - current_length
- # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
- attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
-
- if out_dim == 3:
- if attention_mask.shape[0] < batch_size * head_size:
- attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
- elif out_dim == 4:
- attention_mask = attention_mask.unsqueeze(1)
- attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
-
- return attention_mask
-
- def norm_encoder_hidden_states(self, encoder_hidden_states):
- assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
-
- if isinstance(self.norm_cross, nn.LayerNorm):
- encoder_hidden_states = self.norm_cross(encoder_hidden_states)
- elif isinstance(self.norm_cross, nn.GroupNorm):
- # Group norm norms along the channels dimension and expects
- # input to be in the shape of (N, C, *). In this case, we want
- # to norm along the hidden dimension, so we need to move
- # (batch_size, sequence_length, hidden_size) ->
- # (batch_size, hidden_size, sequence_length)
- encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
- encoder_hidden_states = self.norm_cross(encoder_hidden_states)
- encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
- else:
- assert False
-
- return encoder_hidden_states
-
-
-class AttnProcessor:
- r"""
- Default processor for performing attention-related computations.
- """
-
- # Rich-Text: inject self-attention maps
- def __call__(
- self,
- attn: Attention,
- hidden_states,
- real_attn_probs=None,
- attn_weights=None,
- encoder_hidden_states=None,
- attention_mask=None,
- temb=None,
- ):
- residual = hidden_states
-
- if attn.spatial_norm is not None:
- hidden_states = attn.spatial_norm(hidden_states, temb)
-
- input_ndim = hidden_states.ndim
-
- if input_ndim == 4:
- batch_size, channel, height, width = hidden_states.shape
- hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if attn.group_norm is not None:
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
-
- query = attn.head_to_batch_dim(query)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
-
- if real_attn_probs is None:
- # Rich-Text: font size
- attention_probs = attn.get_attention_scores(query, key, attention_mask, attn_weights=attn_weights)
- else:
- # Rich-Text: inject self-attention maps
- attention_probs = real_attn_probs
- hidden_states = torch.bmm(attention_probs, value)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- if input_ndim == 4:
- hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
- if attn.residual_connection:
- hidden_states = hidden_states + residual
-
- hidden_states = hidden_states / attn.rescale_output_factor
-
- # Rich-Text Modified: return attn probs
- # We return the map averaged over heads to save memory footprint
- attention_probs_avg = attn.reshape_batch_dim_to_heads_and_average(
- attention_probs)
- return hidden_states, [attention_probs_avg, attention_probs]
-
-
-class LoRALinearLayer(nn.Module):
- def __init__(self, in_features, out_features, rank=4, network_alpha=None):
- super().__init__()
-
- if rank > min(in_features, out_features):
- raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}")
-
- self.down = nn.Linear(in_features, rank, bias=False)
- self.up = nn.Linear(rank, out_features, bias=False)
- # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
- # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
- self.network_alpha = network_alpha
- self.rank = rank
-
- nn.init.normal_(self.down.weight, std=1 / rank)
- nn.init.zeros_(self.up.weight)
-
- def forward(self, hidden_states):
- orig_dtype = hidden_states.dtype
- dtype = self.down.weight.dtype
-
- down_hidden_states = self.down(hidden_states.to(dtype))
- up_hidden_states = self.up(down_hidden_states)
-
- if self.network_alpha is not None:
- up_hidden_states *= self.network_alpha / self.rank
-
- return up_hidden_states.to(orig_dtype)
-
-
-class LoRAAttnProcessor(nn.Module):
- r"""
- Processor for implementing the LoRA attention mechanism.
-
- Args:
- hidden_size (`int`, *optional*):
- The hidden size of the attention layer.
- cross_attention_dim (`int`, *optional*):
- The number of channels in the `encoder_hidden_states`.
- rank (`int`, defaults to 4):
- The dimension of the LoRA update matrices.
- network_alpha (`int`, *optional*):
- Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
- """
-
- def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None):
- super().__init__()
-
- self.hidden_size = hidden_size
- self.cross_attention_dim = cross_attention_dim
- self.rank = rank
-
- self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
- self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-
- def __call__(
- self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None
- ):
- residual = hidden_states
-
- if attn.spatial_norm is not None:
- hidden_states = attn.spatial_norm(hidden_states, temb)
-
- input_ndim = hidden_states.ndim
-
- if input_ndim == 4:
- batch_size, channel, height, width = hidden_states.shape
- hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if attn.group_norm is not None:
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
- query = attn.head_to_batch_dim(query)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
-
- attention_probs = attn.get_attention_scores(query, key, attention_mask)
- hidden_states = torch.bmm(attention_probs, value)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- if input_ndim == 4:
- hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
- if attn.residual_connection:
- hidden_states = hidden_states + residual
-
- hidden_states = hidden_states / attn.rescale_output_factor
-
- return hidden_states
-
-
-class CustomDiffusionAttnProcessor(nn.Module):
- r"""
- Processor for implementing attention for the Custom Diffusion method.
-
- Args:
- train_kv (`bool`, defaults to `True`):
- Whether to newly train the key and value matrices corresponding to the text features.
- train_q_out (`bool`, defaults to `True`):
- Whether to newly train query matrices corresponding to the latent image features.
- hidden_size (`int`, *optional*, defaults to `None`):
- The hidden size of the attention layer.
- cross_attention_dim (`int`, *optional*, defaults to `None`):
- The number of channels in the `encoder_hidden_states`.
- out_bias (`bool`, defaults to `True`):
- Whether to include the bias parameter in `train_q_out`.
- dropout (`float`, *optional*, defaults to 0.0):
- The dropout probability to use.
- """
-
- def __init__(
- self,
- train_kv=True,
- train_q_out=True,
- hidden_size=None,
- cross_attention_dim=None,
- out_bias=True,
- dropout=0.0,
- ):
- super().__init__()
- self.train_kv = train_kv
- self.train_q_out = train_q_out
-
- self.hidden_size = hidden_size
- self.cross_attention_dim = cross_attention_dim
-
- # `_custom_diffusion` id for easy serialization and loading.
- if self.train_kv:
- self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
- self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
- if self.train_q_out:
- self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
- self.to_out_custom_diffusion = nn.ModuleList([])
- self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
- self.to_out_custom_diffusion.append(nn.Dropout(dropout))
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
- batch_size, sequence_length, _ = hidden_states.shape
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- if self.train_q_out:
- query = self.to_q_custom_diffusion(hidden_states)
- else:
- query = attn.to_q(hidden_states)
-
- if encoder_hidden_states is None:
- crossattn = False
- encoder_hidden_states = hidden_states
- else:
- crossattn = True
- if attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- if self.train_kv:
- key = self.to_k_custom_diffusion(encoder_hidden_states)
- value = self.to_v_custom_diffusion(encoder_hidden_states)
- else:
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
-
- if crossattn:
- detach = torch.ones_like(key)
- detach[:, :1, :] = detach[:, :1, :] * 0.0
- key = detach * key + (1 - detach) * key.detach()
- value = detach * value + (1 - detach) * value.detach()
-
- query = attn.head_to_batch_dim(query)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
-
- attention_probs = attn.get_attention_scores(query, key, attention_mask)
- hidden_states = torch.bmm(attention_probs, value)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- if self.train_q_out:
- # linear proj
- hidden_states = self.to_out_custom_diffusion[0](hidden_states)
- # dropout
- hidden_states = self.to_out_custom_diffusion[1](hidden_states)
- else:
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- return hidden_states
-
-
-class AttnAddedKVProcessor:
- r"""
- Processor for performing attention-related computations with extra learnable key and value matrices for the text
- encoder.
- """
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
- residual = hidden_states
- hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
- batch_size, sequence_length, _ = hidden_states.shape
-
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
- query = attn.head_to_batch_dim(query)
-
- encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
- encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
- encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
- encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
- if not attn.only_cross_attention:
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
- key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
- value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
- else:
- key = encoder_hidden_states_key_proj
- value = encoder_hidden_states_value_proj
-
- attention_probs = attn.get_attention_scores(query, key, attention_mask)
- hidden_states = torch.bmm(attention_probs, value)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
- hidden_states = hidden_states + residual
-
- return hidden_states
-
-
-class AttnAddedKVProcessor2_0:
- r"""
- Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra
- learnable key and value matrices for the text encoder.
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError(
- "AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
- )
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
- residual = hidden_states
- hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
- batch_size, sequence_length, _ = hidden_states.shape
-
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
- query = attn.head_to_batch_dim(query, out_dim=4)
-
- encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
- encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
- encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)
- encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)
-
- if not attn.only_cross_attention:
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
- key = attn.head_to_batch_dim(key, out_dim=4)
- value = attn.head_to_batch_dim(value, out_dim=4)
- key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
- value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
- else:
- key = encoder_hidden_states_key_proj
- value = encoder_hidden_states_value_proj
-
- # the output of sdp = (batch, num_heads, seq_len, head_dim)
- # TODO: add support for attn.scale when we move to Torch 2.1
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
- hidden_states = hidden_states + residual
-
- return hidden_states
-
-
-class LoRAAttnAddedKVProcessor(nn.Module):
- r"""
- Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text
- encoder.
-
- Args:
- hidden_size (`int`, *optional*):
- The hidden size of the attention layer.
- cross_attention_dim (`int`, *optional*, defaults to `None`):
- The number of channels in the `encoder_hidden_states`.
- rank (`int`, defaults to 4):
- The dimension of the LoRA update matrices.
-
- """
-
- def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None):
- super().__init__()
-
- self.hidden_size = hidden_size
- self.cross_attention_dim = cross_attention_dim
- self.rank = rank
-
- self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
- self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
- self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
- self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
- residual = hidden_states
- hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
- batch_size, sequence_length, _ = hidden_states.shape
-
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
- query = attn.head_to_batch_dim(query)
-
- encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) + scale * self.add_k_proj_lora(
- encoder_hidden_states
- )
- encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) + scale * self.add_v_proj_lora(
- encoder_hidden_states
- )
- encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
- encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
- if not attn.only_cross_attention:
- key = attn.to_k(hidden_states) + scale * self.to_k_lora(hidden_states)
- value = attn.to_v(hidden_states) + scale * self.to_v_lora(hidden_states)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
- key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
- value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
- else:
- key = encoder_hidden_states_key_proj
- value = encoder_hidden_states_value_proj
-
- attention_probs = attn.get_attention_scores(query, key, attention_mask)
- hidden_states = torch.bmm(attention_probs, value)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
- hidden_states = hidden_states + residual
-
- return hidden_states
-
-
-class XFormersAttnAddedKVProcessor:
- r"""
- Processor for implementing memory efficient attention using xFormers.
-
- Args:
- attention_op (`Callable`, *optional*, defaults to `None`):
- The base
- [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
- use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
- operator.
- """
-
- def __init__(self, attention_op: Optional[Callable] = None):
- self.attention_op = attention_op
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
- residual = hidden_states
- hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
- batch_size, sequence_length, _ = hidden_states.shape
-
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
- query = attn.head_to_batch_dim(query)
-
- encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
- encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
- encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
- encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
- if not attn.only_cross_attention:
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
- key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
- value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
- else:
- key = encoder_hidden_states_key_proj
- value = encoder_hidden_states_value_proj
-
- hidden_states = xformers.ops.memory_efficient_attention(
- query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
- )
- hidden_states = hidden_states.to(query.dtype)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
- hidden_states = hidden_states + residual
-
- return hidden_states
-
-
-class XFormersAttnProcessor:
- r"""
- Processor for implementing memory efficient attention using xFormers.
-
- Args:
- attention_op (`Callable`, *optional*, defaults to `None`):
- The base
- [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
- use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
- operator.
- """
-
- def __init__(self, attention_op: Optional[Callable] = None):
- self.attention_op = attention_op
-
- def __call__(
- self,
- attn: Attention,
- hidden_states: torch.FloatTensor,
- encoder_hidden_states: Optional[torch.FloatTensor] = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- temb: Optional[torch.FloatTensor] = None,
- ):
- residual = hidden_states
-
- if attn.spatial_norm is not None:
- hidden_states = attn.spatial_norm(hidden_states, temb)
-
- input_ndim = hidden_states.ndim
-
- if input_ndim == 4:
- batch_size, channel, height, width = hidden_states.shape
- hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
- batch_size, key_tokens, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
- if attention_mask is not None:
- # expand our mask's singleton query_tokens dimension:
- # [batch*heads, 1, key_tokens] ->
- # [batch*heads, query_tokens, key_tokens]
- # so that it can be added as a bias onto the attention scores that xformers computes:
- # [batch*heads, query_tokens, key_tokens]
- # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
- _, query_tokens, _ = hidden_states.shape
- attention_mask = attention_mask.expand(-1, query_tokens, -1)
-
- if attn.group_norm is not None:
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
-
- query = attn.head_to_batch_dim(query).contiguous()
- key = attn.head_to_batch_dim(key).contiguous()
- value = attn.head_to_batch_dim(value).contiguous()
-
- hidden_states = xformers.ops.memory_efficient_attention(
- query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
- )
- hidden_states = hidden_states.to(query.dtype)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- if input_ndim == 4:
- hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
- if attn.residual_connection:
- hidden_states = hidden_states + residual
-
- hidden_states = hidden_states / attn.rescale_output_factor
-
- return hidden_states
-
-
-class AttnProcessor2_0:
- r"""
- Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
- """
-
- def __init__(self):
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
- def __call__(
- self,
- attn: Attention,
- hidden_states,
- encoder_hidden_states=None,
- attention_mask=None,
- temb=None,
- ):
- residual = hidden_states
-
- if attn.spatial_norm is not None:
- hidden_states = attn.spatial_norm(hidden_states, temb)
-
- input_ndim = hidden_states.ndim
-
- if input_ndim == 4:
- batch_size, channel, height, width = hidden_states.shape
- hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
- inner_dim = hidden_states.shape[-1]
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- # scaled_dot_product_attention expects attention_mask shape to be
- # (batch, heads, source_length, target_length)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- if attn.group_norm is not None:
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
-
- head_dim = inner_dim // attn.heads
-
- query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- # the output of sdp = (batch, num_heads, seq_len, head_dim)
- # TODO: add support for attn.scale when we move to Torch 2.1
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
- hidden_states = hidden_states.to(query.dtype)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- if input_ndim == 4:
- hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
- if attn.residual_connection:
- hidden_states = hidden_states + residual
-
- hidden_states = hidden_states / attn.rescale_output_factor
-
- return hidden_states
-
-
-class LoRAXFormersAttnProcessor(nn.Module):
- r"""
- Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.
-
- Args:
- hidden_size (`int`, *optional*):
- The hidden size of the attention layer.
- cross_attention_dim (`int`, *optional*):
- The number of channels in the `encoder_hidden_states`.
- rank (`int`, defaults to 4):
- The dimension of the LoRA update matrices.
- attention_op (`Callable`, *optional*, defaults to `None`):
- The base
- [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
- use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
- operator.
- network_alpha (`int`, *optional*):
- Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
-
- """
-
- def __init__(
- self, hidden_size, cross_attention_dim, rank=4, attention_op: Optional[Callable] = None, network_alpha=None
- ):
- super().__init__()
-
- self.hidden_size = hidden_size
- self.cross_attention_dim = cross_attention_dim
- self.rank = rank
- self.attention_op = attention_op
-
- self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
- self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-
- def __call__(
- self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None
- ):
- residual = hidden_states
-
- if attn.spatial_norm is not None:
- hidden_states = attn.spatial_norm(hidden_states, temb)
-
- input_ndim = hidden_states.ndim
-
- if input_ndim == 4:
- batch_size, channel, height, width = hidden_states.shape
- hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if attn.group_norm is not None:
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
- query = attn.head_to_batch_dim(query).contiguous()
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
- key = attn.head_to_batch_dim(key).contiguous()
- value = attn.head_to_batch_dim(value).contiguous()
-
- hidden_states = xformers.ops.memory_efficient_attention(
- query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
- )
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- if input_ndim == 4:
- hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
- if attn.residual_connection:
- hidden_states = hidden_states + residual
-
- hidden_states = hidden_states / attn.rescale_output_factor
-
- return hidden_states
-
-
-class LoRAAttnProcessor2_0(nn.Module):
- r"""
- Processor for implementing the LoRA attention mechanism using PyTorch 2.0's memory-efficient scaled dot-product
- attention.
-
- Args:
- hidden_size (`int`):
- The hidden size of the attention layer.
- cross_attention_dim (`int`, *optional*):
- The number of channels in the `encoder_hidden_states`.
- rank (`int`, defaults to 4):
- The dimension of the LoRA update matrices.
- network_alpha (`int`, *optional*):
- Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
- """
-
- def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None):
- super().__init__()
- if not hasattr(F, "scaled_dot_product_attention"):
- raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
- self.hidden_size = hidden_size
- self.cross_attention_dim = cross_attention_dim
- self.rank = rank
-
- self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
- self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
- self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
- residual = hidden_states
-
- input_ndim = hidden_states.ndim
-
- if input_ndim == 4:
- batch_size, channel, height, width = hidden_states.shape
- hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
- inner_dim = hidden_states.shape[-1]
-
- if attention_mask is not None:
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- # scaled_dot_product_attention expects attention_mask shape to be
- # (batch, heads, source_length, target_length)
- attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
- if attn.group_norm is not None:
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
-
- head_dim = inner_dim // attn.heads
- query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
- value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
- # TODO: add support for attn.scale when we move to Torch 2.1
- hidden_states = F.scaled_dot_product_attention(
- query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
- )
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
- hidden_states = hidden_states.to(query.dtype)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- if input_ndim == 4:
- hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
- if attn.residual_connection:
- hidden_states = hidden_states + residual
-
- hidden_states = hidden_states / attn.rescale_output_factor
-
- return hidden_states
-
-
-class CustomDiffusionXFormersAttnProcessor(nn.Module):
- r"""
- Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.
-
- Args:
- train_kv (`bool`, defaults to `True`):
- Whether to newly train the key and value matrices corresponding to the text features.
- train_q_out (`bool`, defaults to `True`):
- Whether to newly train query matrices corresponding to the latent image features.
- hidden_size (`int`, *optional*, defaults to `None`):
- The hidden size of the attention layer.
- cross_attention_dim (`int`, *optional*, defaults to `None`):
- The number of channels in the `encoder_hidden_states`.
- out_bias (`bool`, defaults to `True`):
- Whether to include the bias parameter in `train_q_out`.
- dropout (`float`, *optional*, defaults to 0.0):
- The dropout probability to use.
- attention_op (`Callable`, *optional*, defaults to `None`):
- The base
- [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to use
- as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best operator.
- """
-
- def __init__(
- self,
- train_kv=True,
- train_q_out=False,
- hidden_size=None,
- cross_attention_dim=None,
- out_bias=True,
- dropout=0.0,
- attention_op: Optional[Callable] = None,
- ):
- super().__init__()
- self.train_kv = train_kv
- self.train_q_out = train_q_out
-
- self.hidden_size = hidden_size
- self.cross_attention_dim = cross_attention_dim
- self.attention_op = attention_op
-
- # `_custom_diffusion` id for easy serialization and loading.
- if self.train_kv:
- self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
- self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
- if self.train_q_out:
- self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
- self.to_out_custom_diffusion = nn.ModuleList([])
- self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
- self.to_out_custom_diffusion.append(nn.Dropout(dropout))
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
-
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if self.train_q_out:
- query = self.to_q_custom_diffusion(hidden_states)
- else:
- query = attn.to_q(hidden_states)
-
- if encoder_hidden_states is None:
- crossattn = False
- encoder_hidden_states = hidden_states
- else:
- crossattn = True
- if attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- if self.train_kv:
- key = self.to_k_custom_diffusion(encoder_hidden_states)
- value = self.to_v_custom_diffusion(encoder_hidden_states)
- else:
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
-
- if crossattn:
- detach = torch.ones_like(key)
- detach[:, :1, :] = detach[:, :1, :] * 0.0
- key = detach * key + (1 - detach) * key.detach()
- value = detach * value + (1 - detach) * value.detach()
-
- query = attn.head_to_batch_dim(query).contiguous()
- key = attn.head_to_batch_dim(key).contiguous()
- value = attn.head_to_batch_dim(value).contiguous()
-
- hidden_states = xformers.ops.memory_efficient_attention(
- query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
- )
- hidden_states = hidden_states.to(query.dtype)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- if self.train_q_out:
- # linear proj
- hidden_states = self.to_out_custom_diffusion[0](hidden_states)
- # dropout
- hidden_states = self.to_out_custom_diffusion[1](hidden_states)
- else:
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
- return hidden_states
-
-
-class SlicedAttnProcessor:
- r"""
- Processor for implementing sliced attention.
-
- Args:
- slice_size (`int`, *optional*):
- The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
- `attention_head_dim` must be a multiple of the `slice_size`.
- """
-
- def __init__(self, slice_size):
- self.slice_size = slice_size
-
- def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
- residual = hidden_states
-
- input_ndim = hidden_states.ndim
-
- if input_ndim == 4:
- batch_size, channel, height, width = hidden_states.shape
- hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
- batch_size, sequence_length, _ = (
- hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
- )
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if attn.group_norm is not None:
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
- dim = query.shape[-1]
- query = attn.head_to_batch_dim(query)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
-
- batch_size_attention, query_tokens, _ = query.shape
- hidden_states = torch.zeros(
- (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
- )
-
- for i in range(batch_size_attention // self.slice_size):
- start_idx = i * self.slice_size
- end_idx = (i + 1) * self.slice_size
-
- query_slice = query[start_idx:end_idx]
- key_slice = key[start_idx:end_idx]
- attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
-
- attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
-
- attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
-
- hidden_states[start_idx:end_idx] = attn_slice
-
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- if input_ndim == 4:
- hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
- if attn.residual_connection:
- hidden_states = hidden_states + residual
-
- hidden_states = hidden_states / attn.rescale_output_factor
-
- return hidden_states
-
-
-class SlicedAttnAddedKVProcessor:
- r"""
- Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.
-
- Args:
- slice_size (`int`, *optional*):
- The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
- `attention_head_dim` must be a multiple of the `slice_size`.
- """
-
- def __init__(self, slice_size):
- self.slice_size = slice_size
-
- def __call__(self, attn: "Attention", hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
- residual = hidden_states
-
- if attn.spatial_norm is not None:
- hidden_states = attn.spatial_norm(hidden_states, temb)
-
- hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
-
- batch_size, sequence_length, _ = hidden_states.shape
-
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = attn.to_q(hidden_states)
- dim = query.shape[-1]
- query = attn.head_to_batch_dim(query)
-
- encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
- encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
-
- encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
- encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
-
- if not attn.only_cross_attention:
- key = attn.to_k(hidden_states)
- value = attn.to_v(hidden_states)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
- key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
- value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
- else:
- key = encoder_hidden_states_key_proj
- value = encoder_hidden_states_value_proj
-
- batch_size_attention, query_tokens, _ = query.shape
- hidden_states = torch.zeros(
- (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
- )
-
- for i in range(batch_size_attention // self.slice_size):
- start_idx = i * self.slice_size
- end_idx = (i + 1) * self.slice_size
-
- query_slice = query[start_idx:end_idx]
- key_slice = key[start_idx:end_idx]
- attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
-
- attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
-
- attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
-
- hidden_states[start_idx:end_idx] = attn_slice
-
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
- hidden_states = hidden_states + residual
-
- return hidden_states
-
-
-AttentionProcessor = Union[
- AttnProcessor,
- AttnProcessor2_0,
- XFormersAttnProcessor,
- SlicedAttnProcessor,
- AttnAddedKVProcessor,
- SlicedAttnAddedKVProcessor,
- AttnAddedKVProcessor2_0,
- XFormersAttnAddedKVProcessor,
- LoRAAttnProcessor,
- LoRAXFormersAttnProcessor,
- LoRAAttnProcessor2_0,
- LoRAAttnAddedKVProcessor,
- CustomDiffusionAttnProcessor,
- CustomDiffusionXFormersAttnProcessor,
-]
-
-
-class SpatialNorm(nn.Module):
- """
- Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002
- """
-
- def __init__(
- self,
- f_channels,
- zq_channels,
- ):
- super().__init__()
- self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=32, eps=1e-6, affine=True)
- self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
- self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
-
- def forward(self, f, zq):
- f_size = f.shape[-2:]
- zq = F.interpolate(zq, size=f_size, mode="nearest")
- norm_f = self.norm_layer(f)
- new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
- return new_f
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/unsupervised_quality_estimation/repeat_lines.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/unsupervised_quality_estimation/repeat_lines.py
deleted file mode 100644
index 5a04851a74624e9c8ebc259805b7aed6c638b0de..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/unsupervised_quality_estimation/repeat_lines.py
+++ /dev/null
@@ -1,28 +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 argparse
-import sys
-
-
-def _normalize_spaces(line):
- return " ".join(line.split())
-
-
-def main():
- parser = argparse.ArgumentParser()
- parser.add_argument("-i", "--input_file", required=True, type=str)
- parser.add_argument("-n", "--repeat_times", required=True, type=int)
- parser.add_argument("-o", "--output_file", required=False, type=str)
- args = parser.parse_args()
- stream = open(args.output_file, "w") if args.output_file else sys.stdout
-
- for line in open(args.input_file):
- for _ in range(args.repeat_times):
- stream.write(_normalize_spaces(line) + "\n")
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py
deleted file mode 100644
index 1122c88c1964d8beead63bc8dfe21d41602b83bc..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""
-Implement unsupervised metric for decoding hyperparameter selection:
- $$ alpha * LM_PPL + ViterbitUER(%) * 100 $$
-"""
-import argparse
-import logging
-import math
-import sys
-
-import kenlm
-import editdistance
-from g2p_en import G2p
-
-logging.root.setLevel(logging.INFO)
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def get_parser():
- parser = argparse.ArgumentParser()
- parser.add_argument("ref_tra", help="reference pseudo labels")
- parser.add_argument("hyp_tra", help="decoded pseudo labels to be assess")
- parser.add_argument("--kenlm_path", default="/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o5.bin", help="")
- parser.add_argument("--uppercase", action="store_true", help="")
- parser.add_argument("--skipwords", default="", help="")
- parser.add_argument("--gt_tra", default="", help="ground truth pseudo labels for computing oracle WER")
- parser.add_argument("--min_vt_uer", default=0.0, type=float)
- parser.add_argument("--phonemize", action="store_true", help="phonemize word hypotheses, used when reference is phone transcript")
- parser.add_argument("--phonemize_lexicon", default="", type=str, help="use a lexicon for phonemizing")
- return parser
-
-def load_tra(tra_path):
- with open(tra_path, "r") as f:
- uid_to_tra = {}
- for line in f:
- toks = line.rstrip().split()
- uid, tra = toks[0], " ".join(toks[1:])
- uid_to_tra[uid] = tra
- logger.debug(f"loaded {len(uid_to_tra)} utterances from {tra_path}")
- return uid_to_tra
-
-def load_lex(lex_path):
- with open(lex_path, "r") as f:
- w2p = {}
- for line in f:
- w, p = line.rstrip().split(None, 1)
- w2p[w] = p.split()
- return w2p
-
-def compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p, g2p_dict):
- d_cnt = 0
- w_cnt = 0
- w_cnt_h = 0
- for uid in hyp_uid_to_tra:
- ref = ref_uid_to_tra[uid].split()
- if g2p_dict is not None:
- hyp = []
- for word in hyp_uid_to_tra[uid].split():
- if word in g2p_dict:
- hyp = hyp + g2p_dict[word]
- else:
- logger.warning(f"{word} not in g2p_dict")
- elif g2p is not None:
- hyp = g2p(hyp_uid_to_tra[uid])
- hyp = [p for p in hyp if p != "'" and p != " "]
- hyp = [p[:-1] if p[-1].isnumeric() else p for p in hyp]
- else:
- hyp = hyp_uid_to_tra[uid].split()
- logger.debug((
- f"======================\n"
- f"HYP: {' '.join(hyp)}\n"
- f"REF: {' '.join(ref)}"
- ))
- d_cnt += editdistance.eval(ref, hyp)
- w_cnt += len(ref)
- w_cnt_h += len(hyp)
- wer = float(d_cnt) / w_cnt
- logger.debug((
- f"wer = {wer*100:.2f}%; num. of ref words = {w_cnt}; "
- f"num. of hyp words = {w_cnt_h}; num. of sentences = {len(ref_uid_to_tra)}"
- ))
- return wer
-
-def compute_lm_ppl(hyp_uid_to_tra, score_fn):
- lm_score = 0.
- w_cnt = 0
- for hyp in hyp_uid_to_tra.values():
- cur_score = score_fn(hyp)
- cur_cnt = len(hyp.split()) + 1 # plus one for
- lm_score += cur_score
- w_cnt += cur_cnt
- logger.debug((
- f"======================\n"
- f"score sum/avg = {cur_score:.2f}/{cur_score/cur_cnt:.2f}\n"
- f"hyp = {hyp}"
- ))
- lm_ppl = math.pow(10, -lm_score / w_cnt)
- logger.debug(f"lm ppl = {lm_ppl:.2f}; num. of words = {w_cnt}")
- return lm_ppl
-
-def main():
- args = get_parser().parse_args()
- logger.debug(f"Args: {args}")
-
- ref_uid_to_tra = load_tra(args.ref_tra)
- hyp_uid_to_tra = load_tra(args.hyp_tra)
- assert not bool(set(hyp_uid_to_tra.keys()) - set(ref_uid_to_tra.keys()))
-
- lm = kenlm.Model(args.kenlm_path)
- skipwords = set(args.skipwords.split(","))
- def compute_lm_score(s):
- s = " ".join(w for w in s.split() if w not in skipwords)
- s = s.upper() if args.uppercase else s
- return lm.score(s)
-
- g2p, g2p_dict = None, None
- if args.phonemize:
- if args.phonemize_lexicon:
- g2p_dict = load_lex(args.phonemize_lexicon)
- else:
- g2p = G2p()
-
- wer = compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p, g2p_dict)
- lm_ppl = compute_lm_ppl(hyp_uid_to_tra, compute_lm_score)
-
- gt_wer = -math.inf
- if args.gt_tra:
- gt_uid_to_tra = load_tra(args.gt_tra)
- gt_wer = compute_wer(gt_uid_to_tra, hyp_uid_to_tra, None, None)
-
- score = math.log(lm_ppl) * max(wer, args.min_vt_uer)
- logging.info(f"{args.hyp_tra}: score={score:.4f}; wer={wer*100:.2f}%; lm_ppl={lm_ppl:.4f}; gt_wer={gt_wer*100:.2f}%")
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Enderal Forgotten Stories Download For Pc [Crack Serial Key UPDATED.md b/spaces/stomexserde/gpt4-ui/Examples/Enderal Forgotten Stories Download For Pc [Crack Serial Key UPDATED.md
deleted file mode 100644
index 1f35efbc3593873099898180c4884ebff183e647..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Enderal Forgotten Stories Download For Pc [Crack Serial Key UPDATED.md
+++ /dev/null
@@ -1,55 +0,0 @@
-
-
Enderal: Forgotten Stories Download for PC [Crack Serial Key]
-
Enderal: Forgotten Stories is a total conversion mod for Skyrim that is set in its own world with its own lore and story. It offers an immersive open world, new skill systems and gameplay with a dark, psychological storyline and believable characters[^1^] [^2^]. If you are looking for a way to download Enderal: Forgotten Stories for PC with a crack serial key, you have come to the right place.
-
In this article, we will show you how to download Enderal: Forgotten Stories for PC with a crack serial key that will let you play the game for free. You will need to have The Elder Scrolls V: Skyrim or The Elder Scrolls V: Skyrim Special Edition installed on your PC, depending on which version of Enderal you want to play[^1^] [^2^]. You will also need a torrent client and a VPN service to download the game safely and anonymously.
-
Enderal: Forgotten Stories download for pc [Crack Serial Key
How to Download Enderal: Forgotten Stories for PC with a Crack Serial Key
-
Follow these steps to download Enderal: Forgotten Stories for PC with a crack serial key:
-
-
Download and install a torrent client, such as uTorrent or BitTorrent.
-
Download and install a VPN service, such as NordVPN or ExpressVPN.
-
Connect to a VPN server in a country where Enderal: Forgotten Stories is not banned or restricted.
-
Go to a torrent site, such as The Pirate Bay or RARBG, and search for "Enderal: Forgotten Stories".
-
Choose a torrent that has a high number of seeders and leechers, and that includes a crack serial key in the file name or description.
-
Download the torrent file and open it with your torrent client.
-
Wait for the download to finish. It may take some time depending on your internet speed and the size of the file.
-
Once the download is complete, go to the folder where you saved the file and extract it using WinRAR or 7-Zip.
-
Run the setup.exe file and follow the instructions to install Enderal: Forgotten Stories on your PC.
-
Copy the crack serial key from the folder where you extracted the file and paste it into the activation window when prompted.
-
Enjoy playing Enderal: Forgotten Stories for free!
-
-
Disclaimer
-
This article is for educational purposes only. We do not condone or encourage piracy or illegal downloading of any kind. Enderal: Forgotten Stories is a free mod that can be downloaded from Steam or from the official website of SureAI[^1^] [^2^] [^3^]. We recommend that you support the developers and creators of this amazing game by downloading it legally and legitimately. Piracy may expose you to malware, viruses, legal issues, and other risks. Download at your own risk.
-
-
What is Enderal: Forgotten Stories?
-
Enderal: Forgotten Stories is a total conversion mod for Skyrim that is set in its own world with its own lore and story. It is a sequel to the previous mod Enderal: The Shards of Order, which was released in 2016. Enderal: Forgotten Stories was released in 2019 for Skyrim and in 2021 for Skyrim Special Edition[^1^] [^2^].
-
Enderal: Forgotten Stories is not a typical mod. It is a standalone game that does not require Skyrim to run. It has its own launcher, installer, and updater. It also has its own Steam page and achievements. It is completely free to download and play, as long as you own a legal copy of Skyrim or Skyrim Special Edition[^1^] [^2^].
-
What are the Features of Enderal: Forgotten Stories?
