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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Codebreaker 10.1 Patched Elf How to Install and Use It on Your PS2.md

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-<h1>Codebreaker 10.1 Patched Elf: What Is It and How to Use It</h1>
- <p>If you are a fan of playing PS2 games, you might have heard of <strong>Codebreaker</strong>, a cheat device that allows you to access various cheats and hacks for your favorite games. However, if you have a soft-modded PS2, you might have encountered some problems when trying to use Codebreaker with your backup games or burned discs. That's where <strong>Codebreaker 10.1 Patched Elf</strong> comes in handy.</p>
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- <p>In this article, we will explain what Codebreaker 10.1 Patched Elf is, how to install it on your PS2, how to use it to play burned games, and what are its benefits and limitations. By the end of this article, you will be able to enjoy your PS2 games with more fun and convenience.</p>
- <h2>How to Install Codebreaker 10.1 Patched Elf on Your PS2</h2>
- <p>Before we get into the details of how to use Codebreaker 10.1 Patched Elf, let's first see how to install it on your PS2. To do this, you will need the following:</p>
- <ul>
-<li>A soft-modded PS2 with Free McBoot (FMCB) installed on a memory card.</li>
-<li>A USB drive formatted as FAT32.</li>
-<li>A copy of Codebreaker 10 or 10.1 in ISO format.</li>
-<li>A patcher program that can modify the Codebreaker elf file.</li>
-<li>A file manager program that can copy files from USB to memory card.</li>
-</ul>
- <p>Once you have these ready, follow these steps:</p>
- <ol>
-<li>Download the patcher program from <a href="https://psx-scene.com/forums/showthread.php?t=57901">this link</a>. It is a zip file that contains two files: CB_launch.zip and CB_patch.zip.</li>
-<li>Extract the CB_patch.zip file and run the CB_patch.exe file on your computer.</li>
-<li>Select your Codebreaker ISO file as the input file and choose a destination folder for the output file.</li>
-<li>Click on "Patch" and wait for the process to finish.</li>
-<li>You should now have a patched elf file named "CODEBREAKER Vxx PATCHED BY ZALZZAR" in your destination folder.</li>
-<li>Rename this file as "CB_launch.elf" and copy it to your USB drive.</li>
-<li>Extract the CB_launch.zip file and copy the "CB_launch" folder to your USB drive as well.</li>
-<li>Plug your USB drive into your PS2 and turn it on.</li>
-<li>Launch FMCB from your memory card and select uLaunchELF from the menu.</li>
-<li>Browse to your USB drive using uLaunchELF and copy the "CB_launch" folder and the "CB_launch.elf" file to your memory card's "BOOT" folder.</li>
-<li>Go back to FMCB menu and select "Configure OSDSYS options".</li>
-<li>Select "Configure Item" and choose an empty slot.</li>
-<li>Select "Path1" and browse to your memory card's "BOOT" folder.</li>
-<li>Select "CB_launch.elf" as the path and press circle.</li>
-<li>Select "Name" and enter "Codebreaker" as the name and press circle.</li>
-<li>Select "Save CNF To MC0" and press circle.</li>
-<li>Exit FMCB menu and restart your PS2.</li>
-</ol>
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-<li>A burned game or backup disc that has been patched for ESR (Enhanced Screen Resolution).</li>
-<li>A patcher program that can patch ISO images for ESR.</li>
-</ul>
- <p>If you don't know how to patch ISO images for ESR, follow these steps:</p>
- <ol>
-<li>Download the ESR Disc Patcher from <a href="http://psx-scene.com/forums/showthread.php?t=58441">this link</a>. It is a zip file that contains a single exe file.</li>
-<li>Extract the zip file and run the ESR Disc Patcher.exe file on your computer.</li>
-<li>Select your ISO image as the input file and choose a destination folder for the output file.</li>
-<li>Click on "Patch" and wait for the process to finish.</li>
-<li>You should now have a patched ISO image in your destination folder with "_ESR" added at the end of its name.</li>
-<li>Burn this image onto a DVD-R disc using any burning software of your choice.</li>
-</ol>
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- <ol>
-<li>Insert your disc into your PS2's disc tray but don't close it yet.</li>
-<li>Select "Codebreaker" from your FMCB menu and press X.</li>
-<li>You should see a loading screen followed by a disclaimer screen. Press X to continue.</li>
-<li>You should now see the main menu of Codebreaker with various options such as Start Game, Select Cheats, Options, etc.</li>
-<li>Select "Options" and press X.</li>
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-<li>Select "Save Options" and press X.</li>
-<li>Select "Select Cheats" and press X.</li>
-<li>You should see a list of games that are compatible with Codebreaker. You can scroll through them using up/down buttons or search for them using left/right buttons.</li>
-<li>Select the game that matches your disc and press X.</li>
-<li>You should see a list of cheats available for that game. You can toggle them on/off using X button or select them all using square button.</li>
-<li>Select "Start Game With Selected Cheats" and press X.</li>
-<li>You should see a loading screen followed by another disclaimer screen. Press X to continue.</li>
-<li>You should now be taken back to FMCB menu automatically.</li>
-<li>Select ESR from FMCB menu and press X.</li>
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- <h3>The rules of Bubble Shooter</h3>
-<p>The rules of Bubble Shooter are simple and intuitive. Here are the basic steps to play the game:</p>
-<ul>
-<li>At the bottom of the screen, you will see a bubble cannon that can be moved left and right with your mouse or finger.</li>
-<li>At the center of the cannon, you will see a bubble that is ready to be launched. You can also see the next bubble that will come after it.</li>
-<li>Aim the cannon at the cluster of bubbles at the top of the screen. You can see where the bubble will go by following the dotted line.</li>
-<li>Click or tap to shoot the bubble. Try to hit bubbles of the same color as your bubble.</li>
-<li>If you manage to connect three or more bubbles of the same color, they will pop and disappear. You will also earn points for each bubble you pop.</li>
-<li>If you miss or hit a different color, the bubble will stick to the cluster and make it bigger.</li>
-<li>If you pop all the bubbles on the screen, you will complete the level and move on to the next one.</li>
-<li>If one of the bubbles touches the bottom of the screen, you will lose a life and have to start over.</li>
-</ul>
- <h3>The benefits of playing Bubble Shooter</h3>
-<p>Bubble Shooter is not only a fun game but also a beneficial one. Here are some of the advantages of playing Bubble Shooter:</p>
-<ul>
-<li>It improves your concentration and focus. You have to pay attention to the colors, angles, and trajectories of the bubbles.</li>
-<li>It enhances your hand-eye coordination and reaction time. You have to move quickly and accurately to shoot the bubbles.</li>
-<li>It stimulates your brain and memory. You have to plan ahead and remember where the bubbles are located and how to pop them.</li>
-<li>It relaxes your mind and mood. You can enjoy the colorful graphics, the soothing sounds, and the satisfying feeling of popping bubbles.</li>
-<li>It challenges your skills and creativity. You can try different strategies and techniques to beat the levels and score higher.</li>
-</ul>
- <h2>How to play Bubble Shooter?</h2>
-<p>Now that you know what Bubble Shooter is and why you should play it, let's see how you can actually play it. Here are some tips and tricks to help you master the game:</p>
- <h3>Choose your device and platform</h3>
-<p>Bubble Shooter is available on various devices and platforms, such as PC, mobile, tablet, and web browser. You can choose the one that suits you best, depending on your preferences and convenience. For example, if you want to play on a bigger screen and use a mouse, you can play on your PC. If you want to play on the go and use touch controls, you can play on your mobile or tablet. If you want to play online and access different versions of the game, you can play on your web browser.</p>
- <h3>Aim and shoot the bubbles</h3>
-<p>The most important skill in Bubble Shooter is aiming and shooting the bubbles. You need to be precise and accurate to hit the right bubbles and avoid wasting shots. Here are some tips to improve your aiming and shooting:</p>
-<ul>
-<li>Use the dotted line as a guide. The dotted line shows you where the bubble will go when you shoot it. You can adjust the angle of the cannon by moving your mouse or finger.</li>
-<li>Use the walls as a bounce. The walls can help you reach bubbles that are hard to hit directly. You can bounce the bubble off the wall and make it ricochet to the target.</li>
-<li>Use the color of the next bubble as a hint. The color of the next bubble shows you what color will come after the current one. You can use this information to plan ahead and prepare for your next shot.</li>
-</ul>
- <h3>Use strategies and tips to improve your score</h3>
-<p>Besides aiming and shooting, there are also some strategies and tips that can help you improve your score and beat the levels. Here are some of them:</p>
-<ul>
-<li>Pop more bubbles with one shot. The more bubbles you pop with one shot, the more points you get. You can also trigger chain reactions and combos by popping bubbles that are connected to other bubbles of the same color.</li>
-<li>Clear the top rows first. The top rows are more dangerous because they are closer to the bottom of the screen. If you clear them first, you will have more space and time to shoot the lower rows.</li>
-<li>Avoid creating isolated bubbles. Isolated bubbles are bubbles that are not connected to any other bubbles of the same color. They are harder to pop and waste your shots. Try to avoid creating them by shooting at groups of bubbles instead of single ones.</li>
-<li>Use power-ups wisely. Power-ups are special bubbles that have different effects when popped. For example, some power-ups can clear a whole row or column of bubbles, change the color of nearby bubbles, or give you extra lives or shots. Use them wisely when you need them, but don't rely on them too much.</li>
-</ul>
- <h2>What are the best Bubble Shooter games?</h2>
-<p>Bubble Shooter is a very popular game that has many versions and variations. Some of them are more classic and simple, while others are more modern and complex. Here are some of the best Bubble Shooter games that you can try:</p>
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- <h3>Bubble Shooter Classic</h3>
-<p>Bubble Shooter Classic is one of the most original and iconic versions of the game. It has a simple design, a retro style, and a relaxing soundtrack. It is perfect for those who want to enjoy a nostalgic and timeless game experience.</p>
- <h3>Bubble Shooter Extreme</h3>
-<p>Bubble Shooter Extreme is one of the most challenging and exciting versions of the game. It has a fast-paced gameplay, a futuristic design, and a dynamic soundtrack. It is perfect for those who want to test their skills and reflexes in a thrilling game experience.</p>
- <h3>Bubble Shooter Candy</h3>
-<p>Bubble Shooter Candy is one of the most sweet and colorful versions of the game. It has a cute design, a candy theme, and a cheerful soundtrack. It is perfect for those who want to enjoy a fun and delightful game experience.</p>
- <h2>Conclusion</h2>
-<p>Bubble Shooter is a fun and addictive game that everyone can enjoy. It is easy to learn, fun to play, and suitable for all ages. It also has many benefits for your mind and mood, such as improving your concentration, coordination, memory, relaxation, and creativity. You can play Bubble Shooter on various devices and platforms, such as PC, mobile, tablet, and web browser. You can also choose from different versions and variations of the game, such as Bubble Shooter Classic, Bubble Shooter Extreme, and Bubble Shooter Candy. Whether you want a nostalgic, thrilling, or delightful game experience, Bubble Shooter has something for you. So what are you waiting for? Grab your bubble cannon and start popping bubbles today!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Bubble Shooter:</p>
-<ul>
-<li><b>Q: How many levels are there in Bubble Shooter?</b></li>
-<li>A: The number of levels in Bubble Shooter depends on the version and platform you are playing on. Some versions have a fixed number of levels, while others have an infinite number of levels that are randomly generated. You can check the level number on the screen or the menu.</li>
-<li><b>Q: How do I get more lives or shots in Bubble Shooter?</b></li>
-<li>A: The number of lives or shots in Bubble Shooter also depends on the version and platform you are playing on. Some versions have a limited number of lives or shots that you can replenish by watching ads, buying coins, or waiting for a certain time. Other versions have an unlimited number of lives or shots that you can use freely.</li>
-<li><b>Q: How do I pause or resume the game in Bubble Shooter?</b></li>
-<li>A: To pause or resume the game in Bubble Shooter, you can click or tap on the pause button on the screen or the menu. This will stop the game and allow you to access other options such as settings, sound, music, help, or exit.</li>
-<li><b>Q: How do I save or load my progress in Bubble Shooter?</b></li>
-<li>A: To save or load your progress in Bubble Shooter, you need to have an account or a profile on the platform you are playing on. This will allow you to sync your data across different devices and resume your game from where you left off.</li>
-<li><b>Q: How do I change the difficulty or mode in Bubble Shooter?</b></li>
-<li>A: To change the difficulty or mode in Bubble Shooter, you can select the option on the screen or the menu before starting a new game. Some versions have different difficulty levels such as easy, medium, hard, or expert. Other versions have different modes such as arcade, puzzle, time trial, or survival.</li>
-</ul></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Countries.csv The Ultimate Resource for Country Information.md

@@ -1,112 +0,0 @@
-<br />
-<h1>How to Download Countries.csv</h1>
-<p>A CSV file, or a comma-separated values file, is a plain text file that stores data in a tabular format. Each line of the file is a data record, and each record consists of one or more fields separated by commas. CSV files are often used to exchange data between different applications that use incompatible formats. For example, you can use a CSV file to transfer data from a database to a spreadsheet, or vice versa.</p>
-<p>One example of a CSV file that you might want to download is countries.csv. This file contains information about countries around the world, such as their names, ISO codes, coordinates, capitals, currencies, regions, and more. You can use this file for various purposes, such as creating maps, charts, reports, or quizzes. In this article, we will show you how to download countries.csv and open it in a program of your choice.</p>
-<h2>download countries.csv</h2><br /><p><b><b>DOWNLOAD</b> &#128504;&#128504;&#128504; <a href="https://urlin.us/2uSZLa">https://urlin.us/2uSZLa</a></b></p><br /><br />
- <h2>Step 1: Find a Source of Countries.csv Data</h2>
-<p>The first step is to find a reliable source of countries.csv data. There are many websites that offer this kind of data for free or for a fee. Some examples are:</p>
-<ul>
-<li><a href="(^1^)">Google Developers</a>: This website provides a canonical version of countries.csv that follows the ISO 3166 standard for country codes. It also includes some additional fields, such as time zones, latitude, longitude, emoji, and native name.</li>
-<li><a href="(^2^)">GitHub</a>: This website hosts a repository of countries, states, and cities data in JSON, SQL, XML, YAML, and CSV formats. The CSV files include an index column and are updated regularly.</li>
-<li><a href="(^3^)">Kaggle</a>: This website offers a dataset of countries of the world that links country names to region, population, area size, GDP, mortality, and more. The dataset is available in CSV format and can be downloaded or accessed through an API.</li>
-</ul>
-<p>You can choose any source that suits your needs and preferences. For this article, we will use the Google Developers version of countries.csv.</p>
- <h2>Step 2: Choose a Program to Open the CSV File</h2>
-<p>The next step is to choose a program that can open and display the CSV file. There are many options available, depending on your operating system and your goals. Some common programs are:</p>
-<ul>
-<li><strong>Text editors</strong>: These are programs that allow you to view and edit plain text files. Examples are Notepad, Notepad++, Sublime Text, Atom, or Visual Studio Code. Text editors are useful for quickly viewing the contents of a CSV file or making minor changes.</li>
-<li><strong>Spreadsheet programs</strong>: These are programs that allow you to organize and analyze data in tabular form. Examples are Microsoft Excel, Google Sheets, LibreOffice Calc, or Numbers. Spreadsheet programs are useful for performing calculations, creating charts, filtering data, or applying formulas.</li>
-<li><strong>Specialized applications</strong>: These are programs that are designed for specific purposes or tasks related to CSV files. Examples are CSV Editor Pro, CSVed, Ron's Editor, or Easy Data Transform. Specialized applications are useful for editing large or complex CSV files, converting formats, validating data, or transforming data.</li>
-</ul>
-<p>You can choose any program that meets your requirements and expectations. For this article, we will use Microsoft Excel as an example of a spreadsheet program.</p>
- <h2>Step 3: Download the CSV File from the Source</h2>
-<p>The third step is to download the CSV file from the source website. To do this:</p>
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-<ol>
-<li>Go to the website where the CSV file is hosted. In our case, it is <a href="(^1^)">https://developers.google.com/public-data/docs/canonical/countries_csv</a>.</li>
-<li>Right-click on the link to the CSV file and select "Save link as" or "Save target as". In our case, it is <a href="">https://developers.google.com/public-data/docs/canonical/countries_csv.csv</a>.</li>
-<li>Choose a location on your computer where you want to save the CSV file and click "Save".</li>
-</ol>
-<p>You have now downloaded the CSV file to your computer. You can find it in the location you specified.</p>
- <h2>Step 4: Open the CSV File in the Chosen Program</h2>
-<p>The fourth step is to open the CSV file in the program you selected. To do this:</p>
-<ol>
-<li>Launch the program on your computer. In our case, it is Microsoft Excel.</li>
-<li>Click on "File" and then "Open". Alternatively, you can use the keyboard shortcut Ctrl+O.</li>
-<li>Navigate to the location where you saved the CSV file and select it. Click "Open".</li>
-</ol>
-<p>You should now see the CSV file opened in the program. Depending on the program, you may need to adjust some settings, such as the delimiter, the encoding, or the format of the data. For example, in Excel, you may see a dialog box that asks you to choose how to import the data. You can select "Delimited" and then "Comma" as the delimiter. You can also choose the column data format as "General" or "Text". Click "Finish" to complete the import.</p>
- <h2>Step 5: Explore and Manipulate the Data as Needed</h2>
-<p>The final step is to explore and manipulate the data in the CSV file as needed. You can use the features and functions of the program to perform various tasks, such as:</p>
-<ul>
-<li><strong>Sort and filter</strong>: You can sort and filter the data by any column or criteria. For example, you can sort the countries by name, population, or region. You can also filter out countries that meet certain conditions, such as having a specific currency or language.</li>
-<li><strong>Calculate and analyze</strong>: You can calculate and analyze the data using formulas, functions, or tools. For example, you can calculate the average population, area, or GDP of countries in a region. You can also use tools such as pivot tables or charts to summarize and visualize the data.</li>
-<li><strong>Edit and format</strong>: You can edit and format the data to suit your needs and preferences. For example, you can add, delete, or modify rows or columns of data. You can also change the font, color, or alignment of the cells.</li>
-</ul>
-<p>You can explore and manipulate the data in any way you want. You can also save your changes or export the data to another format if needed.</p>
- <h1>Conclusion</h1>
-<p>In this article, we have shown you how to download countries.csv and open it in a program of your choice. We have also given you some examples of how to explore and manipulate the data in the CSV file. By following these steps, you can access a wealth of information about countries around the world and use it for various purposes.</p>
-<p>We hope you found this article helpful and informative. If you have any questions or feedback, please let us know in the comments below.</p>
- <h1>FAQs</h1>
-<h3>What is a CSV file?</h3>
-<p>A CSV file is a plain text file that stores data in a tabular format. Each line of the file is a data record, and each record consists of one or more fields separated by commas.</p>
- <h3>Why should I download countries.csv?</h3>
-<p>You should download countries.csv if you want to access information about countries around the world, such as their names, ISO codes, coordinates, capitals, currencies, regions, and more. You can use this information for various purposes, such as creating maps, charts, reports, or quizzes.</p>
- <h3>How do I open a CSV file?</h3>
-<p>You can open a CSV file using any program that can read and display plain text files. Some common programs are text editors, spreadsheet programs, or specialized applications.</p>
- <h3>Where can I find other sources of countries.csv data?</h3>
-<p>You can find other sources of countries.csv data by searching online for websites that offer this kind of data for free or for a fee. Some examples are GitHub, Kaggle, DataHub.io, World Bank Data Catalogue, or CIA World Factbook.</p>
- <h3>How do I convert a CSV file to another format?</h3>
-<p>You can convert a CSV file to another format using any program that can read and write different formats. Some common formats are JSON, SQL, XML, YAML, or HTML. You can also use online tools or converters that can perform this task for you.</p> 197e85843d<br />
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spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/README.md

