Datasets:

Weyaxi
/

huggingface-spaces-codes

@@ -1,78 +0,0 @@
-# gpt4free gui
-This code provides a Graphical User Interface (GUI) for gpt4free. Users can ask questions and get answers from GPT-4 API's, utilizing multiple API implementations. The project contains two different Streamlit applications: `streamlit_app.py` and `streamlit_chat_app.py`.
-In addition, a new GUI script specifically implemented using PyWebIO has been added and can be found in the pywebio-gui folder. If there are errors with the Streamlit version, you can try using the PyWebIO version instead
-Installation
-------------
-1.  Clone the repository.
-2.  Install the required dependencies with: `pip install -r requirements.txt`.
-3.  To use `streamlit_chat_app.py`, note that it depends on a pull request (PR #24) from the https://github.com/AI-Yash/st-chat/ repository, which may change in the future. The current dependency library can be found at https://github.com/AI-Yash/st-chat/archive/refs/pull/24/head.zip.
-Analytics Disclaimer
------
-The streamlit browser app collects heavy analytics even when running locally. This includes events for every page load, form submission including metadata on queries (like length), browser and client information including host ips. These are all transmitted to a 3rd party analytics group, Segment.com.
-Usage
------
-Choose one of the Streamlit applications to run:
-### streamlit\_app.py
-This application provides a simple interface for asking GPT-4 questions and receiving answers.
-To run the application:
-run:
-```arduino
-streamlit run gui/streamlit_app.py
-```
-<br>
-<img width="724" alt="image" src="https://user-images.githubusercontent.com/98614666/234232449-0d5cd092-a29d-4759-8197-e00ba712cb1a.png">
-<br>
-<br>
-preview:
-<img width="1125" alt="image" src="https://user-images.githubusercontent.com/98614666/234232398-09e9d3c5-08e6-4b8a-b4f2-0666e9790c7d.png">
-### streamlit\_chat\_app.py
-This application provides a chat-like interface for asking GPT-4 questions and receiving answers. It supports multiple query methods, and users can select the desired API for their queries. The application also maintains a conversation history.
-To run the application:
-```arduino
-streamlit run streamlit_chat_app.py
-```
-<br>
-<img width="724" alt="image" src="image1.png">
-<br>
-<br>
-preview:
-<img width="1125" alt="image" src="image2.png">
-Contributing
-------------
-Feel free to submit pull requests, report bugs, or request new features by opening issues on the GitHub repository.
-Bug
-----
-There is a bug in `streamlit_chat_app.py` right now that I haven't pinpointed yet, probably is really simple but havent had the time to look for it. Whenever you open a new conversation or access an old conversation it will only start prompt-answering after the second time you input to the text input, other than that, everything else seems to work accordingly.
-License
--------
-This project is licensed under the MIT License.

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Blaupunkt TravelPilot DX 2013 - 2014 The Best Navigation System for Europe[1].md DELETED Viewed

@@ -1,145 +0,0 @@
-<br />
-<h1>Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013</h1>
- <p>If you are looking for a reliable and convenient navigation system for your car, you might want to check out Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013. This is a digital map that covers all the countries in Europe and provides you with accurate and up-to-date information on roads, traffic, landmarks, and more. In this article, we will tell you everything you need to know about this navigation system, including how to download and install it, how to use it, what are its advantages and disadvantages, and how it compares with other navigation systems. Let's get started!</p>
- <h2>How to Download and Install Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013</h2>
- <p>The first step to use this navigation system is to download the torrent file from a reliable source. You can find many websites that offer this file for free or for a small fee. However, make sure that you choose a reputable and secure site that does not contain any viruses or malware. You can use a torrent client software such as uTorrent or BitTorrent to download the file.</p>
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- <p>The next step is to insert the SD card into your Blaupunkt Dx device and update the navigation software. To do this, you need to turn on your device and go to the main menu. Then, select "Settings" and then "System Update". The device will detect the SD card and ask you if you want to update. Confirm by pressing "Yes". The update process may take several minutes, so do not turn off your device or remove the SD card until it is finished. When it is done, you will see a message that says "Update Successful". Congratulations! You have successfully installed Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013.</p>
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- <p>In the map mode, you can select your desired destination by using one of these methods:</p>
-<ul>
-<li>Enter an address: Press the "Address" button and type in an address or a postcode using the keyboard on the screen. You can also select a country, a city, a street name, or a house number from a list.</li>
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-<li>Select a location from history: Press the "History" button and choose a location that you have previously entered or visited.</li>
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-<li>Select a location from coordinates: Press the "Coordinates" button and enter the latitude and longitude values using the keyboard on the screen.</li>
-</ul>
-<p>Once you have selected your destination, press the "Start" button to begin navigation. The device will calculate the best route for you based on your current location and preferences. You can also change your preferences by pressing the "Settings" button on your device. You can adjust things such as:</p>
-<ul>
-<li>Route type: Choose between fastest, shortest, economical, or easy routes.</li>
-<li>Avoidances: Choose whether to avoid toll roads, highways, ferries, unpaved roads, etc.</li>
-<li>Voice guidance: Choose whether to enable or disable voice guidance and select a language and a volume level.</li>
-<li>Map view: Choose between 2D or 3D view and select a day or night mode.</li>
-<li>Map details: Choose whether to display points of interest, traffic information, speed limits, etc.</li>
-</ul>
-<p>While navigating, you can follow the voice guidance and visual cues on your screen. The device will tell you when to turn left or right, when to enter or exit a highway, when to change lanes, etc. You can also see information such as distance remaining, time remaining, speed limit, current speed, etc. on your screen.</p>
- <p>If you encounter any traffic jams, road closures, or other hazards along your route, the device will alert you and suggest an alternative route if available. You can also press the "Traffic" button on your device to see more details about traffic conditions in your area. You can also press the "Detour" button if you want to manually change your route.</p>
- <h2>Advantages of Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013</h2>
- <p>Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013 is one of the best navigation systems for drivers in Europe because it offers many advantages, such as:</p>
-<ul>
-<li>Accurate and up-to-date maps: This navigation system provides you with detailed and updated maps of all European countries. It covers more than 10 million kilometers of roads and more than 5 million points of interest. It also includes information about speed limits, lane guidance, junction views, cross-border planning, etc.</li>
-<li>Various points of interest: This navigation system offers you various points of interest, such as gas stations, hotels, museums, parks, etc. You can easily find and navigate to any place you want using the POI search function. You can also see ratings and reviews of some places from other users.</li>
-<li>Enhanced driving experience and safety: This navigation system enhances your driving experience and safety by providing you with real-time information on traffic, weather, speed cameras, etc. You can avoid delays and hazards and drive more smoothly and confidently. You can also use the hands-free function to make or receive calls using your device.</li>
-</ul>
- <h2>Disadvantages of Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013</h2>
- <p>However, Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013 also has some disadvantages that you should be aware of, such as:</p>
-<ul>
-<li>Compatibility issues: This navigation system may not be compatible with some older models of Blaupunkt Dx devices. You should check the compatibility list before downloading and installing it. You may also need to update your device's firmware to make it work properly.</li>
-<li>Internet connection requirement: This navigation system may require a high-speed internet connection to download and update. The torrent file is about 3.5 GB in size, so it may take a long time to download depending on your connection speed. You may also incur additional data charges if you use a mobile network.</li>
-<li>Potential errors or glitches: This navigation system may have some errors or glitches in some areas or routes. For example, some roads or POIs may be missing or outdated, some voice commands or directions may be incorrect or unclear, some features or functions may not work properly, etc. You should always use this navigation system with caution and common sense.</li>
-</ul>
- <h2>Comparison with Other Navigation Systems</h2>
- <p>Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013 is not the only navigation system available for drivers in Europe. There are other popular navigation systems, such as Garmin, TomTom, etc. How does Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013 compare with them? Here is a table that summarizes the pros and cons of each system:</p>
- <table>
-<tr>
-<th>System</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013</td>
-<td>- Accurate and up-to-date maps of Europe<br>- Various points of interest<br>- Enhanced driving experience and safety<br>- Free or low-cost download</td>
-<td>- Compatibility issues with some devices<br>- Internet connection requirement<br>- Potential errors or glitches</td>
-</tr>
-<tr>
-<td>Garmin</td>
-<td>- High-quality maps of Europe<br>- Advanced features such as lane assist, junction view, photoReal, etc.<br>- Lifetime map updates<br>- Compatible with most devices</td>
-<td>- Expensive purchase<br>- Limited points of interest<br>- Slow performance and updates<br>- Occasional errors or glitches</td>
-</tr>
-<tr>
-<td>TomTom</td>
-<td>- Detailed maps of Europe<br>- Innovative features such as IQ Routes, HD Traffic, Map Share, etc.<br>- Regular map updates<br>- User-friendly interface and design</td>
-<td>- Costly purchase and subscription<br>- Compatibility issues with some devices<br>- Privacy concerns<br>- Frequent errors or glitches</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>In conclusion, Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013 is a great navigation system for drivers in Europe who want accurate and up-to-date maps, various points of interest, and enhanced driving experience and safety. It is also free or low-cost to download and easy to install and use. However, it also has some drawbacks, such as compatibility issues with some devices, internet connection requirement, and potential errors or glitches. Therefore, you should weigh the pros and cons carefully before deciding whether to use this navigation system or not.</p>
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-<li>Use a software such as WinRAR or 7-Zip to extract the files.</li>
-<li>Use an SD card with enough space (at least 4 GB) and formatted in FAT32.</li>
-<li>Update your device's firmware if necessary.</li>
-<li>Adjust your settings and preferences according to your needs.</li>
-<li>Use the hands-free function to make or receive calls safely.</li>
-<li>Follow the voice guidance and visual cues carefully.</li>
-<li>Avoid traffic jams, road closures, and other hazards using the real-time information.</li>
-<li>Use caution and common sense when using this navigation system.</li>
-<li>Share your feedback with other users.</li>
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- <p>We hope that this article has helped you understand more about Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013 and how to use it effectively. If you have any questions or comments, please feel free to contact us. We would love to hear from you!</p>
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- <ol>
-<li><b>What is Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013?</b><br>Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013 is a digital map that covers all the countries in Europe and provides you with accurate and up-to-date information on roads, traffic, landmarks, and more. It is compatible with most models of Blaupunkt Dx devices.</li>
- <li><b>How do I download and install Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013?</b><br>You need to download the torrent file from a reliable source using a torrent client software such as uTorrent or BitTorrent. Then, you need to extract the files using a software such as WinRAR or 7-Zip and copy them to an SD card formatted in FAT32. Finally, you need to insert the SD card into your device and update the navigation software from the main menu.</li>
- <li><b>How do I use Torrent Sd Navigation Blaupunkt Dx Teleatlas Europe 20122013?</b><br>You need to access the main menu on your device and select "Navigation" to enter the map mode. Then, you need to select your destination by entering an address, selecting a point of interest, choosing a location from history or favorites, or entering coordinates. Then, you need to press "Start" to begin navigation. You can follow the voice guidance and visual cues on your screen and adjust your settings and preferences as needed. You can also avoid traffic jams, road closures, and other hazards using the real-time information.</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/El Silabario Salvadoreno Pdf Download La obra ilustrada que ensea el habla checa y otras.md DELETED Viewed

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-<h1>El Silabario Salvadoreño: A Classic Book for Learning to Read and Write in Spanish</h1>
- <p>If you are looking for a simple, effective, and fun way to learn Spanish, you might want to check out El Silabario Salvadoreño. This is a classic book that has been used by generations of children and adults in El Salvador and other Latin American countries to learn the basics of reading and writing in Spanish. In this article, we will tell you everything you need to know about El Silabario Salvadoreño, including its history, content, benefits, and tips for using it. Whether you are a beginner or an intermediate Spanish learner, you will find this book useful and enjoyable.</p>
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- <ul>
-<li><b>Practice:</b> The key to learning anything is practice. Try to use El Silabario Salvadoreño regularly and consistently. Set a goal for yourself (for example, one page per day) and stick to it. Review what you have learned frequently.</li>
-<li><b>Supplement:</b> While El Silabario Salvadoreño is a great resource for learning Spanish, it is not enough by itself. You should also use other resources and methods for learning Spanish. For example, you can listen to podcasts or songs in Spanish; watch videos or movies in Spanish; read books or articles in Spanish; speak with native speakers or other learners; use apps or websites that teach grammar or vocabulary; etc.</li>
-<li><b>Review:</b> To measure your progress and improvement with El Silabario Salvadoreño, you should test yourself periodically. You can use the exercises at the end of each page or create your own tests based on what you have learned. You can also ask someone else to check your work or give you feedback.</li>
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- <h4>Conclusion</h4>
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spaces/1line/AutoGPT/autogpt/memory/__init__.py DELETED Viewed

@@ -1,99 +0,0 @@
-from autogpt.memory.local import LocalCache
-from autogpt.memory.no_memory import NoMemory
-# List of supported memory backends
-# Add a backend to this list if the import attempt is successful
-supported_memory = ["local", "no_memory"]
-try:
-    from autogpt.memory.redismem import RedisMemory
-    supported_memory.append("redis")
-except ImportError:
-    # print("Redis not installed. Skipping import.")
-    RedisMemory = None
-try:
-    from autogpt.memory.pinecone import PineconeMemory
-    supported_memory.append("pinecone")
-except ImportError:
-    # print("Pinecone not installed. Skipping import.")
-    PineconeMemory = None
-try:
-    from autogpt.memory.weaviate import WeaviateMemory
-    supported_memory.append("weaviate")
-except ImportError:
-    # print("Weaviate not installed. Skipping import.")
-    WeaviateMemory = None
-try:
-    from autogpt.memory.milvus import MilvusMemory
-    supported_memory.append("milvus")
-except ImportError:
-    # print("pymilvus not installed. Skipping import.")
-    MilvusMemory = None
-def get_memory(cfg, init=False):
-    memory = None
-    if cfg.memory_backend == "pinecone":
-        if not PineconeMemory:
-            print(
-                "Error: Pinecone is not installed. Please install pinecone"
-                " to use Pinecone as a memory backend."
-            )
-        else:
-            memory = PineconeMemory(cfg)
-            if init:
-                memory.clear()
-    elif cfg.memory_backend == "redis":
-        if not RedisMemory:
-            print(
-                "Error: Redis is not installed. Please install redis-py to"
-                " use Redis as a memory backend."
-            )
-        else:
-            memory = RedisMemory(cfg)
-    elif cfg.memory_backend == "weaviate":
-        if not WeaviateMemory:
-            print(
-                "Error: Weaviate is not installed. Please install weaviate-client to"
-                " use Weaviate as a memory backend."
-            )
-        else:
-            memory = WeaviateMemory(cfg)
-    elif cfg.memory_backend == "milvus":
-        if not MilvusMemory:
-            print(
-                "Error: Milvus sdk is not installed."
-                "Please install pymilvus to use Milvus as memory backend."
-            )
-        else:
-            memory = MilvusMemory(cfg)
-    elif cfg.memory_backend == "no_memory":
-        memory = NoMemory(cfg)
-    if memory is None:
-        memory = LocalCache(cfg)
-        if init:
-            memory.clear()
-    return memory
-def get_supported_memory_backends():
-    return supported_memory
-__all__ = [
-    "get_memory",
-    "LocalCache",
-    "RedisMemory",
-    "PineconeMemory",
-    "NoMemory",
-    "MilvusMemory",
-    "WeaviateMemory",
-]

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Agar.io Mod Macro Download Enhance Your Gameplay with Agar Tool M PELEA.md DELETED Viewed

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-<h1>Download Agar io Mod Macro: How to Enhance Your Gameplay Experience</h1>
- <p>If you are a fan of online multiplayer games, you might have heard of or played <a href="(^1^)">Agar io</a>, a simple but addictive browser game where you control a cell and try to eat other cells to grow bigger. But did you know that you can also download and install a mod macro for Agar io, which can give you more features and advantages in the game? In this article, we will explain what Agar io is, what a mod macro is, how to download and install it, and how to use it effectively. By the end of this article, you will be able to enjoy Agar io with a new level of fun and excitement.</p>
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- <p>Agar io is a massively multiplayer online action game that was released in 2015 by a Brazilian developer named Matheus Valadares. The game is inspired by the biological phenomenon of agar, which is a gelatinous substance used to culture bacteria. In the game, players control a cell that can move around a map and eat smaller cells, while avoiding being eaten by larger cells. The goal is to become the largest cell in the map and dominate the leaderboard.</p>
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-<li>Using bots or scripts to automate some actions or movements</li>
-</ul>
-<p>A mod macro can make Agar io more fun, easy, or challenging, depending on your preference and play style. However, a mod macro can also be considered as a cheat or a hack by some players or developers, as it can give you an unfair advantage over other players who do not use a mod macro. Therefore, you should be careful and respectful when using a mod macro, and avoid using it in servers or modes that prohibit it.</p>
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- <p>There are many types and features of mod macros for Agar io, each with different functions and purposes. Some of the most popular and widely used mod macros for Agar io are:</p>
-<table>
-<tr>
-<th>Mod Macro Name</th>
-<th>Mod Macro Features</th>
-</tr>
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-<td><a href="">Agar Tool</a></td>
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-</tr>
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-<td>- Zoom in or out with the mouse wheel<br>- Eject mass faster with E key<br>- Split into 16 cells with Z key<br>- Change skin with W key<br>- Show mass and speed with M key<br>- Show coordinates with C key<br>- Show leaderboard with L key<br>- Show chat with Enter key<br>- Show statistics with S key<br>- Use bots with B key</td>
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-</tr>
-</table>
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- <ol>
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-</ol>
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-<li>Splitting into multiple cells: This can help you split your cell into more than two smaller cells, which can be used for various purposes, such as catching other cells, dodging other cells, or spreading your mass. You can usually split into multiple cells with one key, such as A, S, D, F, G, H, J, K, L, Z, X, C, V, B.</li>
-<li>Changing the skin or color of your cell: This can help you change the appearance of your cell, which can be used for various purposes , such as expressing your personality, showing your affiliation, or disguising your identity. You can usually change the skin or color of your cell with the W key or by selecting a skin from the menu.</li>
-<li>Showing the coordinates, mass, or speed of your cell: This can help you see the exact position, size, or velocity of your cell, which can be used for various purposes, such as navigating the map, measuring your growth, or adjusting your movement. You can usually show the coordinates, mass, or speed of your cell with the M or C keys or by enabling an option from the menu.</li>
-<li>Showing the leaderboard, chat, or statistics of the game: This can help you see the ranking, communication, or performance of yourself and other players, which can be used for various purposes, such as competing, socializing, or improving. You can usually show the leaderboard, chat, or statistics of the game with the L, Enter, or S keys or by enabling an option from the menu.</li>
-<li>Using bots or scripts to automate some actions or movements: This can help you use artificial intelligence or code to perform some tasks or behaviors for you or assist you in the game, which can be used for various purposes, such as playing when you are away, helping you when you are stuck, or testing some strategies. You can usually use bots or scripts with the B key or by installing a script from a website.</li>
-</ul>
- <h3>The best practices and strategies for using Agar io mod macro</h3>
- <p>The best practices and strategies for using Agar io mod macro depend on your personal preference and play style. However, some of the general tips and advice are:</p>
-<ul>
-<li>Use a mod macro that suits your needs and goals: There are many types and features of mod macros for Agar io, but not all of them may be useful or enjoyable for you. You should choose a mod macro that offers the features and functions that you want and need in the game, and avoid using a mod macro that has unnecessary or unwanted features and functions.</li>
-<li>Use a mod macro that is compatible and safe: There are many sources and websites that offer mod macros for Agar io, but not all of them may be reliable or secure. You should download and install a mod macro that is compatible with your web browser and user script manager extension, and avoid downloading and installing a mod macro that may contain viruses, malware, or outdated code.</li>
-<li>Use a mod macro that is respectful and ethical: There are many benefits and advantages that a mod macro can provide for Agar io players, but not all of them may be fair or acceptable. You should use a mod macro that is respectful and ethical to other players and the game developer, and avoid using a mod macro that may be banned or frowned upon by the game rules and policies.</li>
-<li>Use a mod macro that is fun and challenging: There are many features and functions that a mod macro can offer for Agar io players, but not all of them may be fun or challenging. You should use a mod macro that is fun and challenging to enhance your gameplay experience, and avoid using a mod macro that may make the game too easy or boring.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Agar io is a simple but addictive online multiplayer game where you control a cell and try to eat other cells to grow bigger. However, if you want to have more features and advantages in the game, you can also download and install a mod macro for Agar io, which can enhance your performance, functionality, or appearance in the game. In this article, we explained what Agar io is, what a mod macro is , how to download and install it, and how to use it effectively. We hope that this article was helpful and informative for you, and that you will enjoy Agar io with a new level of fun and excitement. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions and answers about Agar io mod macro:</p>
-<ol>
-<li>Q: Is Agar io mod macro legal or illegal?<br>A: Agar io mod macro is not illegal, but it may be against the game rules or policies. You should check the terms of service and privacy policy of Agar io before using a mod macro, and respect the rights and wishes of the game developer and other players.</li>
-<li>Q: Is Agar io mod macro safe or risky?<br>A: Agar io mod macro is not risky, but it may be unsafe. You should download and install a mod macro from a trusted and updated source, and avoid downloading and installing a mod macro that may contain viruses, malware, or outdated code. You should also scan your device and game account regularly for any potential threats or issues.</li>
-<li>Q: Is Agar io mod macro free or paid?<br>A: Agar io mod macro is usually free, but it may be paid. You should check the price and payment method of the mod macro before downloading and installing it, and avoid downloading and installing a mod macro that may charge you without your consent or knowledge. You should also support the original game developer by purchasing the game or in-game items if you can.</li>
-<li>Q: Is Agar io mod macro easy or hard?<br>A: Agar io mod macro is usually easy, but it may be hard. You should follow the instructions and requirements of the mod macro carefully, and avoid skipping or missing any steps or tools for installation or usage. You should also practice and experiment with the mod macro features and functions until you master them.</li>
-<li>Q: Is Agar io mod macro fun or boring?<br>A: Agar io mod macro is usually fun, but it may be boring. You should choose a mod macro that suits your needs and goals, and avoid using a mod macro that has unnecessary or unwanted features and functions. You should also use a mod macro that is fun and challenging, and avoid using a mod macro that may make the game too easy or boring.</li>
-</ol></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/CarX Drift Racing 2 How to Master the Art of Tandem Drifting.md DELETED Viewed

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-<h1>CarX Drift Racing 2: A Review of the Best Drift Racing Game</h1>
-<p>If you are a fan of drift racing, you might have heard of CarX Drift Racing 2, the sequel of the most desired drift-game in the world. This game offers an unprecedented and realistic experience of driving real sports cars on one of many race tracks available throughout the game. In this article, we will review CarX Drift Racing 2 and tell you why you should play it, what features it has, and what are its pros and cons.</p>
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-<h2>Introduction</h2>
-<h3>What is CarX Drift Racing 2?</h3>
-<p>CarX Drift Racing 2 is a mobile game developed by CarX Technologies, a company that specializes in creating realistic car physics and graphics for games. It is the second installment of the CarX Drift Racing series, which has over 100 million fans around the world. The game was released in December 2018 for Android and iOS devices, and has since received many updates and improvements.</p>
-<h3>Why should you play CarX Drift Racing 2?</h3>
-<p>CarX Drift Racing 2 is not just another racing game. It is a game that lets you experience the thrill and excitement of drifting, a driving technique where the driver intentionally oversteers the car to make it slide sideways. Drifting requires skill, precision, and practice, and CarX Drift Racing 2 gives you the opportunity to master it. You can compete against real people in online championships, race in tandems with other players, customize your car and track, and enjoy the realistic graphics and physics of the game. Whether you are a beginner or a pro, CarX Drift Racing 2 will challenge you and keep you entertained for hours.</p>
-<h2>Features of CarX Drift Racing 2</h2>
-<h3>Online Rooms</h3>
-<p>This is the game mode that you have been waiting for. You can now drift in real time with your friends or other players from around the world. You can create or join an online room, pick a location, drift, and earn points. You can also watch other players drift using the drone camera. You can earn valuable rewards for achieving different ranks in online rooms.</p>
-<h3>Visual Auto Tuning</h3>
-<p>This feature allows you to customize your car's appearance to suit your style and preferences. You can replace mirrors, lights, running boards, bumpers, and many other parts. You can also create a unique image of your car with body kits, rims, vinyls, and more. The possibilities are endless.</p>
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-<h3>Improved Performance Tuning</h3>
-<p>This feature allows you to fine-tune your car's performance to match your driving skills and needs. You can adjust your suspension, springs, tyre pressure, wheel angle, engine, turbine pressure, gear box, brakes, locking differential, and more. You can show some quality drift only if you have your car fine-tuned to your needs.</p>
-<h3>The Most True to Life Racing on a Mobile Platform</h3>
-<p>This feature makes CarX Drift Racing 2 stand out from other racing games. The game has improved steering control that is perfect for quick side changing, backwards and drift donuts. The game also shows how tyre pressure affects driving physics. The game developers ran a number of field tests with real drift cars to collect data and improve the game physics. The game also has realistic sound effects that make you feel like you are driving a real car. You can hear the sound of engine, turbo, tyres, and exhaust.</p>
-<h3>XDS Mode</h3>
-<p>This feature allows you to enjoy tandem drifting with artificial intelligence. You can select from different modes of difficulty and learn how to drift from the best drivers. You can also improve your own skills by following the leader or leading the follower. You can earn coins and reputation points by performing well in XDS mode.</p>
-<h3>Top-32 Mode</h3>
-<p>This feature allows you to compete in the world championships of drift racing. You can qualify for the Top-32 list of the best drivers from all over the world. You can then challenge them in head-to-head battles and prove your skills. You can win trophies and prizes by advancing in the Top-32 mode.</p>
-<h3>Multiplayer Mode</h3>
-<p>This feature allows you to race against other players in real time. You can join a random race or create your own lobby. You can choose from different modes such as Classic, Time Attack, or Drift Race. You can also chat with other players and make friends. You can earn coins and reputation points by winning races in multiplayer mode.</p>
-<h2>Pros and Cons of CarX Drift Racing 2</h2>
-<h3>Pros</h3>
-<h4>Realistic graphics and physics</h4>
-<p>The game has stunning graphics that make you feel like you are in a real race track. The game also has realistic physics that simulate the behaviour of real cars and tyres. The game is a feast for your eyes and ears.</p>
-<h4>Customizable cars and tracks</h4>
-<p>The game has a wide range of cars and tracks that you can choose from. You can also customize your car's appearance and performance to suit your style and preferences. You can create your own unique car and track with the visual auto tuning and track editor features.</p>
-<h4>Challenging and fun gameplay</h4>
-<p>The game has various game modes that offer different levels of challenge and fun. You can drift solo or with other players, race against time or opponents, or compete in championships. The game also has a dynamic scoring system that rewards you for your style, skill, and speed.</p>
-<h3>Cons</h3>
-<h4>High battery consumption</h4>
-<p>The game has high-quality graphics and physics that require a lot of processing power from your device. This means that the game drains your battery faster than other games. You might need to charge your device more often or lower the graphics settings to save battery life.</p>
-<h4>In-app purchases and ads</h4>
-<p>The game is free to download and play, but it also has in-app purchases and ads that might affect your gaming experience. You might need to spend real money to unlock some cars, tracks, or features, or watch ads to earn some coins or bonuses. You can also disable the ads by purchasing the premium version of the game.</p>
-<h4>Steep learning curve</h4>
-<p>The game is not easy to master, especially for beginners. Drifting requires a lot of practice and patience, and the game does not have a tutorial or a guide to help you learn the basics. You might need to watch some videos or read some tips online to improve your skills.</p>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, CarX Drift Racing 2 is a drift racing game that offers an unprecedented and realistic experience of driving real sports cars on one of many race tracks available throughout the game. The game has many features such as online rooms, visual auto tuning, improved performance tuning, XDS mode, Top-32 mode, multiplayer mode, realistic graphics and physics, customizable cars and tracks, challenging and fun gameplay, etc. The game also has some drawbacks such as high battery consumption, in-app purchases and ads, steep learning curve, etc.</p>
-<h3>Recommendation and rating</h3>
-<p>We recommend CarX Drift Racing 2 to anyone who loves drift racing or wants to try something new and exciting. The game is suitable for both beginners and pros, as it offers different levels of difficulty and challenge. The game is also free to download and play, so you have nothing to lose by giving it a try. We rate CarX Drift Racing 2 4.5 out of 5 stars for its amazing graphics, physics, gameplay, features, etc.</p>
- FAQs Q: How do I download CarX Drift Racing 2? A: You can download CarX Drift Racing 2 from Google Play Store for Android devices or App Store for iOS devices. Q: How do I control my car in CarX Drift Racing 2? A: You can control your car in CarX Drift Racing 2 using different options such as tilt, buttons, or steering wheel. You can also adjust the sensitivity and position of the controls in the settings menu. Q: How do I earn coins and reputation points in CarX Drift Racing 2? A: You can earn coins and reputation points in CarX Drift Racing 2 by drifting, racing, and competing in different game modes. You can also watch ads or complete offers to get some extra coins or bonuses. Q: How do I unlock new cars and tracks in CarX Drift Racing 2? A: You can unlock new cars and tracks in CarX Drift Racing 2 by spending coins or real money. You can also unlock some cars and tracks by achieving certain ranks or completing certain tasks in the game. Q: How do I customize my car and track in CarX Drift Racing 2? A: You can customize your car and track in CarX Drift Racing 2 by using the visual auto tuning and track editor features. You can change the appearance and performance of your car, and create your own unique track with different objects and settings. Q: How do I improve my skills in CarX Drift Racing 2? A: You can improve your skills in CarX Drift Racing 2 by practicing and learning from other players. You can also use the XDS mode to drift with artificial intelligence, or watch some videos or read some tips online to get some advice and tricks.</p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Carros Rebaixados Online A game that lets you change the color wheels and glass of your car.md DELETED Viewed

