Update parquet files (step 60 of 249)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/deepai/README.md

@@ -1,26 +0,0 @@
-# DeepAI Wrapper
-Written by [ading2210](https://github.com/ading2210/).
-
-## Examples:
-These functions are generators which yield strings containing the newly generated text.
-
-### Completion:
-```python
-for chunk in deepai.Completion.create("Who are you?"):
-    print(chunk, end="", flush=True)
-print()
-```
-
-### Chat Completion:
-Use the same format for the messages as you would for the [official OpenAI API](https://platform.openai.com/docs/guides/chat/introduction).
-```python
-messages = [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Who won the world series in 2020?"},
-    {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
-    {"role": "user", "content": "Where was it played?"}
-]
-for chunk in deepai.ChatCompletion.create(messages):
-    print(chunk, end="", flush=True)
-print()
-```

spaces/101-5/gpt4free/g4f/.v1/unfinished/chatpdf/__init__.py

@@ -1,82 +0,0 @@
-import requests
-import json
-
-from queue import Queue, Empty
-from threading import Thread
-from json import loads
-from re import findall
-
-
-class Completion:
-
-    def request(prompt: str):
-        '''TODO: some sort of authentication + upload PDF from URL or local file
-                Then you should get the atoken and chat ID
-                '''
-
-        token = "your_token_here"
-        chat_id = "your_chat_id_here"
-
-        url = "https://chat-pr4yueoqha-ue.a.run.app/"
-
-        payload = json.dumps({
-            "v": 2,
-            "chatSession": {
-                "type": "join",
-                "chatId": chat_id
-            },
-            "history": [
-                {
-                    "id": "VNsSyJIq_0",
-						"author": "p_if2GPSfyN8hjDoA7unYe",
-						"msg": "<start>",
-						"time": 1682672009270
-                },
-                {
-					"id": "Zk8DRUtx_6",
-						"author": "uplaceholder",
-						"msg": prompt,
-						"time": 1682672181339
-                }
-            ]
-        })
-
-        # TODO: fix headers, use random user-agent, streaming response, etc
-        headers = {
-            'authority': 'chat-pr4yueoqha-ue.a.run.app',
-            'accept': '*/*',
-            'accept-language': 'en-US,en;q=0.9',
-            'atoken': token,
-            'content-type': 'application/json',
-            'origin': 'https://www.chatpdf.com',
-            'referer': 'https://www.chatpdf.com/',
-            'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Windows"',
-            'sec-fetch-dest': 'empty',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-site': 'cross-site',
-            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36'
-        }
-
-        response = requests.request(
-            "POST", url, headers=headers, data=payload).text
-        Completion.stream_completed = True
-        return {'response': response}
-
-    @staticmethod
-    def create(prompt: str):
-        Thread(target=Completion.request, args=[prompt]).start()
-
-        while Completion.stream_completed != True or not Completion.message_queue.empty():
-            try:
-                message = Completion.message_queue.get(timeout=0.01)
-                for message in findall(Completion.regex, message):
-                    yield loads(Completion.part1 + message + Completion.part2)['delta']
-
-            except Empty:
-                pass
-
-    @staticmethod
-    def handle_stream_response(response):
-        Completion.message_queue.put(response.decode())

spaces/101-5/gpt4free/testing/wewordle/README.md

@@ -1 +0,0 @@
-original from website https://chat-gpt.com/chat  https://github.com/xtekky/gpt4free/issues/40#issuecomment-1629152431, i got api https://wewordle.org/gptapi/v1/web/turbo but it got limit so i try to try reverse they android app and i got api https://wewordle.org/gptapi/v1/android/turbo and just randomize user id to bypass limit

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Everything You Need to Know About X Particles Download for Cinema 4D.md

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-
-<h1>X Particles Download: How to Get the Best Plugin for Cinema 4D</h1>
-<p>X Particles is a powerful and versatile plugin for Cinema 4D that allows you to create stunning particle effects and simulations. Whether you want to create fire, smoke, fluids, cloth, trails, or abstract art, X Particles has the tools you need to bring your vision to life.</p>
-<p>But how can you get X Particles download for your Cinema 4D project? In this article, we will show you the best way to download and install X Particles, as well as some tips and tricks to make the most of this amazing plugin.</p>
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-<h2>How to Download X Particles</h2>
-<p>The first step to get X Particles download is to visit the official website of the plugin: <a href="https://insydium.ltd/products/x-particles/">https://insydium.ltd/products/x-particles/</a>. Here you can find all the information about the features, pricing, and system requirements of X Particles.</p>
-<p>To download X Particles, you need to purchase a license from the website. You can choose between a perpetual license or a subscription license, depending on your budget and needs. A perpetual license gives you lifetime access to X Particles and all the updates for the current version, while a subscription license gives you access to X Particles and all the updates for as long as you pay the monthly or yearly fee.</p>
-<p>Once you have purchased a license, you will receive an email with a link to download X Particles. You can also log in to your account on the website and access the download link from there. The download file is a zip file that contains the plugin files and an installer.</p>
-<h2>How to Install X Particles</h2>
-<p>The next step to get X Particles download is to install the plugin on your Cinema 4D software. To do this, you need to follow these steps:</p>
-<ol>
-<li>Unzip the download file and run the installer. The installer will guide you through the installation process and ask you to enter your license key.</li>
-<li>Choose the Cinema 4D version that you want to install X Particles on. You can install X Particles on multiple versions of Cinema 4D if you have them on your computer.</li>
-<li>Choose the location where you want to install X Particles. The default location is the plugins folder of your Cinema 4D installation.</li>
-<li>Click on Install and wait for the installation to finish.</li>
-<li>Restart Cinema 4D and check if X Particles is available in your plugins menu.</li>
-</ol>
-<p>Congratulations! You have successfully installed X Particles on your Cinema 4D software. Now you can start creating amazing particle effects and simulations with X Particles.</p>
-<h2>How to Use X Particles</h2>
-<p>X Particles is a very intuitive and user-friendly plugin that lets you create particle effects and simulations with ease. You can use X Particles in two ways: by using the built-in presets or by creating your own custom setups.</p>
-<p>The built-in presets are ready-made particle effects that you can apply to any object or scene in Cinema 4D. You can find them in the content browser of Cinema 4D under the X Particles folder. There are hundreds of presets available for different types of effects, such as fire, smoke, fluids, cloth, trails, and more. You can simply drag and drop a preset onto your object or scene and adjust the parameters as you like.</p>
-<p>The custom setups are particle effects that you can create from scratch using the various tools and modifiers of X Particles. You can find them in the objects menu of Cinema 4D under the X-Particles menu. There are four main types of objects that you can use to create custom setups: emitters, generators, modifiers, and questions & actions.</p>
-<p></p>
-<ul>
-<li>Emitters are objects that emit particles from a source point or area. You can control the number, size, shape, color, speed, direction, and lifespan of the particles using the emitter settings.</li>
-<li>Generators are objects that create geometry from particles. You can use generators to create meshes, splines, trails, or sprites from particles.</li>
-<li>Modifiers are objects that affect particles in various ways. You can use modifiers to add forces, collisions, deformations, dynamics, fields, or shaders to particles.</li>
-<li>Questions & actions are objects that control the behavior of particles based on certain</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Download Nancy Drew Games Full Version The History and Legacy of Nancy Drew.md

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- <h4>Plot</h4>
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-</ul>
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-</ul>
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- <h4>Plot</h4>
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-</ul>
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- <ul>
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-<li>A digital camera that lets you take pictures and edit them.</li>
-<li>A bento box that lets you make your own lunch.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Nancy Drew games are a great way to enjoy mystery, adventure, and puzzle-solving games. They offer engaging stories, immersive environments, challenging puzzles, and educational elements. They are suitable for players of all ages and genders, as they offer different difficulty levels and modes. You can download them for free from GameTop.com, which is a safe and reliable site that has a large collection of Nancy Drew games. You can also try out some of the top 5 Nancy Drew games to play in 2021, such as The Silent Spy, The Haunting of Castle Malloy, Ghost of Thornton Hall, The Captive Curse, and Shadow at the Water's Edge. We hope you have fun playing these games and solving these mysteries!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Nancy Drew games:</p>
- <ol>
-<li>Q: How long does it take to finish a Nancy Drew game?</li>
-<li>A: It depends on the game, the difficulty level, and your playing style. On average, it takes about 10 hours to finish a Nancy Drew game.</li>
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-<li>A: Some Nancy Drew games are compatible with Mac or mobile devices, but not all of them. You can check the system requirements for each game on the official website or on GameTop.com.</li>
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-<li>A: There is no definitive answer to this question, as different players might have different preferences and opinions. However, some of the most popular and highly rated Nancy Drew games are The Final Scene (2001), Curse of Blackmoor Manor (2004), Last Train to Blue Moon Canyon (2005), Warnings at Waverly Academy (2009), and Sea of Darkness (2015).</li>
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spaces/1gistliPinn/ChatGPT4/Examples/Disney Characters 3d Models Free Download Maya.md

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spaces/1gistliPinn/ChatGPT4/Examples/Evermotion - 3D People V.1 - C4D.rar.md

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-<h1>Evermotion - 3D People V.1 - C4D.rar: A Review of the 3D Models Collection for Cinema 4D</h1>
-<p>Are you looking for realistic and high-quality 3D models of people for your Cinema 4D projects? Do you want to create stunning and dynamic scenes with 3D human characters? Do you want to save time and money by using ready-made and shadered models of people? If you answered yes to any of these questions, then you may be interested in Evermotion - 3D People V.1 - C4D.rar. This is a collection of 50 highly detailed and shadered models of people for Cinema 4D. In this article, we will review the features, benefits and drawbacks of Evermotion - 3D People V.1 - C4D.rar.</p>
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-<h2>What is Evermotion - 3D People V.1 - C4D.rar?</h2>
-<p>Evermotion - 3D People V.1 - C4D.rar is a collection of 50 highly detailed and shadered models of people for Cinema 4D. It is part of the Evermotion product range, which is a well-known company that produces high-quality 3D models and assets for architectural visualizations, animations, games and more. Evermotion - 3D People V.1 - C4D.rar contains 50 models of different ages, genders, ethnicities and poses. Each model is shadered and ready to render in Cinema 4D. The models are compatible with Scanline, V-Ray and Mental Ray render engines. The models are also optimized for low polygon count and fast rendering.</p>
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-<p>Evermotion - 3D People V.1 - C4D.rar has many features that make it a valuable and versatile collection of 3D models of people for Cinema 4D. Some of them are:</p>
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-<li>It contains 50 highly detailed and shadered models of people for Cinema 4D.</li>
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-<h2>What are the benefits of Evermotion - 3D People V.1 - C4D.rar?</h2>
-<p>Evermotion - 3D People V.1 - C4D.rar has many benefits that make it a worthwhile investment for Cinema 4D users. Some of them are:</p>
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-<h2>What are the drawbacks of Evermotion - 3D People V.1 - C4D.rar?</h2>
-<p>Evermotion - 3D People V.1 - C4D.rar has some drawbacks that you should be aware of before buying it. Some of them are:</p>
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-<h2>Conclusion</h2>
-<p>Evermotion - 3D People V.1 - C4D.rar is a collection of 50 highly detailed and shadered models of people for Cinema 4D. It is part of the Evermotion product range, which is a well-known company that produces high-quality 3D models and assets for architectural visualizations, animations, games and more. It has many features, benefits and drawbacks that you should consider before buying it. It can save you time, money and creativity by using ready-made and realistic models of people for your Cinema 4D projects. However, it may also not suit your specific needs or preferences, not be updated or supported by Evermotion in the future, or not be legal or ethical to use it for some purposes or projects. You should weigh them carefully and decide what is best for you and your Cinema 4D projects.</p>
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-<li>Extract the .rar file using a software like WinRAR or 7-Zip.</li>
-<li>Open Cinema 4D and create a new project or open an existing one.</li>
-<li>Go to File > Merge and browse to the folder where you extracted the .rar file.</li>
-<li>Select the model of your choice from the list and click Open.</li>
-<li>The model will be imported into your scene with all the shaders and textures applied.</li>
-<li>You can adjust the position, rotation, scale and other parameters of the model as you wish.</li>
-<li>You can also add lights, cameras, animations and other elements to your scene.</li>
-<li>When you are satisfied with your scene, you can render it using your preferred render engine.</li>
-</ol>
-<h2>What are the alternatives to Evermotion - 3D People V.1 - C4D.rar?</h2>
-<p>If you are not satisfied with Evermotion - 3D People V.1 - C4D.rar or you want to try other collections of 3D models of people for Cinema 4D, you have some alternatives to choose from. Some of them are:</p>
-<ul>
-<li>Viz-People: This is a company that offers high-quality 3D models of people, cars, furniture and other objects for various 3D software and applications. They have a free non-commercial version of their HDRI collection that contains 10 high-resolution spherical environmental maps.</li>
-<li>Dosch Design: This is a company that provides high-quality 3D models, textures, HDRI and sound effects for various 3D software and applications. They have a collection of 3D people that contains over 100 realistic and fully textured models of people in different poses and clothing styles.</li>
-<li>Renderpeople: This is a company that specializes in creating realistic and lifelike 3D models of people for various 3D software and applications. They have a collection of 3D people that contains over 5000 models of people in different poses, clothing styles, ethnicities and ages.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Evermotion - 3D People V.1 - C4D.rar is a collection of 50 highly detailed and shadered models of people for Cinema 4D. It is part of the Evermotion product range, which is a well-known company that produces high-quality 3D models and assets for architectural visualizations, animations, games and more. It has many features, benefits and drawbacks that you should consider before buying it. It can save you time, money and creativity by using ready-made and realistic models of people for your Cinema 4D projects. However, it may also not suit your specific needs or preferences, not be updated or supported by Evermotion in the future, or not be legal or ethical to use it for some purposes or projects. You should weigh them carefully and decide what is best for you and your Cinema 4D projects.</p>
-<h2>How to download Evermotion - 3D People V.1 - C4D.rar?</h2>
-<p>If you want to download Evermotion - 3D People V.1 - C4D.rar, you have two options. You can either buy it from the official website of Evermotion or you can download it from a third-party link provided in this article. Both options have their advantages and disadvantages. Let's see them in detail.</p>
-<p>If you buy Evermotion - 3D People V.1 - C4D.rar from the official website of Evermotion, you will get the following benefits:</p>
-<ul>
-<li>You will get the original and updated version of the collection.</li>
-<li>You will get a secure and easy payment method.</li>
-<li>You will get a download link with a .rar file that contains the models in .c4d format.</li>
-<li>You will get access to the catalog with previews and information about each model.</li>
-<li>You will get support and updates from Evermotion in case of any issues or questions.</li>
-</ul>
-<p>However, buying Evermotion - 3D People V.1 - C4D.rar from the official website of Evermotion also has some drawbacks:</p>
-<ul>
-<li>You will have to pay a certain amount of money to buy the collection.</li>
-<li>You will have to register an account on Evermotion website and provide your personal information.</li>
-<li>You will have to agree to the license terms and conditions of Evermotion before using the collection.</li>
-</ul>
-<p>If you download Evermotion - 3D People V.1 - C4D.rar from a third-party link provided in this article, you will get the following benefits:</p>
-<ul>
-<li>You will get the collection for free without paying any money.</li>
-<li>You will get the collection instantly without waiting for any delivery.</li>
-<li>You will not have to register an account or provide any personal information on any website.</li>
-</ul>
-<p>However, downloading Evermotion - 3D People V.1 - C4D.rar from a third-party link also has some drawbacks:</p>
-<ul>
-<li>You may not get the original or updated version of the collection.</li>
-<li>You may not get access to the catalog or any support or updates from Evermotion.</li>
-<li>You may get a virus or malware along with the collection that can harm your computer or steal your data.</li>
-<li>You may violate some laws or terms of service by downloading pirated or illegal content.</li>
-</ul>
-<h2>Is Evermotion - 3D People V.1 - C4D.rar worth it?</h2>
-<p>The answer to this question depends on your needs and preferences. If you are looking for realistic and high-quality 3D models of people for your Cinema 4D projects, Evermotion - 3D People V.1 - C4D.rar may be worth it. It has many features, benefits and drawbacks that you should consider before buying it. It can save you time and money by using ready-made and shadered models of people for your Cinema 4D projects. However, it may also not suit your specific needs or preferences, not be updated or supported by Evermotion in the future, or not be legal or ethical to use it for some purposes or projects. You should weigh them carefully and decide what is best for you and your Cinema 4D projects.</p>
-<h2>Conclusion</h2>
-<p>Evermotion - 3D People V.1 - C4D.rar is a collection of 50 highly detailed and shadered models of people for Cinema 4D. It is part of the Evermotion product range, which is a well-known company that produces high-quality 3D models and assets for architectural visualizations, animations, games and more. It has many features, benefits and drawbacks that you should consider before buying it. It can save you time, money and creativity by using ready-made and realistic models of people for your Cinema 4D projects. However, it may also not suit your specific needs or preferences, not be updated or supported by Evermotion in the future, or not be legal or ethical to use it for some purposes or projects. You can either buy it from the official website of Evermotion or download it from a third-party link provided in this article. Both options have their advantages and disadvantages. You should weigh them carefully and decide what is best for you and your Cinema 4D projects.</p> 3cee63e6c2<br />
-<br />
-<br />

spaces/1line/AutoGPT/Dockerfile

@@ -1,38 +0,0 @@
-# Use an official Python base image from the Docker Hub
-FROM python:3.10-slim
-
-# Install git
-RUN apt-get -y update
-RUN apt-get -y install git chromium-driver
-
-# Install Xvfb and other dependencies for headless browser testing
-RUN apt-get update \
-    && apt-get install -y wget gnupg2 libgtk-3-0 libdbus-glib-1-2 dbus-x11 xvfb ca-certificates
-
-# Install Firefox / Chromium
-RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - \
-    && echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list \
-    && apt-get update \
-    && apt-get install -y chromium firefox-esr
-
-# Set environment variables
-ENV PIP_NO_CACHE_DIR=yes \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1
-
-# Create a non-root user and set permissions
-RUN useradd --create-home appuser
-WORKDIR /home/appuser
-RUN chown appuser:appuser /home/appuser
-USER appuser
-
-# Copy the requirements.txt file and install the requirements
-COPY --chown=appuser:appuser requirements.txt .
-RUN sed -i '/Items below this point will not be included in the Docker Image/,$d' requirements.txt && \
-	pip install --no-cache-dir --user -r requirements.txt
-
-# Copy the application files
-COPY --chown=appuser:appuser autogpt/ ./autogpt
-
-# Set the entrypoint
-ENTRYPOINT ["python", "-m", "autogpt"]

spaces/1phancelerku/anime-remove-background/Emsa-Register-Dll-Tool-Crack.md

@@ -1,84 +0,0 @@
-## Emsa Register Dll Tool Crack
-
-
-
- 
-
-  
-
- 
-
-**Click Here 🌟 [https://www.google.com/url?q=https%3A%2F%2Fblltly.com%2F2txiC0&sa=D&sntz=1&usg=AOvVaw2tOYkFopTq9fhcDyUqgUmE](https://www.google.com/url?q=https%3A%2F%2Fblltly.com%2F2txiC0&sa=D&sntz=1&usg=AOvVaw2tOYkFopTq9fhcDyUqgUmE)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Use EMSA Register DLL Tool to Register and Unregister ActiveX Files
-
- 
-
-EMSA Register DLL Tool is a free and multipurpose tool for Windows that allows you to register and unregister ActiveX files, such as dll, ocx and exe files. ActiveX files are components that enable various functions and features in Windows applications. Sometimes, you may need to manually register or unregister these files if they are corrupted, missing or causing errors. In this article, we will show you how to use EMSA Register DLL Tool to perform these tasks easily and quickly.
-
- 
-
-## Download and Install EMSA Register DLL Tool
-
- 
-
-The first step is to download and install EMSA Register DLL Tool from the official website of Emsai Industrial[^1^]. The tool is compatible with Windows 98/ME/NT/2000/XP/2003. The installation process is simple and straightforward. Just follow the instructions on the screen and choose the destination folder for the tool.
-
- 
-
-## Enable Shell Extensions
-
- 
-
-One of the features of EMSA Register DLL Tool is that it integrates with the Windows Explorer context menu, which means you can right-click on any ActiveX file and choose to register or unregister it. To enable this feature, you need to enable shell extensions in the tool. To do this, launch the tool and go to the Options & Help tab. Check the box that says "Enable Shell Extensions" and click OK. You may need to restart your computer for the changes to take effect.
-
- 
-
-## Register or Unregister ActiveX Files
-
- 
-
-There are two ways to register or unregister ActiveX files with EMSA Register DLL Tool. One way is to use the tool's GUI, and the other way is to use the Windows Explorer context menu.
-
- 
-
-To use the tool's GUI, launch the tool and go to the Reg & Tool File Info tab. Click on the Browse button and select the ActiveX file you want to register or unregister. The tool will display detailed information about the file, such as its name, type, version, description, etc. You can also compare two ActiveX files with identical filenames by using the File Comparison tab. To register or unregister the file, click on the appropriate button at the bottom of the window. You will see a confirmation message if the operation is successful.
-
- 
-
-To use the Windows Explorer context menu, locate the ActiveX file you want to register or unregister in your file system. Right-click on it and select Register or Unregister from the menu. You will see a confirmation message if the operation is successful.
-
- 
-
-## Generate an ActiveX Report
-
- 
-
-Another feature of EMSA Register DLL Tool is that it can generate a report of all ActiveX files in a folder. This can be useful if you want to check the status of multiple files at once. To generate an ActiveX report, right-click on any folder that contains ActiveX files and select ActiveX Report from the menu. The tool will scan the folder for ActiveX files and create a text file (output.txt) containing the report in the same folder. The report will also be opened automatically for viewing. The report will show information such as file name, type, registration status, GUID, etc.
-
- 
-
-## Conclusion
-
- 
-
-EMSA Register DLL Tool is a handy tool for anyone who needs to register or unregister ActiveX files in Windows. It provides extensive information about these files and allows you to perform these tasks easily and quickly. It also integrates with the Windows Explorer context menu for convenience. You can download EMSA Register DLL Tool for free from Emsai Industrial's website[^1^] and try it out yourself.
-
- 1b8d091108
-
- 
-
- 
-

spaces/801artistry/RVC801/infer/lib/infer_pack/modules/F0Predictor/F0Predictor.py

@@ -1,16 +0,0 @@
-class F0Predictor(object):
-    def compute_f0(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length]
-        """
-        pass
-
-    def compute_f0_uv(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
-        """
-        pass

spaces/AIFILMS/Pix2Pix-Video/style.css

@@ -1,101 +0,0 @@
-#col-container {max-width: 820px; margin-left: auto; margin-right: auto;}
-#duplicate-container{
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  line-height: 1em;
-  flex-direction: row-reverse;
-  font-size:1em;
-}
-a, a:hover, a:visited {
-  text-decoration-line: underline; 
-  font-weight: 600;
-  color: #1f2937 !important;
-}
-
-.dark a, .dark a:hover, .dark a:visited {
-  color: #f3f4f6 !important;
-}
-
-.footer {
-  margin-bottom: 45px;
-  margin-top: 10px;
-  text-align: center;
-  border-bottom: 1px solid #e5e5e5;
-}
-
-.footer>p {
-  font-size: .8rem!important;
-  display: inline-block;
-  padding: 0 10px;
-  transform: translateY(26px);
-  background: white;
-}
-.dark .footer {
-  border-color: #303030;
-}
-.dark .footer>p {
-  background: #0b0f19;
-}
-
-div#may-like-container > p {
-  font-size: .8em;
-  margin-bottom: 4px;
-}
-
-.animate-spin {
-  animation: spin 1s linear infinite;
-}
-
-@keyframes spin {
-  from {
-      transform: rotate(0deg);
-  }
-  to {
-      transform: rotate(360deg);
-  }
-}
-
-#share-btn-container {
-  display: flex; 
-  padding-left: 0.5rem !important; 
-  padding-right: 0.5rem !important; 
-  background-color: #000000; 
-  justify-content: center; 
-  align-items: center; 
-  border-radius: 9999px !important; 
-  max-width: 13rem;
-}
-
-#share-btn-container:hover {
-  background-color: #060606;
-}
-
-#share-btn {
-  all: initial; 
-  color: #ffffff;
-  font-weight: 600; 
-  cursor:pointer; 
-  font-family: 'IBM Plex Sans', sans-serif; 
-  margin-left: 0.5rem !important; 
-  padding-top: 0.5rem !important; 
-  padding-bottom: 0.5rem !important;
-  right:0;
-}
-
-#share-btn * {
-  all: unset;
-}
-
-#share-btn-container div:nth-child(-n+2){
-  width: auto !important;
-  min-height: 0px !important;
-}
-
-#share-btn-container .wrap {
-  display: none !important;
-}
-
-#share-btn-container.hidden {
-  display: none!important;
-}

spaces/AIFILMS/generate_human_motion/pyrender/pyrender/__init__.py

@@ -1,24 +0,0 @@
-from .camera import (Camera, PerspectiveCamera, OrthographicCamera,
-                     IntrinsicsCamera)
-from .light import Light, PointLight, DirectionalLight, SpotLight
-from .sampler import Sampler
-from .texture import Texture
-from .material import Material, MetallicRoughnessMaterial
-from .primitive import Primitive
-from .mesh import Mesh
-from .node import Node
-from .scene import Scene
-from .renderer import Renderer
-from .viewer import Viewer
-from .offscreen import OffscreenRenderer
-from .version import __version__
-from .constants import RenderFlags, TextAlign, GLTF
-
-__all__ = [
-    'Camera', 'PerspectiveCamera', 'OrthographicCamera', 'IntrinsicsCamera',
-    'Light', 'PointLight', 'DirectionalLight', 'SpotLight',
-    'Sampler', 'Texture', 'Material', 'MetallicRoughnessMaterial',
-    'Primitive', 'Mesh', 'Node', 'Scene', 'Renderer', 'Viewer',
-    'OffscreenRenderer', '__version__', 'RenderFlags', 'TextAlign',
-    'GLTF'
-]