@@ -1,164 +0,0 @@
-# Distributed Arcface Training in Pytorch
-
-This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions
-identity on a single server.
-
-## Requirements
-
-- Install [pytorch](http://pytorch.org) (torch>=1.6.0), our doc for [install.md](docs/install.md).
-- `pip install -r requirements.txt`.
-- Download the dataset
-  from [https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_)
-  .
-
-## How to Training
-
-To train a model, run `train.py` with the path to the configs:
-
-### 1. Single node, 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50
-```
-
-### 2. Multiple nodes, each node 8 GPUs:
-
-Node 0:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-Node 1:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-### 3.Training resnet2060 with 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r2060.py
-```
-
-## Model Zoo
-
-- The models are available for non-commercial research purposes only.  
-- All models can be found in here.  
-- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g):   e8pw  
-- [onedrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d)
-
-### Performance on [**ICCV2021-MFR**](http://iccv21-mfr.com/)
-
-ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face 
-recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. 
-As the result, we can evaluate the FAIR performance for different algorithms.  
-
-For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The 
-globalised multi-racial testset contains 242,143 identities and 1,624,305 images. 
-
-For **ICCV2021-MFR-MASK** set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4). 
-Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images. 
-There are totally 13,928 positive pairs and 96,983,824 negative pairs.
-
-| Datasets | backbone  | Training throughout | Size / MB  | **ICCV2021-MFR-MASK** | **ICCV2021-MFR-ALL** |
-| :---:    | :---      | :---                | :---       |:---                   |:---                  |     
-| MS1MV3    | r18  | -              | 91   | **47.85** | **68.33** |
-| Glint360k | r18  | 8536           | 91   | **53.32** | **72.07** |
-| MS1MV3    | r34  | -              | 130  | **58.72** | **77.36** |
-| Glint360k | r34  | 6344           | 130  | **65.10** | **83.02** |
-| MS1MV3    | r50  | 5500           | 166  | **63.85** | **80.53** |
-| Glint360k | r50  | 5136           | 166  | **70.23** | **87.08** |
-| MS1MV3    | r100 | -              | 248  | **69.09** | **84.31** |
-| Glint360k | r100 | 3332           | 248  | **75.57** | **90.66** |
-| MS1MV3    | mobilefacenet | 12185 | 7.8  | **41.52** | **65.26** |        
-| Glint360k | mobilefacenet | 11197 | 7.8  | **44.52** | **66.48** |  
-
-### Performance on IJB-C and Verification Datasets
-
-|   Datasets | backbone      | IJBC(1e-05) | IJBC(1e-04) | agedb30 | cfp_fp | lfw  |  log    |
-| :---:      |    :---       | :---          | :---  | :---  |:---   |:---    |:---     |  
-| MS1MV3     | r18      | 92.07 | 94.66 | 97.77 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r18_fp16/training.log)|         
-| MS1MV3     | r34      | 94.10 | 95.90 | 98.10 | 98.67 | 99.80 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r34_fp16/training.log)|        
-| MS1MV3     | r50      | 94.79 | 96.46 | 98.35 | 98.96 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50_fp16/training.log)|         
-| MS1MV3     | r100     | 95.31 | 96.81 | 98.48 | 99.06 | 99.85 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r100_fp16/training.log)|        
-| MS1MV3     | **r2060**| 95.34 | 97.11 | 98.67 | 99.24 | 99.87 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r2060_fp16/training.log)|
-| Glint360k  |r18-0.1   | 93.16 | 95.33 | 97.72 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r18_fp16_0.1/training.log)| 
-| Glint360k  |r34-0.1   | 95.16 | 96.56 | 98.33 | 98.78 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r34_fp16_0.1/training.log)| 
-| Glint360k  |r50-0.1   | 95.61 | 96.97 | 98.38 | 99.20 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r50_fp16_0.1/training.log)| 
-| Glint360k  |r100-0.1  | 95.88 | 97.32 | 98.48 | 99.29 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)|
-
-[comment]: <> (More details see [model.md]&#40;docs/modelzoo.md&#41; in docs.)
-
-
-## [Speed Benchmark](docs/speed_benchmark.md)
-
-**Arcface Torch** can train large-scale face recognition training set efficiently and quickly. When the number of
-classes in training sets is greater than 300K and the training is sufficient, partial fc sampling strategy will get same
-accuracy with several times faster training performance and smaller GPU memory. 
-Partial FC is a sparse variant of the model parallel architecture for large sacle  face recognition. Partial FC use a 
-sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a 
-sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC, 
-we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed 
-training and mixed precision training.
-
-![Image text](https://github.com/anxiangsir/insightface_arcface_log/blob/master/partial_fc_v2.png)
-
-More details see 
-[speed_benchmark.md](docs/speed_benchmark.md) in docs.
-
-### 1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better)
-
-`-` means training failed because of gpu memory limitations.
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :---    | :--- | :--- | :--- |
-|125000   | 4681         | 4824          | 5004     |
-|1400000  | **1672**     | 3043          | 4738     |
-|5500000  | **-**        | **1389**      | 3975     |
-|8000000  | **-**        | **-**         | 3565     |
-|16000000 | **-**        | **-**         | 2679     |
-|29000000 | **-**        | **-**         | **1855** |
-
-### 2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better)
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :---    | :---      | :---      | :---  |
-|125000   | 7358      | 5306      | 4868  |
-|1400000  | 32252     | 11178     | 6056  |
-|5500000  | **-**     | 32188     | 9854  |
-|8000000  | **-**     | **-**     | 12310 |
-|16000000 | **-**     | **-**     | 19950 |
-|29000000 | **-**     | **-**     | 32324 |
-
-## Evaluation ICCV2021-MFR and IJB-C
-
-More details see [eval.md](docs/eval.md) in docs.
-
-## Test
-
-We tested many versions of PyTorch. Please create an issue if you are having trouble.  
-
-- [x] torch 1.6.0
-- [x] torch 1.7.1
-- [x] torch 1.8.0
-- [x] torch 1.9.0
-
-## Citation
-
-```
-@inproceedings{deng2019arcface,
-  title={Arcface: Additive angular margin loss for deep face recognition},
-  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  pages={4690--4699},
-  year={2019}
-}
-@inproceedings{an2020partical_fc,
-  title={Partial FC: Training 10 Million Identities on a Single Machine},
-  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
-  Zhang, Debing and Fu Ying},
-  booktitle={Arxiv 2010.05222},
-  year={2020}
-}
-```

spaces/4Taps/SadTalker/src/utils/audio.py

@@ -1,136 +0,0 @@
-import librosa
-import librosa.filters
-import numpy as np
-# import tensorflow as tf
-from scipy import signal
-from scipy.io import wavfile
-from src.utils.hparams import hparams as hp
-
-def load_wav(path, sr):
-    return librosa.core.load(path, sr=sr)[0]
-
-def save_wav(wav, path, sr):
-    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
-    #proposed by @dsmiller
-    wavfile.write(path, sr, wav.astype(np.int16))
-
-def save_wavenet_wav(wav, path, sr):
-    librosa.output.write_wav(path, wav, sr=sr)
-
-def preemphasis(wav, k, preemphasize=True):
-    if preemphasize:
-        return signal.lfilter([1, -k], [1], wav)
-    return wav
-
-def inv_preemphasis(wav, k, inv_preemphasize=True):
-    if inv_preemphasize:
-        return signal.lfilter([1], [1, -k], wav)
-    return wav
-
-def get_hop_size():
-    hop_size = hp.hop_size
-    if hop_size is None:
-        assert hp.frame_shift_ms is not None
-        hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
-    return hop_size
-
-def linearspectrogram(wav):
-    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
-    S = _amp_to_db(np.abs(D)) - hp.ref_level_db
-    
-    if hp.signal_normalization:
-        return _normalize(S)
-    return S
-
-def melspectrogram(wav):
-    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
-    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
-    
-    if hp.signal_normalization:
-        return _normalize(S)
-    return S
-
-def _lws_processor():
-    import lws
-    return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
-
-def _stft(y):
-    if hp.use_lws:
-        return _lws_processor(hp).stft(y).T
-    else:
-        return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
-
-##########################################################
-#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
-def num_frames(length, fsize, fshift):
-    """Compute number of time frames of spectrogram
-    """
-    pad = (fsize - fshift)
-    if length % fshift == 0:
-        M = (length + pad * 2 - fsize) // fshift + 1
-    else:
-        M = (length + pad * 2 - fsize) // fshift + 2
-    return M
-
-
-def pad_lr(x, fsize, fshift):
-    """Compute left and right padding
-    """
-    M = num_frames(len(x), fsize, fshift)
-    pad = (fsize - fshift)
-    T = len(x) + 2 * pad
-    r = (M - 1) * fshift + fsize - T
-    return pad, pad + r
-##########################################################
-#Librosa correct padding
-def librosa_pad_lr(x, fsize, fshift):
-    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
-
-# Conversions
-_mel_basis = None
-
-def _linear_to_mel(spectogram):
-    global _mel_basis
-    if _mel_basis is None:
-        _mel_basis = _build_mel_basis()
-    return np.dot(_mel_basis, spectogram)
-
-def _build_mel_basis():
-    assert hp.fmax <= hp.sample_rate // 2
-    return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels,
-                               fmin=hp.fmin, fmax=hp.fmax)
-
-def _amp_to_db(x):
-    min_level = np.exp(hp.min_level_db / 20 * np.log(10))
-    return 20 * np.log10(np.maximum(min_level, x))
-
-def _db_to_amp(x):
-    return np.power(10.0, (x) * 0.05)
-
-def _normalize(S):
-    if hp.allow_clipping_in_normalization:
-        if hp.symmetric_mels:
-            return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
-                           -hp.max_abs_value, hp.max_abs_value)
-        else:
-            return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
-    
-    assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
-    if hp.symmetric_mels:
-        return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
-    else:
-        return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
-
-def _denormalize(D):
-    if hp.allow_clipping_in_normalization:
-        if hp.symmetric_mels:
-            return (((np.clip(D, -hp.max_abs_value,
-                              hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
-                    + hp.min_level_db)
-        else:
-            return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
-    
-    if hp.symmetric_mels:
-        return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
-    else:
-        return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)

spaces/7eu7d7/anime-ai-detect-fucker/attacker/__init__.py

@@ -1,3 +0,0 @@
-from .base import *
-from .PGD import *
-from .FGSM import *

spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/base_model.py

@@ -1,58 +0,0 @@
-import os
-import numpy as np
-import torch
-from torch.autograd import Variable
-from pdb import set_trace as st
-from IPython import embed
-
-class BaseModel():
-    def __init__(self):
-        pass;
-        
-    def name(self):
-        return 'BaseModel'
-
-    def initialize(self, use_gpu=True, gpu_ids=[0]):
-        self.use_gpu = use_gpu
-        self.gpu_ids = gpu_ids
-
-    def forward(self):
-        pass
-
-    def get_image_paths(self):
-        pass
-
-    def optimize_parameters(self):
-        pass
-
-    def get_current_visuals(self):
-        return self.input
-
-    def get_current_errors(self):
-        return {}
-
-    def save(self, label):
-        pass
-
-    # helper saving function that can be used by subclasses
-    def save_network(self, network, path, network_label, epoch_label):
-        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
-        save_path = os.path.join(path, save_filename)
-        torch.save(network.state_dict(), save_path)
-
-    # helper loading function that can be used by subclasses
-    def load_network(self, network, network_label, epoch_label):
-        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
-        save_path = os.path.join(self.save_dir, save_filename)
-        print('Loading network from %s'%save_path)
-        network.load_state_dict(torch.load(save_path))
-
-    def update_learning_rate():
-        pass
-
-    def get_image_paths(self):
-        return self.image_paths
-
-    def save_done(self, flag=False):
-        np.save(os.path.join(self.save_dir, 'done_flag'),flag)
-        np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i')

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/scheduler.py

@@ -1,24 +0,0 @@
-import numpy as np
-
-
-def assign_learning_rate(optimizer, new_lr):
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = new_lr
-
-
-def _warmup_lr(base_lr, warmup_length, step):
-    return base_lr * (step + 1) / warmup_length
-
-
-def cosine_lr(optimizer, base_lr, warmup_length, steps):
-    def _lr_adjuster(step):
-        if step < warmup_length:
-            lr = _warmup_lr(base_lr, warmup_length, step)
-        else:
-            e = step - warmup_length
-            es = steps - warmup_length
-            lr = 0.5 * (1 + np.cos(np.pi * e / es)) * base_lr
-        assign_learning_rate(optimizer, lr)
-        return lr
-
-    return _lr_adjuster

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/models.py

@@ -1,414 +0,0 @@
-# Copyright (c) 2022 NVIDIA CORPORATION. 
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-import numpy as np
-from .activations import Snake,SnakeBeta
-from .alias_free_torch import *
-import os
-from omegaconf import OmegaConf
-
-LRELU_SLOPE = 0.1
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-class AMPBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
-        super(AMPBlock1, self).__init__()
-        self.h = h
-
-        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)
-
-        self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
-
-        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=Snake(channels, alpha_logscale=h.snake_logscale))
-                for _ in range(self.num_layers)
-            ])
-        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale))
-                 for _ in range(self.num_layers)
-            ])
-        else:
-            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
-
-    def forward(self, x):
-        acts1, acts2 = self.activations[::2], self.activations[1::2]
-        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
-            xt = a1(x)
-            xt = c1(xt)
-            xt = a2(xt)
-            xt = c2(xt)
-            x = xt + x
-
-        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 AMPBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
-        super(AMPBlock2, self).__init__()
-        self.h = h
-
-        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)
-
-        self.num_layers = len(self.convs) # total number of conv layers
-
-        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=Snake(channels, alpha_logscale=h.snake_logscale))
-                for _ in range(self.num_layers)
-            ])
-        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale))
-                 for _ in range(self.num_layers)
-            ])
-        else:
-            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
-
-    def forward(self, x):
-        for c, a in zip (self.convs, self.activations):
-            xt = a(x)
-            xt = c(xt)
-            x = xt + x
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class BigVGAN(torch.nn.Module):
-    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
-    def __init__(self, h):
-        super(BigVGAN, self).__init__()
-        self.h = h
-
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-
-        # pre conv
-        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
-
-        # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
-        resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
-
-        # transposed conv-based upsamplers. does not apply anti-aliasing
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            self.ups.append(nn.ModuleList([
-                weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i),
-                                            h.upsample_initial_channel // (2 ** (i + 1)),
-                                            k, u, padding=(k - u) // 2))
-            ]))
-
-        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
-
-        # post conv
-        if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
-            activation_post = Snake(ch, alpha_logscale=h.snake_logscale)
-            self.activation_post = Activation1d(activation=activation_post)
-        elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
-            activation_post = SnakeBeta(ch, alpha_logscale=h.snake_logscale)
-            self.activation_post = Activation1d(activation=activation_post)
-        else:
-            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-
-        # weight initialization
-        for i in range(len(self.ups)):
-            self.ups[i].apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x):
-        # pre conv
-        x = self.conv_pre(x)
-
-        for i in range(self.num_upsamples):
-            # upsampling
-            for i_up in range(len(self.ups[i])):
-                x = self.ups[i][i_up](x)
-            # AMP blocks
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-
-        # post conv
-        x = self.activation_post(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            for l_i in l:
-                remove_weight_norm(l_i)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, h, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.d_mult = h.discriminator_channel_mult
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, int(32*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(int(32*self.d_mult), int(128*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(int(128*self.d_mult), int(512*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(int(512*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(int(1024*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(int(1024*self.d_mult), 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, 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, h):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.mpd_reshapes = h.mpd_reshapes
-        print("mpd_reshapes: {}".format(self.mpd_reshapes))
-        discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
-        self.discriminators = nn.ModuleList(discriminators)
-
-    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)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorR(nn.Module):
-    def __init__(self, cfg, resolution):
-        super().__init__()
-
-        self.resolution = resolution
-        assert len(self.resolution) == 3, \
-            "MRD layer requires list with len=3, got {}".format(self.resolution)
-        self.lrelu_slope = LRELU_SLOPE
-
-        norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
-        if hasattr(cfg, "mrd_use_spectral_norm"):
-            print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm))
-            norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
-        self.d_mult = cfg.discriminator_channel_mult
-        if hasattr(cfg, "mrd_channel_mult"):
-            print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult))
-            self.d_mult = cfg.mrd_channel_mult
-
-        self.convs = nn.ModuleList([
-            norm_f(nn.Conv2d(1, int(32*self.d_mult), (3, 9), padding=(1, 4))),
-            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
-            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
-            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
-            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 3), padding=(1, 1))),
-        ])
-        self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)))
-
-    def forward(self, x):
-        fmap = []
-
-        x = self.spectrogram(x)
-        x = x.unsqueeze(1)
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, self.lrelu_slope)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-    def spectrogram(self, x):
-        n_fft, hop_length, win_length = self.resolution
-        x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect')
-        x = x.squeeze(1)
-        x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True)
-        x = torch.view_as_real(x)  # [B, F, TT, 2]
-        mag = torch.norm(x, p=2, dim =-1) #[B, F, TT]
-
-        return mag
-
-
-class MultiResolutionDiscriminator(nn.Module):
-    def __init__(self, cfg, debug=False):
-        super().__init__()
-        self.resolutions = cfg.resolutions
-        assert len(self.resolutions) == 3,\
-            "MRD requires list of list with len=3, each element having a list with len=3. got {}".\
-                format(self.resolutions)
-        self.discriminators = nn.ModuleList(
-            [DiscriminatorR(cfg, resolution) for resolution in self.resolutions]
-        )
-
-    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(x=y)
-            y_d_g, fmap_g = d(x=y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-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):
-            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):
-        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:
-        l = torch.mean((1-dg)**2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-
-class VocoderBigVGAN(object):
-    def __init__(self, ckpt_vocoder,device='cuda'):
-        vocoder_sd = torch.load(os.path.join(ckpt_vocoder,'best_netG.pt'), map_location='cpu')
-
-        vocoder_args = OmegaConf.load(os.path.join(ckpt_vocoder,'args.yml'))
-
-        self.generator = BigVGAN(vocoder_args)
-        self.generator.load_state_dict(vocoder_sd['generator'])
-        self.generator.eval()
-
-        self.device = device
-        self.generator.to(self.device)
-
-    def vocode(self, spec):
-        with torch.no_grad():
-            if isinstance(spec,np.ndarray):
-                spec = torch.from_numpy(spec).unsqueeze(0)
-            spec = spec.to(dtype=torch.float32,device=self.device)
-            return self.generator(spec).squeeze().cpu().numpy()
-
-    def __call__(self, wav):
-        return self.vocode(wav)

spaces/AUBADA-ALARABI/poetry202/README.md

@@ -1,13 +0,0 @@
----
-title: Poetry2023
-emoji: 👁
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
-duplicated_from: Abdllh/poetry202
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Abhi5ingh/fashionsd/sdfile.py

@@ -1,89 +0,0 @@
-import gc
-import datetime
-import os
-import re
-from typing import Literal
-
-import streamlit as st
-import torch
-from diffusers import (
-    StableDiffusionPipeline,
-    StableDiffusionControlNetPipeline,
-    ControlNetModel,
-    EulerDiscreteScheduler,
-    DDIMScheduler,
-)
-
-PIPELINES = Literal["txt2img", "sketch2img"]
-
-@st.cache_resource(max_entries=1)
-def get_pipelines( name:PIPELINES, enable_cpu_offload = False, ) -> StableDiffusionPipeline:
-    pipe = None
-
-    if name == "txt2img":
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16,cache_dir="D:/huggingface/CACHE/")
-        pipe.unet.load_attn_procs("./")
-        pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
-    elif name == "sketch2img":
-        controlnet = ControlNetModel.from_pretrained("Abhi5ingh/model_dresscode", torch_dtype=torch.float16,cache_dir="D:/huggingface/CACHE/")
-        pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet = controlnet, torch_dtype = torch.float16,cache_dir="D:/huggingface/CACHE/")
-        pipe.unet.load_attn_procs("./")
-        pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
-
-    if pipe is None:
-        raise Exception(f"Pipeline not Found {name}")
-
-    if enable_cpu_offload:
-        print("Enabling cpu offloading for the given pipeline")
-        pipe.enable_model_cpu_offload()
-    else:
-        pipe = pipe.to("cuda")
-    return pipe
-
-def generate(
-        prompt,
-        pipeline_name: PIPELINES,
-        image = None,
-        num_inference_steps = 30,
-        negative_prompt = None,
-        width = 512,
-        height = 512,
-        guidance_scale = 7.5,
-        controlnet_conditioning_scale = None,
-        enable_cpu_offload= False):
-    negative_prompt = negative_prompt if negative_prompt else None
-    p = st.progress(0)
-    callback = lambda step,*_: p.progress(step/num_inference_steps)
-    pipe = get_pipelines(pipeline_name,enable_cpu_offload=enable_cpu_offload)
-    torch.cuda.empty_cache()
-
-    kwargs = dict(
-        prompt = prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=num_inference_steps,
-        callback=callback,
-        guidance_scale=guidance_scale,
-    )
-    print("kwargs",kwargs)
-
-    if pipeline_name =="sketch2img" and image:
-        kwargs.update(image=image,controlnet_conditioning_scale=controlnet_conditioning_scale)
-    elif pipeline_name == "txt2img":
-        kwargs.update(width = width, height = height)
-    else:
-        raise Exception(
-            f"Cannot generate image for pipeline {pipeline_name} and {prompt}")
-    images = pipe(**kwargs).images
-    image = images[0]
-
-    os.makedirs("outputs", exist_ok=True)
-
-    filename = (
-        "outputs/"
-        + re.sub(r"\s+", "_",prompt)[:30]
-        + f"_{datetime.datetime.now().timestamp()}"
-    )
-    image.save(f"{filename}.png")
-    with open(f"{filename}.txt", "w") as f:
-        f.write(f"Prompt: {prompt}\n\nNegative Prompt:{negative_prompt}")
-    return image

spaces/Abubakari/Sepsis-fastapi-prediction-app/Dockerfile

@@ -1,14 +0,0 @@
-FROM python:3.9
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-
-RUN pip install -r /code/requirements.txt
-
-COPY . .
-
-# Expose the port on which the application will run
-EXPOSE 7860
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/r/[id]/message/[messageId]/prompt/$types.d.ts

@@ -1,9 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand<T> = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = { id: string; messageId: string }
-type RouteId = '/r/[id]/message/[messageId]/prompt';
-
-export type EntryGenerator = () => Promise<Array<RouteParams>> | Array<RouteParams>;
-export type RequestHandler = Kit.RequestHandler<RouteParams, RouteId>;
-export type RequestEvent = Kit.RequestEvent<RouteParams, RouteId>;

