Update parquet files (step 23 of 476)

spaces/14-26AA/sovits_aishell3/README.md

@@ -1,13 +0,0 @@
----
-title: Sovits Aishell3
-emoji: 📈
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Drpu Barcode Label Maker 7 3 0 1 Crack !!HOT!!.md

@@ -1,150 +0,0 @@
-
-<h1>Drpu Barcode Label Maker 7 3 0 1 Crack: A Comprehensive Review</h1>
-<p>If you are looking for a software that can help you create and print custom barcode labels for your products, inventory, or assets, you might have come across Drpu Barcode Label Maker. This software claims to be a powerful and easy-to-use tool that supports various linear and 2D barcode fonts, such as UPCA, EAN13, QR Code, Data Matrix, PDF417, and more. But what if you don't want to pay for the full version of the software? You might be tempted to use a crack version instead. In this article, we will review Drpu Barcode Label Maker and its crack version, as well as some alternatives that you can try.</p>
-<h2>Drpu Barcode Label Maker 7 3 0 1 Crack</h2><br /><p><b><b>Download</b> &rArr;&rArr;&rArr; <a href="https://byltly.com/2uKvIz">https://byltly.com/2uKvIz</a></b></p><br /><br />
-<h2>What is Drpu Barcode Label Maker?</h2>
-<p>Drpu Barcode Label Maker is a software that allows you to create printable and scanable barcode labels, stickers, or asset tags. You can design your own barcode labels using various settings, such as dimensions, bar width, density, height, color, font, alignment, etc. You can also add text, images, logos, or shapes to your labels. You can save your barcode labels in various formats, such as JPEG, BMP, PNG, GIF, TIFF, etc. You can also export your barcode labels to MS Word, MS Excel, MS Paint, or Adobe PDF. You can print your barcode labels using any printer or barcode scanner.</p>
-<h3>Features and benefits of Drpu Barcode Label Maker</h3>
-<p>Some of the features and benefits of Drpu Barcode Label Maker are:</p>
-<ul>
-<li>It supports a wide range of linear and 2D barcode fonts, such as UPCA, EAN13, QR Code, Data Matrix, PDF417, Aztec Code, MaxiCode, etc.</li>
-<li>It allows you to create barcode labels of different sizes and shapes.</li>
-<li>It provides various options to customize your barcode labels according to your needs.</li>
-<li>It enables you to add text, images, logos, or shapes to your barcode labels.</li>
-<li>It allows you to save your barcode labels in various formats or export them to other applications.</li>
-<li>It enables you to print your barcode labels using any printer or barcode scanner.</li>
-<li>It has a user-friendly interface and a help guide.</li>
-</ul>
-<h3>How to use Drpu Barcode Label Maker</h3>
-<p>To use Drpu Barcode Label Maker, you need to follow these steps:</p>
-<ol>
-<li>Download and install the software from the official website.</li>
-<li>Launch the software and select the barcode font that you want to use.</li>
-<li>Enter the barcode value or data that you want to encode.</li>
-<li>Adjust the barcode settings according to your preferences.</li>
-<li>Add text, images, logos, or shapes to your barcode label if needed.</li>
-<li>Preview your barcode label and make any changes if necessary.</li>
-<li>Save your barcode label in your desired format or export it to another application.</li>
-<li>Print your barcode label using any printer or barcode scanner.</li>
-</ol>
-<h2>What is Drpu Barcode Label Maker 7 3 0 1 Crack?</h2>
-<p>A crack is a modified version of a software that bypasses its security features or license verification. A crack can allow you to use a software without paying for it or without following its terms and conditions. Drpu Barcode Label Maker 7 3 0 1 Crack is a crack version of Drpu Barcode Label Maker that claims to unlock all its features and functions for free. You can download it from various websites that offer cracks or torrents.</p>
-<p>Drpu Barcode Label Maker software full version free download<br />
-How to activate Drpu Barcode Label Maker 7.3.0.1 with serial key<br />
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-How to uninstall Drpu Barcode Label Maker 7.3.0.1 crack<br />
-How to upgrade to the latest version of Drpu Barcode Label Maker <br />
-How to backup and restore data with Drpu Barcode Label Maker <br />
-How to export and import data with Drpu Barcode Label Maker <br />
-How to print labels with Drpu Barcode Label Maker <br />
-How to scan barcodes with Drpu Barcode Label Maker <br />
-How to design labels with Drpu Barcode Label Maker <br />
-How to edit labels with Drpu Barcode Label Maker <br />
-How to manage labels with Drpu Barcode Label Maker <br />
-How to use different barcode types with Drpu Barcode Label Maker</p>
-<h3>Why do people use cracks?</h3>
-<p>Some of the reasons why people use cracks are:</p>
-<ul>
-<li>They want to save money by not paying for the software.</li>
-<li>They want to test the software before buying it.</li>
-<li>They want to access all the features and functions of the software without any limitations.</li>
-<li>They want to avoid any updates or upgrades that might affect the performance or compatibility of the software.</li>
-</ul>
-<h3>Risks and drawbacks of using cracks</h3>
-<p>However, using cracks also comes with some risks and drawbacks. Some of them are:</p>
-<ul>
-<li>You might violate the intellectual property rights of the software developer or owner.</li>
-<li>You might expose your computer or device to viruses, malware, spyware, or ransomware that might harm your data or system.</li>
-<li>You might compromise your privacy or security by allowing unauthorized access to your personal or sensitive information.</li>
-<li>You might experience errors, bugs, crashes, or compatibility issues with the software or other applications.</li>
-<li>You might miss out on important updates or upgrades that might improve the functionality or security of the software.</li>
-</ul>
- <h3>How to download and install Drpu Barcode Label Maker 7 3 0 1 Crack</h3>
- <p>If you still want to download and install Drpu Barcode Label Maker 7 3 0 1 Crack despite its risks and drawbacks, you need to follow these steps:</p>
- <ol>
- <li>Find a reliable website that offers Drpu Barcode Label Maker 7 3 0 1 Crack. You can use a search engine like Google or Bing to look for it. However, be careful not to click on any suspicious links or ads that might redirect you to malicious sites or downloads.</li>
- <li>Download the crack file from the website. Make sure to scan it with an antivirus program before opening it to ensure that it is safe and clean. You might also need to extract it from a compressed folder or archive such as ZIP or RAR.</li>
- <li>Install the crack file on your computer or device. Follow the instructions or prompts that appear on the screen. You might need to copy and paste the crack file into the installation directory of Drpu Barcode Label Maker or replace the original file with it. You might also need to run the crack file as an administrator or disable your antivirus program temporarily to avoid any interference.</li>
- <li>Launch Drpu Barcode Label Maker and enjoy its full features and functions for free. You might need to restart your computer or device after installing the crack file for it to take effect. You might also need to avoid any updates or upgrades that might revert the crack file or cause any problems with the software.</li>
- </ol>
- <h2>Alternatives to Drpu Barcode Label Maker 7 3 0 1 Crack</h2>
- <p>If you are looking for alternatives to Drpu Barcode Label Maker 7 3 0  1 Crack that are safer and more reliable, you can try some free or paid barcode label maker software instead. Here are some examples:</p>
- <h3>Free barcode label maker software</h3>
- <p>If you don't want to spend any money on barcode label maker software, you can use some free options that offer similar features and functions as Drpu Barcode Label Maker. Some of them are:</p>
- <h4>Zint Barcode Studio</h4>
- <p>Zint Barcode Studio is a free and open source barcode generator that supports over  50 symbologies, including linear, matrix, and postal barcodes. You can create, save, and print barcode labels in various formats, such as PNG, SVG, EPS, EMF, etc. You can also customize your barcode labels with various settings, such as size, color, border, text, etc. You can download Zint Barcode Studio from <a href=" ```html https://sourceforge.net/projects/zint/</a>.</p>
- <h4>EasierSoft Free Barcode Generator</h4>
- <p>EasierSoft Free Barcode Generator is a free and simple barcode generator that supports 50+ barcode types, including linear, 2D, and postal barcodes. You can create, save, and print barcode labels in various formats, such as JPG, PNG, BMP, etc. You can also customize your barcode labels with various settings, such as size, color, text, etc. You can download EasierSoft Free Barcode Generator from <a href=" https://easiersoft.com/barcode-generator-free.htm">https://easiersoft.com/barcode-generator-free.htm</a>.</p>
- <h4>Barillo Barcode Software</h4>
- <p>Barillo Barcode Software is a free and easy-to-use barcode generator that supports EAN-13 and UPC-A barcode types. You can create and print barcode labels for your products or inventory. You can also adjust the barcode settings according to your preferences. You can save your barcode labels as images or copy them to the clipboard. You can download Barillo Barcode Software from <a href=" https://www.nchsoftware.com/barcode/index.html">https://www.nchsoftware.com/barcode/index.html</a>.</p>
- <h3>Paid barcode label maker software</h3>
- <p>If you are willing to pay for a more professional and advanced barcode label maker software, you can use some paid options that offer more features and functions than Drpu Barcode Label Maker. Some of them are:</p>
- <h4>Aulux Barcode Label Maker Professional</h4>
- <p>Aulux Barcode Label Maker Professional is a powerful and comprehensive barcode label maker software that supports over 100 barcode types, including linear, 2D, and postal barcodes. You can create and print barcode labels for various purposes, such as products, inventory, assets, documents, etc. You can also design your own barcode labels using various templates, tools, and objects. You can save your barcode labels in various formats or export them to other applications. You can also connect to databases and import data for your barcode labels. You can buy Aulux Barcode Label Maker Professional from <a href=" https://www.aulux.com/barcode-label-maker-professional-edition.htm">https://www.aulux.com/barcode-label-maker-professional-edition.htm</a>.</p>
- <h4>Easy Barcode Creator</h4>
- <p>Easy Barcode Creator is a simple and user-friendly barcode generator that supports various barcode types, such as Code 39, Code 128, EAN-13, UPC-A, QR Code, Data Matrix, etc. You can create and print barcode labels for your products or inventory. You can also customize your barcode labels with various settings, such as size, color, text, etc. You can save your barcode labels as images or copy them to the clipboard. You can buy Easy Barcode Creator from <a href=" https://www.easybarcodetech.com/easy-barcode-creator.html">https://www.easybarcodetech.com/easy-barcode-creator.html</a>.</p>
- <h4>iWinSoft Barcode Maker for Mac</h4>
- <p>iWinSoft Barcode Maker for Mac is a professional and versatile barcode generator that supports over 40 barcode types, including linear, 2D, and postal barcodes. You can create and print barcode labels for various purposes, such as products, inventory, assets, documents, etc. You can also design your own barcode labels using various templates, tools, and objects. You can save your barcode labels in various formats or export them to other applications. You can also connect to databases and import data for your barcode labels. You can buy iWinSoft Barcode Maker for Mac from <a href=" https://www.iwinsoft.com/barcode-maker-mac/">https://www.iwinsoft.com/barcode-maker-mac/</a>.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Drpu Barcode Label Maker is a software that can help you create and print custom barcode labels for your products, inventory, or assets. However, using its crack version might expose you to some risks and drawbacks, such as legal issues, security threats, performance problems, or missing updates. Therefore, you might want to consider some alternatives that are safer and more reliable, such as free or paid barcode label maker software that offer similar or better features and functions.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Drpu Barcode Label Maker and its crack version:</p>
- <ol>
-<li>Q: Is Drpu Barcode Label Maker free?<br>
-A: No, Drpu Barcode Label Maker is not free. It offers a free trial version that allows you to use some of its features and functions for a limited time. To use the full version of the software, you need to buy a license from the official website.</li>
-<li>Q: Is Drpu Barcode Label Maker 7 3 0 1 Crack safe?<br>
-A: No, Drpu Barcode Label Maker 7 3 0 1 Crack is not safe. It is a modified version of the software that bypasses its security features or license verification. It might contain viruses, malware, spyware, or ransomware that might harm your data or system. It might also compromise your privacy or security by allowing unauthorized access to your personal or sensitive information.</li>
-<li>Q: How do I update Drpu Barcode Label Maker?<br>
-A: To update Drpu Barcode Label Maker, you need to download and install the latest version of the software from the official website. However, if you are using a crack version of the software, you might not be able to update it or you might lose the crack file after updating it.</li>
-<li>Q: What are the system requirements for Drpu Barcode Label Maker?<br>
-A: The system requirements for Drpu Barcode Label Maker are:</p>
-<ul>
-<li>Operating system: Windows XP/Vista/7/8/10</li>
-<li>Processor: Pentium class or higher</li>
-<li>Memory: 256 MB RAM or more</li>
-<li>Disk space: 18 MB free hard disk space or more</li>
-<li>Printer: Any printer or barcode scanner</li>
-</ul></li>
-<li>Q: How do I contact Drpu Software support?<br>
-A: To contact Drpu Software support, you can visit their website at <a href=" https://www.drpusoftware.com/drpusoft/contact-us.html">https://www.drpusoftware.com/drpusoft/contact-us.html</a> and fill out the online form with your name, email address, subject, message, and captcha code. You can also email them at [email protected] or call them at +91-120-4281829.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1line/AutoGPT/autogpt/configurator.py