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/discriminator/model.py

@@ -1,295 +0,0 @@
-import functools
-import torch.nn as nn
-
-
-class ActNorm(nn.Module):
-    def __init__(self, num_features, logdet=False, affine=True,
-                 allow_reverse_init=False):
-        assert affine
-        super().__init__()
-        self.logdet = logdet
-        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
-        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
-        self.allow_reverse_init = allow_reverse_init
-
-        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
-
-    def initialize(self, input):
-        with torch.no_grad():
-            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
-            mean = (
-                flatten.mean(1)
-                .unsqueeze(1)
-                .unsqueeze(2)
-                .unsqueeze(3)
-                .permute(1, 0, 2, 3)
-            )
-            std = (
-                flatten.std(1)
-                .unsqueeze(1)
-                .unsqueeze(2)
-                .unsqueeze(3)
-                .permute(1, 0, 2, 3)
-            )
-
-            self.loc.data.copy_(-mean)
-            self.scale.data.copy_(1 / (std + 1e-6))
-
-    def forward(self, input, reverse=False):
-        if reverse:
-            return self.reverse(input)
-        if len(input.shape) == 2:
-            input = input[:, :, None, None]
-            squeeze = True
-        else:
-            squeeze = False
-
-        _, _, height, width = input.shape
-
-        if self.training and self.initialized.item() == 0:
-            self.initialize(input)
-            self.initialized.fill_(1)
-
-        h = self.scale * (input + self.loc)
-
-        if squeeze:
-            h = h.squeeze(-1).squeeze(-1)
-
-        if self.logdet:
-            log_abs = torch.log(torch.abs(self.scale))
-            logdet = height * width * torch.sum(log_abs)
-            logdet = logdet * torch.ones(input.shape[0]).to(input)
-            return h, logdet
-
-        return h
-
-    def reverse(self, output):
-        if self.training and self.initialized.item() == 0:
-            if not self.allow_reverse_init:
-                raise RuntimeError(
-                    "Initializing ActNorm in reverse direction is "
-                    "disabled by default. Use allow_reverse_init=True to enable."
-                )
-            else:
-                self.initialize(output)
-                self.initialized.fill_(1)
-
-        if len(output.shape) == 2:
-            output = output[:, :, None, None]
-            squeeze = True
-        else:
-            squeeze = False
-
-        h = output / self.scale - self.loc
-
-        if squeeze:
-            h = h.squeeze(-1).squeeze(-1)
-        return h
-
-def weights_init(m):
-    classname = m.__class__.__name__
-    if classname.find('Conv') != -1:
-        nn.init.normal_(m.weight.data, 0.0, 0.02)
-    elif classname.find('BatchNorm') != -1:
-        nn.init.normal_(m.weight.data, 1.0, 0.02)
-        nn.init.constant_(m.bias.data, 0)
-
-
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator as in Pix2Pix
-        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
-    """
-    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input images
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super(NLayerDiscriminator, self).__init__()
-        if not use_actnorm:
-            norm_layer = nn.BatchNorm2d
-        else:
-            norm_layer = ActNorm
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
-            use_bias = norm_layer.func != nn.BatchNorm2d
-        else:
-            use_bias = norm_layer != nn.BatchNorm2d
-
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = 1
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(ndf * nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-        # output 1 channel prediction map
-        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
-        self.main = nn.Sequential(*sequence)
-
-    def forward(self, input):
-        """Standard forward."""
-        return self.main(input)
-
-class NLayerDiscriminator1dFeats(NLayerDiscriminator):
-    """Defines a PatchGAN discriminator as in Pix2Pix
-        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
-    """
-    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input feats
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm)
-
-        if not use_actnorm:
-            norm_layer = nn.BatchNorm1d
-        else:
-            norm_layer = ActNorm
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm has affine parameters
-            use_bias = norm_layer.func != nn.BatchNorm1d
-        else:
-            use_bias = norm_layer != nn.BatchNorm1d
-
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv1d(input_nc, input_nc//2, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = input_nc//2
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually decrease the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = max(nf_mult_prev // (2 ** n), 8)
-            sequence += [
-                nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        nf_mult_prev = nf_mult
-        nf_mult = max(nf_mult_prev // (2 ** n), 8)
-        sequence += [
-            nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-        nf_mult_prev = nf_mult
-        nf_mult = max(nf_mult_prev // (2 ** n), 8)
-        sequence += [
-            nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-        # output 1 channel prediction map
-        sequence += [nn.Conv1d(nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
-        self.main = nn.Sequential(*sequence)
-
-
-class NLayerDiscriminator1dSpecs(NLayerDiscriminator):
-    """Defines a PatchGAN discriminator as in Pix2Pix
-        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
-    """
-    def __init__(self, input_nc=80, ndf=64, n_layers=3, use_actnorm=False):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input specs
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm)
-
-        if not use_actnorm:
-            norm_layer = nn.BatchNorm1d
-        else:
-            norm_layer = ActNorm
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm has affine parameters
-            use_bias = norm_layer.func != nn.BatchNorm1d
-        else:
-            use_bias = norm_layer != nn.BatchNorm1d
-
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv1d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = 1
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually decrease the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(ndf * nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-        # output 1 channel prediction map
-        sequence += [nn.Conv1d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
-        self.main = nn.Sequential(*sequence)
-
-    def forward(self, input):
-        """Standard forward."""
-        # (B, C, L)
-        input = input.squeeze(1)
-        input = self.main(input)
-        return input
-
-
-if __name__ == '__main__':
-    import torch
-
-    ## FEATURES
-    disc_in_channels = 2048
-    disc_num_layers = 2
-    use_actnorm = False
-    disc_ndf = 64
-    discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers,
-                                            use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
-    inputs = torch.rand((6, 2048, 212))
-    outputs = discriminator(inputs)
-    print(outputs.shape)
-
-    ## AUDIO
-    disc_in_channels = 1
-    disc_num_layers = 3
-    use_actnorm = False
-    disc_ndf = 64
-    discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers,
-                                        use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
-    inputs = torch.rand((6, 1, 80, 848))
-    outputs = discriminator(inputs)
-    print(outputs.shape)
-
-    ## IMAGE
-    disc_in_channels = 3
-    disc_num_layers = 3
-    use_actnorm = False
-    disc_ndf = 64
-    discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers,
-                                        use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
-    inputs = torch.rand((6, 3, 256, 256))
-    outputs = discriminator(inputs)
-    print(outputs.shape)

spaces/AILab-CVC/SEED-Bench_Leaderboard/constants.py

@@ -1,87 +0,0 @@
-# this is .py for store constants 
-MODEL_INFO = ["Model Type", "Model", "Language Model"]
-TASK_INFO = ["Scene Understanding", "Instance Identity", "Instance Attributes", "Instance Localization", "Instance Counting", "Spatial Relation", "Instance Interaction", "Visual Reasoning", "Text Recognition", "Avg. Img", "Action Recognition", "Action Prediction", "Procedure Understanding", "Avg. Video", "Avg. All"]
-TASK_INFO_v2 = ["Avg. All", "Avg. Img", "Avg. Video", "Scene Understanding", "Instance Identity", "Instance Attributes", "Instance Localization", "Instance Counting", "Spatial Relation", "Instance Interaction", "Visual Reasoning", "Text Recognition",  "Action Recognition", "Action Prediction", "Procedure Understanding"]
-
-AVG_INFO = ["Avg. All", "Avg. Img", "Avg. Video"]
-DATA_TITILE_TYPE = ["markdown", "markdown", "markdown", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]
-CSV_DIR = "./file/result.csv"
-
-# COLUMN_NAMES = MODEL_INFO + TASK_INFO
-COLUMN_NAMES = MODEL_INFO + TASK_INFO_v2
-
-DATA_NUM = [3158, 1831, 4649, 978, 2447, 657, 97, 331, 85, 1740, 2077, 1192]
-
-UNTUNED_MODEL_RESULTS = '''LLM & Flan-T5               & Flan-T5-XL &23.0 &29.0 &32.8 &31.8 &20.5 &31.8 &33.0 &18.2 &19.4 &23.2 &34.9 &25.4 \\
-         LLM                   & Vicuna                & Vicuna-7B  &23.4 &30.7 &29.7 &30.9 &30.8 &28.6 &29.8 &18.5 &13.4 &27.3 &34.5 &23.8 \\
-         LLM                   & LLaMA                 & LLaMA-7B   &26.3 &27.4 &26.2 &28.3 &25.1 &28.8 &19.2 &37.0 & 9.0 &33.0 &23.1 &26.2  \\
-         ImageLLM              & BLIP-2                & Flan-T5-XL &59.1 &53.9 &49.2 &42.3 &43.2 &36.7 &55.7 &45.6 &25.9 &32.6 &47.5 &24.0 \\
-         ImageLLM              & InstructBLIP          & Flan-T5-XL &60.3 &58.5 &63.4 &40.6 &58.4 &38.7 &51.6 &45.9 &25.9 &33.1 &49.1 &27.1 \\
-         ImageLLM              & InstructBLIP-Vicuna   & Vicuna-7B  &60.2 &58.9 &65.6 &43.6 &57.2 &40.3 &52.6 &47.7 &43.5 &34.5 &49.6 &23.1 \\
-         ImageLLM              & LLaVA                 & LLaMA-7B   &42.7 &34.9 &33.5 &28.4 &41.9 &30.8 &27.8 &46.8 &27.7 &29.7 &21.4 &19.1 \\
-         ImageLLM              & MiniGPT-4             & Flan-T5-XL &56.3 &49.2 &45.8 &37.9 &45.3 &32.6 &47.4 &57.1 &11.8 &38.2 &24.5 &27.1 \\
-         ImageLLM              & VPGTrans              & LLaMA-7B   &51.9 &44.1 &39.9 &36.1 &33.7 &36.4 &32.0 &53.2 &30.6 &39.5 &24.3 &31.9 \\
-         ImageLLM              & MultiModal-GPT        & LLaMA-7B   &43.6 &37.9 &31.5 &30.8 &27.3 &30.1 &29.9 &51.4 &18.8 &36.9 &25.8 &24.0 \\
-         ImageLLM              & Otter                 & LLaMA-7B   &44.9 &38.6 &32.2 &30.9 &26.3 &31.8 &32.0 &51.4 &31.8 &37.9 &27.2 &24.8 \\
-         ImageLLM              & OpenFlamingo          & LLaMA-7B   &43.9 &38.1 &31.3 &30.1 &27.3 &30.6 &29.9 &50.2 &20.0 &37.2 &25.4 &24.2 \\
-         ImageLLM              & LLaMA-Adapter V2      & LLaMA-7B   &45.2 &38.5 &29.3 &33.0 &29.7 &35.5 &39.2 &52.0 &24.7 &38.6 &18.5 &19.6 \\
-         ImageLLM              & GVT                   & Vicuna-7B  &41.7 &35.5 &31.8 &29.5 &36.2 &32.0 &32.0 &51.1 &27.1 &33.9 &25.4 &23.0 \\
-         ImageLLM              & mPLUG-Owl             & LLaMA-7B   &49.7 &45.3 &32.5 &36.7 &27.3 &32.7 &44.3 &54.7 &28.8 &26.7 &17.9 &26.5 \\
-         VideoLLM              & VideoChat             & Vicuna-7B  &47.1 &43.8 &34.9 &40.0 &32.8 &34.6 &42.3 &50.5 &17.7 &34.9 &36.4 &27.3 \\
-         VideoLLM              & Video-ChatGPT         & LLaMA-7B   &37.2 &31.4 &33.2 &28.4 &35.5 &29.5 &23.7 &42.3 &25.9 &27.6 &21.3 &21.1 \\
-         VideoLLM              & Valley                & LLaMA-13B  &39.3 &32.9 &31.6 &27.9 &24.2 &30.1 &27.8 &43.8 &11.8 &31.3 &23.2 &20.7 \\'''
-
-
-LEADERBORAD_INTRODUCTION = """# SEED-Bench Leaderboard
-    
-    Welcome to the leaderboard of the SEED-Bench! 🏆 
-    SEED-Bench consists of 19K multiple-choice questions with accurate human annotations for evaluating Multimodal LLMs, covering 12 evaluation dimensions including both the spatial and temporal understanding.
-    Please refer to [our paper](https://arxiv.org/abs/2307.16125) for more details.
-    """
-
-SUBMIT_INTRODUCTION = """# Submit Introduction
-    1. Obtain JSON file from our [github repository](https://github.com/AILab-CVC/SEED-Bench#leaderboard-submit) after evaluation. For example, you can obtain InstructBLIP's JSON file as results/results.json after running 
-    ```shell
-    python eval.py --model instruct_blip --anno_path SEED-Bench.json --output-dir results
-    ```
-    2. If you want to update model performance by uploading new results, please ensure 'Model Name Revision' is the same as what's shown in the leaderboard. For example, if you want to modify InstructBLIP's performance, you need to fill in 'InstructBLIP' in 'Revision Model Name'.
-    3. Please provide the correct link of your model's repository for each submission.  
-    4. For the evaluation dimension, you can choose "All/Image/Video", and the results of dimensions that are not evaluated will be set to zero.
-    5. After clicking 'Submit Eval', you can click 'Refresh' to obtain the latest result in the leaderboard.
-
-    ## Submit Example
-    For example, if you want to upload InstructBLIP's result in the leaderboard, you need to:
-    1. Fill in 'InstructBLIP' in 'Model Name' if it is your first time to submit your result (You can leave 'Revision Model Name' blank).
-    2. Fill in 'InstructBLIP' in 'Revision Model Name' if you want to update your result (You can leave 'Model Name' blank).
-    2. Select 'ImageLLM' in 'Model Type'.
-    3. Fill in 'https://github.com/salesforce/LAVIS' in 'Model Link'.
-    4. Select 'Flan-T5-XL' in 'LLM Type'.
-    5. Select 'All' in 'Evaluation Dimension'.
-    6. Upload results.json.
-    7. Click the 'Submit Eval' button.
-    8. Click 'Refresh' to obtain the uploaded leaderboard.
-"""
-
-TABLE_INTRODUCTION = """In the table below, we summarize each task performance of all the models.
-        We use accurancy(%) as the primary evaluation metric for each tasks. 
-    """
-
-LEADERBORAD_INFO = """
-      Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. 
-      In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment of generative models, by introducing a benchmark named SEED-Bench. 
-      SEED-Bench consists of 19K multiple choice questions with accurate human annotations (x6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality. 
-      We develop an advanced pipeline for generating multiple-choice questions that target specific evaluation dimensions, integrating both automatic filtering and manual verification processes. 
-      Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. 
-      We further evaluate the performance of 18 models across all 12 dimensions, covering both the spatial and temporal understanding. 
-      By revealing the limitations of existing MLLMs through evaluation results, we aim for SEED-Bench to provide insights for motivating future research.
-"""
-
-
-
-CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
-CITATION_BUTTON_TEXT = r"""@article{li2023seed,
-  title={SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension},
-  author={Li, Bohao and Wang, Rui and Wang, Guangzhi and Ge, Yuying and Ge, Yixiao and Shan, Ying},
-  journal={arXiv preprint arXiv:2307.16125},
-  year={2023}
-}"""

spaces/AIQuest/lungCancerVgg19/app.py

@@ -1,34 +0,0 @@
-# this is the custome function to return pre-process the image to size (150 150 3)
-
-import numpy as np
-from tensorflow.keras.preprocessing import image 
-from PIL import Image
-import gradio as gr
-from keras.models import load_model
-
-def custom_Image_preprocessing(image_data, target_size=(150, 150)):
-    img = image.array_to_img(image_data, data_format='channels_last')
-    img = img.resize(target_size)  # Resize the image if needed
-    img_arr = image.img_to_array(img)
-    img_arr = img_arr * 1./255
-    img_arr = np.expand_dims(img_arr, axis=0)
-    return img_arr
-
-# function to predict the custome image
-
-def image_predict(image_path):
-    model = load_model("Second_model.h5")
-    image_preprocess = custom_Image_preprocessing(image_path)
-    result = model.predict(image_preprocess)
-    if ( result <= 0.5  ):
-        return 'Negative',round(result[0][0]*100,2),'%'
-    else:
-        return 'Positive',round(result[0][0]*100,2),'%'
-
-
-# Define Gradio interface
-input_component =  gr.components.Image(label = "Upload the X-Ray")
-output_component = gr.components.Textbox(label = "Result")
-interface = gr.Interface(fn=image_predict, inputs=input_component, outputs=output_component,title = "Lung Cancer x-Ray Classification",description = "This web app provides predictions based on X-Ray images and predict either the X-ray contains sympotms of lung cancer or not ")
-interface.launch()
-

spaces/AlekseyKorshuk/gai-project/README.md

@@ -1,13 +0,0 @@
----
-title: Gai Project
-emoji: 📈
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Alycer/VITS-Umamusume-voice-synthesizer/text/korean.py

@@ -1,210 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄳ', 'ㄱㅅ'),
-    ('ㄵ', 'ㄴㅈ'),
-    ('ㄶ', 'ㄴㅎ'),
-    ('ㄺ', 'ㄹㄱ'),
-    ('ㄻ', 'ㄹㅁ'),
-    ('ㄼ', 'ㄹㅂ'),
-    ('ㄽ', 'ㄹㅅ'),
-    ('ㄾ', 'ㄹㅌ'),
-    ('ㄿ', 'ㄹㅍ'),
-    ('ㅀ', 'ㄹㅎ'),
-    ('ㅄ', 'ㅂㅅ'),
-    ('ㅘ', 'ㅗㅏ'),
-    ('ㅙ', 'ㅗㅐ'),
-    ('ㅚ', 'ㅗㅣ'),
-    ('ㅝ', 'ㅜㅓ'),
-    ('ㅞ', 'ㅜㅔ'),
-    ('ㅟ', 'ㅜㅣ'),
-    ('ㅢ', 'ㅡㅣ'),
-    ('ㅑ', 'ㅣㅏ'),
-    ('ㅒ', 'ㅣㅐ'),
-    ('ㅕ', 'ㅣㅓ'),
-    ('ㅖ', 'ㅣㅔ'),
-    ('ㅛ', 'ㅣㅗ'),
-    ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', '에이'),
-    ('b', '비'),
-    ('c', '시'),
-    ('d', '디'),
-    ('e', '이'),
-    ('f', '에프'),
-    ('g', '지'),
-    ('h', '에이치'),
-    ('i', '아이'),
-    ('j', '제이'),
-    ('k', '케이'),
-    ('l', '엘'),
-    ('m', '엠'),
-    ('n', '엔'),
-    ('o', '오'),
-    ('p', '피'),
-    ('q', '큐'),
-    ('r', '아르'),
-    ('s', '에스'),
-    ('t', '티'),
-    ('u', '유'),
-    ('v', '브이'),
-    ('w', '더블유'),
-    ('x', '엑스'),
-    ('y', '와이'),
-    ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('t͡ɕ','ʧ'),
-    ('d͡ʑ','ʥ'),
-    ('ɲ','n^'),
-    ('ɕ','ʃ'),
-    ('ʷ','w'),
-    ('ɭ','l`'),
-    ('ʎ','ɾ'),
-    ('ɣ','ŋ'),
-    ('ɰ','ɯ'),
-    ('ʝ','j'),
-    ('ʌ','ə'),
-    ('ɡ','g'),
-    ('\u031a','#'),
-    ('\u0348','='),
-    ('\u031e',''),
-    ('\u0320',''),
-    ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
-    for regex, replacement in _latin_to_hangul:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def divide_hangul(text):
-    text = j2hcj(h2j(text))
-    for regex, replacement in _hangul_divided:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def hangul_number(num, sino=True):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    num = re.sub(',', '', num)
-
-    if num == '0':
-        return '영'
-    if not sino and num == '20':
-        return '스무'
-
-    digits = '123456789'
-    names = '일이삼사오육칠팔구'
-    digit2name = {d: n for d, n in zip(digits, names)}
-
-    modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-    decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-    digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-    digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-    spelledout = []
-    for i, digit in enumerate(num):
-        i = len(num) - i - 1
-        if sino:
-            if i == 0:
-                name = digit2name.get(digit, '')
-            elif i == 1:
-                name = digit2name.get(digit, '') + '십'
-                name = name.replace('일십', '십')
-        else:
-            if i == 0:
-                name = digit2mod.get(digit, '')
-            elif i == 1:
-                name = digit2dec.get(digit, '')
-        if digit == '0':
-            if i % 4 == 0:
-                last_three = spelledout[-min(3, len(spelledout)):]
-                if ''.join(last_three) == '':
-                    spelledout.append('')
-                    continue
-            else:
-                spelledout.append('')
-                continue
-        if i == 2:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 3:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 4:
-            name = digit2name.get(digit, '') + '만'
-            name = name.replace('일만', '만')
-        elif i == 5:
-            name = digit2name.get(digit, '') + '십'
-            name = name.replace('일십', '십')
-        elif i == 6:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 7:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 8:
-            name = digit2name.get(digit, '') + '억'
-        elif i == 9:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 10:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 11:
-            name = digit2name.get(digit, '') + '천'
-        elif i == 12:
-            name = digit2name.get(digit, '') + '조'
-        elif i == 13:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 14:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 15:
-            name = digit2name.get(digit, '') + '천'
-        spelledout.append(name)
-    return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-    for token in tokens:
-        num, classifier = token
-        if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-            spelledout = hangul_number(num, sino=False)
-        else:
-            spelledout = hangul_number(num, sino=True)
-        text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-    # digit by digit for remaining digits
-    digits = '0123456789'
-    names = '영일이삼사오육칠팔구'
-    for d, n in zip(digits, names):
-        text = text.replace(d, n)
-    return text
-
-
-def korean_to_lazy_ipa(text):
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
-    for regex, replacement in _ipa_to_lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def korean_to_ipa(text):
-    text = korean_to_lazy_ipa(text)
-    return text.replace('ʧ','tʃ').replace('ʥ','dʑ')

spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/sync_bn.py

@@ -1,279 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.module import Module
-from torch.nn.parameter import Parameter
-
-from annotator.uniformer.mmcv.cnn import NORM_LAYERS
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'sync_bn_forward_mean', 'sync_bn_forward_var', 'sync_bn_forward_output',
-    'sync_bn_backward_param', 'sync_bn_backward_data'
-])
-
-
-class SyncBatchNormFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, running_mean, running_var, weight, bias, momentum,
-                 eps, group, group_size, stats_mode):
-        return g.op(
-            'mmcv::MMCVSyncBatchNorm',
-            input,
-            running_mean,
-            running_var,
-            weight,
-            bias,
-            momentum_f=momentum,
-            eps_f=eps,
-            group_i=group,
-            group_size_i=group_size,
-            stats_mode=stats_mode)
-
-    @staticmethod
-    def forward(self, input, running_mean, running_var, weight, bias, momentum,
-                eps, group, group_size, stats_mode):
-        self.momentum = momentum
-        self.eps = eps
-        self.group = group
-        self.group_size = group_size
-        self.stats_mode = stats_mode
-
-        assert isinstance(
-                   input, (torch.HalfTensor, torch.FloatTensor,
-                           torch.cuda.HalfTensor, torch.cuda.FloatTensor)), \
-               f'only support Half or Float Tensor, but {input.type()}'
-        output = torch.zeros_like(input)
-        input3d = input.flatten(start_dim=2)
-        output3d = output.view_as(input3d)
-        num_channels = input3d.size(1)
-
-        # ensure mean/var/norm/std are initialized as zeros
-        # ``torch.empty()`` does not guarantee that
-        mean = torch.zeros(
-            num_channels, dtype=torch.float, device=input3d.device)
-        var = torch.zeros(
-            num_channels, dtype=torch.float, device=input3d.device)
-        norm = torch.zeros_like(
-            input3d, dtype=torch.float, device=input3d.device)
-        std = torch.zeros(
-            num_channels, dtype=torch.float, device=input3d.device)
-
-        batch_size = input3d.size(0)
-        if batch_size > 0:
-            ext_module.sync_bn_forward_mean(input3d, mean)
-            batch_flag = torch.ones([1], device=mean.device, dtype=mean.dtype)
-        else:
-            # skip updating mean and leave it as zeros when the input is empty
-            batch_flag = torch.zeros([1], device=mean.device, dtype=mean.dtype)
-
-        # synchronize mean and the batch flag
-        vec = torch.cat([mean, batch_flag])
-        if self.stats_mode == 'N':
-            vec *= batch_size
-        if self.group_size > 1:
-            dist.all_reduce(vec, group=self.group)
-        total_batch = vec[-1].detach()
-        mean = vec[:num_channels]
-
-        if self.stats_mode == 'default':
-            mean = mean / self.group_size
-        elif self.stats_mode == 'N':
-            mean = mean / total_batch.clamp(min=1)
-        else:
-            raise NotImplementedError
-
-        # leave var as zeros when the input is empty
-        if batch_size > 0:
-            ext_module.sync_bn_forward_var(input3d, mean, var)
-
-        if self.stats_mode == 'N':
-            var *= batch_size
-        if self.group_size > 1:
-            dist.all_reduce(var, group=self.group)
-
-        if self.stats_mode == 'default':
-            var /= self.group_size
-        elif self.stats_mode == 'N':
-            var /= total_batch.clamp(min=1)
-        else:
-            raise NotImplementedError
-
-        # if the total batch size over all the ranks is zero,
-        # we should not update the statistics in the current batch
-        update_flag = total_batch.clamp(max=1)
-        momentum = update_flag * self.momentum
-        ext_module.sync_bn_forward_output(
-            input3d,
-            mean,
-            var,
-            weight,
-            bias,
-            running_mean,
-            running_var,
-            norm,
-            std,
-            output3d,
-            eps=self.eps,
-            momentum=momentum,
-            group_size=self.group_size)
-        self.save_for_backward(norm, std, weight)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(self, grad_output):
-        norm, std, weight = self.saved_tensors
-        grad_weight = torch.zeros_like(weight)
-        grad_bias = torch.zeros_like(weight)
-        grad_input = torch.zeros_like(grad_output)
-        grad_output3d = grad_output.flatten(start_dim=2)
-        grad_input3d = grad_input.view_as(grad_output3d)
-
-        batch_size = grad_input3d.size(0)
-        if batch_size > 0:
-            ext_module.sync_bn_backward_param(grad_output3d, norm, grad_weight,
-                                              grad_bias)
-
-        # all reduce
-        if self.group_size > 1:
-            dist.all_reduce(grad_weight, group=self.group)
-            dist.all_reduce(grad_bias, group=self.group)
-            grad_weight /= self.group_size
-            grad_bias /= self.group_size
-
-        if batch_size > 0:
-            ext_module.sync_bn_backward_data(grad_output3d, weight,
-                                             grad_weight, grad_bias, norm, std,
-                                             grad_input3d)
-
-        return grad_input, None, None, grad_weight, grad_bias, \
-            None, None, None, None, None
-
-
-@NORM_LAYERS.register_module(name='MMSyncBN')
-class SyncBatchNorm(Module):
-    """Synchronized Batch Normalization.
-
-    Args:
-        num_features (int): number of features/chennels in input tensor
-        eps (float, optional): a value added to the denominator for numerical
-            stability. Defaults to 1e-5.
-        momentum (float, optional): the value used for the running_mean and
-            running_var computation. Defaults to 0.1.
-        affine (bool, optional): whether to use learnable affine parameters.
-            Defaults to True.
-        track_running_stats (bool, optional): whether to track the running
-            mean and variance during training. When set to False, this
-            module does not track such statistics, and initializes statistics
-            buffers ``running_mean`` and ``running_var`` as ``None``. When
-            these buffers are ``None``, this module always uses batch
-            statistics in both training and eval modes. Defaults to True.
-        group (int, optional): synchronization of stats happen within
-            each process group individually. By default it is synchronization
-            across the whole world. Defaults to None.
-        stats_mode (str, optional): The statistical mode. Available options
-            includes ``'default'`` and ``'N'``. Defaults to 'default'.
-            When ``stats_mode=='default'``, it computes the overall statistics
-            using those from each worker with equal weight, i.e., the
-            statistics are synchronized and simply divied by ``group``. This
-            mode will produce inaccurate statistics when empty tensors occur.
-            When ``stats_mode=='N'``, it compute the overall statistics using
-            the total number of batches in each worker ignoring the number of
-            group, i.e., the statistics are synchronized and then divied by
-            the total batch ``N``. This mode is beneficial when empty tensors
-            occur during training, as it average the total mean by the real
-            number of batch.
-    """
-
-    def __init__(self,
-                 num_features,
-                 eps=1e-5,
-                 momentum=0.1,
-                 affine=True,
-                 track_running_stats=True,
-                 group=None,
-                 stats_mode='default'):
-        super(SyncBatchNorm, self).__init__()
-        self.num_features = num_features
-        self.eps = eps
-        self.momentum = momentum
-        self.affine = affine
-        self.track_running_stats = track_running_stats
-        group = dist.group.WORLD if group is None else group
-        self.group = group
-        self.group_size = dist.get_world_size(group)
-        assert stats_mode in ['default', 'N'], \
-            f'"stats_mode" only accepts "default" and "N", got "{stats_mode}"'
-        self.stats_mode = stats_mode
-        if self.affine:
-            self.weight = Parameter(torch.Tensor(num_features))
-            self.bias = Parameter(torch.Tensor(num_features))
-        else:
-            self.register_parameter('weight', None)
-            self.register_parameter('bias', None)
-        if self.track_running_stats:
-            self.register_buffer('running_mean', torch.zeros(num_features))
-            self.register_buffer('running_var', torch.ones(num_features))
-            self.register_buffer('num_batches_tracked',
-                                 torch.tensor(0, dtype=torch.long))
-        else:
-            self.register_buffer('running_mean', None)
-            self.register_buffer('running_var', None)
-            self.register_buffer('num_batches_tracked', None)
-        self.reset_parameters()
-
-    def reset_running_stats(self):
-        if self.track_running_stats:
-            self.running_mean.zero_()
-            self.running_var.fill_(1)
-            self.num_batches_tracked.zero_()
-
-    def reset_parameters(self):
-        self.reset_running_stats()
-        if self.affine:
-            self.weight.data.uniform_()  # pytorch use ones_()
-            self.bias.data.zero_()
-
-    def forward(self, input):
-        if input.dim() < 2:
-            raise ValueError(
-                f'expected at least 2D input, got {input.dim()}D input')
-        if self.momentum is None:
-            exponential_average_factor = 0.0
-        else:
-            exponential_average_factor = self.momentum
-
-        if self.training and self.track_running_stats:
-            if self.num_batches_tracked is not None:
-                self.num_batches_tracked += 1
-                if self.momentum is None:  # use cumulative moving average
-                    exponential_average_factor = 1.0 / float(
-                        self.num_batches_tracked)
-                else:  # use exponential moving average
-                    exponential_average_factor = self.momentum
-
-        if self.training or not self.track_running_stats:
-            return SyncBatchNormFunction.apply(
-                input, self.running_mean, self.running_var, self.weight,
-                self.bias, exponential_average_factor, self.eps, self.group,
-                self.group_size, self.stats_mode)
-        else:
-            return F.batch_norm(input, self.running_mean, self.running_var,
-                                self.weight, self.bias, False,
-                                exponential_average_factor, self.eps)
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'({self.num_features}, '
-        s += f'eps={self.eps}, '
-        s += f'momentum={self.momentum}, '
-        s += f'affine={self.affine}, '
-        s += f'track_running_stats={self.track_running_stats}, '
-        s += f'group_size={self.group_size},'
-        s += f'stats_mode={self.stats_mode})'
-        return s