-
Enderal: Forgotten Stories offers an immersive open world, new skill systems and gameplay with a dark, psychological storyline and believable characters. Some of the features include:
-
-
-
An open world with its own lore and hand-crafted, detailed and diverse landscapes â explore deserts, heathlands, forests, jungles, mountains, and more.
-
An unconventional story with believable characters and psychological undercurrents.
-
German and English voice acting by professional voice over artists.
-
Multi-faceted, believable characters with own ambitions and motivations.
-
Overhauled gameplay with classes, experience points, survival mechanics and challenging combat.
-
An overhauled skill system with classes and new special abilities.
-
A housing system allowing the player to design their own homes.
-
30 to 125 hours of expected playtime.
-
Two "crafting classes" that utilize alchemy and enchanting for combat â use potions to shapeshift into a werewolf as the Lycantrope, use enchanting to craft spirit companions as the Phasmalist.
-
Improved performance and stability for a smooth experience.
-
Twelve new sidequests and two non-linear guild questlines: Work your way up the Golden Sickle, Enderal's merchants guild and infiltrate the ranks of the Rhalâta, the enigmatic cult ruling the Undercity.
-
Reworked and improved crafting, spells, and much more.
-
-
How is Enderal: Forgotten Stories Different from Skyrim?
-
Enderal: Forgotten Stories is not just a mod for Skyrim. It is a completely different game that uses Skyrim's engine and assets. It has its own world, story, characters, gameplay, and mechanics. It does not follow the lore or canon of The Elder Scrolls series. It is a standalone game that does not require Skyrim to run[^1^] [^2^].
-
Enderal: Forgotten Stories is different from Skyrim in many ways. Some of the differences include:
-
-
The world of Enderal is smaller than Skyrim's but more detailed and diverse. It has different regions with different climates, cultures, and wildlife.
-
The story of Enderal is darker and more mature than Skyrim's. It deals with themes such as religion, morality, fate, freedom, and madness.
-
The characters of Enderal are more complex and realistic than Skyrim's. They have their own personalities, backgrounds, goals, and flaws. They can also react differently to the player's actions and choices.
-
The gameplay of Enderal is more challenging and rewarding than Skyrim's. It has a class system that limits the player's skills and abilities based on their chosen role. It also has a survival system that requires the player to eat, drink, sleep, and manage their health.
-
The skill system of Enderal is more dynamic and creative than Skyrim's. It has a talent tree that allows the player to unlock new abilities and perks for their class. It also has two unique crafting classes that use alchemy and enchanting for combat.
-
The housing system of Enderal is more customizable and interactive than Skyrim's. It allows the player to design their own homes by placing furniture, decorations, plants, animals, and more. It also has a gardening system that lets the player grow their own crops. 81aa517590
-
-
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Express Invoice 7.25 Crack Torrent Free Download.md b/spaces/stomexserde/gpt4-ui/Examples/Express Invoice 7.25 Crack Torrent Free Download.md
deleted file mode 100644
index 01c073a0f387c1e5e9b77e26c5808801db7b9f5f..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Express Invoice 7.25 Crack Torrent Free Download.md
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
Express Invoice 7.25 Crack Torrent Free Download: A Simple and Effective Solution for Invoicing and Billing
-
-
If you are looking for a software that can help you create and manage invoices and bills for your business, you might want to check out Express Invoice 7.25 Crack Torrent Free Download. This is a cracked version of the popular Express Invoice software that allows you to use all its features without paying any fees or subscriptions.
Express Invoice 7.25 Crack Torrent Free Download is a simple and effective solution for invoicing and billing. You can easily create invoices, quotes, orders, receipts, and reports with this software. You can also customize your invoices with your logo, colors, and fonts. You can send your invoices by email, fax, or print them out. You can also track payments, partial payments, overdue balances, and taxes with this software.
-
-
Express Invoice 7.25 Crack Torrent Free Download also lets you sync your data across multiple devices and access it from anywhere. You can also integrate it with other software such as Inventoria, Copper, HourGuard, and more. You can also backup your data to the cloud or locally.
-
-
Express Invoice 7.25 Crack Torrent Free Download is a great tool for small businesses, freelancers, contractors, and anyone who needs to create and manage invoices and bills. It is easy to use, reliable, and affordable. You can download it from the link below and start using it right away.
Why should you use Express Invoice 7.25 Crack Torrent Free Download? There are many benefits of using this software for your invoicing and billing needs. Here are some of them:
-
-
-
It saves you time and money. You don't have to spend hours creating and sending invoices manually. You also don't have to pay any fees or subscriptions to use this software. You can use it for free and enjoy all its features.
-
It improves your cash flow and customer satisfaction. You can send your invoices faster and get paid sooner. You can also offer your customers multiple payment options and reminders. You can also create professional-looking invoices that reflect your brand and impress your customers.
-
It simplifies your accounting and reporting. You can easily track and manage your income and expenses with this software. You can also generate various reports such as sales, taxes, payments, and more. You can also export your data to Excel, PDF, or CSV formats.
-
-
-
How to use Express Invoice 7.25 Crack Torrent Free Download? Using this software is very easy and straightforward. Here are the steps you need to follow:
-
-
-
Download and install the software from the link below.
-
Launch the software and enter your business details such as name, address, logo, etc.
-
Create your customers and products lists.
-
Create your invoices by selecting the customer, products, quantity, price, tax, etc.
-
Send your invoices by email, fax, or print them out.
-
Record payments and track balances.
-
Generate reports and backup your data.
-
-
-
As you can see, Express Invoice 7.25 Crack Torrent Free Download is a simple and effective solution for invoicing and billing. It can help you save time and money, improve your cash flow and customer satisfaction, and simplify your accounting and reporting. It is a software that you should not miss if you want to grow your business and streamline your workflow.
-
-
So what are you waiting for? Download Express Invoice 7.25 Crack Torrent Free Download today and start creating and managing invoices and bills like a pro!
81aa517590
-
-
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Jawahir E Khamsa In Urdu Pdf Download.md b/spaces/stomexserde/gpt4-ui/Examples/Jawahir E Khamsa In Urdu Pdf Download.md
deleted file mode 100644
index 8559c59e1abda36bea772a2c64c77f29cb7bf9f2..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Jawahir E Khamsa In Urdu Pdf Download.md
+++ /dev/null
@@ -1,29 +0,0 @@
-
-
How to Download Jawahir E Khamsa in Urdu PDF for Free
-
Jawahir E Khamsa (The Five Jewels) is a collection of five books on Islamic mysticism and spiritual practices by the famous Sufi saint Shah Muhammad Ghaus Gwaliori (d. 1562). The books cover topics such as the names and attributes of Allah, the secrets of the Quran, the virtues of prayers and fasting, the methods of meditation and invocation, and the stages of spiritual journey.
-
If you are interested in reading this classic work of Sufism in Urdu language, you might be wondering how to download it in PDF format for free. Well, you are in luck because there are some online sources that offer this book for free download. Here are some of them:
Jawahir E Khamsa by Mirza Muhammad Abdul Hameed Baig: This is a scanned copy of the book published by Hameediya Steam Press Lahore in 1902. It has 168 pages and is available in PDF, EPUB, Kindle, and other formats. You can download it from the Internet Archive website by clicking on the link above.
-
AL-Jawahir Al-Khamsa by Hazrat Shah Muhammad Ghaus: This is another scanned copy of the book published by Dar-ul-Kitab Arbia in Beirut. It has 344 pages and is also available in PDF, EPUB, Kindle, and other formats. You can download it from the Internet Archive website by clicking on the link above.
-
Jawaahir E Khamsa by Shaikh ,odisha: This is a third scanned copy of the book published by an unknown publisher. It has 54 pages and is only available in PDF format. You can download it from the Internet Archive website by clicking on the link above.
-
-
These are some of the sources that offer Jawahir E Khamsa in Urdu PDF for free download. However, please note that these are old and scanned copies of the book that may have some errors or missing pages. If you want to read a more accurate and updated version of the book, you may have to buy it from a bookstore or an online platform.
-
We hope this article was helpful for you. If you have any questions or feedback, please leave a comment below. Thank you for reading!
-
-
Why You Should Read Jawahir E Khamsa
-
Jawahir E Khamsa is not just a book, but a treasure of wisdom and knowledge that can enrich your mind and soul. It is one of the most influential and authoritative works of Sufism that has been admired and followed by many generations of Muslims. It contains the teachings and experiences of Shah Muhammad Ghaus, who was a renowned scholar, jurist, poet, and spiritual master. He was also the teacher and guide of many famous Sufis, such as Mian Mir, Abdul Qadir Jilani, and Dara Shikoh.
-
By reading Jawahir E Khamsa, you can learn about the core principles and practices of Sufism, such as:
-
-
The concept of tawhid (the oneness of Allah) and its implications for human life.
-
The importance of love, devotion, and obedience to Allah and His Messenger (peace be upon him).
-
The methods of purifying the heart and soul from worldly attachments and sins.
-
The techniques of dhikr (remembrance of Allah) and muraqaba (meditation) to attain spiritual awareness and closeness to Allah.
-
The stages of suluk (spiritual journey) and maqamat (spiritual stations) that lead to the ultimate goal of fana (annihilation in Allah) and baqa (subsistence in Allah).
-
-
Reading Jawahir E Khamsa can also inspire you to follow the example of Shah Muhammad Ghaus, who was a model of piety, humility, generosity, and service. He dedicated his life to spreading the message of Islam and Sufism to the masses. He also helped many people in need, especially during the times of famine and war. He was respected and loved by people from all walks of life, regardless of their religion or sect.
-
Jawahir E Khamsa is a book that can transform your life for the better. It can help you understand the true meaning and purpose of your existence. It can also help you develop a strong connection with Allah and His Messenger (peace be upon him). It can also help you achieve peace, happiness, and success in both worlds.
-
-
So what are you waiting for? Download Jawahir E Khamsa in Urdu PDF for free today and start your journey towards enlightenment!
e93f5a0c3f
-
-
\ No newline at end of file
diff --git a/spaces/supertori/files/lycoris/kohya_utils.py b/spaces/supertori/files/lycoris/kohya_utils.py
deleted file mode 100644
index 3b3f69dc72d70c540e7e3ea8921325968828b804..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/lycoris/kohya_utils.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# part of https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py
-
-import hashlib
-import safetensors
-from io import BytesIO
-
-
-def addnet_hash_legacy(b):
- """Old model hash used by sd-webui-additional-networks for .safetensors format files"""
- m = hashlib.sha256()
-
- b.seek(0x100000)
- m.update(b.read(0x10000))
- return m.hexdigest()[0:8]
-
-
-def addnet_hash_safetensors(b):
- """New model hash used by sd-webui-additional-networks for .safetensors format files"""
- hash_sha256 = hashlib.sha256()
- blksize = 1024 * 1024
-
- b.seek(0)
- header = b.read(8)
- n = int.from_bytes(header, "little")
-
- offset = n + 8
- b.seek(offset)
- for chunk in iter(lambda: b.read(blksize), b""):
- hash_sha256.update(chunk)
-
- return hash_sha256.hexdigest()
-
-
-def precalculate_safetensors_hashes(tensors, metadata):
- """Precalculate the model hashes needed by sd-webui-additional-networks to
- save time on indexing the model later."""
-
- # Because writing user metadata to the file can change the result of
- # sd_models.model_hash(), only retain the training metadata for purposes of
- # calculating the hash, as they are meant to be immutable
- metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")}
-
- bytes = safetensors.torch.save(tensors, metadata)
- b = BytesIO(bytes)
-
- model_hash = addnet_hash_safetensors(b)
- legacy_hash = addnet_hash_legacy(b)
- return model_hash, legacy_hash
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Minecraft Force Op Hack Tool.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Minecraft Force Op Hack Tool.md
deleted file mode 100644
index 8a9d3f6574dfb037c23d127764dcbd4982d3ea98..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Minecraft Force Op Hack Tool.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-to view the contents of an executable file. To use this tool, you must be logged on as an administrator. 4fefd39f24
-
-
-
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Partituras Gratis Para Banda Sinfonica En Pdf.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Partituras Gratis Para Banda Sinfonica En Pdf.md
deleted file mode 100644
index feeaf43cc13fddac62336f84b3dc95d7a83363cd..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Partituras Gratis Para Banda Sinfonica En Pdf.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Tygokus This digital-clock application puts the will toast to sinaoense usefulness Verizon Partituras para banda sinaloense gratis pdf Calling. YTunes may find ... 1fdad05405
-
-
-
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Usbutil V2 00 Full UPDATED Ps2 Ultimate Isorip For Hd.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Usbutil V2 00 Full UPDATED Ps2 Ultimate Isorip For Hd.md
deleted file mode 100644
index 83d2152e1113bea55cc923968fd980f2396a3a2c..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Usbutil V2 00 Full UPDATED Ps2 Ultimate Isorip For Hd.md
+++ /dev/null
@@ -1,14 +0,0 @@
-
-
-November 16, 2016 — USBUtil is a program designed to sample PS2 game images in the ISO9660 8 3 standard format. USBUtil v2.00 Full [English version].rar. Opening program.
-Download torrent.
-Release year: 2011 Genre: Drivers Developer: FxSoft Developer site: http:// Interface language: Multilingual (Russian present) Platform: Windows 2000/XP/Vista/7/8/8.1 Description: A set of drivers for .
-Download the USB Utils program for installation.
-USBUtil is a program designed to sample PS2 game images in ISO9660 8 format. USBUtil v2.00 Full [English version].rar.
-And also you can download all this for free.
-Downloads.
-Games.
-Video. 8a78ff9644
-
-
-
diff --git a/spaces/szukevin/VISOR-GPT/train/scripts/generate_lm_multiple.py b/spaces/szukevin/VISOR-GPT/train/scripts/generate_lm_multiple.py
deleted file mode 100644
index 81bf0ed6b6628fa5d335f406a81be2b6d1e15966..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/scripts/generate_lm_multiple.py
+++ /dev/null
@@ -1,183 +0,0 @@
-"""
- This script provides an exmaple to wrap TencentPretrain for generation.
- Given the beginning of a text, language model generates the rest.
-"""
-import sys
-import os
-import argparse
-import torch
-import torch.nn.functional as F
-
-tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(tencentpretrain_dir)
-
-from tencentpretrain.embeddings import *
-from tencentpretrain.encoders import *
-from tencentpretrain.targets import *
-from tencentpretrain.utils.constants import *
-from tencentpretrain.utils import *
-from tencentpretrain.utils.config import load_hyperparam
-from tencentpretrain.model_loader import load_model
-from tencentpretrain.opts import infer_opts, tokenizer_opts
-from tqdm import tqdm
-
-
-class GenerateLm(torch.nn.Module):
- def __init__(self, args):
- super(GenerateLm, self).__init__()
- self.embedding = Embedding(args)
- for embedding_name in args.embedding:
- tmp_emb = str2embedding[embedding_name](args, len(args.tokenizer.vocab))
- self.embedding.update(tmp_emb, embedding_name)
- self.encoder = str2encoder[args.encoder](args)
- self.target = Target()
- self.target.update(LmTarget(args, len(args.tokenizer.vocab)), "lm")
-
- def forward(self, src, seg):
- emb = self.embedding(src, seg)
- output = self.encoder(emb, seg)
- output = self.target.lm.output_layer(output)
- return output
-
-
-def top_k_top_p_filtering(logits, top_k, top_p):
- top_k = min(top_k, logits.size(-1)) # Safety check
- if top_k > 0:
- # Remove all tokens with a probability less than the last token of the top-k
- indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
- logits[indices_to_remove] = -float("Inf")
-
- if top_p > 0.0:
- sorted_logits, sorted_indices = torch.sort(logits, descending=True)
- cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
-
- # Remove tokens with cumulative probability above the threshold
- sorted_indices_to_remove = cumulative_probs > top_p
- # Shift the indices to the right to keep also the first token above the threshold
- sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
- sorted_indices_to_remove[..., 0] = 0
-
- indices_to_remove = sorted_indices[sorted_indices_to_remove]
- logits[indices_to_remove] = -float("Inf")
- return logits
-
-
-def build_visorgpt(model_path,
- model_config,
- vocab_path='TencentPretrain/models/google_uncased_en_coord_vocab.txt'):
- parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- infer_opts(parser)
- tokenizer_opts(parser)
-
- parser.add_argument("--top_k", type=int, default=70)
- parser.add_argument("--top_p", type=float, default=0)
- parser.add_argument("--temperature", type=float, default=1.0)
-
- args = parser.parse_args()
-
- args.target = "lm"
- args.batch_size = 1
-
- args.load_model_path = model_path
- args.config_path = model_config
- args.vocab_path = vocab_path
- args = load_hyperparam(args)
- args.seq_length = 1024
-
- args.tokenizer = str2tokenizer[args.tokenizer](args)
-
- model = GenerateLm(args)
- model = load_model(model, args.load_model_path).cuda()
- model.eval()
- return args, model
-
-def gen_sequence(args, model, input_text):
-
- lines = [input_text]
- generated_texts = []
- for line in tqdm(lines):
- src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(line))
- seg = [1] * len(src)
- beginning_length = len(src)
- if len(src) > args.seq_length:
- src = src[:args.seq_length]
- seg = seg[:args.seq_length]
- src_tensor, seg_tensor = torch.LongTensor([src]).cuda(), torch.LongTensor([seg]).cuda()
-
- for i in range(args.seq_length - beginning_length):
- output = model(src_tensor, seg_tensor)
- next_token_logits = output[0][-1] / args.temperature
- filtered_logits = top_k_top_p_filtering(next_token_logits, args.top_k, args.top_p)
- next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1).cuda()
-
- src_tensor = torch.cat([src_tensor, next_token.view(1, 1)], dim=1)
- seg_tensor = torch.cat([seg_tensor, torch.tensor([[1]]).cuda()], dim=1)
-
- # generated_texts.append(line)
- generated_sentence = " ".join(
- args.tokenizer.convert_ids_to_tokens([token_id.item() for token_id in src_tensor[0]])
- )
- generated_texts.append(generated_sentence)
- return generated_texts
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
- infer_opts(parser)
-
- parser.add_argument("--top_k", type=int, default=70)
- parser.add_argument("--top_p", type=float, default=0)
- parser.add_argument("--temperature", type=float, default=1.0)
- parser.add_argument("--save_dir", type=str, default='predictions')
-
- tokenizer_opts(parser)
-
- args = parser.parse_args()
-
- args.target = "lm"
- args.batch_size = 1
-
- args = load_hyperparam(args)
-
- args.tokenizer = str2tokenizer[args.tokenizer](args)
-
- model = GenerateLm(args)
- model = load_model(model, args.load_model_path).cuda()
- model.eval()
-
- with open(args.test_path, mode="r", encoding="utf-8") as f:
- lines = [i.strip() for i in f.readlines()]
-
- generated_texts = []
- for line in tqdm(lines):
- src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(line))
- seg = [1] * len(src)
- beginning_length = len(src)
- if len(src) > args.seq_length:
- src = src[:args.seq_length]
- seg = seg[:args.seq_length]
- src_tensor, seg_tensor = torch.LongTensor([src]).cuda(), torch.LongTensor([seg]).cuda()
-
- for i in range(args.seq_length - beginning_length):
- output = model(src_tensor, seg_tensor)
- next_token_logits = output[0][-1] / args.temperature
- filtered_logits = top_k_top_p_filtering(next_token_logits, args.top_k, args.top_p)
- next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1).cuda()
-
- src_tensor = torch.cat([src_tensor, next_token.view(1, 1)], dim=1)
- seg_tensor = torch.cat([seg_tensor, torch.tensor([[1]]).cuda()], dim=1)
-
- # generated_texts.append(line)
- generated_sentence = " ".join(
- args.tokenizer.convert_ids_to_tokens([token_id.item() for token_id in src_tensor[0]])
- )
- generated_texts.append(generated_sentence)
- # import ipdb
- # ipdb.set_trace()
-
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- with open(args.save_dir + '/' + args.prediction_path, mode="w", encoding="utf-8") as f:
- for t in generated_texts:
- f.write(t + "\n")
\ No newline at end of file
diff --git a/spaces/taneemishere/html-code-generation-from-images-with-deep-neural-networks/compiler/__init__.py b/spaces/taneemishere/html-code-generation-from-images-with-deep-neural-networks/compiler/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/templates/flask/static/index.js b/spaces/templates/flask/static/index.js
deleted file mode 100644
index da58d658fda06c7aed1a8384db3cd19e5f8f7a3e..0000000000000000000000000000000000000000
--- a/spaces/templates/flask/static/index.js
+++ /dev/null
@@ -1,126 +0,0 @@
-if (document.location.search.includes('dark-theme=true')) {
- document.body.classList.add('dark-theme');
-}
-
-let cursor = 0;
-const RANGE = 5;
-const LIMIT = 16_000;
-
-const textToImage = async (text) => {
- const inferenceResponse = await fetch(`infer_biggan?input=${text}`);
- const inferenceBlob = await inferenceResponse.blob();
-
- return URL.createObjectURL(inferenceBlob);
-};
-
-const translateText = async (text) => {
- const inferResponse = await fetch(`infer_t5?input=${text}`);
- const inferJson = await inferResponse.json();
-
- return inferJson.output;
-};
-
-const queryDataset = async (start, end) => {
- const queryResponse = await fetch(`query_emotion?start=${start}&end=${end}`);
- const queryJson = await queryResponse.json();
-
- return queryJson.output;
-};
-
-const updateTable = async (cursor, range = RANGE) => {
- const table = document.querySelector('.dataset-output');
-
- const fragment = new DocumentFragment();
-
- const observations = await queryDataset(cursor, cursor + range);
-
- for (const observation of observations) {
- let row = document.createElement('tr');
- let text = document.createElement('td');
- let emotion = document.createElement('td');
-
- text.textContent = observation.text;
- emotion.textContent = observation.emotion;
-
- row.appendChild(text);
- row.appendChild(emotion);
- fragment.appendChild(row);
- }
-
- table.innerHTML = '';
-
- table.appendChild(fragment);
-
- table.insertAdjacentHTML(
- 'afterbegin',
- `
-
-
text
-
emotion
-
- `
- );
-};
-
-const imageGenSelect = document.getElementById('image-gen-input');
-const imageGenImage = document.querySelector('.image-gen-output');
-const textGenForm = document.querySelector('.text-gen-form');
-const tableButtonPrev = document.querySelector('.table-previous');
-const tableButtonNext = document.querySelector('.table-next');
-
-imageGenSelect.addEventListener('change', async (event) => {
- const value = event.target.value;
-
- try {
- imageGenImage.src = await textToImage(value);
- imageGenImage.alt = value + ' generated from BigGAN AI model';
- } catch (err) {
- console.error(err);
- }
-});
-
-textGenForm.addEventListener('submit', async (event) => {
- event.preventDefault();
-
- const textGenInput = document.getElementById('text-gen-input');
- const textGenParagraph = document.querySelector('.text-gen-output');
-
- try {
- textGenParagraph.textContent = await translateText(textGenInput.value);
- } catch (err) {
- console.error(err);
- }
-});
-
-tableButtonPrev.addEventListener('click', () => {
- cursor = cursor > RANGE ? cursor - RANGE : 0;
-
- if (cursor < RANGE) {
- tableButtonPrev.classList.add('hidden');
- }
- if (cursor < LIMIT - RANGE) {
- tableButtonNext.classList.remove('hidden');
- }
-
- updateTable(cursor);
-});
-
-tableButtonNext.addEventListener('click', () => {
- cursor = cursor < LIMIT - RANGE ? cursor + RANGE : cursor;
-
- if (cursor >= RANGE) {
- tableButtonPrev.classList.remove('hidden');
- }
- if (cursor >= LIMIT - RANGE) {
- tableButtonNext.classList.add('hidden');
- }
-
- updateTable(cursor);
-});
-
-textToImage(imageGenSelect.value)
- .then((image) => (imageGenImage.src = image))
- .catch(console.error);
-
-updateTable(cursor)
- .catch(console.error);
diff --git a/spaces/therealcyberlord/abstract-art-generation/SRGAN.py b/spaces/therealcyberlord/abstract-art-generation/SRGAN.py
deleted file mode 100644
index ff178a09a94f08f301cc251ce47f448f14163201..0000000000000000000000000000000000000000
--- a/spaces/therealcyberlord/abstract-art-generation/SRGAN.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import torch.nn as nn
-import torch
-
-class DenseResidualBlock(nn.Module):
- """
- The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
- """
-
- def __init__(self, filters, res_scale=0.2):
- super(DenseResidualBlock, self).__init__()
- self.res_scale = res_scale
-
- def block(in_features, non_linearity=True):
- layers = [nn.Conv2d(in_features, filters, 3, 1, 1, bias=True)]
- if non_linearity:
- layers += [nn.LeakyReLU()]
- return nn.Sequential(*layers)
-
- self.b1 = block(in_features=1 * filters)
- self.b2 = block(in_features=2 * filters)
- self.b3 = block(in_features=3 * filters)
- self.b4 = block(in_features=4 * filters)
- self.b5 = block(in_features=5 * filters, non_linearity=False)
- self.blocks = [self.b1, self.b2, self.b3, self.b4, self.b5]
-
- def forward(self, x):
- inputs = x
- for block in self.blocks:
- out = block(inputs)
- inputs = torch.cat([inputs, out], 1)
- return out.mul(self.res_scale) + x
-
-
-class ResidualInResidualDenseBlock(nn.Module):
- def __init__(self, filters, res_scale=0.2):
- super(ResidualInResidualDenseBlock, self).__init__()
- self.res_scale = res_scale
- self.dense_blocks = nn.Sequential(
- DenseResidualBlock(filters), DenseResidualBlock(filters), DenseResidualBlock(filters)
- )
-
- def forward(self, x):
- return self.dense_blocks(x).mul(self.res_scale) + x
-
-
-class GeneratorRRDB(nn.Module):
- def __init__(self, channels, filters=64, num_res_blocks=16, num_upsample=2):
- super(GeneratorRRDB, self).__init__()
-
- # First layer
- self.conv1 = nn.Conv2d(channels, filters, kernel_size=3, stride=1, padding=1)
- # Residual blocks
- self.res_blocks = nn.Sequential(*[ResidualInResidualDenseBlock(filters) for _ in range(num_res_blocks)])
- # Second conv layer post residual blocks
- self.conv2 = nn.Conv2d(filters, filters, kernel_size=3, stride=1, padding=1)
- # Upsampling layers
- upsample_layers = []
- for _ in range(num_upsample):
- upsample_layers += [
- nn.Conv2d(filters, filters * 4, kernel_size=3, stride=1, padding=1),
- nn.LeakyReLU(),
- nn.PixelShuffle(upscale_factor=2),
- ]
- self.upsampling = nn.Sequential(*upsample_layers)
- # Final output block
- self.conv3 = nn.Sequential(
- nn.Conv2d(filters, filters, kernel_size=3, stride=1, padding=1),
- nn.LeakyReLU(),
- nn.Conv2d(filters, channels, kernel_size=3, stride=1, padding=1),
- )
-
- def forward(self, x):
- out1 = self.conv1(x)
- out = self.res_blocks(out1)
- out2 = self.conv2(out)
- out = torch.add(out1, out2)
- out = self.upsampling(out)
- out = self.conv3(out)
- return out
\ No newline at end of file
diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Celebrate Independence Day with This Quiz in Malayalam 2022 Download PDF and Share with Friends.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Celebrate Independence Day with This Quiz in Malayalam 2022 Download PDF and Share with Friends.md
deleted file mode 100644
index 32a44958f41bb7d3e05ee692ed087a26440fb08f..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Celebrate Independence Day with This Quiz in Malayalam 2022 Download PDF and Share with Friends.md
+++ /dev/null
@@ -1,116 +0,0 @@
-
-
Independence Day Quiz in Malayalam 2022 PDF Download
-
Are you looking for a fun and informative way to celebrate India's 75th Independence Day on August 15, 2022? If yes, then you have come to the right place. In this article, we will provide you with a free PDF download of an Independence Day Quiz in Malayalam that you can take online or offline. This quiz will test your knowledge and awareness of India's history, culture, achievements, and challenges. It will also help you learn more about the freedom fighters, leaders, events, and facts related to India's independence movement. So, what are you waiting for? Read on to find out how to download and take this quiz, and why you should do it.
-
What is Independence Day and why is it celebrated?
-
Independence Day is a national holiday in India that commemorates the end of British colonial rule and the birth of a free and sovereign nation on August 15, 1947. On this day, India became an independent dominion within the Commonwealth of Nations, with Jawaharlal Nehru as its first Prime Minister. The Indian Constitution came into effect on January 26, 1950, making India a republic.
-
independence day quiz in malayalam 2022 pdf download
Independence Day is celebrated with great pride and patriotism across India. It is a day to remember and honour the sacrifices and struggles of millions of Indians who fought for freedom from foreign oppression. It is also a day to celebrate India's diversity, unity, democracy, and development. It is a day to reflect on India's past achievements and future aspirations.
-
How to download the Independence Day Quiz in Malayalam PDF?
-
If you want to download the Independence Day Quiz in Malayalam PDF, you can follow these simple steps:
-
* independence day quiz questions and answers in malayalam 2022 pdf
-* swatantra dinam quiz in malayalam 2022 pdf free download
-* indian independence day quiz malayalam pdf 2022 with solutions
-* 75th independence day quiz competition in malayalam language pdf
-* independence day special quiz in malayalam pdf 2022 for students
-* download malayalam quiz on indian freedom struggle 2022 pdf
-* how to prepare for independence day quiz in malayalam 2022 pdf
-* best independence day quiz in malayalam pdf 2022 for psc exams
-* independence day quiz in malayalam pdf 2022 with images and facts
-* online independence day quiz in malayalam 2022 pdf format
-* easy independence day quiz in malayalam 2022 pdf for kids
-* hard independence day quiz in malayalam 2022 pdf for adults
-* fun independence day quiz in malayalam 2022 pdf with jokes and riddles
-* latest independence day quiz in malayalam 2022 pdf with current affairs
-* history of independence day quiz in malayalam 2022 pdf with timeline
-* importance of independence day quiz in malayalam 2022 pdf with benefits
-* tips and tricks for independence day quiz in malayalam 2022 pdf with examples
-* types of independence day quiz in malayalam 2022 pdf with categories
-* sources of independence day quiz in malayalam 2022 pdf with references
-* reviews of independence day quiz in malayalam 2022 pdf with ratings
-* challenges of independence day quiz in malayalam 2022 pdf with difficulties
-* solutions of independence day quiz in malayalam 2022 pdf with answers
-* results of independence day quiz in malayalam 2022 pdf with scores
-* feedback of independence day quiz in malayalam 2022 pdf with comments
-* suggestions of independence day quiz in malayalam 2022 pdf with improvements
-
-
Click on this link or visit this website [text](^1^).
-
You will see a page with a brief introduction to the quiz and a download button.
-
Click on the download button or right-click on it and select "Save link as".
-
Choose a location on your device where you want to save the file.
-
Open the file with any PDF reader or browser.
-
Enjoy taking the quiz!
-
-
You can also print out the PDF file if you prefer to take the quiz on paper.
What are the benefits of taking the Independence Day Quiz in Malayalam?
-
Taking the Independence Day Quiz in Malayalam can offer you many benefits, such as:
-
-
It can improve your general knowledge and awareness of India's history, culture, achievements, and challenges.
-
It can enhance your cognitive skills, such as memory, recall, reasoning, and problem-solving.
-
It can boost your confidence and self-esteem by challenging yourself and learning new things.
-
It can foster your curiosity and interest in India's independence movement and its heroes.
-
It can increase your respect and appreciation for India's diversity, unity, democracy, and development.
-
It can make you feel proud and patriotic about being an Indian citizen.
-
-
So, don't miss this opportunity to take the Independence Day Quiz in Malayalam and enrich your mind and soul.
-
What are some of the questions and answers in the Independence Day Quiz in Malayalam?
-
The Independence Day Quiz in Malayalam consists of 25 multiple-choice questions that cover various topics related to India's independence movement and its aftermath. Each question has four options, out of which only one is correct. You have to select the correct option and mark it on the answer sheet. You will also find the explanations for the correct answers at the end of the quiz. Here are some examples of the questions and answers in the quiz:
-
-
Question
Options
Answer
-
Who was the first President of independent India?
A) Rajendra Prasad B) C. Rajagopalachari C) B. R. Ambedkar D) Mahatma Gandhi
A) Rajendra Prasad
-
Which slogan was coined by Subhas Chandra Bose?
A) Jai Hind B) Inquilab Zindabad C) Vande Mataram D) Satyameva Jayate
A) Jai Hind
-
Which state or union territory has the largest number of national monuments in India?
A) Delhi B) Uttar Pradesh C) Karnataka D) Tamil Nadu
B) Uttar Pradesh
-
Which Indian sportsperson won the first individual Olympic medal for independent India?
A) Milkha Singh B) K. D. Jadhav C) P. T. Usha D) Leander Paes
B) K. D. Jadhav
-
Which Indian movie won the first Oscar nomination for Best Foreign Language Film?
A) Mother India B) Salaam Bombay! C) Lagaan D) Slumdog Millionaire
A) Mother India
-
-
You can find more questions and answers in the PDF file that you can download from this link or this website [text].
-
How to prepare for the Independence Day Quiz in Malayalam?
-
If you want to ace the Independence Day Quiz in Malayalam, you need to prepare well for it. Here are some tips and resources that can help you:
-
-
Read books, magazines, newspapers, and websites that provide information and insights on India's history, culture, achievements, and challenges. Some examples are [text], [text], [text], and [text].
-
Watch documentaries, movies, shows, and videos that depict or discuss India's independence movement and its heroes. Some examples are [text], [text], [text], and [text].
-
Listen to podcasts, radio programs, songs, and speeches that celebrate or commemorate India's freedom struggle and its achievements. Some examples are [text], [text], [text], and [text].
-
Play games, puzzles, quizzes, and trivia that test your knowledge and awareness of India's history, culture, achievements, and challenges. Some examples are [text], [text], [text]( ), and [text](^12^).
-
Discuss with your friends, family, teachers, or mentors about India's history, culture, achievements, and challenges. You can also join online forums, groups, or communities that share your interest in India's independence movement.
-
-
By following these tips and resources, you can prepare well for the Independence Day Quiz in Malayalam and impress yourself and others with your knowledge and awareness.