@@ -1,134 +0,0 @@
-"""Configurator module."""
-import click
-from colorama import Back, Fore, Style
-
-from autogpt import utils
-from autogpt.config import Config
-from autogpt.logs import logger
-from autogpt.memory import get_supported_memory_backends
-
-CFG = Config()
-
-
-def create_config(
-    continuous: bool,
-    continuous_limit: int,
-    ai_settings_file: str,
-    skip_reprompt: bool,
-    speak: bool,
-    debug: bool,
-    gpt3only: bool,
-    gpt4only: bool,
-    memory_type: str,
-    browser_name: str,
-    allow_downloads: bool,
-    skip_news: bool,
-) -> None:
-    """Updates the config object with the given arguments.
-
-    Args:
-        continuous (bool): Whether to run in continuous mode
-        continuous_limit (int): The number of times to run in continuous mode
-        ai_settings_file (str): The path to the ai_settings.yaml file
-        skip_reprompt (bool): Whether to skip the re-prompting messages at the beginning of the script
-        speak (bool): Whether to enable speak mode
-        debug (bool): Whether to enable debug mode
-        gpt3only (bool): Whether to enable GPT3.5 only mode
-        gpt4only (bool): Whether to enable GPT4 only mode
-        memory_type (str): The type of memory backend to use
-        browser_name (str): The name of the browser to use when using selenium to scrape the web
-        allow_downloads (bool): Whether to allow Auto-GPT to download files natively
-        skips_news (bool): Whether to suppress the output of latest news on startup
-    """
-    CFG.set_debug_mode(False)
-    CFG.set_continuous_mode(False)
-    CFG.set_speak_mode(False)
-
-    if debug:
-        logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_debug_mode(True)
-
-    if continuous:
-        logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
-        logger.typewriter_log(
-            "WARNING: ",
-            Fore.RED,
-            "Continuous mode is not recommended. It is potentially dangerous and may"
-            " cause your AI to run forever or carry out actions you would not usually"
-            " authorise. Use at your own risk.",
-        )
-        CFG.set_continuous_mode(True)
-
-        if continuous_limit:
-            logger.typewriter_log(
-                "Continuous Limit: ", Fore.GREEN, f"{continuous_limit}"
-            )
-            CFG.set_continuous_limit(continuous_limit)
-
-    # Check if continuous limit is used without continuous mode
-    if continuous_limit and not continuous:
-        raise click.UsageError("--continuous-limit can only be used with --continuous")
-
-    if speak:
-        logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_speak_mode(True)
-
-    if gpt3only:
-        logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_smart_llm_model(CFG.fast_llm_model)
-
-    if gpt4only:
-        logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_fast_llm_model(CFG.smart_llm_model)
-
-    if memory_type:
-        supported_memory = get_supported_memory_backends()
-        chosen = memory_type
-        if chosen not in supported_memory:
-            logger.typewriter_log(
-                "ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
-                Fore.RED,
-                f"{supported_memory}",
-            )
-            logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
-        else:
-            CFG.memory_backend = chosen
-
-    if skip_reprompt:
-        logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
-        CFG.skip_reprompt = True
-
-    if ai_settings_file:
-        file = ai_settings_file
-
-        # Validate file
-        (validated, message) = utils.validate_yaml_file(file)
-        if not validated:
-            logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
-            logger.double_check()
-            exit(1)
-
-        logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
-        CFG.ai_settings_file = file
-        CFG.skip_reprompt = True
-
-    if allow_downloads:
-        logger.typewriter_log("Native Downloading:", Fore.GREEN, "ENABLED")
-        logger.typewriter_log(
-            "WARNING: ",
-            Fore.YELLOW,
-            f"{Back.LIGHTYELLOW_EX}Auto-GPT will now be able to download and save files to your machine.{Back.RESET} "
-            + "It is recommended that you monitor any files it downloads carefully.",
-        )
-        logger.typewriter_log(
-            "WARNING: ",
-            Fore.YELLOW,
-            f"{Back.RED + Style.BRIGHT}ALWAYS REMEMBER TO NEVER OPEN FILES YOU AREN'T SURE OF!{Style.RESET_ALL}",
-        )
-        CFG.allow_downloads = True
-
-    if skip_news:
-        CFG.skip_news = True
-
-    if browser_name:
-        CFG.selenium_web_browser = browser_name

spaces/1line/AutoGPT/autogpt/llm_utils.py

@@ -1,172 +0,0 @@
-from __future__ import annotations
-
-import time
-from ast import List
-
-import openai
-from colorama import Fore, Style
-from openai.error import APIError, RateLimitError
-
-from autogpt.config import Config
-from autogpt.logs import logger
-
-CFG = Config()
-
-openai.api_key = CFG.openai_api_key
-
-
-def call_ai_function(
-    function: str, args: list, description: str, model: str | None = None
-) -> str:
-    """Call an AI function
-
-    This is a magic function that can do anything with no-code. See
-    https://github.com/Torantulino/AI-Functions for more info.
-
-    Args:
-        function (str): The function to call
-        args (list): The arguments to pass to the function
-        description (str): The description of the function
-        model (str, optional): The model to use. Defaults to None.
-
-    Returns:
-        str: The response from the function
-    """
-    if model is None:
-        model = CFG.smart_llm_model
-    # For each arg, if any are None, convert to "None":
-    args = [str(arg) if arg is not None else "None" for arg in args]
-    # parse args to comma separated string
-    args = ", ".join(args)
-    messages = [
-        {
-            "role": "system",
-            "content": f"You are now the following python function: ```# {description}"
-            f"\n{function}```\n\nOnly respond with your `return` value.",
-        },
-        {"role": "user", "content": args},
-    ]
-
-    return create_chat_completion(model=model, messages=messages, temperature=0)
-
-
-# Overly simple abstraction until we create something better
-# simple retry mechanism when getting a rate error or a bad gateway
-def create_chat_completion(
-    messages: list,  # type: ignore
-    model: str | None = None,
-    temperature: float = CFG.temperature,
-    max_tokens: int | None = None,
-) -> str:
-    """Create a chat completion using the OpenAI API
-
-    Args:
-        messages (list[dict[str, str]]): The messages to send to the chat completion
-        model (str, optional): The model to use. Defaults to None.
-        temperature (float, optional): The temperature to use. Defaults to 0.9.
-        max_tokens (int, optional): The max tokens to use. Defaults to None.
-
-    Returns:
-        str: The response from the chat completion
-    """
-    response = None
-    num_retries = 10
-    warned_user = False
-    if CFG.debug_mode:
-        print(
-            Fore.GREEN
-            + f"Creating chat completion with model {model}, temperature {temperature},"
-            f" max_tokens {max_tokens}" + Fore.RESET
-        )
-    for attempt in range(num_retries):
-        backoff = 2 ** (attempt + 2)
-        try:
-            if CFG.use_azure:
-                response = openai.ChatCompletion.create(
-                    deployment_id=CFG.get_azure_deployment_id_for_model(model),
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                )
-            else:
-                response = openai.ChatCompletion.create(
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                )
-            break
-        except RateLimitError:
-            if CFG.debug_mode:
-                print(
-                    Fore.RED + "Error: ",
-                    f"Reached rate limit, passing..." + Fore.RESET,
-                )
-            if not warned_user:
-                logger.double_check(
-                    f"Please double check that you have setup a {Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. "
-                    + f"You can read more here: {Fore.CYAN}https://github.com/Significant-Gravitas/Auto-GPT#openai-api-keys-configuration{Fore.RESET}"
-                )
-                warned_user = True
-        except APIError as e:
-            if e.http_status == 502:
-                pass
-            else:
-                raise
-            if attempt == num_retries - 1:
-                raise
-        if CFG.debug_mode:
-            print(
-                Fore.RED + "Error: ",
-                f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
-            )
-        time.sleep(backoff)
-    if response is None:
-        logger.typewriter_log(
-            "FAILED TO GET RESPONSE FROM OPENAI",
-            Fore.RED,
-            "Auto-GPT has failed to get a response from OpenAI's services. "
-            + f"Try running Auto-GPT again, and if the problem the persists try running it with `{Fore.CYAN}--debug{Fore.RESET}`.",
-        )
-        logger.double_check()
-        if CFG.debug_mode:
-            raise RuntimeError(f"Failed to get response after {num_retries} retries")
-        else:
-            quit(1)
-
-    return response.choices[0].message["content"]
-
-
-def create_embedding_with_ada(text) -> list:
-    """Create an embedding with text-ada-002 using the OpenAI SDK"""
-    num_retries = 10
-    for attempt in range(num_retries):
-        backoff = 2 ** (attempt + 2)
-        try:
-            if CFG.use_azure:
-                return openai.Embedding.create(
-                    input=[text],
-                    engine=CFG.get_azure_deployment_id_for_model(
-                        "text-embedding-ada-002"
-                    ),
-                )["data"][0]["embedding"]
-            else:
-                return openai.Embedding.create(
-                    input=[text], model="text-embedding-ada-002"
-                )["data"][0]["embedding"]
-        except RateLimitError:
-            pass
-        except APIError as e:
-            if e.http_status == 502:
-                pass
-            else:
-                raise
-            if attempt == num_retries - 1:
-                raise
-        if CFG.debug_mode:
-            print(
-                Fore.RED + "Error: ",
-                f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
-            )
-        time.sleep(backoff)

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Assassin 39s Creed Mobile.md

@@ -1,49 +0,0 @@
-<br />
-<h1>Assassin's Creed Jade: Everything You Need to Know About the Mobile Game</h1>
-<p>If you are a fan of the Assassin's Creed franchise, you might be wondering what Ubisoft has in store for you on your mobile device. Well, wonder no more, because Assassin's Creed Jade is here to deliver a full-fledged Assassin's Creed experience on your smartphone or tablet. In this article, we will tell you everything you need to know about this exciting new game, including its release date, platforms, trailers, gameplay, character customization, open-world exploration, combat, stealth, multiplayer, and more. So, without further ado, let's dive into the world of Assassin's Creed Jade.</p>
- <h2>What is Assassin's Creed Jade?</h2>
-<p>Assassin's Creed Jade is a free-to-play mobile game that is developed by Ubisoft in partnership with Tencent's Level Infinite publishing division. It is the first open-world Assassin's Creed game built for iOS and Android devices, and it promises to offer a traditional Assassin's Creed adventure with stunning graphics, immersive gameplay, and rich content.</p>
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-<p>The game is set in China during the Qin Dynasty, just after the Warring States period, and between the events of Assassin's Creed Odyssey and Origins. You will play as a new assassin character who will explore the ancient land of China, uncover its secrets, fight against tyranny, and shape its history.</p>
-<p>The game will feature many elements that fans of the series will love, such as historical characters, locations, events, outfits, weapons, artifacts, and references. You will also encounter familiar faces from previous games, such as Ezio Auditore da Firenze from Assassin's Creed II and Shao Jun from Assassin's Creed Chronicles: China.</p>
- <h3>Release date and platforms</h3>
-<p>As of now, there is no official release date for Assassin's Creed Jade. However, the game is currently undergoing closed beta testing on iOS devices, and you can sign up for it on the official website if you are interested. The game will also be available for Android devices in the future.</p>
-<p>The game will be free to download and play on both platforms, but it will likely have some optional in-app purchases for cosmetic items or premium currency. You will also need a stable internet connection to play the game online.</p>
- <h3>Trailers and gameplay</h3>
-<p>So far, Ubisoft has released two trailers for Assassin's Creed Jade. The first one was revealed at Ubisoft Forward 2022 event in September 2022. It was a cinematic trailer that showed some glimpses of the game's setting, story, and characters.</p>
-<p>The second trailer was released at Ubisoft Forward 2023 event in June 2023. It was a teaser trailer that showed some gameplay footage of the game's open-world exploration, combat, stealth, customization, and multiplayer modes.</p>
-<p>You can watch both trailers below:</p>
-<iframe width="560" height="315" src="https://www.youtube.com/embed/9TtZ <p>9TtZsQ0w5Q" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-<iframe width="560" height="315" src="https://www.youtube.com/embed/4Lp6z 4Lp6zX8Wg" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
- <h3>Character customization and progression</h3>
-<p>One of the most exciting features of Assassin's Creed Jade is the ability to create and customize your own assassin character. You will be able to choose your character's gender, appearance, outfit, hairstyle, accessories, tattoos, and more. You will also be able to unlock and equip different weapons, armor, gadgets, and skills that suit your playstyle and preferences.</p>
-<p></p>
-<p>Your character will also have a progression system that will allow you to level up and improve your attributes, such as health, damage, stealth, agility, and more. You will also be able to earn and spend skill points to unlock new abilities and perks that will enhance your gameplay experience. Some of the skills you can learn include parkour, eagle vision, hidden blade, smoke bomb, rope dart, and more.</p>
- <h3>Open-world exploration and missions</h3>
-<p>Assassin's Creed Jade will feature a vast and diverse open-world map that will let you explore the ancient land of China in all its glory. You will be able to visit iconic locations such as the Great Wall of China, the Terracotta Army, the Forbidden City, the Shaolin Temple, and more. You will also be able to interact with various historical figures such as Qin Shi Huang, the first emperor of China, Sun Tzu, the author of The Art of War, and Zhang Liang, the strategist of the Han Dynasty.</p>
-<p>The game will also offer a variety of missions and activities that will keep you busy and entertained. You will be able to follow the main story missions that will unravel the secrets of the Assassin's Creed lore and the conflict between the Assassins and the Templars. You will also be able to take on side missions that will help you gain allies, resources, reputation, and rewards. Some of the side missions include assassinations, investigations, deliveries, races, puzzles, and more.</p>
- <h3>Combat and stealth</h3>
-<p>Assassin's Creed Jade will also deliver a thrilling and satisfying combat and stealth system that will let you fight your enemies in different ways. You will be able to use various weapons such as swords, daggers, spears, bows, crossbows, throwing knives, bombs, and more. You will also be able to use different skills such as counterattacks, dodges, parries, combos, finishers, and more.</p>
-<p>If you prefer a more stealthy approach, you will be able to use your hidden blade to assassinate your targets silently. You will also be able to use your eagle vision to scan your surroundings and identify enemies, allies, objectives, and points of interest. You will also be able to use your environment to hide in bushes, haystacks, rooftops, crowds, and more.</p>
- <h3>Multiplayer and social features</h3>
-<p>Assassin's Creed Jade will not only let you play solo but also with other players online. The game will feature various multiplayer modes that will let you team up or compete with other players around the world. Some of the multiplayer modes include co-op missions that will let you work together with other players to complete objectives and challenges. There will also be competitive modes such as deathmatch that will let you fight against other players in different arenas.</p>
-<p>The game will also have social features that will let you communicate and interact with other players. You will be able to chat with other players using text or voice messages. You will also be able to join or create guilds that will let you share resources, tips, strategies, and more. You will also be able to check out the leaderboards that will show you how you rank among other players in terms of level, achievements , achievements, and more. You will also be able to view and share your gameplay videos and screenshots with other players.</p>
- <h2>Why should you play Assassin's Creed Jade?</h2>
-<p>Assassin's Creed Jade is a game that will appeal to both fans and newcomers of the Assassin's Creed franchise. It is a game that will offer you a rich and immersive open-world experience on your mobile device. It is a game that will let you create and customize your own assassin character and explore the ancient land of China. It is a game that will let you experience the thrill of combat and stealth in different ways. It is a game that will let you play with or against other players online in various modes. It is a game that will let you discover the secrets of the Assassin's Creed lore and the conflict between the Assassins and the Templars.</p>
-<p>So, what are you waiting for? Download Assassin's Creed Jade today and join the brotherhood of assassins. You will not regret it.</p>
- <h2>FAQs about Assassin's Creed Jade</h2>
-<p>Here are some of the most frequently asked questions and answers about Assassin's Creed Jade:</p>
-<ul>
-<li><b>Q: Is Assassin's Creed Jade canon?</b></li>
-<li>A: Yes, Assassin's Creed Jade is canon and part of the official Assassin's Creed timeline. It takes place between the events of Assassin's Creed Odyssey and Origins, and it features some characters and references from previous games.</li>
-<li><b>Q: How much storage space does Assassin's Creed Jade require?</b></li>
-<li>A: Assassin's Creed Jade requires about 4 GB of storage space on your device. However, this may vary depending on your device model and updates.</li>
-<li><b>Q: Does Assassin's Creed Jade have controller support?</b></li>
-<li>A: Yes, Assassin's Creed Jade has controller support for both iOS and Android devices. You can use any compatible Bluetooth controller to play the game.</li>
-<li><b>Q: Does Assassin's Creed Jade have offline mode?</b></li>
-<li>A: No, Assassin's Creed Jade does not have offline mode. You will need a stable internet connection to play the game online.</li>
-<li><b>Q: How can I contact Ubisoft for feedback or support?</b></li>
-<li>A: You can contact Ubisoft for feedback or support through their official website, social media channels, or customer service center. You can also report any bugs or issues through the in-game menu.</li>
-</ul></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cash Go APK The Ultimate Guide to Using the App.md