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/parallel/_functions.py

@@ -1,79 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch.nn.parallel._functions import _get_stream
-
-
-def scatter(input, devices, streams=None):
-    """Scatters tensor across multiple GPUs."""
-    if streams is None:
-        streams = [None] * len(devices)
-
-    if isinstance(input, list):
-        chunk_size = (len(input) - 1) // len(devices) + 1
-        outputs = [
-            scatter(input[i], [devices[i // chunk_size]],
-                    [streams[i // chunk_size]]) for i in range(len(input))
-        ]
-        return outputs
-    elif isinstance(input, torch.Tensor):
-        output = input.contiguous()
-        # TODO: copy to a pinned buffer first (if copying from CPU)
-        stream = streams[0] if output.numel() > 0 else None
-        if devices != [-1]:
-            with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
-                output = output.cuda(devices[0], non_blocking=True)
-        else:
-            # unsqueeze the first dimension thus the tensor's shape is the
-            # same as those scattered with GPU.
-            output = output.unsqueeze(0)
-        return output
-    else:
-        raise Exception(f'Unknown type {type(input)}.')
-
-
-def synchronize_stream(output, devices, streams):
-    if isinstance(output, list):
-        chunk_size = len(output) // len(devices)
-        for i in range(len(devices)):
-            for j in range(chunk_size):
-                synchronize_stream(output[i * chunk_size + j], [devices[i]],
-                                   [streams[i]])
-    elif isinstance(output, torch.Tensor):
-        if output.numel() != 0:
-            with torch.cuda.device(devices[0]):
-                main_stream = torch.cuda.current_stream()
-                main_stream.wait_stream(streams[0])
-                output.record_stream(main_stream)
-    else:
-        raise Exception(f'Unknown type {type(output)}.')
-
-
-def get_input_device(input):
-    if isinstance(input, list):
-        for item in input:
-            input_device = get_input_device(item)
-            if input_device != -1:
-                return input_device
-        return -1
-    elif isinstance(input, torch.Tensor):
-        return input.get_device() if input.is_cuda else -1
-    else:
-        raise Exception(f'Unknown type {type(input)}.')
-
-
-class Scatter:
-
-    @staticmethod
-    def forward(target_gpus, input):
-        input_device = get_input_device(input)
-        streams = None
-        if input_device == -1 and target_gpus != [-1]:
-            # Perform CPU to GPU copies in a background stream
-            streams = [_get_stream(device) for device in target_gpus]
-
-        outputs = scatter(input, target_gpus, streams)
-        # Synchronize with the copy stream
-        if streams is not None:
-            synchronize_stream(outputs, target_gpus, streams)
-
-        return tuple(outputs)

spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/visualize.py

@@ -1,247 +0,0 @@
-import cv2
-import gc
-import requests
-from io import BytesIO
-import base64
-from scipy import misc
-from PIL import Image
-from matplotlib.axes import Axes
-from matplotlib.figure import Figure
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-from typing import Tuple
-
-import torch
-from fastai.core import *
-from fastai.vision import *
-
-from .filters import IFilter, MasterFilter, ColorizerFilter
-from .generators import gen_inference_deep, gen_inference_wide
-
-
-
-# class LoadedModel
-class ModelImageVisualizer:
-    def __init__(self, filter: IFilter, results_dir: str = None):
-        self.filter = filter
-        self.results_dir = None if results_dir is None else Path(results_dir)
-        self.results_dir.mkdir(parents=True, exist_ok=True)
-
-    def _clean_mem(self):
-        torch.cuda.empty_cache()
-        # gc.collect()
-
-    def _open_pil_image(self, path: Path) -> Image:
-        return Image.open(path).convert('RGB')
-
-    def _get_image_from_url(self, url: str) -> Image:
-        response = requests.get(url, timeout=30, headers={'Accept': '*/*;q=0.8'})
-        img = Image.open(BytesIO(response.content)).convert('RGB')
-        return img
-
-    def plot_transformed_image_from_url(
-        self,
-        url: str,
-        path: str = 'test_images/image.png',
-        results_dir:Path = None,
-        figsize: Tuple[int, int] = (20, 20),
-        render_factor: int = None,
-        
-        display_render_factor: bool = False,
-        compare: bool = False,
-        post_process: bool = True,
-        watermarked: bool = True,
-    ) -> Path:
-        img = self._get_image_from_url(url)
-        img.save(path)
-        return self.plot_transformed_image(
-            path=path,
-            results_dir=results_dir,
-            figsize=figsize,
-            render_factor=render_factor,
-            display_render_factor=display_render_factor,
-            compare=compare,
-            post_process = post_process,
-            watermarked=watermarked,
-        )
-
-    def plot_transformed_image(
-        self,
-        path: str,
-        results_dir:Path = None,
-        figsize: Tuple[int, int] = (20, 20),
-        render_factor: int = None,
-        display_render_factor: bool = False,
-        compare: bool = False,
-        post_process: bool = True,
-        watermarked: bool = True,
-    ) -> Path:
-        path = Path(path)
-        if results_dir is None:
-            results_dir = Path(self.results_dir)
-        result = self.get_transformed_image(
-            path, render_factor, post_process=post_process,watermarked=watermarked
-        )
-        orig = self._open_pil_image(path)
-        if compare:
-            self._plot_comparison(
-                figsize, render_factor, display_render_factor, orig, result
-            )
-        else:
-            self._plot_solo(figsize, render_factor, display_render_factor, result)
-
-        orig.close()
-        result_path = self._save_result_image(path, result, results_dir=results_dir)
-        result.close()
-        return result_path
-
-    def plot_transformed_pil_image(
-        self,
-        input_image: Image,
-        figsize: Tuple[int, int] = (20, 20),
-        render_factor: int = None,
-        display_render_factor: bool = False,
-        compare: bool = False,
-        post_process: bool = True,
-    ) -> Image:
-
-        result = self.get_transformed_pil_image(
-            input_image, render_factor, post_process=post_process
-        )
-
-        if compare:
-            self._plot_comparison(
-                figsize, render_factor, display_render_factor, input_image, result
-            )
-        else:
-            self._plot_solo(figsize, render_factor, display_render_factor, result)
-
-        return result
-
-    def _plot_comparison(
-        self,
-        figsize: Tuple[int, int],
-        render_factor: int,
-        display_render_factor: bool,
-        orig: Image,
-        result: Image,
-    ):
-        fig, axes = plt.subplots(1, 2, figsize=figsize)
-        self._plot_image(
-            orig,
-            axes=axes[0],
-            figsize=figsize,
-            render_factor=render_factor,
-            display_render_factor=False,
-        )
-        self._plot_image(
-            result,
-            axes=axes[1],
-            figsize=figsize,
-            render_factor=render_factor,
-            display_render_factor=display_render_factor,
-        )
-
-    def _plot_solo(
-        self,
-        figsize: Tuple[int, int],
-        render_factor: int,
-        display_render_factor: bool,
-        result: Image,
-    ):
-        fig, axes = plt.subplots(1, 1, figsize=figsize)
-        self._plot_image(
-            result,
-            axes=axes,
-            figsize=figsize,
-            render_factor=render_factor,
-            display_render_factor=display_render_factor,
-        )
-
-    def _save_result_image(self, source_path: Path, image: Image, results_dir = None) -> Path:
-        if results_dir is None:
-            results_dir = Path(self.results_dir)
-        result_path = results_dir / source_path.name
-        image.save(result_path)
-        return result_path
-
-    def get_transformed_image(
-        self, path: Path, render_factor: int = None, post_process: bool = True,
-        watermarked: bool = True,
-    ) -> Image:
-        self._clean_mem()
-        orig_image = self._open_pil_image(path)
-        filtered_image = self.filter.filter(
-            orig_image, orig_image, render_factor=render_factor,post_process=post_process
-        )
-
-        return filtered_image
-
-    def get_transformed_pil_image(
-        self, input_image: Image, render_factor: int = None, post_process: bool = True,
-    ) -> Image:
-        self._clean_mem()
-        filtered_image = self.filter.filter(
-            input_image, input_image, render_factor=render_factor,post_process=post_process
-        )
-
-        return filtered_image
-
-    def _plot_image(
-        self,
-        image: Image,
-        render_factor: int,
-        axes: Axes = None,
-        figsize=(20, 20),
-        display_render_factor = False,
-    ):
-        if axes is None:
-            _, axes = plt.subplots(figsize=figsize)
-        axes.imshow(np.asarray(image) / 255)
-        axes.axis('off')
-        if render_factor is not None and display_render_factor:
-            plt.text(
-                10,
-                10,
-                'render_factor: ' + str(render_factor),
-                color='white',
-                backgroundcolor='black',
-            )
-
-    def _get_num_rows_columns(self, num_images: int, max_columns: int) -> Tuple[int, int]:
-        columns = min(num_images, max_columns)
-        rows = num_images // columns
-        rows = rows if rows * columns == num_images else rows + 1
-        return rows, columns
-
-
-def get_image_colorizer(
-    root_folder: Path = Path('./'), render_factor: int = 35, artistic: bool = True
-) -> ModelImageVisualizer:
-    if artistic:
-        return get_artistic_image_colorizer(root_folder=root_folder, render_factor=render_factor)
-    else:
-        return get_stable_image_colorizer(root_folder=root_folder, render_factor=render_factor)
-
-
-def get_stable_image_colorizer(
-    root_folder: Path = Path('./'),
-    weights_name: str = 'ColorizeStable_gen',
-    results_dir='output',
-    render_factor: int = 35
-) -> ModelImageVisualizer:
-    learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
-    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
-    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
-    return vis
-
-
-def get_artistic_image_colorizer(
-    root_folder: Path = Path('./'),
-    weights_name: str = 'ColorizeArtistic_gen',
-    results_dir='output',
-    render_factor: int = 35
-) -> ModelImageVisualizer:
-    learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name)
-    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
-    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
-    return vis

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/distributions/__init__.py

@@ -1,21 +0,0 @@
-from pip._internal.distributions.base import AbstractDistribution
-from pip._internal.distributions.sdist import SourceDistribution
-from pip._internal.distributions.wheel import WheelDistribution
-from pip._internal.req.req_install import InstallRequirement
-
-
-def make_distribution_for_install_requirement(
-    install_req: InstallRequirement,
-) -> AbstractDistribution:
-    """Returns a Distribution for the given InstallRequirement"""
-    # Editable requirements will always be source distributions. They use the
-    # legacy logic until we create a modern standard for them.
-    if install_req.editable:
-        return SourceDistribution(install_req)
-
-    # If it's a wheel, it's a WheelDistribution
-    if install_req.is_wheel:
-        return WheelDistribution(install_req)
-
-    # Otherwise, a SourceDistribution
-    return SourceDistribution(install_req)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/__init__.py

@@ -1,18 +0,0 @@
-"""This is a subpackage because the directory is on sys.path for _in_process.py
-
-The subpackage should stay as empty as possible to avoid shadowing modules that
-the backend might import.
-"""
-
-import importlib.resources as resources
-
-try:
-    resources.files
-except AttributeError:
-    # Python 3.8 compatibility
-    def _in_proc_script_path():
-        return resources.path(__package__, '_in_process.py')
-else:
-    def _in_proc_script_path():
-        return resources.as_file(
-            resources.files(__package__).joinpath('_in_process.py'))

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/status.py

@@ -1,132 +0,0 @@
-from types import TracebackType
-from typing import Optional, Type
-
-from .console import Console, RenderableType
-from .jupyter import JupyterMixin
-from .live import Live
-from .spinner import Spinner
-from .style import StyleType
-
-
-class Status(JupyterMixin):
-    """Displays a status indicator with a 'spinner' animation.
-
-    Args:
-        status (RenderableType): A status renderable (str or Text typically).
-        console (Console, optional): Console instance to use, or None for global console. Defaults to None.
-        spinner (str, optional): Name of spinner animation (see python -m rich.spinner). Defaults to "dots".
-        spinner_style (StyleType, optional): Style of spinner. Defaults to "status.spinner".
-        speed (float, optional): Speed factor for spinner animation. Defaults to 1.0.
-        refresh_per_second (float, optional): Number of refreshes per second. Defaults to 12.5.
-    """
-
-    def __init__(
-        self,
-        status: RenderableType,
-        *,
-        console: Optional[Console] = None,
-        spinner: str = "dots",
-        spinner_style: StyleType = "status.spinner",
-        speed: float = 1.0,
-        refresh_per_second: float = 12.5,
-    ):
-        self.status = status
-        self.spinner_style = spinner_style
-        self.speed = speed
-        self._spinner = Spinner(spinner, text=status, style=spinner_style, speed=speed)
-        self._live = Live(
-            self.renderable,
-            console=console,
-            refresh_per_second=refresh_per_second,
-            transient=True,
-        )
-
-    @property
-    def renderable(self) -> Spinner:
-        return self._spinner
-
-    @property
-    def console(self) -> "Console":
-        """Get the Console used by the Status objects."""
-        return self._live.console
-
-    def update(
-        self,
-        status: Optional[RenderableType] = None,
-        *,
-        spinner: Optional[str] = None,
-        spinner_style: Optional[StyleType] = None,
-        speed: Optional[float] = None,
-    ) -> None:
-        """Update status.
-
-        Args:
-            status (Optional[RenderableType], optional): New status renderable or None for no change. Defaults to None.
-            spinner (Optional[str], optional): New spinner or None for no change. Defaults to None.
-            spinner_style (Optional[StyleType], optional): New spinner style or None for no change. Defaults to None.
-            speed (Optional[float], optional): Speed factor for spinner animation or None for no change. Defaults to None.
-        """
-        if status is not None:
-            self.status = status
-        if spinner_style is not None:
-            self.spinner_style = spinner_style
-        if speed is not None:
-            self.speed = speed
-        if spinner is not None:
-            self._spinner = Spinner(
-                spinner, text=self.status, style=self.spinner_style, speed=self.speed
-            )
-            self._live.update(self.renderable, refresh=True)
-        else:
-            self._spinner.update(
-                text=self.status, style=self.spinner_style, speed=self.speed
-            )
-
-    def start(self) -> None:
-        """Start the status animation."""
-        self._live.start()
-
-    def stop(self) -> None:
-        """Stop the spinner animation."""
-        self._live.stop()
-
-    def __rich__(self) -> RenderableType:
-        return self.renderable
-
-    def __enter__(self) -> "Status":
-        self.start()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.stop()
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from time import sleep
-
-    from .console import Console
-
-    console = Console()
-    with console.status("[magenta]Covid detector booting up") as status:
-        sleep(3)
-        console.log("Importing advanced AI")
-        sleep(3)
-        console.log("Advanced Covid AI Ready")
-        sleep(3)
-        status.update(status="[bold blue] Scanning for Covid", spinner="earth")
-        sleep(3)
-        console.log("Found 10,000,000,000 copies of Covid32.exe")
-        sleep(3)
-        status.update(
-            status="[bold red]Moving Covid32.exe to Trash",
-            spinner="bouncingBall",
-            spinner_style="yellow",
-        )
-        sleep(5)
-    console.print("[bold green]Covid deleted successfully")

spaces/Audio-AGI/AudioSep/models/CLAP/training/params.py

@@ -1,563 +0,0 @@
-import argparse
-
-
-def get_default_params(model_name):
-    # Params from paper (https://arxiv.org/pdf/2103.00020.pdf)
-    model_name = model_name.lower()
-    if "vit" in model_name:
-        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.98, "eps": 1.0e-6}
-    else:
-        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.999, "eps": 1.0e-8}
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--train-data",
-        type=str,
-        default=None,
-        help="Path to h5 filewith training data",
-    )
-    parser.add_argument(
-        "--val-data",
-        type=str,
-        default=None,
-        help="Path to h5 file with validation data",
-    )
-    parser.add_argument(
-        "--freeze-text",
-        default=False,
-        action="store_true",
-        help="if you need to freeze the text encoder, make this True",
-    )
-    parser.add_argument(
-        "--freeze-text-after",
-        type=int,
-        default=-1,
-        help="if you need to freeze the text encoder after (include) epoch x, set this param to x. Set -1 to disable it",
-    )
-    parser.add_argument(
-        "--train-ipc",
-        type=str,
-        default=None,
-        help="Path to npy file of the number of instance per class in training data",
-    )
-    parser.add_argument(
-        "--val-ipc",
-        type=str,
-        default=None,
-        help="Path to npy file of the number of instance per class in validation data",
-    )
-    parser.add_argument(
-        "--train-num-samples",
-        type=int,
-        default=None,
-        help="Number of samples in dataset. Required for webdataset if not available in info file.",
-    )
-    parser.add_argument(
-        "--val-num-samples",
-        type=int,
-        default=None,
-        help="Number of samples in dataset. Useful for webdataset if not available in info file.",
-    )
-    parser.add_argument(
-        "--dataset-type",
-        choices=["webdataset", "csv", "auto", "toy"],
-        default="auto",
-        help="Which type of dataset to process.",
-    )
-    parser.add_argument(
-        "--csv-separator",
-        type=str,
-        default="\t",
-        help="For csv-like datasets, which separator to use.",
-    )
-    parser.add_argument(
-        "--csv-img-key",
-        type=str,
-        default="filepath",
-        help="For csv-like datasets, the name of the key for the image paths.",
-    )
-    parser.add_argument(
-        "--csv-caption-key",
-        type=str,
-        default="title",
-        help="For csv-like datasets, the name of the key for the captions.",
-    )
-    parser.add_argument(
-        "--imagenet-val",
-        type=str,
-        default=None,
-        help="Path to imagenet val set for conducting zero shot evaluation.",
-    )
-    parser.add_argument(
-        "--imagenet-v2",
-        type=str,
-        default=None,
-        help="Path to imagenet v2 for conducting zero shot evaluation.",
-    )
-    parser.add_argument(
-        "--datasetnames",
-        nargs="+",
-        default=None,
-        help="If loading webdataset, spedify the dataset names to load. Can be some of these: Clotho, audioset, audiocaps, BBCSoundEffects",
-    )
-    parser.add_argument(
-        "--full-train-dataset",
-        nargs="+",
-        default=None,
-        help="Which dataset will be trained with all the subsets. (train+test)",
-    )
-    parser.add_argument(
-        "--exclude-eval-dataset",
-        nargs="+",
-        default=None,
-        help="Which dataset will be excluded with evaluation",
-    )
-    parser.add_argument(
-        "--datasetinfos",
-        nargs="+",
-        default=None,
-        help="If loading webdataset, spedify the dataset types to load. Can be some of these: train, test, valid, unbalanced_train, balanced_train, eval",
-    )
-    parser.add_argument(
-        "--dataset-proportion",
-        type=float,
-        default=1.0,
-        help="How much proportion of dataset we want to train.",
-    )
-    parser.add_argument(
-        "--remotedata",
-        default=False,
-        action="store_true",
-        help="if the dataset is remote, set this flag",
-    )
-    parser.add_argument(
-        "--class-label-path",
-        type=str,
-        default=None,
-        help="The path of the class label pickle or csv.",
-    )
-    parser.add_argument(
-        "--datasetpath",
-        type=str,
-        default="/mnt/audio_clip/webdataset_tar",
-        help="The path to the dataset",
-    )
-    parser.add_argument(
-        "--logs",
-        type=str,
-        default="./logs/",
-        help="Where to store tensorboard logs. Use None to avoid storing logs.",
-    )
-    parser.add_argument(
-        "--log-local",
-        action="store_true",
-        default=False,
-        help="log files on local master, otherwise global master only.",
-    )
-    parser.add_argument(
-        "--name",
-        type=str,
-        default=None,
-        help="Optional identifier for the experiment when storing logs. Otherwise use current time.",
-    )
-    parser.add_argument(
-        "--workers", type=int, default=1, help="Number of workers per GPU."
-    )
-    parser.add_argument(
-        "--batch-size", type=int, default=64, help="Batch size per GPU."
-    )
-    parser.add_argument(
-        "--epochs", type=int, default=32, help="Number of epochs to train for."
-    )
-    parser.add_argument("--lr", type=float, default=None, help="Learning rate.")
-    parser.add_argument("--beta1", type=float, default=None, help="Adam beta 1.")
-    parser.add_argument("--beta2", type=float, default=None, help="Adam beta 2.")
-    parser.add_argument("--eps", type=float, default=None, help="Adam epsilon.")
-    parser.add_argument("--momentum", type=float, default=None, help="SGD epsilon.")
-    parser.add_argument("--wd", type=float, default=0.2, help="Weight decay.")
-
-    parser.add_argument(
-        "--split-opt",
-        action="store_true",
-        default=False,
-        help="Use this flag to skip the learning rate decay.",
-    )
-    parser.add_argument(
-        "--lr-pretrained", type=float, default=None, help="Learning rate for text."
-    )
-    parser.add_argument(
-        "--beta1-pretrained", type=float, default=None, help="Adam beta 1 for text."
-    )
-    parser.add_argument(
-        "--beta2-pretrained", type=float, default=None, help="Adam beta 2 for text."
-    )
-    parser.add_argument(
-        "--eps-pretrained", type=float, default=None, help="Adam epsilon for text."
-    )
-    parser.add_argument(
-        "--wd-pretrained", type=float, default=0.2, help="Weight decay for text."
-    )
-    parser.add_argument(
-        "--momentum-pretrained", type=float, default=0.9, help="Momentum for text."
-    )
-    parser.add_argument(
-        "--lr-new", type=float, default=None, help="Learning rate for audio."
-    )
-    parser.add_argument(
-        "--beta1-new", type=float, default=None, help="Adam beta 1 for audio."
-    )
-    parser.add_argument(
-        "--beta2-new", type=float, default=None, help="Adam beta 2 for audio."
-    )
-    parser.add_argument(
-        "--eps-new", type=float, default=None, help="Adam epsilon for audio."
-    )
-    parser.add_argument(
-        "--wd-new", type=float, default=0.2, help="Weight decay for audio."
-    )
-    parser.add_argument(
-        "--momentum-new", type=float, default=0.9, help="Momentum for audio."
-    )
-    parser.add_argument(
-        "--warmup", type=int, default=10000, help="Number of steps to warmup for."
-    )
-    parser.add_argument(
-        "--use-bn-sync",
-        default=False,
-        action="store_true",
-        help="Whether to use batch norm sync.",
-    )
-    parser.add_argument(
-        "--skip-scheduler",
-        action="store_true",
-        default=False,
-        help="Use this flag to skip the learning rate decay.",
-    )
-    parser.add_argument(
-        "--save-frequency", type=int, default=1, help="How often to save checkpoints."
-    )
-    parser.add_argument(
-        "--save-top-performance",
-        type=int,
-        default=0,
-        help="Save the top x performance weights if the value >0",
-    )
-    parser.add_argument(
-        "--save-most-recent",
-        action="store_true",
-        default=False,
-        help="Always save the most recent model trained to epoch_latest.pt.",
-    )
-    parser.add_argument(
-        "--zeroshot-frequency", type=int, default=2, help="How often to run zero shot."
-    )
-    parser.add_argument(
-        "--val-frequency",
-        type=int,
-        default=1,
-        help="How often to run evaluation with val data.",
-    )
-    parser.add_argument(
-        "--resume",
-        default=None,
-        type=str,
-        help="path to latest checkpoint (default: none)",
-    )
-    parser.add_argument(
-        "--precision",
-        choices=["amp", "fp16", "fp32"],
-        default="amp",
-        help="Floating point precision.",
-    )
-    parser.add_argument(
-        "--amodel",
-        type=str,
-        default="RN50",
-        help="Name of the audio backbone to use.",
-    )
-    parser.add_argument(
-        "--tmodel",
-        type=str,
-        default="transformer",
-        help="Name of the text backbone to use. Can be [transformer, bert, roberta, bart]",
-    )
-    parser.add_argument(
-        "--pretrained-audio",
-        default="",
-        type=str,
-        help="Use a pretrained audio model weights for the audio encoder of CLAP",
-    )
-    parser.add_argument(
-        "--pretrained-text",
-        default="",
-        type=str,
-        help="Use a pretrained text model weights for the text encoder of CLAP",
-    )
-    parser.add_argument(
-        "--pretrained",
-        default="",
-        type=str,
-        help="Use a pretrained CLIP model weights with the specified tag or file path.",
-    )
-    parser.add_argument(
-        "--pretrained-image",
-        default=False,
-        action="store_true",
-        help="Load imagenet pretrained weights for image tower backbone if available.",
-    )
-    parser.add_argument(
-        "--lock-image",
-        default=False,
-        action="store_true",
-        help="Lock full image tower by disabling gradients.",
-    )
-    parser.add_argument(
-        "--lock-image-unlocked-groups",
-        type=int,
-        default=0,
-        help="Leave last n image tower layer groups unlocked.",
-    )
-    parser.add_argument(
-        "--lock-image-freeze-bn-stats",
-        default=False,
-        action="store_true",
-        help="Freeze BatchNorm running stats in image tower for any locked layers.",
-    )
-    parser.add_argument(
-        "--local-loss",
-        default=False,
-        action="store_true",
-        help="calculate loss w/ local features @ global (instead of realizing full global @ global matrix)",
-    )
-    parser.add_argument(
-        "--gather-with-grad",
-        default=False,
-        action="store_true",
-        help="enable full distributed gradient for feature gather",
-    )
-    parser.add_argument(
-        "--force-quick-gelu",
-        default=False,
-        action="store_true",
-        help="Force use of QuickGELU activation for non-OpenAI transformer models.",
-    )
-    parser.add_argument(
-        "--torchscript",
-        default=False,
-        action="store_true",
-        help="torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'",
-    )
-    parser.add_argument(
-        "--trace",
-        default=False,
-        action="store_true",
-        help="torch.jit.trace the model for inference / eval only",
-    )
-    # arguments for distributed training
-    parser.add_argument(
-        "--dist-url",
-        default="env://",
-        type=str,
-        help="url used to set up distributed training",
-    )
-    parser.add_argument(
-        "--dist-backend", default="nccl", type=str, help="distributed backend"
-    )
-    parser.add_argument(
-        "--report-to",
-        default="",
-        type=str,
-        help="Options are ['wandb', 'tensorboard', 'wandb,tensorboard']",
-    )
-    parser.add_argument(
-        "--wandb-notes", default="", type=str, help="Notes if logging with wandb"
-    )
-    parser.add_argument(
-        "--C", type=float, default=3.16, help="inverse regularizer for logistic reg."
-    )
-    parser.add_argument(
-        "--debug",
-        default=False,
-        action="store_true",
-        help="If true, more information is logged.",
-    )
-    parser.add_argument(
-        "--copy-codebase",
-        default=False,
-        action="store_true",
-        help="If true, we copy the entire base on the log diretory, and execute from there.",
-    )
-    parser.add_argument(
-        "--horovod",
-        default=False,
-        action="store_true",
-        help="Use horovod for distributed training.",
-    )
-    parser.add_argument(
-        "--ddp-static-graph",
-        default=False,
-        action="store_true",
-        help="Enable static graph optimization for DDP in PyTorch >= 1.11.",
-    )
-    parser.add_argument(
-        "--no-set-device-rank",
-        default=False,
-        action="store_true",
-        help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).",
-    )
-    parser.add_argument("--seed", type=int, default=4242, help="Default random seed.")
-
-    parser.add_argument(
-        "--top-k-checkpoint-select-dataset",
-        type=str,
-        default="all",
-        help="The dataset of selecting top-k checkpoint.",
-    )
-
-    # @R10, @R@5, @R1, mAP@10
-    parser.add_argument(
-        "--top-k-checkpoint-select-metric",
-        type=str,
-        default="_R@10",
-        help="The metric for selecting top-k checkpoint.",
-    )
-    parser.add_argument(
-        "--openai-model-cache-dir",
-        type=str,
-        default="~/.cache/clip",
-        help="Directory to download OpenAI models.",
-    )
-    parser.add_argument(
-        "--optimizer",
-        type=str,
-        default="adamw",
-        help="can be AdamW or SGD",
-    )
-    parser.add_argument(
-        "--parallel-eval",
-        default=False,
-        action="store_true",
-        help="Eval in parallel (multi-GPU, multi-node).",
-    )
-
-    parser.add_argument(
-        "--no-eval",
-        default=False,
-        action="store_true",
-        help="Training without evaluation.",
-    )
-
-    parser.add_argument(
-        "--lp-mlp",
-        default=False,
-        action="store_true",
-        help="Linear Probe using MLP layer or not.",
-    )
-
-    parser.add_argument(
-        "--lp-freeze",
-        default=False,
-        action="store_true",
-        help="Linear Probe using Freeze CLAP or not",
-    )
-
-    parser.add_argument(
-        "--lp-act",
-        default="None",
-        type=str,
-        help="Options are ['relu','elu','prelu','softmax','sigmoid']",
-    )
-
-    parser.add_argument(
-        "--lp-loss", type=str, default="bce", help="Loss func of Linear Probe."
-    )
-
-    parser.add_argument(
-        "--lp-metrics",
-        type=str,
-        default="map,mauc,acc",
-        help="Metrics of Linear Probe.",
-    )
-
-    parser.add_argument(
-        "--lp-lr", type=float, default=1e-4, help="learning rate of linear probe"
-    )
-    parser.add_argument(
-        "--kappa",
-        type=float,
-        default=0,
-        help="the kappa in the weighted contrastive loss, default is to turn off the weighted contrastive loss",
-    )
-
-    parser.add_argument(
-        "--data-filling",
-        type=str,
-        default="pad",
-        help="type of data filling when the audio length is shorter than the max length."
-        "Can be one of the following: repeat, repeatpad, pad",
-    )
-    parser.add_argument(
-        "--data-truncating",
-        type=str,
-        default="rand_trunc",
-        help="type of data truncation when the audio length is longer than the max length."
-        "Can be one of the following: rand_trunc, fusion",
-    )
-
-    parser.add_argument(
-        "--clap-mlploss",
-        default=False,
-        action="store_true",
-        help="Using MLP loss for CLAP model or not",
-    )
-
-    parser.add_argument(
-        "--wandb-id",
-        type=str,
-        default=None,
-        help="the id of wandb experiment to restore.",
-    )
-
-    parser.add_argument(
-        "--sleep", type=float, default=0, help="sleep n seconds before start training"
-    )
-
-    # variable length processing
-    parser.add_argument(
-        "--enable-fusion",
-        default=False,
-        action="store_true",
-        help="Enable feature funsion for variable-length data",
-    )
-
-    parser.add_argument(
-        "--fusion-type",
-        type=str,
-        default="None",
-        help="Type is among ['channel_map', 'daf_1d','aff_1d','iaff_1d','daf_2d','aff_2d','iaff_2d']",
-    )
-
-    parser.add_argument(
-        "--mixup",
-        default=False,
-        action="store_true",
-        help="Enable mixup in finetuning training.",
-    )
-    parser.add_argument(
-        "--text-augment-selection",
-        type=str,
-        default=None,
-        help="For selecting levels of augmented text. Type is among ['all', 'augment_only', 'none']",
-    )
-
-    args = parser.parse_args()
-
-    # If some params are not passed, we use the default values based on model name.
-    default_params = get_default_params(args.amodel)
-    for name, val in default_params.items():
-        if getattr(args, name) is None:
-            setattr(args, name, val)
-
-    return args

spaces/AutoGeneralAI/ChatGPT/README.md

@@ -1,13 +0,0 @@
----
-title: ChatGPT
-emoji: 🐢
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/modeling/test_roi_pooler.py