-
How to celebrate Independence Day in a patriotic and fun way?
-
Independence Day is not only a day to take quizzes and learn about India's history, but also a day to celebrate India's diversity, unity, democracy, and development. There are many ways to observe and enjoy the national holiday with your family and friends. Here are some suggestions:
-
Watch patriotic movies and songs
-
One of the easiest and most entertaining ways to celebrate Independence Day is to watch patriotic movies and songs that depict or inspire India's freedom struggle and its achievements. Some of the popular and inspiring movies and songs are:
-
-
Lagaan (2001): A movie about a group of villagers who challenge the British officers to a cricket match to avoid paying high taxes.
-
Rang De Basanti (2006): A movie about a group of young friends who are influenced by the stories of freedom fighters and decide to take action against corruption and injustice.
-
Chak De! India (2007): A movie about a former hockey player who coaches the Indian women's national hockey team to victory in the World Cup.
-
Vande Mataram: A song composed by Bankim Chandra Chattopadhyay in 1876 and later adopted as the national song of India. It means "I bow to thee, Mother".
-
Jana Gana Mana: A song composed by Rabindranath Tagore in 1911 and later adopted as the national anthem of India. It means "Thou art the ruler of the minds of all people".
-
Saare Jahan Se Achha: A song composed by Muhammad Iqbal in 1904 and later popularized by freedom fighters. It means "Better than the entire world, is our Hindustan".
-
-
Hoist the national flag and sing the national anthem
-
Another way to celebrate Independence Day is to hoist the national flag and sing the national anthem with pride and respect. The national flag of India is a horizontal tricolour of saffron, white, and green, with a navy blue wheel (Ashoka Chakra) in the centre. The saffron represents courage and sacrifice, the white represents peace and truth, the green represents prosperity and faith, and the wheel represents law and justice. The national anthem of India is Jana Gana Mana, which praises the unity and diversity of India and its people. You can hoist the flag at your home, school, college, office, or community centre, and sing the anthem along with others.
-
Participate in cultural programs and competitions
-
A third way to celebrate Independence Day is to participate in cultural programs and competitions that are organized by schools, colleges, communities, or online platforms. You can showcase your talents and skills in various fields such as music, dance, drama, art, literature, sports, quiz, debate, etc. You can also learn from others and appreciate their talents and skills. You can also win prizes and certificates for your participation or performance.
-
Visit historical monuments and museums
-
A fourth way to celebrate Independence Day is to visit historical monuments and museums that showcase India's heritage and culture. You can learn more about the history, architecture, art, religion, science, technology, etc. of India and its people. You can also pay tribute to the freedom fighters, leaders, martyrs, heroes, etc. who contributed to India's independence movement. Some of the places you can visit are:
-
-
The Red Fort in Delhi: The place where Jawaharlal Nehru hoisted the first national flag of independent India on August 15, 1947.
-
The Cellular Jail in Andaman: The place where many freedom fighters were imprisoned and tortured by the British.
-
The Jallianwala Bagh in Amritsar: The place where hundreds of unarmed civilians were massacred by the British troops on April 13, 1919.
-
The Sabarmati Ashram in Ahmedabad: The place where Mahatma Gandhi lived for many years and launched many non-violent movements for India's freedom.
-
The India Gate in Delhi: The place where a memorial for the Indian soldiers who died in World War I and other wars is located.
-
-
Donate to social causes and help the needy
-
A fifth way to celebrate Independence Day is to donate to social causes and help the needy. You can contribute to the welfare and development of India and its people by supporting various organizations and initiatives that work for education, health, environment, women, children, etc. You can also volunteer your time, skills, or resources to help the poor, the homeless, the disabled, the elderly, etc. You can also spread awareness and inspire others to join you in your noble deeds.
-
Conclusion
-
Independence Day is a special day for every Indian citizen. It is a day to remember and honour the sacrifices and struggles of our freedom fighters and leaders. It is also a day to celebrate and appreciate the diversity, unity, democracy, and development of our nation. It is a day to learn and grow as responsible and patriotic citizens. It is a day to have fun and enjoy with our family and friends.
-
One of the ways to celebrate Independence Day is to take the Independence Day Quiz in Malayalam that we have provided in this article. This quiz will test your knowledge and awareness of India's history, culture, achievements, and challenges. It will also help you learn more about the freedom fighters, leaders, events, and facts related to India's independence movement. You can download the PDF file of the quiz from this link or this website [text] and take it online or offline.
-
We hope you enjoyed reading this article and taking the quiz. We wish you a very happy and prosperous Independence Day. Jai Hind!
-
FAQs
-
Here are some frequently asked questions and their answers related to Independence Day and the quiz:
-
-
Q: When was the first Independence Day celebrated in India? A: The first Independence Day was celebrated on August 15, 1947, when India became an independent dominion within the Commonwealth of Nations.
-
Q: Who designed the national flag of India? A: The national flag of India was designed by Pingali Venkayya, a freedom fighter and a farmer from Andhra Pradesh.
-
Q: Who wrote the national anthem of India? A: The national anthem of India was written by Rabindranath Tagore, a Nobel laureate and a poet from Bengal.
-
Q: Who gave the slogan "Quit India" during the freedom movement? A: The slogan "Quit India" was given by Mahatma Gandhi during the Quit India Movement in 1942, which was a mass civil disobedience campaign against the British rule.
-
Q: Who was the first woman to hoist the Indian national flag on foreign soil? A: The first woman to hoist the Indian national flag on foreign soil was Madam Bhikaji Cama, a revolutionary and a feminist from Bombay. She hoisted the flag at the International Socialist Conference in Stuttgart, Germany, in 1907.
- 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Create Custom Shortcuts to Hidden Apps with QuickShortcutMaker APK.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Create Custom Shortcuts to Hidden Apps with QuickShortcutMaker APK.md
deleted file mode 100644
index 3ca440de65c061ed6db98aa938b46a56b587deb2..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Create Custom Shortcuts to Hidden Apps with QuickShortcutMaker APK.md
+++ /dev/null
@@ -1,107 +0,0 @@
-
-
Quick Shortcut Maker APK: How to Download and Use It on Your Android Device
-
If you are looking for a way to access hidden apps and functions on your Android device, you might want to try Quick Shortcut Maker APK. This app allows you to create shortcuts to any app or system service that you want, and customize them with your own names and icons. In this article, we will show you what Quick Shortcut Maker APK is, why you need it, how to download it from Google Drive, and how to use it on your Android device.
Quick Shortcut Maker APK is an app that lets you create shortcuts to hidden apps and functions on your Android device. By default, only the standard apps are listed in the app drawer, and this cannot be changed. However, if you go into your settings and view your list of apps in there, you'll notice a bunch of system apps that have been hidden from the app drawer. Quick Shortcut Maker APK can take these system apps and put them right on your home screen.
-
Some of the features of Quick Shortcut Maker APK are:
-
-
It can create shortcuts to any app or system service that you want.
-
It can customize the names and icons of the shortcuts.
-
It can search for apps and services by keywords or categories.
-
It can sort the shortcuts by name, date, or frequency.
-
It can backup and restore the shortcuts.
-
-
Why Do You Need Quick Shortcut Maker APK?
-
You might be wondering why you need Quick Shortcut Maker APK when you already have a lot of apps on your home screen. Well, there are some reasons why this app can be useful for you:
-
-
It can help you access hidden apps and functions that are not available in the app drawer. For example, you can create shortcuts to system settings, developer options, accessibility settings, etc.
-
It can help you save time and effort by creating shortcuts to frequently used apps and functions. For example, you can create shortcuts to your favorite contacts, websites, documents, etc.
-
It can help you customize your home screen by creating shortcuts with your own names and icons. For example, you can create shortcuts with emojis, symbols, or images that suit your preferences.
-
-
How to Download Quick Shortcut Maker APK from Google Drive?
-
If you are interested in trying out Quick Shortcut Maker APK, you can download it from Google Drive. Here are the steps to do so:
Click on the download button at the top right corner of the screen.
-
Wait for the download to finish and then open the file manager on your device.
-
Navigate to the folder where the APK file has been saved and tap on it.
-
If prompted, enable the installation from unknown sources by going to your device settings > security > unknown sources.
-
Follow the instructions on the screen to complete the installation.
-
-
How to Use Quick Shortcut Maker APK on Your Android Device?
-
Once you have installed Quick Shortcut Maker APK on your device, you can start using it to create and customize shortcuts. Here is how:
-
-
Open the app and - You will see a list of apps and services that you can create shortcuts to. You can also use the search bar at the top to find the app or service you want by typing its name or keyword.
-
Tap on the app or service you want to create a shortcut to and then tap on the "Try" button at the bottom.
-
You will see a pop-up window that shows the details of the app or service, such as its name, icon, package name, and activity name. You can edit these details by tapping on them.
-
When you are satisfied with the details, tap on the "Create" button at the bottom to create the shortcut on your home screen.
-
You can repeat this process for as many shortcuts as you want. You can also delete or modify the shortcuts by long-pressing on them and selecting the appropriate option.
-
-
Conclusion
-
Quick Shortcut Maker APK is a handy app that lets you create shortcuts to hidden apps and functions on your Android device. It can help you access system settings, developer options, accessibility settings, and more. It can also help you save time and effort by creating shortcuts to frequently used apps and functions. Moreover, it can help you customize your home screen by creating shortcuts with your own names and icons. You can download Quick Shortcut Maker APK from Google Drive and install it on your device easily. You can then use it to create and customize shortcuts as you wish.
-
quick shortcut maker apk download google drive
-quick shortcut maker apk for android google drive
-quick shortcut maker apk latest version google drive
-quick shortcut maker apk xda developers google drive
-quick shortcut maker apk free download google drive
-quick shortcut maker apk mod google drive
-quick shortcut maker apk pro google drive
-quick shortcut maker apk no ads google drive
-quick shortcut maker apk old version google drive
-quick shortcut maker apk 2.7 google drive
-quick shortcut maker apk 2.4.0 google drive
-quick shortcut maker apk 2.5.0 google drive
-quick shortcut maker apk 2.6.0 google drive
-quick shortcut maker apk 2.8.0 google drive
-quick shortcut maker apk 2.9.0 google drive
-quick shortcut maker apk for samsung google drive
-quick shortcut maker apk for lg google drive
-quick shortcut maker apk for huawei google drive
-quick shortcut maker apk for oppo google drive
-quick shortcut maker apk for vivo google drive
-quick shortcut maker apk for xiaomi google drive
-quick shortcut maker apk for realme google drive
-quick shortcut maker apk for oneplus google drive
-quick shortcut maker apk for nokia google drive
-quick shortcut maker apk for motorola google drive
-quick shortcut maker apk for frp bypass google drive
-quick shortcut maker apk for frp lock removal google drive
-quick shortcut maker apk for frp unlock tool google drive
-quick shortcut maker apk for frp reset tool google drive
-quick shortcut maker apk for frp flash tool google drive
-quick shortcut maker apk for oreo google drive
-quick shortcut maker apk for nougat google drive
-quick shortcut maker apk for marshmallow google drive
-quick shortcut maker apk for lollipop google drive
-quick shortcut maker apk for kitkat google drive
-quick shortcut maker apk for jelly bean google drive
-quick shortcut maker apk for ice cream sandwich google drive
-quick shortcut maker apk offline installer google drive
-quick shortcut maker apk online installer google drive
-quick shortcut maker apk direct link google drive
-quick shortcut maker apk mirror link google drive
-quick shortcut maker apk alternative link google drive
-how to use quick shortcut maker apk google drive
-how to install quick shortcut maker apk google drive
-how to download quick shortcut maker apk google drive
-how to update quick shortcut maker apk google drive
-how to uninstall quick shortcut maker apk google drive
-how to backup quick shortcut maker apk google drive
-how to restore quick shortcut maker apk google drive
-
If you are looking for a way to make your Android device more convenient and personalized, you should give Quick Shortcut Maker APK a try. It is a simple but powerful app that can make your life easier and more fun. Download it now and enjoy creating shortcuts to your favorite apps and functions!
-
FAQs
-
-
Q: Is Quick Shortcut Maker APK safe to use?
-
A: Yes, Quick Shortcut Maker APK is safe to use as long as you download it from a trusted source like Google Drive. It does not contain any malware or viruses that can harm your device or data.
-
Q: Does Quick Shortcut Maker APK require root access?
-
A: No, Quick Shortcut Maker APK does not require root access to work. It can work on any Android device that runs on Android 1.6 or higher.
-
Q: Can I create shortcuts to apps that are not installed on my device?
-
A: No, Quick Shortcut Maker APK can only create shortcuts to apps that are installed on your device. If you want to create shortcuts to apps that are not installed, you will need to install them first.
-
Q: How many shortcuts can I create with Quick Shortcut Maker APK?
-
A: There is no limit to how many shortcuts you can create with Quick Shortcut Maker APK. You can create as many shortcuts as you want, as long as you have enough space on your home screen.
-
Q: How can I backup and restore my shortcuts created with Quick Shortcut Maker APK?
-
A: You can backup and restore your shortcuts created with Quick Shortcut Maker APK by using the app's built-in feature. To backup your shortcuts, open the app and tap on the menu button at the top left corner. Then tap on "Backup/Restore" and select "Backup". To restore your shortcuts, open the app and tap on the menu button at the top left corner. Then tap on "Backup/Restore" and select "Restore".
-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lokicraft 5 Crafting Game APK for Android - Version 1.19.66.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lokicraft 5 Crafting Game APK for Android - Version 1.19.66.md
deleted file mode 100644
index 3c2966aabef5ac5b41b6f99c57fb22d47bf14e6b..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lokicraft 5 Crafting Game APK for Android - Version 1.19.66.md
+++ /dev/null
@@ -1,150 +0,0 @@
-
-
Lokicraft 5 1.16 APK Download: A Guide for Creative Gamers
-
If you are a fan of sandbox games that let you unleash your creativity and imagination, you might want to check out Lokicraft 5. This game is a free and fun alternative to Minecraft, where you can build, craft, explore, and survive in a 3D block world. In this article, we will show you how to download and install the latest version of Lokicraft 5 1.16 APK on your Android devices, as well as how to play it on PC with MuMu Player. We will also share some tips and tricks for playing this game and making the most out of it.
Lokicraft 5 is a game developed by Denepa, a studio that specializes in crafting and building games. It is the fifth installment in the Lokicraft series, which has been downloaded by millions of players around the world. Lokicraft 5 is inspired by Minecraft, but it has its own unique features and style. You can play it in two modes: creative and survival. In creative mode, you have unlimited resources and can build anything you want without any restrictions. In survival mode, you have to gather resources, craft tools and weapons, fight enemies, and stay alive.
-
Features of Lokicraft 5
-
Some of the features that make Lokicraft 5 stand out from other sandbox games are:
-
-
It has high-quality graphics and realistic lighting effects that create a beautiful and immersive environment.
-
It has a variety of blocks and items that you can use to create different structures and designs.
-
It has a large map with different biomes, such as forests, deserts, mountains, oceans, and more.
-
It has a dynamic weather system that changes according to the time of day and the season.
-
It has animals and monsters that you can interact with or fight against.
-
It has a multiplayer mode that allows you to play with your friends online or offline.
-
It has a user-friendly interface and easy controls that make it suitable for players of all ages.
-
-
How to download and install Lokicraft 5 1.16 APK on Android devices
-
If you want to play the latest version of Lokicraft 5 on your Android devices, you can follow these simple steps:
-
lokicraft 5 crafting game apk download
-lokicraft 5 mod apk download latest version
-lokicraft 5 free download for android
-lokicraft 5 apk download uptodown
-lokicraft 5 adventure game apk download
-lokicraft 5 apk download no ads
-lokicraft 5 unlimited resources apk download
-lokicraft 5 apk download for pc
-lokicraft 5 survival mode apk download
-lokicraft 5 apk download apkpure
-lokicraft 5 creative mode apk download
-lokicraft 5 pro apk download full version
-lokicraft 5 offline apk download
-lokicraft 5 multiplayer apk download
-lokicraft 5 new update apk download
-lokicraft 5 hack apk download unlimited money
-lokicraft 5 premium apk download unlocked
-lokicraft 5 online apk download free
-lokicraft 5 sandbox game apk download
-lokicraft 5 apk download for ios
-lokicraft 5 building game apk download
-lokicraft 5 cheats apk download mod menu
-lokicraft 5 original apk download official
-lokicraft 5 exploration game apk download
-lokicraft 5 beta apk download latest version
-lokicraft 5 cracked apk download full unlocked
-lokicraft 5 simulator game apk download
-lokicraft 5 tips and tricks apk download guide
-lokicraft 5 old version apk download free
-lokicraft 5 skins pack apk download customizer
-lokicraft 5 maps and mods apk download installer
-lokicraft 5 realistic graphics apk download hd
-lokicraft 5 fun game apk download for kids
-lokicraft 5 texture pack apk download changer
-lokicraft 5 seeds and codes apk download generator
-lokicraft 5 best game apk download review
-lokicraft 5 animals and pets apk download addon
-lokicraft 5 weapons and armor apk download modded
-lokicraft 5 vehicles and machines apk download builder
-lokicraft 5 furniture and decorations apk download designer
-lokicraft 5 music and sounds apk download editor
-lokicraft 5 commands and cheats apk download hacker
-lokicraft 5 worlds and biomes apk download creator
-lokicraft 5 characters and skins apk download customizer
-lokicraft 5 blocks and items apk download collector
-lokicraft 5 achievements and trophies apk download unlocker
-lokicraft 5 challenges and quests apk download solver
-
-
Go to [Lokicraft 5 Crafting APK (Android Game) - Free Download - APKCombo](^1^) or [Lokicraft 5 Crafting - Apps on Google Play](^2^) and click on the download button.
-
Once the download is complete, locate the APK file on your device and tap on it.
-
If you see a warning message that says "Install blocked", go to your device settings and enable "Unknown sources" under security options.
-
Follow the instructions on the screen and wait for the installation to finish.
-
Launch the game and enjoy!
-
-
Why play Lokicraft 5 on PC with MuMu Player?
-
While playing Lokicraft 5 on your Android devices is fun and convenient, you might want to experience it on a bigger screen with better performance. That's why we recommend playing it on PC with MuMu Player, an Android emulator that allows you to run mobile games and apps on your computer. MuMu Player is fast, stable, secure, and compatible with most games and apps. It also has many features that enhance your gaming experience, such as keyboard and mouse controls , screen recording and capturing, and more.
-
Benefits of playing Lokicraft 5 on PC
-
Some of the benefits of playing Lokicraft 5 on PC with MuMu Player are:
-
-
You can enjoy the game on a larger and clearer screen, which gives you a better view of the details and the surroundings.
-
You can use the keyboard and mouse to control the game, which gives you more accuracy and flexibility.
-
You can customize the key mapping and settings according to your preferences and needs.
-
You can play the game without worrying about battery drain, storage space, or performance issues.
-
You can access the game from your PC anytime and anywhere, as long as you have an internet connection.
-
-
How to download and install MuMu Player and Lokicraft 5 on PC
-
If you want to play Lokicraft 5 on PC with MuMu Player, you can follow these simple steps:
-
-
Go to [MuMu Player - The Best Free Android Emulator on PC ever] and click on the download button.
-
Once the download is complete, run the installer and follow the instructions on the screen.
-
Launch MuMu Player and sign in with your Google account or create a new one.
-
Go to the Google Play Store or APKPure app on MuMu Player and search for Lokicraft 5.
-
Click on the install button and wait for the installation to finish.
-
Launch the game and enjoy!
-
-
Tips and tricks for playing Lokicraft 5
-
Lokicraft 5 is a game that allows you to express your creativity and imagination, but it also has some challenges and secrets that you might want to know. Here are some tips and tricks that will help you play the game better:
-
How to use the crafting menu
-
The crafting menu is where you can create various items and blocks that you need in the game. To access it, you need to tap on the three dots icon on the top right corner of the screen. You will see different categories of items, such as tools, weapons, armor, food, furniture, etc. To craft an item, you need to have the required materials in your inventory. You can drag and drop the materials into the crafting grid and then tap on the output slot to get the item. You can also use the search bar to find the item you want quickly.
-
How to explore different biomes
-
The world of Lokicraft 5 is divided into different biomes, which are regions with distinct features, such as climate, vegetation, animals, etc. Some of the biomes that you can find in the game are:
-
-
Biome
Description
-
Forest
A biome with many trees, flowers, grass, mushrooms, etc. It is a good place to find wood, apples, seeds, etc.
-
Desert
A biome with sand, cacti, dead bushes, etc. It is a hot and dry place that has no water sources. It is a good place to find sandstone, cactus green dye, etc.
-
Mountain
A biome with high peaks, snow, ice, etc. It is a cold and windy place that has low oxygen levels. It is a good place to find stone, coal, iron, etc.
-
Ocean
A biome with water, coral reefs, fish, dolphins, etc. It is a wet and deep place that has many underwater treasures. It is a good place to find prismarine, sponge, sea lanterns, etc.
-
Jungle
A biome with tall trees, vines, bamboo, melons, etc. It is a humid and lush place that has many exotic animals. It is a good place to find jungle wood, cocoa beans, etc.
-
-
To explore different biomes, you need to travel across the map and look for the changes in the terrain and the environment. You can use a map or a compass to help you navigate. You can also use a boat or a minecart to travel faster. Be careful of the dangers and difficulties that each biome may present, such as hostile mobs, extreme temperatures, lack of resources, etc.
-
How to build your dream home
-
One of the most fun and rewarding aspects of Lokicraft 5 is building your own home. You can design and decorate it however you want, using the blocks and items that you have collected or crafted. You can also add features and functions to your home, such as doors, windows, beds, chests, furnaces, etc. Here are some steps that you can follow to build your dream home:
-
-
Choose a location for your home. It should be flat, spacious, and safe from enemies and natural disasters.
-
Gather the materials that you need for your home. You can use any blocks that you like, but some common ones are wood, stone, brick, glass, etc.
-
Start building the foundation and the walls of your home. You can use a grid or a blueprint to help you plan and measure your home.
-
Add the roof and the floor of your home. You can use stairs, slabs, or other blocks to create different shapes and angles for your roof.
-
Add the doors and windows of your home. You can use wooden or iron doors, glass or iron bars for windows, etc.
-
Add the furniture and appliances of your home. You can use beds, chests, furnaces, crafting tables, bookshelves, paintings, etc.
-
Add the lighting and decoration of your home. You can use torches, lanterns, glowstone, flowers, carpets, etc.
-
-
How to survive in survival mode
-
If you want to challenge yourself and test your skills in Lokicraft 5, you can play in survival mode. In this mode, you have to gather resources, craft tools and weapons, fight enemies, and stay alive. You also have to manage your health and hunger bars, which can be affected by various factors. Here are some tips that will help you survive in survival mode:
-
-
Collect wood as soon as possible. Wood is essential for crafting tools and weapons, as well as building shelters and fires.
-
Craft a wooden pickaxe and mine some stone. Stone is useful for crafting better tools and weapons, as well as building stronger structures.
-
Find food sources and cook them if possible. Food is necessary for restoring your hunger bar and healing your health bar. Some food sources are animals, crops, mushrooms, etc.
-
Build a shelter before nightfall. Nighttime is dangerous because many hostile mobs spawn in the dark. Your shelter should be enclosed and well-lit to prevent mobs from entering.
-
Craft a bed and sleep in it at night. Sleeping in a bed will set your spawn point and skip the night cycle.
-
Craft armor and weapons to protect yourself from enemies. Armor will reduce the damage that you take from attacks. Weapons will help you fight back and kill enemies faster.
-
Explore underground caves and dungeons for rare resources and treasures. Caves and dungeons are full of ores, chests, spawners, etc. that you can use or loot. However, they are also full of dangers, such as lava, traps, mobs, etc. Be careful and prepared when you explore them.
-
Keep an eye on your health and hunger bars. Your health bar will decrease when you take damage from enemies, falls, fire, etc. Your hunger bar will decrease when you perform actions, such as running, jumping, mining, etc. You can restore your health bar by eating food or using potions. You can restore your hunger bar by eating food or drinking water.
-
-
Conclusion
-
Lokicraft 5 is a game that offers endless possibilities and fun for creative gamers. You can download and install the latest version of Lokicraft 5 1.16 APK on your Android devices or play it on PC with MuMu Player. You can also use our tips and tricks to improve your gameplay and enjoy the game more. Whether you want to build, craft, explore, or survive, Lokicraft 5 has something for everyone.
-
FAQs
-
Here are some frequently asked questions about Lokicraft 5:
-
-
Is Lokicraft 5 free to play?
-
Yes, Lokicraft 5 is free to download and play on Android devices and PC. However, it may contain ads and in-app purchases that you can choose to buy or not.
-
Is Lokicraft 5 safe to download and install?
-
Yes, Lokicraft 5 is safe to download and install from the official sources that we have provided in this article. However, you should always be careful when downloading and installing APK files from unknown or untrusted sources, as they may contain viruses or malware that can harm your device.
-
Is Lokicraft 5 compatible with my device?
-
Lokicraft 5 is compatible with most Android devices that have Android 4.4 or higher. However, some devices may not support the game or may experience performance issues due to their specifications. You can check the compatibility of your device by visiting the Google Play Store or APKPure app on MuMu Player.
-
How can I update Lokicraft 5 to the latest version?
-
You can update Lokicraft 5 to the latest version by visiting the Google Play Store or APKPure app on MuMu Player and checking for updates. You can also enable the auto-update feature to get the latest updates automatically.
-
How can I contact the developer of Lokicraft 5?
-
You can contact the developer of Lokicraft 5 by sending an email to denepa.studio@gmail.com or visiting their Facebook page at [Denepa - Home | Facebook]. You can also leave a review or a comment on the Google Play Store or APKPure app on MuMu Player and share your feedback or suggestions.
- 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Automation Studio 5.2 Crack Free Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Automation Studio 5.2 Crack Free Download.md
deleted file mode 100644
index 59989d5bc42baf5cbdea5023f14cdf607d7019dc..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Automation Studio 5.2 Crack Free Download.md
+++ /dev/null
@@ -1,35 +0,0 @@
-
-I can try to help you with that. Here is a possible title and article for your keyword:
-
-
How to Download Automation Studio 5.2 Crack for Free
-
Automation Studio is a software that allows you to design, simulate and document fluid power systems and electrical projects. It is used by engineers, trainers, and maintenance personnel for CAD, training and troubleshooting purposes. [^5^] [^6^]
If you want to download Automation Studio 5.2 crack for free, you might be tempted to use some of the links that appear on the internet. However, you should be aware of the risks and consequences of using cracked software.
-
The Risks of Using Cracked Software
-
Cracked software is software that has been modified to bypass or remove the protection mechanisms that prevent unauthorized copying or use. Cracked software may seem like a convenient and cheap way to get access to premium features and functions, but it comes with many drawbacks and dangers.
-
Some of the risks of using cracked software are:
-
-
-
Legal issues: Cracking software is illegal and violates the intellectual property rights of the software developers. You could face fines, lawsuits or even criminal charges if you are caught using or distributing cracked software. [^7^]
-
Malware infection: Cracked software often contains malicious code that can harm your computer or steal your personal information. Malware can also spread to other devices on your network or online accounts. You could lose your data, compromise your privacy or become a victim of identity theft or fraud. [^8^]
-
Poor performance: Cracked software may not work properly or have bugs and errors that affect its functionality. It may also conflict with other programs or system settings on your computer. You could experience crashes, freezes, slowdowns or other issues that affect your productivity and efficiency.
-
Lack of support and updates: Cracked software does not receive any official support or updates from the software developers. You will not be able to access new features, improvements or bug fixes that are released for the original software. You will also miss out on security patches that protect your software from vulnerabilities and threats.
-
-
The Benefits of Using Genuine Software
-
Instead of using cracked software, you should consider using genuine software that is legally purchased and licensed. Genuine software offers many benefits that outweigh the costs and risks of using cracked software.
-
Some of the benefits of using genuine software are:
-
-
Legal compliance: Genuine software respects the intellectual property rights of the software developers and complies with the law. You will not face any legal issues or penalties for using or distributing genuine software.
-
Malware protection: Genuine software does not contain any malicious code that can harm your computer or steal your information. Genuine software also receives regular security updates that protect it from new vulnerabilities and threats.
-
Optimal performance: Genuine software works properly and has no bugs or errors that affect its functionality. It also works well with other programs and system settings on your computer. You will enjoy a smooth and reliable user experience.
-
Support and updates: Genuine software receives official support and updates from the software developers. You will be able to access new features, improvements and bug fixes that are released for the original software. You will also benefit from customer service and technical assistance if you encounter any problems or questions.
-
-
How to Download Automation Studio 5.2 Crack for Free
-
If you still want to download Automation Studio 5.2 crack for free, despite the risks and consequences, you will need to follow these steps:
-
-
Search for a link that offers Automation Studio 5.2 crack for free on Google or another search engine. For example, you could try this link: automation studio 5.2 crack.rar - Google Drive. [^1^]
-
Download the file from the link and save it on your computer.
-
Extract the file using a program like WinRAR or 7-Zip.
-
7196e7f11a
-
-
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Harts And Flowers Full Movie Hd 1080p Download !!TOP!! Kickass Movie.md b/spaces/tioseFevbu/cartoon-converter/scripts/Harts And Flowers Full Movie Hd 1080p Download !!TOP!! Kickass Movie.md
deleted file mode 100644
index fcc8717d758e44ee8614f5ee90de63260c574e59..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Harts And Flowers Full Movie Hd 1080p Download !!TOP!! Kickass Movie.md
+++ /dev/null
@@ -1,22 +0,0 @@
-
-
Harts and Flowers: A Romantic Comedy Full of Surprises
-
Harts and Flowers is a 2022 American romantic comedy film directed by John Hughes and starring Jennifer Aniston and Jason Bateman. The film follows the lives of two rival florists who fall in love after a series of mishaps and misunderstandings.
-
Harts and Flowers full movie hd 1080p download kickass movie
If you are looking for a fun and lighthearted movie to watch with your date or friends, Harts and Flowers is a good choice. You can download the full movie in HD 1080p quality from KickassTorrents, one of the most popular torrent sites in the world. Here is how you can do it:
Search for "Harts and Flowers" in the search box, or click on the tags "Movies" and "Comedy".
-
You will get a list of torrent files for the movie. Choose the one that has the highest number of seeders and leechers, as well as the best quality (HD 1080p).
-
Click on the torrent name to see more details, such as file size, description, screenshots, etc.
-
Click on the magnet link or the download button below the title to start downloading the movie.
-
You will need a torrent client, such as BitTorrent, uTorrent, BitComet, etc., to download the movie. Install one if you don't have it already.
-
Open the torrent file or copy the magnet link with your torrent client and wait for the download to finish.
-
Enjoy watching Harts and Flowers on your computer or TV.
-
-
Note: Downloading copyrighted material without permission may bring you serious consequences. WonderFox does not encourage illegal downloading and this article is for personal fair-use only. Download Harts and Flowers at your own risk.
-
Harts and Flowers is a movie that will make you laugh and smile with its witty dialogue, hilarious situations, and charming characters. Jennifer Aniston and Jason Bateman play Lucy and Ben, two florists who own competing shops in the same neighborhood. They have been rivals for years, constantly trying to outdo each other with their floral arrangements and business strategies.
-
However, things change when they both sign up for an online dating service and get matched with each other. Unaware of their true identities, they start chatting and flirting online, developing a genuine connection. They decide to meet in person, but when they realize who they are, they are shocked and angry. They try to forget about each other, but their feelings keep growing stronger.
-
Will they be able to overcome their differences and admit their love? Or will their pride and prejudice ruin their chance at happiness? Find out by watching Harts and Flowers, a romantic comedy full of surprises.
7b8c122e87
-
-
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/urllib3/response.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/urllib3/response.py
deleted file mode 100644
index 776e49dd2b2e343a9e336294e5d1604821ff1ba8..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/urllib3/response.py
+++ /dev/null
@@ -1,818 +0,0 @@
-from __future__ import absolute_import
-
-import io
-import logging
-import zlib
-from contextlib import contextmanager
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-brotli = None
-
-from ._collections import HTTPHeaderDict
-from .connection import BaseSSLError, HTTPException
-from .exceptions import (
- BodyNotHttplibCompatible,
- DecodeError,
- HTTPError,
- IncompleteRead,
- InvalidChunkLength,
- InvalidHeader,
- ProtocolError,
- ReadTimeoutError,
- ResponseNotChunked,
- SSLError,
-)
-from .packages import six
-from .util.response import is_fp_closed, is_response_to_head
-
-log = logging.getLogger(__name__)
-
-
-class DeflateDecoder(object):
- def __init__(self):
- self._first_try = True
- self._data = b""
- self._obj = zlib.decompressobj()
-
- def __getattr__(self, name):
- return getattr(self._obj, name)
-
- def decompress(self, data):
- if not data:
- return data
-
- if not self._first_try:
- return self._obj.decompress(data)
-
- self._data += data
- try:
- decompressed = self._obj.decompress(data)
- if decompressed:
- self._first_try = False
- self._data = None
- return decompressed
- except zlib.error:
- self._first_try = False
- self._obj = zlib.decompressobj(-zlib.MAX_WBITS)
- try:
- return self.decompress(self._data)
- finally:
- self._data = None
-
-
-class GzipDecoderState(object):
-
- FIRST_MEMBER = 0
- OTHER_MEMBERS = 1
- SWALLOW_DATA = 2
-
-
-class GzipDecoder(object):
- def __init__(self):
- self._obj = zlib.decompressobj(16 + zlib.MAX_WBITS)
- self._state = GzipDecoderState.FIRST_MEMBER
-
- def __getattr__(self, name):
- return getattr(self._obj, name)
-
- def decompress(self, data):
- ret = bytearray()
- if self._state == GzipDecoderState.SWALLOW_DATA or not data:
- return bytes(ret)
- while True:
- try:
- ret += self._obj.decompress(data)
- except zlib.error:
- previous_state = self._state
- # Ignore data after the first error
- self._state = GzipDecoderState.SWALLOW_DATA
- if previous_state == GzipDecoderState.OTHER_MEMBERS:
- # Allow trailing garbage acceptable in other gzip clients
- return bytes(ret)
- raise
- data = self._obj.unused_data
- if not data:
- return bytes(ret)
- self._state = GzipDecoderState.OTHER_MEMBERS
- self._obj = zlib.decompressobj(16 + zlib.MAX_WBITS)
-
-
-if brotli is not None:
-
- class BrotliDecoder(object):
- # Supports both 'brotlipy' and 'Brotli' packages
- # since they share an import name. The top branches
- # are for 'brotlipy' and bottom branches for 'Brotli'
- def __init__(self):
- self._obj = brotli.Decompressor()
- if hasattr(self._obj, "decompress"):
- self.decompress = self._obj.decompress
- else:
- self.decompress = self._obj.process
-
- def flush(self):
- if hasattr(self._obj, "flush"):
- return self._obj.flush()
- return b""
-
-
-class MultiDecoder(object):
- """
- From RFC7231:
- If one or more encodings have been applied to a representation, the
- sender that applied the encodings MUST generate a Content-Encoding
- header field that lists the content codings in the order in which
- they were applied.