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-<h1>Carros Rebaixados Online APK: A Fun and Customizable Simulation Game</h1>
-<p>If you are a fan of cars and simulation games, you might want to check out <strong>Carros Rebaixados Online APK</strong>, a game that lets you customize and show off your car to your friends. This game is developed by Sebby Games, a Brazilian studio that specializes in creating realistic and immersive car games. In this game, you can choose from various models of cars, modify them according to your preferences, and drive them around in different scenarios. You can also play online with other players, chat with them, and compete with them. In this article, we will tell you everything you need to know about this game, including how to download and install it, what features it offers, how to play it, what are its pros and cons, how it compares to other similar games, and some tips to improve your experience.</p>
-<h2>How to download and install Carros Rebaixados Online APK on your Android device?</h2>
-<p>Downloading and installing Carros Rebaixados Online APK is very easy and straightforward. You can follow these simple steps:</p>
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-<li>Go to [this link](^1^) or [this link](^2^) on your Android device's browser.</li>
-<li>Tap on the download button and wait for the APK file to be downloaded.</li>
-<li>Once the download is complete, tap on the file and allow the installation from unknown sources if prompted.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game from your app drawer or home screen and enjoy!</li>
-</ol>
-<h2>Features of Carros Rebaixados Online APK</h2>
-<p>Carros Rebaixados Online APK is a game that offers a lot of features for car enthusiasts. Here are some of them:</p>
-<h3>Detailed car models and customization options</h3>
-<p>The game features several models of cars that are completely detailed and realistic. You can customize your car in various ways, such as changing its color, wheels, glass, xenon, neon, speakers, LED, etc. You can also choose the size of the car wheel rim and turn up the bass of the song. You can make your car unique and express your personality through it.</p>
-<h3>First or third person perspective and 360 degrees car interiors</h3>
-<p>The game allows you to drive your car from either a first or a third person perspective. You can switch between them anytime you want. You can also see the car interiors in 360 degrees, which adds to the realism and immersion of the game. You can see every detail of your car's dashboard, seats, steering wheel, etc.</p>
-<h3>Interactive elements and realistic physics</h3>
-<p>The game also features many interactive elements in cars, such as opening car doors, hood, trunk, and windows, turning on the car, turning on the lights, etc. The game also has realistic physics that make the car behave according to its weight, speed, suspension, etc. You can feel the difference between driving on asphalt, dirt, or grass.</p>
-<h3>Day and night mode and camera filters</h3>
-<p>The game also has a day and night mode that changes the lighting and atmosphere of the game. You can drive your car in different times of the day and see how it affects the visibility and mood of the game. You can also use different camera filters to change the color and contrast of the game. You can choose from sepia, black and white, vintage, etc.</p>
-<h3>Music and sound effects</h3>
-<p>The game also has a great soundtrack that features various genres of music, such as rap, funk, pop, rock, etc. You can listen to your favorite songs while driving your car and enjoy the rhythm and vibe of the game. You can also hear realistic sound effects of your car's engine, brakes, horn, etc. The game also supports Bluetooth speakers and headphones for a better audio experience.</p>
-<h3>Multiple wheels, neon, speakers, and LED</h3>
-<p>The game also offers multiple options for wheels, neon, speakers, and LED for your car. You can choose from different types and colors of wheels that suit your car's style and performance. You can also add neon lights to your car's body and wheels to make it glow in the dark. You can also install speakers and LED in your car's trunk to create a party atmosphere.</p>
-<h3>Steering wheel, accelerometer, or arrows control</h3>
-<p>The game also gives you three options for controlling your car: steering wheel, accelerometer, or arrows. You can choose the one that you prefer and that is more comfortable for you. You can also adjust the sensitivity and position of the controls according to your preference.</p>
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-<h2>Gameplay of Carros Rebaixados Online APK</h2>
-<p>Carros Rebaixados Online APK is a game that is easy to play but hard to master. Here are some tips on how to play the game:</p>
-<h3>How to start and play the game?</h3>
-<p>To start the game, you need to choose a car model from the garage. You can see the details and stats of each car before choosing it. You can also modify your car in the garage by tapping on the wrench icon. Once you are ready, you can tap on the play button to enter the game world. You can choose from different scenarios, such as city, beach, farm, etc. You can also choose whether you want to play offline or online.</p>
-<h3>How to modify and show off your car?</h3>
-<p>To modify your car, you need to tap on the wrench icon in the garage or in the game world. You can then access various options for customization, such as color, wheels, glass, xenon, neon, speakers, LED, etc. You can also adjust the size of the wheel rim and the bass of the song. You can see the changes in real time and preview them before applying them. To show off your car, you can drive it around in the game world and interact with other cars and objects. You can also use the camera icon to take screenshots or videos of your car and share them with your friends or on social media.</p>
-<h3>How to interact with other cars and objects?</h3>
-<p>To interact with other cars and objects, you need to tap on the hand icon in the game world. You can then access various options for interaction, such as opening car doors, hood, trunk, and windows, turning on the car, turning on the lights, honking the horn, etc. You can also use the chat icon to communicate with other players using text or voice messages. You can also use the emoji icon to express your emotions or reactions.</p>
-<h3>How to switch between modes and perspectives?</h3>
-<p>To switch between modes and perspectives, you need to tap on the gear icon in the game world. You can then access various options for settings, such as day and night mode, camera filters, sound and music volume, language, etc. You can also switch between first or third person perspective by tapping on the eye icon. You can also switch between steering wheel, accelerometer, or arrows control by tapping on the controller icon.</p>
-<h2>Review of Carros Rebaixados Online APK</h2>
-<p>Carros Rebaixados Online APK is a game that has received a lot of positive feedback from its users. Here are some of its pros and cons, ratings and reviews, and comparison with other similar games:</p>
-<h3>Pros and cons of Carros Rebaixados Online APK</h3>
-<p>The game has many pros, such as:</p>
-<ul>
-<li>It has realistic and detailed graphics and physics.</li>
-<li>It has a lot of customization options for cars.</li>
-<li>It has an online mode with chat and voice messages.</li>
-<li>It has a great soundtrack and sound effects.</li>
-<li>It has a simple and intuitive interface and controls.</li>
-</ul>
-<p>The game also has some cons, such as:</p>
-<ul>
-<li>It may have some bugs and glitches.</li>
-<li>It may consume a lot of battery and data.</li>
-<li>It may have some ads and in-app purchases.</li>
-<li>It may not be compatible with some devices or regions.</li>
-<li>It may not have a lot of variety in scenarios or cars.</li>
-</ul>
-<h3>Ratings and reviews of Carros Rebaixados Online APK on Google Play Store</h3>
-<p>The game has a rating of 4.4 out of 5 stars on Google Play Store based on more than 100 thousand reviews. Here are some of the reviews from the users:</p>
- <table style="border: 1px solid black;">
-<tr style="border: 1px solid black;">
-<th style="border: 1px solid black;">User</th>
-<th style="border: 1px solid black;">Rating</th>
-<th style="border: 1px solid black;">Review</th>
-</tr>
-<tr style="border: 1px solid black;">
-<td style="border: 1px solid black;">Lucas Santos</td>
-<td style="border: 1px solid black;">5 stars</td>
-<td style="border: 1px solid black;">"This game is very good, I recommend it to everyone who likes cars and simulation games. The graphics are amazing, the cars are very realistic, and the online mode is very fun. I love this game!"</td>
-</tr>
-<tr style="border: 1px solid black;">
-<td style="border: 1px solid black;">Maria Silva</td>
-<td style="border: 1px solid black;">4 stars</td>
-<td style="border: 1px solid black;">"I like this game a lot, it is very entertaining and addictive. The only thing I don't like is that it has too many ads and it consumes a lot of battery. But other than that, it is a great game."</td>
-</tr>
-<tr style="border: 1px solid black;">
-<td style="border: 1px solid black;">Pedro Oliveira</td>
-<td style="border: 1px solid black;">3 stars</td>
-<td style="border: 1px solid black;">"The game is good, but it could be better. It needs more scenarios, more cars, more customization options, more interaction options, etc. It also has some bugs and glitches that need to be fixed."</td>
-</tr>
-<tr style="border: 1px solid black <h3>How to get more resources and items in the game?</h3>
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- <p>If you are a fan of Brazilian rap music, you might have heard of the song Saudades Mil by 509-E. This song is a classic example of how rap can tell powerful stories and convey deep emotions. In this article, we will explore what this song is about, who are the artists behind it, and how you can download it for free.</p>
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- <h2>What is the song Saudades Mil about?</h2>
- <p>Saudades Mil is a Portuguese expression that means "a thousand sorrows" or "a thousand longings". It is often used to express nostalgia, sadness, or missing someone or something. The song Saudades Mil by 509-E is a letter from a prisoner to his friend, who is also in jail. The prisoner tells his friend about his life, his memories, his regrets, and his hopes. He also expresses his sorrow for losing his wife, his friend's husband, and another inmate. He ends the letter by saying that he will see his friend soon, when they both get out of prison.</p>
- <h3>The story behind the song</h3>
- <p>The song Saudades Mil was released in 1999 as part of the album Provérbios 13 by 509-E. The group name stands for "5th floor, cell number 9, east wing", which was where the two members of the group, Dexter and Afro-X, were incarcerated in Carandiru Penitentiary in São Paulo. They started making rap music in prison as a way to cope with their situation and to denounce the injustices and violence they faced. They recorded their songs using a cassette recorder and smuggled them out of prison with the help of other inmates and visitors. Their songs became popular in the underground rap scene and eventually reached mainstream audiences.</p>
- <h3>The meaning of the lyrics</h3>
- <p>The lyrics of Saudades Mil are written in a mix of Portuguese and slang, which reflects the culture and reality of the Brazilian urban poor. The lyrics are full of references to places, people, events, and expressions that are familiar to those who live in the favelas (slums) or in prison. Some examples are:</p>
- <ul>
-<li>Diadema: A city in the metropolitan area of São Paulo, where Dexter was born and raised.</li>
-<li>Laisla: Dexter's daughter, who was born when he was already in prison.</li>
-<li>Amarildo: Afro-X's brother-in-law, who was killed by rival gang members.</li>
-<li>Jorge: Jorge Ben Jor, a famous Brazilian singer-songwriter, who wrote a song called Charles Anjo 45, about a criminal who escapes from prison.</li>
-<li>Charles: A reference to Charles Anjo 45, as well as to Charles Bronson, an American actor who starred in movies about vigilantes and outlaws.</li>
-</ul>
- <p>The lyrics also convey a range of emotions, such as anger, sadness, frustration, hope, love, and gratitude. The prisoner expresses his anger at the system that put him in jail, his sadness for losing his loved ones, his frustration for wasting his life, his hope for getting out of prison and starting over, his love for his daughter and his friends, and his gratitude for receiving a letter from his friend.</p>
- <h3>The impact of the song</h3>
- <p>The song Saudades Mil had a huge <p>impact on the Brazilian rap scene and society. It was one of the first songs to expose the harsh reality of life in prison and the social problems that lead to crime and violence. It also showed the potential of rap as a form of artistic expression and social criticism. The song inspired many other rap artists to tell their stories and to use rap as a tool for education and empowerment. The song also raised awareness and sympathy among the public and the authorities for the situation of prisoners and their families. The song was praised by critics and fans alike for its authenticity, creativity, and emotion.</p>
- <h2>Who are 509-E and what is their style?</h2>
- <p>509-E is a Brazilian rap group formed by Dexter and Afro-X in 1998, while they were serving time in Carandiru Penitentiary. They are considered one of the pioneers and most influential groups of Brazilian rap music.</p>
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- <h3>The origin and history of 509-E</h3>
- <p>Dexter and Afro-X were both born and raised in poor neighborhoods of São Paulo, where they were exposed to crime, violence, drugs, and racism. They both started rapping at a young age, influenced by American rap artists such as Public Enemy, N.W.A., and Tupac Shakur. They also joined gangs and got involved in criminal activities, which led them to prison. Dexter was arrested for robbery and Afro-X for drug trafficking. They met in prison and decided to form a rap group, using their cell number as their name. They wrote songs about their experiences, their opinions, their dreams, and their struggles. They recorded their songs using a cassette recorder and smuggled them out of prison with the help of other inmates and visitors. They released their first album, Provérbios 13, in 1999, which included the song Saudades Mil. The album was a success and earned them recognition and respect in the rap scene. They continued to make music while in prison, releasing two more albums: MMII DC (2002) (2002 AD) and É Nóis Que Tá (2006) (It's Us Who Are Here). They also participated in several rap festivals and events, such as Hutúz Rap Festival, Rap é Compromisso (Rap is Commitment), and Hip Hop Manifesto. They were released from prison in 2007 and 2008, respectively, after serving their sentences. They resumed their musical careers, both as solo artists and as a group. They also engaged in social projects and initiatives, such as Rap na Escola (Rap in School), Rap na Quebrada (Rap in the Hood), Rap na Febem (Rap in the Juvenile Detention Center), Rap na Cadeia (Rap in Prison), Rap na Rua (Rap on the Street), Rap na Igreja (Rap in Church), Rap na Paz (Rap for Peace), Rap na Vida (Rap for Life), Rap na Luta (Rap for Struggle), Rap na Arte (Rap for Art), Rap na Cultura (Rap for Culture), Rap na História (Rap for History), Rap na Educação (Rap for Education), Rap na Consciência (Rap for Consciousness), Rap na Liberdade (Rap for Freedom), Rap na Esperança (Rap for Hope), Rap na Fé (Rap for Faith), Rap na União (Rap for Unity), Rap na Diversidade (Rap for Diversity), Rap na Resistência (Rap for Resistance), Rap na Transformação (Rap for Transformation), Rap na Revolução (Rap for Revolution), Rap no Amor (Rap for Love), Rap no Respeito (Rap for Respect), Rap no Perdão (Rap for Forgiveness), Rap no Reconhecimento (Rap for Recognition), Rap no Sucesso (Rap for Success), Rap no Futuro (Rap for Future).</p>
- <h3>The influences and inspirations of 509-E</h3>
- <p>509-E is influenced by various musical genres, such as funk, soul, reggae, rock, samba, bossa nova, MPB (Musica Popular Brasileira), and gospel. They are also inspired by various rap artists, such as Racionais MC's, Sabotage, Facção Central, MV Bill, GOG, RZO, Thaíde e DJ Hum, SNJ, Rappin' Hood, Emicida, Criolo, Projota, Rashid, and many others. They also draw inspiration from other sources, such as literature, cinema, philosophy, religion, politics, history, and culture. Some of their references are Machado de Assis, Paulo Freire, Malcolm X, Martin Luther King Jr., Nelson Mandela, Che Guevara, Bob Marley, Jesus Christ, Buddha, Gandhi, Zumbi dos Palmares, Dandara dos Palmares, Chico Mendes, Carlos Marighella, Carlos Drummond de Andrade, Clarice Lispector, Fernando Pessoa, Luís de Camões, Jorge Amado, Gabriel García Márquez, Pablo Neruda, Mario Vargas Llosa, Gabriel O Pensador, Cidade de Deus (City of God), Tropa de Elite (Elite Squad), Carandiru (Carandiru), Pixote (Pixote), O Auto da Compadecida (A Dog's Will), O Pagador de Promessas (The Given Word), O Quatrilho (The Quatrilho), Central do Brasil (Central Station), O Som ao Redor (Neighboring Sounds), Bacurau (Bacurau), Aquarius (Aquarius), Sócrates (Socrates), Platão (Plato), Aristóteles (Aristotle), Descartes (Descartes), Kant (Kant), Hegel (Hegel), Marx (Marx), Nietzsche (Nietzsche), Sartre (Sartre), Foucault (Foucault), Derrida (Derrida), Deleuze (Deleuze), Baudrillard (Baudrillard), Bauman (Bauman), Freire (Freire), Gramsci (Gramsci), Fanon (Fanon), Said (Said), Spivak (Spivak), Bhabha (Bhabha), Hall (Hall), Butler (Butler), hooks (hooks), Lorde (Lorde), Davis (Davis), Anzaldúa (Anzaldúa), Moraga (Moraga), Crenshaw (Crenshaw), and many others.</p>
- <h3>The themes and messages of 509-E</h3>
- <p>509-E is known for addressing various themes and messages in their songs, such as social injustice, racism, violence, poverty, prison, drugs, corruption, education, culture, identity, spirituality, hope, love, friendship, family, and freedom. They use rap as a way to express their feelings, opinions, experiences, and visions. They also use rap as a way to educate, inform, inspire, and empower their listeners. They aim to raise awareness and consciousness about the problems and challenges that affect their communities and society. They also aim to promote positive values and attitudes, such as respect, solidarity, dignity, courage, resilience, creativity, and peace. They believe that rap can be a force for change and transformation.</p>
- <h2>How to download the song Saudades Mil for free?</h2>
- <p>If you want to download the song Saudades Mil by 509-E for free, you need to be aware of some legal and ethical issues. You also need to know the best sites and apps to download music. And you need to follow some simple steps to download the song.</p>
- <h3>The legal and ethical issues of downloading music</h3>
- <p>Downloading music for free can be considered illegal and unethical in some cases. This is because it can violate the intellectual property rights of the artists and the music industry. Intellectual property rights are the legal rights that protect the creations and inventions of individuals and organizations. They include copyrights, trademarks, patents, and trade secrets. By downloading music for free, you can be infringing on these rights and causing harm to the creators and owners of the music. You can also be exposing yourself to legal risks and penalties.</p>
- <p>However, downloading music for free can also be considered legal and ethical in some cases. This is because it can fall under the exceptions and limitations of intellectual property rights. These are the situations where the use of protected works is allowed without permission or payment. They include fair use, fair dealing, public domain, creative commons, and copyleft. By downloading music for free under these situations, you can be respecting the rights of the artists and the music industry. You can also be supporting the culture and the public interest.</p>
- <p>Therefore, before downloading music for free, you should check the legal status and ethical implications of your actions. You should also respect the wishes and interests of the artists and the music industry. You should also acknowledge and credit the sources of the music you download.</p>
- <h3>The best sites and apps to download music</h3>
- <p>There are many sites and apps that allow you to download music for free. However, not all of them are safe, reliable, or legal. Some of them may contain viruses <p>Based on the web search results, I found three sites that are safe and legal to download music: Bandcamp, Jamendo Music, and Internet Archive. These sites offer free music downloads under Creative Commons licenses or public domain. They also have a variety of genres, artists, and songs to choose from. Here is a brief description of each site and how to download Saudades Mil from them.</p>
- <h3>Bandcamp</h3>
- <p>Bandcamp is a site that allows artists to upload their music and set their own prices. You can browse by genre, tag, location, or popularity. You can also stream music online or download it as MP3, FLAC, ALAC, AAC, Ogg Vorbis, WAV, or AIFF files. To download Saudades Mil from Bandcamp, you need to follow these steps:</p>
- <ol>
-<li>Go to the Bandcamp homepage and type "Saudades Mil" in the search box.</li>
-<li>Select the song by 509-E from the results.</li>
-<li>Click on the "Buy Digital Track" button.</li>
-<li>Enter "0" in the name your price field and click on "Download to your computer".</li>
-<li>Choose your preferred format and click on "Download".</li>
-<li>Save the file to your device and enjoy.</li>
-</ol>
- <h3>Jamendo Music</h3>
- <p>Jamendo Music is a site that offers free music downloads under Creative Commons licenses. You can discover new music by browsing through curated playlists, genres, moods, or trending songs. You can also stream music online or download it as MP3 files. To download Saudades Mil from Jamendo Music, you need to follow these steps:</p>
- <ol>
-<li>Go to the Jamendo Music homepage and type "Saudades Mil" in the search box.</li>
-<li>Select the song by 509-E from the results.</li>
-<li>Click on the "Download" button below the song title.</li>
-<li>Create a free account or log in with your existing account.</li>
-<li>Choose your preferred quality and click on "Download".</li>
-<li>Save the file to your device and enjoy.</li>
-</ol>
- <h3>Internet Archive</h3>
- <p>Internet Archive is a site that offers free access to millions of digital files, including music, audio, podcasts, radio programs, and more. You can search by keyword, collection, creator, date, language, or media type. You can also stream music online or download it as MP3, OGG Vorbis, FLAC, or other formats. To download Saudades Mil from Internet Archive, you need to follow these steps:</p>
- <ol>
-<li>Go to the Internet Archive homepage and type "Saudades Mil" in the search box.</li>
-<li>Select the song by 509-E from the results.</li>
-<li>Click on the "VBR MP3" link under the Download Options section.</li>
-<li>Save the file to your device and enjoy.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have learned about the song Saudades Mil by 509-E, one of the most influential rap groups in Brazil. We have explored what this song is about, who are the artists behind it, and how you can download it for free. We have also learned about some legal and ethical issues of downloading music, as well as some of the best sites and apps to do so. We hope you have enjoyed this article and found it useful. If you want to learn more about Brazilian rap music or 509-E, you can check out these links:</p>
- <ul>
-<li>[The History of Brazilian Rap Music]</li>
-<li>[509-E Official Website]</li>
-<li>[509-E YouTube Channel]</li>
-</ul>
- <p>Thank you for reading this article. If you liked it, please share it with your friends and leave a comment below. We would love to hear your feedback and suggestions. And don't forget to check out our other articles on rap music and culture.</p>
- <h3>Frequently Asked Questions</h3>
- <p>Here are some of the most common questions that people ask about Saudades Mil and 509-E:</p>
- <ol>
-<li><b>What does 509-E mean?</b></li>
-<p>509-E is the name of a Brazilian rap group formed by Dexter and Afro-X in 1998. The name stands for "5th floor, cell number 9, east wing", which was where they were incarcerated in Carandiru Penitentiary in São Paulo.</p>
- <li><b>What does Saudades Mil mean?</b></li>
-<p>Saudades Mil is a Portuguese expression that means "a thousand sorrows or "a thousand longings". It is often used to express nostalgia, sadness, or missing someone or something. The song Saudades Mil by 509-E is a letter from a prisoner to his friend, who is also in jail.</p>
- <li><b>How can I listen to Saudades Mil online?</b></li>
-<p>You can listen to Saudades Mil online by streaming it on various platforms, such as YouTube, Spotify, Apple Music, Deezer, or SoundCloud. You can also watch the official video of the song on YouTube.</p>
- <li><b>Is Saudades Mil based on a true story?</b></li>
-<p>Yes, Saudades Mil is based on a true story. The song is a letter from Dexter to Afro-X, who were both imprisoned in Carandiru Penitentiary in São Paulo. The song tells the story of their lives, their memories, their regrets, and their hopes. The song also mentions real people and events that happened to them or around them.</p>
- <li><b>What are some other songs by 509-E that I should listen to?</b></li>
-<p>Some other songs by 509-E that you should listen to are:</p>
- <ul>
-<li>Oitavo Anjo (Eighth Angel)</li>
-<li>Milagre (Miracle)</li>
-<li>Só Os Fortes (Only The Strong)</li>
-<li>Depois da Meia Noite (After Midnight)</li>
-<li>Saudosa Maloca (Nostalgic Shack)</li>
-</ul>
- <li><b>What are some other Brazilian rap artists that I should listen to?</b></li>
-<p>Some other Brazilian rap artists that you should listen to are:</p>
- <ul>
-<li>Racionais MC's</li>
-<li>Sabotage</li>
-<li>Facção Central</li>
-<li>MV Bill</li>
-<li>GOG</li>
-<li>RZO</li>
-<li>Thaíde e DJ Hum</li>
-<li>SNJ</li>
-<li>Rappin' Hood</li>
-<li>Emicida</li>
-<li>Criolo</li>
-<li>Projota</li>
-<li>Rashid</li>
-</ul></p> 401be4b1e0<br />
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-<p>The Final Say by Ada is a gospel song that celebrates the sovereignty and supremacy of Jesus Christ over every situation. It declares that Jesus has the final say in everything, and that nothing can stop His plans and purposes for His children. It also expresses gratitude and praise to God for His love, grace, and power.</p>
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-<p>The message of the song is based on the biblical truth that God is in control of everything, and that He works all things together for good for those who love Him and are called according to His purpose (Romans 8:28). It encourages believers to trust in God's promises and His faithfulness, and to not be afraid or discouraged by the challenges and trials they may face in life. It also reminds them that they are more than conquerors through Christ who loves them (Romans 8:37), and that they have victory over sin, death, and the devil through His blood and resurrection.</p>
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-<ul>
-<li>Strengthen your faith and relationship with God</li>
-<li>Receive comfort, peace, joy, and hope from His presence</li>
-<li>Learn more about His word and His character</li>
-<li>Be inspired to live a godly and fruitful life</li>
-<li>Share the gospel with others through music</li>
-</ul>
- <h3>The quality and availability of the mp3 file</h3>
-<p>When you download The Final Say by Ada mp3, you will get a high-quality audio file that you can enjoy on any device. You will also be able to access it anytime and anywhere, without needing an internet connection or a streaming service. You can also create your own playlist or mixtape with other songs by Ada or other gospel artists.</p>
- <h2>How to download The Final Say by Ada <h2>How to download The Final Say by Ada mp3</h2>
-<p>Downloading The Final Say by Ada mp3 is very easy and fast, as long as you follow these steps:</p>
- <h3>The steps to follow</h3>
-<ol>
-<li>Go to one of the sources that offer the mp3 file for free or for a small fee. We will recommend some of the best sources in the next section.</li>
-<li>Find the song on the website or app, and click on the download button or link. You may need to sign up or log in to some of the sources before you can download.</li>
-<li>Choose the format and quality of the mp3 file that you want to download. The higher the quality, the larger the file size. We suggest you choose at least 128 kbps for a good sound quality.</li>
-<li>Wait for the download to complete, and then save the file to your device or cloud storage. You can also transfer the file to other devices using a USB cable, Bluetooth, or Wi-Fi.</li>
-<li>Enjoy listening to The Final Say by Ada mp3 anytime and anywhere!</li>
-</ol>
- <h3>The best sources to download from</h3>
-<p>There are many sources that offer The Final Say by Ada mp3 for download, but not all of them are reliable and safe. Some of them may contain viruses, malware, or spam that can harm your device or compromise your privacy. To avoid these risks, we recommend you to download from these trusted and verified sources:</p>
-<table>
-<tr>
-<th>Source</th>
-<th>Link</th>
-<th>Price</th>
-<th>Features</th>
-</tr>
-<tr>
-<td>iTunes</td>
-<td></td>
-<td>$0.99</td>
-<td>- High-quality mp3 file<br>- Supports Apple devices<br>- Syncs with iCloud<br>- Supports Ada's ministry</td>
-</tr>
-<tr>
-<td>Amazon Music</td>
-<td></td>
-<td>$0.99</td>
-<td>- High-quality mp3 file<br>- Supports various devices<br>- Syncs with Amazon account<br>- Supports Ada's ministry</td>
-</tr>
-<tr>
-<td>GospelJingle</td>
-<td></td>
-<td>Free</td>
-<td>- Medium-quality mp3 file<br>- Supports various devices<br>- Easy and fast download<br>- No sign up required</td>
-</tr>
-<tr>
-<td>NaijaMusic</td>
-<td></td>
-<td>Free</td>
-<td>- Medium-quality mp3 file<br>- Supports various devices<br>- Easy and fast download<br>- No sign up required</td>
-</tr> <h2>Conclusion</h2>
-<p>We hope that this article has helped you to learn more about The Final Say by Ada, and how to download it as an mp3 file. This song is a wonderful way to worship God and to declare His lordship over your life. It will also bless you with peace, joy, and hope as you listen to it.</p>
- <h4>Summary of the main points</h4>
-<p>Here are the main points that we covered in this article:</p>
-<ul>
-<li>The Final Say by Ada is a gospel song that celebrates the sovereignty and supremacy of Jesus Christ over every situation.</li>
-<li>The song has a powerful message, based on the biblical truth that God is in control of everything, and that He works all things together for good for those who love Him.</li>
-<li>The song has simple yet profound lyrics, using repetition and rhyme to create a catchy and memorable tune.</li>
-<li>Downloading The Final Say by Ada mp3 has many benefits, such as strengthening your faith, receiving comfort and hope, learning more about God, and supporting Ada's ministry.</li>
-<li>Downloading The Final Say by Ada mp3 is easy and fast, as long as you follow the steps and use the trusted sources that we recommended.</li>
-</ul>
- <h4>Call to action</h4>
-<p>Now that you know how to download The Final Say by Ada mp3, what are you waiting for? Go ahead and download it today, and enjoy listening to this amazing song. You can also share it with your friends and family, and let them know about the goodness and greatness of God. You will not regret it!</p>
- <h2>FAQs</h2>
-<h4>Q1: Who is Ada Ehi?</h4>
-<p>A1: Ada Ehi is a Nigerian gospel singer, songwriter, recording and performing artist. She started her musical career at the age of 10 as a backup singer for Tosin Jegede. She later joined the Christ Embassy Church and became a member of the LoveWorld music team. She has released several albums and singles, such as Future Now, Born of God, Only You Jesus, I Testify, and many more. She is also a wife and a mother of two children.</p>
- <h4>Q2: What is ADA's EP (Vol.1)?</h4>
-<p>A2: ADA's EP (Vol.1) is a collection of five songs by Ada Ehi, released in 2019. The songs are The Final Say, Beautiful, See What The Lord Has Done, The Faithful God, and No One Like You. The EP showcases Ada's versatility and creativity as a gospel artist, as well as her passion for God and His people.</p>
- <h4>Q3: How can I watch the official video of The Final Say by Ada?</h4>
-<p>A3: You can watch the official video of The Final Say by Ada on [YouTube] or [Vimeo]. The video features Ada singing and dancing with joy and confidence, surrounded by colorful backgrounds and props. It also has some scenes of people celebrating God's goodness and faithfulness in their lives.</p>
- <h4>Q4: How can I support Ada's ministry?</h4>
-<p>A4: You can support Ada's ministry by downloading her songs, watching her videos, following her on social media, subscribing to her newsletter, attending her concerts and events, praying for her and her family, and giving generously to her projects and causes. You can also share her songs and messages with others, and encourage them to do the same.</p>
- <h4>Q5: Where can I find more songs by Ada?</h4>
-<p>A5: You can find more songs by Ada on her [official website], [Spotify], [Apple Music], [Deezer], [SoundCloud], [Boomplay], [Audiomack], [Napster], [Tidal], or any other music streaming platform. You can also buy her CDs or DVDs from online or offline stores.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download MusicHQ.net The Ultimate Source for Full HD Movies and TV Series Online.md DELETED Viewed