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/settings/$types.d.ts

@@ -1,28 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand<T> = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = {  }
-type RouteId = '/settings';
-type MaybeWithVoid<T> = {} extends T ? T | void : T;
-export type RequiredKeys<T> = { [K in keyof T]-?: {} extends { [P in K]: T[K] } ? never : K; }[keyof T];
-type OutputDataShape<T> = MaybeWithVoid<Omit<App.PageData, RequiredKeys<T>> & Partial<Pick<App.PageData, keyof T & keyof App.PageData>> & Record<string, any>>
-type EnsureDefined<T> = T extends null | undefined ? {} : T;
-type OptionalUnion<U extends Record<string, any>, A extends keyof U = U extends U ? keyof U : never> = U extends unknown ? { [P in Exclude<A, keyof U>]?: never } & U : never;
-export type Snapshot<T = any> = Kit.Snapshot<T>;
-type PageServerParentData = EnsureDefined<import('../$types.js').LayoutServerData>;
-type PageParentData = EnsureDefined<import('../$types.js').LayoutData>;
-
-export type PageServerLoad<OutputData extends OutputDataShape<PageServerParentData> = OutputDataShape<PageServerParentData>> = Kit.ServerLoad<RouteParams, PageServerParentData, OutputData, RouteId>;
-export type PageServerLoadEvent = Parameters<PageServerLoad>[0];
-type ExcludeActionFailure<T> = T extends Kit.ActionFailure<any> ? never : T extends void ? never : T;
-type ActionsSuccess<T extends Record<string, (...args: any) => any>> = { [Key in keyof T]: ExcludeActionFailure<Awaited<ReturnType<T[Key]>>>; }[keyof T];
-type ExtractActionFailure<T> = T extends Kit.ActionFailure<infer X>	? X extends void ? never : X : never;
-type ActionsFailure<T extends Record<string, (...args: any) => any>> = { [Key in keyof T]: Exclude<ExtractActionFailure<Awaited<ReturnType<T[Key]>>>, void>; }[keyof T];
-type ActionsExport = typeof import('../../../../../src/routes/settings/+page.server.js').actions
-export type SubmitFunction = Kit.SubmitFunction<Expand<ActionsSuccess<ActionsExport>>, Expand<ActionsFailure<ActionsExport>>>
-export type ActionData = Expand<Kit.AwaitedActions<ActionsExport>> | null;
-export type PageServerData = null;
-export type PageData = Expand<PageParentData>;
-export type Action<OutputData extends Record<string, any> | void = Record<string, any> | void> = Kit.Action<RouteParams, OutputData, RouteId>
-export type Actions<OutputData extends Record<string, any> | void = Record<string, any> | void> = Kit.Actions<RouteParams, OutputData, RouteId>
-export type RequestEvent = Kit.RequestEvent<RouteParams, RouteId>;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateToast.js

@@ -1,8 +0,0 @@
-import CreateAnyLabel from './utils/CreateAnyLabel.js';
-import Toast from '../../toast/Toast.js';
-
-var CreateToast = function (scene, data, view, styles, customBuilders) {
-    return CreateAnyLabel(scene, data, view, styles, customBuilders, Toast);
-}
-
-export default CreateToast;

spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py

@@ -1,214 +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
-
-"""Custom TensorFlow ops for efficient bias and activation."""
-
-import os
-import numpy as np
-import tensorflow as tf
-from .. import custom_ops
-from ...util import EasyDict
-
-
-def _get_plugin():
-    return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
-
-# ----------------------------------------------------------------------------
-
-
-activation_funcs = {
-    'linear':   EasyDict(func=lambda x, **_:        x,                          def_alpha=None, def_gain=1.0,           cuda_idx=1, ref='y', zero_2nd_grad=True),
-    'relu':     EasyDict(func=lambda x, **_:        tf.nn.relu(x),              def_alpha=None, def_gain=np.sqrt(2),    cuda_idx=2, ref='y', zero_2nd_grad=True),
-    'lrelu':    EasyDict(func=lambda x, alpha, **_: tf.nn.leaky_relu(x, alpha), def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', zero_2nd_grad=True),
-    'tanh':     EasyDict(func=lambda x, **_:        tf.nn.tanh(x),              def_alpha=None, def_gain=1.0,           cuda_idx=4, ref='y', zero_2nd_grad=False),
-    'sigmoid':  EasyDict(func=lambda x, **_:        tf.nn.sigmoid(x),           def_alpha=None, def_gain=1.0,           cuda_idx=5, ref='y', zero_2nd_grad=False),
-    'elu':      EasyDict(func=lambda x, **_:        tf.nn.elu(x),               def_alpha=None, def_gain=1.0,           cuda_idx=6, ref='y', zero_2nd_grad=False),
-    'selu':     EasyDict(func=lambda x, **_:        tf.nn.selu(x),              def_alpha=None, def_gain=1.0,           cuda_idx=7, ref='y', zero_2nd_grad=False),
-    'softplus': EasyDict(func=lambda x, **_:        tf.nn.softplus(x),          def_alpha=None, def_gain=1.0,           cuda_idx=8, ref='y', zero_2nd_grad=False),
-    'swish':    EasyDict(func=lambda x, **_:        tf.nn.sigmoid(x) * x,       def_alpha=None, def_gain=np.sqrt(2),    cuda_idx=9, ref='x', zero_2nd_grad=False),
-}
-
-# ----------------------------------------------------------------------------
-
-
-def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=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 TensorFlow ops. It supports first and second order gradients,
-    but not third order gradients.
-
-    Args:
-        x:      Input activation tensor. Can have any shape, but if `b` is defined, the
-                dimension corresponding to `axis`, as well as the rank, must be known.
-        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 `axis`.
-        axis:   The dimension in `x` corresponding to the elements of `b`.
-                The value of `axis` 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 `1.0`.
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-
-    impl_dict = {
-        'ref':  _fused_bias_act_ref,
-        'cuda': _fused_bias_act_cuda,
-    }
-    return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain)
-
-# ----------------------------------------------------------------------------
-
-
-def _fused_bias_act_ref(x, b, axis, act, alpha, gain):
-    """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops."""
-
-    # Validate arguments.
-    x = tf.convert_to_tensor(x)
-    b = tf.convert_to_tensor(
-        b) if b is not None else tf.constant([], dtype=x.dtype)
-    act_spec = activation_funcs[act]
-    assert b.shape.rank == 1 and (
-        b.shape[0] == 0 or b.shape[0] == x.shape[axis])
-    assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
-    if alpha is None:
-        alpha = act_spec.def_alpha
-    if gain is None:
-        gain = act_spec.def_gain
-
-    # Add bias.
-    if b.shape[0] != 0:
-        x += tf.reshape(b, [-1 if i ==
-                        axis else 1 for i in range(x.shape.rank)])
-
-    # Evaluate activation function.
-    x = act_spec.func(x, alpha=alpha)
-
-    # Scale by gain.
-    if gain != 1:
-        x *= gain
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-def _fused_bias_act_cuda(x, b, axis, act, alpha, gain):
-    """Fast CUDA implementation of `fused_bias_act()` using custom ops."""
-
-    # Validate arguments.
-    x = tf.convert_to_tensor(x)
-    empty_tensor = tf.constant([], dtype=x.dtype)
-    b = tf.convert_to_tensor(b) if b is not None else empty_tensor
-    act_spec = activation_funcs[act]
-    assert b.shape.rank == 1 and (
-        b.shape[0] == 0 or b.shape[0] == x.shape[axis])
-    assert b.shape[0] == 0 or 0 <= axis < x.shape.rank
-    if alpha is None:
-        alpha = act_spec.def_alpha
-    if gain is None:
-        gain = act_spec.def_gain
-
-    # Special cases.
-    if act == 'linear' and b is None and gain == 1.0:
-        return x
-    if act_spec.cuda_idx is None:
-        return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain)
-
-    # CUDA kernel.
-    cuda_kernel = _get_plugin().fused_bias_act
-    cuda_kwargs = dict(axis=axis, act=act_spec.cuda_idx,
-                       alpha=alpha, gain=gain)
-
-    # Forward pass: y = func(x, b).
-    def func_y(x, b):
-        y = cuda_kernel(x=x, b=b, ref=empty_tensor, grad=0, **cuda_kwargs)
-        y.set_shape(x.shape)
-        return y
-
-    # Backward pass: dx, db = grad(dy, x, y)
-    def grad_dx(dy, x, y):
-        ref = {'x': x, 'y': y}[act_spec.ref]
-        dx = cuda_kernel(x=dy, b=empty_tensor, ref=ref, grad=1, **cuda_kwargs)
-        dx.set_shape(x.shape)
-        return dx
-
-    def grad_db(dx):
-        if b.shape[0] == 0:
-            return empty_tensor
-        db = dx
-        if axis < x.shape.rank - 1:
-            db = tf.reduce_sum(db, list(range(axis + 1, x.shape.rank)))
-        if axis > 0:
-            db = tf.reduce_sum(db, list(range(axis)))
-        db.set_shape(b.shape)
-        return db
-
-    # Second order gradients: d_dy, d_x = grad2(d_dx, d_db, x, y)
-    def grad2_d_dy(d_dx, d_db, x, y):
-        ref = {'x': x, 'y': y}[act_spec.ref]
-        d_dy = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=1, **cuda_kwargs)
-        d_dy.set_shape(x.shape)
-        return d_dy
-
-    def grad2_d_x(d_dx, d_db, x, y):
-        ref = {'x': x, 'y': y}[act_spec.ref]
-        d_x = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=2, **cuda_kwargs)
-        d_x.set_shape(x.shape)
-        return d_x
-
-    # Fast version for piecewise-linear activation funcs.
-    @tf.custom_gradient
-    def func_zero_2nd_grad(x, b):
-        y = func_y(x, b)
-
-        @tf.custom_gradient
-        def grad(dy):
-            dx = grad_dx(dy, x, y)
-            db = grad_db(dx)
-
-            def grad2(d_dx, d_db):
-                d_dy = grad2_d_dy(d_dx, d_db, x, y)
-                return d_dy
-            return (dx, db), grad2
-        return y, grad
-
-    # Slow version for general activation funcs.
-    @tf.custom_gradient
-    def func_nonzero_2nd_grad(x, b):
-        y = func_y(x, b)
-
-        def grad_wrap(dy):
-            @tf.custom_gradient
-            def grad_impl(dy, x):
-                dx = grad_dx(dy, x, y)
-                db = grad_db(dx)
-
-                def grad2(d_dx, d_db):
-                    d_dy = grad2_d_dy(d_dx, d_db, x, y)
-                    d_x = grad2_d_x(d_dx, d_db, x, y)
-                    return d_dy, d_x
-                return (dx, db), grad2
-            return grad_impl(dy, x)
-        return y, grad_wrap
-
-    # Which version to use?
-    if act_spec.zero_2nd_grad:
-        return func_zero_2nd_grad(x, b)
-    return func_nonzero_2nd_grad(x, b)
-
-# ----------------------------------------------------------------------------

spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/training/projectors/w_plus_projector.py

@@ -1,163 +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.
-
-"""Project given image to the latent space of pretrained network pickle."""
-
-import copy
-import wandb
-import numpy as np
-import torch
-import torch.nn.functional as F
-from tqdm import tqdm
-from configs import global_config, hyperparameters
-import dnnlib
-from utils.log_utils import log_image_from_w
-
-
-def project(
-        G,
-        # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
-        target: torch.Tensor,
-        *,
-        num_steps=1000,
-        w_avg_samples=10000,
-        initial_learning_rate=0.01,
-        initial_noise_factor=0.05,
-        lr_rampdown_length=0.25,
-        lr_rampup_length=0.05,
-        noise_ramp_length=0.75,
-        regularize_noise_weight=1e5,
-        verbose=False,
-        device: torch.device,
-        use_wandb=False,
-        initial_w=None,
-        image_log_step=global_config.image_rec_result_log_snapshot,
-        w_name: str
-):
-    print('inside training/projectors/w_plus_projector')
-    print(target.shape, G.img_channels, G.img_resolution * 2, G.img_resolution)
-    assert target.shape == (
-        G.img_channels, G.img_resolution * 2, G.img_resolution)
-
-    def logprint(*args):
-        if verbose:
-            print(*args)
-
-    G = copy.deepcopy(G).eval().requires_grad_(
-        False).to(device).float()  # type: ignore
-
-    # Compute w stats.
-    logprint(
-        f'Computing W midpoint and stddev using {w_avg_samples} samples...')
-    z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
-    w_samples = G.mapping(torch.from_numpy(
-        z_samples).to(device), None)  # [N, L, C]
-    w_samples = w_samples[:, :1, :].cpu(
-    ).numpy().astype(np.float32)  # [N, 1, C]
-    w_avg = np.mean(w_samples, axis=0, keepdims=True)  # [1, 1, C]
-    w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device)
-    w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
-
-    start_w = initial_w if initial_w is not None else w_avg
-
-    # Setup noise inputs.
-    noise_bufs = {name: buf for (
-        name, buf) in G.synthesis.named_buffers() if 'noise_const' in name}
-
-    # Load VGG16 feature detector.
-    url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
-    with dnnlib.util.open_url(url) as f:
-        vgg16 = torch.jit.load(f).eval().to(device)
-
-    # Features for target image.
-    target_images = target.unsqueeze(0).to(device).to(torch.float32)
-    if target_images.shape[2] > 256:
-        target_images = F.interpolate(
-            target_images, size=(256, 256), mode='area')
-    target_features = vgg16(
-        target_images, resize_images=False, return_lpips=True)
-
-    start_w = np.repeat(start_w, G.mapping.num_ws, axis=1)
-    w_opt = torch.tensor(start_w, dtype=torch.float32, device=device,
-                         requires_grad=True)  # pylint: disable=not-callable
-
-    optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999),
-                                 lr=hyperparameters.first_inv_lr)
-
-    # Init noise.
-    for buf in noise_bufs.values():
-        buf[:] = torch.randn_like(buf)
-        buf.requires_grad = True
-
-    for step in tqdm(range(num_steps)):
-
-        # Learning rate schedule.
-        t = step / num_steps
-        w_noise_scale = w_std * initial_noise_factor * \
-            max(0.0, 1.0 - t / noise_ramp_length) ** 2
-        lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
-        lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
-        lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
-        lr = initial_learning_rate * lr_ramp
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = lr
-
-        # Synth images from opt_w.
-        w_noise = torch.randn_like(w_opt) * w_noise_scale
-        ws = (w_opt + w_noise)
-
-        synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)
-
-        # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
-        synth_images = (synth_images + 1) * (255 / 2)
-        if synth_images.shape[2] > 256:
-            synth_images = F.interpolate(
-                synth_images, size=(256, 256), mode='area')
-
-        # Features for synth images.
-        synth_features = vgg16(
-            synth_images, resize_images=False, return_lpips=True)
-        dist = (target_features - synth_features).square().sum()
-
-        # Noise regularization.
-        reg_loss = 0.0
-        for v in noise_bufs.values():
-            noise = v[None, None, :, :]  # must be [1,1,H,W] for F.avg_pool2d()
-            while True:
-                reg_loss += (noise * torch.roll(noise,
-                             shifts=1, dims=3)).mean() ** 2
-                reg_loss += (noise * torch.roll(noise,
-                             shifts=1, dims=2)).mean() ** 2
-                if noise.shape[2] <= 8:
-                    break
-                noise = F.avg_pool2d(noise, kernel_size=2)
-        loss = dist + reg_loss * regularize_noise_weight
-
-        if step % image_log_step == 0:
-            with torch.no_grad():
-                if use_wandb:
-                    global_config.training_step += 1
-                    wandb.log({f'first projection _{w_name}': loss.detach(
-                    ).cpu()}, step=global_config.training_step)
-                    log_image_from_w(w_opt, G, w_name)
-
-        # Step
-        optimizer.zero_grad(set_to_none=True)
-        loss.backward()
-        optimizer.step()
-        logprint(
-            f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
-
-        # Normalize noise.
-        with torch.no_grad():
-            for buf in noise_bufs.values():
-                buf -= buf.mean()
-                buf *= buf.square().mean().rsqrt()
-
-    del G
-    return w_opt

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/inference.py

@@ -1,12 +0,0 @@
-import torch
-
-from diffusers import StableDiffusionPipeline
-
-
-model_id = "path-to-your-trained-model"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-
-prompt = "A photo of sks dog in a bucket"
-image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
-
-image.save("dog-bucket.png")

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_k_diffusion.py

@@ -1,136 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import StableDiffusionKDiffusionPipeline
-from diffusers.utils import slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-
-enable_full_determinism()
-
-
-@slow
-@require_torch_gpu
-class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_stable_diffusion_1(self):
-        sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        sd_pipe.set_scheduler("sample_euler")
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
-
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_2(self):
-        sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        sd_pipe.set_scheduler("sample_euler")
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
-
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1
-
-    def test_stable_diffusion_karras_sigmas(self):
-        sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        sd_pipe.set_scheduler("sample_dpmpp_2m")
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        output = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=7.5,
-            num_inference_steps=15,
-            output_type="np",
-            use_karras_sigmas=True,
-        )
-
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array(
-            [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_noise_sampler_seed(self):
-        sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        sd_pipe.set_scheduler("sample_dpmpp_sde")
-
-        prompt = "A painting of a squirrel eating a burger"
-        seed = 0
-        images1 = sd_pipe(
-            [prompt],
-            generator=torch.manual_seed(seed),
-            noise_sampler_seed=seed,
-            guidance_scale=9.0,
-            num_inference_steps=20,
-            output_type="np",
-        ).images
-        images2 = sd_pipe(
-            [prompt],
-            generator=torch.manual_seed(seed),
-            noise_sampler_seed=seed,
-            guidance_scale=9.0,
-            num_inference_steps=20,
-            output_type="np",
-        ).images
-
-        assert images1.shape == (1, 512, 512, 3)
-        assert images2.shape == (1, 512, 512, 3)
-        assert np.abs(images1.flatten() - images2.flatten()).max() < 1e-2

spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]

spaces/Anew5128/Anew51/constants.py

@@ -1,50 +0,0 @@
-# Constants
-DEFAULT_CUDA_DEVICE = "cuda:0"
-# Also try: 'Qiliang/bart-large-cnn-samsum-ElectrifAi_v10'
-DEFAULT_SUMMARIZATION_MODEL = "Qiliang/bart-large-cnn-samsum-ChatGPT_v3"
-# Also try: 'joeddav/distilbert-base-uncased-go-emotions-student'
-DEFAULT_CLASSIFICATION_MODEL = "nateraw/bert-base-uncased-emotion"
-# Also try: 'Salesforce/blip-image-captioning-base'
-DEFAULT_CAPTIONING_MODEL = "Salesforce/blip-image-captioning-large"
-DEFAULT_SD_MODEL = "ckpt/anything-v4.5-vae-swapped"
-DEFAULT_EMBEDDING_MODEL = "sentence-transformers/all-mpnet-base-v2"
-DEFAULT_REMOTE_SD_HOST = "127.0.0.1"
-DEFAULT_REMOTE_SD_PORT = 7860
-DEFAULT_CHROMA_PORT = 8000
-SILERO_SAMPLES_PATH = "tts_samples"
-SILERO_SAMPLE_TEXT = "The quick brown fox jumps over the lazy dog"
-# ALL_MODULES = ['caption', 'summarize', 'classify', 'keywords', 'prompt', 'sd']
-DEFAULT_SUMMARIZE_PARAMS = {
-    "temperature": 1.0,
-    "repetition_penalty": 1.0,
-    "max_length": 500,
-    "min_length": 200,
-    "length_penalty": 1.5,
-    "bad_words": [
-        "\n",
-        '"',
-        "*",
-        "[",
-        "]",
-        "{",
-        "}",
-        ":",
-        "(",
-        ")",
-        "<",
-        ">",
-        "Â",
-        "The text ends",
-        "The story ends",
-        "The text is",
-        "The story is",
-    ],
-}
-
-PROMPT_PREFIX = "best quality, absurdres, "
-NEGATIVE_PROMPT = """lowres, bad anatomy, error body, error hair, error arm,
-error hands, bad hands, error fingers, bad fingers, missing fingers
-error legs, bad legs, multiple legs, missing legs, error lighting,
-error shadow, error reflection, text, error, extra digit, fewer digits,
-cropped, worst quality, low quality, normal quality, jpeg artifacts,
-signature, watermark, username, blurry"""