- """
-
- def __init__(self, modes):
- self._decoders = [_get_decoder(m.strip()) for m in modes.split(",")]
-
- def flush(self):
- return self._decoders[0].flush()
-
- def decompress(self, data):
- for d in reversed(self._decoders):
- data = d.decompress(data)
- return data
-
-
-def _get_decoder(mode):
- if "," in mode:
- return MultiDecoder(mode)
-
- if mode == "gzip":
- return GzipDecoder()
-
- if brotli is not None and mode == "br":
- return BrotliDecoder()
-
- return DeflateDecoder()
-
-
-class HTTPResponse(io.IOBase):
- """
- HTTP Response container.
-
- Backwards-compatible with :class:`http.client.HTTPResponse` but the response ``body`` is
- loaded and decoded on-demand when the ``data`` property is accessed. This
- class is also compatible with the Python standard library's :mod:`io`
- module, and can hence be treated as a readable object in the context of that
- framework.
-
- Extra parameters for behaviour not present in :class:`http.client.HTTPResponse`:
-
- :param preload_content:
- If True, the response's body will be preloaded during construction.
-
- :param decode_content:
- If True, will attempt to decode the body based on the
- 'content-encoding' header.
-
- :param original_response:
- When this HTTPResponse wrapper is generated from an :class:`http.client.HTTPResponse`
- object, it's convenient to include the original for debug purposes. It's
- otherwise unused.
-
- :param retries:
- The retries contains the last :class:`~urllib3.util.retry.Retry` that
- was used during the request.
-
- :param enforce_content_length:
- Enforce content length checking. Body returned by server must match
- value of Content-Length header, if present. Otherwise, raise error.
- """
-
- CONTENT_DECODERS = ["gzip", "deflate"]
- if brotli is not None:
- CONTENT_DECODERS += ["br"]
- REDIRECT_STATUSES = [301, 302, 303, 307, 308]
-
- def __init__(
- self,
- body="",
- headers=None,
- status=0,
- version=0,
- reason=None,
- strict=0,
- preload_content=True,
- decode_content=True,
- original_response=None,
- pool=None,
- connection=None,
- msg=None,
- retries=None,
- enforce_content_length=False,
- request_method=None,
- request_url=None,
- auto_close=True,
- ):
-
- if isinstance(headers, HTTPHeaderDict):
- self.headers = headers
- else:
- self.headers = HTTPHeaderDict(headers)
- self.status = status
- self.version = version
- self.reason = reason
- self.strict = strict
- self.decode_content = decode_content
- self.retries = retries
- self.enforce_content_length = enforce_content_length
- self.auto_close = auto_close
-
- self._decoder = None
- self._body = None
- self._fp = None
- self._original_response = original_response
- self._fp_bytes_read = 0
- self.msg = msg
- self._request_url = request_url
-
- if body and isinstance(body, (six.string_types, bytes)):
- self._body = body
-
- self._pool = pool
- self._connection = connection
-
- if hasattr(body, "read"):
- self._fp = body
-
- # Are we using the chunked-style of transfer encoding?
- self.chunked = False
- self.chunk_left = None
- tr_enc = self.headers.get("transfer-encoding", "").lower()
- # Don't incur the penalty of creating a list and then discarding it
- encodings = (enc.strip() for enc in tr_enc.split(","))
- if "chunked" in encodings:
- self.chunked = True
-
- # Determine length of response
- self.length_remaining = self._init_length(request_method)
-
- # If requested, preload the body.
- if preload_content and not self._body:
- self._body = self.read(decode_content=decode_content)
-
- def get_redirect_location(self):
- """
- Should we redirect and where to?
-
- :returns: Truthy redirect location string if we got a redirect status
- code and valid location. ``None`` if redirect status and no
- location. ``False`` if not a redirect status code.
- """
- if self.status in self.REDIRECT_STATUSES:
- return self.headers.get("location")
-
- return False
-
- def release_conn(self):
- if not self._pool or not self._connection:
- return
-
- self._pool._put_conn(self._connection)
- self._connection = None
-
- def drain_conn(self):
- """
- Read and discard any remaining HTTP response data in the response connection.
-
- Unread data in the HTTPResponse connection blocks the connection from being released back to the pool.
- """
- try:
- self.read()
- except (HTTPError, SocketError, BaseSSLError, HTTPException):
- pass
-
- @property
- def data(self):
- # For backwards-compat with earlier urllib3 0.4 and earlier.
- if self._body:
- return self._body
-
- if self._fp:
- return self.read(cache_content=True)
-
- @property
- def connection(self):
- return self._connection
-
- def isclosed(self):
- return is_fp_closed(self._fp)
-
- def tell(self):
- """
- Obtain the number of bytes pulled over the wire so far. May differ from
- the amount of content returned by :meth:``urllib3.response.HTTPResponse.read``
- if bytes are encoded on the wire (e.g, compressed).
- """
- return self._fp_bytes_read
-
- def _init_length(self, request_method):
- """
- Set initial length value for Response content if available.
- """
- length = self.headers.get("content-length")
-
- if length is not None:
- if self.chunked:
- # This Response will fail with an IncompleteRead if it can't be
- # received as chunked. This method falls back to attempt reading
- # the response before raising an exception.
- log.warning(
- "Received response with both Content-Length and "
- "Transfer-Encoding set. This is expressly forbidden "
- "by RFC 7230 sec 3.3.2. Ignoring Content-Length and "
- "attempting to process response as Transfer-Encoding: "
- "chunked."
- )
- return None
-
- try:
- # RFC 7230 section 3.3.2 specifies multiple content lengths can
- # be sent in a single Content-Length header
- # (e.g. Content-Length: 42, 42). This line ensures the values
- # are all valid ints and that as long as the `set` length is 1,
- # all values are the same. Otherwise, the header is invalid.
- lengths = set([int(val) for val in length.split(",")])
- if len(lengths) > 1:
- raise InvalidHeader(
- "Content-Length contained multiple "
- "unmatching values (%s)" % length
- )
- length = lengths.pop()
- except ValueError:
- length = None
- else:
- if length < 0:
- length = None
-
- # Convert status to int for comparison
- # In some cases, httplib returns a status of "_UNKNOWN"
- try:
- status = int(self.status)
- except ValueError:
- status = 0
-
- # Check for responses that shouldn't include a body
- if status in (204, 304) or 100 <= status < 200 or request_method == "HEAD":
- length = 0
-
- return length
-
- def _init_decoder(self):
- """
- Set-up the _decoder attribute if necessary.
- """
- # Note: content-encoding value should be case-insensitive, per RFC 7230
- # Section 3.2
- content_encoding = self.headers.get("content-encoding", "").lower()
- if self._decoder is None:
- if content_encoding in self.CONTENT_DECODERS:
- self._decoder = _get_decoder(content_encoding)
- elif "," in content_encoding:
- encodings = [
- e.strip()
- for e in content_encoding.split(",")
- if e.strip() in self.CONTENT_DECODERS
- ]
- if len(encodings):
- self._decoder = _get_decoder(content_encoding)
-
- DECODER_ERROR_CLASSES = (IOError, zlib.error)
- if brotli is not None:
- DECODER_ERROR_CLASSES += (brotli.error,)
-
- def _decode(self, data, decode_content, flush_decoder):
- """
- Decode the data passed in and potentially flush the decoder.
- """
- if not decode_content:
- return data
-
- try:
- if self._decoder:
- data = self._decoder.decompress(data)
- except self.DECODER_ERROR_CLASSES as e:
- content_encoding = self.headers.get("content-encoding", "").lower()
- raise DecodeError(
- "Received response with content-encoding: %s, but "
- "failed to decode it." % content_encoding,
- e,
- )
- if flush_decoder:
- data += self._flush_decoder()
-
- return data
-
- def _flush_decoder(self):
- """
- Flushes the decoder. Should only be called if the decoder is actually
- being used.
- """
- if self._decoder:
- buf = self._decoder.decompress(b"")
- return buf + self._decoder.flush()
-
- return b""
-
- @contextmanager
- def _error_catcher(self):
- """
- Catch low-level python exceptions, instead re-raising urllib3
- variants, so that low-level exceptions are not leaked in the
- high-level api.
-
- On exit, release the connection back to the pool.
- """
- clean_exit = False
-
- try:
- try:
- yield
-
- except SocketTimeout:
- # FIXME: Ideally we'd like to include the url in the ReadTimeoutError but
- # there is yet no clean way to get at it from this context.
- raise ReadTimeoutError(self._pool, None, "Read timed out.")
-
- except BaseSSLError as e:
- # FIXME: Is there a better way to differentiate between SSLErrors?
- if "read operation timed out" not in str(e):
- # SSL errors related to framing/MAC get wrapped and reraised here
- raise SSLError(e)
-
- raise ReadTimeoutError(self._pool, None, "Read timed out.")
-
- except (HTTPException, SocketError) as e:
- # This includes IncompleteRead.
- raise ProtocolError("Connection broken: %r" % e, e)
-
- # If no exception is thrown, we should avoid cleaning up
- # unnecessarily.
- clean_exit = True
- finally:
- # If we didn't terminate cleanly, we need to throw away our
- # connection.
- if not clean_exit:
- # The response may not be closed but we're not going to use it
- # anymore so close it now to ensure that the connection is
- # released back to the pool.
- if self._original_response:
- self._original_response.close()
-
- # Closing the response may not actually be sufficient to close
- # everything, so if we have a hold of the connection close that
- # too.
- if self._connection:
- self._connection.close()
-
- # If we hold the original response but it's closed now, we should
- # return the connection back to the pool.
- if self._original_response and self._original_response.isclosed():
- self.release_conn()
-
- def read(self, amt=None, decode_content=None, cache_content=False):
- """
- Similar to :meth:`http.client.HTTPResponse.read`, but with two additional
- parameters: ``decode_content`` and ``cache_content``.
-
- :param amt:
- How much of the content to read. If specified, caching is skipped
- because it doesn't make sense to cache partial content as the full
- response.
-
- :param decode_content:
- If True, will attempt to decode the body based on the
- 'content-encoding' header.
-
- :param cache_content:
- If True, will save the returned data such that the same result is
- returned despite of the state of the underlying file object. This
- is useful if you want the ``.data`` property to continue working
- after having ``.read()`` the file object. (Overridden if ``amt`` is
- set.)
- """
- self._init_decoder()
- if decode_content is None:
- decode_content = self.decode_content
-
- if self._fp is None:
- return
-
- flush_decoder = False
- fp_closed = getattr(self._fp, "closed", False)
-
- with self._error_catcher():
- if amt is None:
- # cStringIO doesn't like amt=None
- data = self._fp.read() if not fp_closed else b""
- flush_decoder = True
- else:
- cache_content = False
- data = self._fp.read(amt) if not fp_closed else b""
- if (
- amt != 0 and not data
- ): # Platform-specific: Buggy versions of Python.
- # Close the connection when no data is returned
- #
- # This is redundant to what httplib/http.client _should_
- # already do. However, versions of python released before
- # December 15, 2012 (http://bugs.python.org/issue16298) do
- # not properly close the connection in all cases. There is
- # no harm in redundantly calling close.
- self._fp.close()
- flush_decoder = True
- if self.enforce_content_length and self.length_remaining not in (
- 0,
- None,
- ):
- # This is an edge case that httplib failed to cover due
- # to concerns of backward compatibility. We're
- # addressing it here to make sure IncompleteRead is
- # raised during streaming, so all calls with incorrect
- # Content-Length are caught.
- raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
-
- if data:
- self._fp_bytes_read += len(data)
- if self.length_remaining is not None:
- self.length_remaining -= len(data)
-
- data = self._decode(data, decode_content, flush_decoder)
-
- if cache_content:
- self._body = data
-
- return data
-
- def stream(self, amt=2 ** 16, decode_content=None):
- """
- A generator wrapper for the read() method. A call will block until
- ``amt`` bytes have been read from the connection or until the
- connection is closed.
-
- :param amt:
- How much of the content to read. The generator will return up to
- much data per iteration, but may return less. This is particularly
- likely when using compressed data. However, the empty string will
- never be returned.
-
- :param decode_content:
- If True, will attempt to decode the body based on the
- 'content-encoding' header.
- """
- if self.chunked and self.supports_chunked_reads():
- for line in self.read_chunked(amt, decode_content=decode_content):
- yield line
- else:
- while not is_fp_closed(self._fp):
- data = self.read(amt=amt, decode_content=decode_content)
-
- if data:
- yield data
-
- @classmethod
- def from_httplib(ResponseCls, r, **response_kw):
- """
- Given an :class:`http.client.HTTPResponse` instance ``r``, return a
- corresponding :class:`urllib3.response.HTTPResponse` object.
-
- Remaining parameters are passed to the HTTPResponse constructor, along
- with ``original_response=r``.
- """
- headers = r.msg
-
- if not isinstance(headers, HTTPHeaderDict):
- if six.PY2:
- # Python 2.7
- headers = HTTPHeaderDict.from_httplib(headers)
- else:
- headers = HTTPHeaderDict(headers.items())
-
- # HTTPResponse objects in Python 3 don't have a .strict attribute
- strict = getattr(r, "strict", 0)
- resp = ResponseCls(
- body=r,
- headers=headers,
- status=r.status,
- version=r.version,
- reason=r.reason,
- strict=strict,
- original_response=r,
- **response_kw
- )
- return resp
-
- # Backwards-compatibility methods for http.client.HTTPResponse
- def getheaders(self):
- return self.headers
-
- def getheader(self, name, default=None):
- return self.headers.get(name, default)
-
- # Backwards compatibility for http.cookiejar
- def info(self):
- return self.headers
-
- # Overrides from io.IOBase
- def close(self):
- if not self.closed:
- self._fp.close()
-
- if self._connection:
- self._connection.close()
-
- if not self.auto_close:
- io.IOBase.close(self)
-
- @property
- def closed(self):
- if not self.auto_close:
- return io.IOBase.closed.__get__(self)
- elif self._fp is None:
- return True
- elif hasattr(self._fp, "isclosed"):
- return self._fp.isclosed()
- elif hasattr(self._fp, "closed"):
- return self._fp.closed
- else:
- return True
-
- def fileno(self):
- if self._fp is None:
- raise IOError("HTTPResponse has no file to get a fileno from")
- elif hasattr(self._fp, "fileno"):
- return self._fp.fileno()
- else:
- raise IOError(
- "The file-like object this HTTPResponse is wrapped "
- "around has no file descriptor"
- )
-
- def flush(self):
- if (
- self._fp is not None
- and hasattr(self._fp, "flush")
- and not getattr(self._fp, "closed", False)
- ):
- return self._fp.flush()
-
- def readable(self):
- # This method is required for `io` module compatibility.
- return True
-
- def readinto(self, b):
- # This method is required for `io` module compatibility.
- temp = self.read(len(b))
- if len(temp) == 0:
- return 0
- else:
- b[: len(temp)] = temp
- return len(temp)
-
- def supports_chunked_reads(self):
- """
- Checks if the underlying file-like object looks like a
- :class:`http.client.HTTPResponse` object. We do this by testing for
- the fp attribute. If it is present we assume it returns raw chunks as
- processed by read_chunked().
- """
- return hasattr(self._fp, "fp")
-
- def _update_chunk_length(self):
- # First, we'll figure out length of a chunk and then
- # we'll try to read it from socket.
- if self.chunk_left is not None:
- return
- line = self._fp.fp.readline()
- line = line.split(b";", 1)[0]
- try:
- self.chunk_left = int(line, 16)
- except ValueError:
- # Invalid chunked protocol response, abort.
- self.close()
- raise InvalidChunkLength(self, line)
-
- def _handle_chunk(self, amt):
- returned_chunk = None
- if amt is None:
- chunk = self._fp._safe_read(self.chunk_left)
- returned_chunk = chunk
- self._fp._safe_read(2) # Toss the CRLF at the end of the chunk.
- self.chunk_left = None
- elif amt < self.chunk_left:
- value = self._fp._safe_read(amt)
- self.chunk_left = self.chunk_left - amt
- returned_chunk = value
- elif amt == self.chunk_left:
- value = self._fp._safe_read(amt)
- self._fp._safe_read(2) # Toss the CRLF at the end of the chunk.
- self.chunk_left = None
- returned_chunk = value
- else: # amt > self.chunk_left
- returned_chunk = self._fp._safe_read(self.chunk_left)
- self._fp._safe_read(2) # Toss the CRLF at the end of the chunk.
- self.chunk_left = None
- return returned_chunk
-
- def read_chunked(self, amt=None, decode_content=None):
- """
- Similar to :meth:`HTTPResponse.read`, but with an additional
- parameter: ``decode_content``.
-
- :param amt:
- How much of the content to read. If specified, caching is skipped
- because it doesn't make sense to cache partial content as the full
- response.
-
- :param decode_content:
- If True, will attempt to decode the body based on the
- 'content-encoding' header.
- """
- self._init_decoder()
- # FIXME: Rewrite this method and make it a class with a better structured logic.
- if not self.chunked:
- raise ResponseNotChunked(
- "Response is not chunked. "
- "Header 'transfer-encoding: chunked' is missing."
- )
- if not self.supports_chunked_reads():
- raise BodyNotHttplibCompatible(
- "Body should be http.client.HTTPResponse like. "
- "It should have have an fp attribute which returns raw chunks."
- )
-
- with self._error_catcher():
- # Don't bother reading the body of a HEAD request.
- if self._original_response and is_response_to_head(self._original_response):
- self._original_response.close()
- return
-
- # If a response is already read and closed
- # then return immediately.
- if self._fp.fp is None:
- return
-
- while True:
- self._update_chunk_length()
- if self.chunk_left == 0:
- break
- chunk = self._handle_chunk(amt)
- decoded = self._decode(
- chunk, decode_content=decode_content, flush_decoder=False
- )
- if decoded:
- yield decoded
-
- if decode_content:
- # On CPython and PyPy, we should never need to flush the
- # decoder. However, on Jython we *might* need to, so
- # lets defensively do it anyway.
- decoded = self._flush_decoder()
- if decoded: # Platform-specific: Jython.
- yield decoded
-
- # Chunk content ends with \r\n: discard it.
- while True:
- line = self._fp.fp.readline()
- if not line:
- # Some sites may not end with '\r\n'.
- break
- if line == b"\r\n":
- break
-
- # We read everything; close the "file".
- if self._original_response:
- self._original_response.close()
-
- def geturl(self):
- """
- Returns the URL that was the source of this response.
- If the request that generated this response redirected, this method
- will return the final redirect location.
- """
- if self.retries is not None and len(self.retries.history):
- return self.retries.history[-1].redirect_location
- else:
- return self._request_url
-
- def __iter__(self):
- buffer = []
- for chunk in self.stream(decode_content=True):
- if b"\n" in chunk:
- chunk = chunk.split(b"\n")
- yield b"".join(buffer) + chunk[0] + b"\n"
- for x in chunk[1:-1]:
- yield x + b"\n"
- if chunk[-1]:
- buffer = [chunk[-1]]
- else:
- buffer = []
- else:
- buffer.append(chunk)
- if buffer:
- yield b"".join(buffer)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/command/check.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/command/check.py
deleted file mode 100644
index 176a8b876469892029e4e0a6261aebed13311ca2..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/command/check.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""distutils.command.check
-
-Implements the Distutils 'check' command.
-"""
-from email.utils import getaddresses
-
-from distutils.core import Command
-from distutils.errors import DistutilsSetupError
-
-try:
- # docutils is installed
- from docutils.utils import Reporter
- from docutils.parsers.rst import Parser
- from docutils import frontend
- from docutils import nodes
-
- class SilentReporter(Reporter):
- def __init__(
- self,
- source,
- report_level,
- halt_level,
- stream=None,
- debug=0,
- encoding='ascii',
- error_handler='replace',
- ):
- self.messages = []
- super().__init__(
- source, report_level, halt_level, stream, debug, encoding, error_handler
- )
-
- def system_message(self, level, message, *children, **kwargs):
- self.messages.append((level, message, children, kwargs))
- return nodes.system_message(
- message, level=level, type=self.levels[level], *children, **kwargs
- )
-
- HAS_DOCUTILS = True
-except Exception:
- # Catch all exceptions because exceptions besides ImportError probably
- # indicate that docutils is not ported to Py3k.
- HAS_DOCUTILS = False
-
-
-class check(Command):
- """This command checks the meta-data of the package."""
-
- description = "perform some checks on the package"
- user_options = [
- ('metadata', 'm', 'Verify meta-data'),
- (
- 'restructuredtext',
- 'r',
- (
- 'Checks if long string meta-data syntax '
- 'are reStructuredText-compliant'
- ),
- ),
- ('strict', 's', 'Will exit with an error if a check fails'),
- ]
-
- boolean_options = ['metadata', 'restructuredtext', 'strict']
-
- def initialize_options(self):
- """Sets default values for options."""
- self.restructuredtext = 0
- self.metadata = 1
- self.strict = 0
- self._warnings = 0
-
- def finalize_options(self):
- pass
-
- def warn(self, msg):
- """Counts the number of warnings that occurs."""
- self._warnings += 1
- return Command.warn(self, msg)
-
- def run(self):
- """Runs the command."""
- # perform the various tests
- if self.metadata:
- self.check_metadata()
- if self.restructuredtext:
- if HAS_DOCUTILS:
- self.check_restructuredtext()
- elif self.strict:
- raise DistutilsSetupError('The docutils package is needed.')
-
- # let's raise an error in strict mode, if we have at least
- # one warning
- if self.strict and self._warnings > 0:
- raise DistutilsSetupError('Please correct your package.')
-
- def check_metadata(self):
- """Ensures that all required elements of meta-data are supplied.
-
- Required fields:
- name, version
-
- Warns if any are missing.
- """
- metadata = self.distribution.metadata
-
- missing = []
- for attr in 'name', 'version':
- if not getattr(metadata, attr, None):
- missing.append(attr)
-
- if missing:
- self.warn("missing required meta-data: %s" % ', '.join(missing))
-
- def check_restructuredtext(self):
- """Checks if the long string fields are reST-compliant."""
- data = self.distribution.get_long_description()
- for warning in self._check_rst_data(data):
- line = warning[-1].get('line')
- if line is None:
- warning = warning[1]
- else:
- warning = '%s (line %s)' % (warning[1], line)
- self.warn(warning)
-
- def _check_rst_data(self, data):
- """Returns warnings when the provided data doesn't compile."""
- # the include and csv_table directives need this to be a path
- source_path = self.distribution.script_name or 'setup.py'
- parser = Parser()
- settings = frontend.OptionParser(components=(Parser,)).get_default_values()
- settings.tab_width = 4
- settings.pep_references = None
- settings.rfc_references = None
- reporter = SilentReporter(
- source_path,
- settings.report_level,
- settings.halt_level,
- stream=settings.warning_stream,
- debug=settings.debug,
- encoding=settings.error_encoding,
- error_handler=settings.error_encoding_error_handler,
- )
-
- document = nodes.document(settings, reporter, source=source_path)
- document.note_source(source_path, -1)
- try:
- parser.parse(data, document)
- except AttributeError as e:
- reporter.messages.append(
- (-1, 'Could not finish the parsing: %s.' % e, '', {})
- )
-
- return reporter.messages
diff --git a/spaces/tmaham/DS-Fusion-Express/ldm/data/imagenet.py b/spaces/tmaham/DS-Fusion-Express/ldm/data/imagenet.py
deleted file mode 100644
index 1c473f9c6965b22315dbb289eff8247c71bdc790..0000000000000000000000000000000000000000
--- a/spaces/tmaham/DS-Fusion-Express/ldm/data/imagenet.py
+++ /dev/null
@@ -1,394 +0,0 @@
-import os, yaml, pickle, shutil, tarfile, glob
-import cv2
-import albumentations
-import PIL
-import numpy as np
-import torchvision.transforms.functional as TF
-from omegaconf import OmegaConf
-from functools import partial
-from PIL import Image
-from tqdm import tqdm
-from torch.utils.data import Dataset, Subset
-
-import taming.data.utils as tdu
-from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
-from taming.data.imagenet import ImagePaths
-
-from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
-
-
-def synset2idx(path_to_yaml="data/index_synset.yaml"):
- with open(path_to_yaml) as f:
- di2s = yaml.load(f)
- return dict((v,k) for k,v in di2s.items())
-
-
-class ImageNetBase(Dataset):
- def __init__(self, config=None):
- self.config = config or OmegaConf.create()
- if not type(self.config)==dict:
- self.config = OmegaConf.to_container(self.config)
- self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
- self.process_images = True # if False we skip loading & processing images and self.data contains filepaths
- self._prepare()
- self._prepare_synset_to_human()
- self._prepare_idx_to_synset()
- self._prepare_human_to_integer_label()
- self._load()
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, i):
- return self.data[i]
-
- def _prepare(self):
- raise NotImplementedError()
-
- def _filter_relpaths(self, relpaths):
- ignore = set([
- "n06596364_9591.JPEG",
- ])
- relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
- if "sub_indices" in self.config:
- indices = str_to_indices(self.config["sub_indices"])
- synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings
- self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
- files = []
- for rpath in relpaths:
- syn = rpath.split("/")[0]
- if syn in synsets:
- files.append(rpath)
- return files
- else:
- return relpaths
-
- def _prepare_synset_to_human(self):
- SIZE = 2655750
- URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
- self.human_dict = os.path.join(self.root, "synset_human.txt")
- if (not os.path.exists(self.human_dict) or
- not os.path.getsize(self.human_dict)==SIZE):
- download(URL, self.human_dict)
-
- def _prepare_idx_to_synset(self):
- URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
- self.idx2syn = os.path.join(self.root, "index_synset.yaml")
- if (not os.path.exists(self.idx2syn)):
- download(URL, self.idx2syn)
-
- def _prepare_human_to_integer_label(self):
- URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
- self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
- if (not os.path.exists(self.human2integer)):
- download(URL, self.human2integer)
- with open(self.human2integer, "r") as f:
- lines = f.read().splitlines()
- assert len(lines) == 1000
- self.human2integer_dict = dict()
- for line in lines:
- value, key = line.split(":")
- self.human2integer_dict[key] = int(value)
-
- def _load(self):
- with open(self.txt_filelist, "r") as f:
- self.relpaths = f.read().splitlines()
- l1 = len(self.relpaths)
- self.relpaths = self._filter_relpaths(self.relpaths)
- print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
-
- self.synsets = [p.split("/")[0] for p in self.relpaths]
- self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
-
- unique_synsets = np.unique(self.synsets)
- class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
- if not self.keep_orig_class_label:
- self.class_labels = [class_dict[s] for s in self.synsets]
- else:
- self.class_labels = [self.synset2idx[s] for s in self.synsets]
-
- with open(self.human_dict, "r") as f:
- human_dict = f.read().splitlines()
- human_dict = dict(line.split(maxsplit=1) for line in human_dict)
-
- self.human_labels = [human_dict[s] for s in self.synsets]
-
- labels = {
- "relpath": np.array(self.relpaths),
- "synsets": np.array(self.synsets),
- "class_label": np.array(self.class_labels),
- "human_label": np.array(self.human_labels),
- }
-
- if self.process_images:
- self.size = retrieve(self.config, "size", default=256)
- self.data = ImagePaths(self.abspaths,
- labels=labels,
- size=self.size,
- random_crop=self.random_crop,
- )
- else:
- self.data = self.abspaths
-
-
-class ImageNetTrain(ImageNetBase):
- NAME = "ILSVRC2012_train"
- URL = "http://www.image-net.org/challenges/LSVRC/2012/"
- AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
- FILES = [
- "ILSVRC2012_img_train.tar",
- ]
- SIZES = [
- 147897477120,
- ]
-
- def __init__(self, process_images=True, data_root=None, **kwargs):
- self.process_images = process_images
- self.data_root = data_root
- super().__init__(**kwargs)
-
- def _prepare(self):
- if self.data_root:
- self.root = os.path.join(self.data_root, self.NAME)
- else:
- cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
- self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-
- self.datadir = os.path.join(self.root, "data")
- self.txt_filelist = os.path.join(self.root, "filelist.txt")
- self.expected_length = 1281167
- self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
- default=True)
- if not tdu.is_prepared(self.root):
- # prep
- print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
- datadir = self.datadir
- if not os.path.exists(datadir):
- path = os.path.join(self.root, self.FILES[0])
- if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
- import academictorrents as at
- atpath = at.get(self.AT_HASH, datastore=self.root)
- assert atpath == path
-
- print("Extracting {} to {}".format(path, datadir))
- os.makedirs(datadir, exist_ok=True)
- with tarfile.open(path, "r:") as tar:
- tar.extractall(path=datadir)
-
- print("Extracting sub-tars.")
- subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
- for subpath in tqdm(subpaths):
- subdir = subpath[:-len(".tar")]
- os.makedirs(subdir, exist_ok=True)
- with tarfile.open(subpath, "r:") as tar:
- tar.extractall(path=subdir)
-
- filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
- filelist = [os.path.relpath(p, start=datadir) for p in filelist]
- filelist = sorted(filelist)
- filelist = "\n".join(filelist)+"\n"
- with open(self.txt_filelist, "w") as f:
- f.write(filelist)
-
- tdu.mark_prepared(self.root)
-
-
-class ImageNetValidation(ImageNetBase):
- NAME = "ILSVRC2012_validation"
- URL = "http://www.image-net.org/challenges/LSVRC/2012/"
- AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
- VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
- FILES = [
- "ILSVRC2012_img_val.tar",
- "validation_synset.txt",
- ]
- SIZES = [
- 6744924160,
- 1950000,
- ]
-
- def __init__(self, process_images=True, data_root=None, **kwargs):
- self.data_root = data_root
- self.process_images = process_images
- super().__init__(**kwargs)
-
- def _prepare(self):
- if self.data_root:
- self.root = os.path.join(self.data_root, self.NAME)
- else:
- cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
- self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
- self.datadir = os.path.join(self.root, "data")
- self.txt_filelist = os.path.join(self.root, "filelist.txt")
- self.expected_length = 50000
- self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
- default=False)
- if not tdu.is_prepared(self.root):
- # prep
- print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
- datadir = self.datadir
- if not os.path.exists(datadir):
- path = os.path.join(self.root, self.FILES[0])
- if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
- import academictorrents as at
- atpath = at.get(self.AT_HASH, datastore=self.root)
- assert atpath == path
-
- print("Extracting {} to {}".format(path, datadir))
- os.makedirs(datadir, exist_ok=True)
- with tarfile.open(path, "r:") as tar:
- tar.extractall(path=datadir)
-
- vspath = os.path.join(self.root, self.FILES[1])
- if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
- download(self.VS_URL, vspath)
-
- with open(vspath, "r") as f:
- synset_dict = f.read().splitlines()
- synset_dict = dict(line.split() for line in synset_dict)
-
- print("Reorganizing into synset folders")
- synsets = np.unique(list(synset_dict.values()))
- for s in synsets:
- os.makedirs(os.path.join(datadir, s), exist_ok=True)
- for k, v in synset_dict.items():
- src = os.path.join(datadir, k)
- dst = os.path.join(datadir, v)
- shutil.move(src, dst)
-
- filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
- filelist = [os.path.relpath(p, start=datadir) for p in filelist]
- filelist = sorted(filelist)
- filelist = "\n".join(filelist)+"\n"
- with open(self.txt_filelist, "w") as f:
- f.write(filelist)
-
- tdu.mark_prepared(self.root)
-
-
-
-class ImageNetSR(Dataset):
- def __init__(self, size=None,
- degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
- random_crop=True):
- """
- Imagenet Superresolution Dataloader
- Performs following ops in order:
- 1. crops a crop of size s from image either as random or center crop
- 2. resizes crop to size with cv2.area_interpolation
- 3. degrades resized crop with degradation_fn
-
- :param size: resizing to size after cropping
- :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
- :param downscale_f: Low Resolution Downsample factor
- :param min_crop_f: determines crop size s,
- where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
- :param max_crop_f: ""
- :param data_root:
- :param random_crop:
- """
- self.base = self.get_base()
- assert size
- assert (size / downscale_f).is_integer()
- self.size = size
- self.LR_size = int(size / downscale_f)
- self.min_crop_f = min_crop_f
- self.max_crop_f = max_crop_f
- assert(max_crop_f <= 1.)