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-<br />
-<h1>Download MusicHQ.net: A Guide to Watch Full HD Movies Online</h1>
-<p>If you are a movie lover, you might have heard of MusicHQ.net, a commercial-free video streaming service that offers full HD movies and TV series online. But did you know that you can also download MusicHQ.net and watch your favorite movies offline? In this article, we will show you what MusicHQ.net is, why you should download it, how to download it, and what are some of the best alternatives to it.</p>
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- <h2>What is MusicHQ.net?</h2>
-<p>MusicHQ.net is a website that provides free access to thousands of movies and TV shows in various genres and languages. You can watch them online with full subtitles and 1080p quality, or you can download them to your device and watch them anytime, anywhere. MusicHQ.net was created in 2019 and has gained popularity among movie fans around the world.</p>
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-<p>Some of the features that make MusicHQ.net stand out from other streaming sites are:</p>
-<ul>
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-</ul>
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-<ul>
-<li>You can watch movies offline without worrying about internet speed, bandwidth, or data usage.</li>
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-<ul>
-<li>You may encounter malware, viruses, or spyware that may harm your device or compromise your privacy.</li>
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- <h2>How to download MusicHQ.net?</h2> <p>If you have decided to download MusicHQ.net, you need to follow some simple steps to do it successfully. Here are the instructions:</p>
- <h3>Step-by-step instructions</h3>
-<ol>
-<li>Go to the official website of MusicHQ.net at www.musichq.net and find the movie or TV show you want to download.</li>
-<li>Click on the movie or TV show poster and you will be redirected to a new page with more details and options.</li>
-<li>Scroll down and look for the download button below the video player. It may have different labels, such as "Download", "Download HD", "Download Full Movie", etc.</li>
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-<li>Click on the link that suits your preference and you will be taken to another page where you can start the download process. You may need to click on another button or link that says "Download Now", "Start Download", "Confirm Download", etc.</li>
-<li>Wait for the download to finish and enjoy your movie offline.</li>
-</ol>
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-<p>Here are some tips and tricks that can help you download MusicHQ.net more easily and safely:</p>
-<ul>
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- <h3>List of top 10 alternatives</h3>
-<ul>
-<li><a href="">Netflix</a>: The most popular and widely used streaming service that offers original and exclusive movies and TV shows, as well as a huge library of licensed content. You can watch online or download offline with a paid subscription.</li>
-<li><a href="">Amazon Prime Video</a>: Another popular and widely used streaming service that offers original and exclusive movies and TV shows, as well as a huge library of licensed content. You can watch online or download offline with a paid subscription.</li>
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-<li><a href="">HBO Max</a>: A streaming service that offers original and exclusive movies and TV shows from HBO, Warner Bros., DC, Cartoon Network, Adult Swim, and more. You can watch online or download offline with a paid subscription.</li>
-<li><a href="">YouTube</a>: The most popular and widely used video-sharing platform that offers millions of user-generated videos, as well as some original and licensed content. You can watch online for free or download offline with a paid subscription.</li>
-<li><a href="">Tubi</a>: A free streaming service that offers thousands of movies and TV shows in various genres and languages. You can watch online for free but you cannot download offline.</li>
-<li><a href="">Crackle</a>: A free streaming service that offers thousands of movies and TV shows in various genres and languages. You can watch online for free but you cannot download offline.</li>
-<li><a href="">Popcornflix</a>: A free streaming service that offers thousands of movies and TV shows in various genres and languages. You can watch online for free but you cannot download offline.</li>
-<li><a href="">Vudu</a>: A streaming service that offers thousands of movies and TV shows in various genres and languages. You can watch online for free or download offline with a paid subscription.</li>
-</ul>
- <h3>Comparison table</h3>
-<table>
-<tr>
-<th>Name</th>
-<th>Price</th>
-<th>Content</th>
-<th>Quality</th>
-<th>Download</th>
-</tr>
-<tr>
-<td>MusicHQ.net</td>
-<td>Free</td>
-<td>Thousands of movies and TV shows in various genres and languages</td>
-<td>Full HD (1080p)</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Netflix</td>
-<td>$8.99-$17.99 per month</td>
-<td>Original and exclusive movies and TV shows, as well as a huge library of licensed content</td>
-<td>Full HD (1080p) or Ultra HD (4K)</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Amazon Prime Video</td>
-<td>$8.99 per month or $119 per year</td>
-<td>Original and exclusive movies and TV shows, as well as a huge library of licensed content</td>
-<td>Full HD (1080p) or Ultra HD (4K)</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Hulu</td>
-<td>$5.99-$11.99 per month or $64.99-$70.99 per month with live TV</td>
-<td>Original and exclusive movies and TV shows, as well as a huge library of licensed content</td>
-<td>Full HD (1080p) or Ultra HD (4K)</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Disney+</td>
-<td>$7.99 per month or $79.99 per year</td>
-<td>Original and exclusive movies and TV shows from Disney, Pixar, Marvel, Star Wars, National Geographic, and more</td>
-<td>Full HD (1080p) or Ultra HD (4K)</td>
-<td>Yes</td>
-</tr>
- <tr> <td>HBO Max</td>
-<td>$9.99-$14.99 per month</td>
-<td>Original and exclusive movies and TV shows from HBO, Warner Bros., DC, Cartoon Network, Adult Swim, and more</td>
-<td>Full HD (1080p) or Ultra HD (4K)</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>YouTube</td>
-<td>Free or $11.99 per month for YouTube Premium</td>
-<td>Millions of user-generated videos, as well as some original and licensed content</td>
-<td>Full HD (1080p) or Ultra HD (4K)</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Tubi</td>
-<td>Free</td>
-<td>Thousands of movies and TV shows in various genres and languages</td>
-<td>Full HD (1080p)</td>
-<td>No</td>
-</tr>
-<tr>
-<td>Crackle</td>
-<td>Free</td>
-<td>Thousands of movies and TV shows in various genres and languages</td>
-<td>Full HD (1080p)</td>
-<td>No</td>
-</tr>
-<tr>
-<td>Popcornflix</td>
-<td>Free</td>
-<td>Thousands of movies and TV shows in various genres and languages</td>
-<td>Full HD (1080p)</td>
-<td>No</td>
-</tr>
-<tr>
-<td>Vudu</td>
-<td>Free or $3.99-$19.99 per movie or TV show</td>
-<td>Thousands of movies and TV shows in various genres and languages</td>
-<td>Full HD (1080p) or Ultra HD (4K)</td>
-<td>Yes</td>
-</tr>
- <h2>Conclusion</h2>
- <p>In conclusion, MusicHQ.net is a great website to watch full HD movies online for free. However, if you want to enjoy your movies offline, you can also download MusicHQ.net and save them on your device. You just need to follow some simple steps and tips to do it safely and easily. However, you should also be aware of the risks and legal issues that may arise from downloading MusicHQ.net. If you are looking for some alternatives to MusicHQ.net, you can check out the list and comparison table above and choose the one that suits your needs and preferences.</p>
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spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_all_in_one.py DELETED Viewed

@@ -1,1294 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import inspect
-import os
-import random
-import re
-import time
-from typing import Callable, List, Optional, Union
-import numpy as np
-import paddle
-import PIL
-import PIL.Image
-from packaging import version
-from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, deprecate, logging
-from ...utils.testing_utils import load_image
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-def save_all(images, FORMAT="jpg", OUTDIR="./outputs/"):
-    if not isinstance(images, (list, tuple)):
-        images = [images]
-    for image in images:
-        PRECISION = "fp32"
-        argument = image.argument
-        os.makedirs(OUTDIR, exist_ok=True)
-        epoch_time = argument["epoch_time"]
-        PROMPT = argument["prompt"]
-        NEGPROMPT = argument["negative_prompt"]
-        HEIGHT = argument["height"]
-        WIDTH = argument["width"]
-        SEED = argument["seed"]
-        STRENGTH = argument.get("strength", 1)
-        INFERENCE_STEPS = argument["num_inference_steps"]
-        GUIDANCE_SCALE = argument["guidance_scale"]
-        filename = f"{str(epoch_time)}_scale_{GUIDANCE_SCALE}_steps_{INFERENCE_STEPS}_seed_{SEED}.{FORMAT}"
-        filedir = f"{OUTDIR}/{filename}"
-        image.save(filedir)
-        with open(f"{OUTDIR}/{epoch_time}_prompt.txt", "w") as file:
-            file.write(
-                f"PROMPT: {PROMPT}\nNEG_PROMPT: {NEGPROMPT}\n\nINFERENCE_STEPS: {INFERENCE_STEPS}\nHeight: {HEIGHT}\nWidth: {WIDTH}\nSeed: {SEED}\n\nPrecision: {PRECISION}\nSTRENGTH: {STRENGTH}\nGUIDANCE_SCALE: {GUIDANCE_SCALE}"
-            )
-re_attention = re.compile(
-    r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""",
-    re.X,
-)
-def parse_prompt_attention(text):
-    """
-    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
-    Accepted tokens are:
-      (abc) - increases attention to abc by a multiplier of 1.1
-      (abc:3.12) - increases attention to abc by a multiplier of 3.12
-      [abc] - decreases attention to abc by a multiplier of 1.1
-      \( - literal character '('
-      \[ - literal character '['
-      \) - literal character ')'
-      \] - literal character ']'
-      \\ - literal character '\'
-      anything else - just text
-    >>> parse_prompt_attention('normal text')
-    [['normal text', 1.0]]
-    >>> parse_prompt_attention('an (important) word')
-    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
-    >>> parse_prompt_attention('(unbalanced')
-    [['unbalanced', 1.1]]
-    >>> parse_prompt_attention('\(literal\]')
-    [['(literal]', 1.0]]
-    >>> parse_prompt_attention('(unnecessary)(parens)')
-    [['unnecessaryparens', 1.1]]
-    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
-    [['a ', 1.0],
-     ['house', 1.5730000000000004],
-     [' ', 1.1],
-     ['on', 1.0],
-     [' a ', 1.1],
-     ['hill', 0.55],
-     [', sun, ', 1.1],
-     ['sky', 1.4641000000000006],
-     ['.', 1.1]]
-    """
-    res = []
-    round_brackets = []
-    square_brackets = []
-    round_bracket_multiplier = 1.1
-    square_bracket_multiplier = 1 / 1.1
-    def multiply_range(start_position, multiplier):
-        for p in range(start_position, len(res)):
-            res[p][1] *= multiplier
-    for m in re_attention.finditer(text):
-        text = m.group(0)
-        weight = m.group(1)
-        if text.startswith("\\"):
-            res.append([text[1:], 1.0])
-        elif text == "(":
-            round_brackets.append(len(res))
-        elif text == "[":
-            square_brackets.append(len(res))
-        elif weight is not None and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), float(weight))
-        elif text == ")" and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), round_bracket_multiplier)
-        elif text == "]" and len(square_brackets) > 0:
-            multiply_range(square_brackets.pop(), square_bracket_multiplier)
-        else:
-            res.append([text, 1.0])
-    for pos in round_brackets:
-        multiply_range(pos, round_bracket_multiplier)
-    for pos in square_brackets:
-        multiply_range(pos, square_bracket_multiplier)
-    if len(res) == 0:
-        res = [["", 1.0]]
-    # merge runs of identical weights
-    i = 0
-    while i + 1 < len(res):
-        if res[i][1] == res[i + 1][1]:
-            res[i][0] += res[i + 1][0]
-            res.pop(i + 1)
-        else:
-            i += 1
-    return res
-def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):
-    r"""
-    Tokenize a list of prompts and return its tokens with weights of each token.
-    No padding, starting or ending token is included.
-    """
-    tokens = []
-    weights = []
-    for text in prompt:
-        texts_and_weights = parse_prompt_attention(text)
-        text_token = []
-        text_weight = []
-        for word, weight in texts_and_weights:
-            # tokenize and discard the starting and the ending token
-            token = pipe.tokenizer(word).input_ids[1:-1]
-            text_token += token
-            # copy the weight by length of token
-            text_weight += [weight] * len(token)
-            # stop if the text is too long (longer than truncation limit)
-            if len(text_token) > max_length:
-                break
-        # truncate
-        if len(text_token) > max_length:
-            text_token = text_token[:max_length]
-            text_weight = text_weight[:max_length]
-        tokens.append(text_token)
-        weights.append(text_weight)
-    return tokens, weights
-def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
-    r"""
-    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
-    """
-    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
-    weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
-    for i in range(len(tokens)):
-        tokens[i] = [bos] + tokens[i] + [eos] + [pad] * (max_length - 2 - len(tokens[i]))
-        if no_boseos_middle:
-            weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
-        else:
-            w = []
-            if len(weights[i]) == 0:
-                w = [1.0] * weights_length
-            else:
-                for j in range((len(weights[i]) - 1) // chunk_length + 1):
-                    w.append(1.0)  # weight for starting token in this chunk
-                    w += weights[i][j * chunk_length : min(len(weights[i]), (j + 1) * chunk_length)]
-                    w.append(1.0)  # weight for ending token in this chunk
-                w += [1.0] * (weights_length - len(w))
-            weights[i] = w[:]
-    return tokens, weights
-def get_unweighted_text_embeddings(
-    pipe: DiffusionPipeline, text_input: paddle.Tensor, chunk_length: int, no_boseos_middle: Optional[bool] = True
-):
-    """
-    When the length of tokens is a multiple of the capacity of the text encoder,
-    it should be split into chunks and sent to the text encoder individually.
-    """
-    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
-    if max_embeddings_multiples > 1:
-        text_embeddings = []
-        for i in range(max_embeddings_multiples):
-            # extract the i-th chunk
-            text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()
-            # cover the head and the tail by the starting and the ending tokens
-            text_input_chunk[:, 0] = text_input[0, 0]
-            text_input_chunk[:, -1] = text_input[0, -1]
-            attention_mask = paddle.ones_like(text_input_chunk)
-            text_embedding = pipe.text_encoder(text_input_chunk, attention_mask=attention_mask)[0]
-            if no_boseos_middle:
-                if i == 0:
-                    # discard the ending token
-                    text_embedding = text_embedding[:, :-1]
-                elif i == max_embeddings_multiples - 1:
-                    # discard the starting token
-                    text_embedding = text_embedding[:, 1:]
-                else:
-                    # discard both starting and ending tokens
-                    text_embedding = text_embedding[:, 1:-1]
-            text_embeddings.append(text_embedding)
-        text_embeddings = paddle.concat(text_embeddings, axis=1)
-    else:
-        attention_mask = paddle.ones_like(text_input)
-        text_embeddings = pipe.text_encoder(text_input, attention_mask=attention_mask)[0]
-    return text_embeddings
-def get_weighted_text_embeddings(
-    pipe: DiffusionPipeline,
-    prompt: Union[str, List[str]],
-    uncond_prompt: Optional[Union[str, List[str]]] = None,
-    max_embeddings_multiples: Optional[int] = 1,
-    no_boseos_middle: Optional[bool] = False,
-    skip_parsing: Optional[bool] = False,
-    skip_weighting: Optional[bool] = False,
-    **kwargs
-):
-    r"""
-    Prompts can be assigned with local weights using brackets. For example,
-    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
-    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
-    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
-    Args:
-        pipe (`DiffusionPipeline`):
-            Pipe to provide access to the tokenizer and the text encoder.
-        prompt (`str` or `List[str]`):
-            The prompt or prompts to guide the image generation.
-        uncond_prompt (`str` or `List[str]`):
-            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
-            is provided, the embeddings of prompt and uncond_prompt are concatenated.
-        max_embeddings_multiples (`int`, *optional*, defaults to `1`):
-            The max multiple length of prompt embeddings compared to the max output length of text encoder.
-        no_boseos_middle (`bool`, *optional*, defaults to `False`):
-            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
-            ending token in each of the chunk in the middle.
-        skip_parsing (`bool`, *optional*, defaults to `False`):
-            Skip the parsing of brackets.
-        skip_weighting (`bool`, *optional*, defaults to `False`):
-            Skip the weighting. When the parsing is skipped, it is forced True.
-    """
-    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
-    if isinstance(prompt, str):
-        prompt = [prompt]
-    if not skip_parsing:
-        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
-        if uncond_prompt is not None:
-            if isinstance(uncond_prompt, str):
-                uncond_prompt = [uncond_prompt]
-            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
-    else:
-        prompt_tokens = [
-            token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
-        ]
-        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
-        if uncond_prompt is not None:
-            if isinstance(uncond_prompt, str):
-                uncond_prompt = [uncond_prompt]
-            uncond_tokens = [
-                token[1:-1]
-                for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
-            ]
-            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
-    # round up the longest length of tokens to a multiple of (model_max_length - 2)
-    max_length = max([len(token) for token in prompt_tokens])
-    if uncond_prompt is not None:
-        max_length = max(max_length, max([len(token) for token in uncond_tokens]))
-    max_embeddings_multiples = min(
-        max_embeddings_multiples, (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1
-    )
-    max_embeddings_multiples = max(1, max_embeddings_multiples)
-    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
-    # pad the length of tokens and weights
-    # support bert tokenizer
-    bos = pipe.tokenizer.bos_token_id if pipe.tokenizer.bos_token_id is not None else pipe.tokenizer.cls_token_id
-    eos = pipe.tokenizer.eos_token_id if pipe.tokenizer.eos_token_id is not None else pipe.tokenizer.sep_token_id
-    pad = pipe.tokenizer.pad_token_id
-    prompt_tokens, prompt_weights = pad_tokens_and_weights(
-        prompt_tokens,
-        prompt_weights,
-        max_length,
-        bos,
-        eos,
-        pad,
-        no_boseos_middle=no_boseos_middle,
-        chunk_length=pipe.tokenizer.model_max_length,
-    )
-    prompt_tokens = paddle.to_tensor(prompt_tokens)
-    if uncond_prompt is not None:
-        uncond_tokens, uncond_weights = pad_tokens_and_weights(
-            uncond_tokens,
-            uncond_weights,
-            max_length,
-            bos,
-            eos,
-            pad,
-            no_boseos_middle=no_boseos_middle,
-            chunk_length=pipe.tokenizer.model_max_length,
-        )
-        uncond_tokens = paddle.to_tensor(uncond_tokens)
-    # get the embeddings
-    text_embeddings = get_unweighted_text_embeddings(
-        pipe, prompt_tokens, pipe.tokenizer.model_max_length, no_boseos_middle=no_boseos_middle
-    )
-    prompt_weights = paddle.to_tensor(prompt_weights, dtype=text_embeddings.dtype)
-    if uncond_prompt is not None:
-        uncond_embeddings = get_unweighted_text_embeddings(
-            pipe, uncond_tokens, pipe.tokenizer.model_max_length, no_boseos_middle=no_boseos_middle
-        )
-        uncond_weights = paddle.to_tensor(uncond_weights, dtype=uncond_embeddings.dtype)
-    # assign weights to the prompts and normalize in the sense of mean
-    # TODO: should we normalize by chunk or in a whole (current implementation)?
-    if (not skip_parsing) and (not skip_weighting):
-        previous_mean = text_embeddings.mean(axis=[-2, -1])
-        text_embeddings *= prompt_weights.unsqueeze(-1)
-        text_embeddings *= previous_mean / text_embeddings.mean(axis=[-2, -1])
-        if uncond_prompt is not None:
-            previous_mean = uncond_embeddings.mean(axis=[-2, -1])
-            uncond_embeddings *= uncond_weights.unsqueeze(-1)
-            uncond_embeddings *= previous_mean / uncond_embeddings.mean(axis=[-2, -1])
-    # For classifier free guidance, we need to do two forward passes.
-    # Here we concatenate the unconditional and text embeddings into a single batch
-    # to avoid doing two forward passes
-    if uncond_prompt is not None:
-        text_embeddings = paddle.concat([uncond_embeddings, text_embeddings])
-    return text_embeddings
-def preprocess_image(image):
-    w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = paddle.to_tensor(image)
-    return 2.0 * image - 1.0
-def preprocess_mask(mask):
-    mask = mask.convert("L")
-    w, h = mask.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    mask = mask.resize((w // 8, h // 8), resample=PIL_INTERPOLATION["nearest"])
-    mask = np.array(mask).astype(np.float32) / 255.0
-    mask = np.tile(mask, (4, 1, 1))
-    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
-    mask = 1 - mask  # repaint white, keep black
-    mask = paddle.to_tensor(mask)
-    return mask
-class StableDiffusionPipelineAllinOne(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image image-to-image inpainting generation using Stable Diffusion.
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular xxxx, etc.)
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`]
-            or [`DPMSolverMultistepScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/junnyu/stable-diffusion-v1-4-paddle) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = False,
-    ):
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_ppdiffusers_version") and version.parse(
-            version.parse(unet.config._ppdiffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-    def __call__(self, *args, **kwargs):
-        return self.text2image(*args, **kwargs)
-    def text2img(self, *args, **kwargs):
-        return self.text2image(*args, **kwargs)
-    def _encode_prompt(
-        self,
-        prompt,
-        negative_prompt,
-        max_embeddings_multiples,
-        no_boseos_middle,
-        skip_parsing,
-        skip_weighting,
-        do_classifier_free_guidance,
-        num_images_per_prompt,
-    ):
-        if do_classifier_free_guidance and negative_prompt is None:
-            negative_prompt = ""
-        text_embeddings = get_weighted_text_embeddings(
-            self, prompt, negative_prompt, max_embeddings_multiples, no_boseos_middle, skip_parsing, skip_weighting
-        )
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.tile([1, num_images_per_prompt, 1])
-        text_embeddings = text_embeddings.reshape([bs_embed * num_images_per_prompt, seq_len, -1])
-        return text_embeddings
-    def run_safety_checker(self, image, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pd")
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.cast(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-    def decode_latents(self, latents):
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clip(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
-        return image
-    def prepare_extra_step_kwargs(self, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-        return extra_step_kwargs
-    def check_inputs_text2img(self, prompt, height, width, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-    def check_inputs_img2img_inpaint(self, prompt, strength, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [1.0, 1.0] but is {strength}")
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-    def prepare_latents_text2img(self, batch_size, num_channels_latents, height, width, dtype, latents=None):
-        shape = [batch_size, num_channels_latents, height // 8, width // 8]
-        if latents is None:
-            latents = paddle.randn(shape, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-    def prepare_latents_img2img(self, image, timestep, num_images_per_prompt, dtype):
-        image = image.cast(dtype=dtype)
-        init_latent_dist = self.vae.encode(image).latent_dist
-        init_latents = init_latent_dist.sample()
-        init_latents = 0.18215 * init_latents
-        b, c, h, w = init_latents.shape
-        init_latents = init_latents.tile([1, num_images_per_prompt, 1, 1])
-        init_latents = init_latents.reshape([b * num_images_per_prompt, c, h, w])
-        # add noise to latents using the timesteps
-        noise = paddle.randn(init_latents.shape, dtype=dtype)
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-        return latents
-    def get_timesteps(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-        return timesteps
-    def prepare_latents_inpaint(self, image, timestep, num_images_per_prompt, dtype):
-        image = image.cast(dtype)
-        init_latent_dist = self.vae.encode(image).latent_dist
-        init_latents = init_latent_dist.sample()
-        init_latents = 0.18215 * init_latents
-        b, c, h, w = init_latents.shape
-        init_latents = init_latents.tile([1, num_images_per_prompt, 1, 1])
-        init_latents = init_latents.reshape([b * num_images_per_prompt, c, h, w])
-        init_latents_orig = init_latents
-        # add noise to latents using the timesteps
-        noise = paddle.randn(init_latents.shape, dtype=dtype)
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-        return latents, init_latents_orig, noise
-    @paddle.no_grad()
-    def text2image(
-        self,
-        prompt: Union[str, List[str]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        seed: Optional[int] = None,
-        latents: Optional[paddle.Tensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-        # new add
-        max_embeddings_multiples: Optional[int] = 1,
-        no_boseos_middle: Optional[bool] = False,
-        skip_parsing: Optional[bool] = False,
-        skip_weighting: Optional[bool] = False,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            seed (`int`, *optional*):
-                Random number seed.
-            latents (`paddle.Tensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `seed`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        seed = random.randint(0, 2**32) if seed is None else seed
-        argument = dict(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            seed=seed,
-            latents=latents,
-            max_embeddings_multiples=max_embeddings_multiples,
-            no_boseos_middle=no_boseos_middle,
-            skip_parsing=skip_parsing,
-            skip_weighting=skip_weighting,
-            epoch_time=time.time(),
-        )
-        paddle.seed(seed)
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs_text2img(prompt, height, width, callback_steps)
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt,
-            negative_prompt,
-            max_embeddings_multiples,
-            no_boseos_middle,
-            skip_parsing,
-            skip_weighting,
-            do_classifier_free_guidance,
-            num_images_per_prompt,
-        )
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-        timesteps = self.scheduler.timesteps
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
-        latents = self.prepare_latents_text2img(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            text_embeddings.dtype,
-            latents,
-        )
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
-        # 7. Denoising loop
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-        # 8. Post-processing
-        image = self.decode_latents(latents)
-        # 9. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image, argument=argument)
-        if not return_dict:
-            return (image, has_nsfw_concept)
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
-    @paddle.no_grad()
-    def img2img(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[paddle.Tensor, PIL.Image.Image],
-        strength: float = 0.8,
-        height=None,
-        width=None,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        seed: Optional[int] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-        # new add
-        max_embeddings_multiples: Optional[int] = 1,
-        no_boseos_middle: Optional[bool] = False,
-        skip_parsing: Optional[bool] = False,
-        skip_weighting: Optional[bool] = False,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`paddle.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
-                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
-                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
-                noise will be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter will be modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            seed (`int`, *optional*):
-                A random seed.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        seed = random.randint(0, 2**32) if seed is None else seed
-        image_str = image
-        if isinstance(image_str, str):
-            image = load_image(image_str)
-        if height is None and width is None:
-            width = (image.size[0] // 8) * 8
-            height = (image.size[1] // 8) * 8
-        elif height is None and width is not None:
-            height = (image.size[1] // 8) * 8
-        elif width is None and height is not None:
-            width = (image.size[0] // 8) * 8
-        else:
-            height = height
-            width = width
-        argument = dict(
-            prompt=prompt,
-            image=image_str,
-            negative_prompt=negative_prompt,
-            height=height,
-            width=width,
-            strength=strength,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            seed=seed,
-            max_embeddings_multiples=max_embeddings_multiples,
-            no_boseos_middle=no_boseos_middle,
-            skip_parsing=skip_parsing,
-            skip_weighting=skip_weighting,
-            epoch_time=time.time(),
-        )
-        paddle.seed(seed)
-        # 1. Check inputs
-        self.check_inputs_img2img_inpaint(prompt, strength, callback_steps)
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt,
-            negative_prompt,
-            max_embeddings_multiples,
-            no_boseos_middle,
-            skip_parsing,
-            skip_weighting,
-            do_classifier_free_guidance,
-            num_images_per_prompt,
-        )
-        # 4. Preprocess image
-        if isinstance(image, PIL.Image.Image):
-            image = image.resize((width, height))
-            image = preprocess_image(image)
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-        timesteps = self.get_timesteps(num_inference_steps, strength)
-        latent_timestep = timesteps[:1].tile([batch_size * num_images_per_prompt])
-        # 6. Prepare latent variables
-        latents = self.prepare_latents_img2img(image, latent_timestep, num_images_per_prompt, text_embeddings.dtype)
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
-        # 8. Denoising loop
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-        # 9. Post-processing
-        image = self.decode_latents(latents)
-        # 10. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
-        # 11. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image, argument=argument)
-        if not return_dict:
-            return (image, has_nsfw_concept)
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
-    @paddle.no_grad()
-    def inpaint(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[paddle.Tensor, PIL.Image.Image],
-        mask_image: Union[paddle.Tensor, PIL.Image.Image],
-        height=None,
-        width=None,
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        seed: Optional[int] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-        # new add
-        max_embeddings_multiples: Optional[int] = 1,
-        no_boseos_middle: Optional[bool] = False,
-        skip_parsing: Optional[bool] = False,
-        skip_weighting: Optional[bool] = False,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`paddle.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process. This is the image whose masked region will be inpainted.
-            mask_image (`paddle.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
-                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
-                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
-                is 1, the denoising process will be run on the masked area for the full number of iterations specified
-                in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
-                noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
-                the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            seed (`int`, *optional*):
-                A random seed.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        seed = random.randint(0, 2**32) if seed is None else seed
-        image_str = image
-        mask_image_str = mask_image
-        if isinstance(image_str, str):
-            image = load_image(image_str)
-        if isinstance(mask_image_str, str):
-            mask_image = load_image(mask_image_str)
-        if height is None and width is None:
-            width = (image.size[0] // 8) * 8
-            height = (image.size[1] // 8) * 8
-        elif height is None and width is not None:
-            height = (image.size[1] // 8) * 8
-        elif width is None and height is not None:
-            width = (image.size[0] // 8) * 8
-        else:
-            height = height
-            width = width
-        argument = dict(
-            prompt=prompt,
-            image=image_str,
-            mask_image=mask_image_str,
-            negative_prompt=negative_prompt,
-            height=height,
-            width=width,
-            strength=strength,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            seed=seed,
-            max_embeddings_multiples=max_embeddings_multiples,
-            no_boseos_middle=no_boseos_middle,
-            skip_parsing=skip_parsing,
-            skip_weighting=skip_weighting,
-            epoch_time=time.time(),
-        )
-        paddle.seed(seed)
-        # 1. Check inputs
-        self.check_inputs_img2img_inpaint(prompt, strength, callback_steps)
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt,
-            negative_prompt,
-            max_embeddings_multiples,
-            no_boseos_middle,
-            skip_parsing,
-            skip_weighting,
-            do_classifier_free_guidance,
-            num_images_per_prompt,
-        )
-        if not isinstance(image, paddle.Tensor):
-            image = image.resize((width, height))
-            image = preprocess_image(image)
-        if not isinstance(mask_image, paddle.Tensor):
-            mask_image = mask_image.resize((width, height))
-            mask_image = preprocess_mask(mask_image)
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-        timesteps = self.get_timesteps(num_inference_steps, strength)
-        latent_timestep = timesteps[:1].tile([batch_size * num_images_per_prompt])
-        # 6. Prepare latent variables
-        # encode the init image into latents and scale the latents
-        latents, init_latents_orig, noise = self.prepare_latents_inpaint(
-            image, latent_timestep, num_images_per_prompt, text_embeddings.dtype
-        )
-        # 7. Prepare mask latent
-        mask = mask_image.cast(latents.dtype)
-        mask = paddle.concat([mask] * batch_size * num_images_per_prompt)
-        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
-        # 9. Denoising loop
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-            # masking
-            init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
-            latents = (init_latents_proper * mask) + (latents * (1 - mask))
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-        # 10. Post-processing
-        image = self.decode_latents(latents)
-        # 11. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
-        # 12. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image, argument=argument)
-        if not return_dict:
-            return (image, has_nsfw_concept)
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
-    @staticmethod
-    def numpy_to_pil(images, **kwargs):
-        """
-        Convert a numpy image or a batch of images to a PIL image.
-        """
-        if images.ndim == 3:
-            images = images[None, ...]
-        images = (images * 255).round().astype("uint8")
-        pil_images = []
-        argument = kwargs.pop("argument", None)
-        for image in images:
-            image = PIL.Image.fromarray(image)
-            if argument is not None:
-                image.argument = argument
-            pil_images.append(image)
-        return pil_images

spaces/7hao/bingo/src/lib/isomorphic/browser.ts DELETED Viewed

@@ -1,11 +0,0 @@
-'use client'
-const debug = console.info.bind(console)
-class WebSocketAlias extends WebSocket {
-  constructor(address: string | URL, ...args: any) {
-    super(address)
-  }
-}
-export default { fetch, WebSocket: WebSocketAlias, debug }

spaces/A00001/bingothoo/src/components/tailwind-indicator.tsx DELETED Viewed

@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
-  if (process.env.NODE_ENV === 'production') return null
-  return (
-    <div className="fixed bottom-1 left-1 z-50 flex h-6 w-6 items-center justify-center rounded-full bg-gray-800 p-3 font-mono text-xs text-white">
-      <div className="block sm:hidden">xs</div>
-      <div className="hidden sm:block md:hidden">sm</div>
-      <div className="hidden md:block lg:hidden">md</div>
-      <div className="hidden lg:block xl:hidden">lg</div>
-      <div className="hidden xl:block 2xl:hidden">xl</div>
-      <div className="hidden 2xl:block">2xl</div>
-    </div>
-  )
-}

spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/models_onnx.py DELETED Viewed