@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.modeling.poolers import ROIPooler
-from detectron2.structures import Boxes, RotatedBoxes
-from detectron2.utils.testing import random_boxes
-
-logger = logging.getLogger(__name__)
-
-
-class TestROIPooler(unittest.TestCase):
-    def _test_roialignv2_roialignrotated_match(self, device):
-        pooler_resolution = 14
-        canonical_level = 4
-        canonical_scale_factor = 2 ** canonical_level
-        pooler_scales = (1.0 / canonical_scale_factor,)
-        sampling_ratio = 0
-
-        N, C, H, W = 2, 4, 10, 8
-        N_rois = 10
-        std = 11
-        mean = 0
-        feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
-
-        features = [feature.to(device)]
-
-        rois = []
-        rois_rotated = []
-        for _ in range(N):
-            boxes = random_boxes(N_rois, W * canonical_scale_factor)
-            rotated_boxes = torch.zeros(N_rois, 5)
-            rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
-            rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
-            rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
-            rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
-            rois.append(Boxes(boxes).to(device))
-            rois_rotated.append(RotatedBoxes(rotated_boxes).to(device))
-
-        roialignv2_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type="ROIAlignV2",
-        )
-
-        roialignv2_out = roialignv2_pooler(features, rois)
-
-        roialignrotated_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type="ROIAlignRotated",
-        )
-
-        roialignrotated_out = roialignrotated_pooler(features, rois_rotated)
-
-        self.assertTrue(torch.allclose(roialignv2_out, roialignrotated_out, atol=1e-4))
-
-    def test_roialignv2_roialignrotated_match_cpu(self):
-        self._test_roialignv2_roialignrotated_match(device="cpu")
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_roialignv2_roialignrotated_match_cuda(self):
-        self._test_roialignv2_roialignrotated_match(device="cuda")
-
-    def _test_scriptability(self, device):
-        pooler_resolution = 14
-        canonical_level = 4
-        canonical_scale_factor = 2 ** canonical_level
-        pooler_scales = (1.0 / canonical_scale_factor,)
-        sampling_ratio = 0
-
-        N, C, H, W = 2, 4, 10, 8
-        N_rois = 10
-        std = 11
-        mean = 0
-        feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
-
-        features = [feature.to(device)]
-
-        rois = []
-        for _ in range(N):
-            boxes = random_boxes(N_rois, W * canonical_scale_factor)
-
-            rois.append(Boxes(boxes).to(device))
-
-        roialignv2_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type="ROIAlignV2",
-        )
-
-        roialignv2_out = roialignv2_pooler(features, rois)
-        scripted_roialignv2_out = torch.jit.script(roialignv2_pooler)(features, rois)
-        self.assertTrue(torch.equal(roialignv2_out, scripted_roialignv2_out))
-
-    def test_scriptability_cpu(self):
-        self._test_scriptability(device="cpu")
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_scriptability_gpu(self):
-        self._test_scriptability(device="cuda")
-
-    def test_no_images(self):
-        N, C, H, W = 0, 32, 32, 32
-        feature = torch.rand(N, C, H, W) - 0.5
-        features = [feature]
-        pooler = ROIPooler(
-            output_size=14, scales=(1.0,), sampling_ratio=0.0, pooler_type="ROIAlignV2"
-        )
-        output = pooler.forward(features, [])
-        self.assertEqual(output.shape, (0, C, 14, 14))
-
-    def test_roi_pooler_tracing(self):
-        class Model(torch.nn.Module):
-            def __init__(self, roi):
-                super(Model, self).__init__()
-                self.roi = roi
-
-            def forward(self, x, boxes):
-                return self.roi(x, [Boxes(boxes)])
-
-        pooler_resolution = 14
-        canonical_level = 4
-        canonical_scale_factor = 2 ** canonical_level
-        pooler_scales = (1.0 / canonical_scale_factor, 0.5 / canonical_scale_factor)
-        sampling_ratio = 0
-
-        N, C, H, W = 1, 4, 10, 8
-        N_rois = 10
-        std = 11
-        mean = 0
-        feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
-        feature = [feature, feature]
-
-        rois = random_boxes(N_rois, W * canonical_scale_factor)
-        # Add one larger box so that this level has only one box.
-        # This may trigger the bug https://github.com/pytorch/pytorch/issues/49852
-        # that we shall workaround.
-        rois = torch.cat([rois, torch.tensor([[0, 0, 448, 448]])])
-
-        model = Model(
-            ROIPooler(
-                output_size=pooler_resolution,
-                scales=pooler_scales,
-                sampling_ratio=sampling_ratio,
-                pooler_type="ROIAlign",
-            )
-        )
-
-        with torch.no_grad():
-            func = torch.jit.trace(model, (feature, rois))
-            o = func(feature, rois)
-            self.assertEqual(o.shape, (11, 4, 14, 14))
-            o = func(feature, rois[:5])
-            self.assertEqual(o.shape, (5, 4, 14, 14))
-            o = func(feature, random_boxes(20, W * canonical_scale_factor))
-            self.assertEqual(o.shape, (20, 4, 14, 14))
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/Ayush113/cricket_matchups/README.md

@@ -1,13 +0,0 @@
----
-title: Cricket Matchups
-emoji: 🏃
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.46.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Bart92/RVC_HF/Fixes/local_fixes.py

@@ -1,136 +0,0 @@
-import os
-import sys
-import time
-import shutil
-import requests
-import zipfile
-
-def insert_new_line(file_name, line_to_find, text_to_insert):
-    lines = []
-    with open(file_name, 'r', encoding='utf-8') as read_obj:
-        lines = read_obj.readlines()
-    already_exists = False
-    with open(file_name + '.tmp', 'w', encoding='utf-8') as write_obj:
-        for i in range(len(lines)):
-            write_obj.write(lines[i])
-            if lines[i].strip() == line_to_find:
-                # If next line exists and starts with sys.path.append, skip
-                if i+1 < len(lines) and lines[i+1].strip().startswith("sys.path.append"):
-                    print('It was already fixed! Skip adding a line...')
-                    already_exists = True
-                    break
-                else:
-                    write_obj.write(text_to_insert + '\n')
-    # If no existing sys.path.append line was found, replace the original file
-    if not already_exists:
-        os.replace(file_name + '.tmp', file_name)
-        return True
-    else:
-        # If existing line was found, delete temporary file
-        os.remove(file_name + '.tmp')
-        return False
-
-def replace_in_file(file_name, old_text, new_text):
-    with open(file_name, 'r', encoding='utf-8') as file:
-        file_contents = file.read()
-
-    if old_text in file_contents:
-        file_contents = file_contents.replace(old_text, new_text)
-        with open(file_name, 'w', encoding='utf-8') as file:
-            file.write(file_contents)
-            return True
-
-    return False
-
-if __name__ == "__main__":
-    current_path = os.getcwd()
-    file_name = os.path.join(current_path, "infer", "modules", "train", "extract", "extract_f0_print.py")
-    line_to_find = 'import numpy as np, logging'
-    text_to_insert = "sys.path.append(r'" + current_path + "')"
-    
-
-    success_1 = insert_new_line(file_name, line_to_find, text_to_insert)
-    if success_1:
-        print('The first operation was successful!')
-    else:
-        print('He skipped the first operation because it was already fixed!')
-
-    file_name = 'infer-web.py'
-    old_text = 'with gr.Blocks(theme=gr.themes.Soft()) as app:'
-    new_text = 'with gr.Blocks() as app:'
-
-    success_2 = replace_in_file(file_name, old_text, new_text)
-    if success_2:
-        print('The second operation was successful!')
-    else:
-        print('The second operation was omitted because it was already fixed!')
-
-    print('Local corrections successful! You should now be able to infer and train locally in Applio RVC Fork.')
-    
-    time.sleep(5)
-
-def find_torchcrepe_directory(directory):
-    """
-    Recursively searches for the topmost folder named 'torchcrepe' within a directory.
-    Returns the path of the directory found or None if none is found.
-    """
-    for root, dirs, files in os.walk(directory):
-        if 'torchcrepe' in dirs:
-            return os.path.join(root, 'torchcrepe')
-    return None
-
-def download_and_extract_torchcrepe():
-    url = 'https://github.com/maxrmorrison/torchcrepe/archive/refs/heads/master.zip'
-    temp_dir = 'temp_torchcrepe'
-    destination_dir = os.getcwd()
-
-    try:
-        torchcrepe_dir_path = os.path.join(destination_dir, 'torchcrepe')
-
-        if os.path.exists(torchcrepe_dir_path):
-            print("Skipping the torchcrepe download. The folder already exists.")
-            return
-
-        # Download the file
-        print("Starting torchcrepe download...")
-        response = requests.get(url)
-
-        # Raise an error if the GET request was unsuccessful
-        response.raise_for_status()
-        print("Download completed.")
-
-        # Save the downloaded file
-        zip_file_path = os.path.join(temp_dir, 'master.zip')
-        os.makedirs(temp_dir, exist_ok=True)
-        with open(zip_file_path, 'wb') as file:
-            file.write(response.content)
-        print(f"Zip file saved to {zip_file_path}")
-
-        # Extract the zip file
-        print("Extracting content...")
-        with zipfile.ZipFile(zip_file_path, 'r') as zip_file:
-            zip_file.extractall(temp_dir)
-        print("Extraction completed.")
-
-        # Locate the torchcrepe folder and move it to the destination directory
-        torchcrepe_dir = find_torchcrepe_directory(temp_dir)
-        if torchcrepe_dir:
-            shutil.move(torchcrepe_dir, destination_dir)
-            print(f"Moved the torchcrepe directory to {destination_dir}!")
-        else:
-            print("The torchcrepe directory could not be located.")
-
-    except Exception as e:
-        print("Torchcrepe not successfully downloaded", e)
-
-    # Clean up temporary directory
-    if os.path.exists(temp_dir):
-        shutil.rmtree(temp_dir)
-
-# Run the function
-download_and_extract_torchcrepe()
-
-temp_dir = 'temp_torchcrepe'
-
-if os.path.exists(temp_dir):
-    shutil.rmtree(temp_dir)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/metadata/base.py

@@ -1,688 +0,0 @@
-import csv
-import email.message
-import functools
-import json
-import logging
-import pathlib
-import re
-import zipfile
-from typing import (
-    IO,
-    TYPE_CHECKING,
-    Any,
-    Collection,
-    Container,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    NamedTuple,
-    Optional,
-    Tuple,
-    Union,
-)
-
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.specifiers import InvalidSpecifier, SpecifierSet
-from pip._vendor.packaging.utils import NormalizedName
-from pip._vendor.packaging.version import LegacyVersion, Version
-
-from pip._internal.exceptions import NoneMetadataError
-from pip._internal.locations import site_packages, user_site
-from pip._internal.models.direct_url import (
-    DIRECT_URL_METADATA_NAME,
-    DirectUrl,
-    DirectUrlValidationError,
-)
-from pip._internal.utils.compat import stdlib_pkgs  # TODO: Move definition here.
-from pip._internal.utils.egg_link import egg_link_path_from_sys_path
-from pip._internal.utils.misc import is_local, normalize_path
-from pip._internal.utils.packaging import safe_extra
-from pip._internal.utils.urls import url_to_path
-
-from ._json import msg_to_json
-
-if TYPE_CHECKING:
-    from typing import Protocol
-else:
-    Protocol = object
-
-DistributionVersion = Union[LegacyVersion, Version]
-
-InfoPath = Union[str, pathlib.PurePath]
-
-logger = logging.getLogger(__name__)
-
-
-class BaseEntryPoint(Protocol):
-    @property
-    def name(self) -> str:
-        raise NotImplementedError()
-
-    @property
-    def value(self) -> str:
-        raise NotImplementedError()
-
-    @property
-    def group(self) -> str:
-        raise NotImplementedError()
-
-
-def _convert_installed_files_path(
-    entry: Tuple[str, ...],
-    info: Tuple[str, ...],
-) -> str:
-    """Convert a legacy installed-files.txt path into modern RECORD path.
-
-    The legacy format stores paths relative to the info directory, while the
-    modern format stores paths relative to the package root, e.g. the
-    site-packages directory.
-
-    :param entry: Path parts of the installed-files.txt entry.
-    :param info: Path parts of the egg-info directory relative to package root.
-    :returns: The converted entry.
-
-    For best compatibility with symlinks, this does not use ``abspath()`` or
-    ``Path.resolve()``, but tries to work with path parts:
-
-    1. While ``entry`` starts with ``..``, remove the equal amounts of parts
-       from ``info``; if ``info`` is empty, start appending ``..`` instead.
-    2. Join the two directly.
-    """
-    while entry and entry[0] == "..":
-        if not info or info[-1] == "..":
-            info += ("..",)
-        else:
-            info = info[:-1]
-        entry = entry[1:]
-    return str(pathlib.Path(*info, *entry))
-
-
-class RequiresEntry(NamedTuple):
-    requirement: str
-    extra: str
-    marker: str
-
-
-class BaseDistribution(Protocol):
-    @classmethod
-    def from_directory(cls, directory: str) -> "BaseDistribution":
-        """Load the distribution from a metadata directory.
-
-        :param directory: Path to a metadata directory, e.g. ``.dist-info``.
-        """
-        raise NotImplementedError()
-
-    @classmethod
-    def from_metadata_file_contents(
-        cls,
-        metadata_contents: bytes,
-        filename: str,
-        project_name: str,
-    ) -> "BaseDistribution":
-        """Load the distribution from the contents of a METADATA file.
-
-        This is used to implement PEP 658 by generating a "shallow" dist object that can
-        be used for resolution without downloading or building the actual dist yet.
-
-        :param metadata_contents: The contents of a METADATA file.
-        :param filename: File name for the dist with this metadata.
-        :param project_name: Name of the project this dist represents.
-        """
-        raise NotImplementedError()
-
-    @classmethod
-    def from_wheel(cls, wheel: "Wheel", name: str) -> "BaseDistribution":
-        """Load the distribution from a given wheel.
-
-        :param wheel: A concrete wheel definition.
-        :param name: File name of the wheel.
-
-        :raises InvalidWheel: Whenever loading of the wheel causes a
-            :py:exc:`zipfile.BadZipFile` exception to be thrown.
-        :raises UnsupportedWheel: If the wheel is a valid zip, but malformed
-            internally.
-        """
-        raise NotImplementedError()
-
-    def __repr__(self) -> str:
-        return f"{self.raw_name} {self.version} ({self.location})"
-
-    def __str__(self) -> str:
-        return f"{self.raw_name} {self.version}"
-
-    @property
-    def location(self) -> Optional[str]:
-        """Where the distribution is loaded from.
-
-        A string value is not necessarily a filesystem path, since distributions
-        can be loaded from other sources, e.g. arbitrary zip archives. ``None``
-        means the distribution is created in-memory.
-
-        Do not canonicalize this value with e.g. ``pathlib.Path.resolve()``. If
-        this is a symbolic link, we want to preserve the relative path between
-        it and files in the distribution.
-        """
-        raise NotImplementedError()
-
-    @property
-    def editable_project_location(self) -> Optional[str]:
-        """The project location for editable distributions.
-
-        This is the directory where pyproject.toml or setup.py is located.
-        None if the distribution is not installed in editable mode.
-        """
-        # TODO: this property is relatively costly to compute, memoize it ?
-        direct_url = self.direct_url
-        if direct_url:
-            if direct_url.is_local_editable():
-                return url_to_path(direct_url.url)
-        else:
-            # Search for an .egg-link file by walking sys.path, as it was
-            # done before by dist_is_editable().
-            egg_link_path = egg_link_path_from_sys_path(self.raw_name)
-            if egg_link_path:
-                # TODO: get project location from second line of egg_link file
-                #       (https://github.com/pypa/pip/issues/10243)
-                return self.location
-        return None
-
-    @property
-    def installed_location(self) -> Optional[str]:
-        """The distribution's "installed" location.
-
-        This should generally be a ``site-packages`` directory. This is
-        usually ``dist.location``, except for legacy develop-installed packages,
-        where ``dist.location`` is the source code location, and this is where
-        the ``.egg-link`` file is.
-
-        The returned location is normalized (in particular, with symlinks removed).
-        """
-        raise NotImplementedError()
-
-    @property
-    def info_location(self) -> Optional[str]:
-        """Location of the .[egg|dist]-info directory or file.
-
-        Similarly to ``location``, a string value is not necessarily a
-        filesystem path. ``None`` means the distribution is created in-memory.
-
-        For a modern .dist-info installation on disk, this should be something
-        like ``{location}/{raw_name}-{version}.dist-info``.
-
-        Do not canonicalize this value with e.g. ``pathlib.Path.resolve()``. If
-        this is a symbolic link, we want to preserve the relative path between
-        it and other files in the distribution.
-        """
-        raise NotImplementedError()
-
-    @property
-    def installed_by_distutils(self) -> bool:
-        """Whether this distribution is installed with legacy distutils format.
-
-        A distribution installed with "raw" distutils not patched by setuptools
-        uses one single file at ``info_location`` to store metadata. We need to
-        treat this specially on uninstallation.
-        """
-        info_location = self.info_location
-        if not info_location:
-            return False
-        return pathlib.Path(info_location).is_file()
-
-    @property
-    def installed_as_egg(self) -> bool:
-        """Whether this distribution is installed as an egg.
-
-        This usually indicates the distribution was installed by (older versions
-        of) easy_install.
-        """
-        location = self.location
-        if not location:
-            return False
-        return location.endswith(".egg")
-
-    @property
-    def installed_with_setuptools_egg_info(self) -> bool:
-        """Whether this distribution is installed with the ``.egg-info`` format.
-
-        This usually indicates the distribution was installed with setuptools
-        with an old pip version or with ``single-version-externally-managed``.
-
-        Note that this ensure the metadata store is a directory. distutils can
-        also installs an ``.egg-info``, but as a file, not a directory. This
-        property is *False* for that case. Also see ``installed_by_distutils``.
-        """
-        info_location = self.info_location
-        if not info_location:
-            return False
-        if not info_location.endswith(".egg-info"):
-            return False
-        return pathlib.Path(info_location).is_dir()
-
-    @property
-    def installed_with_dist_info(self) -> bool:
-        """Whether this distribution is installed with the "modern format".
-
-        This indicates a "modern" installation, e.g. storing metadata in the
-        ``.dist-info`` directory. This applies to installations made by
-        setuptools (but through pip, not directly), or anything using the
-        standardized build backend interface (PEP 517).
-        """
-        info_location = self.info_location
-        if not info_location:
-            return False
-        if not info_location.endswith(".dist-info"):
-            return False
-        return pathlib.Path(info_location).is_dir()
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        raise NotImplementedError()
-
-    @property
-    def version(self) -> DistributionVersion:
-        raise NotImplementedError()
-
-    @property
-    def setuptools_filename(self) -> str:
-        """Convert a project name to its setuptools-compatible filename.
-
-        This is a copy of ``pkg_resources.to_filename()`` for compatibility.
-        """
-        return self.raw_name.replace("-", "_")
-
-    @property
-    def direct_url(self) -> Optional[DirectUrl]:
-        """Obtain a DirectUrl from this distribution.
-
-        Returns None if the distribution has no `direct_url.json` metadata,
-        or if `direct_url.json` is invalid.
-        """
-        try:
-            content = self.read_text(DIRECT_URL_METADATA_NAME)
-        except FileNotFoundError:
-            return None
-        try:
-            return DirectUrl.from_json(content)
-        except (
-            UnicodeDecodeError,
-            json.JSONDecodeError,
-            DirectUrlValidationError,
-        ) as e:
-            logger.warning(
-                "Error parsing %s for %s: %s",
-                DIRECT_URL_METADATA_NAME,
-                self.canonical_name,
-                e,
-            )
-            return None
-
-    @property
-    def installer(self) -> str:
-        try:
-            installer_text = self.read_text("INSTALLER")
-        except (OSError, ValueError, NoneMetadataError):
-            return ""  # Fail silently if the installer file cannot be read.
-        for line in installer_text.splitlines():
-            cleaned_line = line.strip()
-            if cleaned_line:
-                return cleaned_line
-        return ""
-
-    @property
-    def requested(self) -> bool:
-        return self.is_file("REQUESTED")
-
-    @property
-    def editable(self) -> bool:
-        return bool(self.editable_project_location)
-
-    @property
-    def local(self) -> bool:
-        """If distribution is installed in the current virtual environment.
-
-        Always True if we're not in a virtualenv.
-        """
-        if self.installed_location is None:
-            return False
-        return is_local(self.installed_location)
-
-    @property
-    def in_usersite(self) -> bool:
-        if self.installed_location is None or user_site is None:
-            return False
-        return self.installed_location.startswith(normalize_path(user_site))
-
-    @property
-    def in_site_packages(self) -> bool:
-        if self.installed_location is None or site_packages is None:
-            return False
-        return self.installed_location.startswith(normalize_path(site_packages))
-
-    def is_file(self, path: InfoPath) -> bool:
-        """Check whether an entry in the info directory is a file."""
-        raise NotImplementedError()
-
-    def iter_distutils_script_names(self) -> Iterator[str]:
-        """Find distutils 'scripts' entries metadata.
-
-        If 'scripts' is supplied in ``setup.py``, distutils records those in the
-        installed distribution's ``scripts`` directory, a file for each script.
-        """
-        raise NotImplementedError()
-
-    def read_text(self, path: InfoPath) -> str:
-        """Read a file in the info directory.
-
-        :raise FileNotFoundError: If ``path`` does not exist in the directory.
-        :raise NoneMetadataError: If ``path`` exists in the info directory, but
-            cannot be read.
-        """
-        raise NotImplementedError()
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        raise NotImplementedError()
-
-    def _metadata_impl(self) -> email.message.Message:
-        raise NotImplementedError()
-
-    @functools.lru_cache(maxsize=1)
-    def _metadata_cached(self) -> email.message.Message:
-        # When we drop python 3.7 support, move this to the metadata property and use
-        # functools.cached_property instead of lru_cache.
-        metadata = self._metadata_impl()
-        self._add_egg_info_requires(metadata)
-        return metadata
-
-    @property
-    def metadata(self) -> email.message.Message:
-        """Metadata of distribution parsed from e.g. METADATA or PKG-INFO.
-
-        This should return an empty message if the metadata file is unavailable.
-
-        :raises NoneMetadataError: If the metadata file is available, but does
-            not contain valid metadata.
-        """
-        return self._metadata_cached()
-
-    @property
-    def metadata_dict(self) -> Dict[str, Any]:
-        """PEP 566 compliant JSON-serializable representation of METADATA or PKG-INFO.
-
-        This should return an empty dict if the metadata file is unavailable.
-
-        :raises NoneMetadataError: If the metadata file is available, but does
-            not contain valid metadata.
-        """
-        return msg_to_json(self.metadata)
-
-    @property
-    def metadata_version(self) -> Optional[str]:
-        """Value of "Metadata-Version:" in distribution metadata, if available."""
-        return self.metadata.get("Metadata-Version")
-
-    @property
-    def raw_name(self) -> str:
-        """Value of "Name:" in distribution metadata."""
-        # The metadata should NEVER be missing the Name: key, but if it somehow
-        # does, fall back to the known canonical name.
-        return self.metadata.get("Name", self.canonical_name)
-
-    @property
-    def requires_python(self) -> SpecifierSet:
-        """Value of "Requires-Python:" in distribution metadata.
-
-        If the key does not exist or contains an invalid value, an empty
-        SpecifierSet should be returned.
-        """
-        value = self.metadata.get("Requires-Python")
-        if value is None:
-            return SpecifierSet()
-        try:
-            # Convert to str to satisfy the type checker; this can be a Header object.
-            spec = SpecifierSet(str(value))
-        except InvalidSpecifier as e:
-            message = "Package %r has an invalid Requires-Python: %s"
-            logger.warning(message, self.raw_name, e)
-            return SpecifierSet()
-        return spec
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        """Dependencies of this distribution.
-
-        For modern .dist-info distributions, this is the collection of
-        "Requires-Dist:" entries in distribution metadata.
-        """
-        raise NotImplementedError()
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        """Extras provided by this distribution.
-
-        For modern .dist-info distributions, this is the collection of
-        "Provides-Extra:" entries in distribution metadata.
-        """
-        raise NotImplementedError()
-
-    def _iter_declared_entries_from_record(self) -> Optional[Iterator[str]]:
-        try:
-            text = self.read_text("RECORD")
-        except FileNotFoundError:
-            return None
-        # This extra Path-str cast normalizes entries.
-        return (str(pathlib.Path(row[0])) for row in csv.reader(text.splitlines()))
-
-    def _iter_declared_entries_from_legacy(self) -> Optional[Iterator[str]]:
-        try:
-            text = self.read_text("installed-files.txt")
-        except FileNotFoundError:
-            return None
-        paths = (p for p in text.splitlines(keepends=False) if p)
-        root = self.location
-        info = self.info_location
-        if root is None or info is None:
-            return paths
-        try:
-            info_rel = pathlib.Path(info).relative_to(root)
-        except ValueError:  # info is not relative to root.
-            return paths
-        if not info_rel.parts:  # info *is* root.
-            return paths
-        return (
-            _convert_installed_files_path(pathlib.Path(p).parts, info_rel.parts)
-            for p in paths
-        )
-
-    def iter_declared_entries(self) -> Optional[Iterator[str]]:
-        """Iterate through file entries declared in this distribution.
-
-        For modern .dist-info distributions, this is the files listed in the
-        ``RECORD`` metadata file. For legacy setuptools distributions, this
-        comes from ``installed-files.txt``, with entries normalized to be
-        compatible with the format used by ``RECORD``.
-
-        :return: An iterator for listed entries, or None if the distribution
-            contains neither ``RECORD`` nor ``installed-files.txt``.
-        """
-        return (
-            self._iter_declared_entries_from_record()
-            or self._iter_declared_entries_from_legacy()
-        )
-
-    def _iter_requires_txt_entries(self) -> Iterator[RequiresEntry]:
-        """Parse a ``requires.txt`` in an egg-info directory.
-
-        This is an INI-ish format where an egg-info stores dependencies. A
-        section name describes extra other environment markers, while each entry
-        is an arbitrary string (not a key-value pair) representing a dependency
-        as a requirement string (no markers).
-
-        There is a construct in ``importlib.metadata`` called ``Sectioned`` that
-        does mostly the same, but the format is currently considered private.
-        """
-        try:
-            content = self.read_text("requires.txt")
-        except FileNotFoundError:
-            return
-        extra = marker = ""  # Section-less entries don't have markers.
-        for line in content.splitlines():
-            line = line.strip()
-            if not line or line.startswith("#"):  # Comment; ignored.
-                continue
-            if line.startswith("[") and line.endswith("]"):  # A section header.
-                extra, _, marker = line.strip("[]").partition(":")
-                continue
-            yield RequiresEntry(requirement=line, extra=extra, marker=marker)
-
-    def _iter_egg_info_extras(self) -> Iterable[str]:
-        """Get extras from the egg-info directory."""
-        known_extras = {""}
-        for entry in self._iter_requires_txt_entries():
-            if entry.extra in known_extras:
-                continue
-            known_extras.add(entry.extra)
-            yield entry.extra
-
-    def _iter_egg_info_dependencies(self) -> Iterable[str]:
-        """Get distribution dependencies from the egg-info directory.
-
-        To ease parsing, this converts a legacy dependency entry into a PEP 508
-        requirement string. Like ``_iter_requires_txt_entries()``, there is code
-        in ``importlib.metadata`` that does mostly the same, but not do exactly
-        what we need.
-
-        Namely, ``importlib.metadata`` does not normalize the extra name before
-        putting it into the requirement string, which causes marker comparison
-        to fail because the dist-info format do normalize. This is consistent in
-        all currently available PEP 517 backends, although not standardized.
-        """
-        for entry in self._iter_requires_txt_entries():
-            if entry.extra and entry.marker:
-                marker = f'({entry.marker}) and extra == "{safe_extra(entry.extra)}"'
-            elif entry.extra:
-                marker = f'extra == "{safe_extra(entry.extra)}"'
-            elif entry.marker:
-                marker = entry.marker
-            else:
-                marker = ""
-            if marker:
-                yield f"{entry.requirement} ; {marker}"
-            else:
-                yield entry.requirement
-
-    def _add_egg_info_requires(self, metadata: email.message.Message) -> None:
-        """Add egg-info requires.txt information to the metadata."""
-        if not metadata.get_all("Requires-Dist"):
-            for dep in self._iter_egg_info_dependencies():
-                metadata["Requires-Dist"] = dep
-        if not metadata.get_all("Provides-Extra"):
-            for extra in self._iter_egg_info_extras():
-                metadata["Provides-Extra"] = extra
-
-
-class BaseEnvironment:
-    """An environment containing distributions to introspect."""
-
-    @classmethod
-    def default(cls) -> "BaseEnvironment":
-        raise NotImplementedError()
-
-    @classmethod
-    def from_paths(cls, paths: Optional[List[str]]) -> "BaseEnvironment":
-        raise NotImplementedError()
-
-    def get_distribution(self, name: str) -> Optional["BaseDistribution"]:
-        """Given a requirement name, return the installed distributions.
-
-        The name may not be normalized. The implementation must canonicalize
-        it for lookup.
-        """
-        raise NotImplementedError()
-
-    def _iter_distributions(self) -> Iterator["BaseDistribution"]:
-        """Iterate through installed distributions.
-
-        This function should be implemented by subclass, but never called
-        directly. Use the public ``iter_distribution()`` instead, which
-        implements additional logic to make sure the distributions are valid.
-        """
-        raise NotImplementedError()
-
-    def iter_all_distributions(self) -> Iterator[BaseDistribution]:
-        """Iterate through all installed distributions without any filtering."""
-        for dist in self._iter_distributions():
-            # Make sure the distribution actually comes from a valid Python
-            # packaging distribution. Pip's AdjacentTempDirectory leaves folders
-            # e.g. ``~atplotlib.dist-info`` if cleanup was interrupted. The
-            # valid project name pattern is taken from PEP 508.
-            project_name_valid = re.match(
-                r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$",
-                dist.canonical_name,
-                flags=re.IGNORECASE,
-            )
-            if not project_name_valid:
-                logger.warning(
-                    "Ignoring invalid distribution %s (%s)",
-                    dist.canonical_name,
-                    dist.location,
-                )
-                continue
-            yield dist
-
-    def iter_installed_distributions(
-        self,
-        local_only: bool = True,
-        skip: Container[str] = stdlib_pkgs,
-        include_editables: bool = True,
-        editables_only: bool = False,
-        user_only: bool = False,
-    ) -> Iterator[BaseDistribution]:
-        """Return a list of installed distributions.
-
-        This is based on ``iter_all_distributions()`` with additional filtering
-        options. Note that ``iter_installed_distributions()`` without arguments
-        is *not* equal to ``iter_all_distributions()``, since some of the
-        configurations exclude packages by default.
-
-        :param local_only: If True (default), only return installations
-        local to the current virtualenv, if in a virtualenv.
-        :param skip: An iterable of canonicalized project names to ignore;
-            defaults to ``stdlib_pkgs``.
-        :param include_editables: If False, don't report editables.
-        :param editables_only: If True, only report editables.
-        :param user_only: If True, only report installations in the user
-        site directory.
-        """
-        it = self.iter_all_distributions()
-        if local_only:
-            it = (d for d in it if d.local)
-        if not include_editables:
-            it = (d for d in it if not d.editable)
-        if editables_only:
-            it = (d for d in it if d.editable)
-        if user_only:
-            it = (d for d in it if d.in_usersite)
-        return (d for d in it if d.canonical_name not in skip)
-
-
-class Wheel(Protocol):
-    location: str
-
-    def as_zipfile(self) -> zipfile.ZipFile:
-        raise NotImplementedError()
-
-
-class FilesystemWheel(Wheel):
-    def __init__(self, location: str) -> None:
-        self.location = location
-
-    def as_zipfile(self) -> zipfile.ZipFile:
-        return zipfile.ZipFile(self.location, allowZip64=True)
-
-
-class MemoryWheel(Wheel):
-    def __init__(self, location: str, stream: IO[bytes]) -> None:
-        self.location = location
-        self.stream = stream
-
-    def as_zipfile(self) -> zipfile.ZipFile:
-        return zipfile.ZipFile(self.stream, allowZip64=True)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/utf1632prober.py