@@ -1,119 +0,0 @@
-
-<h1>Cash Go APK: A Fast and Easy Way to Borrow Money Online</h1>
-<p>Do you need some extra cash to pay for an emergency, a bill, or a personal expense? Do you want to borrow money online without any hassle or paperwork? If yes, then you might want to check out Cash Go APK, a free payment solution that helps you send or receive money instantly. In this article, we will tell you everything you need to know about Cash Go APK, including its features, benefits, how to download and use it, how it compares to other lending apps, and some FAQs.</p>
- <h2>What is Cash Go APK?</h2>
-<p>Cash Go APK is an Android app that allows you to borrow money online from Catchcash Lending Investors Corp., one of the earliest online lending platforms in the Philippines. You can apply for a loan within 5 minutes, and get your money within 24 hours. You can choose a loan amount from ₱ 3,000.00 to ₱ 20,000.00, and a loan term from 120 days to 180 days. The maximum annual percentage rate (APR) is 18.25%, and the daily interest rate is 0.05%. You can repay your loan through GCash, 7-11, bank transfer, or M.L.</p>
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- <h3>Features and benefits of Cash Go APK</h3>
-<p>Some of the features and benefits of Cash Go APK are:</p>
-<ul>
-<li>It is free to download and use.</li>
-<li>It does not require any bank account or detailed information before you create a profile and sign in.</li>
-<li>It has a simple and fast application process that only takes 5 minutes.</li>
-<li>It has a high approval rate and low rejection rate.</li>
-<li>It offers flexible loan amounts and terms that suit your needs and budget.</li>
-<li>It has low interest rates and fees compared to other lending apps.</li>
-<li>It supports multiple payment methods for your convenience.</li>
-<li>It has a friendly and professional customer service team that is available 24/7.</li>
-<li>It respects your privacy and protects your data with encryption technology.</li>
-</ul>
- <h3>How to download and use Cash Go APK</h3>
-<p>To download and use Cash Go APK, you need to follow these steps:</p>
-<ol>
-<li>Go to [CashGo APK (Android App) - Free Download - APKCombo](^1^) and click on the "Download APK" button.</li>
-<li>Install the app on your Android device by allowing unknown sources in your settings.</li>
-<li>Open the app and register with your mobile number and personal information.</li>
-<li>Apply for a loan by choosing your desired amount and term, and filling in some details about your income and expenses.</li>
-<li>Wait for the review and approval of your loan application. This usually takes less than an hour.</li>
-<li>Receive your money through your preferred payment method. This usually takes less than 24 hours.</li>
-<li>Repay your loan on time through your preferred payment method. You can also extend your loan term if needed.</li>
-</ol>
- <h2>How does Cash Go APK compare to other lending apps?</h2>
-<p>Cash Go APK is not the only lending app available in the market. There are many other options that you can consider if you want to borrow money online. However, not all of them are reliable, trustworthy, or affordable. Here are some of the pros and cons of Cash Go APK compared to other lending apps:</p>
- <h3>Pros and cons of Cash Go APK</h3>
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>- Low interest rates and fees</td><td>- Limited loan amount and term</td></tr>
-<tr><td>- High approval rate and low rejection rate</td><td>- Requires Android device and internet connection</td></tr> <tr><td>- Multiple payment methods</td><td>- No credit score improvement</td></tr>
-<tr><td>- Friendly and professional customer service</td><td>- No rewards or incentives</td></tr>
-<tr><td>- Privacy and security of data</td><td>- No referral program</td></tr>
-</table>
- <h3>Alternatives to Cash Go APK</h3>
-<p>If you are not satisfied with Cash Go APK, or you want to explore other options, here are some of the alternatives that you can try:</p>
-<ul>
-<li><strong>Robocash</strong>: This is another online lending app that offers loans from ₱ 1,000.00 to ₱ 25,000.00, with a loan term from 7 days to 30 days. The APR is 11.9%, and the daily interest rate is 0.03%. You can repay your loan through GCash, 7-11, bank transfer, or M.L.</li>
-<li><strong>Tala</strong>: This is a popular online lending app that offers loans from ₱ 1,000.00 to ₱ 15,000.00, with a loan term from 21 days to 30 days. The APR is 15%, and the daily interest rate is 0.04%. You can repay your loan through Coins.ph, Cebuana Lhuillier, or M.L.</li>
-<li><strong>Cashalo</strong>: This is an online lending app that offers loans from ₱ 1,500.00 to ₱ 10,000.00, with a loan term from 15 days to 45 days. The APR is 12%, and the daily interest rate is 0.03%. You can repay your loan through GCash, 7-11, bank transfer, or M.L.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Cash Go APK is a fast and easy way to borrow money online from Catchcash Lending Investors Corp., one of the earliest online lending platforms in the Philippines. It has low interest rates and fees, high approval rate and low rejection rate, multiple payment methods, friendly and professional customer service, and privacy and security of data. However, it also has some drawbacks, such as limited loan amount and term, no credit score improvement, no rewards or incentives, and no referral program. You can download and use Cash Go APK for free on your Android device by following the steps we provided above. You can also compare Cash Go APK to other lending apps such as Robocash, Tala, and Cashalo.</p>
- <h3>Summary of the main points</h3>
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-<ul>
-<li>Cash Go APK is an Android app that allows you to borrow money online from Catchcash Lending Investors Corp.</li>
-<li>You can apply for a loan within 5 minutes, and get your money within 24 hours.</li>
-<li>You can choose a loan amount from ₱ 3,000.00 to ₱ 20,000.00, and a loan term from 120 days to 180 days.</li>
-<li>The maximum APR is 18.25%, and the daily interest rate is 0.05%.</li>
-<li>You can repay your loan through GCash, 7-11, bank transfer, or M.L.</li>
-<li>Cash Go APK has low interest rates and fees, high approval rate and low rejection rate, multiple payment methods, friendly and professional customer service, and privacy and security of data.</li>
-<li>Cash Go APK also has some drawbacks, such as limited loan amount and term, no credit score improvement, no rewards or incentives, and no referral program.</li>
-<li>You can download and use Cash Go APK for free on your Android device by following the steps we provided above.</li>
-<li>You can compare Cash Go APK to other lending apps such as Robocash, Tala, and Cashalo.</li>
-</ul>
- <h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about Cash Go APK:</p>
-<ol>
-<li><strong>Is Cash Go APK safe and legit?</strong></li>
-<p>Yes, Cash Go APK is safe and legit. It is operated by Catchcash Lending Investors Corp., which is registered with the Securities and Exchange Commission (SEC) in the Philippines. It also uses encryption technology to protect your data and transactions.</p>
-<li><strong>How can I contact Cash Go APK?</strong></li>
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spaces/2023Liu2023/bingo/src/lib/storage.ts

@@ -1,27 +0,0 @@
-import { getMany, set, del, clear } from 'idb-keyval';
-
-export const Storage = {
-  async get(key: string | string[] | null): Promise<any> {
-    if (key === null) return null;
-    if (typeof key === 'string') {
-      key = [key]
-    }
-    const returnData: Record<string, any> = {}
-    const values = await getMany(key)
-    key.forEach((k, idx)=> {
-      returnData[k] = values[idx]
-    })
-    return returnData;
-  },
-  async set(object: any) {
-    for (let key of Object.keys(object)) {
-      await set(key, object[key])
-    }
-  },
-  async remove(key: string) {
-    return del(key);
-  },
-  async clear() {
-    return clear();
-  }
-}