- self.center_crop = not random_crop
-
- self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
-
- self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
-
- if degradation == "bsrgan":
- self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
-
- elif degradation == "bsrgan_light":
- self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
-
- else:
- interpolation_fn = {
- "cv_nearest": cv2.INTER_NEAREST,
- "cv_bilinear": cv2.INTER_LINEAR,
- "cv_bicubic": cv2.INTER_CUBIC,
- "cv_area": cv2.INTER_AREA,
- "cv_lanczos": cv2.INTER_LANCZOS4,
- "pil_nearest": PIL.Image.NEAREST,
- "pil_bilinear": PIL.Image.BILINEAR,
- "pil_bicubic": PIL.Image.BICUBIC,
- "pil_box": PIL.Image.BOX,
- "pil_hamming": PIL.Image.HAMMING,
- "pil_lanczos": PIL.Image.LANCZOS,
- }[degradation]
-
- self.pil_interpolation = degradation.startswith("pil_")
-
- if self.pil_interpolation:
- self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
-
- else:
- self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
- interpolation=interpolation_fn)
-
- def __len__(self):
- return len(self.base)
-
- def __getitem__(self, i):
- example = self.base[i]
- image = Image.open(example["file_path_"])
-
- if not image.mode == "RGB":
- image = image.convert("RGB")
-
- image = np.array(image).astype(np.uint8)
-
- min_side_len = min(image.shape[:2])
- crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
- crop_side_len = int(crop_side_len)
-
- if self.center_crop:
- self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
-
- else:
- self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
-
- image = self.cropper(image=image)["image"]
- image = self.image_rescaler(image=image)["image"]
-
- if self.pil_interpolation:
- image_pil = PIL.Image.fromarray(image)
- LR_image = self.degradation_process(image_pil)
- LR_image = np.array(LR_image).astype(np.uint8)
-
- else:
- LR_image = self.degradation_process(image=image)["image"]
-
- example["image"] = (image/127.5 - 1.0).astype(np.float32)
- example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
-
- return example
-
-
-class ImageNetSRTrain(ImageNetSR):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- def get_base(self):
- with open("data/imagenet_train_hr_indices.p", "rb") as f:
- indices = pickle.load(f)
- dset = ImageNetTrain(process_images=False,)
- return Subset(dset, indices)
-
-
-class ImageNetSRValidation(ImageNetSR):
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
-
- def get_base(self):
- with open("data/imagenet_val_hr_indices.p", "rb") as f:
- indices = pickle.load(f)
- dset = ImageNetValidation(process_images=False,)
- return Subset(dset, indices)
diff --git a/spaces/tomofi/MMOCR/configs/textrecog/satrn/satrn_academic.py b/spaces/tomofi/MMOCR/configs/textrecog/satrn/satrn_academic.py
deleted file mode 100644
index 00a664e2093f4b4c5cbf77708813c66761428814..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/textrecog/satrn/satrn_academic.py
+++ /dev/null
@@ -1,68 +0,0 @@
-_base_ = [
- '../../_base_/default_runtime.py',
- '../../_base_/recog_pipelines/satrn_pipeline.py',
- '../../_base_/recog_datasets/ST_MJ_train.py',
- '../../_base_/recog_datasets/academic_test.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-label_convertor = dict(
- type='AttnConvertor', dict_type='DICT90', with_unknown=True)
-
-model = dict(
- type='SATRN',
- backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=512),
- encoder=dict(
- type='SatrnEncoder',
- n_layers=12,
- n_head=8,
- d_k=512 // 8,
- d_v=512 // 8,
- d_model=512,
- n_position=100,
- d_inner=512 * 4,
- dropout=0.1),
- decoder=dict(
- type='NRTRDecoder',
- n_layers=6,
- d_embedding=512,
- n_head=8,
- d_model=512,
- d_inner=512 * 4,
- d_k=512 // 8,
- d_v=512 // 8),
- loss=dict(type='TFLoss'),
- label_convertor=label_convertor,
- max_seq_len=25)
-
-# optimizer
-optimizer = dict(type='Adam', lr=3e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[3, 4])
-total_epochs = 6
-
-data = dict(
- samples_per_gpu=64,
- workers_per_gpu=4,
- val_dataloader=dict(samples_per_gpu=1),
- test_dataloader=dict(samples_per_gpu=1),
- train=dict(
- type='UniformConcatDataset',
- datasets=train_list,
- pipeline=train_pipeline),
- val=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline),
- test=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
diff --git a/spaces/tomofi/MMOCR/docs/en/changelog.md b/spaces/tomofi/MMOCR/docs/en/changelog.md
deleted file mode 100644
index 4a38ba43ece046fe3f8379a888e8a855347ab599..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/docs/en/changelog.md
+++ /dev/null
@@ -1,377 +0,0 @@
-# Changelog
-
-## v0.4.1 (27/01/2022)
-
-### Highlights
-
-1. Visualizing edge weights in OpenSet KIE is now supported! https://github.com/open-mmlab/mmocr/pull/677
-2. Some configurations have been optimized to significantly speed up the training and testing processes! Don't worry - you can still tune these parameters in case these modifications do not work. https://github.com/open-mmlab/mmocr/pull/757
-3. Now you can use CPU to train/debug your model! https://github.com/open-mmlab/mmocr/pull/752
-4. We have fixed a severe bug that causes users unable to call `mmocr.apis.test` with our pre-built wheels. https://github.com/open-mmlab/mmocr/pull/667
-
-### New Features & Enhancements
-
-* Show edge score for openset kie by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/677
-* Download flake8 from github as pre-commit hooks by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/695
-* Deprecate the support for 'python setup.py test' by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/722
-* Disable multi-processing feature of cv2 to speed up data loading by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/721
-* Extend ctw1500 converter to support text fields by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/729
-* Extend totaltext converter to support text fields by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/728
-* Speed up training by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/739
-* Add setup multi-processing both in train and test.py by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/757
-* Support CPU training/testing by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/752
-* Support specify gpu for testing and training with gpu-id instead of gpu-ids and gpus by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/756
-* Remove unnecessary custom_import from test.py by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/758
-
-### Bug Fixes
-
-* Fix satrn onnxruntime test by @AllentDan in https://github.com/open-mmlab/mmocr/pull/679
-* Support both ConcatDataset and UniformConcatDataset by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/675
-* Fix bugs of show_results in single_gpu_test by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/667
-* Fix a bug for sar decoder when bi-rnn is used by @MhLiao in https://github.com/open-mmlab/mmocr/pull/690
-* Fix opencv version to avoid some bugs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/694
-* Fix py39 ci error by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/707
-* Update visualize.py by @TommyZihao in https://github.com/open-mmlab/mmocr/pull/715
-* Fix link of config by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/726
-* Use yaml.safe_load instead of load by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/753
-* Add necessary keys to test_pipelines to enable test-time visualization by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/754
-
-### Docs
-
-* Fix recog.md by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/674
-* Add config tutorial by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/683
-* Add MMSelfSup/MMRazor/MMDeploy in readme by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/692
-* Add recog & det model summary by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/693
-* Update docs link by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/710
-* add pull request template.md by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/711
-* Add website links to readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/731
-* update readme according to standard by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/742
-
-### New Contributors
-
-* @MhLiao made their first contribution in https://github.com/open-mmlab/mmocr/pull/690
-* @TommyZihao made their first contribution in https://github.com/open-mmlab/mmocr/pull/715
-
-**Full Changelog**: https://github.com/open-mmlab/mmocr/compare/v0.4.0...v0.4.1
-
-## v0.4.0 (15/12/2021)
-
-### Highlights
-
-1. We release a new text recognition model - [ABINet](https://arxiv.org/pdf/2103.06495.pdf) (CVPR 2021, Oral). With it dedicated model design and useful data augmentation transforms, ABINet can achieve the best performance on irregular text recognition tasks. [Check it out!](https://mmocr.readthedocs.io/en/latest/textrecog_models.html#read-like-humans-autonomous-bidirectional-and-iterative-language-modeling-for-scene-text-recognition)
-2. We are also working hard to fulfill the requests from our community.
-[OpenSet KIE](https://mmocr.readthedocs.io/en/latest/kie_models.html#wildreceiptopenset) is one of the achievement, which extends the application of SDMGR from text node classification to node-pair relation extraction. We also provide
-a demo script to convert WildReceipt to open set domain, though it cannot
-take the full advantage of OpenSet format. For more information, please read our
-[tutorial](https://mmocr.readthedocs.io/en/latest/tutorials/kie_closeset_openset.html).
-3. APIs of models can be exposed through TorchServe. [Docs](https://mmocr.readthedocs.io/en/latest/model_serving.html)
-
-### Breaking Changes & Migration Guide
-
-#### Postprocessor
-
-Some refactoring processes are still going on. For all text detection models, we unified their `decode` implementations into a new module category, `POSTPROCESSOR`, which is responsible for decoding different raw outputs into boundary instances. In all text detection configs, the `text_repr_type` argument in `bbox_head` is deprecated and will be removed in the future release.
-
-**Migration Guide**: Find a similar line from detection model's config:
-```
-text_repr_type=xxx,
-```
-And replace it with
-```
-postprocessor=dict(type='{MODEL_NAME}Postprocessor', text_repr_type=xxx)),
-```
-Take a snippet of PANet's config as an example. Before the change, its config for `bbox_head` looks like:
-```
- bbox_head=dict(
- type='PANHead',
- text_repr_type='poly',
- in_channels=[128, 128, 128, 128],
- out_channels=6,
- loss=dict(type='PANLoss')),
-```
-Afterwards:
-```
- bbox_head=dict(
- type='PANHead',
- in_channels=[128, 128, 128, 128],
- out_channels=6,
- loss=dict(type='PANLoss'),
- postprocessor=dict(type='PANPostprocessor', text_repr_type='poly')),
-```
-There are other postprocessors and each takes different arguments. Interested users can find their interfaces or implementations in `mmocr/models/textdet/postprocess` or through our [api docs](https://mmocr.readthedocs.io/en/latest/api.html#textdet-postprocess).
-
-#### New Config Structure
-
-We reorganized the `configs/` directory by extracting reusable sections into `configs/_base_`. Now the directory tree of `configs/_base_` is organized as follows:
-
-```
-_base_
-├── det_datasets
-├── det_models
-├── det_pipelines
-├── recog_datasets
-├── recog_models
-├── recog_pipelines
-└── schedules
-```
-
-Most of model configs are making full use of base configs now, which makes the overall structural clearer and facilitates fair
-comparison across models. Despite the seemingly significant hierarchical difference, **these changes would not break the backward compatibility** as the names of model configs remain the same.
-
-### New Features
-* Support openset kie by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/498
-* Add converter for the Open Images v5 text annotations by Krylov et al. by @baudm in https://github.com/open-mmlab/mmocr/pull/497
-* Support Chinese for kie show result by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/464
-* Add TorchServe support for text detection and recognition by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/522
-* Save filename in text detection test results by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/570
-* Add codespell pre-commit hook and fix typos by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/520
-* Avoid duplicate placeholder docs in CN by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/582
-* Save results to json file for kie. by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/589
-* Add SAR_CN to ocr.py by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/579
-* mim extension for windows by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/641
-* Support muitiple pipelines for different datasets by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/657
-* ABINet Framework by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/651
-
-### Refactoring
-* Refactor textrecog config structure by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/617
-* Refactor text detection config by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/626
-* refactor transformer modules by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/618
-* refactor textdet postprocess by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/640
-
-### Docs
-* C++ example section by @apiaccess21 in https://github.com/open-mmlab/mmocr/pull/593
-* install.md Chinese section by @A465539338 in https://github.com/open-mmlab/mmocr/pull/364
-* Add Chinese Translation of deployment.md. by @fatfishZhao in https://github.com/open-mmlab/mmocr/pull/506
-* Fix a model link and add the metafile for SATRN by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/473
-* Improve docs style by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/474
-* Enhancement & sync Chinese docs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/492
-* TorchServe docs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/539
-* Update docs menu by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/564
-* Docs for KIE CloseSet & OpenSet by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/573
-* Fix broken links by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/576
-* Docstring for text recognition models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/562
-* Add MMFlow & MIM by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/597
-* Add MMFewShot by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/621
-* Update model readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/604
-* Add input size check to model_inference by @mpena-vina in https://github.com/open-mmlab/mmocr/pull/633
-* Docstring for textdet models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/561
-* Add MMHuman3D in readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/644
-* Use shared menu from theme instead by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/655
-* Refactor docs structure by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/662
-* Docs fix by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/664
-
-### Enhancements
-* Use bounding box around polygon instead of within polygon by @alexander-soare in https://github.com/open-mmlab/mmocr/pull/469
-* Add CITATION.cff by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/476
-* Add py3.9 CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/475
-* update model-index.yml by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/484
-* Use container in CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/502
-* CircleCI Setup by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/611
-* Remove unnecessary custom_import from train.py by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/603
-* Change the upper version of mmcv to 1.5.0 by @zhouzaida in https://github.com/open-mmlab/mmocr/pull/628
-* Update CircleCI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/631
-* Pass custom_hooks to MMCV by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/609
-* Skip CI when some specific files were changed by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/642
-* Add markdown linter in pre-commit hook by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/643
-* Use shape from loaded image by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/652
-* Cancel previous runs that are not completed by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/666
-
-### Bug Fixes
-* Modify algorithm "sar" weights path in metafile by @ShoupingShan in https://github.com/open-mmlab/mmocr/pull/581
-* Fix Cuda CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/472
-* Fix image export in test.py for KIE models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/486
-* Allow invalid polygons in intersection and union by default by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/471
-* Update checkpoints' links for SATRN by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/518
-* Fix converting to onnx bug because of changing key from img_shape to resize_shape by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/523
-* Fix PyTorch 1.6 incompatible checkpoints by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/540
-* Fix paper field in metafiles by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/550
-* Unify recognition task names in metafiles by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/548
-* Fix py3.9 CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/563
-* Always map location to cpu when loading checkpoint by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/567
-* Fix wrong model builder in recog_test_imgs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/574
-* Improve dbnet r50 by fixing img std by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/578
-* Fix resource warning: unclosed file by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/577
-* Fix bug that same start_point for different texts in draw_texts_by_pil by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/587
-* Keep original texts for kie by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/588
-* Fix random seed by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/600
-* Fix DBNet_r50 config by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/625
-* Change SBC case to DBC case by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/632
-* Fix kie demo by @innerlee in https://github.com/open-mmlab/mmocr/pull/610
-* fix type check by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/650
-* Remove depreciated image validator in totaltext converter by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/661
-* Fix change locals() dict by @Fei-Wang in https://github.com/open-mmlab/mmocr/pull/663
-* fix #614: textsnake targets by @HolyCrap96 in https://github.com/open-mmlab/mmocr/pull/660
-
-### New Contributors
-* @alexander-soare made their first contribution in https://github.com/open-mmlab/mmocr/pull/469
-* @A465539338 made their first contribution in https://github.com/open-mmlab/mmocr/pull/364
-* @fatfishZhao made their first contribution in https://github.com/open-mmlab/mmocr/pull/506
-* @baudm made their first contribution in https://github.com/open-mmlab/mmocr/pull/497
-* @ShoupingShan made their first contribution in https://github.com/open-mmlab/mmocr/pull/581
-* @apiaccess21 made their first contribution in https://github.com/open-mmlab/mmocr/pull/593
-* @zhouzaida made their first contribution in https://github.com/open-mmlab/mmocr/pull/628
-* @mpena-vina made their first contribution in https://github.com/open-mmlab/mmocr/pull/633
-* @Fei-Wang made their first contribution in https://github.com/open-mmlab/mmocr/pull/663
-
-**Full Changelog**: https://github.com/open-mmlab/mmocr/compare/v0.3.0...0.4.0
-
-## v0.3.0 (25/8/2021)
-
-### Highlights
-1. We add a new text recognition model -- SATRN! Its pretrained checkpoint achieves the best performance over other provided text recognition models. A lighter version of SATRN is also released which can obtain ~98% of the performance of the original model with only 45 MB in size. ([@2793145003](https://github.com/2793145003)) [#405](https://github.com/open-mmlab/mmocr/pull/405)
-2. Improve the demo script, `ocr.py`, which supports applying end-to-end text detection, text recognition and key information extraction models on images with easy-to-use commands. Users can find its full documentation in the demo section. ([@samayala22](https://github.com/samayala22), [@manjrekarom](https://github.com/manjrekarom)) [#371](https://github.com/open-mmlab/mmocr/pull/371), [#386](https://github.com/open-mmlab/mmocr/pull/386), [#400](https://github.com/open-mmlab/mmocr/pull/400), [#374](https://github.com/open-mmlab/mmocr/pull/374), [#428](https://github.com/open-mmlab/mmocr/pull/428)
-3. Our documentation is reorganized into a clearer structure. More useful contents are on the way! [#409](https://github.com/open-mmlab/mmocr/pull/409), [#454](https://github.com/open-mmlab/mmocr/pull/454)
-4. The requirement of `Polygon3` is removed since this project is no longer maintained or distributed. We unified all its references to equivalent substitutions in `shapely` instead. [#448](https://github.com/open-mmlab/mmocr/pull/448)
-
-### Breaking Changes & Migration Guide
-1. Upgrade version requirement of MMDetection to 2.14.0 to avoid bugs [#382](https://github.com/open-mmlab/mmocr/pull/382)
-2. MMOCR now has its own model and layer registries inherited from MMDetection's or MMCV's counterparts. ([#436](https://github.com/open-mmlab/mmocr/pull/436)) The modified hierarchical structure of the model registries are now organized as follows.
-
-```text
-mmcv.MODELS -> mmdet.BACKBONES -> BACKBONES
-mmcv.MODELS -> mmdet.NECKS -> NECKS
-mmcv.MODELS -> mmdet.ROI_EXTRACTORS -> ROI_EXTRACTORS
-mmcv.MODELS -> mmdet.HEADS -> HEADS
-mmcv.MODELS -> mmdet.LOSSES -> LOSSES
-mmcv.MODELS -> mmdet.DETECTORS -> DETECTORS
-mmcv.ACTIVATION_LAYERS -> ACTIVATION_LAYERS
-mmcv.UPSAMPLE_LAYERS -> UPSAMPLE_LAYERS
-```
-
-To migrate your old implementation to our new backend, you need to change the import path of any registries and their corresponding builder functions (including `build_detectors`) from `mmdet.models.builder` to `mmocr.models.builder`. If you have referred to any model or layer of MMDetection or MMCV in your model config, you need to add `mmdet.` or `mmcv.` prefix to its name to inform the model builder of the right namespace to work on.
-
-Interested users may check out [MMCV's tutorial on Registry](https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html) for in-depth explanations on its mechanism.
-
-
-### New Features
-- Automatically replace SyncBN with BN for inference [#420](https://github.com/open-mmlab/mmocr/pull/420), [#453](https://github.com/open-mmlab/mmocr/pull/453)
-- Support batch inference for CRNN and SegOCR [#407](https://github.com/open-mmlab/mmocr/pull/407)
-- Support exporting documentation in pdf or epub format [#406](https://github.com/open-mmlab/mmocr/pull/406)
-- Support `persistent_workers` option in data loader [#459](https://github.com/open-mmlab/mmocr/pull/459)
-
-### Bug Fixes
-- Remove depreciated key in kie_test_imgs.py [#381](https://github.com/open-mmlab/mmocr/pull/381)
-- Fix dimension mismatch in batch testing/inference of DBNet [#383](https://github.com/open-mmlab/mmocr/pull/383)
-- Fix the problem of dice loss which stays at 1 with an empty target given [#408](https://github.com/open-mmlab/mmocr/pull/408)
-- Fix a wrong link in ocr.py ([@naarkhoo](https://github.com/naarkhoo)) [#417](https://github.com/open-mmlab/mmocr/pull/417)
-- Fix undesired assignment to "pretrained" in test.py [#418](https://github.com/open-mmlab/mmocr/pull/418)
-- Fix a problem in polygon generation of DBNet [#421](https://github.com/open-mmlab/mmocr/pull/421), [#443](https://github.com/open-mmlab/mmocr/pull/443)
-- Skip invalid annotations in totaltext_converter [#438](https://github.com/open-mmlab/mmocr/pull/438)
-- Add zero division handler in poly utils, remove Polygon3 [#448](https://github.com/open-mmlab/mmocr/pull/448)
-
-### Improvements
-- Replace lanms-proper with lanms-neo to support installation on Windows (with special thanks to [@gen-ko](https://github.com/gen-ko) who has re-distributed this package!)
-- Support MIM [#394](https://github.com/open-mmlab/mmocr/pull/394)
-- Add tests for PyTorch 1.9 in CI [#401](https://github.com/open-mmlab/mmocr/pull/401)
-- Enables fullscreen layout in readthedocs [#413](https://github.com/open-mmlab/mmocr/pull/413)
-- General documentation enhancement [#395](https://github.com/open-mmlab/mmocr/pull/395)
-- Update version checker [#427](https://github.com/open-mmlab/mmocr/pull/427)
-- Add copyright info [#439](https://github.com/open-mmlab/mmocr/pull/439)
-- Update citation information [#440](https://github.com/open-mmlab/mmocr/pull/440)
-
-### Contributors
-
-We thank [@2793145003](https://github.com/2793145003), [@samayala22](https://github.com/samayala22), [@manjrekarom](https://github.com/manjrekarom), [@naarkhoo](https://github.com/naarkhoo), [@gen-ko](https://github.com/gen-ko), [@duanjiaqi](https://github.com/duanjiaqi), [@gaotongxiao](https://github.com/gaotongxiao), [@cuhk-hbsun](https://github.com/cuhk-hbsun), [@innerlee](https://github.com/innerlee), [@wdsd641417025](https://github.com/wdsd641417025) for their contribution to this release!
-
-## v0.2.1 (20/7/2021)
-
-### Highlights
-1. Upgrade to use MMCV-full **>= 1.3.8** and MMDetection **>= 2.13.0** for latest features
-2. Add ONNX and TensorRT export tool, supporting the deployment of DBNet, PSENet, PANet and CRNN (experimental) [#278](https://github.com/open-mmlab/mmocr/pull/278), [#291](https://github.com/open-mmlab/mmocr/pull/291), [#300](https://github.com/open-mmlab/mmocr/pull/300), [#328](https://github.com/open-mmlab/mmocr/pull/328)
-3. Unified parameter initialization method which uses init_cfg in config files [#365](https://github.com/open-mmlab/mmocr/pull/365)
-
-### New Features
-- Support TextOCR dataset [#293](https://github.com/open-mmlab/mmocr/pull/293)
-- Support Total-Text dataset [#266](https://github.com/open-mmlab/mmocr/pull/266), [#273](https://github.com/open-mmlab/mmocr/pull/273), [#357](https://github.com/open-mmlab/mmocr/pull/357)
-- Support grouping text detection box into lines [#290](https://github.com/open-mmlab/mmocr/pull/290), [#304](https://github.com/open-mmlab/mmocr/pull/304)
-- Add benchmark_processing script that benchmarks data loading process [#261](https://github.com/open-mmlab/mmocr/pull/261)
-- Add SynthText preprocessor for text recognition models [#351](https://github.com/open-mmlab/mmocr/pull/351), [#361](https://github.com/open-mmlab/mmocr/pull/361)
-- Support batch inference during testing [#310](https://github.com/open-mmlab/mmocr/pull/310)
-- Add user-friendly OCR inference script [#366](https://github.com/open-mmlab/mmocr/pull/366)
-
-### Bug Fixes
-
-- Fix improper class ignorance in SDMGR Loss [#221](https://github.com/open-mmlab/mmocr/pull/221)
-- Fix potential numerical zero division error in DRRG [#224](https://github.com/open-mmlab/mmocr/pull/224)
-- Fix installing requirements with pip and mim [#242](https://github.com/open-mmlab/mmocr/pull/242)
-- Fix dynamic input error of DBNet [#269](https://github.com/open-mmlab/mmocr/pull/269)
-- Fix space parsing error in LineStrParser [#285](https://github.com/open-mmlab/mmocr/pull/285)
-- Fix textsnake decode error [#264](https://github.com/open-mmlab/mmocr/pull/264)
-- Correct isort setup [#288](https://github.com/open-mmlab/mmocr/pull/288)
-- Fix a bug in SDMGR config [#316](https://github.com/open-mmlab/mmocr/pull/316)
-- Fix kie_test_img for KIE nonvisual [#319](https://github.com/open-mmlab/mmocr/pull/319)
-- Fix metafiles [#342](https://github.com/open-mmlab/mmocr/pull/342)
-- Fix different device problem in FCENet [#334](https://github.com/open-mmlab/mmocr/pull/334)
-- Ignore improper tailing empty characters in annotation files [#358](https://github.com/open-mmlab/mmocr/pull/358)
-- Docs fixes [#247](https://github.com/open-mmlab/mmocr/pull/247), [#255](https://github.com/open-mmlab/mmocr/pull/255), [#265](https://github.com/open-mmlab/mmocr/pull/265), [#267](https://github.com/open-mmlab/mmocr/pull/267), [#268](https://github.com/open-mmlab/mmocr/pull/268), [#270](https://github.com/open-mmlab/mmocr/pull/270), [#276](https://github.com/open-mmlab/mmocr/pull/276), [#287](https://github.com/open-mmlab/mmocr/pull/287), [#330](https://github.com/open-mmlab/mmocr/pull/330), [#355](https://github.com/open-mmlab/mmocr/pull/355), [#367](https://github.com/open-mmlab/mmocr/pull/367)
-- Fix NRTR config [#356](https://github.com/open-mmlab/mmocr/pull/356), [#370](https://github.com/open-mmlab/mmocr/pull/370)
-
-### Improvements
-- Add backend for resizeocr [#244](https://github.com/open-mmlab/mmocr/pull/244)
-- Skip image processing pipelines in SDMGR novisual [#260](https://github.com/open-mmlab/mmocr/pull/260)
-- Speedup DBNet [#263](https://github.com/open-mmlab/mmocr/pull/263)
-- Update mmcv installation method in workflow [#323](https://github.com/open-mmlab/mmocr/pull/323)
-- Add part of Chinese documentations [#353](https://github.com/open-mmlab/mmocr/pull/353), [#362](https://github.com/open-mmlab/mmocr/pull/362)
-- Add support for ConcatDataset with two workflows [#348](https://github.com/open-mmlab/mmocr/pull/348)
-- Add list_from_file and list_to_file utils [#226](https://github.com/open-mmlab/mmocr/pull/226)
-- Speed up sort_vertex [#239](https://github.com/open-mmlab/mmocr/pull/239)
-- Support distributed evaluation of KIE [#234](https://github.com/open-mmlab/mmocr/pull/234)
-- Add pretrained FCENet on IC15 [#258](https://github.com/open-mmlab/mmocr/pull/258)
-- Support CPU for OCR demo [#227](https://github.com/open-mmlab/mmocr/pull/227)
-- Avoid extra image pre-processing steps [#375](https://github.com/open-mmlab/mmocr/pull/375)
-
-
-## v0.2.0 (18/5/2021)
-
-### Highlights
-
-1. Add the NER approach Bert-softmax (NAACL'2019)
-2. Add the text detection method DRRG (CVPR'2020)
-3. Add the text detection method FCENet (CVPR'2021)
-4. Increase the ease of use via adding text detection and recognition end-to-end demo, and colab online demo.
-5. Simplify the installation.
-
-### New Features
-
-- Add Bert-softmax for Ner task [#148](https://github.com/open-mmlab/mmocr/pull/148)
-- Add DRRG [#189](https://github.com/open-mmlab/mmocr/pull/189)
-- Add FCENet [#133](https://github.com/open-mmlab/mmocr/pull/133)
-- Add end-to-end demo [#105](https://github.com/open-mmlab/mmocr/pull/105)
-- Support batch inference [#86](https://github.com/open-mmlab/mmocr/pull/86) [#87](https://github.com/open-mmlab/mmocr/pull/87) [#178](https://github.com/open-mmlab/mmocr/pull/178)
-- Add TPS preprocessor for text recognition [#117](https://github.com/open-mmlab/mmocr/pull/117) [#135](https://github.com/open-mmlab/mmocr/pull/135)
-- Add demo documentation [#151](https://github.com/open-mmlab/mmocr/pull/151) [#166](https://github.com/open-mmlab/mmocr/pull/166) [#168](https://github.com/open-mmlab/mmocr/pull/168) [#170](https://github.com/open-mmlab/mmocr/pull/170) [#171](https://github.com/open-mmlab/mmocr/pull/171)
-- Add checkpoint for Chinese recognition [#156](https://github.com/open-mmlab/mmocr/pull/156)
-- Add metafile [#175](https://github.com/open-mmlab/mmocr/pull/175) [#176](https://github.com/open-mmlab/mmocr/pull/176) [#177](https://github.com/open-mmlab/mmocr/pull/177) [#182](https://github.com/open-mmlab/mmocr/pull/182) [#183](https://github.com/open-mmlab/mmocr/pull/183)
-- Add support for numpy array inference [#74](https://github.com/open-mmlab/mmocr/pull/74)
-
-### Bug Fixes
-
-- Fix the duplicated point bug due to transform for textsnake [#130](https://github.com/open-mmlab/mmocr/pull/130)
-- Fix CTC loss NaN [#159](https://github.com/open-mmlab/mmocr/pull/159)
-- Fix error raised if result is empty in demo [#144](https://github.com/open-mmlab/mmocr/pull/141)
-- Fix results missing if one image has a large number of boxes [#98](https://github.com/open-mmlab/mmocr/pull/98)
-- Fix package missing in dockerfile [#109](https://github.com/open-mmlab/mmocr/pull/109)
-
-### Improvements
-
-- Simplify installation procedure via removing compiling [#188](https://github.com/open-mmlab/mmocr/pull/188)
-- Speed up panet post processing so that it can detect dense texts [#188](https://github.com/open-mmlab/mmocr/pull/188)
-- Add zh-CN README [#70](https://github.com/open-mmlab/mmocr/pull/70) [#95](https://github.com/open-mmlab/mmocr/pull/95)
-- Support windows [#89](https://github.com/open-mmlab/mmocr/pull/89)
-- Add Colab [#147](https://github.com/open-mmlab/mmocr/pull/147) [#199](https://github.com/open-mmlab/mmocr/pull/199)
-- Add 1-step installation using conda environment [#193](https://github.com/open-mmlab/mmocr/pull/193) [#194](https://github.com/open-mmlab/mmocr/pull/194) [#195](https://github.com/open-mmlab/mmocr/pull/195)
-
-
-## v0.1.0 (7/4/2021)
-
-### Highlights
-
-- MMOCR is released.
-
-### Main Features
-
-- Support text detection, text recognition and the corresponding downstream tasks such as key information extraction.
-- For text detection, support both single-step (`PSENet`, `PANet`, `DBNet`, `TextSnake`) and two-step (`MaskRCNN`) methods.
-- For text recognition, support CTC-loss based method `CRNN`; Encoder-decoder (with attention) based methods `SAR`, `Robustscanner`; Segmentation based method `SegOCR`; Transformer based method `NRTR`.
-- For key information extraction, support GCN based method `SDMG-R`.
-- Provide checkpoints and log files for all of the methods above.
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textdet/dense_heads/__init__.py b/spaces/tomofi/MMOCR/mmocr/models/textdet/dense_heads/__init__.py
deleted file mode 100644
index 3c2eaa7fe29143fce8cbef8d770a1afe6f9c3c24..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textdet/dense_heads/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .db_head import DBHead
-from .drrg_head import DRRGHead
-from .fce_head import FCEHead
-from .head_mixin import HeadMixin
-from .pan_head import PANHead
-from .pse_head import PSEHead
-from .textsnake_head import TextSnakeHead
-
-__all__ = [
- 'PSEHead', 'PANHead', 'DBHead', 'FCEHead', 'TextSnakeHead', 'DRRGHead',
- 'HeadMixin'
-]
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
deleted file mode 100644
index d4189c6fa2a6a3481bf666b713f6ab91812f3d86..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './mask_rcnn_r101_fpn_2x_coco.py'
-model = dict(
- pretrained='open-mmlab://resnext101_32x4d',
- backbone=dict(
- type='ResNeXt',
- depth=101,
- groups=32,
- base_width=4,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- style='pytorch'))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/point_rend_roi_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/point_rend_roi_head.py
deleted file mode 100644
index af43a21a9eabcad73b7b67ef94c1816e55285d38..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/point_rend_roi_head.py
+++ /dev/null
@@ -1,209 +0,0 @@
-# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa
-
-import torch
-import torch.nn.functional as F
-from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
-
-from mmdet.core import bbox2roi, bbox_mapping, merge_aug_masks
-from .. import builder
-from ..builder import HEADS
-from .standard_roi_head import StandardRoIHead
-
-
-@HEADS.register_module()
-class PointRendRoIHead(StandardRoIHead):
- """`PointRend `_."""
-
- def __init__(self, point_head, *args, **kwargs):
- super().__init__(*args, **kwargs)
- assert self.with_bbox and self.with_mask
- self.init_point_head(point_head)
-
- def init_point_head(self, point_head):
- """Initialize ``point_head``"""
- self.point_head = builder.build_head(point_head)
-
- def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
- img_metas):
- """Run forward function and calculate loss for mask head and point head
- in training."""
- mask_results = super()._mask_forward_train(x, sampling_results,
- bbox_feats, gt_masks,
- img_metas)
- if mask_results['loss_mask'] is not None:
- loss_point = self._mask_point_forward_train(
- x, sampling_results, mask_results['mask_pred'], gt_masks,
- img_metas)
- mask_results['loss_mask'].update(loss_point)
-
- return mask_results
-
- def _mask_point_forward_train(self, x, sampling_results, mask_pred,
- gt_masks, img_metas):
- """Run forward function and calculate loss for point head in
- training."""
- pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
- rel_roi_points = self.point_head.get_roi_rel_points_train(
- mask_pred, pos_labels, cfg=self.train_cfg)
- rois = bbox2roi([res.pos_bboxes for res in sampling_results])
-
- fine_grained_point_feats = self._get_fine_grained_point_feats(
- x, rois, rel_roi_points, img_metas)
- coarse_point_feats = point_sample(mask_pred, rel_roi_points)
- mask_point_pred = self.point_head(fine_grained_point_feats,
- coarse_point_feats)
- mask_point_target = self.point_head.get_targets(
- rois, rel_roi_points, sampling_results, gt_masks, self.train_cfg)
- loss_mask_point = self.point_head.loss(mask_point_pred,
- mask_point_target, pos_labels)
-
- return loss_mask_point
-
- def _get_fine_grained_point_feats(self, x, rois, rel_roi_points,
- img_metas):
- """Sample fine grained feats from each level feature map and
- concatenate them together."""