@@ -1,225 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-#
-# Contributor(s):
-#   Jason Zavaglia
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-from typing import List, Union
-
-from .charsetprober import CharSetProber
-from .enums import ProbingState
-
-
-class UTF1632Prober(CharSetProber):
-    """
-    This class simply looks for occurrences of zero bytes, and infers
-    whether the file is UTF16 or UTF32 (low-endian or big-endian)
-    For instance, files looking like ( \0 \0 \0 [nonzero] )+
-    have a good probability to be UTF32BE.  Files looking like ( \0 [nonzero] )+
-    may be guessed to be UTF16BE, and inversely for little-endian varieties.
-    """
-
-    # how many logical characters to scan before feeling confident of prediction
-    MIN_CHARS_FOR_DETECTION = 20
-    # a fixed constant ratio of expected zeros or non-zeros in modulo-position.
-    EXPECTED_RATIO = 0.94
-
-    def __init__(self) -> None:
-        super().__init__()
-        self.position = 0
-        self.zeros_at_mod = [0] * 4
-        self.nonzeros_at_mod = [0] * 4
-        self._state = ProbingState.DETECTING
-        self.quad = [0, 0, 0, 0]
-        self.invalid_utf16be = False
-        self.invalid_utf16le = False
-        self.invalid_utf32be = False
-        self.invalid_utf32le = False
-        self.first_half_surrogate_pair_detected_16be = False
-        self.first_half_surrogate_pair_detected_16le = False
-        self.reset()
-
-    def reset(self) -> None:
-        super().reset()
-        self.position = 0
-        self.zeros_at_mod = [0] * 4
-        self.nonzeros_at_mod = [0] * 4
-        self._state = ProbingState.DETECTING
-        self.invalid_utf16be = False
-        self.invalid_utf16le = False
-        self.invalid_utf32be = False
-        self.invalid_utf32le = False
-        self.first_half_surrogate_pair_detected_16be = False
-        self.first_half_surrogate_pair_detected_16le = False
-        self.quad = [0, 0, 0, 0]
-
-    @property
-    def charset_name(self) -> str:
-        if self.is_likely_utf32be():
-            return "utf-32be"
-        if self.is_likely_utf32le():
-            return "utf-32le"
-        if self.is_likely_utf16be():
-            return "utf-16be"
-        if self.is_likely_utf16le():
-            return "utf-16le"
-        # default to something valid
-        return "utf-16"
-
-    @property
-    def language(self) -> str:
-        return ""
-
-    def approx_32bit_chars(self) -> float:
-        return max(1.0, self.position / 4.0)
-
-    def approx_16bit_chars(self) -> float:
-        return max(1.0, self.position / 2.0)
-
-    def is_likely_utf32be(self) -> bool:
-        approx_chars = self.approx_32bit_chars()
-        return approx_chars >= self.MIN_CHARS_FOR_DETECTION and (
-            self.zeros_at_mod[0] / approx_chars > self.EXPECTED_RATIO
-            and self.zeros_at_mod[1] / approx_chars > self.EXPECTED_RATIO
-            and self.zeros_at_mod[2] / approx_chars > self.EXPECTED_RATIO
-            and self.nonzeros_at_mod[3] / approx_chars > self.EXPECTED_RATIO
-            and not self.invalid_utf32be
-        )
-
-    def is_likely_utf32le(self) -> bool:
-        approx_chars = self.approx_32bit_chars()
-        return approx_chars >= self.MIN_CHARS_FOR_DETECTION and (
-            self.nonzeros_at_mod[0] / approx_chars > self.EXPECTED_RATIO
-            and self.zeros_at_mod[1] / approx_chars > self.EXPECTED_RATIO
-            and self.zeros_at_mod[2] / approx_chars > self.EXPECTED_RATIO
-            and self.zeros_at_mod[3] / approx_chars > self.EXPECTED_RATIO
-            and not self.invalid_utf32le
-        )
-
-    def is_likely_utf16be(self) -> bool:
-        approx_chars = self.approx_16bit_chars()
-        return approx_chars >= self.MIN_CHARS_FOR_DETECTION and (
-            (self.nonzeros_at_mod[1] + self.nonzeros_at_mod[3]) / approx_chars
-            > self.EXPECTED_RATIO
-            and (self.zeros_at_mod[0] + self.zeros_at_mod[2]) / approx_chars
-            > self.EXPECTED_RATIO
-            and not self.invalid_utf16be
-        )
-
-    def is_likely_utf16le(self) -> bool:
-        approx_chars = self.approx_16bit_chars()
-        return approx_chars >= self.MIN_CHARS_FOR_DETECTION and (
-            (self.nonzeros_at_mod[0] + self.nonzeros_at_mod[2]) / approx_chars
-            > self.EXPECTED_RATIO
-            and (self.zeros_at_mod[1] + self.zeros_at_mod[3]) / approx_chars
-            > self.EXPECTED_RATIO
-            and not self.invalid_utf16le
-        )
-
-    def validate_utf32_characters(self, quad: List[int]) -> None:
-        """
-        Validate if the quad of bytes is valid UTF-32.
-
-        UTF-32 is valid in the range 0x00000000 - 0x0010FFFF
-        excluding 0x0000D800 - 0x0000DFFF
-
-        https://en.wikipedia.org/wiki/UTF-32
-        """
-        if (
-            quad[0] != 0
-            or quad[1] > 0x10
-            or (quad[0] == 0 and quad[1] == 0 and 0xD8 <= quad[2] <= 0xDF)
-        ):
-            self.invalid_utf32be = True
-        if (
-            quad[3] != 0
-            or quad[2] > 0x10
-            or (quad[3] == 0 and quad[2] == 0 and 0xD8 <= quad[1] <= 0xDF)
-        ):
-            self.invalid_utf32le = True
-
-    def validate_utf16_characters(self, pair: List[int]) -> None:
-        """
-        Validate if the pair of bytes is  valid UTF-16.
-
-        UTF-16 is valid in the range 0x0000 - 0xFFFF excluding 0xD800 - 0xFFFF
-        with an exception for surrogate pairs, which must be in the range
-        0xD800-0xDBFF followed by 0xDC00-0xDFFF
-
-        https://en.wikipedia.org/wiki/UTF-16
-        """
-        if not self.first_half_surrogate_pair_detected_16be:
-            if 0xD8 <= pair[0] <= 0xDB:
-                self.first_half_surrogate_pair_detected_16be = True
-            elif 0xDC <= pair[0] <= 0xDF:
-                self.invalid_utf16be = True
-        else:
-            if 0xDC <= pair[0] <= 0xDF:
-                self.first_half_surrogate_pair_detected_16be = False
-            else:
-                self.invalid_utf16be = True
-
-        if not self.first_half_surrogate_pair_detected_16le:
-            if 0xD8 <= pair[1] <= 0xDB:
-                self.first_half_surrogate_pair_detected_16le = True
-            elif 0xDC <= pair[1] <= 0xDF:
-                self.invalid_utf16le = True
-        else:
-            if 0xDC <= pair[1] <= 0xDF:
-                self.first_half_surrogate_pair_detected_16le = False
-            else:
-                self.invalid_utf16le = True
-
-    def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
-        for c in byte_str:
-            mod4 = self.position % 4
-            self.quad[mod4] = c
-            if mod4 == 3:
-                self.validate_utf32_characters(self.quad)
-                self.validate_utf16_characters(self.quad[0:2])
-                self.validate_utf16_characters(self.quad[2:4])
-            if c == 0:
-                self.zeros_at_mod[mod4] += 1
-            else:
-                self.nonzeros_at_mod[mod4] += 1
-            self.position += 1
-        return self.state
-
-    @property
-    def state(self) -> ProbingState:
-        if self._state in {ProbingState.NOT_ME, ProbingState.FOUND_IT}:
-            # terminal, decided states
-            return self._state
-        if self.get_confidence() > 0.80:
-            self._state = ProbingState.FOUND_IT
-        elif self.position > 4 * 1024:
-            # if we get to 4kb into the file, and we can't conclude it's UTF,
-            # let's give up
-            self._state = ProbingState.NOT_ME
-        return self._state
-
-    def get_confidence(self) -> float:
-        return (
-            0.85
-            if (
-                self.is_likely_utf16le()
-                or self.is_likely_utf16be()
-                or self.is_likely_utf32le()
-                or self.is_likely_utf32be()
-            )
-            else 0.00
-        )

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/other.py

@@ -1,161 +0,0 @@
-"""
-    pygments.formatters.other
-    ~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Other formatters: NullFormatter, RawTokenFormatter.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.util import get_choice_opt
-from pip._vendor.pygments.token import Token
-from pip._vendor.pygments.console import colorize
-
-__all__ = ['NullFormatter', 'RawTokenFormatter', 'TestcaseFormatter']
-
-
-class NullFormatter(Formatter):
-    """
-    Output the text unchanged without any formatting.
-    """
-    name = 'Text only'
-    aliases = ['text', 'null']
-    filenames = ['*.txt']
-
-    def format(self, tokensource, outfile):
-        enc = self.encoding
-        for ttype, value in tokensource:
-            if enc:
-                outfile.write(value.encode(enc))
-            else:
-                outfile.write(value)
-
-
-class RawTokenFormatter(Formatter):
-    r"""
-    Format tokens as a raw representation for storing token streams.
-
-    The format is ``tokentype<TAB>repr(tokenstring)\n``. The output can later
-    be converted to a token stream with the `RawTokenLexer`, described in the
-    :doc:`lexer list <lexers>`.
-
-    Only two options are accepted:
-
-    `compress`
-        If set to ``'gz'`` or ``'bz2'``, compress the output with the given
-        compression algorithm after encoding (default: ``''``).
-    `error_color`
-        If set to a color name, highlight error tokens using that color.  If
-        set but with no value, defaults to ``'red'``.
-
-        .. versionadded:: 0.11
-
-    """
-    name = 'Raw tokens'
-    aliases = ['raw', 'tokens']
-    filenames = ['*.raw']
-
-    unicodeoutput = False
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        # We ignore self.encoding if it is set, since it gets set for lexer
-        # and formatter if given with -Oencoding on the command line.
-        # The RawTokenFormatter outputs only ASCII. Override here.
-        self.encoding = 'ascii'  # let pygments.format() do the right thing
-        self.compress = get_choice_opt(options, 'compress',
-                                       ['', 'none', 'gz', 'bz2'], '')
-        self.error_color = options.get('error_color', None)
-        if self.error_color is True:
-            self.error_color = 'red'
-        if self.error_color is not None:
-            try:
-                colorize(self.error_color, '')
-            except KeyError:
-                raise ValueError("Invalid color %r specified" %
-                                 self.error_color)
-
-    def format(self, tokensource, outfile):
-        try:
-            outfile.write(b'')
-        except TypeError:
-            raise TypeError('The raw tokens formatter needs a binary '
-                            'output file')
-        if self.compress == 'gz':
-            import gzip
-            outfile = gzip.GzipFile('', 'wb', 9, outfile)
-
-            write = outfile.write
-            flush = outfile.close
-        elif self.compress == 'bz2':
-            import bz2
-            compressor = bz2.BZ2Compressor(9)
-
-            def write(text):
-                outfile.write(compressor.compress(text))
-
-            def flush():
-                outfile.write(compressor.flush())
-                outfile.flush()
-        else:
-            write = outfile.write
-            flush = outfile.flush
-
-        if self.error_color:
-            for ttype, value in tokensource:
-                line = b"%r\t%r\n" % (ttype, value)
-                if ttype is Token.Error:
-                    write(colorize(self.error_color, line))
-                else:
-                    write(line)
-        else:
-            for ttype, value in tokensource:
-                write(b"%r\t%r\n" % (ttype, value))
-        flush()
-
-
-TESTCASE_BEFORE = '''\
-    def testNeedsName(lexer):
-        fragment = %r
-        tokens = [
-'''
-TESTCASE_AFTER = '''\
-        ]
-        assert list(lexer.get_tokens(fragment)) == tokens
-'''
-
-
-class TestcaseFormatter(Formatter):
-    """
-    Format tokens as appropriate for a new testcase.
-
-    .. versionadded:: 2.0
-    """
-    name = 'Testcase'
-    aliases = ['testcase']
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        if self.encoding is not None and self.encoding != 'utf-8':
-            raise ValueError("Only None and utf-8 are allowed encodings.")
-
-    def format(self, tokensource, outfile):
-        indentation = ' ' * 12
-        rawbuf = []
-        outbuf = []
-        for ttype, value in tokensource:
-            rawbuf.append(value)
-            outbuf.append('%s(%s, %r),\n' % (indentation, ttype, value))
-
-        before = TESTCASE_BEFORE % (''.join(rawbuf),)
-        during = ''.join(outbuf)
-        after = TESTCASE_AFTER
-        if self.encoding is None:
-            outfile.write(before + during + after)
-        else:
-            outfile.write(before.encode('utf-8'))
-            outfile.write(during.encode('utf-8'))
-            outfile.write(after.encode('utf-8'))
-        outfile.flush()