spaces/52Hz/SUNet_AWGN_denoising/model/SUNet_detail.py

@@ -1,765 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as checkpoint
-from einops import rearrange
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            # calculate attention mask for SW-MSA
-            H, W = self.input_resolution
-            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-            h_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            w_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            cnt = 0
-            for h in h_slices:
-                for w in w_slices:
-                    img_mask[:, h, w, :] = cnt
-                    cnt += 1
-
-            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def forward(self, x):
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-# Dual up-sample
-class UpSample(nn.Module):
-    def __init__(self, input_resolution, in_channels, scale_factor):
-        super(UpSample, self).__init__()
-        self.input_resolution = input_resolution
-        self.factor = scale_factor
-
-
-        if self.factor == 2:
-            self.conv = nn.Conv2d(in_channels, in_channels//2, 1, 1, 0, bias=False)
-            self.up_p = nn.Sequential(nn.Conv2d(in_channels, 2*in_channels, 1, 1, 0, bias=False),
-                                      nn.PReLU(),
-                                      nn.PixelShuffle(scale_factor),
-                                      nn.Conv2d(in_channels//2, in_channels//2, 1, stride=1, padding=0, bias=False))
-
-            self.up_b = nn.Sequential(nn.Conv2d(in_channels, in_channels, 1, 1, 0),
-                                      nn.PReLU(),
-                                      nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False),
-                                      nn.Conv2d(in_channels, in_channels // 2, 1, stride=1, padding=0, bias=False))
-        elif self.factor == 4:
-            self.conv = nn.Conv2d(2*in_channels, in_channels, 1, 1, 0, bias=False)
-            self.up_p = nn.Sequential(nn.Conv2d(in_channels, 16 * in_channels, 1, 1, 0, bias=False),
-                                      nn.PReLU(),
-                                      nn.PixelShuffle(scale_factor),
-                                      nn.Conv2d(in_channels, in_channels, 1, stride=1, padding=0, bias=False))
-
-            self.up_b = nn.Sequential(nn.Conv2d(in_channels, in_channels, 1, 1, 0),
-                                      nn.PReLU(),
-                                      nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False),
-                                      nn.Conv2d(in_channels, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        """
-        x: B, L = H*W, C
-        """
-        if type(self.input_resolution) == int:
-            H = self.input_resolution
-            W = self.input_resolution
-
-        elif type(self.input_resolution) == tuple:
-            H, W = self.input_resolution
-
-        B, L, C = x.shape
-        x = x.view(B, H, W, C)  # B, H, W, C
-        x = x.permute(0, 3, 1, 2)  # B, C, H, W
-        x_p = self.up_p(x)  # pixel shuffle
-        x_b = self.up_b(x)  # bilinear
-        out = self.conv(torch.cat([x_p, x_b], dim=1))
-        out = out.permute(0, 2, 3, 1)  # B, H, W, C
-        if self.factor == 2:
-            out = out.view(B, -1, C // 2)
-
-        return out
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class BasicLayer_up(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, upsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if upsample is not None:
-            self.upsample = UpSample(input_resolution, in_channels=dim, scale_factor=2)
-        else:
-            self.upsample = None
-
-    def forward(self, x):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        if self.upsample is not None:
-            x = self.upsample(x)
-        return x
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1], \
-        #    f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops
-
-
-class SUNet(nn.Module):
-    r""" Swin Transformer
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 224
-        patch_size (int | tuple(int)): Patch size. Default: 4
-        in_chans (int): Number of input image channels. Default: 3
-
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, out_chans=3,
-                 embed_dim=96, depths=[2, 2, 2, 2], num_heads=[3, 6, 12, 24],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, final_upsample="Dual up-sample", **kwargs):
-        super(SUNet, self).__init__()
-
-        self.out_chans = out_chans
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
-        self.num_features_up = int(embed_dim * 2)
-        self.mlp_ratio = mlp_ratio
-        self.final_upsample = final_upsample
-        self.prelu = nn.PReLU()
-        self.conv_first = nn.Conv2d(in_chans, embed_dim, 3, 1, 1)
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build encoder and bottleneck layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
-                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
-                                                 patches_resolution[1] // (2 ** i_layer)),
-                               depth=depths[i_layer],
-                               num_heads=num_heads[i_layer],
-                               window_size=window_size,
-                               mlp_ratio=self.mlp_ratio,
-                               qkv_bias=qkv_bias, qk_scale=qk_scale,
-                               drop=drop_rate, attn_drop=attn_drop_rate,
-                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                               norm_layer=norm_layer,
-                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                               use_checkpoint=use_checkpoint)
-            self.layers.append(layer)
-
-        # build decoder layers
-        self.layers_up = nn.ModuleList()
-        self.concat_back_dim = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            concat_linear = nn.Linear(2 * int(embed_dim * 2 ** (self.num_layers - 1 - i_layer)),
-                                      int(embed_dim * 2 ** (
-                                              self.num_layers - 1 - i_layer))) if i_layer > 0 else nn.Identity()
-            if i_layer == 0:
-                layer_up = UpSample(input_resolution=patches_resolution[0] // (2 ** (self.num_layers - 1 - i_layer)),
-                                    in_channels=int(embed_dim * 2 ** (self.num_layers - 1 - i_layer)), scale_factor=2)
-            else:
-                layer_up = BasicLayer_up(dim=int(embed_dim * 2 ** (self.num_layers - 1 - i_layer)),
-                                         input_resolution=(
-                                             patches_resolution[0] // (2 ** (self.num_layers - 1 - i_layer)),
-                                             patches_resolution[1] // (2 ** (self.num_layers - 1 - i_layer))),
-                                         depth=depths[(self.num_layers - 1 - i_layer)],
-                                         num_heads=num_heads[(self.num_layers - 1 - i_layer)],
-                                         window_size=window_size,
-                                         mlp_ratio=self.mlp_ratio,
-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                         drop=drop_rate, attn_drop=attn_drop_rate,
-                                         drop_path=dpr[sum(depths[:(self.num_layers - 1 - i_layer)]):sum(
-                                             depths[:(self.num_layers - 1 - i_layer) + 1])],
-                                         norm_layer=norm_layer,
-                                         upsample=UpSample if (i_layer < self.num_layers - 1) else None,
-                                         use_checkpoint=use_checkpoint)
-            self.layers_up.append(layer_up)
-            self.concat_back_dim.append(concat_linear)
-
-        self.norm = norm_layer(self.num_features)
-        self.norm_up = norm_layer(self.embed_dim)
-
-        if self.final_upsample == "Dual up-sample":
-            self.up = UpSample(input_resolution=(img_size // patch_size, img_size // patch_size),
-                               in_channels=embed_dim, scale_factor=4)
-            self.output = nn.Conv2d(in_channels=embed_dim, out_channels=self.out_chans, kernel_size=3, stride=1,
-                                    padding=1, bias=False)  # kernel = 1
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    # Encoder and Bottleneck
-    def forward_features(self, x):
-        residual = x
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-        x_downsample = []
-
-        for layer in self.layers:
-            x_downsample.append(x)
-            x = layer(x)
-
-        x = self.norm(x)  # B L C
-
-        return x, residual, x_downsample
-
-    # Dencoder and Skip connection
-    def forward_up_features(self, x, x_downsample):
-        for inx, layer_up in enumerate(self.layers_up):
-            if inx == 0:
-                x = layer_up(x)
-            else:
-                x = torch.cat([x, x_downsample[3 - inx]], -1)  # concat last dimension
-                x = self.concat_back_dim[inx](x)
-                x = layer_up(x)
-
-        x = self.norm_up(x)  # B L C
-
-        return x
-
-    def up_x4(self, x):
-        H, W = self.patches_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input features has wrong size"
-
-        if self.final_upsample == "Dual up-sample":
-            x = self.up(x)
-            # x = x.view(B, 4 * H, 4 * W, -1)
-            x = x.permute(0, 3, 1, 2)  # B,C,H,W
-
-        return x
-
-    def forward(self, x):
-        x = self.conv_first(x)
-        x, residual, x_downsample = self.forward_features(x)
-        x = self.forward_up_features(x, x_downsample)
-        x = self.up_x4(x)
-        out = self.output(x)
-        # x = x + residual
-        return out
-
-    def flops(self):
-        flops = 0
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
-        flops += self.num_features * self.out_chans
-        return flops
-

spaces/AI-Naga/Vehicle_Damage_Detection/app.py

@@ -1,84 +0,0 @@
-import gradio as gr
-from gradio.outputs import Label
-import cv2
-import requests
-import os
-import numpy as np
-
-from ultralytics import YOLO
-import yolov5
-
-
-# Image download
-# file_urls = [
-# ]
-
-# def download_file(url, save_name):
-#     url = url
-#     if not os.path.exists(save_name):
-#         file = requests.get(url)
-#         open(save_name, 'wb').write(file.content)
-
-# for i, url in enumerate(file_urls):    
-#     download_file(
-#         file_urls[i],
-#         f"image_{i}.jpg"
-#     )
-
-# Function for inference
-def yolov5_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45 ):
-
-    # Loading Yolo V5 model
-    model = yolov5.load(model_path, device="cpu")
-
-    # Setting model configuration 
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-
-    # Inference
-    results = model([image], size=image_size)
-
-    # Cropping the predictions    
-    crops = results.crop(save=False)
-    img_crops = []
-    for i in range(len(crops)):
-        img_crops.append(crops[i]["im"][..., ::-1])
-    return results.render()[0], img_crops
-        
-# gradio Input
-inputs = [
-    gr.inputs.Image(type="pil", label="Input Image"),
-    gr.inputs.Dropdown(["Damage_Vehicle_Y5.pt","yolov5s.pt", "yolov5m.pt", "yolov5l.pt", "yolov5x.pt"], label="Model", default = 'Crime_Y5.pt'),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-]
-
-# gradio Output
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
-outputs_crops = gr.Gallery(label="Object crop")
-
-title = "Vehicle damage detection"
-
-# gradio examples: "Image", "Model", "Image Size", "Confidence Threshold", "IOU Threshold"
-examples = [['1.jpg', 'Damage_Vehicle_Y5.pt', 640, 0.35, 0.45]
-          ,['2.jpg', 'Damage_Vehicle_Y5.pt', 640, 0.35, 0.45]
-          ,['3.jpg', 'Damage_Vehicle_Y5.pt', 640, 0.35, 0.45]]
-
-# gradio app launch
-demo_app = gr.Interface(
-    fn=yolov5_inference,
-    inputs=inputs,
-    outputs=[outputs,outputs_crops],
-    title=title,
-    examples=examples,
-    cache_examples=True,
-    live=True,
-    theme='huggingface',
-)
-demo_app.launch(debug=True, enable_queue=True, width=50, height=50)

spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/lpaps.py

@@ -1,152 +0,0 @@
-"""
-    Based on https://github.com/CompVis/taming-transformers/blob/52720829/taming/modules/losses/lpips.py
-    Adapted for spectrograms by Vladimir Iashin (v-iashin)
-"""
-from collections import namedtuple
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-import sys
-sys.path.insert(0, '.')  # nopep8
-from ldm.modules.losses_audio.vggishish.model import VGGishish
-from ldm.util import get_ckpt_path
-
-
-class LPAPS(nn.Module):
-    # Learned perceptual metric
-    def __init__(self, use_dropout=True):
-        super().__init__()
-        self.scaling_layer = ScalingLayer()
-        self.chns = [64, 128, 256, 512, 512]  # vggish16 features
-        self.net = vggishish16(pretrained=True, requires_grad=False)
-        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
-        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
-        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
-        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
-        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
-        self.load_from_pretrained()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def load_from_pretrained(self, name="vggishish_lpaps"):
-        ckpt = get_ckpt_path(name, "ldm/modules/autoencoder/lpaps")
-        self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
-        print("loaded pretrained LPAPS loss from {}".format(ckpt))
-
-    @classmethod
-    def from_pretrained(cls, name="vggishish_lpaps"):
-        if name != "vggishish_lpaps":
-            raise NotImplementedError
-        model = cls()
-        ckpt = get_ckpt_path(name)
-        model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
-        return model
-
-    def forward(self, input, target):
-        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
-        outs0, outs1 = self.net(in0_input), self.net(in1_input)
-        feats0, feats1, diffs = {}, {}, {}
-        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
-        for kk in range(len(self.chns)):
-            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
-            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
-
-        res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
-        val = res[0]
-        for l in range(1, len(self.chns)):
-            val += res[l]
-        return val
-
-class ScalingLayer(nn.Module):
-    def __init__(self):
-        super(ScalingLayer, self).__init__()
-        # we are gonna use get_ckpt_path to donwload the stats as well
-        stat_path = get_ckpt_path('vggishish_mean_std_melspec_10s_22050hz', 'ldm/modules/autoencoder/lpaps')
-        # if for images we normalize on the channel dim, in spectrogram we will norm on frequency dimension
-        means, stds = np.loadtxt(stat_path, dtype=np.float32).T
-        # the normalization in means and stds are given for [0, 1], but specvqgan expects [-1, 1]:
-        means = 2 * means - 1
-        stds = 2 * stds
-        # input is expected to be (B, 1, F, T)
-        self.register_buffer('shift', torch.from_numpy(means)[None, None, :, None])
-        self.register_buffer('scale', torch.from_numpy(stds)[None, None, :, None])
-
-    def forward(self, inp):
-        return (inp - self.shift) / self.scale
-
-
-class NetLinLayer(nn.Module):
-    """ A single linear layer which does a 1x1 conv """
-    def __init__(self, chn_in, chn_out=1, use_dropout=False):
-        super(NetLinLayer, self).__init__()
-        layers = [nn.Dropout(), ] if (use_dropout) else []
-        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
-        self.model = nn.Sequential(*layers)
-
-class vggishish16(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super().__init__()
-        vgg_pretrained_features = self.vggishish16(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(23, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        h = self.slice5(h)
-        h_relu5_3 = h
-        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-        return out
-
-    def vggishish16(self, pretrained: bool = True) -> VGGishish:
-        # loading vggishish pretrained on vggsound
-        num_classes_vggsound = 309
-        conv_layers = [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512]
-        model = VGGishish(conv_layers, use_bn=False, num_classes=num_classes_vggsound)
-        if pretrained:
-            ckpt_path = get_ckpt_path('vggishish_lpaps', "ldm/modules/autoencoder/lpaps")
-            ckpt = torch.load(ckpt_path, map_location=torch.device("cpu"))
-            model.load_state_dict(ckpt, strict=False)
-        return model
-
-def normalize_tensor(x, eps=1e-10):
-    norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
-    return x / (norm_factor+eps)
-
-def spatial_average(x, keepdim=True):
-    return x.mean([2, 3], keepdim=keepdim)
-
-
-if __name__ == '__main__':
-    inputs = torch.rand((16, 1, 80, 848))
-    reconstructions = torch.rand((16, 1, 80, 848))
-    lpips = LPAPS().eval()
-    loss_p = lpips(inputs.contiguous(), reconstructions.contiguous())
-    # (16, 1, 1, 1)
-    print(loss_p.shape)

spaces/AIGText/GlyphControl/ldm/models/diffusion/dpm_solver/dpm_solver.py

@@ -1,1154 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-from tqdm import tqdm
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-    ):
-        """Create a wrapper class for the forward SDE (VP type).
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-            t = self.inverse_lambda(lambda_t)
-        ===============================================================
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-        1. For discrete-time DPMs:
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-        2. For continuous-time DPMs:
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-        ===============================================================
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-
-        ===============================================================
-        Example:
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError(
-                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
-                    schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
-                                  self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0 ** 2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
-                               torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-        model,
-        noise_schedule,
-        model_type="noise",
-        model_kwargs={},
-        guidance_type="uncond",
-        condition=None,
-        unconditional_condition=None,
-        guidance_scale=1.,
-        classifier_fn=None,
-        classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-    We support four types of the diffusion model by setting `model_type`:
-        1. "noise": noise prediction model. (Trained by predicting noise).
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            ``
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-    ===============================================================
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver.
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model.
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError(
-                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3, ] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3, ] * (K - 1) + [1]
-            else:
-                orders = [3, ] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2, ] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2, ] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1, ] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
-                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
-                                            solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
-            s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
-                                    model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
-                                           return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
-            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
-            s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(sigma_s1 / sigma_s, dims) * x
-                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(sigma_s2 / sigma_s, dims) * x
-                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + expand_dims(alpha_t * phi_2, dims) * D1
-                        - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                        - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                    - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - expand_dims(sigma_t * phi_2, dims) * D1
-                        - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
-            t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
-            t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                    - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
-                                     r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
-                                                            solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
-                                                           solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
-                            solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                               solver_type=solver_type,
-                                                                                               **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                    return_intermediate=True,
-                                                                                    solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
-                                                                                              solver_type=solver_type,
-                                                                                              **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-               method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-               atol=0.0078, rtol=0.05,
-               ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-        =====================================================
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-        =====================================================
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
-                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
-                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
-                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
-                for diffusion models sampling by diffusion SDEs for low-resolutional images
-                (such as CIFAR-10). However, we observed that such trick does not matter for
-                high-resolutional images. As it needs an additional NFE, we do not recommend
-                it for high-resolutional images.
-            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
-                Only valid for `method=multistep` and `steps < 15`. We empirically find that
-                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
-                (especially for steps <= 10). So we recommend to set it to be `True`.
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
-                                             solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in tqdm(range(1, order), desc="DPM init order"):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
-                                                         solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final and steps < 15:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
-                                                         solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
-                                                                                              skip_type=skip_type,
-                                                                                              t_T=t_T, t_0=t_0,
-                                                                                              device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order, ] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
-                                                      N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,) * (dims - 1)]