- num_imgs = len(img_metas)
- fine_grained_feats = []
- for idx in range(self.mask_roi_extractor.num_inputs):
- feats = x[idx]
- spatial_scale = 1. / float(
- self.mask_roi_extractor.featmap_strides[idx])
- point_feats = []
- for batch_ind in range(num_imgs):
- # unravel batch dim
- feat = feats[batch_ind].unsqueeze(0)
- inds = (rois[:, 0].long() == batch_ind)
- if inds.any():
- rel_img_points = rel_roi_point_to_rel_img_point(
- rois[inds], rel_roi_points[inds], feat.shape[2:],
- spatial_scale).unsqueeze(0)
- point_feat = point_sample(feat, rel_img_points)
- point_feat = point_feat.squeeze(0).transpose(0, 1)
- point_feats.append(point_feat)
- fine_grained_feats.append(torch.cat(point_feats, dim=0))
- return torch.cat(fine_grained_feats, dim=1)
-
- def _mask_point_forward_test(self, x, rois, label_pred, mask_pred,
- img_metas):
- """Mask refining process with point head in testing."""
- refined_mask_pred = mask_pred.clone()
- for subdivision_step in range(self.test_cfg.subdivision_steps):
- refined_mask_pred = F.interpolate(
- refined_mask_pred,
- scale_factor=self.test_cfg.scale_factor,
- mode='bilinear',
- align_corners=False)
- # If `subdivision_num_points` is larger or equal to the
- # resolution of the next step, then we can skip this step
- num_rois, channels, mask_height, mask_width = \
- refined_mask_pred.shape
- if (self.test_cfg.subdivision_num_points >=
- self.test_cfg.scale_factor**2 * mask_height * mask_width
- and
- subdivision_step < self.test_cfg.subdivision_steps - 1):
- continue
- point_indices, rel_roi_points = \
- self.point_head.get_roi_rel_points_test(
- refined_mask_pred, label_pred, cfg=self.test_cfg)
- fine_grained_point_feats = self._get_fine_grained_point_feats(
- x, rois, rel_roi_points, img_metas)
- coarse_point_feats = point_sample(mask_pred, rel_roi_points)
- mask_point_pred = self.point_head(fine_grained_point_feats,
- coarse_point_feats)
-
- point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
- refined_mask_pred = refined_mask_pred.reshape(
- num_rois, channels, mask_height * mask_width)
- refined_mask_pred = refined_mask_pred.scatter_(
- 2, point_indices, mask_point_pred)
- refined_mask_pred = refined_mask_pred.view(num_rois, channels,
- mask_height, mask_width)
-
- return refined_mask_pred
-
- def simple_test_mask(self,
- x,
- img_metas,
- det_bboxes,
- det_labels,
- rescale=False):
- """Obtain mask prediction without augmentation."""
- ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
- scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
- num_imgs = len(det_bboxes)
- if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
- segm_results = [[[] for _ in range(self.mask_head.num_classes)]
- for _ in range(num_imgs)]
- else:
- # if det_bboxes is rescaled to the original image size, we need to
- # rescale it back to the testing scale to obtain RoIs.
- if rescale and not isinstance(scale_factors[0], float):
- scale_factors = [
- torch.from_numpy(scale_factor).to(det_bboxes[0].device)
- for scale_factor in scale_factors
- ]
- _bboxes = [
- det_bboxes[i][:, :4] *
- scale_factors[i] if rescale else det_bboxes[i][:, :4]
- for i in range(len(det_bboxes))
- ]
- mask_rois = bbox2roi(_bboxes)
- mask_results = self._mask_forward(x, mask_rois)
- # split batch mask prediction back to each image
- mask_pred = mask_results['mask_pred']
- num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
- mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
- mask_rois = mask_rois.split(num_mask_roi_per_img, 0)
-
- # apply mask post-processing to each image individually
- segm_results = []
- for i in range(num_imgs):
- if det_bboxes[i].shape[0] == 0:
- segm_results.append(
- [[] for _ in range(self.mask_head.num_classes)])
- else:
- x_i = [xx[[i]] for xx in x]
- mask_rois_i = mask_rois[i]
- mask_rois_i[:, 0] = 0 # TODO: remove this hack
- mask_pred_i = self._mask_point_forward_test(
- x_i, mask_rois_i, det_labels[i], mask_preds[i],
- [img_metas])
- segm_result = self.mask_head.get_seg_masks(
- mask_pred_i, _bboxes[i], det_labels[i], self.test_cfg,
- ori_shapes[i], scale_factors[i], rescale)
- segm_results.append(segm_result)
- return segm_results
-
- def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
- """Test for mask head with test time augmentation."""
- if det_bboxes.shape[0] == 0:
- segm_result = [[] for _ in range(self.mask_head.num_classes)]
- else:
- aug_masks = []
- for x, img_meta in zip(feats, img_metas):
- img_shape = img_meta[0]['img_shape']
- scale_factor = img_meta[0]['scale_factor']
- flip = img_meta[0]['flip']
- _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
- scale_factor, flip)
- mask_rois = bbox2roi([_bboxes])
- mask_results = self._mask_forward(x, mask_rois)
- mask_results['mask_pred'] = self._mask_point_forward_test(
- x, mask_rois, det_labels, mask_results['mask_pred'],
- img_metas)
- # convert to numpy array to save memory
- aug_masks.append(
- mask_results['mask_pred'].sigmoid().cpu().numpy())
- merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
-
- ori_shape = img_metas[0][0]['ori_shape']
- segm_result = self.mask_head.get_seg_masks(
- merged_masks,
- det_bboxes,
- det_labels,
- self.test_cfg,
- ori_shape,
- scale_factor=1.0,
- rescale=False)
- return segm_result
diff --git a/spaces/unidata/Chinese-Llama-2-7b/app.py b/spaces/unidata/Chinese-Llama-2-7b/app.py
deleted file mode 100644
index 0a320817de7935bd65bf9fe6ff010f8a13492e14..0000000000000000000000000000000000000000
--- a/spaces/unidata/Chinese-Llama-2-7b/app.py
+++ /dev/null
@@ -1,279 +0,0 @@
-from typing import Iterator
-
-import gradio as gr
-import torch
-
-from model import get_input_token_length, run
-
-DEFAULT_SYSTEM_PROMPT = """\
-You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\
-"""
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = 4000
-
-DESCRIPTION = """
-# Chinese Llama-2 7B Chat
-
-本空间是对于[Chinese-Llama-2-7b-4bit](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b-4bit)和[Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b)模型的演示。该模型由LinkSoul开发，基于Llama 2基础模型和自行收集的大规模中英文指令数据集[instruction_merge_set](https://huggingface.co/datasets/LinkSoul/instruction_merge_set)训练得到。
-"""
-
-LICENSE = """
-
-
----
-As a derivate work of [Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b) by LinkSoul and [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
-this demo is governed by the original [license](https://huggingface.co/spaces/LinkSoul/Chinese-Llama-2-7b/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/LinkSoul/Chinese-Llama-2-7b/blob/main/USE_POLICY.md).
-"""
-
-if not torch.cuda.is_available():
- DESCRIPTION += '\n
-
-Abbey Road Redd Consoles Vst Plugin Torrent ->>->>->> DOWNLOAD (Mirror #1) .. . Home News Top 50 Freeware VST Plugins Of 2015! 4d29de3e1b
-
-
-
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Bioshock 1 Save Editor.md b/spaces/usbethFlerru/sovits-modelsV2/example/Bioshock 1 Save Editor.md
deleted file mode 100644
index f103151878aace6ebf0643d4eed38ef4f97e7969..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Bioshock 1 Save Editor.md
+++ /dev/null
@@ -1,93 +0,0 @@
-
-
Bioshock 1 Save Editor: Everything You Need to Know
-
-
Bioshock 1 is one of the most acclaimed and influential first-person shooter games of all time. It takes you to the dystopian underwater city of Rapture, where you have to fight against splicers, big daddies and little sisters, while uncovering the secrets of the city and its founder, Andrew Ryan. The game offers a rich story, immersive gameplay and stunning graphics, but it also has some challenges and limitations that may frustrate some players.
-
-
That's why some players may want to use a Bioshock 1 save editor, a tool that allows you to modify your save files and customize various aspects of the game, such as your inventory, health, ammo, plasmids, tonics, weapons, upgrades and more. You can also use a Bioshock 1 save editor to unlock achievements, mods and challenges that you may have missed or want to try out.
In this article, we will explain how to use a Bioshock 1 save editor and where to find some useful save files and mods for the game. We will also give you some tips and tricks on how to get the most out of your Bioshock 1 experience.
-
-
How to Use a Bioshock 1 Save Editor
-
-
Using a Bioshock 1 save editor is not very difficult, but you need to follow some steps carefully to avoid any problems or errors. Here are the steps you need to take:
-
-
-
Back up your own save files first. You can find them at: C:\Users\username\Documents\BioshockHD\BioShock\SaveGames
Extract the downloaded file and run the Bioshock 1 save editor as administrator.
-
Open the save file you want to edit and make the changes you want. You can modify your inventory, health, ammo, plasmids, tonics, weapons, upgrades and more. You can also unlock achievements, mods and challenges by changing some values or flags.
-
Save the edited file and replace the original one in your save folder.
-
Launch the game and load the edited save file. Enjoy!
-
-
-
Where to Find Useful Save Files and Mods for Bioshock 1
-
-
If you don't want to edit your own save files or you are looking for some specific save files or mods for Bioshock 1, you can also download them from various sources online. Here are some examples of useful save files and mods for Bioshock 1:
-
-
-
-
BioShock: Save Files for Collectable & Missable Achievements + Worlds of Hurt Challenge!: This is a collection of save files that will help you unlock some of the most difficult or missable achievements in the game, such as Historian, Tonic Collector, Weapon Specialist, Found Cohen’s Room and Irony, Dealt With Every Little Sister and more. It also includes a save file for the Worlds of Hurt challenge, which is a very hard mode that tests your skills and strategies.
-
Bioshock Nexus - Mods and Community: This is a website that hosts hundreds of mods for Bioshock 1, ranging from graphics enhancements, gameplay tweaks, new content, bug fixes and more. You can browse through different categories and find the mods that suit your taste and needs. Some of the most popular mods are BioShock Reborn, which overhauls the gameplay mechanics and balance; BioShock Remastered Texture Pack, which improves the graphics quality; and BioShock Infinite Themed HUD, which changes the user interface to match the style of BioShock Infinite.
-
Bioshock 1 Game Save or Editor - Se7enSins Gaming Community: This is a forum thread where you can request or share Bioshock 1 save files or editors. You can also ask for help or advice from other users who have experience with editing Bioshock 1 save files.
-
-
-
Tips and Tricks for Bioshock 1
-
-
Bioshock 1 is a game that offers a lot of freedom and possibilities for different playstyles and strategies. However, it can also be challenging and frustrating at times. Here are some tips and tricks that will help you get the most out of your Bioshock 1 experience:
-
-
-
Explore every corner of Rapture. The game is full of secrets, hidden items, audio logs, easter eggs and more that will enrich your understanding of the story and the world. You may also find some useful resources or upgrades that will make your life easier.
-
Use your plasmids wisely. Plasmids are powerful abilities that can give you an edge in combat or help you solve puzzles. However, they also consume EVE (energy) which is limited. You should always have some EVE hypos (injectors) with you in case you run out of EVE. You should also experiment with different combinations of plasmids and weapons to find what works best for you.
-
Hack everything you can. Hacking is a mini-game that allows you to take control of machines such as turrets, cameras, vending machines or safes. Hacking can give you many benefits such as lower prices, free items, friendly security or extra loot. However, hacking can also be risky if you fail or trigger an alarm. You should always have some auto-hack tools or buyout options in case hacking is too hard or dangerous.
-
Be careful with your choices. Bioshock 1 is a game that features moral choices that affect the outcome of the story and the gameplay. The most important choice is whether to harvest or rescue little sisters (young girls who carry ADAM). Harvesting them will give you more ADAM (currency) but will kill them; rescuing them will give you less ADAM but will spare their lives. Your choice will also affect how other characters react to you and what ending you get.
-
Save often. Bioshock 1 is a game that can be unpredictable and unforgiving at times. You may encounter enemies that are too strong for you; traps that catch you off guard; glitches that ruin your progress; or choices that you regret later. You should always save your game frequently so that you can reload if something goes wrong or if you want to try something different.
-
-
-
Conclusion
-
-
Bioshock 1 is a game that deserves its reputation as one of the best first-person shooter games ever made. It offers a rich story, an immersive world, a diverse gameplay and a lot of replay value. However, it can also be enhanced by using a Bioshock 1 save editor or downloading some useful save files or mods for the game.
-
-
In this article, we explained how to use a Bioshock 1 save editor and where to find some useful save files and mods for the game. We also gave you some tips and tricks on how to get the most out of your Bioshock 1 experience.
-
-
We hope this article was helpful and informative for you. If you have any questions or feedback about this topic or anything related to Bioshock 1, feel free to leave a comment below.
-
How to Find the Best Bioshock 1 Save Editor for You
-
-
As we mentioned before, there are several options of Bioshock 1 save editors available online, each with its own features and requirements. How do you choose the best Bioshock 1 save editor for you? Here are some factors that you may want to consider:
-
-
-
The compatibility of the Bioshock 1 save editor with your game version and platform. You should check if the Bioshock 1 save editor works with your game version and platform, whether it is PC, Xbox 360 or PS3. You should also check if the Bioshock 1 save editor requires any additional software or files to run properly.
-
The functionality and user-friendliness of the Bioshock 1 save editor. You should check what kind of changes and modifications the Bioshock 1 save editor can make to your save files and how easy or hard it is to use the Bioshock 1 save editor. You should also check if the Bioshock 1 save editor has any bugs or errors that may affect your game or your save files.
-
The reputation and reliability of the Bioshock 1 save editor and its source. You should check if the Bioshock 1 save editor and its source are trustworthy and reputable, as you may be downloading and running a file that may contain viruses, malware or other harmful elements. You should also check if the Bioshock 1 save editor and its source have any positive or negative reviews or feedback from other users who have used it.
-
-
-
Based on these factors, you may want to compare different options of Bioshock 1 save editors and choose the one that suits your needs and preferences best. You may also want to try out different options of Bioshock 1 save editors and see which one works best for you.
-
-
How to Use a Bioshock 1 Save Editor Safely and Ethically
-
-
Using a Bioshock 1 save editor can be fun and rewarding, but it can also be risky and unethical. Here are some tips on how to use a Bioshock 1 save editor safely and ethically:
-
-
-
Always back up your own save files before using a Bioshock 1 save editor. This way, you can restore your original save files if something goes wrong or if you don't like the changes made by the Bioshock 1 save editor.
-
Always scan the downloaded file of the Bioshock 1 save editor with an antivirus or anti-malware program before running it. This way, you can protect your computer and your game from any potential threats or damages caused by the Bioshock 1 save editor.
-
Always follow the instructions and requirements for each Bioshock 1 save editor carefully. This way, you can avoid any errors or glitches that may occur while using the Bioshock 1 save editor or while playing the game.
-
Always use a Bioshock 1 save editor only for personal and non-commercial purposes. This way, you can respect the rights and wishes of the developers and publishers of the game and avoid any legal or ethical issues that may arise from using a Bioshock 1 save editor.
-
Always use a Bioshock 1 save editor sparingly and moderately. This way, you can preserve the challenge and immersion of the game and avoid making the game too easy or boring for yourself.
-
-
-
Conclusion
-
-
Bioshock 1 is a game that offers a rich story, an immersive world, a diverse gameplay and a lot of replay value. However, it can also be enhanced by using a Bioshock 1 save editor or downloading some useful save files or mods for the game.
-
-
In this article, we explained how to use a Bioshock 1 save editor and where to find some useful save files and mods for the game. We also gave you some tips and tricks on how to get the most out of your Bioshock 1 experience. We also discussed some of the benefits and drawbacks of using a Bioshock 1 save editor. We also explained how to find the best Bioshock 1 save editor for you and how to use a Bioshock 1 save editor safely and ethically.
-
-
We hope this article was helpful and informative for you. If you have any questions or feedback about this topic or anything related to Bioshock 1, feel free to leave a comment below.
-
Conclusion
-
-
Bioshock 1 is a game that deserves its reputation as one of the best first-person shooter games ever made. It offers a rich story, an immersive world, a diverse gameplay and a lot of replay value. However, it can also be enhanced by using a Bioshock 1 save editor or downloading some useful save files or mods for the game.
-
-
In this article, we explained how to use a Bioshock 1 save editor and where to find some useful save files and mods for the game. We also gave you some tips and tricks on how to get the most out of your Bioshock 1 experience. We also discussed some of the benefits and drawbacks of using a Bioshock 1 save editor. We also explained how to find the best Bioshock 1 save editor for you and how to use a Bioshock 1 save editor safely and ethically.
-
-
We hope this article was helpful and informative for you. If you have any questions or feedback about this topic or anything related to Bioshock 1, feel free to leave a comment below.
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/video-p2p-library/Video-P2P-Demo/Dockerfile b/spaces/video-p2p-library/Video-P2P-Demo/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/video-p2p-library/Video-P2P-Demo/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/weijiawu/ImageEditAnything/captioner/README.md b/spaces/weijiawu/ImageEditAnything/captioner/README.md
deleted file mode 100644
index e9b387fade5888f6f4330aecfc0d1cdbb1c51703..0000000000000000000000000000000000000000
--- a/spaces/weijiawu/ImageEditAnything/captioner/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-To run BLIP/BLIP2, you should install transformers from source!
-```
-!pip install git+https://github.com/huggingface/transformers.git
-```
-To run filter module, you should install CLIP repo as a Python package as follow:
-```
-!pip install ftfy regex tqdm
-!pip install git+https://github.com/openai/CLIP.git
-```
-To accelerate BLIP2 with int8, you should install accelerate
-```
-!pip install accelerate bitsandbytes
-```
diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/run_code.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/run_code.py
deleted file mode 100644
index f69d2cd1a63d14362ba06388f37a5d20af11a6e3..0000000000000000000000000000000000000000
--- a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/run_code.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time : 2023/5/11 17:46
-@Author : alexanderwu
-@File : run_code.py
-"""
-import os
-import subprocess
-import traceback
-from typing import Tuple
-
-from metagpt.actions.action import Action
-from metagpt.logs import logger
-
-PROMPT_TEMPLATE = """
-Role: You are a senior development and qa engineer, your role is summarize the code running result.
-If the running result does not include an error, you should explicitly approve the result.
-On the other hand, if the running result indicates some error, you should point out which part, the development code or the test code, produces the error,
-and give specific instructions on fixing the errors. Here is the code info:
-{context}
-Now you should begin your analysis
----
-## instruction:
-Please summarize the cause of the errors and give correction instruction
-## File To Rewrite:
-Determine the ONE file to rewrite in order to fix the error, for example, xyz.py, or test_xyz.py
-## Status:
-Determine if all of the code works fine, if so write PASS, else FAIL,
-WRITE ONLY ONE WORD, PASS OR FAIL, IN THIS SECTION
-## Send To:
-Please write Engineer if the errors are due to problematic development codes, and QaEngineer to problematic test codes, and NoOne if there are no errors,
-WRITE ONLY ONE WORD, Engineer OR QaEngineer OR NoOne, IN THIS SECTION.
----
-You should fill in necessary instruction, status, send to, and finally return all content between the --- segment line.
-"""
-
-CONTEXT = """
-## Development Code File Name
-{code_file_name}
-## Development Code
-```python
-{code}
-```
-## Test File Name
-{test_file_name}
-## Test Code
-```python
-{test_code}
-```
-## Running Command
-{command}
-## Running Output
-standard output: {outs};
-standard errors: {errs};
-"""
-
-
-class RunCode(Action):
- def __init__(self, name="RunCode", context=None, llm=None):
- super().__init__(name, context, llm)
-
- @classmethod
- async def run_text(cls, code) -> Tuple[str, str]:
- try:
- # We will document_store the result in this dictionary
- namespace = {}
- exec(code, namespace)
- return namespace.get("result", ""), ""
- except Exception:
- # If there is an error in the code, return the error message
- return "", traceback.format_exc()
-
- @classmethod
- async def run_script(cls, working_directory, additional_python_paths=[], command=[]) -> Tuple[str, str]:
- working_directory = str(working_directory)
- additional_python_paths = [str(path) for path in additional_python_paths]
-
- # Copy the current environment variables
- env = os.environ.copy()
-
- # Modify the PYTHONPATH environment variable
- additional_python_paths = [working_directory] + additional_python_paths
- additional_python_paths = ":".join(additional_python_paths)
- env["PYTHONPATH"] = additional_python_paths + ":" + env.get("PYTHONPATH", "")
-
- # Start the subprocess
- process = subprocess.Popen(
- command, cwd=working_directory, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env
- )
-
- try:
- # Wait for the process to complete, with a timeout
- stdout, stderr = process.communicate(timeout=10)
- except subprocess.TimeoutExpired:
- logger.info("The command did not complete within the given timeout.")
- process.kill() # Kill the process if it times out
- stdout, stderr = process.communicate()
- return stdout.decode("utf-8"), stderr.decode("utf-8")
-
- async def run(
- self, code, mode="script", code_file_name="", test_code="", test_file_name="", command=[], **kwargs
- ) -> str:
- logger.info(f"Running {' '.join(command)}")
- if mode == "script":
- outs, errs = await self.run_script(command=command, **kwargs)
- elif mode == "text":
- outs, errs = await self.run_text(code=code)
-
- logger.info(f"{outs=}")
- logger.info(f"{errs=}")
-
- context = CONTEXT.format(
- code=code,
- code_file_name=code_file_name,
- test_code=test_code,
- test_file_name=test_file_name,
- command=" ".join(command),
- outs=outs[:500], # outs might be long but they are not important, truncate them to avoid token overflow
- errs=errs[:10000], # truncate errors to avoid token overflow
- )
-
- prompt = PROMPT_TEMPLATE.format(context=context)
- rsp = await self._aask(prompt)
-
- result = context + rsp
-
- return result
diff --git a/spaces/whitphx/gradio-static-test/dist/assets/index-1ba997b2.js b/spaces/whitphx/gradio-static-test/dist/assets/index-1ba997b2.js
deleted file mode 100644
index 0c63c32b5f25bcbb0301f93b8c1b69118bcd0e4d..0000000000000000000000000000000000000000
--- a/spaces/whitphx/gradio-static-test/dist/assets/index-1ba997b2.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as L,i as M,s as j,H as w,D as o,N as c,h as g,G as T,r as d,u as S,e as h,m as v,q as b,t as k,o as H,y as q,a0 as z,I as B,j as C,k as D,z as E}from"../lite.js";import{B as G}from"./Button-0391b19a.js";function I(t){let e,n;return{c(){e=w("div"),o(e,"class",n="prose "+t[1].join(" ")+" svelte-1ybaih5"),o(e,"id",t[0]),c(e,"min",t[4]),c(e,"hide",!t[3])},m(s,i){g(s,e,i),e.innerHTML=t[2]},p(s,[i]){i&4&&(e.innerHTML=s[2]),i&2&&n!==(n="prose "+s[1].join(" ")+" svelte-1ybaih5")&&o(e,"class",n),i&1&&o(e,"id",s[0]),i&18&&c(e,"min",s[4]),i&10&&c(e,"hide",!s[3])},i:T,o:T,d(s){s&&d(e)}}}function N(t,e,n){let{elem_id:s=""}=e,{elem_classes:i=[]}=e,{value:m}=e,{visible:u=!0}=e,{min_height:f=!1}=e;const l=S();return t.$$set=a=>{"elem_id"in a&&n(0,s=a.elem_id),"elem_classes"in a&&n(1,i=a.elem_classes),"value"in a&&n(2,m=a.value),"visible"in a&&n(3,u=a.visible),"min_height"in a&&n(4,f=a.min_height)},t.$$.update=()=>{t.$$.dirty&4&&l("change")},[s,i,m,u,f]}class A extends L{constructor(e){super(),M(this,e,N,I,j,{elem_id:0,elem_classes:1,value:2,visible:3,min_height:4})}}function F(t){let e,n,s,i,m;const u=[t[4],{variant:"center"}];let f={};for(let l=0;l{"label"in _&&n(5,s=_.label),"elem_id"in _&&n(0,i=_.elem_id),"elem_classes"in _&&n(1,m=_.elem_classes),"visible"in _&&n(2,u=_.visible),"value"in _&&n(3,f=_.value),"loading_status"in _&&n(4,l=_.loading_status)},t.$$.update=()=>{t.$$.dirty&32&&a("change")},[i,m,u,f,l,s,r]}class O extends L{constructor(e){super(),M(this,e,K,J,j,{label:5,elem_id:0,elem_classes:1,visible:2,value:3,loading_status:4})}}const R=O,U=["static"],V=t=>({type:{payload:"string"},description:{payload:"HTML output"}});export{R as Component,V as document,U as modes};
-//# sourceMappingURL=index-1ba997b2.js.map
diff --git a/spaces/williamberman/stable-diffusion-xl-inpainting/sdxl.py b/spaces/williamberman/stable-diffusion-xl-inpainting/sdxl.py
deleted file mode 100644
index dbd68b0f39f46568b9504c45cf45e3821e15e3ca..0000000000000000000000000000000000000000
--- a/spaces/williamberman/stable-diffusion-xl-inpainting/sdxl.py
+++ /dev/null
@@ -1,1008 +0,0 @@
-import itertools
-import os
-import random
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
-
-import numpy as np
-import safetensors.torch
-import torch
-import torch.nn.functional as F
-import torchvision.transforms
-import torchvision.transforms.functional as TF
-from PIL import Image
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.data import default_collate
-from transformers import (CLIPTextModel, CLIPTextModelWithProjection,
- CLIPTokenizerFast)
-
-from diffusion import (default_num_train_timesteps,
- euler_ode_solver_diffusion_loop, make_sigmas)
-from sdxl_models import (SDXLAdapter, SDXLControlNet, SDXLControlNetFull,
- SDXLControlNetPreEncodedControlnetCond, SDXLUNet,
- SDXLVae)
-
-
-class SDXLTraining:
- text_encoder_one: CLIPTextModel
- text_encoder_two: CLIPTextModelWithProjection
- vae: SDXLVae
- sigmas: torch.Tensor
- unet: SDXLUNet
- adapter: Optional[SDXLAdapter]
- controlnet: Optional[Union[SDXLControlNet, SDXLControlNetFull]]
-
- train_unet: bool
- train_unet_up_blocks: bool
-
- mixed_precision: Optional[torch.dtype]
- timestep_sampling: Literal["uniform", "cubic"]
-
- validation_images_logged: bool
- log_validation_input_images_every_time: bool
-
- get_sdxl_conditioning_images: Callable[[Image.Image], Dict[str, Any]]
-
- def __init__(
- self,
- device,
- train_unet,
- get_sdxl_conditioning_images,
- train_unet_up_blocks=False,
- unet_resume_from=None,
- controlnet_cls=None,
- controlnet_resume_from=None,
- adapter_cls=None,
- adapter_resume_from=None,
- mixed_precision=None,
- timestep_sampling="uniform",
- log_validation_input_images_every_time=True,
- ):
- self.text_encoder_one = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", variant="fp16", torch_dtype=torch.float16)
- self.text_encoder_one.to(device=device)
- self.text_encoder_one.requires_grad_(False)
- self.text_encoder_one.eval()
-
- self.text_encoder_two = CLIPTextModelWithProjection.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder_2", variant="fp16", torch_dtype=torch.float16)
- self.text_encoder_two.to(device=device)
- self.text_encoder_two.requires_grad_(False)
- self.text_encoder_two.eval()
-
- self.vae = SDXLVae.load_fp16_fix(device=device)
- self.vae.requires_grad_(False)
- self.vae.eval()
-
- self.sigmas = make_sigmas(device=device)
-
- if train_unet:
- if unet_resume_from is None:
- self.unet = SDXLUNet.load_fp32(device=device)
- else:
- self.unet = SDXLUNet.load(unet_resume_from, device=device)
- self.unet.requires_grad_(True)
- self.unet.train()
- self.unet = DDP(self.unet, device_ids=[device])
- elif train_unet_up_blocks:
- if unet_resume_from is None:
- self.unet = SDXLUNet.load_fp32(device=device)
- else:
- self.unet = SDXLUNet.load_fp32(device=device, overrides=[unet_resume_from])
- self.unet.requires_grad_(False)
- self.unet.eval()
- self.unet.up_blocks.requires_grad_(True)
- self.unet.up_blocks.train()
- self.unet = DDP(self.unet, device_ids=[device], find_unused_parameters=True)
- else:
- self.unet = SDXLUNet.load_fp16(device=device)
- self.unet.requires_grad_(False)
- self.unet.eval()
-
- if controlnet_cls is not None:
- if controlnet_resume_from is None:
- self.controlnet = controlnet_cls.from_unet(self.unet)
- self.controlnet.to(device)
- else:
- self.controlnet = controlnet_cls.load(controlnet_resume_from, device=device)
- self.controlnet.train()
- self.controlnet.requires_grad_(True)
- # TODO add back
- # controlnet.enable_gradient_checkpointing()
- # TODO - should be able to remove find_unused_parameters. Comes from pre encoded controlnet
- self.controlnet = DDP(self.controlnet, device_ids=[device], find_unused_parameters=True)
- else:
- self.controlnet = None
-
- if adapter_cls is not None:
- if adapter_resume_from is None:
- self.adapter = adapter_cls()
- self.adapter.to(device=device)
- else:
- self.adapter = adapter_cls.load(adapter_resume_from, device=device)
- self.adapter.train()
- self.adapter.requires_grad_(True)
- self.adapter = DDP(self.adapter, device_ids=[device])
- else:
- self.adapter = None
-
- self.mixed_precision = mixed_precision
- self.timestep_sampling = timestep_sampling
-
- self.validation_images_logged = False
- self.log_validation_input_images_every_time = log_validation_input_images_every_time
-
- self.get_sdxl_conditioning_images = get_sdxl_conditioning_images
-
- self.train_unet = train_unet
- self.train_unet_up_blocks = train_unet_up_blocks
-
- def train_step(self, batch):
- with torch.no_grad():
- if isinstance(self.unet, DDP):
- unet_dtype = self.unet.module.dtype
- unet_device = self.unet.module.device
- else:
- unet_dtype = self.unet.dtype
- unet_device = self.unet.device
-
- micro_conditioning = batch["micro_conditioning"].to(device=unet_device)
-
- image = batch["image"].to(self.vae.device, dtype=self.vae.dtype)
- latents = self.vae.encode(image).to(dtype=unet_dtype)
-
- text_input_ids_one = batch["text_input_ids_one"].to(self.text_encoder_one.device)
- text_input_ids_two = batch["text_input_ids_two"].to(self.text_encoder_two.device)
-
- encoder_hidden_states, pooled_encoder_hidden_states = sdxl_text_conditioning(self.text_encoder_one, self.text_encoder_two, text_input_ids_one, text_input_ids_two)
-
- encoder_hidden_states = encoder_hidden_states.to(dtype=unet_dtype)
- pooled_encoder_hidden_states = pooled_encoder_hidden_states.to(dtype=unet_dtype)
-
- bsz = latents.shape[0]
-
- if self.timestep_sampling == "uniform":
- timesteps = torch.randint(0, default_num_train_timesteps, (bsz,), device=unet_device)
- elif self.timestep_sampling == "cubic":
- # Cubic sampling to sample a random timestep for each image
- timesteps = torch.rand((bsz,), device=unet_device)
- timesteps = (1 - timesteps**3) * default_num_train_timesteps
- timesteps = timesteps.long()
- timesteps = timesteps.clamp(0, default_num_train_timesteps - 1)
- else:
- assert False
-
- sigmas_ = self.sigmas[timesteps].to(dtype=latents.dtype)
-
- noise = torch.randn_like(latents)
-
- noisy_latents = latents + noise * sigmas_
-
- scaled_noisy_latents = noisy_latents / ((sigmas_**2 + 1) ** 0.5)
-
- if "conditioning_image" in batch:
- conditioning_image = batch["conditioning_image"].to(unet_device)
-
- if self.controlnet is not None and isinstance(self.controlnet, SDXLControlNetPreEncodedControlnetCond):
- controlnet_device = self.controlnet.module.device
- controlnet_dtype = self.controlnet.module.dtype
- conditioning_image = self.vae.encode(conditioning_image.to(self.vae.dtype)).to(device=controlnet_device, dtype=controlnet_dtype)
- conditioning_image_mask = TF.resize(batch["conditioning_image_mask"], conditioning_image.shape[2:]).to(device=controlnet_device, dtype=controlnet_dtype)
- conditioning_image = torch.concat((conditioning_image, conditioning_image_mask), dim=1)
-
- with torch.autocast(
- "cuda",
- self.mixed_precision,
- enabled=self.mixed_precision is not None,
- ):
- down_block_additional_residuals = None
- mid_block_additional_residual = None
- add_to_down_block_inputs = None
- add_to_output = None
-
- if self.adapter is not None:
- down_block_additional_residuals = self.adapter(conditioning_image)
-
- if self.controlnet is not None:
- controlnet_out = self.controlnet(
- x_t=scaled_noisy_latents,
- t=timesteps,
- encoder_hidden_states=encoder_hidden_states,
- micro_conditioning=micro_conditioning,
- pooled_encoder_hidden_states=pooled_encoder_hidden_states,
- controlnet_cond=conditioning_image,
- )
-
- down_block_additional_residuals = controlnet_out["down_block_res_samples"]
- mid_block_additional_residual = controlnet_out["mid_block_res_sample"]
- add_to_down_block_inputs = controlnet_out.get("add_to_down_block_inputs", None)
- add_to_output = controlnet_out.get("add_to_output", None)
-
- model_pred = self.unet(
- x_t=scaled_noisy_latents,
- t=timesteps,
- encoder_hidden_states=encoder_hidden_states,
- micro_conditioning=micro_conditioning,
- pooled_encoder_hidden_states=pooled_encoder_hidden_states,
- down_block_additional_residuals=down_block_additional_residuals,
- mid_block_additional_residual=mid_block_additional_residual,
- add_to_down_block_inputs=add_to_down_block_inputs,
- add_to_output=add_to_output,
- ).sample
-
- loss = F.mse_loss(model_pred.float(), noise.float(), reduction="mean")
-
- return loss
-
- @torch.no_grad()
- def log_validation(self, step, num_validation_images: int, validation_prompts: Optional[List[str]] = None, validation_images: Optional[List[str]] = None):
- import wandb
-
- if isinstance(self.unet, DDP):
- unet = self.unet.module
- unet.eval()
- unet_set_to_eval = True
- else:
- unet = self.unet
- unet_set_to_eval = False
-
- if self.adapter is not None:
- adapter = self.adapter.module
- adapter.eval()
- else:
- adapter = None
-
- if self.controlnet is not None:
- controlnet = self.controlnet.module
- controlnet.eval()
- else:
- controlnet = None
-
- formatted_validation_images = None
-
- if validation_images is not None:
- formatted_validation_images = []
- wandb_validation_images = []
-
- for validation_image_path in validation_images:
- validation_image = Image.open(validation_image_path)
- validation_image = validation_image.convert("RGB")
- validation_image = validation_image.resize((1024, 1024))
-
- conditioning_images = self.get_sdxl_conditioning_images(validation_image)
-
- conditioning_image = conditioning_images["conditioning_image"]
-
- if self.controlnet is not None and isinstance(self.controlnet, SDXLControlNetPreEncodedControlnetCond):
- conditioning_image = self.vae.encode(conditioning_image[None, :, :, :].to(self.vae.device, dtype=self.vae.dtype))
- conditionin_mask_image = TF.resize(conditioning_images["conditioning_mask_image"], conditioning_image.shape[2:]).to(conditioning_image.dtype, conditioning_image.device)
- conditioning_image = torch.concat(conditioning_image, conditionin_mask_image, dim=1)
-
- formatted_validation_images.append(conditioning_image)
- wandb_validation_images.append(wandb.Image(conditioning_images["conditioning_image_as_pil"]))
-
- if self.log_validation_input_images_every_time or not self.validation_images_logged:
- wandb.log({"validation_conditioning": wandb_validation_images}, step=step)
- self.validation_images_logged = True
-
- generator = torch.Generator().manual_seed(0)
-
- output_validation_images = []
-
- for formatted_validation_image, validation_prompt in zip(formatted_validation_images, validation_prompts):
- for _ in range(num_validation_images):
- with torch.autocast("cuda"):
- x_0 = sdxl_diffusion_loop(
- prompts=validation_prompt,
- images=formatted_validation_image,
- unet=unet,
- text_encoder_one=self.text_encoder_one,
- text_encoder_two=self.text_encoder_two,
- controlnet=controlnet,
- adapter=adapter,
- sigmas=self.sigmas,
- generator=generator,
- )
-
- x_0 = self.vae.decode(x_0)
- x_0 = self.vae.output_tensor_to_pil(x_0)[0]
-
- output_validation_images.append(wandb.Image(x_0, caption=validation_prompt))
-
- wandb.log({"validation": output_validation_images}, step=step)
-
- if unet_set_to_eval:
- unet.train()
-
- if adapter is not None:
- adapter.train()
-
- if controlnet is not None:
- controlnet.train()
-
- def parameters(self):
- if self.train_unet:
- return self.unet.parameters()
-
- if self.controlnet is not None and self.train_unet_up_blocks:
- return itertools.chain(self.controlnet.parameters(), self.unet.up_blocks.parameters())
-
- if self.controlnet is not None:
- return self.controlnet.parameters()
-
- if self.adapter is not None:
- return self.adapter.parameters()
-
- assert False
-
- def save(self, save_to):
- if self.train_unet:
- safetensors.torch.save_file(self.unet.module.state_dict(), os.path.join(save_to, "unet.safetensors"))
-
- if self.controlnet is not None and self.train_unet_up_blocks:
- safetensors.torch.save_file(self.controlnet.module.state_dict(), os.path.join(save_to, "controlnet.safetensors"))
- safetensors.torch.save_file(self.unet.module.up_blocks.state_dict(), os.path.join(save_to, "unet.safetensors"))
-
- if self.controlnet is not None:
- safetensors.torch.save_file(self.controlnet.module.state_dict(), os.path.join(save_to, "controlnet.safetensors"))
-
- if self.adapter is not None:
- safetensors.torch.save_file(self.adapter.module.state_dict(), os.path.join(save_to, "adapter.safetensors"))
-
-
-def get_sdxl_dataset(train_shards: str, shuffle_buffer_size: int, batch_size: int, proportion_empty_prompts: float, get_sdxl_conditioning_images=None):
- import webdataset as wds
-
- dataset = (
- wds.WebDataset(
- train_shards,
- resampled=True,
- handler=wds.ignore_and_continue,
- )
- .shuffle(shuffle_buffer_size)
- .decode("pil", handler=wds.ignore_and_continue)
- .rename(
- image="jpg;png;jpeg;webp",
- text="text;txt;caption",
- metadata="json",
- handler=wds.warn_and_continue,
- )
- .map(lambda d: make_sample(d, proportion_empty_prompts=proportion_empty_prompts, get_sdxl_conditioning_images=get_sdxl_conditioning_images))
- .select(lambda sample: "conditioning_image" not in sample or sample["conditioning_image"] is not None)
- )
-
- dataset = dataset.batched(batch_size, partial=False, collation_fn=default_collate)
-
- return dataset
-
-
-@torch.no_grad()
-def make_sample(d, proportion_empty_prompts, get_sdxl_conditioning_images=None):
- image = d["image"]
- metadata = d["metadata"]
-
- if random.random() < proportion_empty_prompts:
- text = ""
- else:
- text = d["text"]
-
- c_top, c_left, _, _ = get_random_crop_params([image.height, image.width], [1024, 1024])
-
- original_width = int(metadata.get("original_width", 0.0))
- original_height = int(metadata.get("original_height", 0.0))
-
- micro_conditioning = torch.tensor([original_width, original_height, c_top, c_left, 1024, 1024])
-
- text_input_ids_one = sdxl_tokenize_one(text)[0]
-
- text_input_ids_two = sdxl_tokenize_two(text)[0]
-
- image = image.convert("RGB")
-
- image = TF.resize(
- image,
- 1024,
- interpolation=torchvision.transforms.InterpolationMode.BILINEAR,
- )
-
- image = TF.crop(
- image,
- c_top,
- c_left,
- 1024,
- 1024,
- )
-
- sample = {
- "micro_conditioning": micro_conditioning,
- "text_input_ids_one": text_input_ids_one,
- "text_input_ids_two": text_input_ids_two,
- "image": SDXLVae.input_pil_to_tensor(image),
- }
-
- if get_sdxl_conditioning_images is not None:
- conditioning_images = get_sdxl_conditioning_images(image)
-
- sample["conditioning_image"] = conditioning_images["conditioning_image"]
-
- if conditioning_images["conditioning_image_mask"] is not None:
- sample["conditioning_image_mask"] = conditioning_images["conditioning_image_mask"]
-
- return sample
-
-
-def get_random_crop_params(input_size: Tuple[int, int], output_size: Tuple[int, int]) -> Tuple[int, int, int, int]:
- h, w = input_size
-
- th, tw = output_size
-
- if h < th or w < tw:
- raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}")
-
- if w == tw and h == th:
- return 0, 0, h, w
-
- i = torch.randint(0, h - th + 1, size=(1,)).item()
- j = torch.randint(0, w - tw + 1, size=(1,)).item()
-
- return i, j, th, tw
-
-
-def get_adapter_openpose_conditioning_image(image, open_pose):
- resolution = image.width
-
- conditioning_image = open_pose(image, detect_resolution=resolution, image_resolution=resolution, return_pil=False)
-
- if (conditioning_image == 0).all():
- return None, None
-
- conditioning_image_as_pil = Image.fromarray(conditioning_image)
-
- conditioning_image = TF.to_tensor(conditioning_image)
-
- return dict(conditioning_image=conditioning_image, conditioning_image_as_pil=conditioning_image_as_pil)
-
-
-def get_controlnet_canny_conditioning_image(image):
- import cv2
-
- conditioning_image = np.array(image)
- conditioning_image = cv2.Canny(conditioning_image, 100, 200)
- conditioning_image = conditioning_image[:, :, None]
- conditioning_image = np.concatenate([conditioning_image, conditioning_image, conditioning_image], axis=2)
-
- conditioning_image_as_pil = Image.fromarray(conditioning_image)
-
- conditioning_image = TF.to_tensor(conditioning_image)
-
- return dict(conditioning_image=conditioning_image, conditioning_image_as_pil=conditioning_image_as_pil)
-
-
-def get_controlnet_pre_encoded_controlnet_inpainting_conditioning_image(image, conditioning_image_mask):
- resolution = image.width
-
- if conditioning_image_mask is None:
- if random.random() <= 0.25:
- conditioning_image_mask = np.ones((resolution, resolution), np.float32)
- else:
- conditioning_image_mask = random.choice([make_random_rectangle_mask, make_random_irregular_mask, make_outpainting_mask])(resolution, resolution)
-
- conditioning_image_mask = torch.from_numpy(conditioning_image_mask)
-
- conditioning_image_mask = conditioning_image_mask[None, :, :]
-
- conditioning_image = TF.to_tensor(image)
-
- # where mask is 1, zero out the pixels. Note that this requires mask to be concattenated
- # with the mask so that the network knows the zeroed out pixels are from the mask and
- # are not just zero in the original image
- conditioning_image = conditioning_image * (conditioning_image_mask < 0.5)
-
- conditioning_image_as_pil = TF.to_pil_image(conditioning_image)
-
- conditioning_image = TF.normalize(conditioning_image, [0.5], [0.5])
-
- return dict(conditioning_image=conditioning_image, conditioning_image_mask=conditioning_image_mask, conditioning_image_as_pil=conditioning_image_as_pil)
-
-
-def get_controlnet_inpainting_conditioning_image(image, conditioning_image_mask):
- resolution = image.width
-
- if conditioning_image_mask is None:
- if random.random() <= 0.25:
- conditioning_image_mask = np.ones((resolution, resolution), np.float32)
- else:
- conditioning_image_mask = random.choice([make_random_rectangle_mask, make_random_irregular_mask, make_outpainting_mask])(resolution, resolution)
-
- conditioning_image_mask = torch.from_numpy(conditioning_image_mask)
-
- conditioning_image_mask = conditioning_image_mask[None, :, :]
-
- conditioning_image = TF.to_tensor(image)
-
- # Just zero out the pixels which will be masked
- conditioning_image_as_pil = TF.to_pil_image(conditioning_image * (conditioning_image_mask < 0.5))
-
- # where mask is set to 1, set to -1 "special" masked image pixel.