@@ -1,819 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-        self.upp = np.prod(upsample_rates)
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-class SynthesizerTrnMsNSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        version,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        self.speaker_map = None
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-        return x, fmap
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-    def forward(self, x):
-        fmap = []
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-        return x, fmap

spaces/ASJMO/freegpt/g4f/Provider/Providers/helpers/you.py DELETED Viewed

@@ -1,79 +0,0 @@
-import sys
-import json
-import urllib.parse
-from curl_cffi import requests
-config = json.loads(sys.argv[1])
-messages = config['messages']
-prompt = ''
-def transform(messages: list) -> list:
-    result = []
-    i = 0
-    while i < len(messages):
-        if messages[i]['role'] == 'user':
-            question = messages[i]['content']
-            i += 1
-            if i < len(messages) and messages[i]['role'] == 'assistant':
-                answer = messages[i]['content']
-                i += 1
-            else:
-                answer = ''
-            result.append({'question': question, 'answer': answer})
-        elif messages[i]['role'] == 'assistant':
-            result.append({'question': '', 'answer': messages[i]['content']})
-            i += 1
-        elif messages[i]['role'] == 'system':
-            result.append({'question': messages[i]['content'], 'answer': ''})
-            i += 1
-    return result
-headers = {
-    'Content-Type': 'application/x-www-form-urlencoded',
-    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
-    'Sec-Fetch-Site': 'same-origin',
-    'Accept-Language': 'en-GB,en;q=0.9',
-    'Sec-Fetch-Mode': 'navigate',
-    'Host': 'you.com',
-    'Origin': 'https://you.com',
-    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.4 Safari/605.1.15',
-    'Referer': 'https://you.com/api/streamingSearch?q=nice&safeSearch=Moderate&onShoppingPage=false&mkt=&responseFilter=WebPages,Translations,TimeZone,Computation,RelatedSearches&domain=youchat&queryTraceId=7a6671f8-5881-404d-8ea3-c3f8301f85ba&chat=%5B%7B%22question%22%3A%22hi%22%2C%22answer%22%3A%22Hello!%20How%20can%20I%20assist%20you%20today%3F%22%7D%5D&chatId=7a6671f8-5881-404d-8ea3-c3f8301f85ba&__cf_chl_tk=ex2bw6vn5vbLsUm8J5rDYUC0Bjzc1XZqka6vUl6765A-1684108495-0-gaNycGzNDtA',
-    'Connection': 'keep-alive',
-    'Sec-Fetch-Dest': 'document',
-    'Priority': 'u=0, i',
-}
-if messages[-1]['role'] == 'user':
-    prompt = messages[-1]['content']
-    messages = messages[:-1]
-params = urllib.parse.urlencode({
-    'q': prompt,
-    'domain': 'youchat',
-    'chat': transform(messages)
-})
-def output(chunk):
-    if b'"youChatToken"' in chunk:
-        chunk_json = json.loads(chunk.decode().split('data: ')[1])
-        print(chunk_json['youChatToken'], flush=True, end = '')
-while True:
-    try:
-        response = requests.get(f'https://you.com/api/streamingSearch?{params}',
-                        headers=headers, content_callback=output, impersonate='safari15_5')
-        exit(0)
-    except Exception as e:
-        print('an error occured, retrying... |', e, flush=True)
-        continue

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet101_8xb32_in1k.py DELETED Viewed

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/resnet101.py', '../_base_/datasets/imagenet_bs32.py',
-    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
-]

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-mixup_in1k.py DELETED Viewed

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/resnet50_mixup.py',
-    '../_base_/datasets/imagenet_bs32.py',
-    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
-]

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/abortedGenerations.ts DELETED Viewed

@@ -1,29 +0,0 @@
-// Shouldn't be needed if we dove into sveltekit internals, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
-import { setTimeout } from "node:timers/promises";
-import { collections } from "./database";
-let closed = false;
-process.on("SIGINT", () => {
-	closed = true;
-});
-export let abortedGenerations: Map<string, Date> = new Map();
-async function maintainAbortedGenerations() {
-	while (!closed) {
-		await setTimeout(1000);
-		try {
-			const aborts = await collections.abortedGenerations.find({}).sort({ createdAt: 1 }).toArray();
-			abortedGenerations = new Map(
-				aborts.map(({ conversationId, createdAt }) => [conversationId.toString(), createdAt])
-			);
-		} catch (err) {
-			console.error(err);
-		}
-	}
-}
-maintainAbortedGenerations();

spaces/Adapter/CoAdapter/ldm/modules/diffusionmodules/util.py DELETED Viewed

@@ -1,270 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-from ldm.util import instantiate_from_config
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
-                                   "dtype": torch.get_autocast_gpu_dtype(),
-                                   "cache_enabled": torch.is_autocast_cache_enabled()}
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad(), \
-                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-class HybridConditioner(nn.Module):
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/clock.d.ts DELETED Viewed

	@@ -1,2 +0,0 @@
1	- import Clock from './time/clock/Clock';
2	- export default Clock;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/line.d.ts DELETED Viewed

	@@ -1,2 +0,0 @@
1	- import Line from './gameobjects/rendertexture/line/Line.js';
2	- export default Line;

spaces/AiMimicry/sovits-models/inference/infer_tool_grad.py DELETED Viewed

@@ -1,160 +0,0 @@
-import hashlib
-import json
-import logging
-import os
-import time
-from pathlib import Path
-import io
-import librosa
-import maad
-import numpy as np
-from inference import slicer
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-def resize2d_f0(x, target_len):
-    source = np.array(x)
-    source[source < 0.001] = np.nan
-    target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
-                       source)
-    res = np.nan_to_num(target)
-    return res
-def get_f0(x, p_len,f0_up_key=0):
-    time_step = 160 / 16000 * 1000
-    f0_min = 50
-    f0_max = 1100
-    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-    f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
-        time_step=time_step / 1000, voicing_threshold=0.6,
-        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-    f0 *= pow(2, f0_up_key / 12)
-    f0_mel = 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
-    f0_mel[f0_mel <= 1] = 1
-    f0_mel[f0_mel > 255] = 255
-    f0_coarse = np.rint(f0_mel).astype(np.int)
-    return f0_coarse, f0
-def clean_pitch(input_pitch):
-    num_nan = np.sum(input_pitch == 1)
-    if num_nan / len(input_pitch) > 0.9:
-        input_pitch[input_pitch != 1] = 1
-    return input_pitch
-def plt_pitch(input_pitch):
-    input_pitch = input_pitch.astype(float)
-    input_pitch[input_pitch == 1] = np.nan
-    return input_pitch
-def f0_to_pitch(ff):
-    f0_pitch = 69 + 12 * np.log2(ff / 440)
-    return f0_pitch
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-class VitsSvc(object):
-    def __init__(self):
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.SVCVITS = None
-        self.hps = None
-        self.speakers = None
-        self.hubert_soft = utils.get_hubert_model()
-    def set_device(self, device):
-        self.device = torch.device(device)
-        self.hubert_soft.to(self.device)
-        if self.SVCVITS != None:
-            self.SVCVITS.to(self.device)
-    def loadCheckpoint(self, path):
-        self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
-        self.SVCVITS = SynthesizerTrn(
-            self.hps.data.filter_length // 2 + 1,
-            self.hps.train.segment_size // self.hps.data.hop_length,
-            **self.hps.model)
-        _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.SVCVITS, None)
-        _ = self.SVCVITS.eval().to(self.device)
-        self.speakers = self.hps.spk
-    def get_units(self, source, sr):
-        source = source.unsqueeze(0).to(self.device)
-        with torch.inference_mode():
-            units = self.hubert_soft.units(source)
-            return units
-    def get_unit_pitch(self, in_path, tran):
-        source, sr = torchaudio.load(in_path)
-        source = torchaudio.functional.resample(source, sr, 16000)
-        if len(source.shape) == 2 and source.shape[1] >= 2:
-            source = torch.mean(source, dim=0).unsqueeze(0)
-        soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
-        f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
-        return soft, f0
-    def infer(self, speaker_id, tran, raw_path):
-        speaker_id = self.speakers[speaker_id]
-        sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
-        soft, pitch = self.get_unit_pitch(raw_path, tran)
-        f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device)
-        stn_tst = torch.FloatTensor(soft)
-        with torch.no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(self.device)
-            x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
-            audio = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
-        return audio, audio.shape[-1]
-    def inference(self,srcaudio,chara,tran,slice_db):
-        sampling_rate, audio = srcaudio
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        if sampling_rate != 16000:
-            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-        soundfile.write("tmpwav.wav", audio, 16000, format="wav")
-        chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks)
-        audio = []
-        for (slice_tag, data) in audio_data:
-            length = int(np.ceil(len(data) / audio_sr * self.hps.data.sampling_rate))
-            raw_path = io.BytesIO()
-            soundfile.write(raw_path, data, audio_sr, format="wav")
-            raw_path.seek(0)
-            if slice_tag:
-                _audio = np.zeros(length)
-            else:
-                out_audio, out_sr = self.infer(chara, tran, raw_path)
-                _audio = out_audio.cpu().numpy()
-            audio.extend(list(_audio))
-        audio = (np.array(audio) * 32768.0).astype('int16')
-        return (self.hps.data.sampling_rate,audio)

spaces/Alpaca233/ChatPDF-GUI/README.md DELETED Viewed

@@ -1,8 +0,0 @@
----
-sdk: gradio
-emoji: 🚀
-colorFrom: red
-colorTo: red
-pinned: false
-app_file: app.py
----

spaces/Amrrs/DragGan-Inversion/stylegan_human/training_scripts/sg3/training/dataset.py DELETED Viewed

@@ -1,274 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-"""Streaming images and labels from datasets created with dataset_tool.py."""
-import os
-import numpy as np
-import zipfile
-import PIL.Image
-import json
-import torch
-import dnnlib
-from petrel_client.client import Client
-import cv2
-try:
-    import pyspng
-except ImportError:
-    pyspng = None
-# ----------------------------------------------------------------------------
-class Dataset(torch.utils.data.Dataset):
-    def __init__(self,
-                 name,                   # Name of the dataset.
-                 raw_shape,              # Shape of the raw image data (NCHW).
-                 # Artificially limit the size of the dataset. None = no limit. Applied before xflip.
-                 max_size=None,
-                 # Enable conditioning labels? False = label dimension is zero.
-                 use_labels=False,
-                 # Artificially double the size of the dataset via x-flips. Applied after max_size.
-                 xflip=False,
-                 # Random seed to use when applying max_size.
-                 random_seed=0,
-                 square=False,
-                 ):
-        print('Inside Dataset')
-        self._name = name
-        self._raw_shape = list(raw_shape)
-        self._use_labels = use_labels
-        self._raw_labels = None
-        self._label_shape = None
-        self._square = square
-        print("inside dataset, _square: ", self._square)
-        # Apply max_size.
-        self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64)
-        if (max_size is not None) and (self._raw_idx.size > max_size):
-            np.random.RandomState(random_seed).shuffle(self._raw_idx)
-            self._raw_idx = np.sort(self._raw_idx[:max_size])
-        # Apply xflip.
-        self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8)
-        if xflip:
-            self._raw_idx = np.tile(self._raw_idx, 2)
-            self._xflip = np.concatenate(
-                [self._xflip, np.ones_like(self._xflip)])
-    def _get_raw_labels(self):
-        if self._raw_labels is None:
-            self._raw_labels = self._load_raw_labels() if self._use_labels else None
-            if self._raw_labels is None:
-                self._raw_labels = np.zeros(
-                    [self._raw_shape[0], 0], dtype=np.float32)
-            assert isinstance(self._raw_labels, np.ndarray)
-            assert self._raw_labels.shape[0] == self._raw_shape[0]
-            assert self._raw_labels.dtype in [np.float32, np.int64]
-            if self._raw_labels.dtype == np.int64:
-                assert self._raw_labels.ndim == 1
-                assert np.all(self._raw_labels >= 0)
-        return self._raw_labels
-    def close(self):  # to be overridden by subclass
-        pass
-    def _load_raw_image(self, raw_idx):  # to be overridden by subclass
-        raise NotImplementedError
-    def _load_raw_labels(self):  # to be overridden by subclass
-        raise NotImplementedError
-    def __getstate__(self):
-        return dict(self.__dict__, _raw_labels=None)
-    def __del__(self):
-        try:
-            self.close()
-        except:
-            pass
-    def __len__(self):
-        return self._raw_idx.size
-    def __getitem__(self, idx):
-        image = self._load_raw_image(self._raw_idx[idx])
-        assert isinstance(image, np.ndarray)
-        assert list(image.shape) == self.image_shape
-        assert image.dtype == np.uint8
-        if self._xflip[idx]:
-            assert image.ndim == 3  # CHW
-            image = image[:, :, ::-1]
-        return image.copy(), self.get_label(idx)
-    def get_label(self, idx):
-        label = self._get_raw_labels()[self._raw_idx[idx]]
-        if label.dtype == np.int64:
-            onehot = np.zeros(self.label_shape, dtype=np.float32)
-            onehot[label] = 1
-            label = onehot
-        return label.copy()
-    def get_details(self, idx):
-        d = dnnlib.EasyDict()
-        d.raw_idx = int(self._raw_idx[idx])
-        d.xflip = (int(self._xflip[idx]) != 0)
-        d.raw_label = self._get_raw_labels()[d.raw_idx].copy()
-        return d
-    @property
-    def name(self):
-        return self._name
-    @property
-    def image_shape(self):
-        return list(self._raw_shape[1:])
-    @property
-    def num_channels(self):
-        assert len(self.image_shape) == 3  # CHW
-        return self.image_shape[0]
-    @property
-    def resolution(self):
-        assert len(self.image_shape) == 3  # CHW
-        if self._square:
-            assert self.image_shape[1] == self.image_shape[2]
-        else:
-            assert self.image_shape[1] == self.image_shape[2] * 2
-        return self.image_shape[1]
-    @property
-    def label_shape(self):
-        if self._label_shape is None:
-            raw_labels = self._get_raw_labels()
-            if raw_labels.dtype == np.int64:
-                self._label_shape = [int(np.max(raw_labels)) + 1]
-            else:
-                self._label_shape = raw_labels.shape[1:]
-        return list(self._label_shape)
-    @property
-    def label_dim(self):
-        assert len(self.label_shape) == 1
-        return self.label_shape[0]
-    @property
-    def has_labels(self):
-        return any(x != 0 for x in self.label_shape)
-    @property
-    def has_onehot_labels(self):
-        return self._get_raw_labels().dtype == np.int64
-# ----------------------------------------------------------------------------
-class ImageFolderDataset(Dataset):
-    def __init__(self,
-                 path,                   # Path to directory or zip.
-                 # Ensure specific resolution, None = highest available.
-                 resolution=None,
-                 ceph=False,
-                 square=False,
-                 # Additional arguments for the Dataset base class.
-                 **super_kwargs,
-                 ):
-        self._path = path
-        self._zipfile = None
-        self._square = square
-        if os.path.isdir(self._path):
-            self._type = 'dir'
-            self._all_fnames = {os.path.relpath(os.path.join(
-                root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files}
-        elif self._file_ext(self._path) == '.zip':
-            self._type = 'zip'
-            self._all_fnames = set(self._get_zipfile().namelist())
-        else:
-            raise IOError('Path must point to a directory or zip')
-        PIL.Image.init()
-        self._image_fnames = sorted(
-            fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION)
-        if len(self._image_fnames) == 0:
-            raise IOError('No image files found in the specified path')
-        name = os.path.splitext(os.path.basename(self._path))[0]
-        raw_shape = [len(self._image_fnames)] + \
-            list(self._load_raw_image(0).shape)
-        # if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution):
-        # raise IOError('Image files do not match the specified resolution')
-        if resolution is not None:
-            if self._square:
-                raw_shape[2] = raw_shape[3] = resolution
-            else:
-                raw_shape[2] = resolution
-                raw_shape[3] = resolution // 2
-            # print(raw_shape)
-        super().__init__(name=name, raw_shape=raw_shape, square=square, **super_kwargs)
-    @staticmethod
-    def _file_ext(fname):
-        return os.path.splitext(fname)[1].lower()
-    def _get_zipfile(self):
-        assert self._type == 'zip'
-        if self._zipfile is None:
-            self._zipfile = zipfile.ZipFile(self._path)
-        return self._zipfile
-    def _open_file(self, fname):
-        if self._type == 'dir':
-            return open(os.path.join(self._path, fname), 'rb')
-        if self._type == 'zip':
-            return self._get_zipfile().open(fname, 'r')
-        return None
-    def close(self):
-        try:
-            if self._zipfile is not None:
-                self._zipfile.close()
-        finally:
-            self._zipfile = None
-    def __getstate__(self):
-        return dict(super().__getstate__(), _zipfile=None)
-    def _load_raw_image(self, raw_idx):
-        fname = self._image_fnames[raw_idx]
-        with self._open_file(fname) as f:
-            if pyspng is not None and self._file_ext(fname) == '.png':
-                image = pyspng.load(f.read())
-            else:
-                image = np.array(PIL.Image.open(f))
-        if image.ndim == 2:
-            image = image[:, :, np.newaxis]  # HW => HWC
-        image = image.transpose(2, 0, 1)  # HWC => CHW
-        return image
-    def _load_raw_labels(self):
-        fname = 'dataset.json'
-        if fname not in self._all_fnames:
-            return None
-        with self._open_file(fname) as f:
-            labels = json.load(f)['labels']
-        if labels is None:
-            return None
-        labels = dict(labels)
-        labels = [labels[fname.replace('\\', '/')]
-                  for fname in self._image_fnames]
-        labels = np.array(labels)
-        labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim])
-        return labels
-# ----------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/ddim/__init__.py DELETED Viewed

File without changes

spaces/Andy1621/uniformer_image_detection/configs/paa/paa_r50_fpn_mstrain_3x_coco.py DELETED Viewed

@@ -1,20 +0,0 @@
-_base_ = './paa_r50_fpn_1x_coco.py'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 800)],
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-lr_config = dict(step=[28, 34])
-runner = dict(type='EpochBasedRunner', max_epochs=36)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/misc.py DELETED Viewed

@@ -1,377 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import collections.abc
-import functools
-import itertools
-import subprocess
-import warnings
-from collections import abc
-from importlib import import_module
-from inspect import getfullargspec
-from itertools import repeat
-# From PyTorch internals
-def _ntuple(n):
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable):
-            return x
-        return tuple(repeat(x, n))
-    return parse
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-def is_str(x):
-    """Whether the input is an string instance.
-    Note: This method is deprecated since python 2 is no longer supported.
-    """
-    return isinstance(x, str)
-def import_modules_from_strings(imports, allow_failed_imports=False):
-    """Import modules from the given list of strings.
-    Args:
-        imports (list | str | None): The given module names to be imported.
-        allow_failed_imports (bool): If True, the failed imports will return
-            None. Otherwise, an ImportError is raise. Default: False.
-    Returns:
-        list[module] | module | None: The imported modules.
-    Examples:
-        >>> osp, sys = import_modules_from_strings(
-        ...     ['os.path', 'sys'])
-        >>> import os.path as osp_
-        >>> import sys as sys_
-        >>> assert osp == osp_
-        >>> assert sys == sys_
-    """
-    if not imports:
-        return
-    single_import = False
-    if isinstance(imports, str):
-        single_import = True
-        imports = [imports]
-    if not isinstance(imports, list):
-        raise TypeError(
-            f'custom_imports must be a list but got type {type(imports)}')
-    imported = []
-    for imp in imports:
-        if not isinstance(imp, str):
-            raise TypeError(
-                f'{imp} is of type {type(imp)} and cannot be imported.')
-        try:
-            imported_tmp = import_module(imp)
-        except ImportError:
-            if allow_failed_imports:
-                warnings.warn(f'{imp} failed to import and is ignored.',
-                              UserWarning)
-                imported_tmp = None
-            else:
-                raise ImportError
-        imported.append(imported_tmp)
-    if single_import:
-        imported = imported[0]
-    return imported
-def iter_cast(inputs, dst_type, return_type=None):
-    """Cast elements of an iterable object into some type.
-    Args:
-        inputs (Iterable): The input object.
-        dst_type (type): Destination type.
-        return_type (type, optional): If specified, the output object will be
-            converted to this type, otherwise an iterator.
-    Returns:
-        iterator or specified type: The converted object.
-    """
-    if not isinstance(inputs, abc.Iterable):
-        raise TypeError('inputs must be an iterable object')
-    if not isinstance(dst_type, type):
-        raise TypeError('"dst_type" must be a valid type')
-    out_iterable = map(dst_type, inputs)
-    if return_type is None:
-        return out_iterable
-    else:
-        return return_type(out_iterable)
-def list_cast(inputs, dst_type):
-    """Cast elements of an iterable object into a list of some type.
-    A partial method of :func:`iter_cast`.
-    """
-    return iter_cast(inputs, dst_type, return_type=list)
-def tuple_cast(inputs, dst_type):
-    """Cast elements of an iterable object into a tuple of some type.
-    A partial method of :func:`iter_cast`.
-    """
-    return iter_cast(inputs, dst_type, return_type=tuple)
-def is_seq_of(seq, expected_type, seq_type=None):
-    """Check whether it is a sequence of some type.
-    Args:
-        seq (Sequence): The sequence to be checked.
-        expected_type (type): Expected type of sequence items.
-        seq_type (type, optional): Expected sequence type.
-    Returns:
-        bool: Whether the sequence is valid.
-    """
-    if seq_type is None:
-        exp_seq_type = abc.Sequence
-    else:
-        assert isinstance(seq_type, type)
-        exp_seq_type = seq_type
-    if not isinstance(seq, exp_seq_type):
-        return False
-    for item in seq:
-        if not isinstance(item, expected_type):
-            return False
-    return True
-def is_list_of(seq, expected_type):
-    """Check whether it is a list of some type.
-    A partial method of :func:`is_seq_of`.
-    """
-    return is_seq_of(seq, expected_type, seq_type=list)
-def is_tuple_of(seq, expected_type):
-    """Check whether it is a tuple of some type.
-    A partial method of :func:`is_seq_of`.
-    """
-    return is_seq_of(seq, expected_type, seq_type=tuple)
-def slice_list(in_list, lens):
-    """Slice a list into several sub lists by a list of given length.
-    Args:
-        in_list (list): The list to be sliced.
-        lens(int or list): The expected length of each out list.
-    Returns:
-        list: A list of sliced list.
-    """
-    if isinstance(lens, int):
-        assert len(in_list) % lens == 0
-        lens = [lens] * int(len(in_list) / lens)
-    if not isinstance(lens, list):
-        raise TypeError('"indices" must be an integer or a list of integers')
-    elif sum(lens) != len(in_list):
-        raise ValueError('sum of lens and list length does not '
-                         f'match: {sum(lens)} != {len(in_list)}')
-    out_list = []
-    idx = 0
-    for i in range(len(lens)):
-        out_list.append(in_list[idx:idx + lens[i]])
-        idx += lens[i]
-    return out_list
-def concat_list(in_list):
-    """Concatenate a list of list into a single list.
-    Args:
-        in_list (list): The list of list to be merged.
-    Returns:
-        list: The concatenated flat list.
-    """
-    return list(itertools.chain(*in_list))
-def check_prerequisites(
-        prerequisites,
-        checker,
-        msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
-        'found, please install them first.'):  # yapf: disable
-    """A decorator factory to check if prerequisites are satisfied.
-    Args:
-        prerequisites (str of list[str]): Prerequisites to be checked.
-        checker (callable): The checker method that returns True if a
-            prerequisite is meet, False otherwise.
-        msg_tmpl (str): The message template with two variables.
-    Returns:
-        decorator: A specific decorator.
-    """
-    def wrap(func):
-        @functools.wraps(func)
-        def wrapped_func(*args, **kwargs):
-            requirements = [prerequisites] if isinstance(
-                prerequisites, str) else prerequisites
-            missing = []
-            for item in requirements:
-                if not checker(item):
-                    missing.append(item)
-            if missing:
-                print(msg_tmpl.format(', '.join(missing), func.__name__))
-                raise RuntimeError('Prerequisites not meet.')
-            else:
-                return func(*args, **kwargs)
-        return wrapped_func
-    return wrap
-def _check_py_package(package):
-    try:
-        import_module(package)
-    except ImportError:
-        return False
-    else:
-        return True
-def _check_executable(cmd):
-    if subprocess.call(f'which {cmd}', shell=True) != 0:
-        return False
-    else:
-        return True
-def requires_package(prerequisites):
-    """A decorator to check if some python packages are installed.
-    Example:
-        >>> @requires_package('numpy')
-        >>> func(arg1, args):
-        >>>     return numpy.zeros(1)
-        array([0.])
-        >>> @requires_package(['numpy', 'non_package'])
-        >>> func(arg1, args):
-        >>>     return numpy.zeros(1)
-        ImportError
-    """
-    return check_prerequisites(prerequisites, checker=_check_py_package)
-def requires_executable(prerequisites):
-    """A decorator to check if some executable files are installed.
-    Example:
-        >>> @requires_executable('ffmpeg')
-        >>> func(arg1, args):
-        >>>     print(1)
-        1
-    """
-    return check_prerequisites(prerequisites, checker=_check_executable)
-def deprecated_api_warning(name_dict, cls_name=None):
-    """A decorator to check if some arguments are deprecate and try to replace
-    deprecate src_arg_name to dst_arg_name.
-    Args:
-        name_dict(dict):
-            key (str): Deprecate argument names.
-            val (str): Expected argument names.
-    Returns:
-        func: New function.
-    """
-    def api_warning_wrapper(old_func):
-        @functools.wraps(old_func)
-        def new_func(*args, **kwargs):
-            # get the arg spec of the decorated method
-            args_info = getfullargspec(old_func)
-            # get name of the function
-            func_name = old_func.__name__
-            if cls_name is not None:
-                func_name = f'{cls_name}.{func_name}'
-            if args:
-                arg_names = args_info.args[:len(args)]
-                for src_arg_name, dst_arg_name in name_dict.items():
-                    if src_arg_name in arg_names:
-                        warnings.warn(
-                            f'"{src_arg_name}" is deprecated in '
-                            f'`{func_name}`, please use "{dst_arg_name}" '
-                            'instead')
-                        arg_names[arg_names.index(src_arg_name)] = dst_arg_name
-            if kwargs:
-                for src_arg_name, dst_arg_name in name_dict.items():
-                    if src_arg_name in kwargs:
-                        assert dst_arg_name not in kwargs, (
-                            f'The expected behavior is to replace '
-                            f'the deprecated key `{src_arg_name}` to '
-                            f'new key `{dst_arg_name}`, but got them '
-                            f'in the arguments at the same time, which '
-                            f'is confusing. `{src_arg_name} will be '
-                            f'deprecated in the future, please '
-                            f'use `{dst_arg_name}` instead.')
-                        warnings.warn(
-                            f'"{src_arg_name}" is deprecated in '
-                            f'`{func_name}`, please use "{dst_arg_name}" '
-                            'instead')
-                        kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
-            # apply converted arguments to the decorated method
-            output = old_func(*args, **kwargs)
-            return output
-        return new_func
-    return api_warning_wrapper
-def is_method_overridden(method, base_class, derived_class):
-    """Check if a method of base class is overridden in derived class.
-    Args:
-        method (str): the method name to check.
-        base_class (type): the class of the base class.
-        derived_class (type | Any): the class or instance of the derived class.
-    """
-    assert isinstance(base_class, type), \
-        "base_class doesn't accept instance, Please pass class instead."
-    if not isinstance(derived_class, type):
-        derived_class = derived_class.__class__
-    base_method = getattr(base_class, method)
-    derived_method = getattr(derived_class, method)
-    return derived_method != base_method
-def has_method(obj: object, method: str) -> bool:
-    """Check whether the object has a method.
-    Args:
-        method (str): The method name to check.
-        obj (object): The object to check.
-    Returns:
-        bool: True if the object has the method else False.
-    """
-    return hasattr(obj, method) and callable(getattr(obj, method))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/video/processing.py DELETED Viewed