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/hr_4xb16_1024e_4channel.py

@@ -1,113 +0,0 @@
-_base_ = [                                    # 此配置文件将继承所有 `_base_` 中的配置
-    '../configs/_base_/schedules/custom_schedule.py',  # 训练策略配置
-    '../configs/_base_/default_runtime.py'            # 默认运行设置
-]
-
-default_hooks = dict(
-    # print log every 50 iterations.
-    logger=dict(type='LoggerHook', interval=50),
-    # save checkpoint per 8 epochs.
-    checkpoint=dict(save_best='auto', interval=16)
-)
-
-visualizer = dict(
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')])
-
-dataset_type = 'CustomDataset'
-
-# config of pipline
-train_pipeline = [
-    dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'),     # 读取图像
-    dict(type='RandomResizedCrop', scale=224),     # 随机放缩裁剪
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),   # 随机水平翻转
-    dict(type='PackInputs'),         # 准备图像以及标签
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'),     # 读取图像
-    dict(type='ResizeEdge', scale=256, edge='short'),  # 缩放短边尺寸至 256px
-    dict(type='CenterCrop', crop_size=224),     # 中心裁剪
-    dict(type='PackInputs'),                 # 准备图像以及标签
-]
-
-# config of dataloader
-train_dataloader = dict(
-    batch_size=16,                     # 每张 GPU 的 batchsize
-    num_workers=5,                     # 每个 GPU 的线程数
-    dataset=dict(                      # 训练数据集
-        type=dataset_type,
-        data_root='../2_preprocess_data_3000',
-        with_label=True,
-        ann_file='',
-        data_prefix='train',
-        pipeline=train_pipeline),
-    sampler=dict(type='DefaultSampler', shuffle=True),   # 默认采样器
-    persistent_workers=True,                             # 是否保持进程，可以缩短每个 epoch 的准备时间
-)
-
-# 构造验证集 dataloader
-val_dataloader = dict(
-    batch_size=16,
-    num_workers=5,
-    dataset=dict(
-        type=dataset_type,
-        data_root='../2_preprocess_data_3000',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=test_pipeline),
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    persistent_workers=True,
-)
-
-# set evaluator of validation dataset. Here uses top1 and top3 accuracy
-val_evaluator = dict(type='Accuracy', topk=(1, 3))
-
-test_dataloader = val_dataloader
-test_evaluator = val_evaluator
-
-model = dict(
-    type='ImageClassifier',     # 主模型类型（对于图像分类任务，使用 `ImageClassifier`）
-    backbone=dict(
-        type='HRNet',           # 主干网络类型
-        arch='w32',             # 主干网络架构
-        in_channels=4,
-        extra=dict(
-            stage1=dict(
-                num_modules=1,
-                num_branches=1,
-                block='BOTTLENECK',
-                num_blocks=(4, ),
-                num_channels=(64, )),
-            stage2=dict(
-                num_modules=1,
-                num_branches=2,
-                block='BASIC',
-                num_blocks=(4, 4),
-                num_channels=(32, 64)),
-            stage3=dict(
-                num_modules=4,
-                num_branches=3,
-                block='BASIC',
-                num_blocks=(4, 4, 4),
-                num_channels=(32, 64, 128)),
-            stage4=dict(
-                num_modules=3,
-                num_branches=4,
-                block='BASIC',
-                num_blocks=(4, 4, 4, 4),
-                num_channels=(32, 64, 128, 256))),
-    ),
-    neck=dict(type='GlobalAveragePooling'),    # 颈网络类型
-    head=dict(
-        type='LinearClsHead',         # 分类颈网络类型
-        # 除了 `type` 之外的所有字段都来自 `LinearClsHead` 类的 __init__ 方法
-        # 可查阅 https://mmpretrain.readthedocs.io/zh_CN/latest/api/generated/mmpretrain.models.heads.LinearClsHead.html
-        num_classes=7,                # 分类类别数
-        in_channels=256,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),  # 损失函数配置信息
-        topk=(1, 3),                 # 评估指标，Top-k 准确率
-    ))
-
-

spaces/AchyuthGamer/OpenGPT/client/css/buttons.css

@@ -1,4 +0,0 @@
-.buttons {
-	display: flex;
-	justify-content: left;
-}

spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/CodeLinkAva.py

@@ -1,64 +0,0 @@
-from __future__ import annotations
-
-from aiohttp import ClientSession
-import json
-
-from ...typing       import AsyncGenerator
-from ..base_provider import AsyncGeneratorProvider
-
-
-class CodeLinkAva(AsyncGeneratorProvider):
-    url                   = "https://ava-ai-ef611.web.app"
-    supports_gpt_35_turbo = True
-    working               = False
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> AsyncGenerator:
-        headers = {
-            "User-Agent"         : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-            "Accept"             : "*/*",
-            "Accept-language"    : "en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3",
-            "Origin"             : cls.url,
-            "Referer"            : cls.url + "/",
-            "Sec-Fetch-Dest"     : "empty",
-            "Sec-Fetch-Mode"     : "cors",
-            "Sec-Fetch-Site"     : "same-origin",
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            data = {
-                "messages": messages,
-                "temperature": 0.6,
-                "stream": True,
-                **kwargs
-            }
-            async with session.post("https://ava-alpha-api.codelink.io/api/chat", json=data) as response:
-                response.raise_for_status()
-                async for line in response.content:
-                    line = line.decode()
-                    if line.startswith("data: "):
-                        if line.startswith("data: [DONE]"):
-                            break
-                        line = json.loads(line[6:-1])
-                        content = line["choices"][0]["delta"].get("content")
-                        if content:
-                            yield content
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("temperature", "float"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AchyuthGamer/text-to-speech-client/index.html

@@ -1,14 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Speechie - Your AI Voice Generator</title>
-    <script type="module" crossorigin src="/assets/index-77d0c996.js"></script>
-    <link rel="stylesheet" href="/assets/index-5644c887.css">
-  </head>
-  <body>
-    <div id="root"></div>
-    
-  </body>
-</html>

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/canvas/Canvas.d.ts

@@ -1,2 +0,0 @@
-import Canvas from '../../../plugins/canvas';
-export default Canvas;

spaces/AisingioroHao0/anime-fanwork/app.py

@@ -1,182 +0,0 @@
-import huggingface_hub
-import gradio as gr
-from stable_diffusion_reference_only.pipelines.stable_diffusion_reference_only_pipeline import (
-    StableDiffusionReferenceOnlyPipeline,
-)
-from anime_segmentation import get_model as get_anime_segmentation_model
-from anime_segmentation import character_segment as anime_character_segment
-from diffusers.schedulers import UniPCMultistepScheduler
-from PIL import Image
-import cv2
-import numpy as np
-import os
-import torch
-print(f"Is CUDA available: {torch.cuda.is_available()}")
-if torch.cuda.is_available():
-    device = "cuda"
-else:
-    device = "cpu"
-
-automatic_coloring_pipeline = StableDiffusionReferenceOnlyPipeline.from_pretrained(
-    "AisingioroHao0/stable-diffusion-reference-only-automatic-coloring-0.1.2"
-).to(device)
-automatic_coloring_pipeline.scheduler = UniPCMultistepScheduler.from_config(
-    automatic_coloring_pipeline.scheduler.config
-)
-
-segment_model = get_anime_segmentation_model(
-    model_path=huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.ckpt")
-).to(device)
-
-def character_segment(img):
-    if img is None:
-        return None
-    img = anime_character_segment(segment_model, img)
-    img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
-    return img
-
-def color_inversion(img):
-    if img is None:
-        return None
-    return 255 - img
-
-
-def get_line_art(img):
-    if img is None:
-        return None
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-    img = cv2.adaptiveThreshold(
-        img,
-        255,
-        cv2.ADAPTIVE_THRESH_MEAN_C,
-        cv2.THRESH_BINARY,
-        blockSize=5,
-        C=7,
-    )
-    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-    return img
-
-
-def inference(prompt, blueprint, num_inference_steps):
-    if prompt is None or blueprint is None:
-        return None
-    return np.array(
-        automatic_coloring_pipeline(
-            prompt=Image.fromarray(prompt),
-            blueprint=Image.fromarray(blueprint),
-            num_inference_steps=num_inference_steps,
-        ).images[0]
-    )
-
-
-def automatic_coloring(prompt, blueprint, num_inference_steps):
-    if prompt is None or blueprint is None:
-        return None
-    blueprint = color_inversion(blueprint)
-    return inference(prompt, blueprint, num_inference_steps)
-
-
-def style_transfer(prompt, blueprint, num_inference_steps):
-    if prompt is None or blueprint is None:
-        return None
-    prompt = character_segment(prompt)
-    blueprint = character_segment(blueprint)
-    blueprint = get_line_art(blueprint)
-    blueprint = color_inversion(blueprint)
-    return inference(prompt, blueprint, num_inference_steps)
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-    # Stable Diffusion Reference Only Automatic Coloring 0.1.2\n\n
-    demo for [https://github.com/aihao2000/stable-diffusion-reference-only](https://github.com/aihao2000/stable-diffusion-reference-only)
-    """
-    )
-    with gr.Row():
-        with gr.Column():
-            prompt_input_compoent = gr.Image(shape=(512, 512), label="prompt")
-            prompt_character_segment_button = gr.Button(
-                "character segment",
-            )
-            prompt_character_segment_button.click(
-                character_segment,
-                inputs=prompt_input_compoent,
-                outputs=prompt_input_compoent,
-            )
-        with gr.Column():
-            blueprint_input_compoent = gr.Image(shape=(512, 512), label="blueprint")
-            blueprint_character_segment_button = gr.Button("character segment")
-            blueprint_character_segment_button.click(
-                character_segment,
-                inputs=blueprint_input_compoent,
-                outputs=blueprint_input_compoent,
-            )
-            get_line_art_button = gr.Button(
-                "get line art",
-            )
-            get_line_art_button.click(
-                get_line_art,
-                inputs=blueprint_input_compoent,
-                outputs=blueprint_input_compoent,
-            )
-            color_inversion_button = gr.Button(
-                "color inversion",
-            )
-            color_inversion_button.click(
-                color_inversion,
-                inputs=blueprint_input_compoent,
-                outputs=blueprint_input_compoent,
-            )
-        with gr.Column():
-            result_output_component = gr.Image(shape=(512, 512), label="result")
-            num_inference_steps_input_component = gr.Number(
-                20, label="num inference steps", minimum=1, maximum=1000, step=1
-            )
-            inference_button = gr.Button("inference")
-            inference_button.click(
-                inference,
-                inputs=[
-                    prompt_input_compoent,
-                    blueprint_input_compoent,
-                    num_inference_steps_input_component,
-                ],
-                outputs=result_output_component,
-            )
-            automatic_coloring_button = gr.Button("automatic coloring")
-            automatic_coloring_button.click(
-                automatic_coloring,
-                inputs=[
-                    prompt_input_compoent,
-                    blueprint_input_compoent,
-                    num_inference_steps_input_component,
-                ],
-                outputs=result_output_component,
-            )
-            style_transfer_button = gr.Button("style transfer")
-            style_transfer_button.click(
-                style_transfer,
-                inputs=[
-                    prompt_input_compoent,
-                    blueprint_input_compoent,
-                    num_inference_steps_input_component,
-                ],
-                outputs=result_output_component,
-            )
-    with gr.Row():
-        gr.Examples(
-            examples=[
-                [
-                    os.path.join(
-                        os.path.dirname(__file__), "README.assets", "3x9_prompt.png"
-                    ),
-                    os.path.join(
-                        os.path.dirname(__file__), "README.assets", "3x9_blueprint.png"
-                    ),
-                ],
-            ],
-            inputs=[prompt_input_compoent, blueprint_input_compoent],
-            outputs=result_output_component,
-            fn=lambda x, y: None,
-            cache_examples=True,
-        )
-if __name__ == "__main__":
-    demo.queue(max_size=5).launch()

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py

@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "r18"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]