- # -1 is outside of the 0-1 range that the controlnet normalized
- # input is in.
- conditioning_image = conditioning_image * (conditioning_image_mask < 0.5) + -1.0 * (conditioning_image_mask >= 0.5)
-
- return dict(conditioning_image=conditioning_image, conditioning_image_mask=conditioning_image_mask, conditioning_image_as_pil=conditioning_image_as_pil)
-
-
-# TODO: would be nice to just call a function from a tokenizers https://github.com/huggingface/tokenizers
-# i.e. afaik tokenizing shouldn't require holding any state
-
-tokenizer_one = CLIPTokenizerFast.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="tokenizer")
-
-tokenizer_two = CLIPTokenizerFast.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="tokenizer_2")
-
-
-def sdxl_tokenize_one(prompts):
- return tokenizer_one(
- prompts,
- padding="max_length",
- max_length=tokenizer_one.model_max_length,
- truncation=True,
- return_tensors="pt",
- ).input_ids
-
-
-def sdxl_tokenize_two(prompts):
- return tokenizer_two(
- prompts,
- padding="max_length",
- max_length=tokenizer_one.model_max_length,
- truncation=True,
- return_tensors="pt",
- ).input_ids
-
-
-def sdxl_text_conditioning(text_encoder_one, text_encoder_two, text_input_ids_one, text_input_ids_two):
- prompt_embeds_1 = text_encoder_one(
- text_input_ids_one,
- output_hidden_states=True,
- ).hidden_states[-2]
-
- prompt_embeds_1 = prompt_embeds_1.view(prompt_embeds_1.shape[0], prompt_embeds_1.shape[1], -1)
-
- prompt_embeds_2 = text_encoder_two(
- text_input_ids_two,
- output_hidden_states=True,
- )
-
- pooled_encoder_hidden_states = prompt_embeds_2[0]
-
- prompt_embeds_2 = prompt_embeds_2.hidden_states[-2]
-
- prompt_embeds_2 = prompt_embeds_2.view(prompt_embeds_2.shape[0], prompt_embeds_2.shape[1], -1)
-
- encoder_hidden_states = torch.cat((prompt_embeds_1, prompt_embeds_2), dim=-1)
-
- return encoder_hidden_states, pooled_encoder_hidden_states
-
-
-def make_random_rectangle_mask(
- height,
- width,
- margin=10,
- bbox_min_size=100,
- bbox_max_size=512,
- min_times=1,
- max_times=2,
-):
- mask = np.zeros((height, width), np.float32)
-
- bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2)
-
- times = np.random.randint(min_times, max_times + 1)
-
- for i in range(times):
- box_width = np.random.randint(bbox_min_size, bbox_max_size)
- box_height = np.random.randint(bbox_min_size, bbox_max_size)
-
- start_x = np.random.randint(margin, width - margin - box_width + 1)
- start_y = np.random.randint(margin, height - margin - box_height + 1)
-
- mask[start_y : start_y + box_height, start_x : start_x + box_width] = 1
-
- return mask
-
-
-def make_random_irregular_mask(height, width, max_angle=4, max_len=60, max_width=256, min_times=1, max_times=2):
- import cv2
-
- mask = np.zeros((height, width), np.float32)
-
- times = np.random.randint(min_times, max_times + 1)
-
- for i in range(times):
- start_x = np.random.randint(width)
- start_y = np.random.randint(height)
-
- for j in range(1 + np.random.randint(5)):
- angle = 0.01 + np.random.randint(max_angle)
-
- if i % 2 == 0:
- angle = 2 * 3.1415926 - angle
-
- length = 10 + np.random.randint(max_len)
-
- brush_w = 5 + np.random.randint(max_width)
-
- end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width)
- end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height)
-
- choice = random.randint(0, 2)
-
- if choice == 0:
- cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w)
- elif choice == 1:
- cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1.0, thickness=-1)
- elif choice == 2:
- radius = brush_w // 2
- mask[
- start_y - radius : start_y + radius,
- start_x - radius : start_x + radius,
- ] = 1
- else:
- assert False
-
- start_x, start_y = end_x, end_y
-
- return mask
-
-
-def make_outpainting_mask(height, width, probs=[0.5, 0.5, 0.5, 0.5]):
- mask = np.zeros((height, width), np.float32)
- at_least_one_mask_applied = False
-
- coords = [
- [(0, 0), (1, get_padding(height))],
- [(0, 0), (get_padding(width), 1)],
- [(0, 1 - get_padding(height)), (1, 1)],
- [(1 - get_padding(width), 0), (1, 1)],
- ]
-
- for pp, coord in zip(probs, coords):
- if np.random.random() < pp:
- at_least_one_mask_applied = True
- mask = apply_padding(mask=mask, coord=coord)
-
- if not at_least_one_mask_applied:
- idx = np.random.choice(range(len(coords)), p=np.array(probs) / sum(probs))
- mask = apply_padding(mask=mask, coord=coords[idx])
-
- return mask
-
-
-def get_padding(size, min_padding_percent=0.04, max_padding_percent=0.5):
- n1 = int(min_padding_percent * size)
- n2 = int(max_padding_percent * size)
- return np.random.randint(n1, n2) / size
-
-
-def apply_padding(mask, coord):
- height, width = mask.shape
-
- mask[
- int(coord[0][0] * height) : int(coord[1][0] * height),
- int(coord[0][1] * width) : int(coord[1][1] * width),
- ] = 1
-
- return mask
-
-
-@torch.no_grad()
-def sdxl_diffusion_loop(
- prompts: Union[str, List[str]],
- unet,
- text_encoder_one,
- text_encoder_two,
- images=None,
- controlnet=None,
- adapter=None,
- sigmas=None,
- timesteps=None,
- x_T=None,
- micro_conditioning=None,
- guidance_scale=5.0,
- generator=None,
- negative_prompts=None,
- diffusion_loop=euler_ode_solver_diffusion_loop,
-):
- if isinstance(prompts, str):
- prompts = [prompts]
-
- batch_size = len(prompts)
-
- if negative_prompts is not None and guidance_scale > 1.0:
- prompts += negative_prompts
-
- encoder_hidden_states, pooled_encoder_hidden_states = sdxl_text_conditioning(
- text_encoder_one,
- text_encoder_two,
- sdxl_tokenize_one(prompts).to(text_encoder_one.device),
- sdxl_tokenize_two(prompts).to(text_encoder_two.device),
- )
- encoder_hidden_states = encoder_hidden_states.to(unet.dtype)
- pooled_encoder_hidden_states = pooled_encoder_hidden_states.to(unet.dtype)
-
- if guidance_scale > 1.0:
- if negative_prompts is None:
- negative_encoder_hidden_states = torch.zeros_like(encoder_hidden_states)
- negative_pooled_encoder_hidden_states = torch.zeros_like(pooled_encoder_hidden_states)
- else:
- encoder_hidden_states, negative_encoder_hidden_states = torch.chunk(encoder_hidden_states, 2)
- pooled_encoder_hidden_states, negative_pooled_encoder_hidden_states = torch.chunk(pooled_encoder_hidden_states, 2)
- else:
- negative_encoder_hidden_states = None
- negative_pooled_encoder_hidden_states = None
-
- if sigmas is None:
- sigmas = make_sigmas(device=unet.device)
-
- if timesteps is None:
- timesteps = torch.linspace(0, sigmas.numel() - 1, 50, dtype=torch.long, device=unet.device)
-
- if x_T is None:
- x_T = torch.randn((batch_size, 4, 1024 // 8, 1024 // 8), dtype=unet.dtype, device=unet.device, generator=generator)
- x_T = x_T * ((sigmas[timesteps[-1]] ** 2 + 1) ** 0.5)
-
- if micro_conditioning is None:
- micro_conditioning = torch.tensor([[1024, 1024, 0, 0, 1024, 1024]], dtype=torch.long, device=unet.device)
- micro_conditioning = micro_conditioning.expand(batch_size, -1)
-
- if adapter is not None:
- down_block_additional_residuals = adapter(images.to(dtype=adapter.dtype, device=adapter.device))
- else:
- down_block_additional_residuals = None
-
- if controlnet is not None:
- controlnet_cond = images.to(dtype=controlnet.dtype, device=controlnet.device)
- else:
- controlnet_cond = None
-
- eps_theta = lambda *args, **kwargs: sdxl_eps_theta(
- *args,
- **kwargs,
- unet=unet,
- encoder_hidden_states=encoder_hidden_states,
- pooled_encoder_hidden_states=pooled_encoder_hidden_states,
- negative_encoder_hidden_states=negative_encoder_hidden_states,
- negative_pooled_encoder_hidden_states=negative_pooled_encoder_hidden_states,
- micro_conditioning=micro_conditioning,
- guidance_scale=guidance_scale,
- controlnet=controlnet,
- controlnet_cond=controlnet_cond,
- down_block_additional_residuals=down_block_additional_residuals,
- )
-
- x_0 = diffusion_loop(eps_theta=eps_theta, timesteps=timesteps, sigmas=sigmas, x_T=x_T)
-
- return x_0
-
-
-@torch.no_grad()
-def sdxl_eps_theta(
- x_t,
- t,
- sigma,
- unet,
- encoder_hidden_states,
- pooled_encoder_hidden_states,
- negative_encoder_hidden_states,
- negative_pooled_encoder_hidden_states,
- micro_conditioning,
- guidance_scale,
- controlnet=None,
- controlnet_cond=None,
- down_block_additional_residuals=None,
-):
- # TODO - how does this not effect the ode we are solving
- scaled_x_t = x_t / ((sigma**2 + 1) ** 0.5)
-
- if guidance_scale > 1.0:
- scaled_x_t = torch.concat([scaled_x_t, scaled_x_t])
-
- encoder_hidden_states = torch.concat((encoder_hidden_states, negative_encoder_hidden_states))
- pooled_encoder_hidden_states = torch.concat((pooled_encoder_hidden_states, negative_pooled_encoder_hidden_states))
-
- micro_conditioning = torch.concat([micro_conditioning, micro_conditioning])
-
- if controlnet_cond is not None:
- controlnet_cond = torch.concat([controlnet_cond, controlnet_cond])
-
- if controlnet is not None:
- controlnet_out = controlnet(
- x_t=scaled_x_t.to(controlnet.dtype),
- t=t,
- encoder_hidden_states=encoder_hidden_states.to(controlnet.dtype),
- micro_conditioning=micro_conditioning.to(controlnet.dtype),
- pooled_encoder_hidden_states=pooled_encoder_hidden_states.to(controlnet.dtype),
- controlnet_cond=controlnet_cond,
- )
-
- down_block_additional_residuals = [x.to(unet.dtype) for x in controlnet_out["down_block_res_samples"]]
- mid_block_additional_residual = controlnet_out["mid_block_res_sample"].to(unet.dtype)
- add_to_down_block_inputs = controlnet_out.get("add_to_down_block_inputs", None)
- if add_to_down_block_inputs is not None:
- add_to_down_block_inputs = [x.to(unet.dtype) for x in add_to_down_block_inputs]
- add_to_output = controlnet_out.get("add_to_output", None)
- if add_to_output is not None:
- add_to_output = add_to_output.to(unet.dtype)
- else:
- mid_block_additional_residual = None
- add_to_down_block_inputs = None
- add_to_output = None
-
- eps_hat = unet(
- x_t=scaled_x_t,
- t=t,
- encoder_hidden_states=encoder_hidden_states,
- micro_conditioning=micro_conditioning,
- pooled_encoder_hidden_states=pooled_encoder_hidden_states,
- down_block_additional_residuals=down_block_additional_residuals,
- mid_block_additional_residual=mid_block_additional_residual,
- add_to_down_block_inputs=add_to_down_block_inputs,
- add_to_output=add_to_output,
- )
-
- if guidance_scale > 1.0:
- eps_hat, eps_hat_uncond = eps_hat.chunk(2)
-
- eps_hat = eps_hat_uncond + guidance_scale * (eps_hat - eps_hat_uncond)
-
- return eps_hat
-
-
-known_negative_prompt = "text, watermark, low-quality, signature, moiré pattern, downsampling, aliasing, distorted, blurry, glossy, blur, jpeg artifacts, compression artifacts, poorly drawn, low-resolution, bad, distortion, twisted, excessive, exaggerated pose, exaggerated limbs, grainy, symmetrical, duplicate, error, pattern, beginner, pixelated, fake, hyper, glitch, overexposed, high-contrast, bad-contrast"
-
-if __name__ == "__main__":
- from argparse import ArgumentParser
-
- args = ArgumentParser()
- args.add_argument("--prompts", required=True, type=str, nargs="+")
- args.add_argument("--negative_prompts", required=False, type=str, nargs="+")
- args.add_argument("--use_known_negative_prompt", action="store_true")
- args.add_argument("--num_images_per_prompt", required=True, type=int, default=1)
- args.add_argument("--num_inference_steps", required=False, type=int, default=50)
- args.add_argument("--images", required=False, type=str, default=None, nargs="+")
- args.add_argument("--masks", required=False, type=str, default=None, nargs="+")
- args.add_argument("--controlnet_checkpoint", required=False, type=str, default=None)
- args.add_argument("--controlnet", required=False, choices=["SDXLControlNet", "SDXLControlNetFull", "SDXLControNetPreEncodedControlnetCond"], default=None)
- args.add_argument("--adapter_checkpoint", required=False, type=str, default=None)
- args.add_argument("--device", required=False, default=None)
- args.add_argument("--dtype", required=False, default="fp16", choices=["fp16", "fp32"])
- args.add_argument("--guidance_scale", required=False, default=5.0, type=float)
- args.add_argument("--seed", required=False, type=int)
- args = args.parse_args()
-
- if args.device is None:
- if torch.cuda.is_available():
- device = "cuda"
- elif torch.backends.mps.is_available():
- device = "mps"
-
- if args.dtype == "fp16":
- dtype = torch.float16
-
- text_encoder_one = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", variant="fp16", torch_dtype=torch.float16)
- text_encoder_one.to(device=device)
-
- text_encoder_two = CLIPTextModelWithProjection.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder_2", variant="fp16", torch_dtype=torch.float16)
- text_encoder_two.to(device=device)
-
- vae = SDXLVae.load_fp16_fix(device=device)
- vae.to(torch.float16)
-
- unet = SDXLUNet.load_fp16(device=device)
- elif args.dtype == "fp32":
- dtype = torch.float32
-
- text_encoder_one = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder")
- text_encoder_one.to(device=device)
-
- text_encoder_two = CLIPTextModelWithProjection.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder_2")
- text_encoder_two.to(device=device)
-
- vae = SDXLVae.load_fp16_fix(device=device)
-
- unet = SDXLUNet.load_fp32(device=device)
- else:
- assert False
-
- if args.controlnet == "SDXLControlNet":
- controlnet = SDXLControlNet.load(args.controlnet_checkpoint, device=device)
- controlnet.to(dtype)
- elif args.controlnet == "SDXLControlNetFull":
- controlnet = SDXLControlNetFull.load(args.controlnet_checkpoint, device=device)
- controlnet.to(dtype)
- elif args.controlnet == "SDXLControlNetPreEncodedControlnetCond":
- controlnet = SDXLControlNetPreEncodedControlnetCond.load(args.controlnet_checkpoint, device=device)
- controlnet.to(dtype)
- else:
- controlnet = None
-
- if args.adapter_checkpoint is not None:
- adapter = SDXLAdapter.load(args.adapter_checkpoint, device=device)
- adapter.to(dtype)
- else:
- adapter = None
-
- sigmas = make_sigmas(device=device).to(unet.dtype)
-
- timesteps = torch.linspace(0, sigmas.numel() - 1, args.num_inference_steps, dtype=torch.long, device=unet.device)
-
- prompts = []
- for prompt in args.prompts:
- prompts += [prompt] * args.num_images_per_prompt
-
- if args.use_known_negative_prompt:
- args.negative_prompts = [known_negative_prompt]
-
- if args.negative_prompts is None:
- negative_prompts = None
- elif len(args.negative_prompts) == 1:
- negative_prompts = args.negative_prompts * len(prompts)
- elif len(args.negative_prompts) == len(args.prompts):
- negative_prompts = []
- for negative_prompt in args.negative_prompts:
- negative_prompts += [negative_prompt] * args.num_images_per_prompt
- else:
- assert False
-
- if args.images is not None:
- images = []
-
- for image_idx, image in enumerate(args.images):
- image = Image.open(image)
- image = image.convert("RGB")
- image = image.resize((1024, 1024))
- image = TF.to_tensor(image)
-
- if args.masks is not None:
- mask = args.masks[image_idx]
- mask = Image.open(mask)
- mask = mask.convert("L")
- mask = mask.resize((1024, 1024))
- mask = TF.to_tensor(mask)
-
- if isinstance(controlnet, SDXLControlNetPreEncodedControlnetCond):
- image = image * (mask < 0.5)
- image = TF.normalize(image, [0.5], [0.5])
- image = vae.encode(image[None, :, :, :].to(dtype=vae.dtype, device=vae.device)).to(dtype=controlnet.dtype, device=controlnet.device)
- mask = TF.resize(mask, (1024 // 8, 1024 // 8))[None, :, :, :].to(dtype=image.dtype, device=image.device)
- image = torch.concat((image, mask), dim=1)
- else:
- image = (image * (mask < 0.5) + -1.0 * (mask >= 0.5)).to(dtype=dtype, device=device)
- image = image[None, :, :, :]
-
- images += [image] * args.num_images_per_prompt
-
- images = torch.concat(images)
- else:
- images = None
-
- if args.seed is None:
- generator = None
- else:
- generator = torch.Generator(device).manual_seed(args.seed)
-
- images = sdxl_diffusion_loop(
- prompts=prompts,
- unet=unet,
- text_encoder_one=text_encoder_one,
- text_encoder_two=text_encoder_two,
- images=images,
- controlnet=controlnet,
- adapter=adapter,
- sigmas=sigmas,
- timesteps=timesteps,
- guidance_scale=args.guidance_scale,
- negative_prompts=negative_prompts,
- generator=generator,
- )
-
- images = vae.output_tensor_to_pil(vae.decode(images))
-
- for i, image in enumerate(images):
- image.save(f"out_{i}.png")
diff --git a/spaces/xiaorong/fork2-so-vits/text/cleaners.py b/spaces/xiaorong/fork2-so-vits/text/cleaners.py
deleted file mode 100644
index d26581deb399609163518054718ad80ecca5d934..0000000000000000000000000000000000000000
--- a/spaces/xiaorong/fork2-so-vits/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄳ', 'ㄱㅅ'),
- ('ㄵ', 'ㄴㅈ'),
- ('ㄶ', 'ㄴㅎ'),
- ('ㄺ', 'ㄹㄱ'),
- ('ㄻ', 'ㄹㅁ'),
- ('ㄼ', 'ㄹㅂ'),
- ('ㄽ', 'ㄹㅅ'),
- ('ㄾ', 'ㄹㅌ'),
- ('ㄿ', 'ㄹㅍ'),
- ('ㅀ', 'ㄹㅎ'),
- ('ㅄ', 'ㅂㅅ'),
- ('ㅘ', 'ㅗㅏ'),
- ('ㅙ', 'ㅗㅐ'),
- ('ㅚ', 'ㅗㅣ'),
- ('ㅝ', 'ㅜㅓ'),
- ('ㅞ', 'ㅜㅔ'),
- ('ㅟ', 'ㅜㅣ'),
- ('ㅢ', 'ㅡㅣ'),
- ('ㅑ', 'ㅣㅏ'),
- ('ㅒ', 'ㅣㅐ'),
- ('ㅕ', 'ㅣㅓ'),
- ('ㅖ', 'ㅣㅔ'),
- ('ㅛ', 'ㅣㅗ'),
- ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', '에이'),
- ('b', '비'),
- ('c', '시'),
- ('d', '디'),
- ('e', '이'),
- ('f', '에프'),
- ('g', '지'),
- ('h', '에이치'),
- ('i', '아이'),
- ('j', '제이'),
- ('k', '케이'),
- ('l', '엘'),
- ('m', '엠'),
- ('n', '엔'),
- ('o', '오'),
- ('p', '피'),
- ('q', '큐'),
- ('r', '아르'),
- ('s', '에스'),
- ('t', '티'),
- ('u', '유'),
- ('v', '브이'),
- ('w', '더블유'),
- ('x', '엑스'),
- ('y', '와이'),
- ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'ㄟˉ'),
- ('b', 'ㄅㄧˋ'),
- ('c', 'ㄙㄧˉ'),
- ('d', 'ㄉㄧˋ'),
- ('e', 'ㄧˋ'),
- ('f', 'ㄝˊㄈㄨˋ'),
- ('g', 'ㄐㄧˋ'),
- ('h', 'ㄝˇㄑㄩˋ'),
- ('i', 'ㄞˋ'),
- ('j', 'ㄐㄟˋ'),
- ('k', 'ㄎㄟˋ'),
- ('l', 'ㄝˊㄛˋ'),
- ('m', 'ㄝˊㄇㄨˋ'),
- ('n', 'ㄣˉ'),
- ('o', 'ㄡˉ'),
- ('p', 'ㄆㄧˉ'),
- ('q', 'ㄎㄧㄡˉ'),
- ('r', 'ㄚˋ'),
- ('s', 'ㄝˊㄙˋ'),
- ('t', 'ㄊㄧˋ'),
- ('u', 'ㄧㄡˉ'),
- ('v', 'ㄨㄧˉ'),
- ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
- ('x', 'ㄝˉㄎㄨˋㄙˋ'),
- ('y', 'ㄨㄞˋ'),
- ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'ʧ⁼'),
- ('ㄑ', 'ʧʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ʦ`⁼'),
- ('ㄔ', 'ʦ`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ʦ⁼'),
- ('ㄘ', 'ʦʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'e'),
- ('ㄞ', 'ai'),
- ('ㄟ', 'ei'),
- ('ㄠ', 'au'),
- ('ㄡ', 'ou'),
- ('ㄧㄢ', 'yeNN'),
- ('ㄢ', 'aNN'),
- ('ㄧㄣ', 'iNN'),
- ('ㄣ', 'əNN'),
- ('ㄤ', 'aNg'),
- ('ㄧㄥ', 'iNg'),
- ('ㄨㄥ', 'uNg'),
- ('ㄩㄥ', 'yuNg'),
- ('ㄥ', 'əNg'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def lowercase(text):
- return text.lower()
-
-
-def collapse_whitespace(text):
- return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
- return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text!='':
- text+=' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil','pau']:
- text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
- else:
- continue
- n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
- a2_next=-1
- else:
- a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i Tuple[Dict, Dict]:
- """Function that takes the fairseq Musicgen state dict and renames it according to the HF
- module names. It further partitions the state dict into the decoder (LM) state dict, and that for the
- encoder-decoder projection."""
- keys = list(state_dict.keys())
- enc_dec_proj_state_dict = {}
- for key in keys:
- val = state_dict.pop(key)
- key = rename_keys(key)
- if "in_proj_weight" in key:
- # split fused qkv proj
- state_dict[key.replace("in_proj_weight", "q_proj.weight")] = val[:hidden_size, :]
- state_dict[key.replace("in_proj_weight", "k_proj.weight")] = val[hidden_size : 2 * hidden_size, :]
- state_dict[key.replace("in_proj_weight", "v_proj.weight")] = val[-hidden_size:, :]
- elif "enc_to_dec_proj" in key:
- enc_dec_proj_state_dict[key[len("enc_to_dec_proj.") :]] = val
- else:
- state_dict[key] = val
- return state_dict, enc_dec_proj_state_dict
-
-
-def decoder_config_from_checkpoint(checkpoint: str) -> MusicgenDecoderConfig:
- if checkpoint == "small":
- # default config values
- hidden_size = 1024
- num_hidden_layers = 24
- num_attention_heads = 16
- elif checkpoint == "medium":
- hidden_size = 1536
- num_hidden_layers = 48
- num_attention_heads = 24
- elif checkpoint == "large":
- hidden_size = 2048
- num_hidden_layers = 48
- num_attention_heads = 32
- else:
- raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.")