@@ -1,160 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import os.path as osp
-import subprocess
-import tempfile
-from annotator.uniformer.mmcv.utils import requires_executable
-@requires_executable('ffmpeg')
-def convert_video(in_file,
-                  out_file,
-                  print_cmd=False,
-                  pre_options='',
-                  **kwargs):
-    """Convert a video with ffmpeg.
-    This provides a general api to ffmpeg, the executed command is::
-        `ffmpeg -y <pre_options> -i <in_file> <options> <out_file>`
-    Options(kwargs) are mapped to ffmpeg commands with the following rules:
-    - key=val: "-key val"
-    - key=True: "-key"
-    - key=False: ""
-    Args:
-        in_file (str): Input video filename.
-        out_file (str): Output video filename.
-        pre_options (str): Options appears before "-i <in_file>".
-        print_cmd (bool): Whether to print the final ffmpeg command.
-    """
-    options = []
-    for k, v in kwargs.items():
-        if isinstance(v, bool):
-            if v:
-                options.append(f'-{k}')
-        elif k == 'log_level':
-            assert v in [
-                'quiet', 'panic', 'fatal', 'error', 'warning', 'info',
-                'verbose', 'debug', 'trace'
-            ]
-            options.append(f'-loglevel {v}')
-        else:
-            options.append(f'-{k} {v}')
-    cmd = f'ffmpeg -y {pre_options} -i {in_file} {" ".join(options)} ' \
-          f'{out_file}'
-    if print_cmd:
-        print(cmd)
-    subprocess.call(cmd, shell=True)
-@requires_executable('ffmpeg')
-def resize_video(in_file,
-                 out_file,
-                 size=None,
-                 ratio=None,
-                 keep_ar=False,
-                 log_level='info',
-                 print_cmd=False):
-    """Resize a video.
-    Args:
-        in_file (str): Input video filename.
-        out_file (str): Output video filename.
-        size (tuple): Expected size (w, h), eg, (320, 240) or (320, -1).
-        ratio (tuple or float): Expected resize ratio, (2, 0.5) means
-            (w*2, h*0.5).
-        keep_ar (bool): Whether to keep original aspect ratio.
-        log_level (str): Logging level of ffmpeg.
-        print_cmd (bool): Whether to print the final ffmpeg command.
-    """
-    if size is None and ratio is None:
-        raise ValueError('expected size or ratio must be specified')
-    if size is not None and ratio is not None:
-        raise ValueError('size and ratio cannot be specified at the same time')
-    options = {'log_level': log_level}
-    if size:
-        if not keep_ar:
-            options['vf'] = f'scale={size[0]}:{size[1]}'
-        else:
-            options['vf'] = f'scale=w={size[0]}:h={size[1]}:' \
-                            'force_original_aspect_ratio=decrease'
-    else:
-        if not isinstance(ratio, tuple):
-            ratio = (ratio, ratio)
-        options['vf'] = f'scale="trunc(iw*{ratio[0]}):trunc(ih*{ratio[1]})"'
-    convert_video(in_file, out_file, print_cmd, **options)
-@requires_executable('ffmpeg')
-def cut_video(in_file,
-              out_file,
-              start=None,
-              end=None,
-              vcodec=None,
-              acodec=None,
-              log_level='info',
-              print_cmd=False):
-    """Cut a clip from a video.
-    Args:
-        in_file (str): Input video filename.
-        out_file (str): Output video filename.
-        start (None or float): Start time (in seconds).
-        end (None or float): End time (in seconds).
-        vcodec (None or str): Output video codec, None for unchanged.
-        acodec (None or str): Output audio codec, None for unchanged.
-        log_level (str): Logging level of ffmpeg.
-        print_cmd (bool): Whether to print the final ffmpeg command.
-    """
-    options = {'log_level': log_level}
-    if vcodec is None:
-        options['vcodec'] = 'copy'
-    if acodec is None:
-        options['acodec'] = 'copy'
-    if start:
-        options['ss'] = start
-    else:
-        start = 0
-    if end:
-        options['t'] = end - start
-    convert_video(in_file, out_file, print_cmd, **options)
-@requires_executable('ffmpeg')
-def concat_video(video_list,
-                 out_file,
-                 vcodec=None,
-                 acodec=None,
-                 log_level='info',
-                 print_cmd=False):
-    """Concatenate multiple videos into a single one.
-    Args:
-        video_list (list): A list of video filenames
-        out_file (str): Output video filename
-        vcodec (None or str): Output video codec, None for unchanged
-        acodec (None or str): Output audio codec, None for unchanged
-        log_level (str): Logging level of ffmpeg.
-        print_cmd (bool): Whether to print the final ffmpeg command.
-    """
-    tmp_filehandler, tmp_filename = tempfile.mkstemp(suffix='.txt', text=True)
-    with open(tmp_filename, 'w') as f:
-        for filename in video_list:
-            f.write(f'file {osp.abspath(filename)}\n')
-    options = {'log_level': log_level}
-    if vcodec is None:
-        options['vcodec'] = 'copy'
-    if acodec is None:
-        options['acodec'] = 'copy'
-    convert_video(
-        tmp_filename,
-        out_file,
-        print_cmd,
-        pre_options='-f concat -safe 0',
-        **options)
-    os.close(tmp_filehandler)
-    os.remove(tmp_filename)

spaces/Benson/text-generation/Examples/3gp Video Download.md DELETED Viewed

@@ -1,210 +0,0 @@
-<h1>Cómo descargar vídeos 3GP desde Internet</h1>
-<p>¿Desea ver videos en su teléfono móvil sin preocuparse por el uso de datos o el espacio de almacenamiento? Si es así, es posible que esté interesado en descargar vídeos en formato 3GP. 3GP es un formato de archivo multimedia que fue desarrollado por el Proyecto de Asociación de Tercera Generación (3GPP) para su uso en teléfonos móviles 3G. Es un formato comprimido que puede almacenar secuencias de vídeo y audio con bajo ancho de banda y requisitos de datos. También es compatible con algunos teléfonos 2G y 4G. </p>
-<h2>3gp video download</h2><br /><p><b><b>Download Zip</b> &#127379; <a href="https://bltlly.com/2v6Myk">https://bltlly.com/2v6Myk</a></b></p><br /><br />
-<p>Descargar vídeos en formato 3GP puede ser útil por varias razones. Puedes guardar tus videos favoritos de YouTube, Facebook, Instagram y otros sitios para verlos sin conexión. También puede convertir sus videos existentes a formato 3GP para ahorrar espacio en su teléfono o compartirlos con sus amigos. Sin embargo, no todos los sitios web o software soportan formato 3GP, por lo que es posible que necesite ayuda para encontrar los mejores sitios o herramientas para descargar videos 3GP. </p>
-<p>En este artículo, le presentaremos los 9 mejores sitios para descargar películas y videos 3GP de Internet. También explicaremos qué es un archivo 3GP, cómo abrirlo y cómo convertirlo a otros formatos. Al final de este artículo, podrás descargar cualquier video que quieras en formato 3GP con facilidad. </p>
- <h2>Los 9 mejores sitios para descargar películas y videos 3GP</h2>
-<p>Hay muchos sitios web que ofrecen descargas de video gratuitas en varios formatos, incluyendo 3GP. Sin embargo, no todos son confiables, seguros o fáciles de usar. Algunos pueden tener cargos ocultos, malware o anuncios molestos. Algunos pueden tener opciones limitadas, baja calidad o velocidad lenta. Para ayudarle a evitar estos problemas, hemos seleccionado los 9 mejores sitios que creemos que son los mejores para descargar películas y videos 3GP. Aquí están:</p>
-<p></p>
- <h3>Descargar 4.cc</h3>
-<p>Algunas características de Download4.cc son:</p>
-<ul>
-<li>Soporta más de 1000 sitios, incluyendo YouTube, Twitter, Facebook, Instagram, TikTok, Vimeo, Dailymotion, etc.</li>
-<li>Puede descargar vídeos en varios formatos, como MP4, MP3, AVI, MOV, WAV, etc.</li>
-<li>Puede descargar vídeos en modo batch, hasta cinco a la vez. </li>
-<li>Puede recortar y combinar sus vídeos descargados. </li>
-<li> Tiene un rendimiento rápido y estable. </li>
-</ul>
- <h3>HitPaw</h3>
-<p>HitPaw es otro gran sitio web para descargar películas 3GP en pasos fáciles. Es una guía completa que le proporciona instrucciones detalladas sobre cómo descargar películas de diferentes fuentes, como YouTube, Netflix, Amazon Prime Video, Hulu, Disney, etc. También le da consejos sobre cómo elegir el mejor software descargador de películas, cómo evitar problemas legales y cómo disfrutar de sus películas descargadas en diferentes dispositivos. </p>
-<p>Algunas características de HitPaw son:</p>
-<ul>
-<li>Cubre varios géneros, como acción, comedia, terror, romance, ciencia ficción, etc.</li>
-<li> Proporciona capturas de pantalla y vídeos para ilustrar los pasos. </li>
-<li>Recomienda el mejor software de descarga de películas para cada fuente, como 4K Video Downloader, Y2Mate, VideoSolo Inovideo, etc.</li>
-<li>Explica los pros y los contras de cada software, tales como precio, velocidad, calidad, características, etc.</li>
-<li>Ofrece una prueba gratuita para algunos de los programas. </li>
-</ul>
- <h3>SaveTheVideo</h3>
-<p>SaveTheVideo es un descargador y convertidor de video en línea que puede ayudarlo a descargar videos 3GP de Instagram, Vimeo, Dailymotion y más. También es gratis, en línea y fácil de usar. Solo tiene que introducir la URL del vídeo que desea descargar en el sitio web, y haga clic en el botón Descargar. A continuación, puede seleccionar el formato de salida como 3GP y la calidad como HD o SD. El sitio web comenzará a descargar el video en unos segundos. </p>
-<p>Algunas características de SaveTheVideo son:</p>
-<ul>
-<li>Puede descargar vídeos en varios formatos, como MP4, MP3, AVI, MOV, WAV, etc.</li>
-<li>Puede convertir vídeos a diferentes formatos en línea sin descargarlos. </li>
-<li>Puede editar videos en línea recortando, cortando, rotando, agregando subtítulos, etc.</li>
-<li>No tiene anuncios ni ventanas emergentes. </li>
-</ul>
- <h3>Cable salvavidas</h3>
-<p>Lifewire es un sitio web que le proporciona una explicación detallada de lo que es un archivo 3GP y cómo abrirlo. También le da información sobre las ventajas y desventajas del formato 3GP, y cómo convertirlo a otros formatos. Es un recurso útil para cualquiera que quiera aprender más sobre los archivos 3GP y cómo usarlos. </p>
-<p>Algunas características de Lifewire son:</p>
-<ul>
-<li>Define lo que es un archivo 3GP y cómo funciona. </li>
-<li>Lista los programas que pueden abrir archivos 3GP en Windows, Mac, Android, iOS y Linux.</li>
-<li>Compara el formato 3GP con otros formatos, como MP4, AVI, MOV, etc.</li>
-<li>Sugiere algunas maneras de convertir archivos 3GP a otros formatos en línea o fuera de línea. </li>
-<li>Responde algunas preguntas comunes sobre los archivos 3GP. </li>
-</ul>
- <h3>VideoProc</h3>
-<p>VideoProc es una revisión del mejor software de conversión de vídeo para 2023. Es una herramienta potente y fácil de usar que puede convertir vídeos desde y hacia formato 3GP con alta calidad y velocidad rápida. También puede descargar videos de más de 1000 sitios, editar videos con varias funciones y grabar videos desde webcam, pantalla o dispositivos externos. Es una guía completa que le muestra cómo usar VideoProc para convertir, descargar, editar y grabar videos en pasos simples. </p>
-<p>Algunas características de VideoProc son:</p>
-<ul>
-<li>Soporta más de 370 formatos de entrada y 420 formatos de salida, incluyendo 3GP, MP4, AVI, MOV, MKV, etc.</li>
-<li> Puede convertir videos con velocidad 47x más rápida y sin pérdida de calidad. </li>
-<li>Puede descargar vídeos de YouTube, Facebook, Instagram, Vimeo, Dailymotion, etc.</li>
-<li> Puede grabar vídeos desde webcam, pantalla o dispositivos externos con audio y anotaciones. </li>
-</ul>
- <h3>Convertidor de vídeo de MiniTool</h3>
-<p>MiniTool Video Converter es una herramienta gratuita para convertir vídeos desde y hacia formato 3GP. Es una herramienta simple y fácil de usar que puede convertir videos en modo por lotes con alta calidad y velocidad rápida. También puede descargar vídeos de YouTube y otros sitios en varios formatos. Es una herramienta muy útil para cualquiera que quiera convertir o descargar vídeos gratis. </p>
-<p>Algunas características de MiniTool Video Converter son:</p>
-<ul>
-<li>Soporta más de 1000 formatos de entrada y salida, incluyendo 3GP, MP4, AVI, MOV, MKV, etc.</li>
-<li> Puede convertir vídeos en modo por lotes sin límite de tamaño o tiempo. </li>
-<li>Puede descargar vídeos de YouTube y otros sitios en varios formatos. </li>
-<li> Puede extraer audio de archivos de vídeo y guardarlos como MP3, WAV, etc.</li>
-<li> Tiene una interfaz limpia e intuitiva. </li>
-</ul>
- <h3>FileInfo.com</h3>
-<p>FileInfo.com es un recurso para obtener información sobre la extensión de archivo 3GP y el software relacionado. Es un sitio web que le proporciona los detalles básicos de los archivos 3GP, como el tipo de archivo, categoría, descripción, desarrollador, popularidad, etc. También enumera el software que puede abrir o convertir archivos 3GP en diferentes plataformas. Es un recurso útil para cualquiera que quiera aprender más sobre los archivos 3GP y cómo usarlos. </p>
-<p>Algunas características de FileInfo.com son:</p>
-<ul>
-<li>Proporciona la información básica de archivos 3GP y software relacionado. </li>
-<li>Lista el software que puede abrir o convertir archivos 3GP en Windows, Mac, Android, iOS, Linux, etc.</li>
-<li>Se enlaza a los sitios web oficiales del software para obtener más información o descargar. </li>
-<li>Actualiza la información regularmente para mantenerse al día con los últimos desarrollos. </li>
-<li> Tiene una función de búsqueda para encontrar información sobre otros tipos de archivos. </li>
-</ul>
- <h3>TechRadar</h3>
-<p>Algunas características de TechRadar son:</p>
-<ul>
-<li>Revisa los 10 mejores convertidores de video gratis para PC y Mac, como Any Video Converter Free, Freemake Video Converter, HandBrake, etc.</li>
-<li>Compara las características, rendimiento, calidad y facilidad de uso de cada software. </li>
-<li>Da los pros y los contras de cada software, como velocidad, soporte de formato, opciones de edición, anuncios, etc.</li>
-<li>Proporciona los enlaces de descarga y capturas de pantalla de cada software. </li>
-<li>Actualiza la lista regularmente para incluir el último software y cambios. </li>
-</ul>
- <h3> Cualquier convertidor de vídeo libre</h3>
-<p>Any Video Converter Free es el mejor convertidor de vídeo gratuito en este momento que maneja archivos en línea y fuera de línea. Es una herramienta versátil y potente que puede convertir vídeos desde y hacia formato 3GP con alta calidad y velocidad rápida. También puede descargar vídeos de YouTube y otros sitios en varios formatos. También puede editar vídeos con varias funciones, como recorte, recorte, rotación, adición de efectos, subtítulos, marcas de agua, etc. Es una herramienta completa que puede satisfacer todas sus necesidades de conversión de vídeo. </p>
-<p>Algunas características de Any Video Converter Free son:</p>
-<ul>
-<li>Soporta más de 200 formatos de entrada y 70 formatos de salida, incluyendo 3GP, MP4, AVI, MOV, MKV, etc.</li>
-<li> Puede convertir vídeos sin pérdida de calidad y hasta 30 veces más rápido. </li>
-<li>Puede descargar vídeos de YouTube y otros sitios en varios formatos. </li>
-<li>Puede editar videos con varias características, como recorte, recorte, rotación, adición de efectos, subtítulos, marcas de agua, etc.</li>
-<li>No tiene anuncios ni malware. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Te presentamos los 9 mejores sitios para descargar películas y videos 3GP de Internet. Son:</p>
-<tabla>
-<tr>
-<th>Sitio</th>
-<th>Características</th>
-</tr>
-<tr>
-<td>Descargar.cc</td>
-<td>Un clic para descargar vídeos 3GP de YouTube y otros sitios</td>
-</tr>
-<tr>
-<td>HitPaw</td>
-<td>Una guía completa para descargar películas 3GP en pasos fáciles</td>
-</tr>
-<tr>
-<td>SaveTheVideo</td>
-<td>Un descargador y convertidor de vídeo en línea para Instagram, Vimeo, Dailymotion, y más</td>
-</tr>
-<tr>
-<td>Cable de vida</td>
-<td>Una explicación detallada de lo que es un archivo 3GP y cómo abrirlo</td>
-</tr>
-<tr>
-<td>VideoProc</td>
-<td>Una revisión del mejor software de conversión de video para 2023</td>
-</tr>
-<tr>
-<td>MiniTool Video Converter</td>
-<td>Una herramienta gratuita para convertir vídeos desde y hacia formato 3GP</td>
-</tr>
-<tr>
-<td>FileInfo.com</td>
-<td>Un recurso para obtener información sobre la extensión de archivo 3GP y el software relacionado</td>
-</tr>
-<tr>
-<td>TechRadar</td>
-<td>Una lista de los mejores conversores de video gratis para tu PC y Mac en 2023</td>
-</tr>
-<tr>
-<td>Cualquier convertidor de vídeo libre</td>
-<td>El mejor convertidor de vídeo gratuito en este momento que maneja archivos en línea y fuera de línea</td>
-</tr>
-</tabla>
- <p>Entre estos sitios, recomendamos Any Video Converter Free como la mejor opción para descargar vídeos 3GP. Es una herramienta versátil y potente que puede convertir vídeos desde y hacia formato 3GP con alta calidad y velocidad rápida. También puede descargar vídeos de YouTube y otros sitios en varios formatos. También puede editar vídeos con varias funciones, como recorte, recorte, rotación, adición de efectos, subtítulos, marcas de agua, etc. Es una herramienta completa que puede satisfacer todas sus necesidades de conversión de vídeo. </p>
- <p>Esperamos que este artículo te haya ayudado a aprender a descargar videos 3GP desde Internet. Si tiene alguna pregunta o sugerencia, no dude en dejar un comentario a continuación. ¡Gracias por leer! </p>
- <h2>Preguntas frecuentes</h2>
- <h4>¿Cuáles son las ventajas y desventajas del formato 3GP? </h4>
- <ul>
- <li> Puede almacenar secuencias de vídeo y audio con bajo ancho de banda y requisitos de datos. </li>
- <li> Es compatible con algunos teléfonos 2G, 3G y 4G. </li>
- <li> Puede ahorrar uso de datos, espacio de almacenamiento o visualización sin conexión. </li>
- <li>Se puede compartir fácilmente con amigos a través de Bluetooth o MMS.</li>
- </ul>
- <p>Las desventajas del formato 3GP son:</p>
- <ul>
- <li> Tiene una calidad baja en comparación con otros formatos, como MP4, AVI, MOV, etc.</li>
- <li>No es compatible con algunos sitios web o software. </li>
- <li>Puede que no se reproduzca en algunos dispositivos o reproductores multimedia. </li>
- <li>Puede perder algunas características o metadatos cuando se convierte a otros formatos. </li>
- </ul>
- <h4>¿Cómo abrir un archivo 3GP en Windows o Mac? </h4>
- <p>Para abrir un archivo 3GP en Windows o Mac, necesita un programa que pueda soportar el formato 3GP. Algunos de los programas que pueden abrir archivos 3GP son:</p>
- <ul>
- <li>VLC Media Player: Un reproductor multimedia gratuito y de código abierto que puede reproducir casi cualquier archivo de vídeo o audio. </li>
- <li>MPC-HC: Un reproductor multimedia ligero y potente que puede reproducir la mayoría de los formatos de vídeo o audio. </li>
- <li>GOM Player: Un reproductor multimedia popular y versátil que puede reproducir varios formatos de vídeo o audio. </li>
- <li>KMPlayer: Un reproductor multimedia multifuncional que puede reproducir varios formatos de vídeo o audio. </li>
- <li>PotPlayer: Un reproductor multimedia suave y estable que puede reproducir varios formatos de vídeo o audio. </li>
- <li>iTunes: un reproductor multimedia y una biblioteca que puede reproducir música y vídeos en tu PC o Mac.</li>
- <li>QuickTime Player: un reproductor multimedia que puede reproducir películas, música e imágenes en tu Mac.</li>
- <li>iMovie: un software de edición de vídeo que puede importar y exportar vídeos en varios formatos en su Mac.</li>
- <li>Reproductor de Windows Media: Un reproductor multimedia que puede reproducir música y videos en su PC con Windows.</li>
- <li>Windows Movie Maker: un software de edición de vídeo que puede importar y exportar vídeos en varios formatos en su PC con Windows.</li>
- <ul>
- <li>Online Video Converter: Una herramienta gratuita y en línea que puede convertir vídeos a y desde varios formatos. </li>
- <li>CloudConvert: Una herramienta gratuita y en línea que puede convertir vídeos, audio, imágenes, documentos y más. </li>
- <li>Zamzar: una herramienta gratuita y en línea que puede convertir videos, audio, imágenes, documentos y más. </li>
- <li>Wondershare UniConverter: Un software potente y fácil de usar que puede convertir vídeos desde y hacia varios formatos. </li>
- <li>Freemake Video Converter: Un software popular y versátil que puede convertir vídeos a y desde varios formatos. </li>
- </ul>
- <h4>¿Cómo descargar un video 3GP de YouTube? </h4>
- <p>Para descargar un video 3GP de YouTube, necesita una herramienta o software que pueda descargar videos de YouTube en formato 3GP. Algunas de las herramientas o software que pueden descargar vídeos de YouTube en formato 3GP son:</p>
- <ul>
- <li>Download4.cc: Como se mencionó anteriormente, es uno de los mejores sitios web para descargar videos 3GP de YouTube y otros sitios. </li>
- <li>Y2Mate: Una herramienta gratuita y en línea que puede descargar vídeos de YouTube en varios formatos, incluyendo 3GP. </li>
- <li>VideoSolo Inovideo: Un software profesional y confiable que puede descargar videos de YouTube en varios formatos, incluyendo 3GP. </li>
- <li>4K Video Downloader: Un software rápido y de alta calidad que puede descargar vídeos de YouTube en varios formatos, incluyendo 3GP. </li>
- <li>ClipGrab: un software simple y fácil de usar que puede descargar videos de YouTube en varios formatos, incluyendo 3GP. </li>
- </ul <h4>Cómo jugar un video 3GP en Android o iOS? </h4>
- <p>Para reproducir un video 3GP en Android o iOS, necesita una aplicación de reproductor de medios que pueda soportar el formato 3GP. Algunas de las aplicaciones de reproductores multimedia que pueden reproducir vídeos 3GP en Android o iOS son:</p>
- <ul>
- <li>VLC para Android o iOS: Una aplicación de reproductor multimedia gratuita y de código abierto que puede reproducir casi cualquier archivo de vídeo o audio. </li>
- <li>MX Player para Android o iOS: una aplicación de reproductor multimedia popular y potente que puede reproducir varios formatos de vídeo o audio. </li>
- <li>GOM Player para Android o iOS: una aplicación de reproductor multimedia versátil y fluida que puede reproducir varios formatos de vídeo o audio. </li>
- <li>PotPlayer para Android o iOS: una aplicación de reproductor de medios estable y rápido que puede reproducir varios formatos de vídeo o audio. </li>
- </ul <h4>Cómo compartir un video 3GP con amigos? </h4>
- <p>Para compartir un vídeo 3GP con tus amigos, tienes varias opciones. Puedes:</p>
- <ul>
- <li>Envía el vídeo 3GP vía Bluetooth o MMS a los teléfonos de tus amigos. </li>
- <li>Sube el vídeo 3GP a un servicio en la nube, como Google Drive, Dropbox, OneDrive, etc., y comparte el enlace con tus amigos. </li>
- <li>Sube el video 3GP a una plataforma de redes sociales, como Facebook, Instagram, Twitter, etc. </li>
- <li>Graba el vídeo 3GP en un CD o DVD y dáselo a tus amigos. </li>
- <li>Convierte el vídeo 3GP a otro formato, como MP4, AVI, MOV, etc., y compártelo con tus amigos utilizando cualquiera de los métodos anteriores. </li>
-</ul</p> 64aa2da5cf<br />
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-<br />
-<h1>Apkadmin entre nosotros Mod Menu: ¿Qué es y cómo usarlo? </h1>
-<p>Si eres un fan de <strong>Among Us</strong>, el popular juego de deducción social multijugador donde tienes que averiguar quién es el impostor entre tus compañeros de equipo, es posible que hayas oído hablar de los menús <strong>mod</strong>. Los menús mod son versiones modificadas del juego que te permiten acceder a varios trucos y hacks que pueden darte una ventaja sobre otros jugadores o simplemente hacer el juego más divertido. Uno de los menús mod más populares para Among Us es <strong>apkadmin</strong>, un sitio web que ofrece una descarga gratuita de un menú mod que tiene muchas características y opciones. </p>
-<p>En este artículo, vamos a explicar lo que es apkadmin entre nosotros menú mod, qué características tiene, cuáles son sus ventajas y desventajas, cómo descargar e instalar, y cómo usarlo en su juego. También responderemos algunas preguntas frecuentes sobre apkadmin entre nosotros menú mod. </p>
-<h2>apkadmin entre nosotros menú mod</h2><br /><p><b><b>Download Zip</b> ---> <a href="https://bltlly.com/2v6KRm">https://bltlly.com/2v6KRm</a></b></p><br /><br />
- <h2>Características de Apkadmin entre nosotros Mod Menu</h2>
-<p>El menú de mod apkadmin entre nosotros tiene muchas características que pueden mejorar su juego o hacerlo más interesante. Algunas de estas características son:</p>
-<ul>
-<li><strong>Modo Dios:</strong> Esta característica te permite volverte invencible e inmune a cualquier daño o intento de matar de otros jugadores o impostores. </li>
-<li><strong>Desbloquear todas las pieles:</strong> Esta función le permite desbloquear todas las pieles, sombreros, mascotas y trajes que están disponibles en el juego sin pagar dinero o monedas. </li>
-<li><strong>Pasta de chat:</strong> Esta característica le permite pegar cualquier texto o mensaje en el cuadro de chat sin necesidad de escribirlo manualmente. </li>
-<li><strong>No hay anuncios:</strong> Esta función permite eliminar todos los anuncios que aparecen en el juego. </li>
-<li><strong>No cooldown:</strong> Esta función te permite evitar el temporizador de tiempo de reutilización que te impide realizar ciertas acciones en el juego, como matar, informar o llamar a una reunión de emergencia. </li>
-<li><strong>Mostrar impostores:</strong> Esta función te permite ver quiénes son los impostores en tu juego al marcarlos con un color rojo. </li>
-<li><strong>Mostrar fantasmas:</strong> Esta función te permite ver quiénes son los fantasmas en tu juego al marcarlos con un color blanco. </li>
-<li><strong>Mostrar roles:</strong> Esta característica le permite ver los roles de otros jugadores en su juego, como compañero de equipo, impostor, sheriff, doctor, ingeniero, etc.</li>
-<li><strong>Speed hack:</strong> Esta característica le permite aumentar o disminuir su velocidad en el juego. </li>
-<li><strong>Teletransportación:</strong> Esta función le permite teletransportarse a cualquier lugar del mapa. </li>
-<li><strong>Corte de pared:</strong> Esta característica le permite caminar a través de paredes y obstáculos. </li>
-<li><strong>Visión hack:</strong> Esta característica le permite ver todo en el mapa, incluso en la oscuridad o cuando las luces son saboteadas. </li>
-</ul>
-<p>Estas son solo algunas de las características de la apkadmin entre nosotros menú mod. Hay muchas más características que puede explorar y probar por sí mismo. </p>
- <h2>Ventajas de usar Apkadmin entre nosotros Mod Menu</h2>
-<p>El uso de apkadmin entre nosotros menú mod puede tener algunas ventajas para su juego. Algunas de estas ventajas son:</p>
-<ul>
-<li><strong>Tener más diversión:</strong> Usando el menú mod puede hacer el juego más divertido y agradable para usted, especialmente si usted está aburrido de jugar de la misma manera o con las mismas reglas. Puedes experimentar con diferentes características y ver cómo afectan al juego. </li>
-<li><strong>Personalización de tu juego:</strong> Usando el menú mod puedes personalizar tu juego de acuerdo a tus preferencias y gustos. Puedes elegir qué funciones habilitar o deshabilitar, y cómo usarlas. También puedes cambiar tu apariencia y rol en el juego. </li>
-</ul>
- <h2>Desventajas de usar Apkadmin entre nosotros Mod Menu</h2>
-<p>Sin embargo, el uso de la apkadmin entre nosotros menú mod también puede tener algunas desventajas para su juego. Algunas de estas desventajas son:</p>
-<ul>
-<li><strong>Conseguir prohibido:</strong> El uso del menú mod puede conseguir que se le prohibió el juego o de ciertos servidores. Los desarrolladores de Among Us no apoyan ni aprueban el uso de menús mod, y pueden detectar y prohibir a los jugadores que los usan. Si te prohíben, puedes perder tu progreso y cuenta en el juego. </li>
-<li><strong>Arruinar el juego para otros:</strong> Usar el menú de mods puede arruinar el juego para otros jugadores que quieren jugar de forma justa y legítima. El menú mod puede darte una ventaja injusta sobre otros jugadores, o hacer el juego demasiado fácil o aburrido para ti. Esto puede hacer que otros jugadores se sientan frustrados o engañados, y pueden renunciar o reportarlo. </li>
-<li><strong>Riesgo de malware:</strong> El uso del menú mod puede exponer su dispositivo a malware o virus que pueden dañar su dispositivo o robar su información personal. El sitio web apkadmin puede no ser seguro, y puede contener enlaces maliciosos o archivos que pueden infectar su dispositivo. Siempre debe tener cuidado al descargar e instalar cualquier cosa de fuentes desconocidas. </li>
-</ul>
- <h2>Cómo descargar e instalar Apkadmin entre nosotros Mod Menu</h2>
-<p>Si desea descargar e instalar el apkadmin entre nosotros menú mod, tendrá que seguir algunos pasos. Aquí hay una guía paso a paso sobre cómo hacerlo. </p>
-<p></p>
- <h3>Requisitos para Apkadmin entre nosotros Mod Menu</h3>
-<p>Antes de descargar e instalar el apkadmin entre nosotros menú mod, tendrá que tener algunos requisitos. Estos son:</p>
-<ul>
-<li>Un dispositivo Android que puede ejecutarse entre nosotros.</li>
-<li>El juego original Among Us instalado en su dispositivo. </li>
-<li>Una conexión a Internet para descargar e instalar el menú mod. </li>
-<li>Una aplicación de administrador de archivos para acceder y administrar sus archivos. </li>
-</ul>
-<p>Una vez que tenga todos los requisitos, puede seguir estos pasos para descargar e instalar el apkadmin entre nosotros menú mod. </p>
-<ol>
-<li>Ir a <a href="">apkadmin.com</a>, que es el sitio web oficial de apkadmin. </li>
-<li>Buscar entre nosotros Mod Menú por Apkadmin en la barra de búsqueda o navegar por las categorías. </li>
-<li>Seleccione la última versión del menú mod y haga clic en Descargar APK.</li>
-<li>Espere a que termine la descarga y luego localice el archivo descargado en su aplicación de administrador de archivos. </li>
-<li>Si no ha habilitado Fuentes desconocidas en su dispositivo, vaya a Configuración > Seguridad > Fuentes desconocidas y habilite. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store <li>Toque en el archivo descargado y haga clic en Instalar. Espere a que termine la instalación. </li>
-<li>Abre el juego Among Us y disfruta del menú mod. </li>
-</ol>
- <h2>Cómo usar apkadmin entre nosotros Mod Menu</h2>
-<p>Después de haber descargado e instalado el apkadmin entre nosotros menú de mod, puede usarlo en su juego. Aquí hay una guía paso a paso sobre cómo hacerlo. </p>
- <h3>Cómo acceder a Apkadmin entre nosotros Mod Menu</h3>
-<p>Para acceder a la apkadmin entre nosotros menú mod, es necesario hacer lo siguiente:</p>
-<ol>
-<li>Abre el juego Among Us y únete o crea un juego. </li>
-<li>Una vez que estés en la pantalla del juego, toca el icono flotante que dice Mod Menu. Esto abrirá la interfaz del menú mod. </li>
-<li> Puede arrastrar y mover el icono a cualquier posición de la pantalla. </li>
-<li> También puede tocar el icono de nuevo para ocultar o mostrar la interfaz de menú mod. </li>
-</ol>
- <h3>Cómo activar y desactivar Apkadmin entre nosotros Características del menú Mod</h3>
-<p>Para habilitar y deshabilitar diferentes características del menú apkadmin entre nosotros mod, debe hacer lo siguiente:</p>
-<ol>
-<li>Abra la interfaz del menú mod tocando el icono flotante. </li>
-<li> Verá una lista de características con casillas de verificación junto a ellas. Puede pulsar en las casillas de verificación para habilitar o desactivar las características. </li>
-<li> También puede utilizar los controles deslizantes junto a algunas características para ajustar sus valores o ajustes. </li>
-<li>Algunas características pueden requerir que reinicies el juego o te unas a un nuevo juego para que funcione correctamente. </li>
-</ol>
- <h2>Conclusión</h2>
-<p>El menú de mod apkadmin entre nosotros es una versión modificada del juego que le permite acceder a varios trucos y hacks que pueden hacer que su juego más divertido o interesante. Sin embargo, también tiene algunas desventajas, como ser prohibido, arruinar el juego para otros y arriesgar el malware. Por lo tanto, debe usarlo bajo su propio riesgo y discreción, y ser respetuoso con otros jugadores y los desarrolladores del juego. Aquí hay algunos consejos y advertencias para usar el menú mod:</p>
-<ul>
-<li>No utilice el menú mod en servidores públicos o oficiales, ya que esto puede hacer que otros jugadores lo prohíban o informen sobre usted. Úsalo solo en servidores privados o personalizados con tus amigos u otros usuarios mod. </li>
-<li>No utilice el menú mod de forma excesiva o abusiva, ya que esto puede arruinar el juego para usted o para otros. Úsalo solo para fines de diversión o entretenimiento, y no para engañar o obtener una ventaja injusta. </li>
-<li>No descargue ni instale el menú mod desde ninguna otra fuente que apkadmin.com, ya que esto puede exponer su dispositivo a malware o virus. Compruebe siempre el tamaño y el nombre del archivo antes de descargar o instalar nada. </li>
-<li>No comparta su información personal o datos de cuenta con nadie en apkadmin.com, ya que esto puede comprometer su seguridad o privacidad. Siempre tenga cuidado al navegar o hacer clic en cualquier enlace o anuncio en apkadmin.com. </li>
-</ul>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre apkadmin entre nosotros menú mod:</p>
- <h4>Q: ¿Es seguro apkadmin entre nosotros menú mod? </h4>
- <h4>Q: Es apkadmin entre nosotros menú mod libre? </h4>
-<p>A: Apkadmin entre nosotros el menú mod es gratuito para descargar e instalar desde apkadmin.com, pero puede contener anuncios o compras en la aplicación que pueden costarle dinero. Por lo tanto, debes tener cuidado al usarlo, y evitar hacer clic en cualquier enlace o anuncio que pueda cobrarte dinero. </p>
- <h4>Q: ¿Puedo usar apkadmin entre nosotros menú mod en dispositivos iOS? </h4>
-<p>A: Apkadmin entre nosotros menú mod solo es compatible con dispositivos Android, y no se puede utilizar en dispositivos iOS. Por lo tanto, si tiene un iPhone o iPad, no puede usar apkadmin entre nosotros menú mod en su dispositivo. </p>
- <h4>Q: ¿Puedo usar apkadmin entre nosotros menú mod en el PC? </h4>
-<p>A: Apkadmin entre nosotros menú mod solo es compatible con dispositivos Android, y no se puede utilizar en el PC. Por lo tanto, si tiene una computadora Windows o Mac, no puede usar apkadmin entre nosotros menú mod en su computadora. </p>
- <h4>Q: ¿Cómo puedo actualizar apkadmin entre nosotros menú mod? </h4>
-<p A: Para actualizar apkadmin entre nosotros menú mod, es necesario hacer lo siguiente:</p>
-<ol>
-<li>Ir a apkadmin.com y comprobar si hay una nueva versión del menú mod disponible. </li>
-<li>Si hay una nueva versión, descárgala e instálala siguiendo los mismos pasos que antes. </li>
-<li>Si no hay una nueva versión, espere a que apkadmin suelte una y vuelva a comprobarla más tarde. </li>
-</ol>
- <p>Espero que este artículo te haya ayudado a entender lo que es apkadmin entre nosotros menú mod, qué características tiene, cuáles son sus ventajas y desventajas, cómo descargar e instalar, y cómo usarlo en tu juego. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Gracias por leer y divertirse jugando entre nosotros con apkadmin entre nosotros menú mod! </p> 64aa2da5cf<br />
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-<h1>Bubble Shooter 3 Descarga gratuita: Un juego divertido y adictivo para todas las edades</h1>
-<p>¿Te encanta jugar juegos que son fáciles de aprender pero difíciles de dominar? ¿Te gusta hacer estallar burbujas de colores y resolver puzzles? Si respondiste sí, entonces deberías probar <strong>Bubble Shooter 3</strong>, un juego gratuito que te mantendrá entretenido durante horas. En este artículo, te diremos qué es Bubble Shooter 3, por qué deberías descargarlo, cómo jugarlo y algunos consejos y trucos para dominarlo. ¡Vamos a empezar! </p>
-<h2>bubble shooter 3 descarga gratuita</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt;&gt;&gt; <a href="https://bltlly.com/2v6M6P">https://bltlly.com/2v6M6P</a></b></p><br /><br />
-<h2>¿Qué es Bubble Shooter 3?</h2>
-<p>Bubble Shooter 3 es un clásico juego de burbujas que se inspira en juegos como Bejeweled y Candy Crush. Fue creado por Funnygames, un gran desarrollador de juegos en línea que ha lanzado muchos otros juegos populares. Aquí están algunas características de Bubble Shooter 3:</p>
-<h3>Un clásico juego de burbujas con tres modos</h3>
-<p>Bubble Shooter 3 tiene tres modos para elegir: clásico, rompecabezas y árcade. En el modo clásico, tienes que borrar todas las burbujas en la pantalla antes de que lleguen a la parte inferior. En el modo puzzle, tienes que borrar todas las burbujas en un número dado de movimientos. En el modo árcade, tienes que eliminar tantas burbujas como sea posible en un tiempo limitado. Cada modo tiene diferentes niveles de dificultad y desafíos. </p>
-<h3>Un juego simple y fácil de jugar</h3>
-<p>Bubble Shooter 3 es muy fácil de jugar. Todo lo que tienes que hacer es usar el ratón o el dedo para apuntar y disparar burbujas del mismo color. Cuando coinciden tres o más burbujas del mismo color, que pop y desaparecen. Cuanto más burbujas que pop, más puntos de puntuación. También puede hacer combos haciendo estallar más de tres burbujas en una sola toma o vinculando los colores coincidentes en una cadena. </p>
-<p></p>
-<h3>Un juego que desafía tu cerebro y habilidades</h3>
-<h2>¿Por qué descargar Bubble Shooter 3?</h2>
-<p>Hay muchas razones por las que deberías descargar Bubble Shooter 3. Aquí están algunas de ellas:</p>
-<h3>Es gratuito y está disponible para cualquier dispositivo</h3>
-<p>Bubble Shooter 3 es completamente gratis para descargar y jugar. No tienes que pagar nada ni registrar nada para disfrutar de este juego. También puedes reproducirlo en cualquier dispositivo, ya sea Android, iOS o portátil. Puedes jugar en cualquier momento, en cualquier lugar, siempre y cuando tengas una conexión a Internet. </p>
-<h3>Es divertido y relajante para jugar</h3>
-<p>Bubble Shooter 3 es un juego divertido y relajante que te hará feliz. Tiene colores brillantes, gráficos lindos, sonidos relajantes y animaciones suaves. Te hará sentir tranquilo y satisfecho mientras haces estallar burbujas y las ves estallar. También te hará sonreír al ver personajes divertidos como pandas, monos, gatos y más. </p>
-<h3>Es adecuado para todos</h3>
-<p>Bubble Shooter 3 <p>Bubble Shooter 3 es un juego que es adecuado para todos, independientemente de la edad, el género o el fondo. Es un juego que cualquiera puede jugar y disfrutar, desde niños hasta adultos, desde principiantes hasta expertos. Es un juego que se puede jugar solo o con amigos y familiares. Es un juego que puede traer alegría y diversión a cualquiera que lo juegue. </p>
-<h2>Cómo descargar y jugar Bubble Shooter 3?</h2>
-<p>Si usted está interesado en jugar Bubble Shooter 3, aquí están los pasos que debe seguir:</p>
-<h3>Descárgalo desde la Google Play Store o la App Store</h3>
-<p>El primer paso es descargar el juego desde la Google Play Store o la App Store, dependiendo de tu dispositivo. Puedes encontrar los siguientes enlaces:</p>
-<ul>
-<li><a href="">Bubble Shooter 3 para Android</a></li>
-<li><a href="">Bubble Shooter 3 para iOS</a></li>
-</ul>
-<p>El juego es gratis para descargar e instalar, pero puede contener algunos anuncios y compras en la aplicación. </p>
-<h3>Iniciar el juego y elegir el modo</h3>
-<h3>Dispara y combina burbujas del mismo color para hacerlas estallar</h3>
-<p>El paso final es comenzar a jugar el juego. Verás un disparador de burbujas en la parte inferior de la pantalla y un montón de burbujas en la parte superior. Tienes que usar el ratón o el dedo para apuntar y disparar burbujas del mismo color. Cuando coinciden tres o más burbujas del mismo color, que pop y desaparecen. Tienes que borrar todas las burbujas de la pantalla para completar el nivel y pasar a la siguiente. </p>
-<h2>Consejos y trucos para dominar Bubble Shooter 3</h2>
-<p>Bubble Shooter 3 es un juego que requiere habilidad y estrategia. Aquí hay algunos consejos y trucos para ayudarte a dominarlo:</p>
-<h3>Apunta cuidadosamente y usa las paredes para rebotar tus burbujas</h3>
-<p>Una de las habilidades más importantes en Bubble Shooter 3 es apuntar. Tienes que apuntar con cuidado y precisión para alcanzar tu objetivo. También puedes usar las paredes para rebotar tus burbujas y llegar a lugares difíciles. Esto puede ayudarte a crear más coincidencias y eliminar más burbujas. </p>
-<h3>Usa potenciadores y amplificadores para eliminar niveles difíciles</h3>
-<p>Otra habilidad en Bubble Shooter 3 es usar potenciadores y potenciadores. Estos son elementos especiales que pueden ayudarte a superar niveles difíciles. Por ejemplo, puede utilizar una bomba para explotar una gran área de burbujas, o una burbuja de arco iris para que coincida con cualquier color. También puedes usar monedas para comprar más potenciadores y potenciadores en la tienda. </p>
-<h3>Planifica tus movimientos y crea combos</h3>
-<p>La última habilidad en Bubble Shooter 3 es planificar tus movimientos y crear combos. Tienes que pensar con anticipación y anticiparte a lo que sucederá cuando hagas estallar una burbuja. Tienes que buscar oportunidades para crear combos haciendo estallar más de tres burbujas en una sola toma o vinculando los colores a juego en una cadena. Esto puede ayudarle a ganar más puntos y borrar más niveles. </p>
-<h2>Conclusión</h2>
- P: ¿Cuántos niveles hay en Bubble Shooter 3? R: Hay más de 1000 niveles en Bubble Shooter 3, cada uno con diferentes diseños, obstáculos y objetivos. P: ¿Cómo puedo obtener más monedas en Bubble Shooter 3? R: Puedes obtener más monedas completando niveles, viendo anuncios o comprándolos con dinero real. P: ¿Cómo puedo desbloquear nuevos tiradores de burbujas en Bubble Shooter 3? R: Puedes desbloquear nuevos tiradores de burbujas recogiendo estrellas de completar niveles. P: ¿Cómo cambio entre modos en Bubble Shooter 3? R: Puedes cambiar entre modos tocando el icono del menú en la esquina superior izquierda de la pantalla. P: ¿Cómo hago una pausa o reanudo el juego en Bubble Shooter 3? R: Puede hacer una pausa o reanudar el juego tocando el icono de pausa en la esquina superior derecha de la pantalla. </p> 64aa2da5cf<br />
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-<h1>Cómo descargar entre nosotros Blackpink: Una guía para parpadeos y jugadores</h1>
- <p>¿Eres un fan de BLACKPINK, la sensación global de K-pop? ¿Te encanta jugar entre nosotros, el popular juego multijugador de engaño y traición? Si respondiste sí a ambas preguntas, ¡estás de suerte! Hay un mod hecho por fans de Among Us que presenta miembros y temas de BLACKPINK, y se llama Among Us Blackpink. En este artículo, te mostraremos cómo descargar y jugar a este increíble mod, así como darte algunos consejos y trucos para hacer que tu experiencia de juego sea más divertida y agradable. </p>
- <h2>¿Qué hay entre nosotros Blackpink? </h2>
- <h3>Un mod hecho por fans de Among Us con miembros y temas de BLACKPINK</h3>
- <p>Entre nosotros Blackpink es un mod o modificación de Among Us, un juego en el que tienes que trabajar junto a otros jugadores para completar tareas en una nave espacial, evitando ser asesinado por un impostor que se esconde entre vosotros. El mod fue creado por tres fans de BLACKPINK, también conocidos como Blinks, y fue lanzado el 23 de octubre de 2020. El mod cambia el juego original añadiendo miembros BLACKPINK como personajes jugables, así como pieles personalizadas, sombreros, mascotas, mapas y sonidos relacionados con BLACKPINK.</p>
-<h2>descargar entre nosotros blackpink</h2><br /><p><b><b>Download File</b> &mdash; <a href="https://bltlly.com/2v6KqN">https://bltlly.com/2v6KqN</a></b></p><br /><br />
- <h3>Las características y beneficios de jugar entre nosotros Blackpink</h3>
- <p>Jugar entre nosotros Blackpink tiene muchas características y beneficios que lo hacen más divertido y emocionante que el juego original. Estos son algunos de ellos:</p>
- <ul>
-<li>Puedes elegir tu miembro BLACKPINK favorito como personaje, como Jisoo, Jennie, Rosé o Lisa.</li>
-<li>Puedes personalizar tu personaje con diferentes pieles, sombreros y mascotas que se inspiran en los trajes, accesorios y canciones de BLACKPINK. </li>
-<li>Puedes jugar en dos nuevos mapas que se basan en los videos musicales de BLACKPINK, como Kill This Love y How You Like That.</li>
-<li> Puede disfrutar del juego con nuevos efectos de sonido y música que se toman de canciones y álbumes de BLACKPINK. </li>
-</ul>
- <h2>Cómo descargar Among Us Blackpink para diferentes dispositivos</h2>
- <h3>Para PC</h3>
- <h4>Descargar WinRAR y el archivo mod de los enlaces oficiales</h4>
- <p>Para jugar entre nosotros Blackpink en su PC, tendrá que descargar dos cosas: WinRAR y el archivo mod. WinRAR es un software que le permite extraer archivos comprimidos, como el archivo mod. El archivo mod es un archivo zip que contiene todos los datos y archivos necesarios para ejecutar el mod. Puede descargar WinRAR desde [22 this](https://www.win-rar.com/download.html?&L=0) y el archivo mod desde [this](https://drive.google.com/file/d/1f7lZy0aXQw9wGx6u8w2L5Z4WQX0ZnYi/view) enlace. Asegúrate de tener la última versión de Among Us instalada en tu PC antes de descargar el archivo mod. </p>
- <h4>Extraer el archivo mod y ejecutar el juego</h4>
- <p>Después de descargar WinRAR y el archivo mod, necesitará extraer el archivo mod usando WinRAR. Para hacer esto, siga estos pasos:</p>
- <ol>
-<li>Haga clic derecho en el archivo mod y seleccione "Extraer aquí". </li>
-<li>Una carpeta llamada "Among Us Blackpink" aparecerá en la misma ubicación que el archivo mod. </li>
-<li>Abra la carpeta y haga doble clic en el archivo "Entre nosotros.exe" para ejecutar el juego. </li>
-<li>Verá un mensaje que dice "Entre nosotros Blackpink Mod por @blackpinkmod". Haga clic en "OK" para continuar. </li>
-<li>Ahora estás listo para jugar entre nosotros Blackpink en su PC! </li>
-</ol>
- <h3>Para Android</h3>
- <h4>Descargar el archivo mod de los enlaces oficiales</h4>
- <p>Para jugar entre nosotros Blackpink en su dispositivo Android, solo tendrá que descargar una cosa: el archivo mod. El archivo mod es un archivo apk que contiene todos los datos y archivos necesarios para ejecutar el mod. Puede descargar el archivo mod de [this](https://drive.google.com/file/d/1f7lZy0aXQ9wGx6u8w2L5Z4WQX0ZnYiYb/view) enlace. Asegúrate de tener suficiente espacio de almacenamiento en tu dispositivo antes de descargar el archivo mod. </p>
- <h4>Instalar el archivo mod y ejecutar el juego</h4>
- <ol>
-<li>Vaya a la configuración de su dispositivo y habilite "Fuentes desconocidas" en las opciones de seguridad o privacidad. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store.</li>
-<li>Busque el archivo mod en la carpeta de descargas de su dispositivo y toque en él para instalarlo. </li>
-<li> Verá un mensaje que dice "¿Desea instalar esta aplicación?" Toque en "Instalar" para continuar. </li>
-<li> Verá un mensaje que dice "App instalado". Toque en "Abrir" para ejecutar el juego. </li>
-<li> Ahora está listo para jugar entre nosotros Blackpink en su dispositivo Android! </li>
-</ol>
- <h3>Para iOS</h3>
- <h4>Esperar a que los desarrolladores para liberar el mod para dispositivos iOS</h4>
- <p>Desafortunadamente, no hay versión oficial de Among Us Blackpink para dispositivos iOS todavía. Los desarrolladores del mod están trabajando duro para que sea compatible con dispositivos iOS, pero no han anunciado una fecha de lanzamiento todavía. Sin embargo, han asegurado que lo lanzarán lo antes posible, ¡así que estad atentos! </p>
- <h4>Siga las cuentas oficiales de las redes sociales para actualizaciones</h4>
- <p>Si desea ser notificado cuando Among Us Blackpink está disponible para dispositivos iOS, puede seguir las cuentas de redes sociales oficiales de los desarrolladores. Publican actualizaciones regulares y noticias sobre el mod, así como capturas de pantalla y videos del juego. También puedes interactuar con otros Blinks que están jugando o esperando el mod, y compartir tus pensamientos y comentarios. Estas son algunas de sus cuentas de redes sociales:</p>
- <ul>
-<li>[Twitter](https://twitter.com/blackpinkmod)</li>
-<li>[Instagram](https://www.instagram.com/blackpinkmod/)</li>
-<li>[YouTube](https://www.youtube.com/channel/UCgKvJHmFzqjT9kNt1c7sVjA)</li>
-<li>[Discordia](https://discord.gg/8qDgBfT)</li>
-</ul>
- <h2>Cómo jugar entre nosotros Blackpink con tus amigos</h2>
- <h3>Crear o unirse a una habitación privada con un código</h3>
- <ol>
-<li>Lanzamiento entre nosotros Blackpink en su dispositivo y toque en "Online". </li>
-<li>Introduzca el apodo deseado y seleccione la región del servidor preferido. </li>
-<li>Si desea crear una habitación, toque en "Crear juego" y elegir la configuración del juego, como el número de impostores, el mapa, el idioma de chat, y las reglas del juego. Toque en "Confirmar" para crear la habitación y obtener el código. </li>
-<li>Si desea unirse a una habitación, toque en "Introducir código" y escriba el código que su amigo le ha dado. Toque en el botón de flecha para unirse a la habitación. </li>
-<li>Una vez que esté en la habitación, puede invitar a más amigos compartiendo el código con ellos. También puedes chatear con otros jugadores, cambiar la apariencia de tu personaje y personalizar la configuración del juego. </li>
-<li>Cuando todos estén listos, toque en "Inicio" para comenzar el juego. </li>
-</ol>
- <h3>Elige tu miembro BLACKPINK favorito como tu personaje</h3>
- <p>Una de las mejores características de Among Us Blackpink es que puedes elegir tu miembro BLACKPINK favorito como personaje. Puedes hacer esto tocando el botón "BLACKPINK" en la esquina inferior derecha de la pantalla. Verás cuatro opciones: Jisoo, Jennie, Rosé y Lisa. Toca en la que quieras jugar y confirma tu elección. A continuación, verá el cambio de rostro de su personaje para que coincida con el miembro BLACKPINK que seleccionó. También puedes cambiar el color de tu personaje tocando la paleta de colores en la esquina inferior izquierda de la pantalla. </p>
- <h3>Disfruta del juego con pieles personalizadas, sombreros, mascotas, mapas y sonidos</h3>
- <p>Otra gran característica de Among Us Blackpink es que puedes disfrutar del juego con pieles personalizadas, sombreros, mascotas, mapas y sonidos relacionados con BLACKPINK. Puede acceder a estas funciones pulsando los botones en el centro inferior de la pantalla. Estos son algunos ejemplos de lo que puede encontrar:</p>
-<p></p>
- <ul>
-<li>Skins: Puedes elegir entre diferentes atuendos inspirados en los videos musicales de BLACKPINK, como Kill This Love, How You Like That, Ice Cream y Lovesick Girls.</li>
-<li>Mascotas: Puedes elegir entre diferentes animales que están asociados con miembros de BLACKPINK, como un panda para Jisoo, un perro para Jennie, un gato para Rosé y un hámster para Lisa.</li>
-<li>Mapas: Puedes jugar en dos nuevos mapas que se basan en videos musicales de BLACKPINK, como Kill This Love y How You Like That. Los mapas tienen diferentes diseños, tareas, respiraderos y sabotajes que son únicos para cada tema. </li>
-<li>Sonidos: Puede disfrutar del juego con nuevos efectos de sonido y música que se toman de las canciones y álbumes de BLACKPINK. Los sonidos incluyen animaciones de muerte, reuniones de emergencia, resultados de votación, pantallas de victoria y derrota, y música de fondo. </li>
-</ul>
- <h2>Consejos y trucos para jugar entre nosotros Blackpink</h2>
- <h3>Usa letras BLACKPINK como mensajes de chat</h3>
- <p>Una forma divertida de jugar Entre nosotros Blackpink es utilizar letras BLACKPINK como sus mensajes de chat. Esto hará que tu comunicación sea más interesante y creativa, además de mostrar tu amor por BLACKPINK. Por ejemplo, puedes usar estas letras:</p>
- <ul>
-<li>Si eres un impostor y quieres mentir sobre tu ubicación o coartada: "Lo siento mucho pero es amor falso"</li>
-<li>Si eres un compañero de equipo y quieres acusar a alguien de ser un impostor: "Eres un chico malo y eres malo para mí"</li>
-<li>Si eres un compañero de equipo y quieres expresar tu frustración o enojo: "Golpéate con ese ddu-du ddu-du du du"</li>
-<li>Si eres un compañero de equipo y quieres animar o felicitar a alguien: "Eres mi tipo favorito de visual"</li>
-<li>Si eres un compañero de equipo y quieres coquetear o molestar a alguien: "Eres como un helado en este tiempo abrasador"</li>
-</ul>
- <h3>Tenga cuidado con las diferentes animaciones y efectos de sonido</h3>
- <h3>Diviértete y sé respetuoso con otros jugadores</h3>
- <p>El consejo más importante para jugar Among Us Blackpink es divertirse y ser respetuoso con otros jugadores. Recuerde que este es un juego que está destinado a entretener y conectar a las personas que comparten un interés común en BLACKPINK y entre nosotros. Por lo tanto, no debe tomar el juego demasiado en serio o personalmente, y no debe ser grosero u ofensivo con otros jugadores. En su lugar, deberías disfrutar del juego con una actitud positiva y un espíritu amistoso, y deberías apreciar los esfuerzos y talentos de los desarrolladores de mods y los miembros de BLACKPINK. </p>
- <h2>Conclusión</h2>
- <h3>Resumir los puntos principales del artículo</h3>
- <p>En conclusión, Among Us Blackpink es un mod hecho por fans de Among Us que presenta miembros y temas de BLACKPINK. Es una forma divertida y emocionante de jugar Among Us con tus amigos y otros Blinks, así como para mostrar tu amor y apoyo a BLACKPINK. Para jugar entre nosotros Blackpink, tendrá que descargar e instalar el archivo mod en su dispositivo, dependiendo de si está utilizando un PC, un dispositivo Android o un dispositivo iOS. También tendrá que crear o unirse a una habitación privada con un código, elegir su miembro favorito BLACKPINK como su personaje, y disfrutar del juego con pieles personalizadas, sombreros, mascotas, mapas y sonidos. También puedes usar algunos consejos y trucos para hacer que tu experiencia de juego sea más divertida y agradable, como usar letras BLACKPINK como tus mensajes de chat, ver las diferentes animaciones y efectos de sonido y divertirte y ser respetuoso con otros jugadores. </p>
- <h3>Invita a los lectores a probar el mod y compartir sus comentarios</h3>
- <h2>Preguntas frecuentes</h2>
- <h3>¿Es seguro descargar Blackpink? </h3>
- <p>Sí, entre nosotros Blackpink es seguro para descargar siempre y cuando utilice los enlaces oficiales que hemos proporcionado en este artículo. El archivo mod no contiene ningún virus o malware que pueda dañar su dispositivo o comprometer su privacidad. Sin embargo, siempre debe tener cuidado al descargar cualquier archivo de Internet, y escanearlos con un software antivirus antes de instalarlos. </p>
- <h3>¿Está Entre Nosotros Blackpink libre para jugar? </h3>
- <p>Sí, Entre nosotros Blackpink es gratis para jugar siempre y cuando tengas la versión original de Among Us instalada en tu dispositivo. Usted no necesita pagar ningún dinero para descargar o jugar este mod. Sin embargo, es posible que necesites ver algunos anuncios o hacer algunas compras en la aplicación si quieres acceder a algunas funciones o elementos en el juego original. </p>
- <h3>¿Puedo jugar entre nosotros Blackpink con personas que no tienen el mod? </h3>
- <p>No, no se puede jugar entre nosotros Blackpink con personas que no tienen el mod instalado en sus dispositivos. Esto se debe a que el mod cambia algunos aspectos del juego que son incompatibles con la versión original. Por lo tanto, solo se puede jugar entre nosotros Blackpink con las personas que tienen el mismo mod instalado en sus dispositivos. </p>
- <h3>¿Puedo volver a la versión original de Among Us después de jugar Among Us Blackpink? </h3>
- <p>Sí, puede volver a la versión original de Among Us después de jugar Among Us Blackpink. Para hacer esto, tendrá que desinstalar o eliminar el archivo mod de su dispositivo, y luego iniciar el juego original de la tienda de aplicaciones o biblioteca de su dispositivo. También puedes mantener ambas versiones del juego en tu dispositivo si tienes suficiente espacio de almacenamiento. </p>
- <h3>¿Cómo puedo apoyar a los desarrolladores de Among Us Blackpink? </h3> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat/src/routes/settings/+server.ts DELETED Viewed