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/sphinxext.py

@@ -1,217 +0,0 @@
-"""
-    pygments.sphinxext
-    ~~~~~~~~~~~~~~~~~~
-
-    Sphinx extension to generate automatic documentation of lexers,
-    formatters and filters.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import sys
-
-from docutils import nodes
-from docutils.statemachine import ViewList
-from docutils.parsers.rst import Directive
-from sphinx.util.nodes import nested_parse_with_titles
-
-
-MODULEDOC = '''
-.. module:: %s
-
-%s
-%s
-'''
-
-LEXERDOC = '''
-.. class:: %s
-
-    :Short names: %s
-    :Filenames:   %s
-    :MIME types:  %s
-
-    %s
-
-'''
-
-FMTERDOC = '''
-.. class:: %s
-
-    :Short names: %s
-    :Filenames: %s
-
-    %s
-
-'''
-
-FILTERDOC = '''
-.. class:: %s
-
-    :Name: %s
-
-    %s
-
-'''
-
-
-class PygmentsDoc(Directive):
-    """
-    A directive to collect all lexers/formatters/filters and generate
-    autoclass directives for them.
-    """
-    has_content = False
-    required_arguments = 1
-    optional_arguments = 0
-    final_argument_whitespace = False
-    option_spec = {}
-
-    def run(self):
-        self.filenames = set()
-        if self.arguments[0] == 'lexers':
-            out = self.document_lexers()
-        elif self.arguments[0] == 'formatters':
-            out = self.document_formatters()
-        elif self.arguments[0] == 'filters':
-            out = self.document_filters()
-        elif self.arguments[0] == 'lexers_overview':
-            out = self.document_lexers_overview()
-        else:
-            raise Exception('invalid argument for "pygmentsdoc" directive')
-        node = nodes.compound()
-        vl = ViewList(out.split('\n'), source='')
-        nested_parse_with_titles(self.state, vl, node)
-        for fn in self.filenames:
-            self.state.document.settings.record_dependencies.add(fn)
-        return node.children
-
-    def document_lexers_overview(self):
-        """Generate a tabular overview of all lexers.
-
-        The columns are the lexer name, the extensions handled by this lexer
-        (or "None"), the aliases and a link to the lexer class."""
-        from pip._vendor.pygments.lexers._mapping import LEXERS
-        from pip._vendor.pygments.lexers import find_lexer_class
-        out = []
-
-        table = []
-
-        def format_link(name, url):
-            if url:
-                return f'`{name} <{url}>`_'
-            return name
-
-        for classname, data in sorted(LEXERS.items(), key=lambda x: x[1][1].lower()):
-            lexer_cls = find_lexer_class(data[1])
-            extensions = lexer_cls.filenames + lexer_cls.alias_filenames
-
-            table.append({
-                'name': format_link(data[1], lexer_cls.url),
-                'extensions': ', '.join(extensions).replace('*', '\\*').replace('_', '\\') or 'None',
-                'aliases': ', '.join(data[2]),
-                'class': f'{data[0]}.{classname}'
-            })
-
-        column_names = ['name', 'extensions', 'aliases', 'class']
-        column_lengths = [max([len(row[column]) for row in table if row[column]])
-                          for column in column_names]
-
-        def write_row(*columns):
-            """Format a table row"""
-            out = []
-            for l, c in zip(column_lengths, columns):
-                if c:
-                    out.append(c.ljust(l))
-                else:
-                    out.append(' '*l)
-
-            return ' '.join(out)
-
-        def write_seperator():
-            """Write a table separator row"""
-            sep = ['='*c for c in column_lengths]
-            return write_row(*sep)
-
-        out.append(write_seperator())
-        out.append(write_row('Name', 'Extension(s)', 'Short name(s)', 'Lexer class'))
-        out.append(write_seperator())
-        for row in table:
-            out.append(write_row(
-                row['name'],
-                row['extensions'],
-                row['aliases'],
-                f':class:`~{row["class"]}`'))
-        out.append(write_seperator())
-
-        return '\n'.join(out)
-
-    def document_lexers(self):
-        from pip._vendor.pygments.lexers._mapping import LEXERS
-        out = []
-        modules = {}
-        moduledocstrings = {}
-        for classname, data in sorted(LEXERS.items(), key=lambda x: x[0]):
-            module = data[0]
-            mod = __import__(module, None, None, [classname])
-            self.filenames.add(mod.__file__)
-            cls = getattr(mod, classname)
-            if not cls.__doc__:
-                print("Warning: %s does not have a docstring." % classname)
-            docstring = cls.__doc__
-            if isinstance(docstring, bytes):
-                docstring = docstring.decode('utf8')
-            modules.setdefault(module, []).append((
-                classname,
-                ', '.join(data[2]) or 'None',
-                ', '.join(data[3]).replace('*', '\\*').replace('_', '\\') or 'None',
-                ', '.join(data[4]) or 'None',
-                docstring))
-            if module not in moduledocstrings:
-                moddoc = mod.__doc__
-                if isinstance(moddoc, bytes):
-                    moddoc = moddoc.decode('utf8')
-                moduledocstrings[module] = moddoc
-
-        for module, lexers in sorted(modules.items(), key=lambda x: x[0]):
-            if moduledocstrings[module] is None:
-                raise Exception("Missing docstring for %s" % (module,))
-            heading = moduledocstrings[module].splitlines()[4].strip().rstrip('.')
-            out.append(MODULEDOC % (module, heading, '-'*len(heading)))
-            for data in lexers:
-                out.append(LEXERDOC % data)
-
-        return ''.join(out)
-
-    def document_formatters(self):
-        from pip._vendor.pygments.formatters import FORMATTERS
-
-        out = []
-        for classname, data in sorted(FORMATTERS.items(), key=lambda x: x[0]):
-            module = data[0]
-            mod = __import__(module, None, None, [classname])
-            self.filenames.add(mod.__file__)
-            cls = getattr(mod, classname)
-            docstring = cls.__doc__
-            if isinstance(docstring, bytes):
-                docstring = docstring.decode('utf8')
-            heading = cls.__name__
-            out.append(FMTERDOC % (heading, ', '.join(data[2]) or 'None',
-                                   ', '.join(data[3]).replace('*', '\\*') or 'None',
-                                   docstring))
-        return ''.join(out)
-
-    def document_filters(self):
-        from pip._vendor.pygments.filters import FILTERS
-
-        out = []
-        for name, cls in FILTERS.items():
-            self.filenames.add(sys.modules[cls.__module__].__file__)
-            docstring = cls.__doc__
-            if isinstance(docstring, bytes):
-                docstring = docstring.decode('utf8')
-            out.append(FILTERDOC % (cls.__name__, name, docstring))
-        return ''.join(out)
-
-
-def setup(app):
-    app.add_directive('pygmentsdoc', PygmentsDoc)

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

@@ -1,331 +0,0 @@
-# testing.py
-
-from contextlib import contextmanager
-import typing
-
-from .core import (
-    ParserElement,
-    ParseException,
-    Keyword,
-    __diag__,
-    __compat__,
-)
-
-
-class pyparsing_test:
-    """
-    namespace class for classes useful in writing unit tests
-    """
-
-    class reset_pyparsing_context:
-        """
-        Context manager to be used when writing unit tests that modify pyparsing config values:
-        - packrat parsing
-        - bounded recursion parsing
-        - default whitespace characters.
-        - default keyword characters
-        - literal string auto-conversion class
-        - __diag__ settings
-
-        Example::
-
-            with reset_pyparsing_context():
-                # test that literals used to construct a grammar are automatically suppressed
-                ParserElement.inlineLiteralsUsing(Suppress)
-
-                term = Word(alphas) | Word(nums)
-                group = Group('(' + term[...] + ')')
-
-                # assert that the '()' characters are not included in the parsed tokens
-                self.assertParseAndCheckList(group, "(abc 123 def)", ['abc', '123', 'def'])
-
-            # after exiting context manager, literals are converted to Literal expressions again
-        """
-
-        def __init__(self):
-            self._save_context = {}
-
-        def save(self):
-            self._save_context["default_whitespace"] = ParserElement.DEFAULT_WHITE_CHARS
-            self._save_context["default_keyword_chars"] = Keyword.DEFAULT_KEYWORD_CHARS
-
-            self._save_context[
-                "literal_string_class"
-            ] = ParserElement._literalStringClass
-
-            self._save_context["verbose_stacktrace"] = ParserElement.verbose_stacktrace
-
-            self._save_context["packrat_enabled"] = ParserElement._packratEnabled
-            if ParserElement._packratEnabled:
-                self._save_context[
-                    "packrat_cache_size"
-                ] = ParserElement.packrat_cache.size
-            else:
-                self._save_context["packrat_cache_size"] = None
-            self._save_context["packrat_parse"] = ParserElement._parse
-            self._save_context[
-                "recursion_enabled"
-            ] = ParserElement._left_recursion_enabled
-
-            self._save_context["__diag__"] = {
-                name: getattr(__diag__, name) for name in __diag__._all_names
-            }
-
-            self._save_context["__compat__"] = {
-                "collect_all_And_tokens": __compat__.collect_all_And_tokens
-            }
-
-            return self
-
-        def restore(self):
-            # reset pyparsing global state
-            if (
-                ParserElement.DEFAULT_WHITE_CHARS
-                != self._save_context["default_whitespace"]
-            ):
-                ParserElement.set_default_whitespace_chars(
-                    self._save_context["default_whitespace"]
-                )
-
-            ParserElement.verbose_stacktrace = self._save_context["verbose_stacktrace"]
-
-            Keyword.DEFAULT_KEYWORD_CHARS = self._save_context["default_keyword_chars"]
-            ParserElement.inlineLiteralsUsing(
-                self._save_context["literal_string_class"]
-            )
-
-            for name, value in self._save_context["__diag__"].items():
-                (__diag__.enable if value else __diag__.disable)(name)
-
-            ParserElement._packratEnabled = False
-            if self._save_context["packrat_enabled"]:
-                ParserElement.enable_packrat(self._save_context["packrat_cache_size"])
-            else:
-                ParserElement._parse = self._save_context["packrat_parse"]
-            ParserElement._left_recursion_enabled = self._save_context[
-                "recursion_enabled"
-            ]
-
-            __compat__.collect_all_And_tokens = self._save_context["__compat__"]
-
-            return self
-
-        def copy(self):
-            ret = type(self)()
-            ret._save_context.update(self._save_context)
-            return ret
-
-        def __enter__(self):
-            return self.save()
-
-        def __exit__(self, *args):
-            self.restore()
-
-    class TestParseResultsAsserts:
-        """
-        A mixin class to add parse results assertion methods to normal unittest.TestCase classes.
-        """
-
-        def assertParseResultsEquals(
-            self, result, expected_list=None, expected_dict=None, msg=None
-        ):
-            """
-            Unit test assertion to compare a :class:`ParseResults` object with an optional ``expected_list``,
-            and compare any defined results names with an optional ``expected_dict``.
-            """
-            if expected_list is not None:
-                self.assertEqual(expected_list, result.as_list(), msg=msg)
-            if expected_dict is not None:
-                self.assertEqual(expected_dict, result.as_dict(), msg=msg)
-
-        def assertParseAndCheckList(
-            self, expr, test_string, expected_list, msg=None, verbose=True
-        ):
-            """
-            Convenience wrapper assert to test a parser element and input string, and assert that
-            the resulting ``ParseResults.asList()`` is equal to the ``expected_list``.
-            """
-            result = expr.parse_string(test_string, parse_all=True)
-            if verbose:
-                print(result.dump())
-            else:
-                print(result.as_list())
-            self.assertParseResultsEquals(result, expected_list=expected_list, msg=msg)
-
-        def assertParseAndCheckDict(
-            self, expr, test_string, expected_dict, msg=None, verbose=True
-        ):
-            """
-            Convenience wrapper assert to test a parser element and input string, and assert that
-            the resulting ``ParseResults.asDict()`` is equal to the ``expected_dict``.
-            """
-            result = expr.parse_string(test_string, parseAll=True)
-            if verbose:
-                print(result.dump())
-            else:
-                print(result.as_list())
-            self.assertParseResultsEquals(result, expected_dict=expected_dict, msg=msg)
-
-        def assertRunTestResults(
-            self, run_tests_report, expected_parse_results=None, msg=None
-        ):
-            """
-            Unit test assertion to evaluate output of ``ParserElement.runTests()``. If a list of
-            list-dict tuples is given as the ``expected_parse_results`` argument, then these are zipped
-            with the report tuples returned by ``runTests`` and evaluated using ``assertParseResultsEquals``.
-            Finally, asserts that the overall ``runTests()`` success value is ``True``.
-
-            :param run_tests_report: tuple(bool, [tuple(str, ParseResults or Exception)]) returned from runTests
-            :param expected_parse_results (optional): [tuple(str, list, dict, Exception)]
-            """
-            run_test_success, run_test_results = run_tests_report
-
-            if expected_parse_results is not None:
-                merged = [
-                    (*rpt, expected)
-                    for rpt, expected in zip(run_test_results, expected_parse_results)
-                ]
-                for test_string, result, expected in merged:
-                    # expected should be a tuple containing a list and/or a dict or an exception,
-                    # and optional failure message string
-                    # an empty tuple will skip any result validation
-                    fail_msg = next(
-                        (exp for exp in expected if isinstance(exp, str)), None
-                    )
-                    expected_exception = next(
-                        (
-                            exp
-                            for exp in expected
-                            if isinstance(exp, type) and issubclass(exp, Exception)
-                        ),
-                        None,
-                    )
-                    if expected_exception is not None:
-                        with self.assertRaises(
-                            expected_exception=expected_exception, msg=fail_msg or msg
-                        ):
-                            if isinstance(result, Exception):
-                                raise result
-                    else:
-                        expected_list = next(
-                            (exp for exp in expected if isinstance(exp, list)), None
-                        )
-                        expected_dict = next(
-                            (exp for exp in expected if isinstance(exp, dict)), None
-                        )
-                        if (expected_list, expected_dict) != (None, None):
-                            self.assertParseResultsEquals(
-                                result,
-                                expected_list=expected_list,
-                                expected_dict=expected_dict,
-                                msg=fail_msg or msg,
-                            )
-                        else:
-                            # warning here maybe?
-                            print("no validation for {!r}".format(test_string))
-
-            # do this last, in case some specific test results can be reported instead
-            self.assertTrue(
-                run_test_success, msg=msg if msg is not None else "failed runTests"
-            )
-
-        @contextmanager
-        def assertRaisesParseException(self, exc_type=ParseException, msg=None):
-            with self.assertRaises(exc_type, msg=msg):
-                yield
-
-    @staticmethod
-    def with_line_numbers(
-        s: str,
-        start_line: typing.Optional[int] = None,
-        end_line: typing.Optional[int] = None,
-        expand_tabs: bool = True,
-        eol_mark: str = "|",
-        mark_spaces: typing.Optional[str] = None,
-        mark_control: typing.Optional[str] = None,
-    ) -> str:
-        """
-        Helpful method for debugging a parser - prints a string with line and column numbers.
-        (Line and column numbers are 1-based.)
-
-        :param s: tuple(bool, str - string to be printed with line and column numbers
-        :param start_line: int - (optional) starting line number in s to print (default=1)
-        :param end_line: int - (optional) ending line number in s to print (default=len(s))
-        :param expand_tabs: bool - (optional) expand tabs to spaces, to match the pyparsing default
-        :param eol_mark: str - (optional) string to mark the end of lines, helps visualize trailing spaces (default="|")
-        :param mark_spaces: str - (optional) special character to display in place of spaces
-        :param mark_control: str - (optional) convert non-printing control characters to a placeholding
-                                 character; valid values:
-                                 - "unicode" - replaces control chars with Unicode symbols, such as "␍" and "␊"
-                                 - any single character string - replace control characters with given string
-                                 - None (default) - string is displayed as-is
-
-        :return: str - input string with leading line numbers and column number headers
-        """
-        if expand_tabs:
-            s = s.expandtabs()
-        if mark_control is not None:
-            if mark_control == "unicode":
-                tbl = str.maketrans(
-                    {c: u for c, u in zip(range(0, 33), range(0x2400, 0x2433))}
-                    | {127: 0x2421}
-                )
-                eol_mark = ""
-            else:
-                tbl = str.maketrans(
-                    {c: mark_control for c in list(range(0, 32)) + [127]}
-                )
-            s = s.translate(tbl)
-        if mark_spaces is not None and mark_spaces != " ":
-            if mark_spaces == "unicode":
-                tbl = str.maketrans({9: 0x2409, 32: 0x2423})
-                s = s.translate(tbl)
-            else:
-                s = s.replace(" ", mark_spaces)
-        if start_line is None:
-            start_line = 1
-        if end_line is None:
-            end_line = len(s)
-        end_line = min(end_line, len(s))
-        start_line = min(max(1, start_line), end_line)
-
-        if mark_control != "unicode":
-            s_lines = s.splitlines()[start_line - 1 : end_line]
-        else:
-            s_lines = [line + "␊" for line in s.split("␊")[start_line - 1 : end_line]]
-        if not s_lines:
-            return ""
-
-        lineno_width = len(str(end_line))
-        max_line_len = max(len(line) for line in s_lines)
-        lead = " " * (lineno_width + 1)
-        if max_line_len >= 99:
-            header0 = (
-                lead
-                + "".join(
-                    "{}{}".format(" " * 99, (i + 1) % 100)
-                    for i in range(max(max_line_len // 100, 1))
-                )
-                + "\n"
-            )
-        else:
-            header0 = ""
-        header1 = (
-            header0
-            + lead
-            + "".join(
-                "         {}".format((i + 1) % 10)
-                for i in range(-(-max_line_len // 10))
-            )
-            + "\n"
-        )
-        header2 = lead + "1234567890" * (-(-max_line_len // 10)) + "\n"
-        return (
-            header1
-            + header2
-            + "\n".join(
-                "{:{}d}:{}{}".format(i, lineno_width, line, eol_mark)
-                for i, line in enumerate(s_lines, start=start_line)
-            )
-            + "\n"
-        )

spaces/Blessin/drama-director/app.py

@@ -1,56 +0,0 @@
-import gradio as gr
-import openai
-from gtts import gTTS
-import tempfile
-
-def generate_stage_directions(location, situation, api_key):
-    prompt = (
-        f"Write detailed stage directions for a scene with 5 characters, set in a {location}. "
-        "You are reading these directions out loud to an audience, so keep the stage directions conversational. "
-        "Do not break it down into different sections. Each character enters one by one, two of them enter as a couple. "
-        "As they enter, tell us their name and describe their physical characteristics, their emotional state and their actions, "
-        "gestures and movements in the scene. Write detailed stage direction on how they interact with the location they are in "
-        "and with each other, with detailed description on their movements, actions and gestures in the scene. Make the overall "
-        "scene highly dramatic, full of twists and turns, with lots of movement by the characters that keep changing positions "
-        "and moving around. At some point, a {situation} happens in the scene. Show the characters interacting with elements of "
-        "the location. Describe in vivid detail their emotion, facial expressions and emotions. You will also write dialogues for "
-        "each character. Keep the dialogues short. Keep the scene mostly non-verbal, with only a few dialogues. Make the scene "
-        "very dramatic, emotional, thrilling. Keep your response limited to 750 words."
-    )
-    
-    openai.api_key = api_key  # Set the API key from the user input
-    
-    try:
-        response = openai.Completion.create(
-            engine="text-davinci-003",
-            prompt=prompt,
-            max_tokens=750,
-            temperature=0.7,
-        )
-        stage_directions = response.choices[0].text.strip()
-        response_audio_path = text_to_audio(stage_directions)
-        return response_audio_path
-    except Exception as e:
-        return str(e)
-
-def text_to_audio(text):
-    tts = gTTS(text, lang='en')
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3')
-    tts.save(temp_file.name)
-    return temp_file.name
-
-# Create Gradio UI
-iface = gr.Interface(
-    fn=generate_stage_directions,
-    inputs=[
-        gr.Textbox(label="Location"),
-        gr.Textbox(label="Situation"),
-        gr.Textbox(label="API Key") 
-    ],
-    outputs=gr.Audio(type='filepath', label="Stage Directions"),
-    live=True,
-    title="DramaDirector",
-    description="Input a location, situation, and your OpenAI API key to generate stage directions.",
-)
-
-iface.launch()

spaces/Boadiwaa/Recipes/openai/error.py

@@ -1,164 +0,0 @@
-import openai
-
-
-class OpenAIError(Exception):
-    def __init__(
-        self,
-        message=None,
-        http_body=None,
-        http_status=None,
-        json_body=None,
-        headers=None,
-        code=None,
-    ):
-        super(OpenAIError, self).__init__(message)
-
-        if http_body and hasattr(http_body, "decode"):
-            try:
-                http_body = http_body.decode("utf-8")
-            except BaseException:
-                http_body = (
-                    "<Could not decode body as utf-8. "
-                    "Please report to [email protected]>"
-                )
-
-        self._message = message
-        self.http_body = http_body
-        self.http_status = http_status
-        self.json_body = json_body
-        self.headers = headers or {}
-        self.code = code
-        self.request_id = self.headers.get("request-id", None)
-        self.error = self.construct_error_object()
-        self.organization = self.headers.get("openai-organization", None)
-
-    def __str__(self):
-        msg = self._message or "<empty message>"
-        if self.request_id is not None:
-            return "Request {0}: {1}".format(self.request_id, msg)
-        else:
-            return msg
-
-    # Returns the underlying `Exception` (base class) message, which is usually
-    # the raw message returned by OpenAI's API. This was previously available
-    # in python2 via `error.message`. Unlike `str(error)`, it omits "Request
-    # req_..." from the beginning of the string.
-    @property
-    def user_message(self):
-        return self._message
-
-    def __repr__(self):
-        return "%s(message=%r, http_status=%r, request_id=%r)" % (
-            self.__class__.__name__,
-            self._message,
-            self.http_status,
-            self.request_id,
-        )
-
-    def construct_error_object(self):
-        if (
-            self.json_body is None
-            or "error" not in self.json_body
-            or not isinstance(self.json_body["error"], dict)
-        ):
-            return None
-
-        return openai.api_resources.error_object.ErrorObject.construct_from(
-            self.json_body["error"]
-        )
-
-
-class APIError(OpenAIError):
-    pass
-
-
-class TryAgain(OpenAIError):
-    pass
-
-
-class APIConnectionError(OpenAIError):
-    def __init__(
-        self,
-        message,
-        http_body=None,
-        http_status=None,
-        json_body=None,
-        headers=None,
-        code=None,
-        should_retry=False,
-    ):
-        super(APIConnectionError, self).__init__(
-            message, http_body, http_status, json_body, headers, code
-        )
-        self.should_retry = should_retry
-
-
-class InvalidRequestError(OpenAIError):
-    def __init__(
-        self,
-        message,
-        param,
-        code=None,
-        http_body=None,
-        http_status=None,
-        json_body=None,
-        headers=None,
-    ):
-        super(InvalidRequestError, self).__init__(
-            message, http_body, http_status, json_body, headers, code
-        )
-        self.param = param
-
-    def __repr__(self):
-        return "%s(message=%r, param=%r, code=%r, http_status=%r, " "request_id=%r)" % (
-            self.__class__.__name__,
-            self._message,
-            self.param,
-            self.code,
-            self.http_status,
-            self.request_id,
-        )
-
-    def __reduce__(self):
-        return type(self), (
-            self._message,
-            self.param,
-            self.code,
-            self.http_body,
-            self.http_status,
-            self.json_body,
-            self.headers,
-        )
-
-
-class AuthenticationError(OpenAIError):
-    pass
-
-
-class PermissionError(OpenAIError):
-    pass
-
-
-class RateLimitError(OpenAIError):
-    pass
-
-
-class ServiceUnavailableError(OpenAIError):
-    pass
-
-
-class InvalidAPIType(OpenAIError):
-    pass
-
-
-class SignatureVerificationError(OpenAIError):
-    def __init__(self, message, sig_header, http_body=None):
-        super(SignatureVerificationError, self).__init__(message, http_body)
-        self.sig_header = sig_header
-
-    def __reduce__(self):
-        return type(self), (
-            self._message,
-            self.sig_header,
-            self.http_body,
-        )

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/__init__.py

@@ -1,18 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from . import transforms  # isort:skip
-
-from .build import (
-    build_detection_test_loader,
-    build_detection_train_loader,
-    get_detection_dataset_dicts,
-    load_proposals_into_dataset,
-    print_instances_class_histogram,
-)
-from .catalog import DatasetCatalog, MetadataCatalog
-from .common import DatasetFromList, MapDataset
-from .dataset_mapper import DatasetMapper
-
-# ensure the builtin datasets are registered
-from . import datasets, samplers  # isort:skip
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]

spaces/CVPR/LIVE/pybind11/tests/test_kwargs_and_defaults.py

@@ -1,192 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-
-import env  # noqa: F401
-
-from pybind11_tests import kwargs_and_defaults as m
-
-
-def test_function_signatures(doc):
-    assert doc(m.kw_func0) == "kw_func0(arg0: int, arg1: int) -> str"
-    assert doc(m.kw_func1) == "kw_func1(x: int, y: int) -> str"
-    assert doc(m.kw_func2) == "kw_func2(x: int = 100, y: int = 200) -> str"
-    assert doc(m.kw_func3) == "kw_func3(data: str = 'Hello world!') -> None"
-    assert doc(m.kw_func4) == "kw_func4(myList: List[int] = [13, 17]) -> str"
-    assert doc(m.kw_func_udl) == "kw_func_udl(x: int, y: int = 300) -> str"
-    assert doc(m.kw_func_udl_z) == "kw_func_udl_z(x: int, y: int = 0) -> str"
-    assert doc(m.args_function) == "args_function(*args) -> tuple"
-    assert doc(m.args_kwargs_function) == "args_kwargs_function(*args, **kwargs) -> tuple"
-    assert doc(m.KWClass.foo0) == \
-        "foo0(self: m.kwargs_and_defaults.KWClass, arg0: int, arg1: float) -> None"
-    assert doc(m.KWClass.foo1) == \
-        "foo1(self: m.kwargs_and_defaults.KWClass, x: int, y: float) -> None"
-
-
-def test_named_arguments(msg):
-    assert m.kw_func0(5, 10) == "x=5, y=10"
-
-    assert m.kw_func1(5, 10) == "x=5, y=10"
-    assert m.kw_func1(5, y=10) == "x=5, y=10"
-    assert m.kw_func1(y=10, x=5) == "x=5, y=10"
-
-    assert m.kw_func2() == "x=100, y=200"
-    assert m.kw_func2(5) == "x=5, y=200"
-    assert m.kw_func2(x=5) == "x=5, y=200"
-    assert m.kw_func2(y=10) == "x=100, y=10"
-    assert m.kw_func2(5, 10) == "x=5, y=10"
-    assert m.kw_func2(x=5, y=10) == "x=5, y=10"
-
-    with pytest.raises(TypeError) as excinfo:
-        # noinspection PyArgumentList
-        m.kw_func2(x=5, y=10, z=12)
-    assert excinfo.match(
-        r'(?s)^kw_func2\(\): incompatible.*Invoked with: kwargs: ((x=5|y=10|z=12)(, |$))' + '{3}$')
-
-    assert m.kw_func4() == "{13 17}"
-    assert m.kw_func4(myList=[1, 2, 3]) == "{1 2 3}"
-
-    assert m.kw_func_udl(x=5, y=10) == "x=5, y=10"
-    assert m.kw_func_udl_z(x=5) == "x=5, y=0"
-
-
-def test_arg_and_kwargs():
-    args = 'arg1_value', 'arg2_value', 3
-    assert m.args_function(*args) == args
-
-    args = 'a1', 'a2'
-    kwargs = dict(arg3='a3', arg4=4)
-    assert m.args_kwargs_function(*args, **kwargs) == (args, kwargs)
-
-
-def test_mixed_args_and_kwargs(msg):
-    mpa = m.mixed_plus_args
-    mpk = m.mixed_plus_kwargs
-    mpak = m.mixed_plus_args_kwargs
-    mpakd = m.mixed_plus_args_kwargs_defaults
-
-    assert mpa(1, 2.5, 4, 99.5, None) == (1, 2.5, (4, 99.5, None))
-    assert mpa(1, 2.5) == (1, 2.5, ())
-    with pytest.raises(TypeError) as excinfo:
-        assert mpa(1)
-    assert msg(excinfo.value) == """
-        mixed_plus_args(): incompatible function arguments. The following argument types are supported:
-            1. (arg0: int, arg1: float, *args) -> tuple
-
-        Invoked with: 1
-    """  # noqa: E501 line too long
-    with pytest.raises(TypeError) as excinfo:
-        assert mpa()
-    assert msg(excinfo.value) == """
-        mixed_plus_args(): incompatible function arguments. The following argument types are supported:
-            1. (arg0: int, arg1: float, *args) -> tuple
-
-        Invoked with:
-    """  # noqa: E501 line too long
-
-    assert mpk(-2, 3.5, pi=3.14159, e=2.71828) == (-2, 3.5, {'e': 2.71828, 'pi': 3.14159})
-    assert mpak(7, 7.7, 7.77, 7.777, 7.7777, minusseven=-7) == (
-        7, 7.7, (7.77, 7.777, 7.7777), {'minusseven': -7})
-    assert mpakd() == (1, 3.14159, (), {})
-    assert mpakd(3) == (3, 3.14159, (), {})
-    assert mpakd(j=2.71828) == (1, 2.71828, (), {})
-    assert mpakd(k=42) == (1, 3.14159, (), {'k': 42})
-    assert mpakd(1, 1, 2, 3, 5, 8, then=13, followedby=21) == (
-        1, 1, (2, 3, 5, 8), {'then': 13, 'followedby': 21})
-    # Arguments specified both positionally and via kwargs should fail:
-    with pytest.raises(TypeError) as excinfo:
-        assert mpakd(1, i=1)
-    assert msg(excinfo.value) == """
-        mixed_plus_args_kwargs_defaults(): incompatible function arguments. The following argument types are supported:
-            1. (i: int = 1, j: float = 3.14159, *args, **kwargs) -> tuple
-
-        Invoked with: 1; kwargs: i=1
-    """  # noqa: E501 line too long
-    with pytest.raises(TypeError) as excinfo:
-        assert mpakd(1, 2, j=1)
-    assert msg(excinfo.value) == """
-        mixed_plus_args_kwargs_defaults(): incompatible function arguments. The following argument types are supported:
-            1. (i: int = 1, j: float = 3.14159, *args, **kwargs) -> tuple
-
-        Invoked with: 1, 2; kwargs: j=1
-    """  # noqa: E501 line too long
-
-
-def test_keyword_only_args(msg):
-    assert m.kwonly_all(i=1, j=2) == (1, 2)
-    assert m.kwonly_all(j=1, i=2) == (2, 1)
-
-    with pytest.raises(TypeError) as excinfo:
-        assert m.kwonly_all(i=1) == (1,)
-    assert "incompatible function arguments" in str(excinfo.value)
-
-    with pytest.raises(TypeError) as excinfo:
-        assert m.kwonly_all(1, 2) == (1, 2)
-    assert "incompatible function arguments" in str(excinfo.value)
-
-    assert m.kwonly_some(1, k=3, j=2) == (1, 2, 3)
-
-    assert m.kwonly_with_defaults(z=8) == (3, 4, 5, 8)
-    assert m.kwonly_with_defaults(2, z=8) == (2, 4, 5, 8)
-    assert m.kwonly_with_defaults(2, j=7, k=8, z=9) == (2, 7, 8, 9)
-    assert m.kwonly_with_defaults(2, 7, z=9, k=8) == (2, 7, 8, 9)
-
-    assert m.kwonly_mixed(1, j=2) == (1, 2)
-    assert m.kwonly_mixed(j=2, i=3) == (3, 2)
-    assert m.kwonly_mixed(i=2, j=3) == (2, 3)
-
-    assert m.kwonly_plus_more(4, 5, k=6, extra=7) == (4, 5, 6, {'extra': 7})
-    assert m.kwonly_plus_more(3, k=5, j=4, extra=6) == (3, 4, 5, {'extra': 6})
-    assert m.kwonly_plus_more(2, k=3, extra=4) == (2, -1, 3, {'extra': 4})
-
-    with pytest.raises(TypeError) as excinfo:
-        assert m.kwonly_mixed(i=1) == (1,)
-    assert "incompatible function arguments" in str(excinfo.value)
-
-    with pytest.raises(RuntimeError) as excinfo:
-        m.register_invalid_kwonly(m)
-    assert msg(excinfo.value) == """
-        arg(): cannot specify an unnamed argument after an kwonly() annotation
-    """
-
-
-@pytest.mark.xfail("env.PYPY and env.PY2", reason="PyPy2 doesn't double count")
-def test_args_refcount():
-    """Issue/PR #1216 - py::args elements get double-inc_ref()ed when combined with regular
-    arguments"""
-    refcount = m.arg_refcount_h
-
-    myval = 54321
-    expected = refcount(myval)
-    assert m.arg_refcount_h(myval) == expected
-    assert m.arg_refcount_o(myval) == expected + 1
-    assert m.arg_refcount_h(myval) == expected
-    assert refcount(myval) == expected
-
-    assert m.mixed_plus_args(1, 2.0, "a", myval) == (1, 2.0, ("a", myval))
-    assert refcount(myval) == expected
-
-    assert m.mixed_plus_kwargs(3, 4.0, a=1, b=myval) == (3, 4.0, {"a": 1, "b": myval})
-    assert refcount(myval) == expected
-
-    assert m.args_function(-1, myval) == (-1, myval)
-    assert refcount(myval) == expected
-
-    assert m.mixed_plus_args_kwargs(5, 6.0, myval, a=myval) == (5, 6.0, (myval,), {"a": myval})
-    assert refcount(myval) == expected
-
-    assert m.args_kwargs_function(7, 8, myval, a=1, b=myval) == \
-        ((7, 8, myval), {"a": 1, "b": myval})
-    assert refcount(myval) == expected
-
-    exp3 = refcount(myval, myval, myval)
-    assert m.args_refcount(myval, myval, myval) == (exp3, exp3, exp3)
-    assert refcount(myval) == expected
-
-    # This function takes the first arg as a `py::object` and the rest as a `py::args`.  Unlike the
-    # previous case, when we have both positional and `py::args` we need to construct a new tuple
-    # for the `py::args`; in the previous case, we could simply inc_ref and pass on Python's input
-    # tuple without having to inc_ref the individual elements, but here we can't, hence the extra
-    # refs.
-    assert m.mixed_args_refcount(myval, myval, myval) == (exp3 + 3, exp3 + 3, exp3 + 3)
-
-    assert m.class_default_argument() == "<class 'decimal.Decimal'>"

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/par.h

@@ -1,125 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/cuda/detail/guarded_cuda_runtime_api.h>
-#include <thrust/system/cuda/detail/execution_policy.h>
-#include <thrust/system/cuda/detail/util.h>
-
-#include <thrust/detail/allocator_aware_execution_policy.h>
-
-#if THRUST_CPP_DIALECT >= 2011
-#  include <thrust/detail/dependencies_aware_execution_policy.h>
-#endif
-
-
-namespace thrust
-{
-namespace cuda_cub {
-
-template <class Derived>
-struct execute_on_stream_base : execution_policy<Derived>
-{
-private:
-  cudaStream_t stream;
-
-public:
-  __host__ __device__
-  execute_on_stream_base(cudaStream_t stream_ = default_stream())
-      : stream(stream_) {}
-
-  THRUST_RUNTIME_FUNCTION
-  Derived
-  on(cudaStream_t const &s) const
-  {
-    Derived result = derived_cast(*this);
-    result.stream  = s;
-    return result;
-  }
-
-private:
-  friend __host__ __device__
-  cudaStream_t
-  get_stream(const execute_on_stream_base &exec)
-  {
-    return exec.stream;
-  }
-};
-
-struct execute_on_stream : execute_on_stream_base<execute_on_stream>
-{
-  typedef execute_on_stream_base<execute_on_stream> base_t;
-
-  __host__ __device__
-  execute_on_stream() : base_t(){};
-  __host__ __device__
-  execute_on_stream(cudaStream_t stream) : base_t(stream){};
-};
-
-
-struct par_t : execution_policy<par_t>,
-  thrust::detail::allocator_aware_execution_policy<
-    execute_on_stream_base>
-#if THRUST_CPP_DIALECT >= 2011
-, thrust::detail::dependencies_aware_execution_policy<
-    execute_on_stream_base>
-#endif
-{
-  typedef execution_policy<par_t> base_t;
-
-  __host__ __device__
-  THRUST_CONSTEXPR par_t() : base_t() {}
-
-  typedef execute_on_stream stream_attachment_type;
-
-  THRUST_RUNTIME_FUNCTION
-  stream_attachment_type
-  on(cudaStream_t const &stream) const
-  {
-    return execute_on_stream(stream);
-  }
-};
-
-THRUST_INLINE_CONSTANT par_t par;
-}    // namespace cuda_
-
-namespace system {
-namespace cuda {
-  using thrust::cuda_cub::par;
-  namespace detail {
-    using thrust::cuda_cub::par_t;
-  }
-} // namesapce cuda
-} // namespace system
-
-namespace cuda {
-using thrust::cuda_cub::par;