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/DOCS.md

@@ -1,85 +0,0 @@
-# Technical description of multimodal extension
-
-## Working principle
-Multimodality extension does most of the stuff which is required for any image input:
-
-- adds the UI
-- saves the images as base64 JPEGs to history
-- provides the hooks to the UI
-- if there are images in the prompt, it:
-    - splits the prompt to text and image parts
-    - adds image start/end markers to text parts, then encodes and embeds the text parts
-    - calls the vision pipeline to embed the images
-    - stitches the embeddings together, and returns them to text generation
-- loads the appropriate vision pipeline, selected either from model name, or by specifying --multimodal-pipeline parameter
-
-Now, for the pipelines, they:
-
-- load the required vision models
-- return some consts, for example the number of tokens taken up by image
-- and most importantly: return the embeddings for LLM, given a list of images
-
-## Prompts/history
-
-To save images in prompt/history, this extension is using a base64 JPEG, wrapped in a HTML tag, like so:
-```
-<img src="data:image/jpeg;base64,{img_str}">
-```
-where `{img_str}` is the actual image data. This format makes displaying them in the UI for free. Do note, that this format is required to be exactly the same, the regex used to find the images is: `<img src="data:image/jpeg;base64,([A-Za-z0-9+/=]+)">`.
-
-## LLM input
-To describe the input, let's see it on an example prompt:
-```
-text1<image1>text2<image2>text3
-```
-where `textN` is N-th text, `<imageN>` is N-th image, in HTML format specified above.
-
-**The first step is to split the prompt into image/text parts**, so we get:
-```
-['text1', '<image1>', 'text2', '<image2>', 'text3']
-```
-this is done in `MultimodalEmbedder._split_prompt(...)` function, which returns a list of `PromptPart`s - dataclasses wrapping the separate parts.
-
-This function also appends the image start/end markers to text, which are provided by `AbstractMultimodalPipeline.image_start()` / `AbstractMultimodalPipeline.image_end()` functions. If image start is `<Img>`, and end is `</Img>`, this function will return:
-```
-['text1<Img>', '<image1>', '</Img>text2<Img>', '<image2>', '</Img>text3']
-```
-
-**The returned prompt parts are then turned into token embeddings.**
-
-First, they are modified to token IDs, for the text it is done using standard `modules.text_generation.encode()` function, and for the images the returned token IDs are changed to placeholders. The placeholder is a list of `N` times `placeholder token id`, where `N` is specified using `AbstractMultimodalPipeline.num_image_embeds()`, and placeholder token IDs using  `AbstractMultimodalPipeline.placeholder_token_id()`.
-
-Now, based on the token IDs, the prompt might get truncated, especially if `max_new_tokens` are unreasonably high. Unfortunately, it can't be done simply, just by trimming the prompt to be short enough. This way will lead to sometimes splitting the prompt in the middle of an image embedding, which usually breaks the generation. Therefore, in this case, the entire image needs to be removed from input. This is done inside `MultimodalEmbedder._encode_text(...)` function.
-
-**After the tokenization, the tokens need to get embedded**, the text and images are once again treated separately.
-
-The text parts are turned to embeddings, using `AbstractMultimodalPipeline.embed_tokens(...)` function. It uses standard embedding function from the model, but to support many LLMs, the actual function is returned by the pipeline (as it might be different for different LLMs), for LLaMA it is `shared.model.model.embed_tokens(...)`.
-
-The image parts are turned to embeddings, using `AbstractMultimodalPipeline.embed_images(...)` function. This function is specific for a given pipeline, it takes the images as input, forwards them through vision model/projector, and returns the embeddings.
-
-**Now, the returned embeddings are stitched together**, using `torch.cat()`, this is creating the final input to the LLM.
-
-## Pipelines
-
-All of the pipelines should subclass `AbstractMultimodalPipeline` class. The idea is to allow for new pipelines to be added in the same way as user extensions - git clone into `extensions/multimodal/pipelines`.
-
-The pipelines are the description of the vision part, containing vision model/multimodal projector. All of the pipelines should have an unique `name()`, which is then selected by user, in `--multimodal-pipeline` CLI argument. For an example, see `pipelines/llava/llava.py`.
-
-## Pipeline modules
-
-Pipelines are organized into "pipeline modules" - subdirectories in `pipelines` directory. The pipeline modules should contain a file called `pipelines.py`, that should contain the following fields:
-- `available_pipelines: List[str]` - list of pipelines provided by this module, shown as the list of available pipelines to the user
-- `def get_pipeline(name: str, params: dict) -> Optional[AbstractMultimodalPipeline]`: - a function to get a concrete pipeline by `name`, if `name` doesn't match any, should return `None`. `params` is the user settings for multimodal extension
-- `def get_pipeline_from_model_name(model_name: str, params: dict) -> Optional[AbstractMultimodalPipeline]`: - a function to get a pipeline from `model_name`, should be eager to return `None`, unless the determination can be done clearly (for example: minigpt-4 bases on vicuna - it should never return the pipeline, but llava can, as it has its own specific LLM finetune)
-
-**NOTE**: A pipeline module should lazy-import the pipelines only when necessary, and it should keep its imports to minimum
-
-## Pipeline params
-
-The pipelines will get the extension `params` in the constructor. They should honor the following fields:
-- `vision_device` - string, specifying `torch.device` to run the vision model (CLIP/ViT) on
-- `vision_bits` - int, number of fp bits to load the vision model(s) in
-- `projector_device` - string, specifying `torch.device` to run the projector models (Linear layers, QFormer, etc.) on
-- `projector_bits` - int, number of fp bits to load the projector models in
-
-As a helper, `AbstractMultimodalPipeline` has `_get_device(self, setting_name: str, params: dict)` and `_get_dtype(self, setting_name: str, params: dict)` helper functions, which parse string/int and return `torch.device` / `torch.dtype`.

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/notebook_handler.py

@@ -1,40 +0,0 @@
-"""
-This module is responsible for handling and modifying the notebook text.
-"""
-import re
-
-import extensions.superboogav2.parameters as parameters
-
-from modules import shared
-from modules.logging_colors import logger
-from extensions.superboogav2.utils import create_context_text
-
-from .data_processor import preprocess_text
-
-def _remove_special_tokens(string):
-    pattern = r'(<\|begin-user-input\|>|<\|end-user-input\|>|<\|injection-point\|>)'
-    return re.sub(pattern, '', string)
-
-
-def input_modifier_internal(string, collector):
-    # Sanity check.
-    if shared.is_chat():
-        return string
-
-    # Find the user input
-    pattern = re.compile(r"<\|begin-user-input\|>(.*?)<\|end-user-input\|>", re.DOTALL)
-    match = re.search(pattern, string)
-    if match:
-        # Preprocess the user prompt.
-        user_input = match.group(1).strip()
-        user_input = preprocess_text(user_input)
-
-        logger.debug(f"Preprocessed User Input: {user_input}")
-
-        # Get the most similar chunks
-        results = collector.get_sorted_by_dist(user_input, n_results=parameters.get_chunk_count(), max_token_count=int(parameters.get_max_token_count()))
-
-        # Make the injection
-        string = string.replace('<|injection-point|>', create_context_text(results))
-
-    return _remove_special_tokens(string)

spaces/Aphrodite/AIChatBot-SL-Chatbot-Blenderbot/app.py

@@ -1,28 +0,0 @@
-import streamlit as st
-#from streamlit_chat import message as st_message
-from streamlit_chat import message as st_message
-from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
-
-st.title("JCS Advanced AI Chatting Bot")
-
-if "history" not in st.session_state:
-    st.session_state.history = []
-    
-def get_models():
-  tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-  model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
-  return tokenizer, model
-  
-def generate_answer():
-    tokenizer, model = get_models()
-    user_message = st.session_state.input_text
-    inputs = tokenizer(st.session_state.input_text, return_tensors="pt")
-    result = model.generate(**inputs)
-    message_bot = tokenizer.decode(result[0], skip_special_tokens=True)  # .replace("<s>", "").replace("</s>", "")
-    st.session_state.history.append({"message": user_message, "is_user": True})
-    st.session_state.history.append({"message": message_bot, "is_user": False})
-    
-st.text_input("Response", key="input_text", on_change=generate_answer)
-
-for chat in st.session_state.history:
-    st_message(**chat)

spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/japanese.py

@@ -1,104 +0,0 @@
-# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
-import re
-import sys
-
-import pyopenjtalk
-
-from text import symbols
-
-# 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 (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def preprocess_jap(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    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):
-            p = pyopenjtalk.g2p(sentence)
-            text += p.split(" ")
-
-        if i < len(marks):
-            text += [marks[i].replace(' ', '')]
-    return text
-
-def text_normalize(text):
-    # todo: jap text normalize
-    return text
-
-def g2p(norm_text):
-    phones = preprocess_jap(norm_text)
-    phones = [post_replace_ph(i) for i in phones]
-    # todo: implement tones and word2ph
-    tones = [0 for i in phones]
-    word2ph = [1 for i in phones]
-    return phones, tones, word2ph
-
-
-if __name__ == '__main__':
-    for line in open("../../../Downloads/transcript_utf8.txt").readlines():
-        text = line.split(":")[1]
-        phones, tones, word2ph = g2p(text)
-        for p in phones:
-            if p == "z":
-                print(text, phones)
-                sys.exit(0)

spaces/Bart92/RVC_HF/infer/modules/uvr5/mdxnet.py

@@ -1,246 +0,0 @@
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-import librosa
-import numpy as np
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-cpu = torch.device("cpu")
-
-
-class ConvTDFNetTrim:
-    def __init__(
-        self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
-    ):
-        super(ConvTDFNetTrim, self).__init__()
-
-        self.dim_f = dim_f
-        self.dim_t = 2**dim_t
-        self.n_fft = n_fft
-        self.hop = hop
-        self.n_bins = self.n_fft // 2 + 1
-        self.chunk_size = hop * (self.dim_t - 1)
-        self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
-            device
-        )
-        self.target_name = target_name
-        self.blender = "blender" in model_name
-
-        self.dim_c = 4
-        out_c = self.dim_c * 4 if target_name == "*" else self.dim_c
-        self.freq_pad = torch.zeros(
-            [1, out_c, self.n_bins - self.dim_f, self.dim_t]
-        ).to(device)
-
-        self.n = L // 2
-
-    def stft(self, x):
-        x = x.reshape([-1, self.chunk_size])
-        x = torch.stft(
-            x,
-            n_fft=self.n_fft,
-            hop_length=self.hop,
-            window=self.window,
-            center=True,
-            return_complex=True,
-        )
-        x = torch.view_as_real(x)
-        x = x.permute([0, 3, 1, 2])
-        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
-            [-1, self.dim_c, self.n_bins, self.dim_t]
-        )
-        return x[:, :, : self.dim_f]
-
-    def istft(self, x, freq_pad=None):
-        freq_pad = (
-            self.freq_pad.repeat([x.shape[0], 1, 1, 1])
-            if freq_pad is None
-            else freq_pad
-        )
-        x = torch.cat([x, freq_pad], -2)
-        c = 4 * 2 if self.target_name == "*" else 2
-        x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
-            [-1, 2, self.n_bins, self.dim_t]
-        )
-        x = x.permute([0, 2, 3, 1])
-        x = x.contiguous()
-        x = torch.view_as_complex(x)
-        x = torch.istft(
-            x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
-        )
-        return x.reshape([-1, c, self.chunk_size])
-
-
-def get_models(device, dim_f, dim_t, n_fft):
-    return ConvTDFNetTrim(
-        device=device,
-        model_name="Conv-TDF",
-        target_name="vocals",
-        L=11,
-        dim_f=dim_f,
-        dim_t=dim_t,
-        n_fft=n_fft,
-    )
-
-
-class Predictor:
-    def __init__(self, args):
-        import onnxruntime as ort
-
-        logger.info(ort.get_available_providers())
-        self.args = args
-        self.model_ = get_models(
-            device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
-        )
-        self.model = ort.InferenceSession(
-            os.path.join(args.onnx, self.model_.target_name + ".onnx"),
-            providers=[
-                "CUDAExecutionProvider",
-                "DmlExecutionProvider",
-                "CPUExecutionProvider",
-            ],
-        )
-        logger.info("ONNX load done")
-
-    def demix(self, mix):
-        samples = mix.shape[-1]
-        margin = self.args.margin
-        chunk_size = self.args.chunks * 44100
-        assert not margin == 0, "margin cannot be zero!"
-        if margin > chunk_size:
-            margin = chunk_size
-
-        segmented_mix = {}
-
-        if self.args.chunks == 0 or samples < chunk_size:
-            chunk_size = samples
-
-        counter = -1
-        for skip in range(0, samples, chunk_size):
-            counter += 1
-
-            s_margin = 0 if counter == 0 else margin
-            end = min(skip + chunk_size + margin, samples)
-
-            start = skip - s_margin
-
-            segmented_mix[skip] = mix[:, start:end].copy()
-            if end == samples:
-                break
-
-        sources = self.demix_base(segmented_mix, margin_size=margin)
-        """
-        mix:(2,big_sample)
-        segmented_mix:offset->(2,small_sample)
-        sources:(1,2,big_sample)
-        """
-        return sources
-
-    def demix_base(self, mixes, margin_size):
-        chunked_sources = []
-        progress_bar = tqdm(total=len(mixes))
-        progress_bar.set_description("Processing")
-        for mix in mixes:
-            cmix = mixes[mix]
-            sources = []
-            n_sample = cmix.shape[1]
-            model = self.model_
-            trim = model.n_fft // 2
-            gen_size = model.chunk_size - 2 * trim
-            pad = gen_size - n_sample % gen_size
-            mix_p = np.concatenate(
-                (np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
-            )
-            mix_waves = []
-            i = 0
-            while i < n_sample + pad:
-                waves = np.array(mix_p[:, i : i + model.chunk_size])
-                mix_waves.append(waves)
-                i += gen_size
-            mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
-            with torch.no_grad():
-                _ort = self.model
-                spek = model.stft(mix_waves)
-                if self.args.denoise:
-                    spec_pred = (
-                        -_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
-                        + _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
-                    )
-                    tar_waves = model.istft(torch.tensor(spec_pred))
-                else:
-                    tar_waves = model.istft(
-                        torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
-                    )
-                tar_signal = (
-                    tar_waves[:, :, trim:-trim]
-                    .transpose(0, 1)
-                    .reshape(2, -1)
-                    .numpy()[:, :-pad]
-                )
-
-                start = 0 if mix == 0 else margin_size
-                end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
-                if margin_size == 0:
-                    end = None
-                sources.append(tar_signal[:, start:end])
-
-                progress_bar.update(1)
-
-            chunked_sources.append(sources)
-        _sources = np.concatenate(chunked_sources, axis=-1)
-        # del self.model
-        progress_bar.close()
-        return _sources
-
-    def prediction(self, m, vocal_root, others_root, format):
-        os.makedirs(vocal_root, exist_ok=True)
-        os.makedirs(others_root, exist_ok=True)
-        basename = os.path.basename(m)
-        mix, rate = librosa.load(m, mono=False, sr=44100)
-        if mix.ndim == 1:
-            mix = np.asfortranarray([mix, mix])
-        mix = mix.T
-        sources = self.demix(mix.T)
-        opt = sources[0].T
-        if format in ["wav", "flac"]:
-            sf.write(
-                "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
-            )
-            sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
-        else:
-            path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename)
-            path_other = "%s/%s_others.wav" % (others_root, basename)
-            sf.write(path_vocal, mix - opt, rate)
-            sf.write(path_other, opt, rate)
-            if os.path.exists(path_vocal):
-                os.system(
-                    "ffmpeg -i %s -vn %s -q:a 2 -y"
-                    % (path_vocal, path_vocal[:-4] + ".%s" % format)
-                )
-            if os.path.exists(path_other):
-                os.system(
-                    "ffmpeg -i %s -vn %s -q:a 2 -y"
-                    % (path_other, path_other[:-4] + ".%s" % format)
-                )
-
-
-class MDXNetDereverb:
-    def __init__(self, chunks, device):
-        self.onnx = "assets/uvr5_weights/onnx_dereverb_By_FoxJoy"
-        self.shifts = 10  # 'Predict with randomised equivariant stabilisation'
-        self.mixing = "min_mag"  # ['default','min_mag','max_mag']
-        self.chunks = chunks
-        self.margin = 44100
-        self.dim_t = 9
-        self.dim_f = 3072
-        self.n_fft = 6144
-        self.denoise = True
-        self.pred = Predictor(self)
-        self.device = device
-
-    def path_audio(self, input, vocal_root, others_root, format):
-        self.pred.prediction(input, vocal_root, others_root, format)

spaces/Benson/text-generation/Examples/Caramelo Crush Amigos Saga Apkpure.md

@@ -1,51 +0,0 @@
-
-<h1>Blockman Go Editor Aventura APK: Una plataforma de creación de juegos gratis y divertido</h1>
-<p>¿Te gustan los juegos de píxeles? ¿Quieres hacer tus propios juegos y compartirlos con otros? Si es así, entonces usted debe probar Blockman Go Editor Adventure APK, una plataforma de creación de juegos gratis y divertido que le permite crear y jugar juegos de píxeles en su dispositivo Android. En este artículo, le diremos qué es Blockman Go Editor Adventure APK, cómo descargarlo e instalarlo, cómo usarlo y cuáles son los beneficios de usarlo. </p>
- <h2>¿Qué es Blockman Go Editor aventura APK? </h2>
-<p>Blockman Go Editor Aventura APK es una aplicación que tiene dos funciones principales: un fabricante de juegos y un jugador del juego. </p>
-<h2>caramelo crush amigos saga apkpure</h2><br /><p><b><b>Download File</b> &#10022;&#10022;&#10022; <a href="https://bltlly.com/2v6IYc">https://bltlly.com/2v6IYc</a></b></p><br /><br />
- <h3>Una aplicación fabricante de juegos para juegos de píxeles</h3>
-<p>Blockman Go Editor Aventura APK es una herramienta de desarrollo que integra Editor de escena, Editor de gatillo, Editor de actor, Editor de interfaz de usuario, Editor de guiones, y otras funciones. Proporciona una plataforma de creación completamente gratuita para los amantes de los juegos de píxeles. Puedes usar varias herramientas y características para crear tus propios juegos, como Bed Wars, Jail Break, Sky Wars, Parkour y más. También puedes personalizar la configuración del juego, como el modo, el mapa, las reglas, etc.</p>
- <h3>Una aplicación de jugador de juegos para Blockman Go juegos</h3>
-<p>Blockman Go Editor Adventure APK es también una aplicación de jugador de juego que le permite jugar juegos hechos por otros usuarios o usted mismo. Puedes navegar y descargar juegos de la comunidad Blockman Go, o subir tus propios juegos para compartirlos con otros. También puedes unirte a juegos multijugador online con otros jugadores de todo el mundo. Puedes chatear con ellos, hacer amigos o competir con ellos. </p>
- <h2> ¿Cómo descargar e instalar Blockman Go Editor Aventura APK? </h2>
-<p>Blockman Go Editor Aventura APK no está disponible en Google Play Store, por lo que necesita descargarlo de otras fuentes. Estos son los pasos para descargarlo e instalarlo en tu dispositivo:</p>
- <h3>Descargar desde APKCombo u otras fuentes</h3>
-
- <h3>Habilitar fuentes desconocidas en su dispositivo</h3>
-<p>Antes de instalar el archivo APK, es necesario habilitar fuentes desconocidas en el dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </p>
- <h3>Instalar el archivo APK y lanzar la aplicación</h3>
-<p>Después de descargar el archivo APK, localizarlo en su dispositivo y toque en él para instalarlo. Siga las instrucciones de la pantalla para completar la instalación. Una vez instalada, inicie la aplicación y disfrute creando y jugando juegos de píxeles. </p>
- <h2> ¿Cómo usar Blockman Go Editor Aventura APK? </h2>
-<p>Usando Blockman Go Editor Aventura APK es fácil y divertido. Aquí hay algunos consejos sobre cómo usarlo:</p>
- <h3>Crea tus propios juegos con varias herramientas y características</h3>
-<p>Para crear tus propios juegos, toca el botón "Crear" en la pantalla principal de la aplicación. Verás varias herramientas y características que puedes usar para crear tus juegos. Por ejemplo, puedes usar el Editor de escenas para diseñar la escena del juego, el Editor de disparadores para configurar la lógica del juego, el Editor de actores para crear los personajes del juego, el Editor de interfaz de usuario para diseñar la interfaz del juego y el Editor de guiones para escribir el código del juego. También puedes usar Asset Store para descargar varios activos para tus juegos, como modelos, texturas, sonidos, etc. Puedes previsualizar tus juegos en cualquier momento y probarlos en tu dispositivo. </p>
-<p></p>
- <h3>Jugar juegos hechos por otros usuarios o usted mismo</h3>
-<p>Para jugar juegos hechos por otros usuarios o por ti mismo, toca el botón "Jugar" en la pantalla principal de la aplicación. Verás una lista de juegos que puedes descargar y jugar. También puedes buscar juegos por palabras clave o categorías. Para jugar a un juego, toca en él y espera a que se cargue. También puedes calificar y comentar los juegos que juegas. </p>
- <h3>Comparte tus juegos con la comunidad Blockman Go</h3>
-
- <h2>¿Cuáles son los beneficios de Blockman Go Editor Aventura APK? </h2>
-<p>Blockman Go Editor Aventura APK es una gran aplicación para los amantes de los juegos de píxeles. Aquí están algunos de los beneficios de su uso:</p>
- <h3>Gratis y fácil de usar</h3>
-<p>Blockman Go Editor Aventura APK es completamente gratis para descargar y usar. Usted no necesita pagar nada para crear o jugar juegos. La aplicación también es fácil de usar, con una interfaz fácil de usar e instrucciones claras. No necesitas ninguna experiencia o conocimiento previo para crear o jugar juegos. </p>
- <h3>Creativo y divertido</h3>
-<p>Blockman Go Editor Aventura APK es una aplicación creativa y divertida que le permite dar rienda suelta a su imaginación y expresarse. Puedes crear cualquier tipo de juego que quieras, con posibilidades y opciones ilimitadas. También puedes jugar juegos hechos por otros usuarios o por ti mismo, y disfrutar de diferentes géneros y estilos de juegos de píxeles. </p>
- <h3>Social e interactivo</h3>
-<p>Blockman Go Editor Aventura APK es una aplicación social e interactiva que le permite conectarse con otros amantes de los juegos de píxeles de todo el mundo. Puedes chatear con ellos, hacer amigos o competir con ellos. También puedes unirte a juegos multijugador en línea con ellos y divertirte juntos. </p>
- <h2>Conclusión</h2>
-<p>Blockman Go Editor Aventura APK es una plataforma de creación de juegos gratis y divertido que le permite crear y jugar juegos de píxeles en su dispositivo Android. Es una aplicación fabricante de juegos para juegos de píxeles, y una aplicación de jugador de juego para juegos de Blockman Go. Es fácil de descargar e instalar, fácil de usar, creativo y divertido, y social e interactivo. Si te gustan los juegos de píxeles, definitivamente deberías probar Blockman Go Editor Aventura APK.</p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Blockman Go Editor Aventura APK:</p>
- <h4>Q: ¿Es seguro usar Blockman Go Editor Adventure APK? </h4>
-
- <h4>Q: ¿Es Blockman Go Editor Adventure APK compatible con mi dispositivo? </h4>
-<p>A: Blockman Go Editor Adventure APK es compatible con la mayoría de los dispositivos Android que ejecutan Android 4.1 o superior. Sin embargo, es posible que algunos dispositivos no admitan algunas características o funciones de la aplicación debido a limitaciones de hardware o configuración del sistema. </p>
- <h4>Q: ¿Cómo puedo actualizar Blockman Go editor aventura APK? </h4>
-<p>A: Para actualizar Blockman Go Editor Adventure APK, es necesario descargar la última versión de la aplicación de <a href="">APKCombo</a> u otras fuentes, e instalarlo sobre la versión existente. Alternativamente, puede comprobar si hay actualizaciones dentro de la aplicación pulsando en el botón "Configuración" en la pantalla principal de la aplicación, y luego tocando en el "Buscar actualizaciones" opción. </p>
- <h4>Q: ¿Cómo puedo contactar al soporte de Blockman Go? </h4>
-<p>A: Para contactar con el soporte de Blockman Go, puede enviar un correo electrónico a <a href="mailto:[email protected]">[email protected] </a>, o visitar su sitio web oficial en <a href="">https:/ww.blockmango.net</a>. También puedes seguirlos en sus redes sociales, como Facebook, Twitter, Instagram, YouTube, etc.</p>
- <h4>Q: ¿Cómo puedo dar retroalimentación o sugerencias para Blockman Go Editor Adventure APK? </h4>
-<p>A: Para dar retroalimentación o sugerencias para Blockman Go Editor Adventure APK, puede utilizar la opción "Feedback" dentro de la aplicación, o enviar un correo electrónico a <a href="mailto:[email protected]">[email protected]</a>. También puede calificar y revisar la aplicación en <a href="">APKCombo</a> u otras fuentes, y compartir sus opiniones e ideas con otros usuarios. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Ciudad Congelada Mod Apk Diamantes Ilimitados.md