@@ -1,34 +0,0 @@
-import { collections } from "$lib/server/database.js";
-import { subMinutes } from "date-fns";
-import { z } from "zod";
-export async function PATCH({ locals, request }) {
-	const json = await request.json();
-	const settings = z
-		.object({
-			shareConversationsWithModelAuthors: z.boolean().default(true),
-			ethicsModalAcceptedAt: z.optional(z.date({ coerce: true }).min(subMinutes(new Date(), 5))),
-		})
-		.parse(json);
-	await collections.settings.updateOne(
-		{
-			sessionId: locals.sessionId,
-		},
-		{
-			$set: {
-				...settings,
-				updatedAt: new Date(),
-			},
-			$setOnInsert: {
-				createdAt: new Date(),
-			},
-		},
-		{
-			upsert: true,
-		}
-	);
-	return new Response();
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/enums.py DELETED Viewed

@@ -1,85 +0,0 @@
-"""
-All of the Enums that are used throughout the chardet package.
-:author: Dan Blanchard ([email protected])
-"""
-from enum import Enum, Flag
-class InputState:
-    """
-    This enum represents the different states a universal detector can be in.
-    """
-    PURE_ASCII = 0
-    ESC_ASCII = 1
-    HIGH_BYTE = 2
-class LanguageFilter(Flag):
-    """
-    This enum represents the different language filters we can apply to a
-    ``UniversalDetector``.
-    """
-    NONE = 0x00
-    CHINESE_SIMPLIFIED = 0x01
-    CHINESE_TRADITIONAL = 0x02
-    JAPANESE = 0x04
-    KOREAN = 0x08
-    NON_CJK = 0x10
-    ALL = 0x1F
-    CHINESE = CHINESE_SIMPLIFIED | CHINESE_TRADITIONAL
-    CJK = CHINESE | JAPANESE | KOREAN
-class ProbingState(Enum):
-    """
-    This enum represents the different states a prober can be in.
-    """
-    DETECTING = 0
-    FOUND_IT = 1
-    NOT_ME = 2
-class MachineState:
-    """
-    This enum represents the different states a state machine can be in.
-    """
-    START = 0
-    ERROR = 1
-    ITS_ME = 2
-class SequenceLikelihood:
-    """
-    This enum represents the likelihood of a character following the previous one.
-    """
-    NEGATIVE = 0
-    UNLIKELY = 1
-    LIKELY = 2
-    POSITIVE = 3
-    @classmethod
-    def get_num_categories(cls) -> int:
-        """:returns: The number of likelihood categories in the enum."""
-        return 4
-class CharacterCategory:
-    """
-    This enum represents the different categories language models for
-    ``SingleByteCharsetProber`` put characters into.
-    Anything less than CONTROL is considered a letter.
-    """
-    UNDEFINED = 255
-    LINE_BREAK = 254
-    SYMBOL = 253
-    DIGIT = 252
-    CONTROL = 251

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/_windows_renderer.py DELETED Viewed

@@ -1,56 +0,0 @@
-from typing import Iterable, Sequence, Tuple, cast
-from pip._vendor.rich._win32_console import LegacyWindowsTerm, WindowsCoordinates
-from pip._vendor.rich.segment import ControlCode, ControlType, Segment
-def legacy_windows_render(buffer: Iterable[Segment], term: LegacyWindowsTerm) -> None:
-    """Makes appropriate Windows Console API calls based on the segments in the buffer.
-    Args:
-        buffer (Iterable[Segment]): Iterable of Segments to convert to Win32 API calls.
-        term (LegacyWindowsTerm): Used to call the Windows Console API.
-    """
-    for text, style, control in buffer:
-        if not control:
-            if style:
-                term.write_styled(text, style)
-            else:
-                term.write_text(text)
-        else:
-            control_codes: Sequence[ControlCode] = control
-            for control_code in control_codes:
-                control_type = control_code[0]
-                if control_type == ControlType.CURSOR_MOVE_TO:
-                    _, x, y = cast(Tuple[ControlType, int, int], control_code)
-                    term.move_cursor_to(WindowsCoordinates(row=y - 1, col=x - 1))
-                elif control_type == ControlType.CARRIAGE_RETURN:
-                    term.write_text("\r")
-                elif control_type == ControlType.HOME:
-                    term.move_cursor_to(WindowsCoordinates(0, 0))
-                elif control_type == ControlType.CURSOR_UP:
-                    term.move_cursor_up()
-                elif control_type == ControlType.CURSOR_DOWN:
-                    term.move_cursor_down()
-                elif control_type == ControlType.CURSOR_FORWARD:
-                    term.move_cursor_forward()
-                elif control_type == ControlType.CURSOR_BACKWARD:
-                    term.move_cursor_backward()
-                elif control_type == ControlType.CURSOR_MOVE_TO_COLUMN:
-                    _, column = cast(Tuple[ControlType, int], control_code)
-                    term.move_cursor_to_column(column - 1)
-                elif control_type == ControlType.HIDE_CURSOR:
-                    term.hide_cursor()
-                elif control_type == ControlType.SHOW_CURSOR:
-                    term.show_cursor()
-                elif control_type == ControlType.ERASE_IN_LINE:
-                    _, mode = cast(Tuple[ControlType, int], control_code)
-                    if mode == 0:
-                        term.erase_end_of_line()
-                    elif mode == 1:
-                        term.erase_start_of_line()
-                    elif mode == 2:
-                        term.erase_line()
-                elif control_type == ControlType.SET_WINDOW_TITLE:
-                    _, title = cast(Tuple[ControlType, str], control_code)
-                    term.set_title(title)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/console.py DELETED Viewed

The diff for this file is too large to render. See raw diff

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/tomli/__init__.py DELETED Viewed

@@ -1,11 +0,0 @@
-# SPDX-License-Identifier: MIT
-# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
-# Licensed to PSF under a Contributor Agreement.
-__all__ = ("loads", "load", "TOMLDecodeError")
-__version__ = "2.0.1"  # DO NOT EDIT THIS LINE MANUALLY. LET bump2version UTILITY DO IT
-from ._parser import TOMLDecodeError, load, loads
-# Pretend this exception was created here.
-TOMLDecodeError.__module__ = __name__