-} // namespace cuda
-
-} // end namespace thrust
-

spaces/CVPR/regionclip-demo/detectron2/layers/roi_align_rotated.py

@@ -1,93 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from detectron2 import _C
-
-
-class _ROIAlignRotated(Function):
-    @staticmethod
-    def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
-        ctx.save_for_backward(roi)
-        ctx.output_size = _pair(output_size)
-        ctx.spatial_scale = spatial_scale
-        ctx.sampling_ratio = sampling_ratio
-        ctx.input_shape = input.size()
-        output = _C.roi_align_rotated_forward(
-            input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio
-        )
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        (rois,) = ctx.saved_tensors
-        output_size = ctx.output_size
-        spatial_scale = ctx.spatial_scale
-        sampling_ratio = ctx.sampling_ratio
-        bs, ch, h, w = ctx.input_shape
-        grad_input = _C.roi_align_rotated_backward(
-            grad_output,
-            rois,
-            spatial_scale,
-            output_size[0],
-            output_size[1],
-            bs,
-            ch,
-            h,
-            w,
-            sampling_ratio,
-        )
-        return grad_input, None, None, None, None, None
-
-
-roi_align_rotated = _ROIAlignRotated.apply
-
-
-class ROIAlignRotated(nn.Module):
-    def __init__(self, output_size, spatial_scale, sampling_ratio):
-        """
-        Args:
-            output_size (tuple): h, w
-            spatial_scale (float): scale the input boxes by this number
-            sampling_ratio (int): number of inputs samples to take for each output
-                sample. 0 to take samples densely.
-
-        Note:
-            ROIAlignRotated supports continuous coordinate by default:
-            Given a continuous coordinate c, its two neighboring pixel indices (in our
-            pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
-            c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
-            from the underlying signal at continuous coordinates 0.5 and 1.5).
-        """
-        super(ROIAlignRotated, self).__init__()
-        self.output_size = output_size
-        self.spatial_scale = spatial_scale
-        self.sampling_ratio = sampling_ratio
-
-    def forward(self, input, rois):
-        """
-        Args:
-            input: NCHW images
-            rois: Bx6 boxes. First column is the index into N.
-                The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees).
-        """
-        assert rois.dim() == 2 and rois.size(1) == 6
-        orig_dtype = input.dtype
-        if orig_dtype == torch.float16:
-            input = input.float()
-            rois = rois.float()
-        return roi_align_rotated(
-            input, rois, self.output_size, self.spatial_scale, self.sampling_ratio
-        ).to(dtype=orig_dtype)
-
-    def __repr__(self):
-        tmpstr = self.__class__.__name__ + "("
-        tmpstr += "output_size=" + str(self.output_size)
-        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
-        tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
-        tmpstr += ")"
-        return tmpstr

spaces/ChrisPreston/diff-svc_minato_aqua/modules/commons/common_layers.py

@@ -1,675 +0,0 @@
-import math
-
-import torch
-import torch.nn.functional as F
-import torch.onnx.operators
-from torch import nn
-from torch.nn import Parameter
-
-import utils
-
-
-class Reshape(nn.Module):
-    def __init__(self, *args):
-        super(Reshape, self).__init__()
-        self.shape = args
-
-    def forward(self, x):
-        return x.view(self.shape)
-
-
-class Permute(nn.Module):
-    def __init__(self, *args):
-        super(Permute, self).__init__()
-        self.args = args
-
-    def forward(self, x):
-        return x.permute(self.args)
-
-
-class LinearNorm(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
-        super(LinearNorm, self).__init__()
-        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, x):
-        return self.linear_layer(x)
-
-
-class ConvNorm(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
-                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
-        super(ConvNorm, self).__init__()
-        if padding is None:
-            assert (kernel_size % 2 == 1)
-            padding = int(dilation * (kernel_size - 1) / 2)
-
-        self.conv = torch.nn.Conv1d(in_channels, out_channels,
-                                    kernel_size=kernel_size, stride=stride,
-                                    padding=padding, dilation=dilation,
-                                    bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, signal):
-        conv_signal = self.conv(signal)
-        return conv_signal
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx=None):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    if padding_idx is not None:
-        nn.init.constant_(m.weight[padding_idx], 0)
-    return m
-
-
-def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
-    if not export and torch.cuda.is_available():
-        try:
-            from apex.normalization import FusedLayerNorm
-            return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
-        except ImportError:
-            pass
-    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
-
-
-def Linear(in_features, out_features, bias=True):
-    m = nn.Linear(in_features, out_features, bias)
-    nn.init.xavier_uniform_(m.weight)
-    if bias:
-        nn.init.constant_(m.bias, 0.)
-    return m
-
-
-class SinusoidalPositionalEmbedding(nn.Module):
-    """This module produces sinusoidal positional embeddings of any length.
-
-    Padding symbols are ignored.
-    """
-
-    def __init__(self, embedding_dim, padding_idx, init_size=1024):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.padding_idx = padding_idx
-        self.weights = SinusoidalPositionalEmbedding.get_embedding(
-            init_size,
-            embedding_dim,
-            padding_idx,
-        )
-        self.register_buffer('_float_tensor', torch.FloatTensor(1))
-
-    @staticmethod
-    def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
-        """Build sinusoidal embeddings.
-
-        This matches the implementation in tensor2tensor, but differs slightly
-        from the description in Section 3.5 of "Attention Is All You Need".
-        """
-        half_dim = embedding_dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
-        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
-        if embedding_dim % 2 == 1:
-            # zero pad
-            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
-        if padding_idx is not None:
-            emb[padding_idx, :] = 0
-        return emb
-
-    def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs):
-        """Input is expected to be of size [bsz x seqlen]."""
-        bsz, seq_len = input.shape[:2]
-        max_pos = self.padding_idx + 1 + seq_len
-        if self.weights is None or max_pos > self.weights.size(0):
-            # recompute/expand embeddings if needed
-            self.weights = SinusoidalPositionalEmbedding.get_embedding(
-                max_pos,
-                self.embedding_dim,
-                self.padding_idx,
-            )
-        self.weights = self.weights.to(self._float_tensor)
-
-        if incremental_state is not None:
-            # positions is the same for every token when decoding a single step
-            pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
-            return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
-
-        positions = utils.make_positions(input, self.padding_idx) if positions is None else positions
-        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
-
-    def max_positions(self):
-        """Maximum number of supported positions."""
-        return int(1e5)  # an arbitrary large number
-
-
-class ConvTBC(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, padding=0):
-        super(ConvTBC, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.padding = padding
-
-        self.weight = torch.nn.Parameter(torch.Tensor(
-            self.kernel_size, in_channels, out_channels))
-        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
-
-    def forward(self, input):
-        return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding)
-
-
-class MultiheadAttention(nn.Module):
-    def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
-                 add_bias_kv=False, add_zero_attn=False, self_attention=False,
-                 encoder_decoder_attention=False):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.kdim = kdim if kdim is not None else embed_dim
-        self.vdim = vdim if vdim is not None else embed_dim
-        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
-
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
-        self.scaling = self.head_dim ** -0.5
-
-        self.self_attention = self_attention
-        self.encoder_decoder_attention = encoder_decoder_attention
-
-        assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \
-                                                             'value to be of the same size'
-
-        if self.qkv_same_dim:
-            self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
-        else:
-            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
-            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
-            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
-
-        if bias:
-            self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
-        else:
-            self.register_parameter('in_proj_bias', None)
-
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-        if add_bias_kv:
-            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
-            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
-        else:
-            self.bias_k = self.bias_v = None
-
-        self.add_zero_attn = add_zero_attn
-
-        self.reset_parameters()
-
-        self.enable_torch_version = False
-        if hasattr(F, "multi_head_attention_forward"):
-            self.enable_torch_version = True
-        else:
-            self.enable_torch_version = False
-        self.last_attn_probs = None
-
-    def reset_parameters(self):
-        if self.qkv_same_dim:
-            nn.init.xavier_uniform_(self.in_proj_weight)
-        else:
-            nn.init.xavier_uniform_(self.k_proj_weight)
-            nn.init.xavier_uniform_(self.v_proj_weight)
-            nn.init.xavier_uniform_(self.q_proj_weight)
-
-        nn.init.xavier_uniform_(self.out_proj.weight)
-        if self.in_proj_bias is not None:
-            nn.init.constant_(self.in_proj_bias, 0.)
-            nn.init.constant_(self.out_proj.bias, 0.)
-        if self.bias_k is not None:
-            nn.init.xavier_normal_(self.bias_k)
-        if self.bias_v is not None:
-            nn.init.xavier_normal_(self.bias_v)
-
-    def forward(
-            self,
-            query, key, value,
-            key_padding_mask=None,
-            incremental_state=None,
-            need_weights=True,
-            static_kv=False,
-            attn_mask=None,
-            before_softmax=False,
-            need_head_weights=False,
-            enc_dec_attn_constraint_mask=None,
-            reset_attn_weight=None
-    ):
-        """Input shape: Time x Batch x Channel
-
-        Args:
-            key_padding_mask (ByteTensor, optional): mask to exclude
-                keys that are pads, of shape `(batch, src_len)`, where
-                padding elements are indicated by 1s.
-            need_weights (bool, optional): return the attention weights,
-                averaged over heads (default: False).
-            attn_mask (ByteTensor, optional): typically used to
-                implement causal attention, where the mask prevents the
-                attention from looking forward in time (default: None).
-            before_softmax (bool, optional): return the raw attention
-                weights and values before the attention softmax.
-            need_head_weights (bool, optional): return the attention
-                weights for each head. Implies *need_weights*. Default:
-                return the average attention weights over all heads.
-        """
-        if need_head_weights:
-            need_weights = True
-
-        tgt_len, bsz, embed_dim = query.size()
-        assert embed_dim == self.embed_dim
-        assert list(query.size()) == [tgt_len, bsz, embed_dim]
-
-        if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None:
-            if self.qkv_same_dim:
-                return F.multi_head_attention_forward(query, key, value,
-                                                      self.embed_dim, self.num_heads,
-                                                      self.in_proj_weight,
-                                                      self.in_proj_bias, self.bias_k, self.bias_v,
-                                                      self.add_zero_attn, self.dropout,
-                                                      self.out_proj.weight, self.out_proj.bias,
-                                                      self.training, key_padding_mask, need_weights,
-                                                      attn_mask)
-            else:
-                return F.multi_head_attention_forward(query, key, value,
-                                                      self.embed_dim, self.num_heads,
-                                                      torch.empty([0]),
-                                                      self.in_proj_bias, self.bias_k, self.bias_v,
-                                                      self.add_zero_attn, self.dropout,
-                                                      self.out_proj.weight, self.out_proj.bias,
-                                                      self.training, key_padding_mask, need_weights,
-                                                      attn_mask, use_separate_proj_weight=True,
-                                                      q_proj_weight=self.q_proj_weight,
-                                                      k_proj_weight=self.k_proj_weight,
-                                                      v_proj_weight=self.v_proj_weight)
-
-        if incremental_state is not None:
-            print('Not implemented error.')
-            exit()
-        else:
-            saved_state = None
-
-        if self.self_attention:
-            # self-attention
-            q, k, v = self.in_proj_qkv(query)
-        elif self.encoder_decoder_attention:
-            # encoder-decoder attention
-            q = self.in_proj_q(query)
-            if key is None:
-                assert value is None
-                k = v = None
-            else:
-                k = self.in_proj_k(key)
-                v = self.in_proj_v(key)
-
-        else:
-            q = self.in_proj_q(query)
-            k = self.in_proj_k(key)
-            v = self.in_proj_v(value)
-        q *= self.scaling
-
-        if self.bias_k is not None:
-            assert self.bias_v is not None
-            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
-            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
-            if attn_mask is not None:
-                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
-            if key_padding_mask is not None:
-                key_padding_mask = torch.cat(
-                    [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
-
-        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-        if k is not None:
-            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-        if v is not None:
-            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-
-        if saved_state is not None:
-            print('Not implemented error.')
-            exit()
-
-        src_len = k.size(1)
-
-        # This is part of a workaround to get around fork/join parallelism
-        # not supporting Optional types.
-        if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]):
-            key_padding_mask = None
-
-        if key_padding_mask is not None:
-            assert key_padding_mask.size(0) == bsz
-            assert key_padding_mask.size(1) == src_len
-
-        if self.add_zero_attn:
-            src_len += 1
-            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
-            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
-            if attn_mask is not None:
-                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
-            if key_padding_mask is not None:
-                key_padding_mask = torch.cat(
-                    [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
-
-        attn_weights = torch.bmm(q, k.transpose(1, 2))
-        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
-
-        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
-
-        if attn_mask is not None:
-            if len(attn_mask.shape) == 2:
-                attn_mask = attn_mask.unsqueeze(0)
-            elif len(attn_mask.shape) == 3:
-                attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape(
-                    bsz * self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights + attn_mask
-
-        if enc_dec_attn_constraint_mask is not None:  # bs x head x L_kv
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.masked_fill(
-                enc_dec_attn_constraint_mask.unsqueeze(2).bool(),
-                -1e9,
-            )
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if key_padding_mask is not None:
-            # don't attend to padding symbols
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.masked_fill(
-                key_padding_mask.unsqueeze(1).unsqueeze(2),
-                -1e9,
-            )
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-
-        if before_softmax:
-            return attn_weights, v
-
-        attn_weights_float = utils.softmax(attn_weights, dim=-1)
-        attn_weights = attn_weights_float.type_as(attn_weights)
-        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
-
-        if reset_attn_weight is not None:
-            if reset_attn_weight:
-                self.last_attn_probs = attn_probs.detach()
-            else:
-                assert self.last_attn_probs is not None
-                attn_probs = self.last_attn_probs
-        attn = torch.bmm(attn_probs, v)
-        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
-        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-        attn = self.out_proj(attn)
-
-        if need_weights:
-            attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
-            if not need_head_weights:
-                # average attention weights over heads
-                attn_weights = attn_weights.mean(dim=0)
-        else:
-            attn_weights = None
-
-        return attn, (attn_weights, attn_logits)
-
-    def in_proj_qkv(self, query):
-        return self._in_proj(query).chunk(3, dim=-1)
-
-    def in_proj_q(self, query):
-        if self.qkv_same_dim:
-            return self._in_proj(query, end=self.embed_dim)
-        else:
-            bias = self.in_proj_bias
-            if bias is not None:
-                bias = bias[:self.embed_dim]
-            return F.linear(query, self.q_proj_weight, bias)
-
-    def in_proj_k(self, key):
-        if self.qkv_same_dim:
-            return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
-        else:
-            weight = self.k_proj_weight
-            bias = self.in_proj_bias
-            if bias is not None:
-                bias = bias[self.embed_dim:2 * self.embed_dim]
-            return F.linear(key, weight, bias)
-
-    def in_proj_v(self, value):
-        if self.qkv_same_dim:
-            return self._in_proj(value, start=2 * self.embed_dim)
-        else:
-            weight = self.v_proj_weight
-            bias = self.in_proj_bias
-            if bias is not None:
-                bias = bias[2 * self.embed_dim:]
-            return F.linear(value, weight, bias)
-
-    def _in_proj(self, input, start=0, end=None):
-        weight = self.in_proj_weight
-        bias = self.in_proj_bias
-        weight = weight[start:end, :]
-        if bias is not None:
-            bias = bias[start:end]
-        return F.linear(input, weight, bias)
-
-    def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
-        return attn_weights
-
-
-class Swish(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, i):
-        result = i * torch.sigmoid(i)
-        ctx.save_for_backward(i)
-        return result
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        i = ctx.saved_variables[0]
-        sigmoid_i = torch.sigmoid(i)
-        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
-
-
-class CustomSwish(nn.Module):
-    def forward(self, input_tensor):
-        return Swish.apply(input_tensor)
-
-
-class Mish(nn.Module):
-    def forward(self, x):
-        return x * torch.tanh(F.softplus(x))
-
-
-class TransformerFFNLayer(nn.Module):
-    def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'):
-        super().__init__()
-        self.kernel_size = kernel_size
-        self.dropout = dropout
-        self.act = act
-        if padding == 'SAME':
-            self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2)
-        elif padding == 'LEFT':
-            self.ffn_1 = nn.Sequential(
-                nn.ConstantPad1d((kernel_size - 1, 0), 0.0),
-                nn.Conv1d(hidden_size, filter_size, kernel_size)
-            )
-        self.ffn_2 = Linear(filter_size, hidden_size)
-        if self.act == 'swish':
-            self.swish_fn = CustomSwish()
-
-    def forward(self, x, incremental_state=None):
-        # x: T x B x C
-        if incremental_state is not None:
-            assert incremental_state is None, 'Nar-generation does not allow this.'
-            exit(1)
-
-        x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1)
-        x = x * self.kernel_size ** -0.5
-
-        if incremental_state is not None:
-            x = x[-1:]
-        if self.act == 'gelu':
-            x = F.gelu(x)
-        if self.act == 'relu':
-            x = F.relu(x)
-        if self.act == 'swish':
-            x = self.swish_fn(x)
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = self.ffn_2(x)
-        return x
-
-
-class BatchNorm1dTBC(nn.Module):
-    def __init__(self, c):
-        super(BatchNorm1dTBC, self).__init__()
-        self.bn = nn.BatchNorm1d(c)
-
-    def forward(self, x):
-        """
-
-        :param x: [T, B, C]
-        :return: [T, B, C]
-        """
-        x = x.permute(1, 2, 0)  # [B, C, T]
-        x = self.bn(x)  # [B, C, T]
-        x = x.permute(2, 0, 1)  # [T, B, C]
-        return x
-
-
-class EncSALayer(nn.Module):
-    def __init__(self, c, num_heads, dropout, attention_dropout=0.1,
-                 relu_dropout=0.1, kernel_size=9, padding='SAME', norm='ln', act='gelu'):
-        super().__init__()
-        self.c = c
-        self.dropout = dropout
-        self.num_heads = num_heads
-        if num_heads > 0:
-            if norm == 'ln':
-                self.layer_norm1 = LayerNorm(c)
-            elif norm == 'bn':
-                self.layer_norm1 = BatchNorm1dTBC(c)
-            self.self_attn = MultiheadAttention(
-                self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False,
-            )
-        if norm == 'ln':
-            self.layer_norm2 = LayerNorm(c)
-        elif norm == 'bn':
-            self.layer_norm2 = BatchNorm1dTBC(c)
-        self.ffn = TransformerFFNLayer(
-            c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act)
-
-    def forward(self, x, encoder_padding_mask=None, **kwargs):
-        layer_norm_training = kwargs.get('layer_norm_training', None)
-        if layer_norm_training is not None:
-            self.layer_norm1.training = layer_norm_training
-            self.layer_norm2.training = layer_norm_training
-        if self.num_heads > 0:
-            residual = x
-            x = self.layer_norm1(x)
-            x, _, = self.self_attn(
-                query=x,
-                key=x,
-                value=x,
-                key_padding_mask=encoder_padding_mask
-            )
-            x = F.dropout(x, self.dropout, training=self.training)
-            x = residual + x
-            x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
-
-        residual = x
-        x = self.layer_norm2(x)
-        x = self.ffn(x)
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-        x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
-        return x
-
-
-class DecSALayer(nn.Module):
-    def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'):
-        super().__init__()
-        self.c = c
-        self.dropout = dropout
-        self.layer_norm1 = LayerNorm(c)
-        self.self_attn = MultiheadAttention(
-            c, num_heads, self_attention=True, dropout=attention_dropout, bias=False
-        )
-        self.layer_norm2 = LayerNorm(c)
-        self.encoder_attn = MultiheadAttention(
-            c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False,
-        )
-        self.layer_norm3 = LayerNorm(c)
-        self.ffn = TransformerFFNLayer(
-            c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act)
-
-    def forward(
-            self,
-            x,
-            encoder_out=None,
-            encoder_padding_mask=None,
-            incremental_state=None,
-            self_attn_mask=None,
-            self_attn_padding_mask=None,
-            attn_out=None,
-            reset_attn_weight=None,
-            **kwargs,
-    ):
-        layer_norm_training = kwargs.get('layer_norm_training', None)
-        if layer_norm_training is not None:
-            self.layer_norm1.training = layer_norm_training
-            self.layer_norm2.training = layer_norm_training
-            self.layer_norm3.training = layer_norm_training
-        residual = x
-        x = self.layer_norm1(x)
-        x, _ = self.self_attn(
-            query=x,
-            key=x,
-            value=x,
-            key_padding_mask=self_attn_padding_mask,
-            incremental_state=incremental_state,
-            attn_mask=self_attn_mask
-        )
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-
-        residual = x
-        x = self.layer_norm2(x)
-        if encoder_out is not None:
-            x, attn = self.encoder_attn(
-                query=x,
-                key=encoder_out,
-                value=encoder_out,
-                key_padding_mask=encoder_padding_mask,
-                incremental_state=incremental_state,
-                static_kv=True,
-                enc_dec_attn_constraint_mask=None,
-                # utils.get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'),
-                reset_attn_weight=reset_attn_weight
-            )
-            attn_logits = attn[1]
-        else:
-            assert attn_out is not None
-            x = self.encoder_attn.in_proj_v(attn_out.transpose(0, 1))
-            attn_logits = None
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-
-        residual = x
-        x = self.layer_norm3(x)
-        x = self.ffn(x, incremental_state=incremental_state)
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-        # if len(attn_logits.size()) > 3:
-        #    indices = attn_logits.softmax(-1).max(-1).values.sum(-1).argmax(-1)
-        #    attn_logits = attn_logits.gather(1,
-        #        indices[:, None, None, None].repeat(1, 1, attn_logits.size(-2), attn_logits.size(-1))).squeeze(1)
-        return x, attn_logits

spaces/CikeyQI/Yunzai/Yunzai/lib/config/config.js

@@ -1,174 +0,0 @@
-import YAML from "yaml"
-import fs from "node:fs"
-import chokidar from "chokidar"
-
-/** 配置文件 */
-class Cfg {
-  constructor () {
-    this.config = {}
-
-    /** 监听文件 */
-    this.watcher = { config: {}, defSet: {} }
-
-    this.initCfg()
-  }
-
-  /** 初始化配置 */
-  initCfg () {
-    let path = "config/config/"
-    let pathDef = "config/default_config/"
-    const files = fs.readdirSync(pathDef).filter(file => file.endsWith(".yaml"))
-    for (let file of files)
-      if (!fs.existsSync(`${path}${file}`))
-        fs.copyFileSync(`${pathDef}${file}`, `${path}${file}`)
-    if (!fs.existsSync("data")) fs.mkdirSync("data")
-    if (!fs.existsSync("resources")) fs.mkdirSync("resources")
-  }
-
-  /** Bot配置 */
-  get bot () {
-    let bot = this.getConfig("bot")
-    let defbot = this.getdefSet("bot")
-    bot = { ...defbot, ...bot }
-
-    return bot
-  }
-
-  get other () {
-    return this.getConfig("other")
-  }
-
-  get redis () {
-    return this.getConfig("redis")
-  }
-
-  get renderer() {
-    return this.getConfig("renderer");
-  }
-
-  /** 主人账号 */
-  get masterQQ () {
-    let masterQQ = this.getConfig("other").masterQQ || []
-
-    if (!Array.isArray(masterQQ))
-      masterQQ = [masterQQ]
-
-    const masters = []
-    for (const i of masterQQ)
-      masters.push(Number(i) || String(i))
-    return masters
-  }
-
-  /** Bot账号:[主人帐号] */
-  get master () {
-    let master = this.getConfig("other").master || []
-
-    if (!Array.isArray(master))
-      master = [master]
-
-    const masters = {}
-    for (let i of master) {
-      i = i.split(":")
-      if (Array.isArray(masters[i[0]]))
-        masters[i[0]].push(i[1])
-      else
-        masters[i[0]] = [i[1]]
-    }
-    return masters
-  }
-
-  /** 机器人账号 */
-  get uin () {
-    return Object.keys(this.master)
-  }
-  get qq () {
-    return this.uin
-  }
-
-  /** package.json */
-  get package () {
-    if (this._package) return this._package
-
-    this._package = JSON.parse(fs.readFileSync("package.json", "utf8"))
-    return this._package
-  }
-
-  /** 群配置 */
-  getGroup (bot_id = "", group_id = "") {
-    const config = this.getConfig("group")
-    const defCfg = this.getdefSet("group")
-    return {
-      ...defCfg.default,
-      ...config.default,
-      ...config[`${bot_id}:default`],
-      ...config[group_id],
-      ...config[`${bot_id}:${group_id}`],
-    }
-  }
-
-  /** other配置 */
-  getOther () {
-    let def = this.getdefSet("other")
-    let config = this.getConfig("other")
-    return { ...def, ...config }
-  }
-
-  /**
-   * @param app  功能
-   * @param name 配置文件名称
-   */
-  getdefSet (name) {
-    return this.getYaml("default_config", name)
-  }
-
-  /** 用户配置 */
-  getConfig (name) {
-    return this.getYaml("config", name)
-  }
-
-  /**
-   * 获取配置yaml
-   * @param type 默认跑配置-defSet，用户配置-config
-   * @param name 名称
-   */
-  getYaml (type, name) {
-    let file = `config/${type}/${name}.yaml`
-    let key = `${type}.${name}`
-    if (this.config[key]) return this.config[key]
-
-    this.config[key] = YAML.parse(
-      fs.readFileSync(file, "utf8")
-    )
-
-    this.watch(file, name, type)
-
-    return this.config[key]
-  }
-
-  /** 监听配置文件 */
-  watch (file, name, type = "default_config") {
-    let key = `${type}.${name}`
-
-    if (this.watcher[key]) return
-
-    const watcher = chokidar.watch(file)
-    watcher.on("change", path => {
-      delete this.config[key]
-      if (typeof Bot == "undefined") return
-      logger.mark(`[修改配置文件][${type}][${name}]`)
-      if (this[`change_${name}`]) {
-        this[`change_${name}`]()
-      }
-    })
-
-    this.watcher[key] = watcher
-  }
-
-  async change_bot () {
-    /** 修改日志等级 */
-    let log = await import("./log.js")
-    log.default()
-  }
-}
-
-export default new Cfg()

spaces/CjangCjengh/Sanskrit-TTS/text/__init__.py

@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text

spaces/Cong723/gpt-academic-public/crazy_functions/crazy_utils.py

@@ -1,608 +0,0 @@
-from toolbox import update_ui, get_conf, trimmed_format_exc
-
-def input_clipping(inputs, history, max_token_limit):
-    import numpy as np
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-
-    mode = 'input-and-history'
-    # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
-    input_token_num = get_token_num(inputs)
-    if input_token_num < max_token_limit//2: 
-        mode = 'only-history'
-        max_token_limit = max_token_limit - input_token_num
-
-    everything = [inputs] if mode == 'input-and-history' else ['']
-    everything.extend(history)
-    n_token = get_token_num('\n'.join(everything))
-    everything_token = [get_token_num(e) for e in everything]
-    delta = max(everything_token) // 16 # 截断时的颗粒度
-        
-    while n_token > max_token_limit:
-        where = np.argmax(everything_token)
-        encoded = enc.encode(everything[where], disallowed_special=())
-        clipped_encoded = encoded[:len(encoded)-delta]
-        everything[where] = enc.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char
-        everything_token[where] = get_token_num(everything[where])
-        n_token = get_token_num('\n'.join(everything))
-
-    if mode == 'input-and-history':
-        inputs = everything[0]
-    else:
-        pass
-    history = everything[1:]
-    return inputs, history
-
-def request_gpt_model_in_new_thread_with_ui_alive(
-        inputs, inputs_show_user, llm_kwargs, 
-        chatbot, history, sys_prompt, refresh_interval=0.2,
-        handle_token_exceed=True, 
-        retry_times_at_unknown_error=2,
-        ):
-    """
-    Request GPT model，请求GPT模型同时维持用户界面活跃。
-
-    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
-        inputs (string): List of inputs （输入）
-        inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
-        top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）
-        temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）
-        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
-        history (list): List of chat history （历史，对话历史列表）
-        sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
-        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
-        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
-        retry_times_at_unknown_error：失败时的重试次数
-
-    输出 Returns:
-        future: 输出，GPT返回的结果
-    """
-    import time
-    from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
-    # 用户反馈
-    chatbot.append([inputs_show_user, ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    executor = ThreadPoolExecutor(max_workers=16)
-    mutable = ["", time.time(), ""]
-    def _req_gpt(inputs, history, sys_prompt):
-        retry_op = retry_times_at_unknown_error
-        exceeded_cnt = 0
-        while True:
-            # watchdog error
-            if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: 
-                raise RuntimeError("检测到程序终止。")
-            try:
-                # 【第一种情况】：顺利完成
-                result = predict_no_ui_long_connection(
-                    inputs=inputs, llm_kwargs=llm_kwargs,
-                    history=history, sys_prompt=sys_prompt, observe_window=mutable)
-                return result
-            except ConnectionAbortedError as token_exceeded_error:
-                # 【第二种情况】：Token溢出
-                if handle_token_exceed:
-                    exceeded_cnt += 1
-                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
-                    from toolbox import get_reduce_token_percent
-                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = 4096
-                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
-                    mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
-                    continue # 返回重试
-                else:
-                    # 【选择放弃】
-                    tb_str = '```\n' + trimmed_format_exc() + '```'
-                    mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    return mutable[0] # 放弃
-            except:
-                # 【第三种情况】：其他错误：重试几次
-                tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
-                mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                if retry_op > 0:
-                    retry_op -= 1
-                    mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
-                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
-                        time.sleep(30)
-                    time.sleep(5)
-                    continue # 返回重试
-                else:
-                    time.sleep(5)
-                    return mutable[0] # 放弃
-
-    # 提交任务
-    future = executor.submit(_req_gpt, inputs, history, sys_prompt)
-    while True:
-        # yield一次以刷新前端页面
-        time.sleep(refresh_interval)
-        # “喂狗”（看门狗）
-        mutable[1] = time.time()
-        if future.done():
-            break
-        chatbot[-1] = [chatbot[-1][0], mutable[0]]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-
-    final_result = future.result()
-    chatbot[-1] = [chatbot[-1][0], final_result]
-    yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
-    return final_result
-
-
-def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array, inputs_show_user_array, llm_kwargs, 
-        chatbot, history_array, sys_prompt_array, 
-        refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
-        handle_token_exceed=True, show_user_at_complete=False,
-        retry_times_at_unknown_error=2,
-        ):
-    """
-    Request GPT model using multiple threads with UI and high efficiency
-    请求GPT模型的[多线程]版。
-    具备以下功能：
-        实时在UI上反馈远程数据流
-        使用线程池，可调节线程池的大小避免openai的流量限制错误
-        处理中途中止的情况
-        网络等出问题时，会把traceback和已经接收的数据转入输出
-
-    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
-        inputs_array (list): List of inputs （每个子任务的输入）
-        inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
-        llm_kwargs: llm_kwargs参数
-        chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化）
-        history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史）
-        sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
-        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
-        max_workers (int, optional): Maximum number of threads (default: see config.py) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误）
-        scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果）
-        handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本）
-        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
-        show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)
-        retry_times_at_unknown_error：子任务失败时的重试次数
-
-    输出 Returns:
-        list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）
-    """
-    import time, random
-    from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
-    assert len(inputs_array) == len(history_array)
-    assert len(inputs_array) == len(sys_prompt_array)
-    if max_workers == -1: # 读取配置文件