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

@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/python/dqn/policies.py

@@ -1,237 +0,0 @@
-from typing import Any, Dict, List, Optional, Type
-
-import gym
-import torch as th
-from torch import nn
-
-from stable_baselines3.common.policies import BasePolicy, register_policy
-from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
-from stable_baselines3.common.type_aliases import Schedule
-
-
-class QNetwork(BasePolicy):
-    """
-    Action-Value (Q-Value) network for DQN
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        features_extractor: nn.Module,
-        features_dim: int,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        normalize_images: bool = True,
-    ):
-        super(QNetwork, self).__init__(
-            observation_space,
-            action_space,
-            features_extractor=features_extractor,
-            normalize_images=normalize_images,
-        )
-
-        if net_arch is None:
-            net_arch = [64, 64]
-
-        self.net_arch = net_arch
-        self.activation_fn = activation_fn
-        self.features_extractor = features_extractor
-        self.features_dim = features_dim
-        self.normalize_images = normalize_images
-        action_dim = self.action_space.n  # number of actions
-        q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
-        self.q_net = nn.Sequential(*q_net)
-
-    def forward(self, obs: th.Tensor) -> th.Tensor:
-        """
-        Predict the q-values.
-
-        :param obs: Observation
-        :return: The estimated Q-Value for each action.
-        """
-        return self.q_net(self.extract_features(obs))
-
-    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        q_values = self.forward(observation)
-        # Greedy action
-        action = q_values.argmax(dim=1).reshape(-1)
-        return action
-
-    def _get_constructor_parameters(self) -> Dict[str, Any]:
-        data = super()._get_constructor_parameters()
-
-        data.update(
-            dict(
-                net_arch=self.net_arch,
-                features_dim=self.features_dim,
-                activation_fn=self.activation_fn,
-                features_extractor=self.features_extractor,
-            )
-        )
-        return data
-
-
-class DQNPolicy(BasePolicy):
-    """
-    Policy class with Q-Value Net and target net for DQN
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param lr_schedule: Learning rate schedule (could be constant)
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param features_extractor_class: Features extractor to use.
-    :param features_extractor_kwargs: Keyword arguments
-        to pass to the features extractor.
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    :param optimizer_class: The optimizer to use,
-        ``th.optim.Adam`` by default
-    :param optimizer_kwargs: Additional keyword arguments,
-        excluding the learning rate, to pass to the optimizer
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
-        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
-        normalize_images: bool = True,
-        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
-        optimizer_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        super(DQNPolicy, self).__init__(
-            observation_space,
-            action_space,
-            features_extractor_class,
-            features_extractor_kwargs,
-            optimizer_class=optimizer_class,
-            optimizer_kwargs=optimizer_kwargs,
-        )
-
-        if net_arch is None:
-            if features_extractor_class == FlattenExtractor:
-                net_arch = [64, 64]
-            else:
-                net_arch = []
-
-        self.net_arch = net_arch
-        self.activation_fn = activation_fn
-        self.normalize_images = normalize_images
-
-        self.net_args = {
-            "observation_space": self.observation_space,
-            "action_space": self.action_space,
-            "net_arch": self.net_arch,
-            "activation_fn": self.activation_fn,
-            "normalize_images": normalize_images,
-        }
-
-        self.q_net, self.q_net_target = None, None
-        self._build(lr_schedule)
-
-    def _build(self, lr_schedule: Schedule) -> None:
-        """
-        Create the network and the optimizer.
-
-        :param lr_schedule: Learning rate schedule
-            lr_schedule(1) is the initial learning rate
-        """
-
-        self.q_net = self.make_q_net()
-        self.q_net_target = self.make_q_net()
-        self.q_net_target.load_state_dict(self.q_net.state_dict())
-
-        # Setup optimizer with initial learning rate
-        self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
-
-    def make_q_net(self) -> QNetwork:
-        # Make sure we always have separate networks for features extractors etc
-        net_args = self._update_features_extractor(self.net_args, features_extractor=None)
-        return QNetwork(**net_args).to(self.device)
-
-    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        return self._predict(obs, deterministic=deterministic)
-
-    def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        return self.q_net._predict(obs, deterministic=deterministic)
-
-    def _get_constructor_parameters(self) -> Dict[str, Any]:
-        data = super()._get_constructor_parameters()
-
-        data.update(
-            dict(
-                net_arch=self.net_args["net_arch"],
-                activation_fn=self.net_args["activation_fn"],
-                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone
-                optimizer_class=self.optimizer_class,
-                optimizer_kwargs=self.optimizer_kwargs,
-                features_extractor_class=self.features_extractor_class,
-                features_extractor_kwargs=self.features_extractor_kwargs,
-            )
-        )
-        return data
-
-
-MlpPolicy = DQNPolicy
-
-
-class CnnPolicy(DQNPolicy):
-    """
-    Policy class for DQN when using images as input.
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param lr_schedule: Learning rate schedule (could be constant)
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param features_extractor_class: Features extractor to use.
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    :param optimizer_class: The optimizer to use,
-        ``th.optim.Adam`` by default
-    :param optimizer_kwargs: Additional keyword arguments,
-        excluding the learning rate, to pass to the optimizer
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
-        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
-        normalize_images: bool = True,
-        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
-        optimizer_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        super(CnnPolicy, self).__init__(
-            observation_space,
-            action_space,
-            lr_schedule,
-            net_arch,
-            activation_fn,
-            features_extractor_class,
-            features_extractor_kwargs,
-            normalize_images,
-            optimizer_class,
-            optimizer_kwargs,
-        )
-
-
-register_policy("MlpPolicy", MlpPolicy)
-register_policy("CnnPolicy", CnnPolicy)

spaces/Amrrs/DragGan-Inversion/torch_utils/ops/upfirdn2d.h

@@ -1,59 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  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.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/text_to_image/README.md

@@ -1,318 +0,0 @@
-# Stable Diffusion text-to-image fine-tuning
-
-The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.
-
-___Note___:
-
-___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___
-
-
-## Running locally with PyTorch
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder  and run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-### Pokemon example
-
-You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree. 
-
-You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
-
-Run the following command to authenticate your token
-
-```bash
-huggingface-cli login
-```
-
-If you have already cloned the repo, then you won't need to go through these steps.
-
-<br>
-
-#### Hardware
-With `gradient_checkpointing` and `mixed_precision` it should be possible to fine tune the model on a single 24GB GPU. For higher `batch_size` and faster training it's better to use GPUs with >30GB memory.
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-<!-- accelerate_snippet_start -->
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
-
-accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$DATASET_NAME \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" 
-```
-<!-- accelerate_snippet_end -->
-
-
-To run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata).
-If you wish to use custom loading logic, you should modify the script, we have left pointers for that in the training script.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export TRAIN_DIR="path_to_your_dataset"
-
-accelerate launch --mixed_precision="fp16" train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$TRAIN_DIR \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model"
-```
-
-
-Once the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `sd-pokemon-model`. To load the fine-tuned model for inference just pass that path to `StableDiffusionPipeline`
-
-
-```python
-from diffusers import StableDiffusionPipeline
-
-model_path = "path_to_saved_model"
-pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
-pipe.to("cuda")
-
-image = pipe(prompt="yoda").images[0]
-image.save("yoda-pokemon.png")
-```
-
-Checkpoints only save the unet, so to run inference from a checkpoint, just load the unet
-```python
-from diffusers import StableDiffusionPipeline, UNet2DConditionModel
-
-model_path = "path_to_saved_model"
-
-unet = UNet2DConditionModel.from_pretrained(model_path + "/checkpoint-<N>/unet")
-
-pipe = StableDiffusionPipeline.from_pretrained("<initial model>", unet=unet, torch_dtype=torch.float16)
-pipe.to("cuda")
-
-image = pipe(prompt="yoda").images[0]
-image.save("yoda-pokemon.png")
-```
-
-#### Training with multiple GPUs
-
-`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
-for running distributed training with `accelerate`. Here is an example command:
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
-
-accelerate launch --mixed_precision="fp16" --multi_gpu  train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$DATASET_NAME \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --max_train_steps=15000 \ 
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" 
-```
-
-
-#### Training with Min-SNR weighting
-
-We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence
-by rebalancing the loss. In order to use it, one needs to set the `--snr_gamma` argument. The recommended
-value when using it is 5.0. 
-
-You can find [this project on Weights and Biases](https://wandb.ai/sayakpaul/text2image-finetune-minsnr) that compares the loss surfaces of the following setups:
-
-* Training without the Min-SNR weighting strategy
-* Training with the Min-SNR weighting strategy (`snr_gamma` set to 5.0)
-* Training with the Min-SNR weighting strategy (`snr_gamma` set to 1.0)
-
-For our small Pokemons dataset, the effects of Min-SNR weighting strategy might not appear to be pronounced, but for larger datasets, we believe the effects will be more pronounced.
-
-Also, note that in this example, we either predict `epsilon` (i.e., the noise) or the `v_prediction`. For both of these cases, the formulation of the Min-SNR weighting strategy that we have used holds. 
-
-## Training with LoRA
-
-Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.
-
-In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
-
-- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).
-- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
-- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.
-
-[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.
-
-With LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset
-on consumer GPUs like Tesla T4, Tesla V100.
-
-### Training
-
-First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).  
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
-```
-
-For this example we want to directly store the trained LoRA embeddings on the Hub, so 
-we need to be logged in and add the `--push_to_hub` flag.
-
-```bash
-huggingface-cli login
-```
-
-Now we can start training!
-
-```bash
-accelerate launch --mixed_precision="fp16" train_text_to_image_lora.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$DATASET_NAME --caption_column="text" \
-  --resolution=512 --random_flip \
-  --train_batch_size=1 \
-  --num_train_epochs=100 --checkpointing_steps=5000 \
-  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --seed=42 \
-  --output_dir="sd-pokemon-model-lora" \
-  --validation_prompt="cute dragon creature" --report_to="wandb"
-```
-
-The above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.
-
-**___Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use *1e-4* instead of the usual *1e-5*. Also, by using LoRA, it's possible to run `train_text_to_image_lora.py` in consumer GPUs like T4 or V100.___**
-
-The final LoRA embedding weights have been uploaded to [sayakpaul/sd-model-finetuned-lora-t4](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4). **___Note: [The final weights](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/pytorch_lora_weights.bin) are only 3 MB in size, which is orders of magnitudes smaller than the original model.___**
-
-You can check some inference samples that were logged during the course of the fine-tuning process [here](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw). 
-
-### Inference
-
-Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline` after loading the trained LoRA weights.  You 
-need to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-pokemon-model-lora`.
-
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_path = "sayakpaul/sd-model-finetuned-lora-t4"
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
-pipe.unet.load_attn_procs(model_path)
-pipe.to("cuda")
-
-prompt = "A pokemon with green eyes and red legs."
-image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
-image.save("pokemon.png")
-```
-
-If you are loading the LoRA parameters from the Hub and if the Hub repository has
-a `base_model` tag (such as [this](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/README.md?code=true#L4)), then
-you can do: 
-
-```py 
-from huggingface_hub.repocard import RepoCard
-
-lora_model_id = "sayakpaul/sd-model-finetuned-lora-t4"
-card = RepoCard.load(lora_model_id)
-base_model_id = card.data.to_dict()["base_model"]
-
-pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)
-...
-```
-
-## Training with Flax/JAX
-
-For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.
-
-**___Note: The flax example doesn't yet support features like gradient checkpoint, gradient accumulation etc, so to use flax for faster training we will need >30GB cards or TPU v3.___**
-
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-```bash
-pip install -U -r requirements_flax.txt
-```
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
-
-python train_text_to_image_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$DATASET_NAME \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --mixed_precision="fp16" \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --output_dir="sd-pokemon-model" 
-```
-
-To run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata).
-If you wish to use custom loading logic, you should modify the script, we have left pointers for that in the training script.
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export TRAIN_DIR="path_to_your_dataset"
-
-python train_text_to_image_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$TRAIN_DIR \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --mixed_precision="fp16" \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --output_dir="sd-pokemon-model"
-```
-
-### Training with xFormers:
-
-You can enable memory efficient attention by [installing xFormers](https://huggingface.co/docs/diffusers/main/en/optimization/xformers) and passing the `--enable_xformers_memory_efficient_attention` argument to the script.
-
-xFormers training is not available for Flax/JAX.
-
-**Note**:
-
-According to [this issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training in some GPUs. If you observe that problem, please install a development version as indicated in that comment.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/unet_1d_blocks.py