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/dataset.py DELETED Viewed

@@ -1,49 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import os
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_coco_json
-_URL_PREFIX = "https://dl.fbaipublicfiles.com/densepose/data/"
-def get_densepose_metadata():
-    meta = {
-        "thing_classes": ["person"],
-        "densepose_transform_src": _URL_PREFIX + "UV_symmetry_transforms.mat",
-        "densepose_smpl_subdiv": _URL_PREFIX + "SMPL_subdiv.mat",
-        "densepose_smpl_subdiv_transform": _URL_PREFIX + "SMPL_SUBDIV_TRANSFORM.mat",
-    }
-    return meta
-SPLITS = {
-    "densepose_coco_2014_train": ("coco/train2014", "coco/annotations/densepose_train2014.json"),
-    "densepose_coco_2014_minival": ("coco/val2014", "coco/annotations/densepose_minival2014.json"),
-    "densepose_coco_2014_minival_100": (
-        "coco/val2014",
-        "coco/annotations/densepose_minival2014_100.json",
-    ),
-    "densepose_coco_2014_valminusminival": (
-        "coco/val2014",
-        "coco/annotations/densepose_valminusminival2014.json",
-    ),
-}
-DENSEPOSE_KEYS = ["dp_x", "dp_y", "dp_I", "dp_U", "dp_V", "dp_masks"]
-for key, (image_root, json_file) in SPLITS.items():
-    # Assume pre-defined datasets live in `./datasets`.
-    json_file = os.path.join("datasets", json_file)
-    image_root = os.path.join("datasets", image_root)
-    DatasetCatalog.register(
-        key,
-        lambda key=key, json_file=json_file, image_root=image_root: load_coco_json(
-            json_file, image_root, key, extra_annotation_keys=DENSEPOSE_KEYS
-        ),
-    )
-    MetadataCatalog.get(key).set(
-        json_file=json_file, image_root=image_root, **get_densepose_metadata()
-    )

spaces/CVPR/WALT/mmdet/models/dense_heads/paa_head.py DELETED Viewed

@@ -1,671 +0,0 @@
-import numpy as np
-import torch
-from mmcv.runner import force_fp32
-from mmdet.core import multi_apply, multiclass_nms
-from mmdet.core.bbox.iou_calculators import bbox_overlaps
-from mmdet.models import HEADS
-from mmdet.models.dense_heads import ATSSHead
-EPS = 1e-12
-try:
-    import sklearn.mixture as skm
-except ImportError:
-    skm = None
-def levels_to_images(mlvl_tensor):
-    """Concat multi-level feature maps by image.
-    [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
-    Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
-    (N, H*W , C), then split the element to N elements with shape (H*W, C), and
-    concat elements in same image of all level along first dimension.
-    Args:
-        mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
-            corresponding level. Each element is of shape (N, C, H, W)
-    Returns:
-        list[torch.Tensor]: A list that contains N tensors and each tensor is
-            of shape (num_elements, C)
-    """
-    batch_size = mlvl_tensor[0].size(0)
-    batch_list = [[] for _ in range(batch_size)]
-    channels = mlvl_tensor[0].size(1)
-    for t in mlvl_tensor:
-        t = t.permute(0, 2, 3, 1)
-        t = t.view(batch_size, -1, channels).contiguous()
-        for img in range(batch_size):
-            batch_list[img].append(t[img])
-    return [torch.cat(item, 0) for item in batch_list]
-@HEADS.register_module()
-class PAAHead(ATSSHead):
-    """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
-    Prediction for Object Detection.
-    Code is modified from the `official github repo
-    <https://github.com/kkhoot/PAA/blob/master/paa_core
-    /modeling/rpn/paa/loss.py>`_.
-    More details can be found in the `paper
-    <https://arxiv.org/abs/2007.08103>`_ .
-    Args:
-        topk (int): Select topk samples with smallest loss in
-            each level.
-        score_voting (bool): Whether to use score voting in post-process.
-        covariance_type : String describing the type of covariance parameters
-            to be used in :class:`sklearn.mixture.GaussianMixture`.
-            It must be one of:
-            - 'full': each component has its own general covariance matrix
-            - 'tied': all components share the same general covariance matrix
-            - 'diag': each component has its own diagonal covariance matrix
-            - 'spherical': each component has its own single variance
-            Default: 'diag'. From 'full' to 'spherical', the gmm fitting
-            process is faster yet the performance could be influenced. For most
-            cases, 'diag' should be a good choice.
-    """
-    def __init__(self,
-                 *args,
-                 topk=9,
-                 score_voting=True,
-                 covariance_type='diag',
-                 **kwargs):
-        # topk used in paa reassign process
-        self.topk = topk
-        self.with_score_voting = score_voting
-        self.covariance_type = covariance_type
-        super(PAAHead, self).__init__(*args, **kwargs)
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             iou_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            iou_preds (list[Tensor]): iou_preds for each scale
-                level with shape (N, num_anchors * 1, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
-                boxes can be ignored when are computing the loss.
-        Returns:
-            dict[str, Tensor]: A dictionary of loss gmm_assignment.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-        )
-        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
-         pos_gt_index) = cls_reg_targets
-        cls_scores = levels_to_images(cls_scores)
-        cls_scores = [
-            item.reshape(-1, self.cls_out_channels) for item in cls_scores
-        ]
-        bbox_preds = levels_to_images(bbox_preds)
-        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
-        iou_preds = levels_to_images(iou_preds)
-        iou_preds = [item.reshape(-1, 1) for item in iou_preds]
-        pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
-                                       cls_scores, bbox_preds, labels,
-                                       labels_weight, bboxes_target,
-                                       bboxes_weight, pos_inds)
-        with torch.no_grad():
-            reassign_labels, reassign_label_weight, \
-                reassign_bbox_weights, num_pos = multi_apply(
-                    self.paa_reassign,
-                    pos_losses_list,
-                    labels,
-                    labels_weight,
-                    bboxes_weight,
-                    pos_inds,
-                    pos_gt_index,
-                    anchor_list)
-            num_pos = sum(num_pos)
-        # convert all tensor list to a flatten tensor
-        cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
-        bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
-        iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))
-        labels = torch.cat(reassign_labels, 0).view(-1)
-        flatten_anchors = torch.cat(
-            [torch.cat(item, 0) for item in anchor_list])
-        labels_weight = torch.cat(reassign_label_weight, 0).view(-1)
-        bboxes_target = torch.cat(bboxes_target,
-                                  0).view(-1, bboxes_target[0].size(-1))
-        pos_inds_flatten = ((labels >= 0)
-                            &
-                            (labels < self.num_classes)).nonzero().reshape(-1)
-        losses_cls = self.loss_cls(
-            cls_scores,
-            labels,
-            labels_weight,
-            avg_factor=max(num_pos, len(img_metas)))  # avoid num_pos=0
-        if num_pos:
-            pos_bbox_pred = self.bbox_coder.decode(
-                flatten_anchors[pos_inds_flatten],
-                bbox_preds[pos_inds_flatten])
-            pos_bbox_target = bboxes_target[pos_inds_flatten]
-            iou_target = bbox_overlaps(
-                pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
-            losses_iou = self.loss_centerness(
-                iou_preds[pos_inds_flatten],
-                iou_target.unsqueeze(-1),
-                avg_factor=num_pos)
-            losses_bbox = self.loss_bbox(
-                pos_bbox_pred,
-                pos_bbox_target,
-                iou_target.clamp(min=EPS),
-                avg_factor=iou_target.sum())
-        else:
-            losses_iou = iou_preds.sum() * 0
-            losses_bbox = bbox_preds.sum() * 0
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
-    def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight,
-                     bbox_target, bbox_weight, pos_inds):
-        """Calculate loss of all potential positive samples obtained from first
-        match process.
-        Args:
-            anchors (list[Tensor]): Anchors of each scale.
-            cls_score (Tensor): Box scores of single image with shape
-                (num_anchors, num_classes)
-            bbox_pred (Tensor): Box energies / deltas of single image
-                with shape (num_anchors, 4)
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_target (dict): Regression target of each anchor with
-                shape (num_anchors, 4).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-        Returns:
-            Tensor: Losses of all positive samples in single image.
-        """
-        if not len(pos_inds):
-            return cls_score.new([]),
-        anchors_all_level = torch.cat(anchors, 0)
-        pos_scores = cls_score[pos_inds]
-        pos_bbox_pred = bbox_pred[pos_inds]
-        pos_label = label[pos_inds]
-        pos_label_weight = label_weight[pos_inds]
-        pos_bbox_target = bbox_target[pos_inds]
-        pos_bbox_weight = bbox_weight[pos_inds]
-        pos_anchors = anchors_all_level[pos_inds]
-        pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
-        # to keep loss dimension
-        loss_cls = self.loss_cls(
-            pos_scores,
-            pos_label,
-            pos_label_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-        loss_bbox = self.loss_bbox(
-            pos_bbox_pred,
-            pos_bbox_target,
-            pos_bbox_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-        loss_cls = loss_cls.sum(-1)
-        pos_loss = loss_bbox + loss_cls
-        return pos_loss,
-    def paa_reassign(self, pos_losses, label, label_weight, bbox_weight,
-                     pos_inds, pos_gt_inds, anchors):
-        """Fit loss to GMM distribution and separate positive, ignore, negative
-        samples again with GMM model.
-        Args:
-            pos_losses (Tensor): Losses of all positive samples in
-                single image.
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-            pos_gt_inds (Tensor): Gt_index of all positive samples got
-                from first assign process.
-            anchors (list[Tensor]): Anchors of each scale.
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-                - label (Tensor): classification target of each anchor after
-                  paa assign, with shape (num_anchors,)
-                - label_weight (Tensor): Classification loss weight of each
-                  anchor after paa assign, with shape (num_anchors).
-                - bbox_weight (Tensor): Bbox weight of each anchor with shape
-                  (num_anchors, 4).
-                - num_pos (int): The number of positive samples after paa
-                  assign.
-        """
-        if not len(pos_inds):
-            return label, label_weight, bbox_weight, 0
-        label = label.clone()
-        label_weight = label_weight.clone()
-        bbox_weight = bbox_weight.clone()
-        num_gt = pos_gt_inds.max() + 1
-        num_level = len(anchors)
-        num_anchors_each_level = [item.size(0) for item in anchors]
-        num_anchors_each_level.insert(0, 0)
-        inds_level_interval = np.cumsum(num_anchors_each_level)
-        pos_level_mask = []
-        for i in range(num_level):
-            mask = (pos_inds >= inds_level_interval[i]) & (
-                pos_inds < inds_level_interval[i + 1])
-            pos_level_mask.append(mask)
-        pos_inds_after_paa = [label.new_tensor([])]
-        ignore_inds_after_paa = [label.new_tensor([])]
-        for gt_ind in range(num_gt):
-            pos_inds_gmm = []
-            pos_loss_gmm = []
-            gt_mask = pos_gt_inds == gt_ind
-            for level in range(num_level):
-                level_mask = pos_level_mask[level]
-                level_gt_mask = level_mask & gt_mask
-                value, topk_inds = pos_losses[level_gt_mask].topk(
-                    min(level_gt_mask.sum(), self.topk), largest=False)
-                pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds])
-                pos_loss_gmm.append(value)
-            pos_inds_gmm = torch.cat(pos_inds_gmm)
-            pos_loss_gmm = torch.cat(pos_loss_gmm)
-            # fix gmm need at least two sample
-            if len(pos_inds_gmm) < 2:
-                continue
-            device = pos_inds_gmm.device
-            pos_loss_gmm, sort_inds = pos_loss_gmm.sort()
-            pos_inds_gmm = pos_inds_gmm[sort_inds]
-            pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy()
-            min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max()
-            means_init = np.array([min_loss, max_loss]).reshape(2, 1)
-            weights_init = np.array([0.5, 0.5])
-            precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1)  # full
-            if self.covariance_type == 'spherical':
-                precisions_init = precisions_init.reshape(2)
-            elif self.covariance_type == 'diag':
-                precisions_init = precisions_init.reshape(2, 1)
-            elif self.covariance_type == 'tied':
-                precisions_init = np.array([[1.0]])
-            if skm is None:
-                raise ImportError('Please run "pip install sklearn" '
-                                  'to install sklearn first.')
-            gmm = skm.GaussianMixture(
-                2,
-                weights_init=weights_init,
-                means_init=means_init,
-                precisions_init=precisions_init,
-                covariance_type=self.covariance_type)
-            gmm.fit(pos_loss_gmm)
-            gmm_assignment = gmm.predict(pos_loss_gmm)
-            scores = gmm.score_samples(pos_loss_gmm)
-            gmm_assignment = torch.from_numpy(gmm_assignment).to(device)
-            scores = torch.from_numpy(scores).to(device)
-            pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme(
-                gmm_assignment, scores, pos_inds_gmm)
-            pos_inds_after_paa.append(pos_inds_temp)
-            ignore_inds_after_paa.append(ignore_inds_temp)
-        pos_inds_after_paa = torch.cat(pos_inds_after_paa)
-        ignore_inds_after_paa = torch.cat(ignore_inds_after_paa)
-        reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1)
-        reassign_ids = pos_inds[reassign_mask]
-        label[reassign_ids] = self.num_classes
-        label_weight[ignore_inds_after_paa] = 0
-        bbox_weight[reassign_ids] = 0
-        num_pos = len(pos_inds_after_paa)
-        return label, label_weight, bbox_weight, num_pos
-    def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm):
-        """A general separation scheme for gmm model.
-        It separates a GMM distribution of candidate samples into three
-        parts, 0 1 and uncertain areas, and you can implement other
-        separation schemes by rewriting this function.
-        Args:
-            gmm_assignment (Tensor): The prediction of GMM which is of shape
-                (num_samples,). The 0/1 value indicates the distribution
-                that each sample comes from.
-            scores (Tensor): The probability of sample coming from the
-                fit GMM distribution. The tensor is of shape (num_samples,).
-            pos_inds_gmm (Tensor): All the indexes of samples which are used
-                to fit GMM model. The tensor is of shape (num_samples,)
-        Returns:
-            tuple[Tensor]: The indices of positive and ignored samples.
-                - pos_inds_temp (Tensor): Indices of positive samples.
-                - ignore_inds_temp (Tensor): Indices of ignore samples.
-        """
-        # The implementation is (c) in Fig.3 in origin paper instead of (b).
-        # You can refer to issues such as
-        # https://github.com/kkhoot/PAA/issues/8 and
-        # https://github.com/kkhoot/PAA/issues/9.
-        fgs = gmm_assignment == 0
-        pos_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        ignore_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        if fgs.nonzero().numel():
-            _, pos_thr_ind = scores[fgs].topk(1)
-            pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1]
-            ignore_inds_temp = pos_inds_gmm.new_tensor([])
-        return pos_inds_temp, ignore_inds_temp
-    def get_targets(
-        self,
-        anchor_list,
-        valid_flag_list,
-        gt_bboxes_list,
-        img_metas,
-        gt_bboxes_ignore_list=None,
-        gt_labels_list=None,
-        label_channels=1,
-        unmap_outputs=True,
-    ):
-        """Get targets for PAA head.
-        This method is almost the same as `AnchorHead.get_targets()`. We direct
-        return the results from _get_targets_single instead map it to levels
-        by images_to_levels function.
-        Args:
-            anchor_list (list[list[Tensor]]): Multi level anchors of each
-                image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, 4).
-            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
-                each image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, )
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
-                ignored.
-            gt_labels_list (list[Tensor]): Ground truth labels of each box.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-                - labels (list[Tensor]): Labels of all anchors, each with
-                    shape (num_anchors,).
-                - label_weights (list[Tensor]): Label weights of all anchor.
-                    each with shape (num_anchors,).
-                - bbox_targets (list[Tensor]): BBox targets of all anchors.
-                    each with shape (num_anchors, 4).
-                - bbox_weights (list[Tensor]): BBox weights of all anchors.
-                    each with shape (num_anchors, 4).
-                - pos_inds (list[Tensor]): Contains all index of positive
-                    sample in all anchor.
-                - gt_inds (list[Tensor]): Contains all gt_index of positive
-                    sample in all anchor.
-        """
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-        concat_anchor_list = []
-        concat_valid_flag_list = []
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            concat_anchor_list.append(torch.cat(anchor_list[i]))
-            concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        results = multi_apply(
-            self._get_targets_single,
-            concat_anchor_list,
-            concat_valid_flag_list,
-            gt_bboxes_list,
-            gt_bboxes_ignore_list,
-            gt_labels_list,
-            img_metas,
-            label_channels=label_channels,
-            unmap_outputs=unmap_outputs)
-        (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds,
-         valid_neg_inds, sampling_result) = results
-        # Due to valid flag of anchors, we have to calculate the real pos_inds
-        # in origin anchor set.
-        pos_inds = []
-        for i, single_labels in enumerate(labels):
-            pos_mask = (0 <= single_labels) & (
-                single_labels < self.num_classes)
-            pos_inds.append(pos_mask.nonzero().view(-1))
-        gt_inds = [item.pos_assigned_gt_inds for item in sampling_result]
-        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
-                gt_inds)
-    def _get_targets_single(self,
-                            flat_anchors,
-                            valid_flags,
-                            gt_bboxes,
-                            gt_bboxes_ignore,
-                            gt_labels,
-                            img_meta,
-                            label_channels=1,
-                            unmap_outputs=True):
-        """Compute regression and classification targets for anchors in a
-        single image.
-        This method is same as `AnchorHead._get_targets_single()`.
-        """
-        assert unmap_outputs, 'We must map outputs back to the original' \
-            'set of anchors in PAAhead'
-        return super(ATSSHead, self)._get_targets_single(
-            flat_anchors,
-            valid_flags,
-            gt_bboxes,
-            gt_bboxes_ignore,
-            gt_labels,
-            img_meta,
-            label_channels=1,
-            unmap_outputs=True)
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    iou_preds,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-        This method is almost same as `ATSSHead._get_bboxes()`.
-        We use sqrt(iou_preds * cls_scores) in NMS process instead of just
-        cls_scores. Besides, score voting is used when `` score_voting``
-        is set to True.
-        """
-        assert with_nms, 'PAA only supports "with_nms=True" now'
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        batch_size = cls_scores[0].shape[0]
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_iou_preds = []
-        for cls_score, bbox_pred, iou_preds, anchors in zip(
-                cls_scores, bbox_preds, iou_preds, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3,
-                                          1).reshape(batch_size, -1, 4)
-            iou_preds = iou_preds.permute(0, 2, 3, 1).reshape(batch_size,
-                                                              -1).sigmoid()
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[1] > nms_pre:
-                max_scores, _ = (scores * iou_preds[..., None]).sqrt().max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-                iou_preds = iou_preds[batch_inds, topk_inds]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_iou_preds.append(iou_preds)
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        # Add a dummy background class to the backend when using sigmoid
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-        batch_mlvl_iou_preds = torch.cat(mlvl_iou_preds, dim=1)
-        batch_mlvl_nms_scores = (batch_mlvl_scores *
-                                 batch_mlvl_iou_preds[..., None]).sqrt()
-        det_results = []
-        for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
-                                              batch_mlvl_nms_scores):
-            det_bbox, det_label = multiclass_nms(
-                mlvl_bboxes,
-                mlvl_scores,
-                cfg.score_thr,
-                cfg.nms,
-                cfg.max_per_img,
-                score_factors=None)
-            if self.with_score_voting and len(det_bbox) > 0:
-                det_bbox, det_label = self.score_voting(
-                    det_bbox, det_label, mlvl_bboxes, mlvl_scores,
-                    cfg.score_thr)
-            det_results.append(tuple([det_bbox, det_label]))
-        return det_results
-    def score_voting(self, det_bboxes, det_labels, mlvl_bboxes,
-                     mlvl_nms_scores, score_thr):
-        """Implementation of score voting method works on each remaining boxes
-        after NMS procedure.
-        Args:
-            det_bboxes (Tensor): Remaining boxes after NMS procedure,
-                with shape (k, 5), each dimension means
-                (x1, y1, x2, y2, score).
-            det_labels (Tensor): The label of remaining boxes, with shape
-                (k, 1),Labels are 0-based.
-            mlvl_bboxes (Tensor): All boxes before the NMS procedure,
-                with shape (num_anchors,4).
-            mlvl_nms_scores (Tensor): The scores of all boxes which is used
-                in the NMS procedure, with shape (num_anchors, num_class)
-            mlvl_iou_preds (Tensor): The predictions of IOU of all boxes
-                before the NMS procedure, with shape (num_anchors, 1)
-            score_thr (float): The score threshold of bboxes.
-        Returns:
-            tuple: Usually returns a tuple containing voting results.
-                - det_bboxes_voted (Tensor): Remaining boxes after
-                    score voting procedure, with shape (k, 5), each
-                    dimension means (x1, y1, x2, y2, score).
-                - det_labels_voted (Tensor): Label of remaining bboxes
-                    after voting, with shape (num_anchors,).
-        """
-        candidate_mask = mlvl_nms_scores > score_thr
-        candidate_mask_nonzeros = candidate_mask.nonzero()
-        candidate_inds = candidate_mask_nonzeros[:, 0]
-        candidate_labels = candidate_mask_nonzeros[:, 1]
-        candidate_bboxes = mlvl_bboxes[candidate_inds]
-        candidate_scores = mlvl_nms_scores[candidate_mask]
-        det_bboxes_voted = []
-        det_labels_voted = []
-        for cls in range(self.cls_out_channels):
-            candidate_cls_mask = candidate_labels == cls
-            if not candidate_cls_mask.any():
-                continue
-            candidate_cls_scores = candidate_scores[candidate_cls_mask]
-            candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask]
-            det_cls_mask = det_labels == cls
-            det_cls_bboxes = det_bboxes[det_cls_mask].view(
-                -1, det_bboxes.size(-1))
-            det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4],
-                                               candidate_cls_bboxes)
-            for det_ind in range(len(det_cls_bboxes)):
-                single_det_ious = det_candidate_ious[det_ind]
-                pos_ious_mask = single_det_ious > 0.01
-                pos_ious = single_det_ious[pos_ious_mask]
-                pos_bboxes = candidate_cls_bboxes[pos_ious_mask]
-                pos_scores = candidate_cls_scores[pos_ious_mask]
-                pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) *
-                       pos_scores)[:, None]
-                voted_box = torch.sum(
-                    pis * pos_bboxes, dim=0) / torch.sum(
-                        pis, dim=0)
-                voted_score = det_cls_bboxes[det_ind][-1:][None, :]
-                det_bboxes_voted.append(
-                    torch.cat((voted_box[None, :], voted_score), dim=1))
-                det_labels_voted.append(cls)
-        det_bboxes_voted = torch.cat(det_bboxes_voted, dim=0)
-        det_labels_voted = det_labels.new_tensor(det_labels_voted)
-        return det_bboxes_voted, det_labels_voted

spaces/CVPR/regionclip-demo/detectron2/data/datasets/clip_prompt_utils.py DELETED Viewed

@@ -1,441 +0,0 @@
-import gzip
-import html
-import os
-from functools import lru_cache
-import ftfy
-import regex as re
-import torch
-import numpy as np
-from typing import Union, List
-from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES
-from .coco_zeroshot_categories import COCO_UNSEEN_CLS, COCO_SEEN_CLS, COCO_OVD_ALL_CLS, COCO_80_ALL_CLS
-# https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        self.vocab = vocab
-        for merge in merges:
-            vocab.append(''.join(merge))
-        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
-        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-        if not pairs:
-            return token+'</w>'
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-    def encode(self, text, return_link=False):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        str2id_links = []  #  link original sentence word to the tokenized ids of its subwords
-        for token in re.findall(self.pat, text):
-            this_link = [token]
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            ids = [self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')]
-            bpe_tokens.extend(ids)
-            this_link.append(ids)
-            str2id_links.append(this_link)
-        if return_link:
-            return bpe_tokens, str2id_links
-        return bpe_tokens
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text
-# https://github.com/openai/CLIP/blob/main/clip/clip.py
-#_tokenizer = SimpleTokenizer()
-def tokenize(texts: Union[str, List[str]], context_length: int = 77):
-    if isinstance(texts, str):
-        texts = [texts]
-    sot_token = _tokenizer.encoder["<|startoftext|>"]
-    eot_token = _tokenizer.encoder["<|endoftext|>"]
-    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
-    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-    for i, tokens in enumerate(all_tokens):
-        if len(tokens) > context_length:
-            raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
-        result[i, :len(tokens)] = torch.tensor(tokens)
-    return result
-# prompt_engineering.py
-def get_prompt_templates():
-    # prompt_templates = [
-    #     'There is a {} in the scene.',
-    #     'There is the {} in the scene.',
-    #     'a photo of a {} in the scene.',
-    #     'a photo of the {} in the scene.',
-    #     'a photo of one {} in the scene.',
-    #     'itap of a {}.',
-    #     'itap of my {}.',  # itap: I took a picture of
-    #     'itap of the {}.',
-    #     'a photo of a {}.',
-    #     'a photo of my {}.',
-    #     'a photo of the {}.',
-    #     'a photo of one {}.',
-    #     'a photo of many {}.',
-    #     'a good photo of a {}.',
-    #     'a good photo of the {}.',
-    #     'a bad photo of a {}.',
-    #     'a bad photo of the {}.',
-    #     'a photo of a nice {}.',
-    #     'a photo of the nice {}.',
-    #     'a photo of a cool {}.',
-    #     'a photo of the cool {}.',
-    #     'a photo of a weird {}.',
-    #     'a photo of the weird {}.',
-    #     'a photo of a small {}.',
-    #     'a photo of the small {}.',
-    #     'a photo of a large {}.',
-    #     'a photo of the large {}.',
-    #     'a photo of a clean {}.',
-    #     'a photo of the clean {}.',
-    #     'a photo of a dirty {}.',
-    #     'a photo of the dirty {}.',
-    #     'a bright photo of a {}.',
-    #     'a bright photo of the {}.',
-    #     'a dark photo of a {}.',
-    #     'a dark photo of the {}.',
-    #     'a photo of a hard to see {}.',
-    #     'a photo of the hard to see {}.',
-    #     'a low resolution photo of a {}.',
-    #     'a low resolution photo of the {}.',
-    #     'a cropped photo of a {}.',
-    #     'a cropped photo of the {}.',
-    #     'a close-up photo of a {}.',
-    #     'a close-up photo of the {}.',
-    #     'a jpeg corrupted photo of a {}.',
-    #     'a jpeg corrupted photo of the {}.',
-    #     'a blurry photo of a {}.',
-    #     'a blurry photo of the {}.',
-    #     'a pixelated photo of a {}.',
-    #     'a pixelated photo of the {}.',
-    #     'a black and white photo of the {}.',
-    #     'a black and white photo of a {}.',
-    #     'a plastic {}.',
-    #     'the plastic {}.',
-    #     'a toy {}.',
-    #     'the toy {}.',
-    #     'a plushie {}.',
-    #     'the plushie {}.',
-    #     'a cartoon {}.',
-    #     'the cartoon {}.',
-    #     'an embroidered {}.',
-    #     'the embroidered {}.',
-    #     'a painting of the {}.',
-    #     'a painting of a {}.',
-    # ]
-    prompt_templates = [
-        '{}.',
-        'a photo of a {}.',
-        'a bad photo of a {}.',
-        'a photo of many {}.',
-        'a sculpture of a {}.',
-        'a photo of the hard to see {}.',
-        'a low resolution photo of the {}.',
-        'a rendering of a {}.',
-        'graffiti of a {}.',
-        'a bad photo of the {}.',
-        'a cropped photo of the {}.',
-        'a tattoo of a {}.',
-        'the embroidered {}.',
-        'a photo of a hard to see {}.',
-        'a bright photo of a {}.',
-        'a photo of a clean {}.',
-        'a photo of a dirty {}.',
-        'a dark photo of the {}.',
-        'a drawing of a {}.',
-        'a photo of my {}.',
-        'the plastic {}.',
-        'a photo of the cool {}.',
-        'a close-up photo of a {}.',
-        'a black and white photo of the {}.',
-        'a painting of the {}.',
-        'a painting of a {}.',
-        'a pixelated photo of the {}.',
-        'a sculpture of the {}.',
-        'a bright photo of the {}.',
-        'a cropped photo of a {}.',
-        'a plastic {}.',
-        'a photo of the dirty {}.',
-        'a jpeg corrupted photo of a {}.',
-        'a blurry photo of the {}.',
-        'a photo of the {}.',
-        'a good photo of the {}.',
-        'a rendering of the {}.',
-        'a {} in a video game.',
-        'a photo of one {}.',
-        'a doodle of a {}.',
-        'a close-up photo of the {}.',
-        'the origami {}.',
-        'the {} in a video game.',
-        'a sketch of a {}.',
-        'a doodle of the {}.',
-        'a origami {}.',
-        'a low resolution photo of a {}.',
-        'the toy {}.',
-        'a rendition of the {}.',
-        'a photo of the clean {}.',
-        'a photo of a large {}.',
-        'a rendition of a {}.',
-        'a photo of a nice {}.',
-        'a photo of a weird {}.',
-        'a blurry photo of a {}.',
-        'a cartoon {}.',
-        'art of a {}.',
-        'a sketch of the {}.',
-        'a embroidered {}.',
-        'a pixelated photo of a {}.',
-        'itap of the {}.',
-        'a jpeg corrupted photo of the {}.',
-        'a good photo of a {}.',
-        'a plushie {}.',
-        'a photo of the nice {}.',
-        'a photo of the small {}.',
-        'a photo of the weird {}.',
-        'the cartoon {}.',
-        'art of the {}.',
-        'a drawing of the {}.',
-        'a photo of the large {}.',
-        'a black and white photo of a {}.',
-        'the plushie {}.',
-        'a dark photo of a {}.',
-        'itap of a {}.',
-        'graffiti of the {}.',
-        'a toy {}.',
-        'itap of my {}.',
-        'a photo of a cool {}.',
-        'a photo of a small {}.',
-        'a tattoo of the {}.',
-    ]
-    return prompt_templates
-def prompt_engineering(classnames, template=""):
-    return template.replace('{}', classnames.replace(',', '').replace('+', ' '))
-# clip_img_tsv.py
-def convert_example_to_features_bpe(text, tokenizer, sot_token, eot_token, context_length=77):
-    """
-    Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample.
-    :param tokenizer: Tokenizer
-    :return: List, a list containing token id, padded by 0
-    """
-    assert isinstance(text, str)
-    input_ids = [sot_token] + tokenizer.encode(text) + [eot_token]
-    if len(input_ids) > context_length:
-        input_ids = input_ids[:context_length]
-    input_ids = np.array(input_ids)
-    pad_input_ids = np.zeros(context_length)
-    pad_input_ids[:input_ids.shape[0]] = input_ids
-    return pad_input_ids
-def get_cls_names(filter_novel=False, coco=None, from_file=False):
-    """ return a list of strings with each string as name of a class
-    """
-    # the names are stored in a txt file
-    if from_file:
-        # coco_det_cls = {COCO_80_ALL_CLS[key]: key for key in COCO_80_ALL_CLS}
-        # # not found in nouns {'skis': 31, 'sports ball': 33, 'hot dog': 53, 'potted plant': 59, 'scissors': 77, 'hair drier': 79}
-        # coco_det_cls['ski'] = 81
-        # coco_det_cls['scissor'] = 82
-        # with open('/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/COCO_Caption_nouns_4688.txt','w') as g:
-        #     with open(from_file, 'r') as f:
-        #         cnt = 0
-        #         for row in f:
-        #             if row.split(",")[0] not in coco_det_cls:
-        #                 g.write(row)
-        #                 cnt += 1
-        #             else:
-        #                 coco_det_cls.pop(row.split(",")[0])
-        names = []
-        with open(from_file, 'r') as f:
-            for row in f:
-                names.append(row.split(",")[0])
-        return names
-    # classes' names
-    if coco == 'target':
-        return COCO_UNSEEN_CLS
-    elif coco == 'base':
-        return COCO_SEEN_CLS
-    elif coco == 'all':
-        return COCO_OVD_ALL_CLS
-    elif coco == 'all_80':
-        return [COCO_80_ALL_CLS[i+1] for i in range(80)]
-    assert len(LVIS_V1_CATEGORIES) == 1203
-    cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES]
-    assert min(cat_ids) == 1 and max(cat_ids) == len(
-        cat_ids
-    ), "Category ids are not in [1, #categories], as expected"
-    # Ensure that the category list is sorted by id
-    lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"])
-    if filter_novel:
-        class_names = [cls_meta['name'] for cls_meta in lvis_categories if cls_meta['frequency'] != 'r']
-    else:
-        class_names = [cls_meta['name'] for cls_meta in lvis_categories]
-    # remove or replace special symbols
-    class_names = [cls_n.replace("_", " ") for cls_n in class_names]
-    class_names = [cls_n.replace("(", "") for cls_n in class_names]
-    class_names = [cls_n.replace(")", "") for cls_n in class_names]
-    return class_names
-def pre_tokenize(class_names):
-    """
-    pre-tokenize class names
-    :param class_names: List, a list of class names
-    :param tokenizer: Tokenizer, SimpleTokenizer()
-    :return: Tensor, containing all prompts for all classes, [#cls, #prompts, context_length]
-    """
-    # tokenizer
-    tokenizer = SimpleTokenizer()
-    sot_token = tokenizer.encoder["<|startoftext|>"]
-    eot_token = tokenizer.encoder["<|endoftext|>"]
-    # prompt engineering
-    prompt_templates = get_prompt_templates()
-    input_ids_all = []
-    for k in range(len(class_names)):
-        v = class_names[k]
-        if isinstance(v, str):
-            vs = [v]
-        elif isinstance(v, list):
-            vs = v
-        t1s = []
-        for v in vs:
-            for pt in prompt_templates:
-                t1s.append(prompt_engineering(v, template=pt))
-        input_ids = []
-        for t1 in t1s:
-            this_input_ids = convert_example_to_features_bpe(t1, tokenizer, sot_token, eot_token)
-            input_ids.append(torch.tensor(this_input_ids, dtype=torch.long))
-        input_ids_all.append(torch.stack(input_ids, 0))
-    input_ids_all_classes = torch.stack(input_ids_all, 0)
-    return input_ids_all_classes
-if __name__ == "__main__":
-    flatten_input_ids = pre_tokenize()

spaces/CVPR/regionclip-demo/detectron2/data/transforms/transform.py DELETED Viewed