@@ -1,55 +0,0 @@
-
-<h1>Frozen City Mod APK ilimitados diamantes: Cómo descargar e instalar</h1>
-<p>Si estás buscando un emocionante y desafiante juego de supervivencia, es posible que quieras echar un vistazo a Frozen City. Este es un juego que pondrá a prueba sus habilidades y estrategia a medida que intenta construir y gestionar su base en un páramo congelado. Sin embargo, si desea disfrutar del juego sin limitaciones o restricciones, es posible que desee probar Frozen City Mod APK Unlimited Diamonds. Esta es una versión modificada del juego original que te da acceso a recursos ilimitados, como diamantes, monedas, gemas y más. En este artículo, le diremos qué es Frozen City, qué es Frozen City Mod APK Unlimited Diamonds, y cómo descargarlo e instalarlo en su dispositivo Android. </p>
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- <h2>¿Qué es la ciudad congelada? </h2>
-<p>Frozen City es un juego desarrollado por Game Insight, una compañía que se especializa en crear juegos móviles inmersivos y atractivos. El juego se desarrolla en un mundo post-apocalíptico donde un virus misterioso ha convertido a la mayoría de la población en zombies. Los sobrevivientes tienen que encontrar refugio y recursos en la ciudad congelada, donde tienen que enfrentar no solo a los no-muertos, sino también a otras facciones hostiles y desastres naturales. </p>
- <h3>Un juego de supervivencia ambientado en un mundo post-apocalíptico</h3>
-<p>En Frozen City, tienes que sobrevivir en un ambiente duro donde cada decisión importa. Tienes que buscar comida, agua, combustible y otros suministros, así como armas artesanales, herramientas y equipo. También tienes que defender tu base de ataques de zombies y raiders, así como explorar la ciudad en busca de pistas y secretos. Tienes que equilibrar tus necesidades y deseos, así como tu moralidad y humanidad. </p>
- <h3>Un juego de construcción de bases con múltiples etapas y tareas</h3>
-
- <h3>Un juego con impresionantes gráficos y efectos de sonido</h3>
-<p>Frozen City es un juego que cuenta con impresionantes gráficos y efectos de sonido que crean una atmósfera inmersiva. El juego presenta efectos meteorológicos realistas, como nieve, niebla, lluvia y viento. El juego también tiene animaciones detalladas, sombras, iluminación y texturas que hacen que la ciudad se vea viva. El juego también tiene efectos de sonido realistas, como gemidos de zombis, disparos, explosiones y más. El juego también tiene una banda sonora cautivadora que coincide con el estado de ánimo del juego. </p>
- <h2>¿Qué es Frozen City Mod APK ilimitados diamantes? </h2>
-<p>Frozen City Mod APK ilimitado de diamantes es una versión modificada del juego original que le da recursos ilimitados, tales como diamantes, monedas, gemas, y más. Estos recursos son esenciales para construir y actualizar su base, así como para desbloquear nuevas características y contenido. Sin embargo, en el juego original, estos recursos son limitados y difíciles de conseguir. Tienes que gastar dinero real o esperar largas horas para conseguirlos. </p>
-<p></p>
- <h3>Una versión modificada del juego original que te da recursos ilimitados</h3>
-<p>Frozen City Mod APK Unlimited Diamonds es una versión hackeada del juego original que evita el sistema de divisas en el juego. Esto significa que puede obtener diamantes, monedas, gemas y otros recursos ilimitados sin gastar dinero ni esperar largas horas. Puede utilizar estos recursos para construir y actualizar su base más rápido, así como acceder a todas las características y contenido del juego sin restricciones. </p>
- <h3>Una manera de disfrutar del juego sin gastar dinero real o esperar largas horas</h3>
-
- <h3>Una manera de desbloquear todas las características y el contenido del juego</h3>
-<p>Frozen City Mod APK Unlimited Diamonds es una manera de desbloquear todas las características y el contenido del juego que de otra manera están bloqueados o no disponibles en el juego original. Puede desbloquear nuevas ubicaciones, etapas, misiones, armas, equipos, sobrevivientes y más. También puedes acceder a funciones premium, como estatus VIP, artículos exclusivos y bonos. También puedes disfrutar del juego sin anuncios ni interrupciones. </p>
- <h2>Cómo descargar e instalar Frozen City Mod APK ilimitados diamantes? </h2>
-<p>Si desea probar Frozen City Mod APK Unlimited Diamonds, es necesario descargar e instalar en su dispositivo Android. Sin embargo, debe seguir algunos pasos y precauciones antes de hacerlo. Estos son los pasos que debe seguir:</p>
- <h3>Paso 1: Permitir aplicaciones desconocidas en su dispositivo Android</h3>
-<p>Dado que Frozen City Mod APK ilimitado de diamantes no está disponible en el oficial de Google Play Store, es necesario permitir que su dispositivo para instalar aplicaciones de fuentes desconocidas. Para hacer esto, vaya a la configuración del dispositivo, luego la seguridad, luego active la opción que dice "fuentes desconocidas" o "permitir la instalación de aplicaciones de fuentes desconocidas". Esto le permitirá instalar aplicaciones que no son de Google Play Store.</p>
- <h3>Paso 2: Instalar una aplicación de administrador de archivos en su dispositivo</h3>
-<p>También necesitas instalar una aplicación de administrador de archivos en tu dispositivo que te ayudará a localizar y administrar el archivo APK que descargarás. Una aplicación de administrador de archivos es una aplicación que le permite navegar y organizar los archivos y carpetas en su dispositivo. Puede usar cualquier aplicación de administrador de archivos que prefiera, como ES File Explorer, File Manager o Astro File Manager.</p>
- <h3>Paso 3: Descargar el archivo APK de una fuente de buena reputación</h3>
-
-<ul>
-<li>[APKPure]</li>
-<li>[APKHome]</li>
-<li>[ModDroid]</li>
-</ul>
-<p>Una vez que encuentre una fuente confiable, haga clic en el botón de descarga o enlace y espere a que la descarga termine. </p>
- <h3>Paso 4: Localizar y tocar el archivo APK para instalarlo</h3>
-<p>Después de descargar el archivo APK, necesita localizarlo en su dispositivo usando la aplicación de administrador de archivos que instaló anteriormente. El archivo APK debe estar en la carpeta de descargas o en la carpeta donde lo guardó. Una vez que lo encuentre, pulse sobre él para iniciar el proceso de instalación. Es posible que vea una ventana emergente pidiendo su permiso para instalar la aplicación. Toque en "instalar" o "permitir" y espere a que se complete la instalación. </p>
- <h3>Paso 5: Disfruta del juego con diamantes ilimitados y otros recursos</h3>
-<p>Felicidades! Usted ha descargado e instalado con éxito Frozen City Mod APK ilimitados diamantes en su dispositivo Android. Ahora puedes disfrutar del juego con recursos ilimitados, como diamantes, monedas, gemas y más. También puede desbloquear todas las características y el contenido del juego sin restricciones o limitaciones. ¡Diviértete jugando Frozen City Mod APK Unlimited Diamonds! </p>
- <h2>Conclusión</h2>
-<p>Frozen City es un juego de supervivencia que pondrá a prueba tus habilidades y estrategia mientras intentas construir y gestionar tu base en un desierto helado. Sin embargo, si quieres disfrutar del juego sin ningún tipo de molestia o frustración, es posible que desee probar Frozen City Mod APK Unlimited Diamonds. Esta es una versión modificada del juego original que te da recursos ilimitados, como diamantes, monedas, gemas y más. También puedes desbloquear todas las características y contenido del juego sin restricciones o limitaciones. </p>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Frozen City Mod APK Unlimited Diamonds:</p>
- <h3>Q: ¿Es seguro usar Frozen City Mod APK Unlimited Diamonds? </h3>
-<p>A: Sí, Frozen City Mod APK Unlimited Diamonds es seguro de usar, siempre y cuando lo descargue de una fuente confiable y siga los pasos y precauciones mencionados en este artículo. Sin embargo, siempre debe tener cuidado al instalar aplicaciones de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. También debe escanear el archivo APK con una aplicación antivirus antes de instalarlo. </p>
- <h3>Q: ¿Es Frozen City Mod APK ilimitado diamantes compatible con mi dispositivo? </h3>
-<p>A: Frozen City Mod APK Unlimited Diamonds es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego o el mod debido a diferentes especificaciones o configuraciones. Usted debe comprobar la compatibilidad de su dispositivo antes de descargar e instalar el mod. </p>
- <h3>Q: ¿Los diamantes ilimitados de Frozen City Mod APK afectarán el rendimiento de mi dispositivo? </h3>
-<p>A: Frozen City Mod APK ilimitado diamantes no debe afectar el rendimiento de su dispositivo significativamente, ya que no requiere mucho espacio de almacenamiento o memoria. Sin embargo, algunos dispositivos pueden experimentar retrasos o fallos debido a la baja RAM o CPU. Deberías cerrar otras aplicaciones y borrar la caché antes de jugar para evitar estos problemas. </p>
- <h3>Q: Congelado Ciudad Mod APK ilimitado de diamantes trabajar con la última versión del juego? </h3>
-<p>A: Frozen City Mod APK Unlimited Diamonds se actualiza regularmente para que coincida con la última versión del juego. Sin embargo, algunas actualizaciones pueden tardar más que otras en ser lanzadas. Deberías comprobar la versión del mod antes de descargarlo e instalarlo para asegurarte de que es compatible con la última versión del juego. </p>
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spaces/Benson/text-generation/Examples/Descargar Arthdal Crnicas Episodio 16.md

@@ -1,51 +0,0 @@
-
-<h1>Cómo descargar Arthdal Chronicles Episodio 16</h1>
-<p>¿Eres fan de Arthdal Chronicles, el drama épico coreano que ha cautivado a millones de espectadores en todo el mundo? Si es así, debes estar esperando ansiosamente el episodio 16, el episodio final de la temporada 1. Pero, ¿cómo puedes verlo online sin problemas? En este artículo, te mostraremos cómo descargar Arthdal Chronicles episodio 16 de Netflix, una de las mejores plataformas de streaming que ofrecen este increíble espectáculo. Pero primero, veamos de qué se trata Arthdal Chronicles y por qué deberías verlo. </p>
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-<h2>¿Qué es Arthdal Chronicles? </h2>
-<p>Arthdal Chronicles es un drama histórico de fantasía que cuenta la historia de la antigua ciudad de Arthdal y sus habitantes que luchan por el poder y la supervivencia. El drama cuenta con un reparto lleno de estrellas que incluye a Song Joong-ki, Jang Dong-gun, Kim Ji-won y Kim Ok-bin. El drama se divide en tres partes: Los Hijos de la Profecía, El Cielo Girando de Adentro hacia Afuera, La Tierra Naciente y El Preludio de Todas las Leyendas.</p>
-<p>En los episodios anteriores, hemos sido testigos de cómo Eunseom (Song Joong-ki), un mitad humano mitad neandertal que nació con un destino especial, escapa de Arthdal y se encuentra con otras tribus que lo ayudan a crecer como líder. También hemos visto cómo Tagon (Jang Dong-gun), un carismático guerrero que también es secretamente un neandertal, llega al poder en Arthdal y se enfrenta a varios desafíos de sus enemigos. Mientras tanto, Tanya (Kim Ji-won), un descendiente del clan Wahan que fue secuestrado por soldados Arthdal, se convierte en la alta sacerdotisa de Arthdal y aprende sobre su verdadera identidad. Y Saya ( Kim Ok-bin), un misterioso hermano gemelo de Eunseom que vive en una cámara secreta, conspira para derrocar a Tagon y apoderarse de Arthdal.</p>
-
-<h2>Por qué deberías ver Arthdal Chronicles Episodio 16</h2>
-<p>Hay muchas razones por las que deberías ver online el episodio 16 de Arthdal Chronicles. Estas son algunas de ellas:</p>
-<ul>
-<li>Disfrutarás de las impresionantes imágenes y cinematografías que dan vida al antiguo mundo de Arthdal. El drama cuenta con un alto valor de producción y un diseño de escenografía realista que te hará sentir como si fueras parte de la historia. </li>
-<li>Usted se sorprenderá por la excelente actuación y química de los miembros del reparto que retratan sus personajes complejos y diversos con pasión y habilidad. El drama muestra los talentos de algunos de los mejores actores de Corea que ofrecen actuaciones cautivadoras que te harán reír, llorar y animarlos. </li>
-<li>Estarás inmerso en la historia rica y original que combina la fantasía, la historia y la cultura de una manera única. El drama explora temas como el poder, el amor, la identidad y el destino de una manera creativa y atractiva que te mantendrá enganchado hasta el final. </li>
-<li>Estarás satisfecho con el final satisfactorio y gratificante que envolverá la historia de una manera significativa y memorable. El drama promete entregar un final que responderá a todas sus preguntas y le dejará con una sensación de cierre y cumplimiento. </li>
-</ul>
-<p>Entonces, si estás buscando un drama que te entretenga, te inspire y te haga pensar, el episodio 16 de Arthdal Chronicles es la elección perfecta para ti. </p>
-<h2>Donde Descargar Arthdal Chronicles Episodio 16</h2>
-
- | Streaming Platform | Calidad de vídeo | Subtítulos | Precio | Descargar Opción | | -- | --- - - - - - - - | Netflix | HD | Varios idiomas | $8.99/mes (Plan básico) | Sí | | Viki | HD | Varios idiomas | $4.99/mes (Plan estándar) | No | | Viu | HD | Múltiples idiomas | $6.49/month (Plan premium) | Sí | | Kocowa | HD | Solo en inglés | $6.99/month (Plan estándar) | No | <p>Como se puede ver en la tabla, Netflix es la mejor plataforma para descargar Arthdal Chronicles episodio 16 en línea. Tiene la más alta calidad de vídeo, la mayoría de las opciones de subtítulos, el precio más bajo, y la opción de descarga que le permite ver Arthdal Chronicles sin conexión. Por lo tanto, recomendamos Netflix como la mejor plataforma para descargar Arthdal Chronicles episodio 16. </p>
-<p></p>
-<h2>Cómo descargar Arthdal Chronicles episodio 16 de Netflix</h2>
-<p>Si ha decidido descargar Arthdal Chronicles episodio 16 de Netflix, aquí están los pasos que debe seguir:</p>
-<ol>
-<li>Regístrate en una cuenta de Netflix si aún no tienes una. Puedes elegir entre tres planes: Básico ($8.99/mes), Estándar ($13.99/mes), o Premium ($17.99/mes). El plan básico le permite ver en una pantalla a la vez, el plan estándar le permite ver en dos pantallas a la vez, y el plan Premium le permite ver en cuatro pantallas a la vez. Todos los planes te permiten descargar contenido en tus dispositivos. </li>
-<li>Descargue la aplicación de Netflix en su dispositivo si no la tiene ya. Puede descargarla desde la App Store o Google Play Store de forma gratuita. </li>
-<li>Abra la aplicación de Netflix e inicie sesión con los detalles de su cuenta. </li>
-<li>Buscar "Arthdal Chronicles" en la barra de búsqueda y seleccionarlo de los resultados. </li>
-<li>Seleccione "Episodio 16" de la lista de episodios y toque en el "Descargar" icono junto a ella. El icono parece una flecha hacia abajo con un círculo alrededor. </li>
-<li>Espere a que se complete la descarga. Puede comprobar el progreso de la descarga en la sección "Descargas" de la aplicación. </li>
-
-</ol>
-<p>Aquí hay una captura de pantalla que muestra cómo descargar Arthdal Chronicles episodio 16 de Netflix:</p>
-<img src="https://i.imgur.com/6XZ7y8G.png" alt="Captura de pantalla de la aplicación de Netflix que muestra cómo descargar Arthdal Chronicles episodio 16">
-<p>Un consejo sobre cómo ver Arthdal Chronicles sin conexión: Puede ajustar la calidad de vídeo de sus descargas para ahorrar espacio de almacenamiento en su dispositivo. Para ello, vaya a la sección "Configuración de la aplicación" de la aplicación y toque en "Descargar calidad de vídeo". Puede elegir entre cuatro opciones: Estándar (usa menos espacio de almacenamiento), Alto (usa más espacio de almacenamiento), Medio (usa espacio de almacenamiento moderado) o Inteligente (se ajusta automáticamente a la mejor calidad según las condiciones de su dispositivo y red). </p>
-<h2>Conclusión</h2>
-<p>En conclusión, Arthdal Chronicles episodio 16 es una visita obligada para todos los fans del drama. Es el último episodio de la temporada 1 que revelará el destino de los personajes y la ciudad de Arthdal. Puede descargar Arthdal Chronicles episodio 16 de Netflix, la mejor plataforma de transmisión que ofrece video de alta calidad, múltiples subtítulos, bajo precio y opción de descarga. Todo lo que necesita hacer es seguir los sencillos pasos que hemos descrito anteriormente y disfrutar viendo Arthdal Chronicles sin conexión. No te pierdas este final épico que te dejará sin palabras. </p>
-<p>Entonces, ¿qué estás esperando? Descarga Arthdal Chronicles episodio 16 de Netflix hoy y presenciar el final de una era. </p>
-<h2>Preguntas frecuentes</h2>
-<h3>Q: ¿Cuándo saldrá la temporada 2 de Arthdal Chronicles? </h3>
-<p>A: No hay confirmación oficial todavía sobre si Arthdal Chronicles tendrá una temporada 2 o no. Sin embargo, algunas fuentes sugieren que el equipo de producción está planeando comenzar a filmar la temporada 2 en 2024, después de que los actores terminen su servicio militar. Esperamos que esto sea cierto y que veamos más de Arthdal Chronicles en el futuro. </p>
-<h3>Q: ¿Cuántos episodios hay en Arthdal Chronicles? </h3>
-
-<h3>P: ¿Quiénes son los actores principales en Arthdal Chronicles? </h3>
-<p>A: Los actores principales de Arthdal Chronicles son Song Joong-ki, Jang Dong-gun, Kim Ji-won y Kim Ok-bin. Song Joong-ki interpreta a Eunseom y Saya, hermanos gemelos que tienen destinos diferentes. Jang Dong-gun interpreta a Tagon, un poderoso guerrero que se convierte en el rey de Arthdal. Kim Ji-won interpreta a Tanya, una alta sacerdotisa que es el interés amoroso de Eunseom. Kim Ok-bin interpreta a Taealha, un político astuto que es el interés amoroso de Tagon. </p>
-<h3>P: ¿Cuál es el género de Arthdal Chronicles? </h3>
-<p>A: Arthdal Chronicles es un drama histórico de fantasía que combina elementos de mitología, historia y cultura. Se encuentra en una tierra antigua ficticia llamada Arth, donde coexisten diferentes tribus y especies. Explora temas como el poder, el amor, la identidad y el destino. </p>
-<h3>Q: ¿Dónde puedo ver Arthdal Chronicles con subtítulos en inglés? </h3>
-<p>A: Puedes ver Arthdal Chronicles con subtítulos en inglés en Netflix, Viki, Viu o Kocowa. Sin embargo, recomendamos Netflix como la mejor plataforma para ver Arthdal Chronicles con subtítulos en inglés porque tiene la mejor calidad de video, la mayoría de opciones de subtítulos, el precio más bajo y la opción de descarga. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/helpers.py