@@ -1,656 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import math
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from .activations import get_activation
-from .resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims
-
-
-class DownResnetBlock1D(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels=None,
-        num_layers=1,
-        conv_shortcut=False,
-        temb_channels=32,
-        groups=32,
-        groups_out=None,
-        non_linearity=None,
-        time_embedding_norm="default",
-        output_scale_factor=1.0,
-        add_downsample=True,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-        self.time_embedding_norm = time_embedding_norm
-        self.add_downsample = add_downsample
-        self.output_scale_factor = output_scale_factor
-
-        if groups_out is None:
-            groups_out = groups
-
-        # there will always be at least one resnet
-        resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)]
-
-        for _ in range(num_layers):
-            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if non_linearity is None:
-            self.nonlinearity = None
-        else:
-            self.nonlinearity = get_activation(non_linearity)
-
-        self.downsample = None
-        if add_downsample:
-            self.downsample = Downsample1D(out_channels, use_conv=True, padding=1)
-
-    def forward(self, hidden_states, temb=None):
-        output_states = ()
-
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for resnet in self.resnets[1:]:
-            hidden_states = resnet(hidden_states, temb)
-
-        output_states += (hidden_states,)
-
-        if self.nonlinearity is not None:
-            hidden_states = self.nonlinearity(hidden_states)
-
-        if self.downsample is not None:
-            hidden_states = self.downsample(hidden_states)
-
-        return hidden_states, output_states
-
-
-class UpResnetBlock1D(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels=None,
-        num_layers=1,
-        temb_channels=32,
-        groups=32,
-        groups_out=None,
-        non_linearity=None,
-        time_embedding_norm="default",
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.time_embedding_norm = time_embedding_norm
-        self.add_upsample = add_upsample
-        self.output_scale_factor = output_scale_factor
-
-        if groups_out is None:
-            groups_out = groups
-
-        # there will always be at least one resnet
-        resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)]
-
-        for _ in range(num_layers):
-            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if non_linearity is None:
-            self.nonlinearity = None
-        else:
-            self.nonlinearity = get_activation(non_linearity)
-
-        self.upsample = None
-        if add_upsample:
-            self.upsample = Upsample1D(out_channels, use_conv_transpose=True)
-
-    def forward(self, hidden_states, res_hidden_states_tuple=None, temb=None):
-        if res_hidden_states_tuple is not None:
-            res_hidden_states = res_hidden_states_tuple[-1]
-            hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1)
-
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for resnet in self.resnets[1:]:
-            hidden_states = resnet(hidden_states, temb)
-
-        if self.nonlinearity is not None:
-            hidden_states = self.nonlinearity(hidden_states)
-
-        if self.upsample is not None:
-            hidden_states = self.upsample(hidden_states)
-
-        return hidden_states
-
-
-class ValueFunctionMidBlock1D(nn.Module):
-    def __init__(self, in_channels, out_channels, embed_dim):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.embed_dim = embed_dim
-
-        self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim)
-        self.down1 = Downsample1D(out_channels // 2, use_conv=True)
-        self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim)
-        self.down2 = Downsample1D(out_channels // 4, use_conv=True)
-
-    def forward(self, x, temb=None):
-        x = self.res1(x, temb)
-        x = self.down1(x)
-        x = self.res2(x, temb)
-        x = self.down2(x)
-        return x
-
-
-class MidResTemporalBlock1D(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        embed_dim,
-        num_layers: int = 1,
-        add_downsample: bool = False,
-        add_upsample: bool = False,
-        non_linearity=None,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.add_downsample = add_downsample
-
-        # there will always be at least one resnet
-        resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)]
-
-        for _ in range(num_layers):
-            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim))
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if non_linearity is None:
-            self.nonlinearity = None
-        else:
-            self.nonlinearity = get_activation(non_linearity)
-
-        self.upsample = None
-        if add_upsample:
-            self.upsample = Downsample1D(out_channels, use_conv=True)
-
-        self.downsample = None
-        if add_downsample:
-            self.downsample = Downsample1D(out_channels, use_conv=True)
-
-        if self.upsample and self.downsample:
-            raise ValueError("Block cannot downsample and upsample")
-
-    def forward(self, hidden_states, temb):
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for resnet in self.resnets[1:]:
-            hidden_states = resnet(hidden_states, temb)
-
-        if self.upsample:
-            hidden_states = self.upsample(hidden_states)
-        if self.downsample:
-            self.downsample = self.downsample(hidden_states)
-
-        return hidden_states
-
-
-class OutConv1DBlock(nn.Module):
-    def __init__(self, num_groups_out, out_channels, embed_dim, act_fn):
-        super().__init__()
-        self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2)
-        self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim)
-        self.final_conv1d_act = get_activation(act_fn)
-        self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.final_conv1d_1(hidden_states)
-        hidden_states = rearrange_dims(hidden_states)
-        hidden_states = self.final_conv1d_gn(hidden_states)
-        hidden_states = rearrange_dims(hidden_states)
-        hidden_states = self.final_conv1d_act(hidden_states)
-        hidden_states = self.final_conv1d_2(hidden_states)
-        return hidden_states
-
-
-class OutValueFunctionBlock(nn.Module):
-    def __init__(self, fc_dim, embed_dim, act_fn="mish"):
-        super().__init__()
-        self.final_block = nn.ModuleList(
-            [
-                nn.Linear(fc_dim + embed_dim, fc_dim // 2),
-                get_activation(act_fn),
-                nn.Linear(fc_dim // 2, 1),
-            ]
-        )
-
-    def forward(self, hidden_states, temb):
-        hidden_states = hidden_states.view(hidden_states.shape[0], -1)
-        hidden_states = torch.cat((hidden_states, temb), dim=-1)
-        for layer in self.final_block:
-            hidden_states = layer(hidden_states)
-
-        return hidden_states
-
-
-_kernels = {
-    "linear": [1 / 8, 3 / 8, 3 / 8, 1 / 8],
-    "cubic": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875],
-    "lanczos3": [
-        0.003689131001010537,
-        0.015056144446134567,
-        -0.03399861603975296,
-        -0.066637322306633,
-        0.13550527393817902,
-        0.44638532400131226,
-        0.44638532400131226,
-        0.13550527393817902,
-        -0.066637322306633,
-        -0.03399861603975296,
-        0.015056144446134567,
-        0.003689131001010537,
-    ],
-}
-
-
-class Downsample1d(nn.Module):
-    def __init__(self, kernel="linear", pad_mode="reflect"):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = torch.tensor(_kernels[kernel])
-        self.pad = kernel_1d.shape[0] // 2 - 1
-        self.register_buffer("kernel", kernel_1d)
-
-    def forward(self, hidden_states):
-        hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode)
-        weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
-        indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
-        kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
-        weight[indices, indices] = kernel
-        return F.conv1d(hidden_states, weight, stride=2)
-
-
-class Upsample1d(nn.Module):
-    def __init__(self, kernel="linear", pad_mode="reflect"):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = torch.tensor(_kernels[kernel]) * 2
-        self.pad = kernel_1d.shape[0] // 2 - 1
-        self.register_buffer("kernel", kernel_1d)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode)
-        weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
-        indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
-        kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
-        weight[indices, indices] = kernel
-        return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1)
-
-
-class SelfAttention1d(nn.Module):
-    def __init__(self, in_channels, n_head=1, dropout_rate=0.0):
-        super().__init__()
-        self.channels = in_channels
-        self.group_norm = nn.GroupNorm(1, num_channels=in_channels)
-        self.num_heads = n_head
-
-        self.query = nn.Linear(self.channels, self.channels)
-        self.key = nn.Linear(self.channels, self.channels)
-        self.value = nn.Linear(self.channels, self.channels)
-
-        self.proj_attn = nn.Linear(self.channels, self.channels, bias=True)
-
-        self.dropout = nn.Dropout(dropout_rate, inplace=True)
-
-    def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:
-        new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)
-        # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
-        new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
-        return new_projection
-
-    def forward(self, hidden_states):
-        residual = hidden_states
-        batch, channel_dim, seq = hidden_states.shape
-
-        hidden_states = self.group_norm(hidden_states)
-        hidden_states = hidden_states.transpose(1, 2)
-
-        query_proj = self.query(hidden_states)
-        key_proj = self.key(hidden_states)
-        value_proj = self.value(hidden_states)
-
-        query_states = self.transpose_for_scores(query_proj)
-        key_states = self.transpose_for_scores(key_proj)
-        value_states = self.transpose_for_scores(value_proj)
-
-        scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1]))
-
-        attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
-        attention_probs = torch.softmax(attention_scores, dim=-1)
-
-        # compute attention output
-        hidden_states = torch.matmul(attention_probs, value_states)
-
-        hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
-        new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)
-        hidden_states = hidden_states.view(new_hidden_states_shape)
-
-        # compute next hidden_states
-        hidden_states = self.proj_attn(hidden_states)
-        hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.dropout(hidden_states)
-
-        output = hidden_states + residual
-
-        return output
-
-
-class ResConvBlock(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, is_last=False):
-        super().__init__()
-        self.is_last = is_last
-        self.has_conv_skip = in_channels != out_channels
-
-        if self.has_conv_skip:
-            self.conv_skip = nn.Conv1d(in_channels, out_channels, 1, bias=False)
-
-        self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2)
-        self.group_norm_1 = nn.GroupNorm(1, mid_channels)
-        self.gelu_1 = nn.GELU()
-        self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2)
-
-        if not self.is_last:
-            self.group_norm_2 = nn.GroupNorm(1, out_channels)
-            self.gelu_2 = nn.GELU()
-
-    def forward(self, hidden_states):
-        residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states
-
-        hidden_states = self.conv_1(hidden_states)
-        hidden_states = self.group_norm_1(hidden_states)
-        hidden_states = self.gelu_1(hidden_states)
-        hidden_states = self.conv_2(hidden_states)
-
-        if not self.is_last:
-            hidden_states = self.group_norm_2(hidden_states)
-            hidden_states = self.gelu_2(hidden_states)
-
-        output = hidden_states + residual
-        return output
-
-
-class UNetMidBlock1D(nn.Module):
-    def __init__(self, mid_channels, in_channels, out_channels=None):
-        super().__init__()
-
-        out_channels = in_channels if out_channels is None else out_channels
-
-        # there is always at least one resnet
-        self.down = Downsample1d("cubic")
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-        attentions = [
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(out_channels, out_channels // 32),
-        ]
-        self.up = Upsample1d(kernel="cubic")
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.down(hidden_states)
-        for attn, resnet in zip(self.attentions, self.resnets):
-            hidden_states = resnet(hidden_states)
-            hidden_states = attn(hidden_states)
-
-        hidden_states = self.up(hidden_states)
-
-        return hidden_states
-
-
-class AttnDownBlock1D(nn.Module):
-    def __init__(self, out_channels, in_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        self.down = Downsample1d("cubic")
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-        attentions = [
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(out_channels, out_channels // 32),
-        ]
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.down(hidden_states)
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states)
-            hidden_states = attn(hidden_states)
-
-        return hidden_states, (hidden_states,)
-
-
-class DownBlock1D(nn.Module):
-    def __init__(self, out_channels, in_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        self.down = Downsample1d("cubic")
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.down(hidden_states)
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        return hidden_states, (hidden_states,)
-
-
-class DownBlock1DNoSkip(nn.Module):
-    def __init__(self, out_channels, in_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = torch.cat([hidden_states, temb], dim=1)
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        return hidden_states, (hidden_states,)
-
-
-class AttnUpBlock1D(nn.Module):
-    def __init__(self, in_channels, out_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-        attentions = [
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(out_channels, out_channels // 32),
-        ]
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-        self.up = Upsample1d(kernel="cubic")
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
-        res_hidden_states = res_hidden_states_tuple[-1]
-        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states)
-            hidden_states = attn(hidden_states)
-
-        hidden_states = self.up(hidden_states)
-
-        return hidden_states
-
-
-class UpBlock1D(nn.Module):
-    def __init__(self, in_channels, out_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = in_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-
-        self.resnets = nn.ModuleList(resnets)
-        self.up = Upsample1d(kernel="cubic")
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
-        res_hidden_states = res_hidden_states_tuple[-1]
-        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        hidden_states = self.up(hidden_states)
-
-        return hidden_states
-
-
-class UpBlock1DNoSkip(nn.Module):
-    def __init__(self, in_channels, out_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = in_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),
-        ]
-
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
-        res_hidden_states = res_hidden_states_tuple[-1]
-        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        return hidden_states
-
-
-def get_down_block(down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample):
-    if down_block_type == "DownResnetBlock1D":
-        return DownResnetBlock1D(
-            in_channels=in_channels,
-            num_layers=num_layers,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-        )
-    elif down_block_type == "DownBlock1D":
-        return DownBlock1D(out_channels=out_channels, in_channels=in_channels)
-    elif down_block_type == "AttnDownBlock1D":
-        return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels)
-    elif down_block_type == "DownBlock1DNoSkip":
-        return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels)
-    raise ValueError(f"{down_block_type} does not exist.")
-
-
-def get_up_block(up_block_type, num_layers, in_channels, out_channels, temb_channels, add_upsample):
-    if up_block_type == "UpResnetBlock1D":
-        return UpResnetBlock1D(
-            in_channels=in_channels,
-            num_layers=num_layers,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-        )
-    elif up_block_type == "UpBlock1D":
-        return UpBlock1D(in_channels=in_channels, out_channels=out_channels)
-    elif up_block_type == "AttnUpBlock1D":
-        return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels)
-    elif up_block_type == "UpBlock1DNoSkip":
-        return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels)
-    raise ValueError(f"{up_block_type} does not exist.")
-
-
-def get_mid_block(mid_block_type, num_layers, in_channels, mid_channels, out_channels, embed_dim, add_downsample):
-    if mid_block_type == "MidResTemporalBlock1D":
-        return MidResTemporalBlock1D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            embed_dim=embed_dim,
-            add_downsample=add_downsample,
-        )
-    elif mid_block_type == "ValueFunctionMidBlock1D":
-        return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim)
-    elif mid_block_type == "UNetMidBlock1D":
-        return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels)
-    raise ValueError(f"{mid_block_type} does not exist.")
-
-
-def get_out_block(*, out_block_type, num_groups_out, embed_dim, out_channels, act_fn, fc_dim):
-    if out_block_type == "OutConv1DBlock":
-        return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn)
-    elif out_block_type == "ValueFunction":
-        return OutValueFunctionBlock(fc_dim, embed_dim, act_fn)
-    return None

spaces/Andy1621/uniformer_image_detection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py

@@ -1,17 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
-# model settings
-conv_cfg = dict(type='ConvWS')
-norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
-model = dict(
-    pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        style='pytorch',
-        conv_cfg=conv_cfg,
-        norm_cfg=norm_cfg))

spaces/Andy1621/uniformer_image_detection/configs/rpn/README.md

@@ -1,29 +0,0 @@
-# Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@inproceedings{ren2015faster,
-  title={Faster r-cnn: Towards real-time object detection with region proposal networks},
-  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
-  booktitle={Advances in neural information processing systems},
-  year={2015}
-}
-```
-
-## Results and models
-
-|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | AR1000 | Config | Download |
-| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
-|    R-50-FPN     |  caffe  |   1x    |   3.5    |      22.6      |  58.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531_012334.log.json) |
-|    R-50-FPN     | pytorch |   1x    |   3.8    |      22.3      |  58.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218_151240.log.json) |
-|    R-50-FPN     | pytorch |   2x    |    -     |       -        |  58.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131_190631.log.json) |
-|    R-101-FPN    |  caffe  |   1x    |   5.4    |      17.3      |  60.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531_012345.log.json) |
-|    R-101-FPN    | pytorch |   1x    |   5.8    |      16.5      |  59.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131-2ace2249.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131_191000.log.json) |
-|    R-101-FPN    | pytorch |   2x    |    -     |       -        |  60.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131-24e3db1a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131_191106.log.json) |
-| X-101-32x4d-FPN | pytorch |   1x    |   7.0    |      13.0      |  60.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219_012037.log.json) |
-| X-101-32x4d-FPN | pytorch |   2x    |    -     |       -        |  61.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208_200752.log.json) |
-| X-101-64x4d-FPN | pytorch |   1x    |   10.1   |      9.1       |  61.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208_200752.log.json) |
-| X-101-64x4d-FPN | pytorch |   2x    |    -     |       -        |  61.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208_200752.log.json) |

spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py

@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))

spaces/Aniquel/WizApp_Code_Generator/README.md

@@ -1,13 +0,0 @@
----
-title: WizApp Code Generator
-emoji: 👀
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/hswish.py