@@ -1,351 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-See "Data Augmentation" tutorial for an overview of the system:
-https://detectron2.readthedocs.io/tutorials/augmentation.html
-"""
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fvcore.transforms.transform import (
-    CropTransform,
-    HFlipTransform,
-    NoOpTransform,
-    Transform,
-    TransformList,
-)
-from PIL import Image
-try:
-    import cv2  # noqa
-except ImportError:
-    # OpenCV is an optional dependency at the moment
-    pass
-__all__ = [
-    "ExtentTransform",
-    "ResizeTransform",
-    "RotationTransform",
-    "ColorTransform",
-    "PILColorTransform",
-]
-class ExtentTransform(Transform):
-    """
-    Extracts a subregion from the source image and scales it to the output size.
-    The fill color is used to map pixels from the source rect that fall outside
-    the source image.
-    See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform
-    """
-    def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0):
-        """
-        Args:
-            src_rect (x0, y0, x1, y1): src coordinates
-            output_size (h, w): dst image size
-            interp: PIL interpolation methods
-            fill: Fill color used when src_rect extends outside image
-        """
-        super().__init__()
-        self._set_attributes(locals())
-    def apply_image(self, img, interp=None):
-        h, w = self.output_size
-        if len(img.shape) > 2 and img.shape[2] == 1:
-            pil_image = Image.fromarray(img[:, :, 0], mode="L")
-        else:
-            pil_image = Image.fromarray(img)
-        pil_image = pil_image.transform(
-            size=(w, h),
-            method=Image.EXTENT,
-            data=self.src_rect,
-            resample=interp if interp else self.interp,
-            fill=self.fill,
-        )
-        ret = np.asarray(pil_image)
-        if len(img.shape) > 2 and img.shape[2] == 1:
-            ret = np.expand_dims(ret, -1)
-        return ret
-    def apply_coords(self, coords):
-        # Transform image center from source coordinates into output coordinates
-        # and then map the new origin to the corner of the output image.
-        h, w = self.output_size
-        x0, y0, x1, y1 = self.src_rect
-        new_coords = coords.astype(np.float32)
-        new_coords[:, 0] -= 0.5 * (x0 + x1)
-        new_coords[:, 1] -= 0.5 * (y0 + y1)
-        new_coords[:, 0] *= w / (x1 - x0)
-        new_coords[:, 1] *= h / (y1 - y0)
-        new_coords[:, 0] += 0.5 * w
-        new_coords[:, 1] += 0.5 * h
-        return new_coords
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
-        return segmentation
-class ResizeTransform(Transform):
-    """
-    Resize the image to a target size.
-    """
-    def __init__(self, h, w, new_h, new_w, interp=None):
-        """
-        Args:
-            h, w (int): original image size
-            new_h, new_w (int): new image size
-            interp: PIL interpolation methods, defaults to bilinear.
-        """
-        # TODO decide on PIL vs opencv
-        super().__init__()
-        if interp is None:
-            interp = Image.BILINEAR
-        self._set_attributes(locals())
-    def apply_image(self, img, interp=None):
-        assert img.shape[:2] == (self.h, self.w)
-        assert len(img.shape) <= 4
-        interp_method = interp if interp is not None else self.interp
-        if img.dtype == np.uint8:
-            if len(img.shape) > 2 and img.shape[2] == 1:
-                pil_image = Image.fromarray(img[:, :, 0], mode="L")
-            else:
-                pil_image = Image.fromarray(img)
-            pil_image = pil_image.resize((self.new_w, self.new_h), interp_method)
-            ret = np.asarray(pil_image)
-            if len(img.shape) > 2 and img.shape[2] == 1:
-                ret = np.expand_dims(ret, -1)
-        else:
-            # PIL only supports uint8
-            if any(x < 0 for x in img.strides):
-                img = np.ascontiguousarray(img)
-            img = torch.from_numpy(img)
-            shape = list(img.shape)
-            shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:]
-            img = img.view(shape_4d).permute(2, 3, 0, 1)  # hw(c) -> nchw
-            _PIL_RESIZE_TO_INTERPOLATE_MODE = {
-                Image.NEAREST: "nearest",
-                Image.BILINEAR: "bilinear",
-                Image.BICUBIC: "bicubic",
-            }
-            mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method]
-            align_corners = None if mode == "nearest" else False
-            img = F.interpolate(
-                img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners
-            )
-            shape[:2] = (self.new_h, self.new_w)
-            ret = img.permute(2, 3, 0, 1).view(shape).numpy()  # nchw -> hw(c)
-        return ret
-    def apply_coords(self, coords):
-        coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w)
-        coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h)
-        return coords
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
-        return segmentation
-    def inverse(self):
-        return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp)
-class RotationTransform(Transform):
-    """
-    This method returns a copy of this image, rotated the given
-    number of degrees counter clockwise around its center.
-    """
-    def __init__(self, h, w, angle, expand=True, center=None, interp=None):
-        """
-        Args:
-            h, w (int): original image size
-            angle (float): degrees for rotation
-            expand (bool): choose if the image should be resized to fit the whole
-                rotated image (default), or simply cropped
-            center (tuple (width, height)): coordinates of the rotation center
-                if left to None, the center will be fit to the center of each image
-                center has no effect if expand=True because it only affects shifting
-            interp: cv2 interpolation method, default cv2.INTER_LINEAR
-        """
-        super().__init__()
-        image_center = np.array((w / 2, h / 2))
-        if center is None:
-            center = image_center
-        if interp is None:
-            interp = cv2.INTER_LINEAR
-        abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle))))
-        if expand:
-            # find the new width and height bounds
-            bound_w, bound_h = np.rint(
-                [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin]
-            ).astype(int)
-        else:
-            bound_w, bound_h = w, h
-        self._set_attributes(locals())
-        self.rm_coords = self.create_rotation_matrix()
-        # Needed because of this problem https://github.com/opencv/opencv/issues/11784
-        self.rm_image = self.create_rotation_matrix(offset=-0.5)
-    def apply_image(self, img, interp=None):
-        """
-        img should be a numpy array, formatted as Height * Width * Nchannels
-        """
-        if len(img) == 0 or self.angle % 360 == 0:
-            return img
-        assert img.shape[:2] == (self.h, self.w)
-        interp = interp if interp is not None else self.interp
-        return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp)
-    def apply_coords(self, coords):
-        """
-        coords should be a N * 2 array-like, containing N couples of (x, y) points
-        """
-        coords = np.asarray(coords, dtype=float)
-        if len(coords) == 0 or self.angle % 360 == 0:
-            return coords
-        return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :]
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST)
-        return segmentation
-    def create_rotation_matrix(self, offset=0):
-        center = (self.center[0] + offset, self.center[1] + offset)
-        rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1)
-        if self.expand:
-            # Find the coordinates of the center of rotation in the new image
-            # The only point for which we know the future coordinates is the center of the image
-            rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :]
-            new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center
-            # shift the rotation center to the new coordinates
-            rm[:, 2] += new_center
-        return rm
-    def inverse(self):
-        """
-        The inverse is to rotate it back with expand, and crop to get the original shape.
-        """
-        if not self.expand:  # Not possible to inverse if a part of the image is lost
-            raise NotImplementedError()
-        rotation = RotationTransform(
-            self.bound_h, self.bound_w, -self.angle, True, None, self.interp
-        )
-        crop = CropTransform(
-            (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h
-        )
-        return TransformList([rotation, crop])
-class ColorTransform(Transform):
-    """
-    Generic wrapper for any photometric transforms.
-    These transformations should only affect the color space and
-        not the coordinate space of the image (e.g. annotation
-        coordinates such as bounding boxes should not be changed)
-    """
-    def __init__(self, op):
-        """
-        Args:
-            op (Callable): operation to be applied to the image,
-                which takes in an ndarray and returns an ndarray.
-        """
-        if not callable(op):
-            raise ValueError("op parameter should be callable")
-        super().__init__()
-        self._set_attributes(locals())
-    def apply_image(self, img):
-        return self.op(img)
-    def apply_coords(self, coords):
-        return coords
-    def inverse(self):
-        return NoOpTransform()
-    def apply_segmentation(self, segmentation):
-        return segmentation
-class PILColorTransform(ColorTransform):
-    """
-    Generic wrapper for PIL Photometric image transforms,
-        which affect the color space and not the coordinate
-        space of the image
-    """
-    def __init__(self, op):
-        """
-        Args:
-            op (Callable): operation to be applied to the image,
-                which takes in a PIL Image and returns a transformed
-                PIL Image.
-                For reference on possible operations see:
-                - https://pillow.readthedocs.io/en/stable/
-        """
-        if not callable(op):
-            raise ValueError("op parameter should be callable")
-        super().__init__(op)
-    def apply_image(self, img):
-        img = Image.fromarray(img)
-        return np.asarray(super().apply_image(img))
-def HFlip_rotated_box(transform, rotated_boxes):
-    """
-    Apply the horizontal flip transform on rotated boxes.
-    Args:
-        rotated_boxes (ndarray): Nx5 floating point array of
-            (x_center, y_center, width, height, angle_degrees) format
-            in absolute coordinates.
-    """
-    # Transform x_center
-    rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0]
-    # Transform angle
-    rotated_boxes[:, 4] = -rotated_boxes[:, 4]
-    return rotated_boxes
-def Resize_rotated_box(transform, rotated_boxes):
-    """
-    Apply the resizing transform on rotated boxes. For details of how these (approximation)
-    formulas are derived, please refer to :meth:`RotatedBoxes.scale`.
-    Args:
-        rotated_boxes (ndarray): Nx5 floating point array of
-            (x_center, y_center, width, height, angle_degrees) format
-            in absolute coordinates.
-    """
-    scale_factor_x = transform.new_w * 1.0 / transform.w
-    scale_factor_y = transform.new_h * 1.0 / transform.h
-    rotated_boxes[:, 0] *= scale_factor_x
-    rotated_boxes[:, 1] *= scale_factor_y
-    theta = rotated_boxes[:, 4] * np.pi / 180.0
-    c = np.cos(theta)
-    s = np.sin(theta)
-    rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s))
-    rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c))
-    rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi
-    return rotated_boxes
-HFlipTransform.register_type("rotated_box", HFlip_rotated_box)
-ResizeTransform.register_type("rotated_box", Resize_rotated_box)
-# not necessary any more with latest fvcore
-NoOpTransform.register_type("rotated_box", lambda t, x: x)

spaces/CVPR/regionclip-demo/detectron2/modeling/poolers.py DELETED Viewed

@@ -1,250 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import math
-from typing import List
-import torch
-from torch import nn
-from torchvision.ops import RoIPool
-from detectron2.layers import ROIAlign, ROIAlignRotated, cat, nonzero_tuple
-from detectron2.structures import Boxes
-"""
-To export ROIPooler to torchscript, in this file, variables that should be annotated with
-`Union[List[Boxes], List[RotatedBoxes]]` are only annotated with `List[Boxes]`.
-TODO: Correct these annotations when torchscript support `Union`.
-https://github.com/pytorch/pytorch/issues/41412
-"""
-__all__ = ["ROIPooler"]
-def assign_boxes_to_levels(
-    box_lists: List[Boxes],
-    min_level: int,
-    max_level: int,
-    canonical_box_size: int,
-    canonical_level: int,
-):
-    """
-    Map each box in `box_lists` to a feature map level index and return the assignment
-    vector.
-    Args:
-        box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes,
-            where N is the number of images in the batch.
-        min_level (int): Smallest feature map level index. The input is considered index 0,
-            the output of stage 1 is index 1, and so.
-        max_level (int): Largest feature map level index.
-        canonical_box_size (int): A canonical box size in pixels (sqrt(box area)).
-        canonical_level (int): The feature map level index on which a canonically-sized box
-            should be placed.
-    Returns:
-        A tensor of length M, where M is the total number of boxes aggregated over all
-            N batch images. The memory layout corresponds to the concatenation of boxes
-            from all images. Each element is the feature map index, as an offset from
-            `self.min_level`, for the corresponding box (so value i means the box is at
-            `self.min_level + i`).
-    """
-    box_sizes = torch.sqrt(cat([boxes.area() for boxes in box_lists]))
-    # Eqn.(1) in FPN paper
-    level_assignments = torch.floor(
-        canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8)
-    )
-    # clamp level to (min, max), in case the box size is too large or too small
-    # for the available feature maps
-    level_assignments = torch.clamp(level_assignments, min=min_level, max=max_level)
-    return level_assignments.to(torch.int64) - min_level
-def _fmt_box_list(box_tensor, batch_index: int):
-    repeated_index = torch.full_like(
-        box_tensor[:, :1], batch_index, dtype=box_tensor.dtype, device=box_tensor.device
-    )
-    return cat((repeated_index, box_tensor), dim=1)
-def convert_boxes_to_pooler_format(box_lists: List[Boxes]):
-    """
-    Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops
-    (see description under Returns).
-    Args:
-        box_lists (list[Boxes] | list[RotatedBoxes]):
-            A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch.
-    Returns:
-        When input is list[Boxes]:
-            A tensor of shape (M, 5), where M is the total number of boxes aggregated over all
-            N batch images.
-            The 5 columns are (batch index, x0, y0, x1, y1), where batch index
-            is the index in [0, N) identifying which batch image the box with corners at
-            (x0, y0, x1, y1) comes from.
-        When input is list[RotatedBoxes]:
-            A tensor of shape (M, 6), where M is the total number of boxes aggregated over all
-            N batch images.
-            The 6 columns are (batch index, x_ctr, y_ctr, width, height, angle_degrees),
-            where batch index is the index in [0, N) identifying which batch image the
-            rotated box (x_ctr, y_ctr, width, height, angle_degrees) comes from.
-    """
-    pooler_fmt_boxes = cat(
-        [_fmt_box_list(box_list.tensor, i) for i, box_list in enumerate(box_lists)], dim=0
-    )
-    return pooler_fmt_boxes
-class ROIPooler(nn.Module):
-    """
-    Region of interest feature map pooler that supports pooling from one or more
-    feature maps.
-    """
-    def __init__(
-        self,
-        output_size,
-        scales,
-        sampling_ratio,
-        pooler_type,
-        canonical_box_size=224,
-        canonical_level=4,
-    ):
-        """
-        Args:
-            output_size (int, tuple[int] or list[int]): output size of the pooled region,
-                e.g., 14 x 14. If tuple or list is given, the length must be 2.
-            scales (list[float]): The scale for each low-level pooling op relative to
-                the input image. For a feature map with stride s relative to the input
-                image, scale is defined as 1/s. The stride must be power of 2.
-                When there are multiple scales, they must form a pyramid, i.e. they must be
-                a monotically decreasing geometric sequence with a factor of 1/2.
-            sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op.
-            pooler_type (string): Name of the type of pooling operation that should be applied.
-                For instance, "ROIPool" or "ROIAlignV2".
-            canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). The default
-                is heuristically defined as 224 pixels in the FPN paper (based on ImageNet
-                pre-training).
-            canonical_level (int): The feature map level index from which a canonically-sized box
-                should be placed. The default is defined as level 4 (stride=16) in the FPN paper,
-                i.e., a box of size 224x224 will be placed on the feature with stride=16.
-                The box placement for all boxes will be determined from their sizes w.r.t
-                canonical_box_size. For example, a box whose area is 4x that of a canonical box
-                should be used to pool features from feature level ``canonical_level+1``.
-                Note that the actual input feature maps given to this module may not have
-                sufficiently many levels for the input boxes. If the boxes are too large or too
-                small for the input feature maps, the closest level will be used.
-        """
-        super().__init__()
-        if isinstance(output_size, int):
-            output_size = (output_size, output_size)
-        assert len(output_size) == 2
-        assert isinstance(output_size[0], int) and isinstance(output_size[1], int)
-        self.output_size = output_size
-        if pooler_type == "ROIAlign":
-            self.level_poolers = nn.ModuleList(
-                ROIAlign(
-                    output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False
-                )
-                for scale in scales
-            )
-        elif pooler_type == "ROIAlignV2":
-            self.level_poolers = nn.ModuleList(
-                ROIAlign(
-                    output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True
-                )
-                for scale in scales
-            )
-        elif pooler_type == "ROIPool":
-            self.level_poolers = nn.ModuleList(
-                RoIPool(output_size, spatial_scale=scale) for scale in scales
-            )
-        elif pooler_type == "ROIAlignRotated":
-            self.level_poolers = nn.ModuleList(
-                ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio)
-                for scale in scales
-            )
-        else:
-            raise ValueError("Unknown pooler type: {}".format(pooler_type))
-        # Map scale (defined as 1 / stride) to its feature map level under the
-        # assumption that stride is a power of 2.
-        min_level = -(math.log2(scales[0]))
-        max_level = -(math.log2(scales[-1]))
-        assert math.isclose(min_level, int(min_level)) and math.isclose(
-            max_level, int(max_level)
-        ), "Featuremap stride is not power of 2!"
-        self.min_level = int(min_level)
-        self.max_level = int(max_level)
-        assert (
-            len(scales) == self.max_level - self.min_level + 1
-        ), "[ROIPooler] Sizes of input featuremaps do not form a pyramid!"
-        assert 0 <= self.min_level and self.min_level <= self.max_level
-        self.canonical_level = canonical_level
-        assert canonical_box_size > 0
-        self.canonical_box_size = canonical_box_size
-    def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]):
-        """
-        Args:
-            x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those
-                used to construct this module.
-            box_lists (list[Boxes] | list[RotatedBoxes]):
-                A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch.
-                The box coordinates are defined on the original image and
-                will be scaled by the `scales` argument of :class:`ROIPooler`.
-        Returns:
-            Tensor:
-                A tensor of shape (M, C, output_size, output_size) where M is the total number of
-                boxes aggregated over all N batch images and C is the number of channels in `x`.
-        """
-        num_level_assignments = len(self.level_poolers)
-        assert isinstance(x, list) and isinstance(
-            box_lists, list
-        ), "Arguments to pooler must be lists"
-        assert (
-            len(x) == num_level_assignments
-        ), "unequal value, num_level_assignments={}, but x is list of {} Tensors".format(
-            num_level_assignments, len(x)
-        )
-        assert len(box_lists) == x[0].size(
-            0
-        ), "unequal value, x[0] batch dim 0 is {}, but box_list has length {}".format(
-            x[0].size(0), len(box_lists)
-        )
-        if len(box_lists) == 0:
-            return torch.zeros(
-                (0, x[0].shape[1]) + self.output_size, device=x[0].device, dtype=x[0].dtype
-            )
-        pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists)
-        if num_level_assignments == 1:
-            return self.level_poolers[0](x[0], pooler_fmt_boxes)
-        level_assignments = assign_boxes_to_levels(
-            box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level
-        )
-        num_boxes = pooler_fmt_boxes.size(0)
-        num_channels = x[0].shape[1]
-        output_size = self.output_size[0]
-        dtype, device = x[0].dtype, x[0].device
-        output = torch.zeros(
-            (num_boxes, num_channels, output_size, output_size), dtype=dtype, device=device
-        )
-        for level, pooler in enumerate(self.level_poolers):
-            inds = nonzero_tuple(level_assignments == level)[0]
-            pooler_fmt_boxes_level = pooler_fmt_boxes[inds]
-            # Use index_put_ instead of advance indexing, to avoid pytorch/issues/49852
-            output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
-        return output

spaces/CVPR/unicl-zero-shot-img-recog/README.md DELETED Viewed

@@ -1,13 +0,0 @@
----
-title: Unicl Zero-Shot Image Recognition Demo
-emoji: 🏢
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.13
-app_file: app.py
-pinned: false
-license: mit
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/datasets/transforms.py DELETED Viewed

@@ -1,311 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Transforms and data augmentation for both image + bbox.
-"""
-import os
-import random
-import PIL
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as F
-from groundingdino.util.box_ops import box_xyxy_to_cxcywh
-from groundingdino.util.misc import interpolate
-def crop(image, target, region):
-    cropped_image = F.crop(image, *region)
-    target = target.copy()
-    i, j, h, w = region
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor([h, w])
-    fields = ["labels", "area", "iscrowd", "positive_map"]
-    if "boxes" in target:
-        boxes = target["boxes"]
-        max_size = torch.as_tensor([w, h], dtype=torch.float32)
-        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
-        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
-        cropped_boxes = cropped_boxes.clamp(min=0)
-        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
-        target["boxes"] = cropped_boxes.reshape(-1, 4)
-        target["area"] = area
-        fields.append("boxes")
-    if "masks" in target:
-        # FIXME should we update the area here if there are no boxes?
-        target["masks"] = target["masks"][:, i : i + h, j : j + w]
-        fields.append("masks")
-    # remove elements for which the boxes or masks that have zero area
-    if "boxes" in target or "masks" in target:
-        # favor boxes selection when defining which elements to keep
-        # this is compatible with previous implementation
-        if "boxes" in target:
-            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
-            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
-        else:
-            keep = target["masks"].flatten(1).any(1)
-        for field in fields:
-            if field in target:
-                target[field] = target[field][keep]
-    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
-        # for debug and visualization only.
-        if "strings_positive" in target:
-            target["strings_positive"] = [
-                _i for _i, _j in zip(target["strings_positive"], keep) if _j
-            ]
-    return cropped_image, target
-def hflip(image, target):
-    flipped_image = F.hflip(image)
-    w, h = image.size
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
-            [w, 0, w, 0]
-        )
-        target["boxes"] = boxes
-    if "masks" in target:
-        target["masks"] = target["masks"].flip(-1)
-    return flipped_image, target
-def resize(image, target, size, max_size=None):
-    # size can be min_size (scalar) or (w, h) tuple
-    def get_size_with_aspect_ratio(image_size, size, max_size=None):
-        w, h = image_size
-        if max_size is not None:
-            min_original_size = float(min((w, h)))
-            max_original_size = float(max((w, h)))
-            if max_original_size / min_original_size * size > max_size:
-                size = int(round(max_size * min_original_size / max_original_size))
-        if (w <= h and w == size) or (h <= w and h == size):
-            return (h, w)
-        if w < h:
-            ow = size
-            oh = int(size * h / w)
-        else:
-            oh = size
-            ow = int(size * w / h)
-        return (oh, ow)
-    def get_size(image_size, size, max_size=None):
-        if isinstance(size, (list, tuple)):
-            return size[::-1]
-        else:
-            return get_size_with_aspect_ratio(image_size, size, max_size)
-    size = get_size(image.size, size, max_size)
-    rescaled_image = F.resize(image, size)
-    if target is None:
-        return rescaled_image, None
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
-    ratio_width, ratio_height = ratios
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        scaled_boxes = boxes * torch.as_tensor(
-            [ratio_width, ratio_height, ratio_width, ratio_height]
-        )
-        target["boxes"] = scaled_boxes
-    if "area" in target:
-        area = target["area"]
-        scaled_area = area * (ratio_width * ratio_height)
-        target["area"] = scaled_area
-    h, w = size
-    target["size"] = torch.tensor([h, w])
-    if "masks" in target:
-        target["masks"] = (
-            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
-        )
-    return rescaled_image, target
-def pad(image, target, padding):
-    # assumes that we only pad on the bottom right corners
-    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
-    if target is None:
-        return padded_image, None
-    target = target.copy()
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor(padded_image.size[::-1])
-    if "masks" in target:
-        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
-    return padded_image, target
-class ResizeDebug(object):
-    def __init__(self, size):
-        self.size = size
-    def __call__(self, img, target):
-        return resize(img, target, self.size)
-class RandomCrop(object):
-    def __init__(self, size):
-        self.size = size
-    def __call__(self, img, target):
-        region = T.RandomCrop.get_params(img, self.size)
-        return crop(img, target, region)
-class RandomSizeCrop(object):
-    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
-        # respect_boxes:    True to keep all boxes
-        #                   False to tolerence box filter
-        self.min_size = min_size
-        self.max_size = max_size
-        self.respect_boxes = respect_boxes
-    def __call__(self, img: PIL.Image.Image, target: dict):
-        init_boxes = len(target["boxes"])
-        max_patience = 10
-        for i in range(max_patience):
-            w = random.randint(self.min_size, min(img.width, self.max_size))
-            h = random.randint(self.min_size, min(img.height, self.max_size))
-            region = T.RandomCrop.get_params(img, [h, w])
-            result_img, result_target = crop(img, target, region)
-            if (
-                not self.respect_boxes
-                or len(result_target["boxes"]) == init_boxes
-                or i == max_patience - 1
-            ):
-                return result_img, result_target
-        return result_img, result_target
-class CenterCrop(object):
-    def __init__(self, size):
-        self.size = size
-    def __call__(self, img, target):
-        image_width, image_height = img.size
-        crop_height, crop_width = self.size
-        crop_top = int(round((image_height - crop_height) / 2.0))
-        crop_left = int(round((image_width - crop_width) / 2.0))
-        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
-class RandomHorizontalFlip(object):
-    def __init__(self, p=0.5):
-        self.p = p
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return hflip(img, target)
-        return img, target
-class RandomResize(object):
-    def __init__(self, sizes, max_size=None):
-        assert isinstance(sizes, (list, tuple))
-        self.sizes = sizes
-        self.max_size = max_size
-    def __call__(self, img, target=None):
-        size = random.choice(self.sizes)
-        return resize(img, target, size, self.max_size)
-class RandomPad(object):
-    def __init__(self, max_pad):
-        self.max_pad = max_pad
-    def __call__(self, img, target):
-        pad_x = random.randint(0, self.max_pad)
-        pad_y = random.randint(0, self.max_pad)
-        return pad(img, target, (pad_x, pad_y))
-class RandomSelect(object):
-    """
-    Randomly selects between transforms1 and transforms2,
-    with probability p for transforms1 and (1 - p) for transforms2
-    """
-    def __init__(self, transforms1, transforms2, p=0.5):
-        self.transforms1 = transforms1
-        self.transforms2 = transforms2
-        self.p = p
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return self.transforms1(img, target)
-        return self.transforms2(img, target)
-class ToTensor(object):
-    def __call__(self, img, target):
-        return F.to_tensor(img), target
-class RandomErasing(object):
-    def __init__(self, *args, **kwargs):
-        self.eraser = T.RandomErasing(*args, **kwargs)
-    def __call__(self, img, target):
-        return self.eraser(img), target
-class Normalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-    def __call__(self, image, target=None):
-        image = F.normalize(image, mean=self.mean, std=self.std)
-        if target is None:
-            return image, None
-        target = target.copy()
-        h, w = image.shape[-2:]
-        if "boxes" in target:
-            boxes = target["boxes"]
-            boxes = box_xyxy_to_cxcywh(boxes)
-            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
-            target["boxes"] = boxes
-        return image, target
-class Compose(object):
-    def __init__(self, transforms):
-        self.transforms = transforms
-    def __call__(self, image, target):
-        for t in self.transforms:
-            image, target = t(image, target)
-        return image, target
-    def __repr__(self):
-        format_string = self.__class__.__name__ + "("
-        for t in self.transforms:
-            format_string += "\n"
-            format_string += "    {0}".format(t)
-        format_string += "\n)"
-        return format_string

spaces/CarlDennis/Lovelive-VITS-JPZH/transforms.py DELETED Viewed

@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-import numpy as np
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-def piecewise_rational_quadratic_transform(inputs,
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None,
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-    return outputs, logabsdet
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-    num_bins = unnormalized_widths.shape[-1]
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-        return outputs, logabsdet

spaces/CikeyQI/meme-api/Dockerfile DELETED Viewed

@@ -1,51 +0,0 @@
-FROM python:3.10 as tmp
-WORKDIR /tmp
-ENV PATH="${PATH}:/root/.local/bin"
-COPY ./pyproject.toml ./poetry.lock* /tmp/
-RUN pip install poetry \
-  && poetry config virtualenvs.in-project true \
-  && poetry install --only main --no-interaction --no-ansi
-FROM python:3.10-slim as app
-WORKDIR /app
-EXPOSE 7860
-VOLUME /data
-COPY --from=tmp /tmp/.venv /app/.venv
-COPY ./resources/fonts/* /usr/share/fonts/meme-fonts/
-RUN apt-get update \
-  && apt-get install -y --no-install-recommends locales fontconfig fonts-noto-cjk fonts-noto-color-emoji gettext \
-  && localedef -i zh_CN -c -f UTF-8 -A /usr/share/locale/locale.alias zh_CN.UTF-8 \
-  && fc-cache -fv \
-  && apt-get purge -y --auto-remove \
-  && rm -rf /var/lib/apt/lists/*
-ENV TZ=Asia/Shanghai \
-  LC_ALL=zh_CN.UTF-8 \
-  PATH="/app/.venv/bin:${PATH}" \
-  VIRTUAL_ENV="/app/.venv" \
-  LOAD_BUILTIN_MEMES=true \
-  MEME_DIRS="[\"/data/memes\"]" \
-  MEME_DISABLED_LIST="[]" \
-  GIF_MAX_SIZE=10.0 \
-  GIF_MAX_FRAMES=100 \
-  BAIDU_TRANS_APPID="" \
-  BAIDU_TRANS_APIKEY="" \
-  LOG_LEVEL="INFO"
-COPY ./meme_generator /app/meme_generator
-COPY ./docker/config.toml.template /app/config.toml.template
-COPY ./docker/start.sh /app/start.sh
-RUN mkdir -p /.config
-RUN chmod -R 777 /.config
-RUN chmod +x /app/start.sh
-CMD ["/app/start.sh"]