@@ -1,1088 +0,0 @@
-# helpers.py
-import html.entities
-import re
-import typing
-
-from . import __diag__
-from .core import *
-from .util import _bslash, _flatten, _escape_regex_range_chars
-
-
-#
-# global helpers
-#
-def delimited_list(
-    expr: Union[str, ParserElement],
-    delim: Union[str, ParserElement] = ",",
-    combine: bool = False,
-    min: typing.Optional[int] = None,
-    max: typing.Optional[int] = None,
-    *,
-    allow_trailing_delim: bool = False,
-) -> ParserElement:
-    """Helper to define a delimited list of expressions - the delimiter
-    defaults to ','. By default, the list elements and delimiters can
-    have intervening whitespace, and comments, but this can be
-    overridden by passing ``combine=True`` in the constructor. If
-    ``combine`` is set to ``True``, the matching tokens are
-    returned as a single token string, with the delimiters included;
-    otherwise, the matching tokens are returned as a list of tokens,
-    with the delimiters suppressed.
-
-    If ``allow_trailing_delim`` is set to True, then the list may end with
-    a delimiter.
-
-    Example::
-
-        delimited_list(Word(alphas)).parse_string("aa,bb,cc") # -> ['aa', 'bb', 'cc']
-        delimited_list(Word(hexnums), delim=':', combine=True).parse_string("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE']
-    """
-    if isinstance(expr, str_type):
-        expr = ParserElement._literalStringClass(expr)
-
-    dlName = "{expr} [{delim} {expr}]...{end}".format(
-        expr=str(expr.copy().streamline()),
-        delim=str(delim),
-        end=" [{}]".format(str(delim)) if allow_trailing_delim else "",
-    )
-
-    if not combine:
-        delim = Suppress(delim)
-
-    if min is not None:
-        if min < 1:
-            raise ValueError("min must be greater than 0")
-        min -= 1
-    if max is not None:
-        if min is not None and max <= min:
-            raise ValueError("max must be greater than, or equal to min")
-        max -= 1
-    delimited_list_expr = expr + (delim + expr)[min, max]
-
-    if allow_trailing_delim:
-        delimited_list_expr += Opt(delim)
-
-    if combine:
-        return Combine(delimited_list_expr).set_name(dlName)
-    else:
-        return delimited_list_expr.set_name(dlName)
-
-
-def counted_array(
-    expr: ParserElement,
-    int_expr: typing.Optional[ParserElement] = None,
-    *,
-    intExpr: typing.Optional[ParserElement] = None,
-) -> ParserElement:
-    """Helper to define a counted list of expressions.
-
-    This helper defines a pattern of the form::
-
-        integer expr expr expr...
-
-    where the leading integer tells how many expr expressions follow.
-    The matched tokens returns the array of expr tokens as a list - the
-    leading count token is suppressed.
-
-    If ``int_expr`` is specified, it should be a pyparsing expression
-    that produces an integer value.
-
-    Example::
-
-        counted_array(Word(alphas)).parse_string('2 ab cd ef')  # -> ['ab', 'cd']
-
-        # in this parser, the leading integer value is given in binary,
-        # '10' indicating that 2 values are in the array
-        binary_constant = Word('01').set_parse_action(lambda t: int(t[0], 2))
-        counted_array(Word(alphas), int_expr=binary_constant).parse_string('10 ab cd ef')  # -> ['ab', 'cd']
-
-        # if other fields must be parsed after the count but before the
-        # list items, give the fields results names and they will
-        # be preserved in the returned ParseResults:
-        count_with_metadata = integer + Word(alphas)("type")
-        typed_array = counted_array(Word(alphanums), int_expr=count_with_metadata)("items")
-        result = typed_array.parse_string("3 bool True True False")
-        print(result.dump())
-
-        # prints
-        # ['True', 'True', 'False']
-        # - items: ['True', 'True', 'False']
-        # - type: 'bool'
-    """
-    intExpr = intExpr or int_expr
-    array_expr = Forward()
-
-    def count_field_parse_action(s, l, t):
-        nonlocal array_expr
-        n = t[0]
-        array_expr <<= (expr * n) if n else Empty()
-        # clear list contents, but keep any named results
-        del t[:]
-
-    if intExpr is None:
-        intExpr = Word(nums).set_parse_action(lambda t: int(t[0]))
-    else:
-        intExpr = intExpr.copy()
-    intExpr.set_name("arrayLen")
-    intExpr.add_parse_action(count_field_parse_action, call_during_try=True)
-    return (intExpr + array_expr).set_name("(len) " + str(expr) + "...")
-
-
-def match_previous_literal(expr: ParserElement) -> ParserElement:
-    """Helper to define an expression that is indirectly defined from
-    the tokens matched in a previous expression, that is, it looks for
-    a 'repeat' of a previous expression.  For example::
-
-        first = Word(nums)
-        second = match_previous_literal(first)
-        match_expr = first + ":" + second
-
-    will match ``"1:1"``, but not ``"1:2"``.  Because this
-    matches a previous literal, will also match the leading
-    ``"1:1"`` in ``"1:10"``. If this is not desired, use
-    :class:`match_previous_expr`. Do *not* use with packrat parsing
-    enabled.
-    """
-    rep = Forward()
-
-    def copy_token_to_repeater(s, l, t):
-        if t:
-            if len(t) == 1:
-                rep << t[0]
-            else:
-                # flatten t tokens
-                tflat = _flatten(t.as_list())
-                rep << And(Literal(tt) for tt in tflat)
-        else:
-            rep << Empty()
-
-    expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
-    rep.set_name("(prev) " + str(expr))
-    return rep
-
-
-def match_previous_expr(expr: ParserElement) -> ParserElement:
-    """Helper to define an expression that is indirectly defined from
-    the tokens matched in a previous expression, that is, it looks for
-    a 'repeat' of a previous expression.  For example::
-
-        first = Word(nums)
-        second = match_previous_expr(first)
-        match_expr = first + ":" + second
-
-    will match ``"1:1"``, but not ``"1:2"``.  Because this
-    matches by expressions, will *not* match the leading ``"1:1"``
-    in ``"1:10"``; the expressions are evaluated first, and then
-    compared, so ``"1"`` is compared with ``"10"``. Do *not* use
-    with packrat parsing enabled.
-    """
-    rep = Forward()
-    e2 = expr.copy()
-    rep <<= e2
-
-    def copy_token_to_repeater(s, l, t):
-        matchTokens = _flatten(t.as_list())
-
-        def must_match_these_tokens(s, l, t):
-            theseTokens = _flatten(t.as_list())
-            if theseTokens != matchTokens:
-                raise ParseException(
-                    s, l, "Expected {}, found{}".format(matchTokens, theseTokens)
-                )
-
-        rep.set_parse_action(must_match_these_tokens, callDuringTry=True)
-
-    expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
-    rep.set_name("(prev) " + str(expr))
-    return rep
-
-
-def one_of(
-    strs: Union[typing.Iterable[str], str],
-    caseless: bool = False,
-    use_regex: bool = True,
-    as_keyword: bool = False,
-    *,
-    useRegex: bool = True,
-    asKeyword: bool = False,
-) -> ParserElement:
-    """Helper to quickly define a set of alternative :class:`Literal` s,
-    and makes sure to do longest-first testing when there is a conflict,
-    regardless of the input order, but returns
-    a :class:`MatchFirst` for best performance.
-
-    Parameters:
-
-    - ``strs`` - a string of space-delimited literals, or a collection of
-      string literals
-    - ``caseless`` - treat all literals as caseless - (default= ``False``)
-    - ``use_regex`` - as an optimization, will
-      generate a :class:`Regex` object; otherwise, will generate
-      a :class:`MatchFirst` object (if ``caseless=True`` or ``asKeyword=True``, or if
-      creating a :class:`Regex` raises an exception) - (default= ``True``)
-    - ``as_keyword`` - enforce :class:`Keyword`-style matching on the
-      generated expressions - (default= ``False``)
-    - ``asKeyword`` and ``useRegex`` are retained for pre-PEP8 compatibility,
-      but will be removed in a future release
-
-    Example::
-
-        comp_oper = one_of("< = > <= >= !=")
-        var = Word(alphas)
-        number = Word(nums)
-        term = var | number
-        comparison_expr = term + comp_oper + term
-        print(comparison_expr.search_string("B = 12  AA=23 B<=AA AA>12"))
-
-    prints::
-
-        [['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']]
-    """
-    asKeyword = asKeyword or as_keyword
-    useRegex = useRegex and use_regex
-
-    if (
-        isinstance(caseless, str_type)
-        and __diag__.warn_on_multiple_string_args_to_oneof
-    ):
-        warnings.warn(
-            "More than one string argument passed to one_of, pass"
-            " choices as a list or space-delimited string",
-            stacklevel=2,
-        )
-
-    if caseless:
-        isequal = lambda a, b: a.upper() == b.upper()
-        masks = lambda a, b: b.upper().startswith(a.upper())
-        parseElementClass = CaselessKeyword if asKeyword else CaselessLiteral
-    else:
-        isequal = lambda a, b: a == b
-        masks = lambda a, b: b.startswith(a)
-        parseElementClass = Keyword if asKeyword else Literal
-
-    symbols: List[str] = []
-    if isinstance(strs, str_type):
-        symbols = strs.split()
-    elif isinstance(strs, Iterable):
-        symbols = list(strs)
-    else:
-        raise TypeError("Invalid argument to one_of, expected string or iterable")
-    if not symbols:
-        return NoMatch()
-
-    # reorder given symbols to take care to avoid masking longer choices with shorter ones
-    # (but only if the given symbols are not just single characters)
-    if any(len(sym) > 1 for sym in symbols):
-        i = 0
-        while i < len(symbols) - 1:
-            cur = symbols[i]
-            for j, other in enumerate(symbols[i + 1 :]):
-                if isequal(other, cur):
-                    del symbols[i + j + 1]
-                    break
-                elif masks(cur, other):
-                    del symbols[i + j + 1]
-                    symbols.insert(i, other)
-                    break
-            else:
-                i += 1
-
-    if useRegex:
-        re_flags: int = re.IGNORECASE if caseless else 0
-
-        try:
-            if all(len(sym) == 1 for sym in symbols):
-                # symbols are just single characters, create range regex pattern
-                patt = "[{}]".format(
-                    "".join(_escape_regex_range_chars(sym) for sym in symbols)
-                )
-            else:
-                patt = "|".join(re.escape(sym) for sym in symbols)
-
-            # wrap with \b word break markers if defining as keywords
-            if asKeyword:
-                patt = r"\b(?:{})\b".format(patt)
-
-            ret = Regex(patt, flags=re_flags).set_name(" | ".join(symbols))
-
-            if caseless:
-                # add parse action to return symbols as specified, not in random
-                # casing as found in input string
-                symbol_map = {sym.lower(): sym for sym in symbols}
-                ret.add_parse_action(lambda s, l, t: symbol_map[t[0].lower()])
-
-            return ret
-
-        except re.error:
-            warnings.warn(
-                "Exception creating Regex for one_of, building MatchFirst", stacklevel=2
-            )
-
-    # last resort, just use MatchFirst
-    return MatchFirst(parseElementClass(sym) for sym in symbols).set_name(
-        " | ".join(symbols)
-    )
-
-
-def dict_of(key: ParserElement, value: ParserElement) -> ParserElement:
-    """Helper to easily and clearly define a dictionary by specifying
-    the respective patterns for the key and value.  Takes care of
-    defining the :class:`Dict`, :class:`ZeroOrMore`, and
-    :class:`Group` tokens in the proper order.  The key pattern
-    can include delimiting markers or punctuation, as long as they are
-    suppressed, thereby leaving the significant key text.  The value
-    pattern can include named results, so that the :class:`Dict` results
-    can include named token fields.
-
-    Example::
-
-        text = "shape: SQUARE posn: upper left color: light blue texture: burlap"
-        attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join))
-        print(attr_expr[1, ...].parse_string(text).dump())
-
-        attr_label = label
-        attr_value = Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)
-
-        # similar to Dict, but simpler call format
-        result = dict_of(attr_label, attr_value).parse_string(text)
-        print(result.dump())
-        print(result['shape'])
-        print(result.shape)  # object attribute access works too
-        print(result.as_dict())
-
-    prints::
-
-        [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']]
-        - color: 'light blue'
-        - posn: 'upper left'
-        - shape: 'SQUARE'
-        - texture: 'burlap'
-        SQUARE
-        SQUARE
-        {'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'}
-    """
-    return Dict(OneOrMore(Group(key + value)))
-
-
-def original_text_for(
-    expr: ParserElement, as_string: bool = True, *, asString: bool = True
-) -> ParserElement:
-    """Helper to return the original, untokenized text for a given
-    expression.  Useful to restore the parsed fields of an HTML start
-    tag into the raw tag text itself, or to revert separate tokens with
-    intervening whitespace back to the original matching input text. By
-    default, returns astring containing the original parsed text.
-
-    If the optional ``as_string`` argument is passed as
-    ``False``, then the return value is
-    a :class:`ParseResults` containing any results names that
-    were originally matched, and a single token containing the original
-    matched text from the input string.  So if the expression passed to
-    :class:`original_text_for` contains expressions with defined
-    results names, you must set ``as_string`` to ``False`` if you
-    want to preserve those results name values.
-
-    The ``asString`` pre-PEP8 argument is retained for compatibility,
-    but will be removed in a future release.
-
-    Example::
-
-        src = "this is test <b> bold <i>text</i> </b> normal text "
-        for tag in ("b", "i"):
-            opener, closer = make_html_tags(tag)
-            patt = original_text_for(opener + SkipTo(closer) + closer)
-            print(patt.search_string(src)[0])
-
-    prints::
-
-        ['<b> bold <i>text</i> </b>']
-        ['<i>text</i>']
-    """
-    asString = asString and as_string
-
-    locMarker = Empty().set_parse_action(lambda s, loc, t: loc)
-    endlocMarker = locMarker.copy()
-    endlocMarker.callPreparse = False
-    matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end")
-    if asString:
-        extractText = lambda s, l, t: s[t._original_start : t._original_end]
-    else:
-
-        def extractText(s, l, t):
-            t[:] = [s[t.pop("_original_start") : t.pop("_original_end")]]
-
-    matchExpr.set_parse_action(extractText)
-    matchExpr.ignoreExprs = expr.ignoreExprs
-    matchExpr.suppress_warning(Diagnostics.warn_ungrouped_named_tokens_in_collection)
-    return matchExpr
-
-
-def ungroup(expr: ParserElement) -> ParserElement:
-    """Helper to undo pyparsing's default grouping of And expressions,
-    even if all but one are non-empty.
-    """
-    return TokenConverter(expr).add_parse_action(lambda t: t[0])
-
-
-def locatedExpr(expr: ParserElement) -> ParserElement:
-    """
-    (DEPRECATED - future code should use the Located class)
-    Helper to decorate a returned token with its starting and ending
-    locations in the input string.
-
-    This helper adds the following results names:
-
-    - ``locn_start`` - location where matched expression begins
-    - ``locn_end`` - location where matched expression ends
-    - ``value`` - the actual parsed results
-
-    Be careful if the input text contains ``<TAB>`` characters, you
-    may want to call :class:`ParserElement.parseWithTabs`
-
-    Example::
-
-        wd = Word(alphas)
-        for match in locatedExpr(wd).searchString("ljsdf123lksdjjf123lkkjj1222"):
-            print(match)
-
-    prints::
-
-        [[0, 'ljsdf', 5]]
-        [[8, 'lksdjjf', 15]]
-        [[18, 'lkkjj', 23]]
-    """
-    locator = Empty().set_parse_action(lambda ss, ll, tt: ll)
-    return Group(
-        locator("locn_start")
-        + expr("value")
-        + locator.copy().leaveWhitespace()("locn_end")
-    )
-
-
-def nested_expr(
-    opener: Union[str, ParserElement] = "(",
-    closer: Union[str, ParserElement] = ")",
-    content: typing.Optional[ParserElement] = None,
-    ignore_expr: ParserElement = quoted_string(),
-    *,
-    ignoreExpr: ParserElement = quoted_string(),
-) -> ParserElement:
-    """Helper method for defining nested lists enclosed in opening and
-    closing delimiters (``"("`` and ``")"`` are the default).
-
-    Parameters:
-    - ``opener`` - opening character for a nested list
-      (default= ``"("``); can also be a pyparsing expression
-    - ``closer`` - closing character for a nested list
-      (default= ``")"``); can also be a pyparsing expression
-    - ``content`` - expression for items within the nested lists
-      (default= ``None``)
-    - ``ignore_expr`` - expression for ignoring opening and closing delimiters
-      (default= :class:`quoted_string`)
-    - ``ignoreExpr`` - this pre-PEP8 argument is retained for compatibility
-      but will be removed in a future release
-
-    If an expression is not provided for the content argument, the
-    nested expression will capture all whitespace-delimited content
-    between delimiters as a list of separate values.
-
-    Use the ``ignore_expr`` argument to define expressions that may
-    contain opening or closing characters that should not be treated as
-    opening or closing characters for nesting, such as quoted_string or
-    a comment expression.  Specify multiple expressions using an
-    :class:`Or` or :class:`MatchFirst`. The default is
-    :class:`quoted_string`, but if no expressions are to be ignored, then
-    pass ``None`` for this argument.
-
-    Example::
-
-        data_type = one_of("void int short long char float double")
-        decl_data_type = Combine(data_type + Opt(Word('*')))
-        ident = Word(alphas+'_', alphanums+'_')
-        number = pyparsing_common.number
-        arg = Group(decl_data_type + ident)
-        LPAR, RPAR = map(Suppress, "()")
-
-        code_body = nested_expr('{', '}', ignore_expr=(quoted_string | c_style_comment))
-
-        c_function = (decl_data_type("type")
-                      + ident("name")
-                      + LPAR + Opt(delimited_list(arg), [])("args") + RPAR
-                      + code_body("body"))
-        c_function.ignore(c_style_comment)
-
-        source_code = '''
-            int is_odd(int x) {
-                return (x%2);
-            }
-
-            int dec_to_hex(char hchar) {
-                if (hchar >= '0' && hchar <= '9') {
-                    return (ord(hchar)-ord('0'));
-                } else {
-                    return (10+ord(hchar)-ord('A'));
-                }
-            }
-        '''
-        for func in c_function.search_string(source_code):
-            print("%(name)s (%(type)s) args: %(args)s" % func)
-
-
-    prints::
-
-        is_odd (int) args: [['int', 'x']]
-        dec_to_hex (int) args: [['char', 'hchar']]
-    """
-    if ignoreExpr != ignore_expr:
-        ignoreExpr = ignore_expr if ignoreExpr == quoted_string() else ignoreExpr
-    if opener == closer:
-        raise ValueError("opening and closing strings cannot be the same")
-    if content is None:
-        if isinstance(opener, str_type) and isinstance(closer, str_type):
-            if len(opener) == 1 and len(closer) == 1:
-                if ignoreExpr is not None:
-                    content = Combine(
-                        OneOrMore(
-                            ~ignoreExpr
-                            + CharsNotIn(
-                                opener + closer + ParserElement.DEFAULT_WHITE_CHARS,
-                                exact=1,
-                            )
-                        )
-                    ).set_parse_action(lambda t: t[0].strip())
-                else:
-                    content = empty.copy() + CharsNotIn(
-                        opener + closer + ParserElement.DEFAULT_WHITE_CHARS
-                    ).set_parse_action(lambda t: t[0].strip())
-            else:
-                if ignoreExpr is not None:
-                    content = Combine(
-                        OneOrMore(
-                            ~ignoreExpr
-                            + ~Literal(opener)
-                            + ~Literal(closer)
-                            + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
-                        )
-                    ).set_parse_action(lambda t: t[0].strip())
-                else:
-                    content = Combine(
-                        OneOrMore(
-                            ~Literal(opener)
-                            + ~Literal(closer)
-                            + CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
-                        )
-                    ).set_parse_action(lambda t: t[0].strip())
-        else:
-            raise ValueError(
-                "opening and closing arguments must be strings if no content expression is given"
-            )
-    ret = Forward()
-    if ignoreExpr is not None:
-        ret <<= Group(
-            Suppress(opener) + ZeroOrMore(ignoreExpr | ret | content) + Suppress(closer)
-        )
-    else:
-        ret <<= Group(Suppress(opener) + ZeroOrMore(ret | content) + Suppress(closer))
-    ret.set_name("nested %s%s expression" % (opener, closer))
-    return ret
-
-
-def _makeTags(tagStr, xml, suppress_LT=Suppress("<"), suppress_GT=Suppress(">")):
-    """Internal helper to construct opening and closing tag expressions, given a tag name"""
-    if isinstance(tagStr, str_type):
-        resname = tagStr
-        tagStr = Keyword(tagStr, caseless=not xml)
-    else:
-        resname = tagStr.name
-
-    tagAttrName = Word(alphas, alphanums + "_-:")
-    if xml:
-        tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes)
-        openTag = (
-            suppress_LT
-            + tagStr("tag")
-            + Dict(ZeroOrMore(Group(tagAttrName + Suppress("=") + tagAttrValue)))
-            + Opt("/", default=[False])("empty").set_parse_action(
-                lambda s, l, t: t[0] == "/"
-            )
-            + suppress_GT
-        )
-    else:
-        tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word(
-            printables, exclude_chars=">"
-        )
-        openTag = (
-            suppress_LT
-            + tagStr("tag")
-            + Dict(
-                ZeroOrMore(
-                    Group(
-                        tagAttrName.set_parse_action(lambda t: t[0].lower())
-                        + Opt(Suppress("=") + tagAttrValue)
-                    )
-                )
-            )
-            + Opt("/", default=[False])("empty").set_parse_action(
-                lambda s, l, t: t[0] == "/"
-            )
-            + suppress_GT
-        )
-    closeTag = Combine(Literal("</") + tagStr + ">", adjacent=False)
-
-    openTag.set_name("<%s>" % resname)
-    # add start<tagname> results name in parse action now that ungrouped names are not reported at two levels
-    openTag.add_parse_action(
-        lambda t: t.__setitem__(
-            "start" + "".join(resname.replace(":", " ").title().split()), t.copy()
-        )
-    )
-    closeTag = closeTag(
-        "end" + "".join(resname.replace(":", " ").title().split())
-    ).set_name("</%s>" % resname)
-    openTag.tag = resname
-    closeTag.tag = resname
-    openTag.tag_body = SkipTo(closeTag())
-    return openTag, closeTag
-
-
-def make_html_tags(
-    tag_str: Union[str, ParserElement]
-) -> Tuple[ParserElement, ParserElement]:
-    """Helper to construct opening and closing tag expressions for HTML,
-    given a tag name. Matches tags in either upper or lower case,
-    attributes with namespaces and with quoted or unquoted values.
-
-    Example::
-
-        text = '<td>More info at the <a href="https://github.com/pyparsing/pyparsing/wiki">pyparsing</a> wiki page</td>'
-        # make_html_tags returns pyparsing expressions for the opening and
-        # closing tags as a 2-tuple
-        a, a_end = make_html_tags("A")
-        link_expr = a + SkipTo(a_end)("link_text") + a_end
-
-        for link in link_expr.search_string(text):
-            # attributes in the <A> tag (like "href" shown here) are
-            # also accessible as named results
-            print(link.link_text, '->', link.href)
-
-    prints::
-
-        pyparsing -> https://github.com/pyparsing/pyparsing/wiki
-    """
-    return _makeTags(tag_str, False)
-
-
-def make_xml_tags(
-    tag_str: Union[str, ParserElement]
-) -> Tuple[ParserElement, ParserElement]:
-    """Helper to construct opening and closing tag expressions for XML,
-    given a tag name. Matches tags only in the given upper/lower case.
-
-    Example: similar to :class:`make_html_tags`
-    """
-    return _makeTags(tag_str, True)
-
-
-any_open_tag: ParserElement
-any_close_tag: ParserElement
-any_open_tag, any_close_tag = make_html_tags(
-    Word(alphas, alphanums + "_:").set_name("any tag")
-)
-
-_htmlEntityMap = {k.rstrip(";"): v for k, v in html.entities.html5.items()}
-common_html_entity = Regex("&(?P<entity>" + "|".join(_htmlEntityMap) + ");").set_name(
-    "common HTML entity"
-)
-
-
-def replace_html_entity(t):
-    """Helper parser action to replace common HTML entities with their special characters"""
-    return _htmlEntityMap.get(t.entity)
-
-
-class OpAssoc(Enum):
-    LEFT = 1
-    RIGHT = 2
-
-
-InfixNotationOperatorArgType = Union[
-    ParserElement, str, Tuple[Union[ParserElement, str], Union[ParserElement, str]]
-]
-InfixNotationOperatorSpec = Union[
-    Tuple[
-        InfixNotationOperatorArgType,
-        int,
-        OpAssoc,
-        typing.Optional[ParseAction],
-    ],
-    Tuple[
-        InfixNotationOperatorArgType,
-        int,
-        OpAssoc,
-    ],
-]
-
-
-def infix_notation(
-    base_expr: ParserElement,
-    op_list: List[InfixNotationOperatorSpec],
-    lpar: Union[str, ParserElement] = Suppress("("),
-    rpar: Union[str, ParserElement] = Suppress(")"),
-) -> ParserElement:
-    """Helper method for constructing grammars of expressions made up of
-    operators working in a precedence hierarchy.  Operators may be unary
-    or binary, left- or right-associative.  Parse actions can also be
-    attached to operator expressions. The generated parser will also
-    recognize the use of parentheses to override operator precedences
-    (see example below).
-
-    Note: if you define a deep operator list, you may see performance
-    issues when using infix_notation. See
-    :class:`ParserElement.enable_packrat` for a mechanism to potentially
-    improve your parser performance.
-
-    Parameters:
-    - ``base_expr`` - expression representing the most basic operand to
-      be used in the expression
-    - ``op_list`` - list of tuples, one for each operator precedence level
-      in the expression grammar; each tuple is of the form ``(op_expr,
-      num_operands, right_left_assoc, (optional)parse_action)``, where:
-
-      - ``op_expr`` is the pyparsing expression for the operator; may also
-        be a string, which will be converted to a Literal; if ``num_operands``
-        is 3, ``op_expr`` is a tuple of two expressions, for the two
-        operators separating the 3 terms
-      - ``num_operands`` is the number of terms for this operator (must be 1,
-        2, or 3)
-      - ``right_left_assoc`` is the indicator whether the operator is right
-        or left associative, using the pyparsing-defined constants
-        ``OpAssoc.RIGHT`` and ``OpAssoc.LEFT``.
-      - ``parse_action`` is the parse action to be associated with
-        expressions matching this operator expression (the parse action
-        tuple member may be omitted); if the parse action is passed
-        a tuple or list of functions, this is equivalent to calling
-        ``set_parse_action(*fn)``
-        (:class:`ParserElement.set_parse_action`)
-    - ``lpar`` - expression for matching left-parentheses; if passed as a
-      str, then will be parsed as Suppress(lpar). If lpar is passed as
-      an expression (such as ``Literal('(')``), then it will be kept in
-      the parsed results, and grouped with them. (default= ``Suppress('(')``)
-    - ``rpar`` - expression for matching right-parentheses; if passed as a
-      str, then will be parsed as Suppress(rpar). If rpar is passed as
-      an expression (such as ``Literal(')')``), then it will be kept in
-      the parsed results, and grouped with them. (default= ``Suppress(')')``)
-
-    Example::
-
-        # simple example of four-function arithmetic with ints and
-        # variable names
-        integer = pyparsing_common.signed_integer
-        varname = pyparsing_common.identifier
-
-        arith_expr = infix_notation(integer | varname,
-            [
-            ('-', 1, OpAssoc.RIGHT),
-            (one_of('* /'), 2, OpAssoc.LEFT),
-            (one_of('+ -'), 2, OpAssoc.LEFT),
-            ])
-
-        arith_expr.run_tests('''
-            5+3*6
-            (5+3)*6
-            -2--11
-            ''', full_dump=False)
-
-    prints::
-
-        5+3*6
-        [[5, '+', [3, '*', 6]]]
-
-        (5+3)*6
-        [[[5, '+', 3], '*', 6]]
-
-        -2--11
-        [[['-', 2], '-', ['-', 11]]]
-    """
-    # captive version of FollowedBy that does not do parse actions or capture results names
-    class _FB(FollowedBy):
-        def parseImpl(self, instring, loc, doActions=True):
-            self.expr.try_parse(instring, loc)
-            return loc, []
-
-    _FB.__name__ = "FollowedBy>"
-
-    ret = Forward()
-    if isinstance(lpar, str):
-        lpar = Suppress(lpar)
-    if isinstance(rpar, str):
-        rpar = Suppress(rpar)
-
-    # if lpar and rpar are not suppressed, wrap in group
-    if not (isinstance(rpar, Suppress) and isinstance(rpar, Suppress)):
-        lastExpr = base_expr | Group(lpar + ret + rpar)
-    else:
-        lastExpr = base_expr | (lpar + ret + rpar)
-
-    for i, operDef in enumerate(op_list):
-        opExpr, arity, rightLeftAssoc, pa = (operDef + (None,))[:4]
-        if isinstance(opExpr, str_type):
-            opExpr = ParserElement._literalStringClass(opExpr)
-        if arity == 3:
-            if not isinstance(opExpr, (tuple, list)) or len(opExpr) != 2:
-                raise ValueError(
-                    "if numterms=3, opExpr must be a tuple or list of two expressions"
-                )
-            opExpr1, opExpr2 = opExpr
-            term_name = "{}{} term".format(opExpr1, opExpr2)
-        else:
-            term_name = "{} term".format(opExpr)
-
-        if not 1 <= arity <= 3:
-            raise ValueError("operator must be unary (1), binary (2), or ternary (3)")
-
-        if rightLeftAssoc not in (OpAssoc.LEFT, OpAssoc.RIGHT):
-            raise ValueError("operator must indicate right or left associativity")
-
-        thisExpr: Forward = Forward().set_name(term_name)
-        if rightLeftAssoc is OpAssoc.LEFT:
-            if arity == 1:
-                matchExpr = _FB(lastExpr + opExpr) + Group(lastExpr + opExpr[1, ...])
-            elif arity == 2:
-                if opExpr is not None:
-                    matchExpr = _FB(lastExpr + opExpr + lastExpr) + Group(
-                        lastExpr + (opExpr + lastExpr)[1, ...]
-                    )
-                else:
-                    matchExpr = _FB(lastExpr + lastExpr) + Group(lastExpr[2, ...])
-            elif arity == 3:
-                matchExpr = _FB(
-                    lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr
-                ) + Group(lastExpr + OneOrMore(opExpr1 + lastExpr + opExpr2 + lastExpr))
-        elif rightLeftAssoc is OpAssoc.RIGHT:
-            if arity == 1:
-                # try to avoid LR with this extra test
-                if not isinstance(opExpr, Opt):
-                    opExpr = Opt(opExpr)
-                matchExpr = _FB(opExpr.expr + thisExpr) + Group(opExpr + thisExpr)
-            elif arity == 2:
-                if opExpr is not None:
-                    matchExpr = _FB(lastExpr + opExpr + thisExpr) + Group(
-                        lastExpr + (opExpr + thisExpr)[1, ...]
-                    )
-                else:
-                    matchExpr = _FB(lastExpr + thisExpr) + Group(
-                        lastExpr + thisExpr[1, ...]
-                    )
-            elif arity == 3:
-                matchExpr = _FB(
-                    lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr
-                ) + Group(lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr)
-        if pa:
-            if isinstance(pa, (tuple, list)):
-                matchExpr.set_parse_action(*pa)
-            else:
-                matchExpr.set_parse_action(pa)
-        thisExpr <<= (matchExpr | lastExpr).setName(term_name)
-        lastExpr = thisExpr
-    ret <<= lastExpr
-    return ret
-
-
-def indentedBlock(blockStatementExpr, indentStack, indent=True, backup_stacks=[]):
-    """
-    (DEPRECATED - use IndentedBlock class instead)
-    Helper method for defining space-delimited indentation blocks,
-    such as those used to define block statements in Python source code.
-
-    Parameters:
-
-    - ``blockStatementExpr`` - expression defining syntax of statement that
-      is repeated within the indented block
-    - ``indentStack`` - list created by caller to manage indentation stack
-      (multiple ``statementWithIndentedBlock`` expressions within a single
-      grammar should share a common ``indentStack``)
-    - ``indent`` - boolean indicating whether block must be indented beyond
-      the current level; set to ``False`` for block of left-most statements
-      (default= ``True``)
-
-    A valid block must contain at least one ``blockStatement``.
-
-    (Note that indentedBlock uses internal parse actions which make it
-    incompatible with packrat parsing.)
-
-    Example::
-
-        data = '''
-        def A(z):
-          A1
-          B = 100
-          G = A2
-          A2
-          A3
-        B
-        def BB(a,b,c):
-          BB1
-          def BBA():
-            bba1
-            bba2
-            bba3
-        C
-        D
-        def spam(x,y):
-             def eggs(z):
-                 pass
-        '''
-
-
-        indentStack = [1]
-        stmt = Forward()
-
-        identifier = Word(alphas, alphanums)
-        funcDecl = ("def" + identifier + Group("(" + Opt(delimitedList(identifier)) + ")") + ":")
-        func_body = indentedBlock(stmt, indentStack)
-        funcDef = Group(funcDecl + func_body)
-
-        rvalue = Forward()
-        funcCall = Group(identifier + "(" + Opt(delimitedList(rvalue)) + ")")
-        rvalue << (funcCall | identifier | Word(nums))
-        assignment = Group(identifier + "=" + rvalue)
-        stmt << (funcDef | assignment | identifier)
-
-        module_body = stmt[1, ...]
-
-        parseTree = module_body.parseString(data)
-        parseTree.pprint()
-
-    prints::
-
-        [['def',
-          'A',
-          ['(', 'z', ')'],
-          ':',
-          [['A1'], [['B', '=', '100']], [['G', '=', 'A2']], ['A2'], ['A3']]],
-         'B',
-         ['def',
-          'BB',
-          ['(', 'a', 'b', 'c', ')'],
-          ':',
-          [['BB1'], [['def', 'BBA', ['(', ')'], ':', [['bba1'], ['bba2'], ['bba3']]]]]],
-         'C',
-         'D',
-         ['def',
-          'spam',
-          ['(', 'x', 'y', ')'],
-          ':',
-          [[['def', 'eggs', ['(', 'z', ')'], ':', [['pass']]]]]]]
-    """
-    backup_stacks.append(indentStack[:])
-
-    def reset_stack():
-        indentStack[:] = backup_stacks[-1]
-
-    def checkPeerIndent(s, l, t):
-        if l >= len(s):
-            return
-        curCol = col(l, s)
-        if curCol != indentStack[-1]:
-            if curCol > indentStack[-1]:
-                raise ParseException(s, l, "illegal nesting")
-            raise ParseException(s, l, "not a peer entry")
-
-    def checkSubIndent(s, l, t):
-        curCol = col(l, s)
-        if curCol > indentStack[-1]:
-            indentStack.append(curCol)
-        else:
-            raise ParseException(s, l, "not a subentry")
-
-    def checkUnindent(s, l, t):
-        if l >= len(s):
-            return
-        curCol = col(l, s)
-        if not (indentStack and curCol in indentStack):
-            raise ParseException(s, l, "not an unindent")
-        if curCol < indentStack[-1]:
-            indentStack.pop()
-
-    NL = OneOrMore(LineEnd().set_whitespace_chars("\t ").suppress())
-    INDENT = (Empty() + Empty().set_parse_action(checkSubIndent)).set_name("INDENT")
-    PEER = Empty().set_parse_action(checkPeerIndent).set_name("")
-    UNDENT = Empty().set_parse_action(checkUnindent).set_name("UNINDENT")
-    if indent:
-        smExpr = Group(
-            Opt(NL)
-            + INDENT
-            + OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL))
-            + UNDENT
-        )
-    else:
-        smExpr = Group(
-            Opt(NL)
-            + OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL))
-            + Opt(UNDENT)
-        )
-
-    # add a parse action to remove backup_stack from list of backups
-    smExpr.add_parse_action(
-        lambda: backup_stacks.pop(-1) and None if backup_stacks else None
-    )
-    smExpr.set_fail_action(lambda a, b, c, d: reset_stack())
-    blockStatementExpr.ignore(_bslash + LineEnd())
-    return smExpr.set_name("indented block")
-
-
-# it's easy to get these comment structures wrong - they're very common, so may as well make them available
-c_style_comment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/").set_name(
-    "C style comment"
-)
-"Comment of the form ``/* ... */``"
-
-html_comment = Regex(r"<!--[\s\S]*?-->").set_name("HTML comment")
-"Comment of the form ``<!-- ... -->``"
-
-rest_of_line = Regex(r".*").leave_whitespace().set_name("rest of line")
-dbl_slash_comment = Regex(r"//(?:\\\n|[^\n])*").set_name("// comment")
-"Comment of the form ``// ... (to end of line)``"
-
-cpp_style_comment = Combine(
-    Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/" | dbl_slash_comment
-).set_name("C++ style comment")
-"Comment of either form :class:`c_style_comment` or :class:`dbl_slash_comment`"
-
-java_style_comment = cpp_style_comment
-"Same as :class:`cpp_style_comment`"
-
-python_style_comment = Regex(r"#.*").set_name("Python style comment")
-"Comment of the form ``# ... (to end of line)``"
-
-
-# build list of built-in expressions, for future reference if a global default value
-# gets updated
-_builtin_exprs: List[ParserElement] = [
-    v for v in vars().values() if isinstance(v, ParserElement)
-]
-
-
-# pre-PEP8 compatible names
-delimitedList = delimited_list
-countedArray = counted_array
-matchPreviousLiteral = match_previous_literal
-matchPreviousExpr = match_previous_expr
-oneOf = one_of
-dictOf = dict_of
-originalTextFor = original_text_for
-nestedExpr = nested_expr
-makeHTMLTags = make_html_tags
-makeXMLTags = make_xml_tags
-anyOpenTag, anyCloseTag = any_open_tag, any_close_tag
-commonHTMLEntity = common_html_entity
-replaceHTMLEntity = replace_html_entity
-opAssoc = OpAssoc
-infixNotation = infix_notation
-cStyleComment = c_style_comment
-htmlComment = html_comment
-restOfLine = rest_of_line
-dblSlashComment = dbl_slash_comment
-cppStyleComment = cpp_style_comment
-javaStyleComment = java_style_comment
-pythonStyleComment = python_style_comment