-        try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
-        except: max_workers = 8
-        if max_workers <= 0: max_workers = 3
-    # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
-    if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
-        max_workers = 1
-        
-    executor = ThreadPoolExecutor(max_workers=max_workers)
-    n_frag = len(inputs_array)
-    # 用户反馈
-    chatbot.append(["请开始多线程操作。", ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    # 跨线程传递
-    mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
-
-    # 子线程任务
-    def _req_gpt(index, inputs, history, sys_prompt):
-        gpt_say = ""
-        retry_op = retry_times_at_unknown_error
-        exceeded_cnt = 0
-        mutable[index][2] = "执行中"
-        while True:
-            # watchdog error
-            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: 
-                raise RuntimeError("检测到程序终止。")
-            try:
-                # 【第一种情况】：顺利完成
-                # time.sleep(10); raise RuntimeError("测试")
-                gpt_say = predict_no_ui_long_connection(
-                    inputs=inputs, llm_kwargs=llm_kwargs, history=history, 
-                    sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
-                )
-                mutable[index][2] = "已成功"
-                return gpt_say
-            except ConnectionAbortedError as token_exceeded_error:
-                # 【第二种情况】：Token溢出，
-                if handle_token_exceed:
-                    exceeded_cnt += 1
-                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
-                    from toolbox import get_reduce_token_percent
-                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = 4096
-                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
-                    gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
-                    mutable[index][2] = f"截断重试"
-                    continue # 返回重试
-                else:
-                    # 【选择放弃】
-                    tb_str = '```\n' + trimmed_format_exc() + '```'
-                    gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
-                    mutable[index][2] = "输入过长已放弃"
-                    return gpt_say # 放弃
-            except:
-                # 【第三种情况】：其他错误
-                tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
-                gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
-                if retry_op > 0: 
-                    retry_op -= 1
-                    wait = random.randint(5, 20)
-                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
-                        wait = wait * 3
-                        fail_info = "OpenAI绑定信用卡可解除频率限制 "
-                    else:
-                        fail_info = ""
-                    # 也许等待十几秒后，情况会好转
-                    for i in range(wait):
-                        mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1)
-                    # 开始重试
-                    mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
-                    continue # 返回重试
-                else:
-                    mutable[index][2] = "已失败"
-                    wait = 5
-                    time.sleep(5)
-                    return gpt_say # 放弃
-
-    # 异步任务开始
-    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
-        range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
-    cnt = 0
-    while True:
-        # yield一次以刷新前端页面
-        time.sleep(refresh_interval)
-        cnt += 1
-        worker_done = [h.done() for h in futures]
-        if all(worker_done):
-            executor.shutdown()
-            break
-        # 更好的UI视觉效果
-        observe_win = []
-        # 每个线程都要“喂狗”（看门狗）
-        for thread_index, _ in enumerate(worker_done):
-            mutable[thread_index][1] = time.time()
-        # 在前端打印些好玩的东西
-        for thread_index, _ in enumerate(worker_done):
-            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
-                replace('\n', '').replace('```', '...').replace(
-                    ' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
-            observe_win.append(print_something_really_funny)
-        # 在前端打印些好玩的东西
-        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' 
-                            if not done else f'`{mutable[thread_index][2]}`\n\n' 
-                            for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
-        # 在前端打印些好玩的东西
-        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    
-    # 异步任务结束
-    gpt_response_collection = []
-    for inputs_show_user, f in zip(inputs_show_user_array, futures):
-        gpt_res = f.result()
-        gpt_response_collection.extend([inputs_show_user, gpt_res])
-    
-    # 是否在结束时，在界面上显示结果
-    if show_user_at_complete:
-        for inputs_show_user, f in zip(inputs_show_user_array, futures):
-            gpt_res = f.result()
-            chatbot.append([inputs_show_user, gpt_res])
-            yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-            time.sleep(0.3)
-    return gpt_response_collection
-
-
-def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
-    def cut(txt_tocut, must_break_at_empty_line):  # 递归
-        if get_token_fn(txt_tocut) <= limit:
-            return [txt_tocut]
-        else:
-            lines = txt_tocut.split('\n')
-            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    if lines[cnt] != "":
-                        continue
-                print(cnt)
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-            if cnt == 0:
-                raise RuntimeError("存在一行极长的文本！")
-            # print(len(post))
-            # 列表递归接龙
-            result = [prev]
-            result.extend(cut(post, must_break_at_empty_line))
-            return result
-    try:
-        return cut(txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        return cut(txt, must_break_at_empty_line=False)
-
-
-def force_breakdown(txt, limit, get_token_fn):
-    """
-    当无法用标点、空行分割时，我们用最暴力的方法切割
-    """
-    for i in reversed(range(len(txt))):
-        if get_token_fn(txt[:i]) < limit:
-            return txt[:i], txt[i:]
-    return "Tiktoken未知错误", "Tiktoken未知错误"
-
-def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
-    # 递归
-    def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):  
-        if get_token_fn(txt_tocut) <= limit:
-            return [txt_tocut]
-        else:
-            lines = txt_tocut.split('\n')
-            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-            cnt = 0
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    if lines[cnt] != "":
-                        continue
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-            if cnt == 0:
-                if break_anyway:
-                    prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
-                else:
-                    raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
-            # print(len(post))
-            # 列表递归接龙
-            result = [prev]
-            result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
-            return result
-    try:
-        # 第1次尝试，将双空行（\n\n）作为切分点
-        return cut(txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        try:
-            # 第2次尝试，将单空行（\n）作为切分点
-            return cut(txt, must_break_at_empty_line=False)
-        except RuntimeError:
-            try:
-                # 第3次尝试，将英文句号（.）作为切分点
-                res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
-                return [r.replace('。\n', '.') for r in res]
-            except RuntimeError as e:
-                try:
-                    # 第4次尝试，将中文句号（。）作为切分点
-                    res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False)
-                    return [r.replace('。。\n', '。') for r in res]
-                except RuntimeError as e:
-                    # 第5次尝试，没办法了，随便切一下敷衍吧
-                    return cut(txt, must_break_at_empty_line=False, break_anyway=True)
-
-
-
-def read_and_clean_pdf_text(fp):
-    """
-    这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好
-
-    **输入参数说明**
-    - `fp`：需要读取和清理文本的pdf文件路径
-
-    **输出参数说明**
-    - `meta_txt`：清理后的文本内容字符串
-    - `page_one_meta`：第一页清理后的文本内容列表
-
-    **函数功能**
-    读取pdf文件并清理其中的文本内容，清理规则包括：
-    - 提取所有块元的文本信息，并合并为一个字符串
-    - 去除短块（字符数小于100）并替换为回车符
-    - 清理多余的空行
-    - 合并小写字母开头的段落块并替换为空格
-    - 清除重复的换行
-    - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
-    """
-    import fitz, copy
-    import re
-    import numpy as np
-    from colorful import print亮黄, print亮绿
-    fc = 0  # Index 0 文本
-    fs = 1  # Index 1 字体
-    fb = 2  # Index 2 框框
-    REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
-    REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
-    def primary_ffsize(l):
-        """
-        提取文本块主字体
-        """
-        fsize_statiscs = {}
-        for wtf in l['spans']:
-            if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
-            fsize_statiscs[wtf['size']] += len(wtf['text'])
-        return max(fsize_statiscs, key=fsize_statiscs.get)
-        
-    def ffsize_same(a,b):
-        """
-        提取字体大小是否近似相等
-        """
-        return abs((a-b)/max(a,b)) < 0.02
-
-    with fitz.open(fp) as doc:
-        meta_txt = []
-        meta_font = []
-
-        meta_line = []
-        meta_span = []
-        ############################## <第 1 步，搜集初始信息> ##################################
-        for index, page in enumerate(doc):
-            # file_content += page.get_text()
-            text_areas = page.get_text("dict")  # 获取页面上的文本信息
-            for t in text_areas['blocks']:
-                if 'lines' in t:
-                    pf = 998
-                    for l in t['lines']:
-                        txt_line = "".join([wtf['text'] for wtf in l['spans']])
-                        if len(txt_line) == 0: continue
-                        pf = primary_ffsize(l)
-                        meta_line.append([txt_line, pf, l['bbox'], l])
-                        for wtf in l['spans']: # for l in t['lines']:
-                            meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
-                    # meta_line.append(["NEW_BLOCK", pf])
-            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block
-            meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
-                '- ', '') for t in text_areas['blocks'] if 'lines' in t])
-            meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
-                             for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
-            if index == 0:
-                page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
-                    '- ', '') for t in text_areas['blocks'] if 'lines' in t]
-                
-        ############################## <第 2 步，获取正文主字体> ##################################
-        fsize_statiscs = {}
-        for span in meta_span:
-            if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
-            fsize_statiscs[span[1]] += span[2]
-        main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
-        if REMOVE_FOOT_NOTE:
-            give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
-
-        ############################## <第 3 步，切分和重新整合> ##################################
-        mega_sec = []
-        sec = []
-        for index, line in enumerate(meta_line):
-            if index == 0: 
-                sec.append(line[fc])
-                continue
-            if REMOVE_FOOT_NOTE:
-                if meta_line[index][fs] <= give_up_fize_threshold:
-                    continue
-            if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
-                # 尝试识别段落
-                if meta_line[index][fc].endswith('.') and\
-                    (meta_line[index-1][fc] != 'NEW_BLOCK') and \
-                    (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
-                    sec[-1] += line[fc]
-                    sec[-1] += "\n\n"
-                else:
-                    sec[-1] += " "
-                    sec[-1] += line[fc]
-            else:
-                if (index+1 < len(meta_line)) and \
-                    meta_line[index][fs] > main_fsize:
-                    # 单行 + 字体大
-                    mega_sec.append(copy.deepcopy(sec))
-                    sec = []
-                    sec.append("# " + line[fc])
-                else:
-                    # 尝试识别section
-                    if meta_line[index-1][fs] > meta_line[index][fs]:
-                        sec.append("\n" + line[fc])
-                    else:
-                        sec.append(line[fc])
-        mega_sec.append(copy.deepcopy(sec))
-
-        finals = []
-        for ms in mega_sec:
-            final = " ".join(ms)
-            final = final.replace('- ', ' ')
-            finals.append(final)
-        meta_txt = finals
-
-        ############################## <第 4 步，乱七八糟的后处理> ##################################
-        def 把字符太少的块清除为回车(meta_txt):
-            for index, block_txt in enumerate(meta_txt):
-                if len(block_txt) < 100:
-                    meta_txt[index] = '\n'
-            return meta_txt
-        meta_txt = 把字符太少的块清除为回车(meta_txt)
-
-        def 清理多余的空行(meta_txt):
-            for index in reversed(range(1, len(meta_txt))):
-                if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
-                    meta_txt.pop(index)
-            return meta_txt
-        meta_txt = 清理多余的空行(meta_txt)
-
-        def 合并小写开头的段落块(meta_txt):
-            def starts_with_lowercase_word(s):
-                pattern = r"^[a-z]+"
-                match = re.match(pattern, s)
-                if match:
-                    return True
-                else:
-                    return False
-            for _ in range(100):
-                for index, block_txt in enumerate(meta_txt):
-                    if starts_with_lowercase_word(block_txt):
-                        if meta_txt[index-1] != '\n':
-                            meta_txt[index-1] += ' '
-                        else:
-                            meta_txt[index-1] = ''
-                        meta_txt[index-1] += meta_txt[index]
-                        meta_txt[index] = '\n'
-            return meta_txt
-        meta_txt = 合并小写开头的段落块(meta_txt)
-        meta_txt = 清理多余的空行(meta_txt)
-
-        meta_txt = '\n'.join(meta_txt)
-        # 清除重复的换行
-        for _ in range(5):
-            meta_txt = meta_txt.replace('\n\n', '\n')
-
-        # 换行 -> 双换行
-        meta_txt = meta_txt.replace('\n', '\n\n')
-
-        ############################## <第 5 步，展示分割效果> ##################################
-        # for f in finals:
-        #    print亮黄(f)
-        #    print亮绿('***************************')
-
-    return meta_txt, page_one_meta
-
-
-def get_files_from_everything(txt, type): # type='.md'
-    """
-    这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
-    下面是对每个参数和返回值的说明：
-    参数 
-    - txt: 路径或网址，表示要搜索的文件或者文件夹路径或网络上的文件。 
-    - type: 字符串，表示要搜索的文件类型。默认是.md。
-    返回值 
-    - success: 布尔值，表示函数是否成功执行。 
-    - file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。 
-    - project_folder: 字符串，表示文件所在的文件夹路径。如果是网络上的文件，就是临时文件夹的路径。
-    该函数详细注释已添加，请确认是否满足您的需要。
-    """
-    import glob, os
-
-    success = True
-    if txt.startswith('http'):
-        # 网络的远程文件
-        import requests
-        from toolbox import get_conf
-        proxies, = get_conf('proxies')
-        r = requests.get(txt, proxies=proxies)
-        with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)
-        project_folder = './gpt_log/'
-        file_manifest = ['./gpt_log/temp'+type]
-    elif txt.endswith(type):
-        # 直接给定文件
-        file_manifest = [txt]
-        project_folder = os.path.dirname(txt)
-    elif os.path.exists(txt):
-        # 本地路径，递归搜索
-        project_folder = txt
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
-        if len(file_manifest) == 0:
-            success = False
-    else:
-        project_folder = None
-        file_manifest = []
-        success = False
-
-    return success, file_manifest, project_folder

spaces/Cyril666/ContourNet-ABI/modules/model_alignment.py

@@ -1,34 +0,0 @@
-import torch
-import torch.nn as nn
-from fastai.vision import *
-
-from modules.model import Model, _default_tfmer_cfg
-
-
-class BaseAlignment(Model):
-    def __init__(self, config):
-        super().__init__(config)
-        d_model = ifnone(config.model_alignment_d_model, _default_tfmer_cfg['d_model'])
-
-        self.loss_weight = ifnone(config.model_alignment_loss_weight, 1.0)
-        self.max_length = config.dataset_max_length + 1  # additional stop token
-        self.w_att = nn.Linear(2 * d_model, d_model)
-        self.cls = nn.Linear(d_model, self.charset.num_classes)
-
-    def forward(self, l_feature, v_feature):
-        """
-        Args:
-            l_feature: (N, T, E) where T is length, N is batch size and d is dim of model
-            v_feature: (N, T, E) shape the same as l_feature 
-            l_lengths: (N,)
-            v_lengths: (N,)
-        """
-        f = torch.cat((l_feature, v_feature), dim=2)
-        f_att = torch.sigmoid(self.w_att(f))
-        output = f_att * v_feature + (1 - f_att) * l_feature
-
-        logits = self.cls(output)  # (N, T, C)
-        pt_lengths = self._get_length(logits)
-
-        return {'logits': logits, 'pt_lengths': pt_lengths, 'loss_weight':self.loss_weight,
-                'name': 'alignment'}

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/linear-58a44b5e.js

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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/UploadText-28892309.js

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-import{S as h,e as S,s as T,N as g,P as c,O as y,K as U,p as q,M as l,R as v,n as b,A as w,a4 as A}from"./index-3370be2a.js";import{X as C}from"./Blocks-f0129fcd.js";function K(t){let e,o=t[1](t[2][t[0]])+"",i,r,s,n,_=t[1]("or")+"",d,m,k,f=t[1]("interface.click_to_upload")+"",u;return{c(){e=g("div"),i=c(o),r=y(),s=g("span"),n=c("- "),d=c(_),m=c(" -"),k=y(),u=c(f),U(s,"class","or svelte-1ck5uk8"),U(e,"class","wrap svelte-1ck5uk8")},m(a,p){q(a,e,p),l(e,i),l(e,r),l(e,s),l(s,n),l(s,d),l(s,m),l(e,k),l(e,u)},p(a,[p]){p&3&&o!==(o=a[1](a[2][a[0]])+"")&&v(i,o),p&2&&_!==(_=a[1]("or")+"")&&v(d,_),p&2&&f!==(f=a[1]("interface.click_to_upload")+"")&&v(u,f)},i:b,o:b,d(a){a&&w(e)}}}function M(t,e,o){let i;A(t,C,n=>o(1,i=n));let{type:r="file"}=e;const s={image:"interface.drop_image",video:"interface.drop_video",audio:"interface.drop_audio",file:"interface.drop_file",csv:"interface.drop_csv"};return t.$$set=n=>{"type"in n&&o(0,r=n.type)},[r,i,s]}class P extends h{constructor(e){super(),S(this,e,M,K,T,{type:0})}}export{P as U};
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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio_client/utils.py

@@ -1,575 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import base64
-import json
-import mimetypes
-import os
-import pkgutil
-import secrets
-import shutil
-import tempfile
-import warnings
-from concurrent.futures import CancelledError
-from dataclasses import dataclass, field
-from datetime import datetime
-from enum import Enum
-from pathlib import Path
-from threading import Lock
-from typing import Any, Callable, Optional
-
-import fsspec.asyn
-import httpx
-import huggingface_hub
-import requests
-from huggingface_hub import SpaceStage
-from websockets.legacy.protocol import WebSocketCommonProtocol
-
-API_URL = "api/predict/"
-WS_URL = "queue/join"
-UPLOAD_URL = "upload"
-CONFIG_URL = "config"
-API_INFO_URL = "info"
-RAW_API_INFO_URL = "info?serialize=False"
-SPACE_FETCHER_URL = "https://gradio-space-api-fetcher-v2.hf.space/api"
-RESET_URL = "reset"
-SPACE_URL = "https://hf.space/{}"
-
-SKIP_COMPONENTS = {
-    "state",
-    "row",
-    "column",
-    "tabs",
-    "tab",
-    "tabitem",
-    "box",
-    "form",
-    "accordion",
-    "group",
-    "interpretation",
-    "dataset",
-}
-STATE_COMPONENT = "state"
-INVALID_RUNTIME = [
-    SpaceStage.NO_APP_FILE,
-    SpaceStage.CONFIG_ERROR,
-    SpaceStage.BUILD_ERROR,
-    SpaceStage.RUNTIME_ERROR,
-    SpaceStage.PAUSED,
-]
-
-__version__ = (pkgutil.get_data(__name__, "version.txt") or b"").decode("ascii").strip()
-
-
-class TooManyRequestsError(Exception):
-    """Raised when the API returns a 429 status code."""
-
-    pass
-
-
-class QueueError(Exception):
-    """Raised when the queue is full or there is an issue adding a job to the queue."""
-
-    pass
-
-
-class InvalidAPIEndpointError(Exception):
-    """Raised when the API endpoint is invalid."""
-
-    pass
-
-
-class SpaceDuplicationError(Exception):
-    """Raised when something goes wrong with a Space Duplication."""
-
-    pass
-
-
-class Status(Enum):
-    """Status codes presented to client users."""
-
-    STARTING = "STARTING"
-    JOINING_QUEUE = "JOINING_QUEUE"
-    QUEUE_FULL = "QUEUE_FULL"
-    IN_QUEUE = "IN_QUEUE"
-    SENDING_DATA = "SENDING_DATA"
-    PROCESSING = "PROCESSING"
-    ITERATING = "ITERATING"
-    PROGRESS = "PROGRESS"
-    FINISHED = "FINISHED"
-    CANCELLED = "CANCELLED"
-
-    @staticmethod
-    def ordering(status: Status) -> int:
-        """Order of messages. Helpful for testing."""
-        order = [
-            Status.STARTING,
-            Status.JOINING_QUEUE,
-            Status.QUEUE_FULL,
-            Status.IN_QUEUE,
-            Status.SENDING_DATA,
-            Status.PROCESSING,
-            Status.PROGRESS,
-            Status.ITERATING,
-            Status.FINISHED,
-            Status.CANCELLED,
-        ]
-        return order.index(status)
-
-    def __lt__(self, other: Status):
-        return self.ordering(self) < self.ordering(other)
-
-    @staticmethod
-    def msg_to_status(msg: str) -> Status:
-        """Map the raw message from the backend to the status code presented to users."""
-        return {
-            "send_hash": Status.JOINING_QUEUE,
-            "queue_full": Status.QUEUE_FULL,
-            "estimation": Status.IN_QUEUE,
-            "send_data": Status.SENDING_DATA,
-            "process_starts": Status.PROCESSING,
-            "process_generating": Status.ITERATING,
-            "process_completed": Status.FINISHED,
-            "progress": Status.PROGRESS,
-        }[msg]
-
-
-@dataclass
-class ProgressUnit:
-    index: Optional[int]
-    length: Optional[int]
-    unit: Optional[str]
-    progress: Optional[float]
-    desc: Optional[str]
-
-    @classmethod
-    def from_ws_msg(cls, data: list[dict]) -> list[ProgressUnit]:
-        return [
-            cls(
-                index=d.get("index"),
-                length=d.get("length"),
-                unit=d.get("unit"),
-                progress=d.get("progress"),
-                desc=d.get("desc"),
-            )
-            for d in data
-        ]
-
-
-@dataclass
-class StatusUpdate:
-    """Update message sent from the worker thread to the Job on the main thread."""
-
-    code: Status
-    rank: int | None
-    queue_size: int | None
-    eta: float | None
-    success: bool | None
-    time: datetime | None
-    progress_data: list[ProgressUnit] | None
-
-
-def create_initial_status_update():
-    return StatusUpdate(
-        code=Status.STARTING,
-        rank=None,
-        queue_size=None,
-        eta=None,
-        success=None,
-        time=datetime.now(),
-        progress_data=None,
-    )
-
-
-@dataclass
-class JobStatus:
-    """The job status.
-
-    Keeps track of the latest status update and intermediate outputs (not yet implements).
-    """
-
-    latest_status: StatusUpdate = field(default_factory=create_initial_status_update)
-    outputs: list[Any] = field(default_factory=list)
-
-
-@dataclass
-class Communicator:
-    """Helper class to help communicate between the worker thread and main thread."""
-
-    lock: Lock
-    job: JobStatus
-    prediction_processor: Callable[..., tuple]
-    reset_url: str
-    should_cancel: bool = False
-
-
-########################
-# Network utils
-########################
-
-
-def is_http_url_like(possible_url: str) -> bool:
-    """
-    Check if the given string looks like an HTTP(S) URL.
-    """
-    return possible_url.startswith(("http://", "https://"))
-
-
-def probe_url(possible_url: str) -> bool:
-    """
-    Probe the given URL to see if it responds with a 200 status code (to HEAD, then to GET).
-    """
-    headers = {"User-Agent": "gradio (https://gradio.app/; [email protected])"}
-    try:
-        with requests.session() as sess:
-            head_request = sess.head(possible_url, headers=headers)
-            if head_request.status_code == 405:
-                return sess.get(possible_url, headers=headers).ok
-            return head_request.ok
-    except Exception:
-        return False
-
-
-def is_valid_url(possible_url: str) -> bool:
-    """
-    Check if the given string is a valid URL.
-    """
-    warnings.warn(
-        "is_valid_url should not be used. "
-        "Use is_http_url_like() and probe_url(), as suitable, instead.",
-    )
-    return is_http_url_like(possible_url) and probe_url(possible_url)
-
-
-async def get_pred_from_ws(
-    websocket: WebSocketCommonProtocol,
-    data: str,
-    hash_data: str,
-    helper: Communicator | None = None,
-) -> dict[str, Any]:
-    completed = False
-    resp = {}
-    while not completed:
-        # Receive message in the background so that we can
-        # cancel even while running a long pred
-        task = asyncio.create_task(websocket.recv())
-        while not task.done():
-            if helper:
-                with helper.lock:
-                    if helper.should_cancel:
-                        # Need to reset the iterator state since the client
-                        # will not reset the session
-                        async with httpx.AsyncClient() as http:
-                            reset = http.post(
-                                helper.reset_url, json=json.loads(hash_data)
-                            )
-                            # Retrieve cancel exception from task
-                            # otherwise will get nasty warning in console
-                            task.cancel()
-                            await asyncio.gather(task, reset, return_exceptions=True)
-                        raise CancelledError()
-            # Need to suspend this coroutine so that task actually runs
-            await asyncio.sleep(0.01)
-        msg = task.result()
-        resp = json.loads(msg)
-        if helper:
-            with helper.lock:
-                has_progress = "progress_data" in resp
-                status_update = StatusUpdate(
-                    code=Status.msg_to_status(resp["msg"]),
-                    queue_size=resp.get("queue_size"),
-                    rank=resp.get("rank", None),
-                    success=resp.get("success"),
-                    time=datetime.now(),
-                    eta=resp.get("rank_eta"),
-                    progress_data=ProgressUnit.from_ws_msg(resp["progress_data"])
-                    if has_progress
-                    else None,
-                )
-                output = resp.get("output", {}).get("data", [])
-                if output and status_update.code != Status.FINISHED:
-                    try:
-                        result = helper.prediction_processor(*output)
-                    except Exception as e:
-                        result = [e]
-                    helper.job.outputs.append(result)
-                helper.job.latest_status = status_update
-        if resp["msg"] == "queue_full":
-            raise QueueError("Queue is full! Please try again.")
-        if resp["msg"] == "send_hash":
-            await websocket.send(hash_data)
-        elif resp["msg"] == "send_data":
-            await websocket.send(data)
-        completed = resp["msg"] == "process_completed"
-    return resp["output"]
-
-
-########################
-# Data processing utils
-########################
-
-
-def download_tmp_copy_of_file(
-    url_path: str, hf_token: str | None = None, dir: str | None = None
-) -> str:
-    if dir is not None:
-        os.makedirs(dir, exist_ok=True)
-    headers = {"Authorization": "Bearer " + hf_token} if hf_token else {}
-    directory = Path(dir or tempfile.gettempdir()) / secrets.token_hex(20)
-    directory.mkdir(exist_ok=True, parents=True)
-    file_path = directory / Path(url_path).name
-
-    with requests.get(url_path, headers=headers, stream=True) as r:
-        r.raise_for_status()
-        with open(file_path, "wb") as f:
-            shutil.copyfileobj(r.raw, f)
-    return str(file_path.resolve())
-
-
-def create_tmp_copy_of_file(file_path: str, dir: str | None = None) -> str:
-    directory = Path(dir or tempfile.gettempdir()) / secrets.token_hex(20)
-    directory.mkdir(exist_ok=True, parents=True)
-    dest = directory / Path(file_path).name
-    shutil.copy2(file_path, dest)
-    return str(dest.resolve())
-
-
-def get_mimetype(filename: str) -> str | None:
-    if filename.endswith(".vtt"):
-        return "text/vtt"
-    mimetype = mimetypes.guess_type(filename)[0]
-    if mimetype is not None:
-        mimetype = mimetype.replace("x-wav", "wav").replace("x-flac", "flac")
-    return mimetype
-
-
-def get_extension(encoding: str) -> str | None:
-    encoding = encoding.replace("audio/wav", "audio/x-wav")
-    type = mimetypes.guess_type(encoding)[0]
-    if type == "audio/flac":  # flac is not supported by mimetypes
-        return "flac"
-    elif type is None:
-        return None
-    extension = mimetypes.guess_extension(type)
-    if extension is not None and extension.startswith("."):
-        extension = extension[1:]
-    return extension
-
-
-def encode_file_to_base64(f: str | Path):
-    with open(f, "rb") as file:
-        encoded_string = base64.b64encode(file.read())
-        base64_str = str(encoded_string, "utf-8")
-        mimetype = get_mimetype(str(f))
-        return (
-            "data:"
-            + (mimetype if mimetype is not None else "")
-            + ";base64,"
-            + base64_str
-        )
-
-
-def encode_url_to_base64(url: str):
-    resp = requests.get(url)
-    resp.raise_for_status()
-    encoded_string = base64.b64encode(resp.content)
-    base64_str = str(encoded_string, "utf-8")
-    mimetype = get_mimetype(url)
-    return (
-        "data:" + (mimetype if mimetype is not None else "") + ";base64," + base64_str
-    )
-
-
-def encode_url_or_file_to_base64(path: str | Path):
-    path = str(path)
-    if is_http_url_like(path):
-        return encode_url_to_base64(path)
-    return encode_file_to_base64(path)
-
-
-def decode_base64_to_binary(encoding: str) -> tuple[bytes, str | None]:
-    extension = get_extension(encoding)
-    data = encoding.rsplit(",", 1)[-1]
-    return base64.b64decode(data), extension
-
-
-def strip_invalid_filename_characters(filename: str, max_bytes: int = 200) -> str:
-    """Strips invalid characters from a filename and ensures that the file_length is less than `max_bytes` bytes."""
-    filename = "".join([char for char in filename if char.isalnum() or char in "._- "])
-    filename_len = len(filename.encode())
-    if filename_len > max_bytes:
-        while filename_len > max_bytes:
-            if len(filename) == 0:
-                break
-            filename = filename[:-1]
-            filename_len = len(filename.encode())
-    return filename
-
-
-def sanitize_parameter_names(original_name: str) -> str:
-    """Cleans up a Python parameter name to make the API info more readable."""
-    return (
-        "".join([char for char in original_name if char.isalnum() or char in " _"])
-        .replace(" ", "_")
-        .lower()
-    )
-
-
-def decode_base64_to_file(
-    encoding: str,
-    file_path: str | None = None,
-    dir: str | Path | None = None,
-    prefix: str | None = None,
-):
-    directory = Path(dir or tempfile.gettempdir()) / secrets.token_hex(20)
-    directory.mkdir(exist_ok=True, parents=True)
-    data, extension = decode_base64_to_binary(encoding)
-    if file_path is not None and prefix is None:
-        filename = Path(file_path).name
-        prefix = filename
-        if "." in filename:
-            prefix = filename[0 : filename.index(".")]
-            extension = filename[filename.index(".") + 1 :]
-
-    if prefix is not None:
-        prefix = strip_invalid_filename_characters(prefix)
-
-    if extension is None:
-        file_obj = tempfile.NamedTemporaryFile(
-            delete=False, prefix=prefix, dir=directory
-        )
-    else:
-        file_obj = tempfile.NamedTemporaryFile(
-            delete=False,
-            prefix=prefix,
-            suffix="." + extension,
-            dir=directory,
-        )
-    file_obj.write(data)
-    file_obj.flush()
-    return file_obj
-
-
-def dict_or_str_to_json_file(jsn: str | dict | list, dir: str | Path | None = None):
-    if dir is not None:
-        os.makedirs(dir, exist_ok=True)
-
-    file_obj = tempfile.NamedTemporaryFile(
-        delete=False, suffix=".json", dir=dir, mode="w+"
-    )
-    if isinstance(jsn, str):
-        jsn = json.loads(jsn)
-    json.dump(jsn, file_obj)
-    file_obj.flush()
-    return file_obj
-
-
-def file_to_json(file_path: str | Path) -> dict | list:
-    with open(file_path) as f:
-        return json.load(f)
-
-
-###########################
-# HuggingFace Hub API Utils
-###########################
-def set_space_timeout(
-    space_id: str,
-    hf_token: str | None = None,
-    timeout_in_seconds: int = 300,
-):
-    headers = huggingface_hub.utils.build_hf_headers(
-        token=hf_token,
-        library_name="gradio_client",
-        library_version=__version__,
-    )
-    req = requests.post(
-        f"https://huggingface.co/api/spaces/{space_id}/sleeptime",
-        json={"seconds": timeout_in_seconds},
-        headers=headers,
-    )
-    try:
-        huggingface_hub.utils.hf_raise_for_status(req)
-    except huggingface_hub.utils.HfHubHTTPError as err:
-        raise SpaceDuplicationError(
-            f"Could not set sleep timeout on duplicated Space. Please visit {SPACE_URL.format(space_id)} "
-            "to set a timeout manually to reduce billing charges."
-        ) from err
-
-
-########################
-# Misc utils
-########################
-
-
-def synchronize_async(func: Callable, *args, **kwargs) -> Any:
-    """
-    Runs async functions in sync scopes. Can be used in any scope.
-
-    Example:
-        if inspect.iscoroutinefunction(block_fn.fn):
-            predictions = utils.synchronize_async(block_fn.fn, *processed_input)
-
-    Args:
-        func:
-        *args:
-        **kwargs:
-    """
-    return fsspec.asyn.sync(fsspec.asyn.get_loop(), func, *args, **kwargs)  # type: ignore
-
-
-class APIInfoParseError(ValueError):
-    pass
-
-
-def get_type(schema: dict):
-    if "type" in schema:
-        return schema["type"]
-    elif schema.get("oneOf"):
-        return "oneOf"
-    elif schema.get("anyOf"):
-        return "anyOf"
-    else:
-        raise APIInfoParseError(f"Cannot parse type for {schema}")
-
-
-def json_schema_to_python_type(schema: Any) -> str:
-    """Convert the json schema into a python type hint"""
-    type_ = get_type(schema)
-    if type_ == {}:
-        if "json" in schema["description"]:
-            return "Dict[Any, Any]"
-        else:
-            return "Any"
-    elif type_ == "null":
-        return "None"
-    elif type_ == "integer":
-        return "int"
-    elif type_ == "string":
-        return "str"
-    elif type_ == "boolean":
-        return "bool"
-    elif type_ == "number":
-        return "int | float"
-    elif type_ == "array":
-        items = schema.get("items")
-        if "prefixItems" in items:
-            elements = ", ".join(
-                [json_schema_to_python_type(i) for i in items["prefixItems"]]
-            )
-            return f"Tuple[{elements}]"
-        else:
-            elements = json_schema_to_python_type(items)
-            return f"List[{elements}]"
-    elif type_ == "object":
-        des = ", ".join(
-            [
-                f"{n}: {json_schema_to_python_type(v)} ({v.get('description')})"
-                for n, v in schema["properties"].items()
-            ]
-        )
-        return f"Dict({des})"
-    elif type_ in ["oneOf", "anyOf"]:
-        desc = " | ".join([json_schema_to_python_type(i) for i in schema[type_]])
-        return desc
-    else:
-        raise APIInfoParseError(f"Cannot parse schema {schema}")

spaces/Deci/DeciLM-6b-instruct/USE_POLICY.md

@@ -1,50 +0,0 @@
-# Llama 2 Acceptable Use Policy
-
-Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
-
-## Prohibited Uses
-We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
-
-1. Violate the law or others’ rights, including to:
-    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
-        1. Violence or terrorism 
-        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
-        3. Human trafficking, exploitation, and sexual violence
-        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
-        5. Sexual solicitation
-        6. Any other criminal activity
-    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
-    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
-    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
-    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
-    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
-    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 
-
-
-
-2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
-    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
-    2. Guns and illegal weapons (including weapon development)
-    3. Illegal drugs and regulated/controlled substances
-    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
-    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
-    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
-
-
-
-3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
-    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
-    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
-    3. Generating, promoting, or further distributing spam
-    4. Impersonating another individual without consent, authorization, or legal right
-    5. Representing that the use of Llama 2 or outputs are human-generated
-    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
-4. Fail to appropriately disclose to end users any known dangers of your AI system 
-
-Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
-
-* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
-* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
-* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
-* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected])
-