- config = MusicgenDecoderConfig(
- hidden_size=hidden_size,
- ffn_dim=hidden_size * 4,
- num_hidden_layers=num_hidden_layers,
- num_attention_heads=num_attention_heads,
- )
- return config
-
-
-@torch.no_grad()
-def convert_musicgen_checkpoint(checkpoint, pytorch_dump_folder=None, repo_id=None, device="cpu"):
- fairseq_model = MusicGen.get_pretrained(checkpoint, device=device)
- decoder_config = decoder_config_from_checkpoint(checkpoint)
-
- decoder_state_dict = fairseq_model.lm.state_dict()
- decoder_state_dict, enc_dec_proj_state_dict = rename_state_dict(
- decoder_state_dict, hidden_size=decoder_config.hidden_size
- )
-
- text_encoder = T5EncoderModel.from_pretrained("t5-base")
- audio_encoder = EncodecModel.from_pretrained("facebook/encodec_32khz")
- decoder = MusicgenForCausalLM(decoder_config).eval()
-
- # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
- missing_keys, unexpected_keys = decoder.load_state_dict(decoder_state_dict, strict=False)
-
- for key in missing_keys.copy():
- if key.startswith(("text_encoder", "audio_encoder")) or key in EXPECTED_MISSING_KEYS:
- missing_keys.remove(key)
-
- if len(missing_keys) > 0:
- raise ValueError(f"Missing key(s) in state_dict: {missing_keys}")
-
- if len(unexpected_keys) > 0:
- raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}")
-
- # init the composite model
- model = MusicgenForConditionalGeneration(text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder)
-
- # load the pre-trained enc-dec projection (from the decoder state dict)
- model.enc_to_dec_proj.load_state_dict(enc_dec_proj_state_dict)
-
- # check we can do a forward pass
- input_ids = torch.arange(0, 8, dtype=torch.long).reshape(2, -1)
- decoder_input_ids = input_ids.reshape(2 * 4, -1)
-
- with torch.no_grad():
- logits = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids).logits
-
- if logits.shape != (8, 1, 2048):
- raise ValueError("Incorrect shape for logits")
-
- # now construct the processor
- tokenizer = AutoTokenizer.from_pretrained("t5-base")
- feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz", padding_side="left")
-
- processor = MusicgenProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
-
- # set the appropriate bos/pad token ids
- model.generation_config.decoder_start_token_id = 2048
- model.generation_config.pad_token_id = 2048
-
- # set other default generation config params
- model.generation_config.max_length = int(30 * audio_encoder.config.frame_rate)
- model.generation_config.do_sample = True
- model.generation_config.guidance_scale = 3.0
-
- if pytorch_dump_folder is not None:
- Path(pytorch_dump_folder).mkdir(exist_ok=True)
- logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}")
- model.save_pretrained(pytorch_dump_folder)
- processor.save_pretrained(pytorch_dump_folder)
-
- if repo_id:
- logger.info(f"Pushing model {checkpoint} to {repo_id}")
- model.push_to_hub(repo_id)
- processor.push_to_hub(repo_id)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- # Required parameters
- parser.add_argument(
- "--checkpoint",
- default="small",
- type=str,
- help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
- )
- parser.add_argument(
- "--pytorch_dump_folder",
- required=True,
- default=None,
- type=str,
- help="Path to the output PyTorch model directory.",
- )
- parser.add_argument(
- "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
- )
- parser.add_argument(
- "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
- )
-
- args = parser.parse_args()
- convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
diff --git a/spaces/yizhangliu/Text-to-Image/baidu_translate/module.py b/spaces/yizhangliu/Text-to-Image/baidu_translate/module.py
deleted file mode 100644
index 531e4e0a7de487f6e82f4e55ff869739faf1abc0..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Text-to-Image/baidu_translate/module.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import argparse
-import random, os
-from hashlib import md5
-from typing import Optional
-
-import requests
-
-import paddlehub as hub
-from paddlehub.module.module import moduleinfo
-from paddlehub.module.module import runnable
-from paddlehub.module.module import serving
-
-def make_md5(s, encoding='utf-8'):
- return md5(s.encode(encoding)).hexdigest()
-
-@moduleinfo(name="baidu_translate",
- version="1.0.0",
- type="text/machine_translation",
- summary="",
- author="baidu-nlp",
- author_email="paddle-dev@baidu.com")
-class BaiduTranslate:
-
- def __init__(self, appid=None, appkey=None):
- """
- :param appid: appid for requesting Baidu translation service.
- :param appkey: appkey for requesting Baidu translation service.
- """
- appid = os.environ.get('baidu_translate_appid')
- appkey = os.environ.get('baidu_translate_appkey')
- # Set your own appid/appkey.
- if appid is None:
- self.appid = ''
- else:
- self.appid = appid
- if appkey is None:
- self.appkey = ''
- else:
- self.appkey = appkey
- self.url = 'http://api.fanyi.baidu.com/api/trans/vip/translate'
-
- def translate(self, query: str, from_lang: Optional[str] = "en", to_lang: Optional[int] = "zh"):
- """
- Create image by text prompts using ErnieVilG model.
- :param query: Text to be translated.
- :param from_lang: Source language.
- :param to_lang: Dst language.
- Return translated string.
- """
- # Generate salt and sign
- salt = random.randint(32768, 65536)
- sign = make_md5(self.appid + query + str(salt) + self.appkey)
-
- # Build request
- headers = {'Content-Type': 'application/x-www-form-urlencoded'}
- payload = {'appid': self.appid, 'q': query, 'from': from_lang, 'to': to_lang, 'salt': salt, 'sign': sign}
-
- # Send request
- try:
- r = requests.post(self.url, params=payload, headers=headers)
- result = r.json()
- except Exception as e:
- error_msg = str(e)
- raise RuntimeError(error_msg)
- if 'error_code' in result:
- raise RuntimeError(result['error_msg'])
- return result['trans_result'][0]['dst']
-
- @runnable
- def run_cmd(self, argvs):
- """
- Run as a command.
- """
- self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name),
- prog='hub run {}'.format(self.name),
- usage='%(prog)s',
- add_help=True)
- self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
- self.add_module_input_arg()
- args = self.parser.parse_args(argvs)
- if args.appid is not None and args.appkey is not None:
- self.appid = args.appid
- self.appkey = args.appkey
- result = self.translate(args.query, args.from_lang, args.to_lang)
- return result
-
- @serving
- def serving_method(self, query, from_lang, to_lang):
- """
- Run as a service.
- """
- return self.translate(query, from_lang, to_lang)
-
- def add_module_input_arg(self):
- """
- Add the command input options.
- """
- self.arg_input_group.add_argument('--query', type=str)
- self.arg_input_group.add_argument('--from_lang', type=str, default='en', help="源语言")
- self.arg_input_group.add_argument('--to_lang', type=str, default='zh', help="目标语言")
- self.arg_input_group.add_argument('--appid', type=str, default=None, help="注册得到的个人appid")
- self.arg_input_group.add_argument('--appkey', type=str, default=None, help="注册得到的个人appkey")
-
diff --git a/spaces/ykilcher/apes/torch_utils/ops/bias_act.py b/spaces/ykilcher/apes/torch_utils/ops/bias_act.py
deleted file mode 100644
index 4bcb409a89ccf6c6f6ecfca5962683df2d280b1f..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/torch_utils/ops/bias_act.py
+++ /dev/null
@@ -1,212 +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.
-
-"""Custom PyTorch ops for efficient bias and activation."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import dnnlib
-import traceback
-
-from .. import custom_ops
-from .. import misc
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
- 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False),
- 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False),
- 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False),
- 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True),
- 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True),
- 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True),
- 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True),
- 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True),
- 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True),
-}
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-_null_tensor = torch.empty([0])
-
-def _init():
- global _inited, _plugin
- if not _inited:
- _inited = True
- sources = ['bias_act.cpp', 'bias_act.cu']
- sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
- try:
- _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
- except:
- warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
- return _plugin is not None
-
-#----------------------------------------------------------------------------
-
-def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
- r"""Fused bias and activation function.
-
- Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
- and scales the result by `gain`. Each of the steps is optional. In most cases,
- the fused op is considerably more efficient than performing the same calculation
- using standard PyTorch ops. It supports first and second order gradients,
- but not third order gradients.
-
- Args:
- x: Input activation tensor. Can be of any shape.
- b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
- as `x`. The shape must be known, and it must match the dimension of `x`
- corresponding to `dim`.
- dim: The dimension in `x` corresponding to the elements of `b`.
- The value of `dim` is ignored if `b` is not specified.
- act: Name of the activation function to evaluate, or `"linear"` to disable.
- Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
- See `activation_funcs` for a full list. `None` is not allowed.
- alpha: Shape parameter for the activation function, or `None` to use the default.
- gain: Scaling factor for the output tensor, or `None` to use default.
- See `activation_funcs` for the default scaling of each activation function.
- If unsure, consider specifying 1.
- clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
- the clamping (default).
- impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
- Returns:
- Tensor of the same shape and datatype as `x`.
- """
- assert isinstance(x, torch.Tensor)
- assert impl in ['ref', 'cuda']
- if impl == 'cuda' and x.device.type == 'cuda' and _init():
- return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
- return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
- """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
- """
- assert isinstance(x, torch.Tensor)
- assert clamp is None or clamp >= 0
- spec = activation_funcs[act]
- alpha = float(alpha if alpha is not None else spec.def_alpha)
- gain = float(gain if gain is not None else spec.def_gain)
- clamp = float(clamp if clamp is not None else -1)
-
- # Add bias.
- if b is not None:
- assert isinstance(b, torch.Tensor) and b.ndim == 1
- assert 0 <= dim < x.ndim
- assert b.shape[0] == x.shape[dim]
- x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
-
- # Evaluate activation function.
- alpha = float(alpha)
- x = spec.func(x, alpha=alpha)
-
- # Scale by gain.
- gain = float(gain)
- if gain != 1:
- x = x * gain
-
- # Clamp.
- if clamp >= 0:
- x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
- return x
-
-#----------------------------------------------------------------------------
-
-_bias_act_cuda_cache = dict()
-
-def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
- """Fast CUDA implementation of `bias_act()` using custom ops.
- """
- # Parse arguments.
- assert clamp is None or clamp >= 0
- spec = activation_funcs[act]
- alpha = float(alpha if alpha is not None else spec.def_alpha)
- gain = float(gain if gain is not None else spec.def_gain)
- clamp = float(clamp if clamp is not None else -1)
-
- # Lookup from cache.
- key = (dim, act, alpha, gain, clamp)
- if key in _bias_act_cuda_cache:
- return _bias_act_cuda_cache[key]
-
- # Forward op.
- class BiasActCuda(torch.autograd.Function):
- @staticmethod
- def forward(ctx, x, b): # pylint: disable=arguments-differ
- ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format
- x = x.contiguous(memory_format=ctx.memory_format)
- b = b.contiguous() if b is not None else _null_tensor
- y = x
- if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
- y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
- ctx.save_for_backward(
- x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
- b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
- y if 'y' in spec.ref else _null_tensor)
- return y
-
- @staticmethod
- def backward(ctx, dy): # pylint: disable=arguments-differ
- dy = dy.contiguous(memory_format=ctx.memory_format)
- x, b, y = ctx.saved_tensors
- dx = None
- db = None
-
- if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
- dx = dy
- if act != 'linear' or gain != 1 or clamp >= 0:
- dx = BiasActCudaGrad.apply(dy, x, b, y)
-
- if ctx.needs_input_grad[1]:
- db = dx.sum([i for i in range(dx.ndim) if i != dim])
-
- return dx, db
-
- # Backward op.
- class BiasActCudaGrad(torch.autograd.Function):
- @staticmethod
- def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
- ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format
- dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
- ctx.save_for_backward(
- dy if spec.has_2nd_grad else _null_tensor,
- x, b, y)
- return dx
-
- @staticmethod
- def backward(ctx, d_dx): # pylint: disable=arguments-differ
- d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
- dy, x, b, y = ctx.saved_tensors
- d_dy = None
- d_x = None
- d_b = None
- d_y = None
-
- if ctx.needs_input_grad[0]:
- d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
-
- if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
- d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
-
- if spec.has_2nd_grad and ctx.needs_input_grad[2]:
- d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
-
- return d_dy, d_x, d_b, d_y
-
- # Add to cache.
- _bias_act_cuda_cache[key] = BiasActCuda
- return BiasActCuda
-
-#----------------------------------------------------------------------------
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/utils.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/utils.py
deleted file mode 100644
index 8f463a7c4011b1eed5f4f200fab1f6b744af60e7..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/utils.py
+++ /dev/null
@@ -1,539 +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
-import faiss
-import tqdm
-
-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_Onnx(device = device)
- elif speech_encoder == "vec256l12-onnx":
- from vencoder.ContentVec256L12_Onnx import ContentVec256L12_Onnx
- speech_encoder_object = ContentVec256L12_Onnx(device = device)
- elif speech_encoder == "vec768l9-onnx":
- from vencoder.ContentVec768L9_Onnx import ContentVec768L9_Onnx
- speech_encoder_object = ContentVec768L9_Onnx(device = device)
- elif speech_encoder == "vec768l12-onnx":
- from vencoder.ContentVec768L12_Onnx import ContentVec768L12_Onnx
- speech_encoder_object = ContentVec768L12_Onnx(device = device)
- elif speech_encoder == "hubertsoft-onnx":
- from vencoder.HubertSoft_Onnx import HubertSoft_Onnx
- speech_encoder_object = HubertSoft_Onnx(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)
- elif speech_encoder == "cnhubertlarge":
- from vencoder.CNHubertLarge import CNHubertLarge
- speech_encoder_object = CNHubertLarge(device = device)
- elif speech_encoder == "dphubert":
- from vencoder.DPHubert import DPHubert
- speech_encoder_object = DPHubert(device = device)
- elif speech_encoder == "whisper-ppg-large":
- from vencoder.WhisperPPGLarge import WhisperPPGLarge
- speech_encoder_object = WhisperPPGLarge(device = device)
- elif speech_encoder == "wavlmbase+":
- from vencoder.WavLMBasePlus import WavLMBasePlus
- speech_encoder_object = WavLMBasePlus(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, infer_mode = False):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
- hparams =HParams(**config) if not infer_mode else InferHParams(**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, mode = 'left'):
- # content : [h, t]
- return repeat_expand_2d_left(content, target_len) if mode == 'left' else repeat_expand_2d_other(content, target_len, mode)
-
-
-
-def repeat_expand_2d_left(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
-
-
-# mode : 'nearest'| 'linear'| 'bilinear'| 'bicubic'| 'trilinear'| 'area'
-def repeat_expand_2d_other(content, target_len, mode = 'nearest'):
- # content : [h, t]
- content = content[None,:,:]
- target = F.interpolate(content,size=target_len,mode=mode)[0]
- 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")
-
-def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比 from RVC
- # print(data1.max(),data2.max())
- rms1 = librosa.feature.rms(
- y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
- ) # 每半秒一个点
- rms2 = librosa.feature.rms(y=data2.detach().cpu().numpy(), frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
- rms1 = torch.from_numpy(rms1).to(data2.device)
- rms1 = F.interpolate(
- rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.from_numpy(rms2).to(data2.device)
- rms2 = F.interpolate(
- rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
- data2 *= (
- torch.pow(rms1, torch.tensor(1 - rate))
- * torch.pow(rms2, torch.tensor(rate - 1))
- )
- return data2
-
-def train_index(spk_name,root_dir = "dataset/44k/"): #from: RVC https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
- print("The feature index is constructing.")
- exp_dir = os.path.join(root_dir,spk_name)
- listdir_res = []
- for file in os.listdir(exp_dir):
- if ".wav.soft.pt" in file:
- listdir_res.append(os.path.join(exp_dir,file))
- if len(listdir_res) == 0:
- raise Exception("You need to run preprocess_hubert_f0.py!")
- npys = []
- for name in sorted(listdir_res):
- phone = torch.load(name)[0].transpose(-1,-2).numpy()
- npys.append(phone)
- big_npy = np.concatenate(npys, 0)
- big_npy_idx = np.arange(big_npy.shape[0])
- np.random.shuffle(big_npy_idx)
- big_npy = big_npy[big_npy_idx]
- n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
- index = faiss.index_factory(big_npy.shape[1] , "IVF%s,Flat" % n_ivf)
- index_ivf = faiss.extract_index_ivf(index) #
- index_ivf.nprobe = 1
- index.train(big_npy)
- batch_size_add = 8192
- for i in range(0, big_npy.shape[0], batch_size_add):
- index.add(big_npy[i : i + batch_size_add])
- # faiss.write_index(
- # index,
- # f"added_{spk_name}.index"
- # )
- print("Successfully build index")
- return index
-
-
-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 InferHParams(HParams):
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = InferHParams(**v)
- self[k] = v
-
- def __getattr__(self,index):
- return self.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
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/diffusion/logger/utils.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/diffusion/logger/utils.py
deleted file mode 100644
index 485681ced897980dc0bf5b149308245bbd708de9..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/diffusion/logger/utils.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import yaml
-import json
-import pickle
-import torch
-
-def traverse_dir(
- root_dir,
- extensions,
- amount=None,
- str_include=None,
- str_exclude=None,
- is_pure=False,
- is_sort=False,
- is_ext=True):
-
- file_list = []
- cnt = 0
- for root, _, files in os.walk(root_dir):
- for file in files:
- if any([file.endswith(f".{ext}") for ext in extensions]):
- # path
- mix_path = os.path.join(root, file)
- pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
- # amount
- if (amount is not None) and (cnt == amount):
- if is_sort:
- file_list.sort()
- return file_list
-
- # check string
- if (str_include is not None) and (str_include not in pure_path):
- continue
- if (str_exclude is not None) and (str_exclude in pure_path):
- continue
-
- if not is_ext:
- ext = pure_path.split('.')[-1]
- pure_path = pure_path[:-(len(ext)+1)]
- file_list.append(pure_path)
- cnt += 1
- if is_sort:
- file_list.sort()
- return file_list
-
-
-
-class DotDict(dict):
- def __getattr__(*args):
- val = dict.get(*args)
- return DotDict(val) if type(val) is dict else val
-
- __setattr__ = dict.__setitem__
- __delattr__ = dict.__delitem__
-
-
-def get_network_paras_amount(model_dict):
- info = dict()
- for model_name, model in model_dict.items():
- # all_params = sum(p.numel() for p in model.parameters())
- trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-
- info[model_name] = trainable_params
- return info
-
-
-def load_config(path_config):
- with open(path_config, "r") as config:
- args = yaml.safe_load(config)
- args = DotDict(args)
- # print(args)
- return args
-
-def save_config(path_config,config):
- config = dict(config)
- with open(path_config, "w") as f:
- yaml.dump(config, f)
-
-def to_json(path_params, path_json):
- params = torch.load(path_params, map_location=torch.device('cpu'))
- raw_state_dict = {}
- for k, v in params.items():
- val = v.flatten().numpy().tolist()
- raw_state_dict[k] = val
-
- with open(path_json, 'w') as outfile:
- json.dump(raw_state_dict, outfile,indent= "\t")
-
-
-def convert_tensor_to_numpy(tensor, is_squeeze=True):
- if is_squeeze:
- tensor = tensor.squeeze()
- if tensor.requires_grad:
- tensor = tensor.detach()
- if tensor.is_cuda:
- tensor = tensor.cpu()
- return tensor.numpy()
-
-
-def load_model(
- expdir,
- model,
- optimizer,
- name='model',
- postfix='',
- device='cpu'):
- if postfix == '':
- postfix = '_' + postfix
- path = os.path.join(expdir, name+postfix)
- path_pt = traverse_dir(expdir, ['pt'], is_ext=False)
- global_step = 0
- if len(path_pt) > 0:
- steps = [s[len(path):] for s in path_pt]
- maxstep = max([int(s) if s.isdigit() else 0 for s in steps])
- if maxstep >= 0:
- path_pt = path+str(maxstep)+'.pt'
- else:
- path_pt = path+'best.pt'
- print(' [*] restoring model from', path_pt)
- ckpt = torch.load(path_pt, map_location=torch.device(device))
- global_step = ckpt['global_step']
- model.load_state_dict(ckpt['model'], strict=False)
- if ckpt.get('optimizer') != None:
- optimizer.load_state_dict(ckpt['optimizer'])
- return global_step, model, optimizer
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/__init__.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/__init__.py
deleted file mode 100644
index 6b0668157052ce7b796ef50bc7ee85361e7605b9..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .build import META_ARCH_REGISTRY, build_model # isort:skip
-
-from .panoptic_fpn import PanopticFPN
-
-# import all the meta_arch, so they will be registered
-from .rcnn import GeneralizedRCNN, ProposalNetwork
-from .dense_detector import DenseDetector
-from .retinanet import RetinaNet
-from .fcos import FCOS
-from .semantic_seg import SEM_SEG_HEADS_REGISTRY, SemanticSegmentor, build_sem_seg_head
-
-
-__all__ = list(globals().keys())
diff --git a/spaces/ysharma/LLaVA_v1/llava/__init__.py b/spaces/ysharma/LLaVA_v1/llava/__init__.py
deleted file mode 100644
index 4d1f016db1028101d45ba7d68cb3f0bcb558c2bb..0000000000000000000000000000000000000000
--- a/spaces/ysharma/LLaVA_v1/llava/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .model import LlavaLlamaForCausalLM
diff --git a/spaces/zestyoreo/vtryon/options/__init__.py b/spaces/zestyoreo/vtryon/options/__init__.py
deleted file mode 100644
index 95a8a3001b66b872e8f4a111df2c5cabee1e4c56..0000000000000000000000000000000000000000
--- a/spaces/zestyoreo/vtryon/options/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# options_init
\ No newline at end of file
diff --git a/spaces/zhang-wei-jian/docker/node_modules/type-is/index.js b/spaces/zhang-wei-jian/docker/node_modules/type-is/index.js
deleted file mode 100644
index 890ad76c7eb990c75e4601becab263df961318fb..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/type-is/index.js
+++ /dev/null
@@ -1,266 +0,0 @@
-/*!
- * type-is
- * Copyright(c) 2014 Jonathan Ong
- * Copyright(c) 2014-2015 Douglas Christopher Wilson
- * MIT Licensed
- */
-
-'use strict'
-
-/**
- * Module dependencies.
- * @private
- */
-
-var typer = require('media-typer')
-var mime = require('mime-types')
-
-/**
- * Module exports.
- * @public
- */
-
-module.exports = typeofrequest
-module.exports.is = typeis
-module.exports.hasBody = hasbody
-module.exports.normalize = normalize
-module.exports.match = mimeMatch
-
-/**
- * Compare a `value` content-type with `types`.
- * Each `type` can be an extension like `html`,
- * a special shortcut like `multipart` or `urlencoded`,
- * or a mime type.
- *
- * If no types match, `false` is returned.
- * Otherwise, the first `type` that matches is returned.
- *
- * @param {String} value
- * @param {Array} types
- * @public
- */
-
-function typeis (value, types_) {
- var i
- var types = types_
-
- // remove parameters and normalize
- var val = tryNormalizeType(value)
-
- // no type or invalid
- if (!val) {
- return false
- }
-
- // support flattened arguments
- if (types && !Array.isArray(types)) {
- types = new Array(arguments.length - 1)
- for (i = 0; i < types.length; i++) {
- types[i] = arguments[i + 1]
- }
- }
-
- // no types, return the content type
- if (!types || !types.length) {
- return val
- }
-
- var type
- for (i = 0; i < types.length; i++) {
- if (mimeMatch(normalize(type = types[i]), val)) {
- return type[0] === '+' || type.indexOf('*') !== -1
- ? val
- : type
- }
- }
-
- // no matches
- return false
-}
-
-/**
- * Check if a request has a request body.
- * A request with a body __must__ either have `transfer-encoding`
- * or `content-length` headers set.
- * http://www.w3.org/Protocols/rfc2616/rfc2616-sec4.html#sec4.3
- *
- * @param {Object} request
- * @return {Boolean}
- * @public
- */
-
-function hasbody (req) {
- return req.headers['transfer-encoding'] !== undefined ||
- !isNaN(req.headers['content-length'])
-}
-
-/**
- * Check if the incoming request contains the "Content-Type"
- * header field, and it contains any of the give mime `type`s.
- * If there is no request body, `null` is returned.
- * If there is no content type, `false` is returned.
- * Otherwise, it returns the first `type` that matches.
- *
- * Examples:
- *
- * // With Content-Type: text/html; charset=utf-8
- * this.is('html'); // => 'html'
- * this.is('text/html'); // => 'text/html'
- * this.is('text/*', 'application/json'); // => 'text/html'
- *
- * // When Content-Type is application/json
- * this.is('json', 'urlencoded'); // => 'json'
- * this.is('application/json'); // => 'application/json'
- * this.is('html', 'application/*'); // => 'application/json'
- *
- * this.is('html'); // => false
- *
- * @param {String|Array} types...
- * @return {String|false|null}
- * @public
- */
-
-function typeofrequest (req, types_) {
- var types = types_
-
- // no body
- if (!hasbody(req)) {
- return null
- }
-
- // support flattened arguments
- if (arguments.length > 2) {
- types = new Array(arguments.length - 1)
- for (var i = 0; i < types.length; i++) {
- types[i] = arguments[i + 1]
- }
- }
-
- // request content type
- var value = req.headers['content-type']
-
- return typeis(value, types)
-}
-
-/**
- * Normalize a mime type.
- * If it's a shorthand, expand it to a valid mime type.
- *
- * In general, you probably want:
- *
- * var type = is(req, ['urlencoded', 'json', 'multipart']);
- *
- * Then use the appropriate body parsers.
- * These three are the most common request body types
- * and are thus ensured to work.
- *
- * @param {String} type
- * @private
- */
-
-function normalize (type) {
- if (typeof type !== 'string') {
- // invalid type
- return false
- }
-
- switch (type) {
- case 'urlencoded':
- return 'application/x-www-form-urlencoded'
- case 'multipart':
- return 'multipart/*'
- }
-
- if (type[0] === '+') {
- // "+json" -> "*/*+json" expando
- return '*/*' + type
- }
-
- return type.indexOf('/') === -1
- ? mime.lookup(type)
- : type
-}
-
-/**
- * Check if `expected` mime type
- * matches `actual` mime type with
- * wildcard and +suffix support.
- *
- * @param {String} expected
- * @param {String} actual
- * @return {Boolean}
- * @private
- */
-
-function mimeMatch (expected, actual) {
- // invalid type
- if (expected === false) {
- return false
- }
-
- // split types
- var actualParts = actual.split('/')
- var expectedParts = expected.split('/')
-
- // invalid format
- if (actualParts.length !== 2 || expectedParts.length !== 2) {
- return false
- }
-
- // validate type
- if (expectedParts[0] !== '*' && expectedParts[0] !== actualParts[0]) {
- return false
- }
-
- // validate suffix wildcard
- if (expectedParts[1].substr(0, 2) === '*+') {
- return expectedParts[1].length <= actualParts[1].length + 1 &&
- expectedParts[1].substr(1) === actualParts[1].substr(1 - expectedParts[1].length)
- }
-
- // validate subtype
- if (expectedParts[1] !== '*' && expectedParts[1] !== actualParts[1]) {
- return false
- }
-
- return true
-}
-
-/**
- * Normalize a type and remove parameters.
- *
- * @param {string} value
- * @return {string}
- * @private
- */
-
-function normalizeType (value) {
- // parse the type
- var type = typer.parse(value)
-
- // remove the parameters
- type.parameters = undefined
-
- // reformat it
- return typer.format(type)
-}
-
-/**
- * Try to normalize a type and remove parameters.
- *
- * @param {string} value
- * @return {string}
- * @private
- */
-
-function tryNormalizeType (value) {
- if (!value) {
- return null
- }
-
- try {
- return normalizeType(value)
- } catch (err) {
- return null
- }
-}
diff --git a/spaces/zideliu/styledrop/timm/utils/metrics.py b/spaces/zideliu/styledrop/timm/utils/metrics.py
deleted file mode 100644
index 8e0b1f9989a9dc95708a0dbb42e747f9a8565378..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/timm/utils/metrics.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" Eval metrics and related
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-
-
-class AverageMeter:
- """Computes and stores the average and current value"""
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = self.sum / self.count
-
-
-def accuracy(output, target, topk=(1,)):
- """Computes the accuracy over the k top predictions for the specified values of k"""
- maxk = max(topk)
- batch_size = target.size(0)
- _, pred = output.topk(maxk, 1, True, True)
- pred = pred.t()
- correct = pred.eq(target.reshape(1, -1).expand_as(pred))
- return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]
diff --git a/spaces/zomehwh/sovits-tannhauser/modules/__init__.py b/spaces/zomehwh/sovits-tannhauser/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/env.py b/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/env.py
deleted file mode 100644
index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/env.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import shutil
-
-
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
-
-
-def build_env(config, config_name, path):
- t_path = os.path.join(path, config_name)
- if config != t_path:
- os.makedirs(path, exist_ok=True)
- shutil.copyfile(config, os.path.join(path, config_name))
diff --git a/spaces/zxy666/bingo-chatai666/cloudflare/worker.js b/spaces/zxy666/bingo-chatai666/cloudflare/worker.js
deleted file mode 100644
index e0debd750615f1329b2c72fbce73e1b9291f7137..0000000000000000000000000000000000000000
--- a/spaces/zxy666/bingo-chatai666/cloudflare/worker.js
+++ /dev/null
@@ -1,18 +0,0 @@
-const TRAGET_HOST='hf4all-bingo.hf.space' // 请将此域名改成你自己的，域名信息在设置》站点域名查看。
-
-export default {
- async fetch(request) {
- const uri = new URL(request.url);
- if (uri.protocol === 'http:') {
- uri.protocol = 'https:';
- return new Response('', {
- status: 301,
- headers: {
- location: uri.toString(),
- },
- })
- }
- uri.host = TRAGET_HOST
- return fetch(new Request(uri.toString(), request));
- },
-};
diff --git a/spaces/zxy666/bingo-chatai666/src/lib/hooks/use-enter-submit.tsx b/spaces/zxy666/bingo-chatai666/src/lib/hooks/use-enter-submit.tsx
deleted file mode 100644
index d66b2d3253baff164235d4ca791aae6d84721835..0000000000000000000000000000000000000000
--- a/spaces/zxy666/bingo-chatai666/src/lib/hooks/use-enter-submit.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { useRef, type RefObject } from 'react'
-
-export function useEnterSubmit(): {
- formRef: RefObject
- onKeyDown: (event: React.KeyboardEvent) => void
-} {
- const formRef = useRef(null)
-
- const handleKeyDown = (
- event: React.KeyboardEvent
- ): void => {
- if (
- event.key === 'Enter' &&
- !event.shiftKey &&
- !event.nativeEvent.isComposing
- ) {
- formRef.current?.requestSubmit()
- event.preventDefault()
- }
- }
-
- return { formRef, onKeyDown: handleKeyDown }
-}
''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}.", freeze_for, gr.update(visible=False)]
- elif(option == "person"):
- instance_prompt_example = "julcto"
- freeze_for = 70
- #show_prior_preservation = True if base != "v2-1-768" else False
- show_prior_preservation=False
- if(show_prior_preservation):
- prior_preservation_box_update = gr.update(visible=show_prior_preservation)
- else:
- prior_preservation_box_update = gr.update(visible=show_prior_preservation, value=False)
- return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. You can use services like 
''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}.", freeze_for, prior_preservation_box_update]
- elif(option == "style"):
- instance_prompt_example = "trsldamrl"
- freeze_for = 10
- return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. You can use services like 
''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}", freeze_for, gr.update(visible=False)]
-
-def swap_base_model(selected_model):
- if(is_gpu_associated):
- global model_to_load
- if(selected_model == "v1-5"):
- model_to_load = model_v1
- elif(selected_model == "v2-1-768"):
- model_to_load = model_v2
- else:
- model_to_load = model_v2_512
-
-def count_files(*inputs):
- file_counter = 0
- concept_counter = 0
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- files = inputs[i]
- if(files):
- concept_counter+=1
- file_counter+=len(files)
- uses_custom = inputs[-1]
- type_of_thing = inputs[-4]
- selected_model = inputs[-5]
- experimental_faces = inputs[-6]
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- else:
- Training_Steps = file_counter*150
- if(type_of_thing == "person" and Training_Steps > 2400):
- Training_Steps = 2400 #Avoid overfitting on person faces
- if(is_spaces):
- if(selected_model == "v1-5"):
- its = 1.1 if which_gpu == "T4" else 1.8
- if(experimental_faces):
- its = 1
- elif(selected_model == "v2-1-512"):
- its = 0.8 if which_gpu == "T4" else 1.5
- if(experimental_faces):
- its = 0.7
- elif(selected_model == "v2-1-768"):
- its = 0.48 if which_gpu == "T4" else 0.85
-
- gpu_price = 0.60 if which_gpu == "T4" else 1.10
- summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. The training should take around {round(Training_Steps/its, 2)} seconds, or {round((Training_Steps/its)/60, 2)} minutes.
- The setup, compression and uploading the model can take up to 20 minutes.
- 
- 
-
-ChatResponse是一款根据审稿人的评论自动生成作者回复的AI助手。其用途为：
-
-⭐️根据输入的审稿意见，ChatResponse会自动提取其中各个审稿人的问题和担忧，并生成点对点的回复。
-
-如果觉得很卡，可以点击右上角的Duplicate this Space，把ChatResponse复制到你自己的Space中！
-
-本项目的[Github](https://github.com/nishiwen1214/ChatReviewer)，欢迎Star和Fork，也欢迎大佬赞助让本项目快速成长！💗
-
-
- 
- 
- 
- 
- 
diff --git a/spaces/bioriAsaeru/text-to-voice/3DMark Port Royal Upgrade Torrent Download !FREE! [key].md b/spaces/bioriAsaeru/text-to-voice/3DMark Port Royal Upgrade Torrent Download !FREE! [key].md
deleted file mode 100644
index 2d8cdf1ef971441aade6dfaef8f3ff92f1aab83e..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/3DMark Port Royal Upgrade Torrent Download !FREE! [key].md
+++ /dev/null
@@ -1,34 +0,0 @@
-