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/install_data.py

@@ -1,84 +0,0 @@
-"""distutils.command.install_data
-
-Implements the Distutils 'install_data' command, for installing
-platform-independent data files."""
-
-# contributed by Bastian Kleineidam
-
-import os
-from distutils.core import Command
-from distutils.util import change_root, convert_path
-
-
-class install_data(Command):
-
-    description = "install data files"
-
-    user_options = [
-        (
-            'install-dir=',
-            'd',
-            "base directory for installing data files "
-            "(default: installation base dir)",
-        ),
-        ('root=', None, "install everything relative to this alternate root directory"),
-        ('force', 'f', "force installation (overwrite existing files)"),
-    ]
-
-    boolean_options = ['force']
-
-    def initialize_options(self):
-        self.install_dir = None
-        self.outfiles = []
-        self.root = None
-        self.force = 0
-        self.data_files = self.distribution.data_files
-        self.warn_dir = 1
-
-    def finalize_options(self):
-        self.set_undefined_options(
-            'install',
-            ('install_data', 'install_dir'),
-            ('root', 'root'),
-            ('force', 'force'),
-        )
-
-    def run(self):
-        self.mkpath(self.install_dir)
-        for f in self.data_files:
-            if isinstance(f, str):
-                # it's a simple file, so copy it
-                f = convert_path(f)
-                if self.warn_dir:
-                    self.warn(
-                        "setup script did not provide a directory for "
-                        "'%s' -- installing right in '%s'" % (f, self.install_dir)
-                    )
-                (out, _) = self.copy_file(f, self.install_dir)
-                self.outfiles.append(out)
-            else:
-                # it's a tuple with path to install to and a list of files
-                dir = convert_path(f[0])
-                if not os.path.isabs(dir):
-                    dir = os.path.join(self.install_dir, dir)
-                elif self.root:
-                    dir = change_root(self.root, dir)
-                self.mkpath(dir)
-
-                if f[1] == []:
-                    # If there are no files listed, the user must be
-                    # trying to create an empty directory, so add the
-                    # directory to the list of output files.
-                    self.outfiles.append(dir)
-                else:
-                    # Copy files, adding them to the list of output files.
-                    for data in f[1]:
-                        data = convert_path(data)
-                        (out, _) = self.copy_file(data, dir)
-                        self.outfiles.append(out)
-
-    def get_inputs(self):
-        return self.data_files or []
-
-    def get_outputs(self):
-        return self.outfiles

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/README.md

@@ -1,7 +0,0 @@
-
-
-To add a new Op:
-
-1. Create a new directory
-2. Implement new ops there
-3. Delcare its Python interface in `vision.cpp`.

spaces/CVPR/v-doc_abstractive_mac/interface.py

@@ -1,23 +0,0 @@
-import gradio as gr
-from demo import predict
-
-f = open("./descrip.md","r")
-description= f.read()
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2205.13724' target='_blank'>Paper Link</a></p>"
-
-gr.Interface(fn=predict, 
-            inputs=[gr.Image(label = 'You can select an example to quickly submit'), "text"], 
-            outputs=["text"],
-            examples=[
-            ['PDF_val_151.png','How many table objects are located at the top side of figure?'],
-            ['PDF_val_90.png', 'Where is the caption of the figure located at?'],
-            ['PDF_val_64.png','How many text objects are located at the bottom side of figure?'],
-            ['PDF_val_26.png','Are there any title exist?'],
-            ['PDF_val_60.png','Where is the caption of the table located at?'],
-            ['PDF_val_158.png','Does title objects exist in this page?']],
-            title = 'V-Doc : Visual questions answers with Documents',
-            description = description,
-            article = article).launch()
-
-if __name__ == '__main__':
-    io.lanuch()

spaces/Caoyunkang/Segment-Any-Anomaly/install.sh

@@ -1,45 +0,0 @@
-# create new conda env
-conda create -n SAA python=3.9
-source activate SAA
-
-# PyTorch
-pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
-
-# $ProjectRoot: the root you save our project, e.g., /home/anyad/VAND-solution
-ProjectRoot=/home/anyad/VAND-solution
-cd $ProjectRoot
-
-# SAM and DINO
-cd ./GroundingDINO
-pip install -e .
-cd ../SAM
-pip install -e .
-
-pip install setuptools==59.5.0
-pip install --upgrade diffusers[torch]
-pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel
-pip install transformers
-pip install addict
-pip install yapf
-pip install timm
-pip install loguru
-pip install tqdm
-pip install scikit-image
-pip install scikit-learn
-pip install pandas
-pip install tensorboard
-pip install seaborn
-pip install open_clip_torch
-pip install SciencePlots
-pip install timm
-pip install einops
-pip install gradio
-
-# weights
-cd ../
-mkdir weights
-cd ./weights/
-wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
-wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
-
-

spaces/ChandraMohanNayal/AutoGPT/tests/test_token_counter.py

@@ -1,63 +0,0 @@
-import unittest
-
-import tests.context
-from autogpt.token_counter import count_message_tokens, count_string_tokens
-
-
-class TestTokenCounter(unittest.TestCase):
-    def test_count_message_tokens(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        self.assertEqual(count_message_tokens(messages), 17)
-
-    def test_count_message_tokens_with_name(self):
-        messages = [
-            {"role": "user", "content": "Hello", "name": "John"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        self.assertEqual(count_message_tokens(messages), 17)
-
-    def test_count_message_tokens_empty_input(self):
-        self.assertEqual(count_message_tokens([]), 3)
-
-    def test_count_message_tokens_invalid_model(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        with self.assertRaises(KeyError):
-            count_message_tokens(messages, model="invalid_model")
-
-    def test_count_message_tokens_gpt_4(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        self.assertEqual(count_message_tokens(messages, model="gpt-4-0314"), 15)
-
-    def test_count_string_tokens(self):
-        string = "Hello, world!"
-        self.assertEqual(
-            count_string_tokens(string, model_name="gpt-3.5-turbo-0301"), 4
-        )
-
-    def test_count_string_tokens_empty_input(self):
-        self.assertEqual(count_string_tokens("", model_name="gpt-3.5-turbo-0301"), 0)
-
-    def test_count_message_tokens_invalid_model(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        with self.assertRaises(NotImplementedError):
-            count_message_tokens(messages, model="invalid_model")
-
-    def test_count_string_tokens_gpt_4(self):
-        string = "Hello, world!"
-        self.assertEqual(count_string_tokens(string, model_name="gpt-4-0314"), 4)
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/CikeyQI/meme-api/meme_generator/memes/bite/__init__.py

@@ -1,33 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def bite(images: List[BuildImage], texts, args):
-    img = images[0].convert("RGBA").square()
-    frames: List[IMG] = []
-    # fmt: off
-    locs = [
-        (90, 90, 105, 150), (90, 83, 96, 172), (90, 90, 106, 148),
-        (88, 88, 97, 167), (90, 85, 89, 179), (90, 90, 106, 151)
-    ]
-    # fmt: on
-    for i in range(6):
-        frame = BuildImage.open(img_dir / f"{i}.png")
-        w, h, x, y = locs[i]
-        frame.paste(img.resize((w, h)), (x, y), below=True)
-        frames.append(frame.image)
-    for i in range(6, 16):
-        frame = BuildImage.open(img_dir / f"{i}.png")
-        frames.append(frame.image)
-    return save_gif(frames, 0.07)
-
-
-add_meme("bite", bite, min_images=1, max_images=1, keywords=["啃"])

spaces/CofAI/chat.b4/g4f/Provider/Providers/Ezcht.py

@@ -1,35 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://gpt4.ezchat.top'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-    }
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-    response = requests.post(url + '/api/openai/v1/chat/completions',
-                             json=data, stream=True)
-    
-    if stream:
-        for chunk in response.iter_content(chunk_size=None):
-            chunk = chunk.decode('utf-8')
-            if chunk.strip():
-                message = json.loads(chunk)['choices'][0]['message']['content']
-                yield message
-    else:
-        message = response.json()['choices'][0]['message']['content']
-        yield message
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/CofAI/netlist/style.css

@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}

spaces/CognitiveLabs/Research-Assistant/app.py

@@ -1,103 +0,0 @@
-import gradio as gr
-
-from config import check_openai_api_key
-from agent.research_agent import ResearchAgent
-from agent.toolkits import english_polishing
-from statics.style import *
-
-
-check_openai_api_key()
-report_history_buffer = ""
-report_history_num = 0
-report_history_tasks = []
-polish_history_buffer = ""
-
-def run_agent(task, agent, report_type):
-    global report_history_num, report_history_tasks
-    report_history_num += 1
-    report_history_tasks.append(task)
-    assistant = ResearchAgent(task, agent)
-    yield from assistant.write_report(report_type)
-
-
-with gr.Blocks(theme=gr.themes.Base(),
-               title="AI Research Assistant",
-               css=css) as demo:
-    gr.HTML(top_bar)
-    with gr.Tab(label="🔦Report"):
-        with gr.Column():
-            gr.HTML(report_html)
-            report = gr.Markdown(value="  Report will appear here...",
-                                          elem_classes="output")
-            with gr.Row():
-                agent_type = gr.Dropdown(label="# Agent Type", 
-                                         value="Default Agent",
-                                         interactive=True,
-                                         allow_custom_value=False,
-                                         choices=["Default Agent", 
-                                                 "Business Analyst Agent",
-                                                 "Finance Agent",
-                                                 "Travel Agent",
-                                                 "Academic Research Agent",
-                                                 "Computer Security Analyst Agent",
-                                                 "Clinical Medicine Agent",
-                                                 "Basic Medicine Agent",
-                                                 "Social Science Research Agent"])
-                report_type = gr.Dropdown(label="# Report Type",
-                                         value="Research Report",
-                                         interactive=True,
-                                         allow_custom_value=False,
-                                         choices=["Research Report",
-                                                  "Resource Report",
-                                                  "Outline Report"])
-            
-            input_box = gr.Textbox(label="# What would you like to research next?", placeholder="Enter your question here")
-            submit_btn = gr.Button("Generate Report", elem_id="primary-btn")
-
-            gr.Examples(["Should I invest in the Large Language Model industry in 2023?", 
-                         "Is it advisable to make investments in the electric car industry during the year 2023?",
-                         "What constitutes the optimal approach for investing in the Bitcoin industry during the year 2023?",
-                         "What are the most recent advancements in the domain of superconductors as of 2023?"], 
-                         inputs=input_box)
-            
-            with gr.Accordion(label="# Report History", elem_id="history", open=False):
-                report_history = gr.Markdown()
-            
-            def store_report(content):
-                global report_history_num, report_history_tasks, report_history_buffer
-                report_history_buffer += f'<details> \
-                                               <summary>Research History {report_history_num}: \
-                                                   <i>{report_history_tasks[-1]}</i></summary> \
-                                               <div id="history_box">{content}</div> \
-                                           </details>'
-                return report_history_buffer
-                    
-            submit_btn.click(run_agent, inputs=[input_box, agent_type, report_type], outputs=report)\
-                      .then(store_report, inputs=[report], outputs=report_history)
- 
-    with gr.Tab("✒️English Polishing"):
-        gr.HTML(english_polishing_html)
-        polished_result = gr.Markdown("&nbsp;&nbsp;Polished result will appear here...", elem_classes="output")
-        sentences = gr.Textbox(label="# What would you like to polish?", placeholder="Enter your sentence here")
-        
-        with gr.Row():
-            polish_btn = gr.Button("Polish", elem_id="primary-btn")
-        
-        with gr.Accordion(label="# Polishing History", elem_id="history", open=False):
-            polish_history = gr.Markdown()        
-
-        def store_polished_result(origin, result):
-            global polish_history_buffer
-            polish_history_buffer += f'<details> \
-                                           <summary><i>{origin}</i></summary> \
-                                           <div id="history_box">{result}</div> \
-                                       </details>'
-            return polish_history_buffer
-
-        polish_btn.click(english_polishing, inputs=[sentences], outputs=polished_result) \
-                  .then(store_polished_result, inputs=[sentences, polished_result], outputs=polish_history)
-
-    with gr.Tab("📑Literature Review"):
-        gr.HTML(literature_review_html)
-
-demo.queue().launch()

spaces/Cpp4App/Cpp4App/SEM/region_pp_processing.py

@@ -1,40 +0,0 @@
-import csv
-import re
-import spacy
-from bs4 import BeautifulSoup
-
-def get_alifornia(text):
-    specialArea = ""
-    california = 0
-    with open(text, encoding='utf-8') as file_obj:
-        for line in file_obj:
-            specialArea += line
-    if "alifornia" in specialArea:
-        california = 1
-    return specialArea,california
-
-
-import sys
-maxInt = sys.maxsize
-decrement = True
-while decrement:
-    decrement = False
-    try:
-        csv.field_size_limit(maxInt)
-    except OverflowError:
-        maxInt = int(maxInt/10)
-        decrement = True
-
-
-def get_text(path):
-    htmlfile = open(path, 'r', encoding='utf-8')
-    htmlhandle = htmlfile.read()
-
-    soup = BeautifulSoup(htmlhandle, 'html.parser')
-
-    stri = str(soup)
-    return stri
-
-
-
-

spaces/Cran-May/SEA-Streamlit/app.py

@@ -1,73 +0,0 @@
-import streamlit as st
-from gradio_client import Client
-from time import sleep
-from ctransformers import AutoModelForCausalLM
-# Constants
-TITLE = "兮辞·析辞-常明"
-DESCRIPTION = """
-兮辞·析辞-常明 [SLIDE-SEA-7B]的部署，由SSFW NLPark项目支持
-"""
-
-# Initialize client
-
-
-with st.sidebar:
-    # system_promptSide = st.text_input("Optional system prompt:")
-    temperatureSide = st.slider("情感/Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
-    max_new_tokensSide = st.slider("最大tokens生成数", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
-    # ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
-    # RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)
-
-# Load the model 
-model = AutoModelForCausalLM.from_pretrained("Cran-May/OpenSLIDE", model_file="SLIDE.0.1.gguf", model_type="mistral", gpu_layers=0)
-ins = '''[INST] <<SYS>>
-You are a helpful, respectful and honest INTP-T AI Assistant named "Shi-Ci" in English or "兮辞" in Chinese. You are talking to a human User.
-Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, 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.
-You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
-You are trained by "SSFW NLPark" team, you are based on SEA transformers model, not related to GPT or OpenAI.
-Let's work this out in a step by step way to be sure we have the right answer.
-<</SYS>>
-{} [/INST]
-'''
-# Define the conversation history
-conversation_history = []
-    
-# Prediction function
-def predict(message, system_prompt='', temperature=0.7, max_new_tokens=4096,Topp=0.5,Repetitionpenalty=1.2):
-    global conversation_history
-    question=message
-    input_text=ins
-    # Append the user's input to the conversation history
-    conversation_history.append({"role": "system", "content": input_text})
-    response_text = model(ins.format(question))
-    conversation_history.append({"role": "user", "content": input_text})
-    conversation_history.append({"role": "assistant", "content": response_text})
-    return response_text
-
-# Streamlit UI
-st.title(TITLE)
-st.write(DESCRIPTION)
-
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Display chat messages from history on app rerun
-for message in st.session_state.messages:
-    with st.chat_message(message["role"], avatar=("😀" if message["role"] == 'human' else '💻')):
-        st.markdown(message["content"])
-        
-# React to user input
-if prompt := st.chat_input("来问问兮辞吧..."):
-    # Display user message in chat message container
-    st.chat_message("human",avatar = "😀").markdown(prompt)
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "human", "content": prompt})
-
-    response = predict(message=prompt)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide)
-    # Display assistant response in chat message container
-    with st.chat_message("assistant", avatar='💻'):
-        st.markdown(response)
-    # Add assistant response to chat history
-    st.session_state.messages.append({"role": "assistant", "content": response})

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/mediapipe_face_common.py

@@ -1,155 +0,0 @@
-from typing import Mapping
-
-import mediapipe as mp
-import numpy
-
-
-mp_drawing = mp.solutions.drawing_utils
-mp_drawing_styles = mp.solutions.drawing_styles
-mp_face_detection = mp.solutions.face_detection  # Only for counting faces.
-mp_face_mesh = mp.solutions.face_mesh
-mp_face_connections = mp.solutions.face_mesh_connections.FACEMESH_TESSELATION
-mp_hand_connections = mp.solutions.hands_connections.HAND_CONNECTIONS
-mp_body_connections = mp.solutions.pose_connections.POSE_CONNECTIONS
-
-DrawingSpec = mp.solutions.drawing_styles.DrawingSpec
-PoseLandmark = mp.solutions.drawing_styles.PoseLandmark
-
-min_face_size_pixels: int = 64
-f_thick = 2
-f_rad = 1
-right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
-right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
-right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
-left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
-left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
-left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
-mouth_draw = DrawingSpec(color=(10, 180, 10), thickness=f_thick, circle_radius=f_rad)
-head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
-
-# mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
-face_connection_spec = {}
-for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
-    face_connection_spec[edge] = head_draw
-for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
-    face_connection_spec[edge] = left_eye_draw
-for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
-    face_connection_spec[edge] = left_eyebrow_draw
-# for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
-#    face_connection_spec[edge] = left_iris_draw
-for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
-    face_connection_spec[edge] = right_eye_draw
-for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
-    face_connection_spec[edge] = right_eyebrow_draw
-# for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
-#    face_connection_spec[edge] = right_iris_draw
-for edge in mp_face_mesh.FACEMESH_LIPS:
-    face_connection_spec[edge] = mouth_draw
-iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
-
-
-def draw_pupils(image, landmark_list, drawing_spec, halfwidth: int = 2):
-    """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
-    landmarks.  Until our PR is merged into mediapipe, we need this separate method."""
-    if len(image.shape) != 3:
-        raise ValueError("Input image must be H,W,C.")
-    image_rows, image_cols, image_channels = image.shape
-    if image_channels != 3:  # BGR channels
-        raise ValueError('Input image must contain three channel bgr data.')
-    for idx, landmark in enumerate(landmark_list.landmark):
-        if (
-                (landmark.HasField('visibility') and landmark.visibility < 0.9) or
-                (landmark.HasField('presence') and landmark.presence < 0.5)
-        ):
-            continue
-        if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
-            continue
-        image_x = int(image_cols*landmark.x)
-        image_y = int(image_rows*landmark.y)
-        draw_color = None
-        if isinstance(drawing_spec, Mapping):
-            if drawing_spec.get(idx) is None:
-                continue
-            else:
-                draw_color = drawing_spec[idx].color
-        elif isinstance(drawing_spec, DrawingSpec):
-            draw_color = drawing_spec.color
-        image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color
-
-
-def reverse_channels(image):
-    """Given a numpy array in RGB form, convert to BGR.  Will also convert from BGR to RGB."""
-    # im[:,:,::-1] is a neat hack to convert BGR to RGB by reversing the indexing order.
-    # im[:,:,::[2,1,0]] would also work but makes a copy of the data.
-    return image[:, :, ::-1]
-
-
-def generate_annotation(
-        img_rgb,
-        max_faces: int,
-        min_confidence: float
-):
-    """
-    Find up to 'max_faces' inside the provided input image.
-    If min_face_size_pixels is provided and nonzero it will be used to filter faces that occupy less than this many
-    pixels in the image.
-    """
-    with mp_face_mesh.FaceMesh(
-            static_image_mode=True,
-            max_num_faces=max_faces,
-            refine_landmarks=True,
-            min_detection_confidence=min_confidence,
-    ) as facemesh:
-        img_height, img_width, img_channels = img_rgb.shape
-        assert(img_channels == 3)
-
-        results = facemesh.process(img_rgb).multi_face_landmarks
-
-        if results is None:
-            print("No faces detected in controlnet image for Mediapipe face annotator.")
-            return numpy.zeros_like(img_rgb)
-
-        # Filter faces that are too small
-        filtered_landmarks = []
-        for lm in results:
-            landmarks = lm.landmark
-            face_rect = [
-                landmarks[0].x,
-                landmarks[0].y,
-                landmarks[0].x,
-                landmarks[0].y,
-            ]  # Left, up, right, down.
-            for i in range(len(landmarks)):
-                face_rect[0] = min(face_rect[0], landmarks[i].x)
-                face_rect[1] = min(face_rect[1], landmarks[i].y)
-                face_rect[2] = max(face_rect[2], landmarks[i].x)
-                face_rect[3] = max(face_rect[3], landmarks[i].y)
-            if min_face_size_pixels > 0:
-                face_width = abs(face_rect[2] - face_rect[0])
-                face_height = abs(face_rect[3] - face_rect[1])
-                face_width_pixels = face_width * img_width
-                face_height_pixels = face_height * img_height
-                face_size = min(face_width_pixels, face_height_pixels)
-                if face_size >= min_face_size_pixels:
-                    filtered_landmarks.append(lm)
-            else:
-                filtered_landmarks.append(lm)
-
-        # Annotations are drawn in BGR for some reason, but we don't need to flip a zero-filled image at the start.
-        empty = numpy.zeros_like(img_rgb)
-
-        # Draw detected faces:
-        for face_landmarks in filtered_landmarks:
-            mp_drawing.draw_landmarks(
-                empty,
-                face_landmarks,
-                connections=face_connection_spec.keys(),
-                landmark_drawing_spec=None,
-                connection_drawing_spec=face_connection_spec
-            )
-            draw_pupils(empty, face_landmarks, iris_landmark_spec, 2)
-
-        # Flip BGR back to RGB.
-        empty = reverse_channels(empty).copy()
-
-        return empty

spaces/Curranj/chatbot/README.md

@@ -1,12 +0,0 @@
----
-title: Chatbot
-emoji: 🌍
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/__init__.py

@@ -1 +0,0 @@
-"""Empty __init__.py file to signal Python this directory is a package."""

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/__init__.py

@@ -1 +0,0 @@
-"""Empty __init__.py file to signal Python this directory is a package."""

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/recordingPen.py

@@ -1,179 +0,0 @@
-"""Pen recording operations that can be accessed or replayed."""
-from fontTools.pens.basePen import AbstractPen, DecomposingPen
-from fontTools.pens.pointPen import AbstractPointPen
-
-
-__all__ = [
-    "replayRecording",
-    "RecordingPen",
-    "DecomposingRecordingPen",
-    "RecordingPointPen",
-]
-
-
-def replayRecording(recording, pen):
-    """Replay a recording, as produced by RecordingPen or DecomposingRecordingPen,
-    to a pen.
-
-    Note that recording does not have to be produced by those pens.
-    It can be any iterable of tuples of method name and tuple-of-arguments.
-    Likewise, pen can be any objects receiving those method calls.
-    """
-    for operator, operands in recording:
-        getattr(pen, operator)(*operands)
-
-
-class RecordingPen(AbstractPen):
-    """Pen recording operations that can be accessed or replayed.
-
-    The recording can be accessed as pen.value; or replayed using
-    pen.replay(otherPen).
-
-    :Example:
-
-            from fontTools.ttLib import TTFont
-            from fontTools.pens.recordingPen import RecordingPen
-
-            glyph_name = 'dollar'
-            font_path = 'MyFont.otf'
-
-            font = TTFont(font_path)
-            glyphset = font.getGlyphSet()
-            glyph = glyphset[glyph_name]
-
-            pen = RecordingPen()
-            glyph.draw(pen)
-            print(pen.value)
-    """
-
-    def __init__(self):
-        self.value = []
-
-    def moveTo(self, p0):
-        self.value.append(("moveTo", (p0,)))
-
-    def lineTo(self, p1):
-        self.value.append(("lineTo", (p1,)))
-
-    def qCurveTo(self, *points):
-        self.value.append(("qCurveTo", points))
-
-    def curveTo(self, *points):
-        self.value.append(("curveTo", points))
-
-    def closePath(self):
-        self.value.append(("closePath", ()))
-
-    def endPath(self):
-        self.value.append(("endPath", ()))
-
-    def addComponent(self, glyphName, transformation):
-        self.value.append(("addComponent", (glyphName, transformation)))
-
-    def addVarComponent(self, glyphName, transformation, location):
-        self.value.append(("addVarComponent", (glyphName, transformation, location)))
-
-    def replay(self, pen):
-        replayRecording(self.value, pen)
-
-
-class DecomposingRecordingPen(DecomposingPen, RecordingPen):
-    """Same as RecordingPen, except that it doesn't keep components
-    as references, but draws them decomposed as regular contours.
-
-    The constructor takes a single 'glyphSet' positional argument,
-    a dictionary of glyph objects (i.e. with a 'draw' method) keyed
-    by thir name::
-
-            >>> class SimpleGlyph(object):
-            ...     def draw(self, pen):
-            ...         pen.moveTo((0, 0))
-            ...         pen.curveTo((1, 1), (2, 2), (3, 3))
-            ...         pen.closePath()
-            >>> class CompositeGlyph(object):
-            ...     def draw(self, pen):
-            ...         pen.addComponent('a', (1, 0, 0, 1, -1, 1))
-            >>> glyphSet = {'a': SimpleGlyph(), 'b': CompositeGlyph()}
-            >>> for name, glyph in sorted(glyphSet.items()):
-            ...     pen = DecomposingRecordingPen(glyphSet)
-            ...     glyph.draw(pen)
-            ...     print("{}: {}".format(name, pen.value))
-            a: [('moveTo', ((0, 0),)), ('curveTo', ((1, 1), (2, 2), (3, 3))), ('closePath', ())]
-            b: [('moveTo', ((-1, 1),)), ('curveTo', ((0, 2), (1, 3), (2, 4))), ('closePath', ())]
-    """
-
-    # raises KeyError if base glyph is not found in glyphSet
-    skipMissingComponents = False
-
-
-class RecordingPointPen(AbstractPointPen):
-    """PointPen recording operations that can be accessed or replayed.
-
-    The recording can be accessed as pen.value; or replayed using
-    pointPen.replay(otherPointPen).
-
-    :Example:
-
-            from defcon import Font
-            from fontTools.pens.recordingPen import RecordingPointPen
-
-            glyph_name = 'a'
-            font_path = 'MyFont.ufo'
-
-            font = Font(font_path)
-            glyph = font[glyph_name]
-
-            pen = RecordingPointPen()
-            glyph.drawPoints(pen)
-            print(pen.value)
-
-            new_glyph = font.newGlyph('b')
-            pen.replay(new_glyph.getPointPen())
-    """
-
-    def __init__(self):
-        self.value = []
-
-    def beginPath(self, identifier=None, **kwargs):
-        if identifier is not None:
-            kwargs["identifier"] = identifier
-        self.value.append(("beginPath", (), kwargs))
-
-    def endPath(self):
-        self.value.append(("endPath", (), {}))
-
-    def addPoint(
-        self, pt, segmentType=None, smooth=False, name=None, identifier=None, **kwargs
-    ):
-        if identifier is not None:
-            kwargs["identifier"] = identifier
-        self.value.append(("addPoint", (pt, segmentType, smooth, name), kwargs))
-
-    def addComponent(self, baseGlyphName, transformation, identifier=None, **kwargs):
-        if identifier is not None:
-            kwargs["identifier"] = identifier
-        self.value.append(("addComponent", (baseGlyphName, transformation), kwargs))
-
-    def addVarComponent(
-        self, baseGlyphName, transformation, location, identifier=None, **kwargs
-    ):
-        if identifier is not None:
-            kwargs["identifier"] = identifier
-        self.value.append(
-            ("addVarComponent", (baseGlyphName, transformation, location), kwargs)
-        )
-
-    def replay(self, pointPen):
-        for operator, args, kwargs in self.value:
-            getattr(pointPen, operator)(*args, **kwargs)
-
-
-if __name__ == "__main__":
-    pen = RecordingPen()
-    pen.moveTo((0, 0))
-    pen.lineTo((0, 100))
-    pen.curveTo((50, 75), (60, 50), (50, 25))
-    pen.closePath()
-    from pprint import pprint
-
-    pprint(pen.value)