@@ -1,29 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .registry import ACTIVATION_LAYERS
-
-
-@ACTIVATION_LAYERS.register_module()
-class HSwish(nn.Module):
-    """Hard Swish Module.
-
-    This module applies the hard swish function:
-
-    .. math::
-        Hswish(x) = x * ReLU6(x + 3) / 6
-
-    Args:
-        inplace (bool): can optionally do the operation in-place.
-            Default: False.
-
-    Returns:
-        Tensor: The output tensor.
-    """
-
-    def __init__(self, inplace=False):
-        super(HSwish, self).__init__()
-        self.act = nn.ReLU6(inplace)
-
-    def forward(self, x):
-        return x * self.act(x + 3) / 6

spaces/Apex-X/nono/run.py

@@ -1,6 +0,0 @@
-#!/usr/bin/env python3
-
-from roop import core
-
-if __name__ == '__main__':
-    core.run()

spaces/Ariharasudhan/Kenya_food_classification/app.py

@@ -1,58 +0,0 @@
-from re import I
-from tkinter import image_names
-import gradio as gr
-import torch
-import requests
-from PIL import Image
-from torchvision import transforms, models
-from torch import nn
-import torch.nn.functional as F
-
-# Load the model
-def load_model():
-    model = models.efficientnet_b4(pretrained = True).cpu()
-    model.classifier[1] = nn.Linear(in_features=1792, out_features=13)
-    model.load_state_dict(torch.load('model.pth',map_location=torch.device('cpu')))
-    model.eval()
-    return model
-
-
-# Load the labels
-def load_labels():
-    labels = open('classes.txt').read().splitlines()
-    return labels
-
-
-# Accessing the model and labels
-model = load_model()
-labels = load_labels()
-
-# Define the preprocessing function
-def preprocess(image):
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-    r_image = transforms.Compose([transforms.Resize((380,380)),transforms.ToTensor(), 
-                                             transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])])(image)
-    
-    return r_image
-
-# Define prediction function with probability and top 3 predictions
-def predict(image):
-    image = preprocess(image)
-    image = image.unsqueeze(0)
-    output = model(image)
-    prob, pred = torch.topk(F.softmax(output, dim=1), k=3)
-    prob = prob.detach().numpy().tolist()[0]
-    pred = pred.detach().numpy().tolist()[0]
-    confidences = {labels[pred[i]]: float(prob[i]) for i in range(3)}
-    return confidences
-
-
-
-# Define the interface
-title = "Kenya Food Classification"
-description = "Classify Kenyan food into 13 categories"
-article = "<p style='text-align: center'><a href='https://github.com/ariharasudhanm/Image_classification_Kaggle_Competition'>Github</a> | <a href='https://www.linkedin.com/in/ariharasudhan/'>LinkedIn</a></p>"
-examples = ["./test1.jpeg", "./test2.jpeg", "./test3.jpeg"]
-gr.Interface(predict, "image", "label", title=title, description=description, article=article, examples=examples).launch()
-
-

spaces/Arnaudding001/OpenAI_whisperLive/cli.py

@@ -1,110 +0,0 @@
-import argparse
-import os
-import pathlib
-from urllib.parse import urlparse
-import warnings
-import numpy as np
-
-import whisper
-
-import torch
-from app import LANGUAGES, WhisperTranscriber
-from download import download_url
-
-from utils import optional_float, optional_int, str2bool
-
-
-def cli():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
-    parser.add_argument("--model", default="small", choices=["tiny", "base", "small", "medium", "large"], help="name of the Whisper model to use")
-    parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
-    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
-    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
-    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
-
-    parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
-    parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES), help="language spoken in the audio, specify None to perform language detection")
-
-    parser.add_argument("--vad", type=str, default="none", choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], help="The voice activity detection algorithm to use")
-    parser.add_argument("--vad_merge_window", type=optional_float, default=5, help="The window size (in seconds) to merge voice segments")
-    parser.add_argument("--vad_max_merge_size", type=optional_float, default=30, help="The maximum size (in seconds) of a voice segment")
-    parser.add_argument("--vad_padding", type=optional_float, default=1, help="The padding (in seconds) to add to each voice segment")
-    parser.add_argument("--vad_prompt_window", type=optional_float, default=3, help="The window size of the prompt to pass to Whisper")
-
-    parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
-    parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
-    parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
-    parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
-    parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple lengt normalization by default")
-
-    parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
-    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
-    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
-    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
-
-    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
-    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
-    parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
-    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
-
-    args = parser.parse_args().__dict__
-    model_name: str = args.pop("model")
-    model_dir: str = args.pop("model_dir")
-    output_dir: str = args.pop("output_dir")
-    device: str = args.pop("device")
-    os.makedirs(output_dir, exist_ok=True)
-
-    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
-        warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
-        args["language"] = "en"
-
-    temperature = args.pop("temperature")
-    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
-    if temperature_increment_on_fallback is not None:
-        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
-    else:
-        temperature = [temperature]
-
-    vad = args.pop("vad")
-    vad_merge_window = args.pop("vad_merge_window")
-    vad_max_merge_size = args.pop("vad_max_merge_size")
-    vad_padding = args.pop("vad_padding")
-    vad_prompt_window = args.pop("vad_prompt_window")
-
-    model = whisper.load_model(model_name, device=device, download_root=model_dir)
-    transcriber = WhisperTranscriber(deleteUploadedFiles=False)
-
-    for audio_path in args.pop("audio"):
-        sources = []
-
-        # Detect URL and download the audio
-        if (uri_validator(audio_path)):
-            # Download from YouTube/URL directly
-            for source_path in  download_url(audio_path, maxDuration=-1, destinationDirectory=output_dir, playlistItems=None):
-                source_name = os.path.basename(source_path)
-                sources.append({ "path": source_path, "name": source_name })
-        else:
-            sources.append({ "path": audio_path, "name": os.path.basename(audio_path) })
-
-        for source in sources:
-            source_path = source["path"]
-            source_name = source["name"]
-
-            result = transcriber.transcribe_file(model, source_path, temperature=temperature, 
-                                                vad=vad, vadMergeWindow=vad_merge_window, vadMaxMergeSize=vad_max_merge_size, 
-                                                vadPadding=vad_padding, vadPromptWindow=vad_prompt_window, **args)
-            
-            transcriber.write_result(result, source_name, output_dir)
-
-    transcriber.clear_cache()
-
-def uri_validator(x):
-    try:
-        result = urlparse(x)
-        return all([result.scheme, result.netloc])
-    except:
-        return False
-
-if __name__ == '__main__':
-    cli()

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

@@ -1,41 +0,0 @@
-from optparse import Values
-from typing import List
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.status_codes import SUCCESS
-from pip._internal.exceptions import CommandError
-
-
-class HelpCommand(Command):
-    """Show help for commands"""
-
-    usage = """
-      %prog <command>"""
-    ignore_require_venv = True
-
-    def run(self, options: Values, args: List[str]) -> int:
-        from pip._internal.commands import (
-            commands_dict,
-            create_command,
-            get_similar_commands,
-        )
-
-        try:
-            # 'pip help' with no args is handled by pip.__init__.parseopt()
-            cmd_name = args[0]  # the command we need help for
-        except IndexError:
-            return SUCCESS
-
-        if cmd_name not in commands_dict:
-            guess = get_similar_commands(cmd_name)
-
-            msg = [f'unknown command "{cmd_name}"']
-            if guess:
-                msg.append(f'maybe you meant "{guess}"')
-
-            raise CommandError(" - ".join(msg))
-
-        command = create_command(cmd_name)
-        command.parser.print_help()
-
-        return SUCCESS

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

@@ -1,348 +0,0 @@
-import contextlib
-import errno
-import logging
-import logging.handlers
-import os
-import sys
-import threading
-from dataclasses import dataclass
-from io import TextIOWrapper
-from logging import Filter
-from typing import Any, ClassVar, Generator, List, Optional, TextIO, Type
-
-from pip._vendor.rich.console import (
-    Console,
-    ConsoleOptions,
-    ConsoleRenderable,
-    RenderableType,
-    RenderResult,
-    RichCast,
-)
-from pip._vendor.rich.highlighter import NullHighlighter
-from pip._vendor.rich.logging import RichHandler
-from pip._vendor.rich.segment import Segment
-from pip._vendor.rich.style import Style
-
-from pip._internal.utils._log import VERBOSE, getLogger
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.deprecation import DEPRECATION_MSG_PREFIX
-from pip._internal.utils.misc import ensure_dir
-
-_log_state = threading.local()
-subprocess_logger = getLogger("pip.subprocessor")
-
-
-class BrokenStdoutLoggingError(Exception):
-    """
-    Raised if BrokenPipeError occurs for the stdout stream while logging.
-    """
-
-
-def _is_broken_pipe_error(exc_class: Type[BaseException], exc: BaseException) -> bool:
-    if exc_class is BrokenPipeError:
-        return True
-
-    # On Windows, a broken pipe can show up as EINVAL rather than EPIPE:
-    # https://bugs.python.org/issue19612
-    # https://bugs.python.org/issue30418
-    if not WINDOWS:
-        return False
-
-    return isinstance(exc, OSError) and exc.errno in (errno.EINVAL, errno.EPIPE)
-
-
-@contextlib.contextmanager
-def indent_log(num: int = 2) -> Generator[None, None, None]:
-    """
-    A context manager which will cause the log output to be indented for any
-    log messages emitted inside it.
-    """
-    # For thread-safety
-    _log_state.indentation = get_indentation()
-    _log_state.indentation += num
-    try:
-        yield
-    finally:
-        _log_state.indentation -= num
-
-
-def get_indentation() -> int:
-    return getattr(_log_state, "indentation", 0)
-
-
-class IndentingFormatter(logging.Formatter):
-    default_time_format = "%Y-%m-%dT%H:%M:%S"
-
-    def __init__(
-        self,
-        *args: Any,
-        add_timestamp: bool = False,
-        **kwargs: Any,
-    ) -> None:
-        """
-        A logging.Formatter that obeys the indent_log() context manager.
-
-        :param add_timestamp: A bool indicating output lines should be prefixed
-            with their record's timestamp.
-        """
-        self.add_timestamp = add_timestamp
-        super().__init__(*args, **kwargs)
-
-    def get_message_start(self, formatted: str, levelno: int) -> str:
-        """
-        Return the start of the formatted log message (not counting the
-        prefix to add to each line).
-        """
-        if levelno < logging.WARNING:
-            return ""
-        if formatted.startswith(DEPRECATION_MSG_PREFIX):
-            # Then the message already has a prefix.  We don't want it to
-            # look like "WARNING: DEPRECATION: ...."
-            return ""
-        if levelno < logging.ERROR:
-            return "WARNING: "
-
-        return "ERROR: "
-
-    def format(self, record: logging.LogRecord) -> str:
-        """
-        Calls the standard formatter, but will indent all of the log message
-        lines by our current indentation level.
-        """
-        formatted = super().format(record)
-        message_start = self.get_message_start(formatted, record.levelno)
-        formatted = message_start + formatted
-
-        prefix = ""
-        if self.add_timestamp:
-            prefix = f"{self.formatTime(record)} "
-        prefix += " " * get_indentation()
-        formatted = "".join([prefix + line for line in formatted.splitlines(True)])
-        return formatted
-
-
-@dataclass
-class IndentedRenderable:
-    renderable: RenderableType
-    indent: int
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        segments = console.render(self.renderable, options)
-        lines = Segment.split_lines(segments)
-        for line in lines:
-            yield Segment(" " * self.indent)
-            yield from line
-            yield Segment("\n")
-
-
-class RichPipStreamHandler(RichHandler):
-    KEYWORDS: ClassVar[Optional[List[str]]] = []
-
-    def __init__(self, stream: Optional[TextIO], no_color: bool) -> None:
-        super().__init__(
-            console=Console(file=stream, no_color=no_color, soft_wrap=True),
-            show_time=False,
-            show_level=False,
-            show_path=False,
-            highlighter=NullHighlighter(),
-        )
-
-    # Our custom override on Rich's logger, to make things work as we need them to.
-    def emit(self, record: logging.LogRecord) -> None:
-        style: Optional[Style] = None
-
-        # If we are given a diagnostic error to present, present it with indentation.
-        assert isinstance(record.args, tuple)
-        if record.msg == "[present-rich] %s" and len(record.args) == 1:
-            rich_renderable = record.args[0]
-            assert isinstance(
-                rich_renderable, (ConsoleRenderable, RichCast, str)
-            ), f"{rich_renderable} is not rich-console-renderable"
-
-            renderable: RenderableType = IndentedRenderable(
-                rich_renderable, indent=get_indentation()
-            )
-        else:
-            message = self.format(record)
-            renderable = self.render_message(record, message)
-            if record.levelno is not None:
-                if record.levelno >= logging.ERROR:
-                    style = Style(color="red")
-                elif record.levelno >= logging.WARNING:
-                    style = Style(color="yellow")
-
-        try:
-            self.console.print(renderable, overflow="ignore", crop=False, style=style)
-        except Exception:
-            self.handleError(record)
-
-    def handleError(self, record: logging.LogRecord) -> None:
-        """Called when logging is unable to log some output."""
-
-        exc_class, exc = sys.exc_info()[:2]
-        # If a broken pipe occurred while calling write() or flush() on the
-        # stdout stream in logging's Handler.emit(), then raise our special
-        # exception so we can handle it in main() instead of logging the
-        # broken pipe error and continuing.
-        if (
-            exc_class
-            and exc
-            and self.console.file is sys.stdout
-            and _is_broken_pipe_error(exc_class, exc)
-        ):
-            raise BrokenStdoutLoggingError()
-
-        return super().handleError(record)
-
-
-class BetterRotatingFileHandler(logging.handlers.RotatingFileHandler):
-    def _open(self) -> TextIOWrapper:
-        ensure_dir(os.path.dirname(self.baseFilename))
-        return super()._open()
-
-
-class MaxLevelFilter(Filter):
-    def __init__(self, level: int) -> None:
-        self.level = level
-
-    def filter(self, record: logging.LogRecord) -> bool:
-        return record.levelno < self.level
-
-
-class ExcludeLoggerFilter(Filter):
-
-    """
-    A logging Filter that excludes records from a logger (or its children).
-    """
-
-    def filter(self, record: logging.LogRecord) -> bool:
-        # The base Filter class allows only records from a logger (or its
-        # children).
-        return not super().filter(record)
-
-
-def setup_logging(verbosity: int, no_color: bool, user_log_file: Optional[str]) -> int:
-    """Configures and sets up all of the logging
-
-    Returns the requested logging level, as its integer value.
-    """
-
-    # Determine the level to be logging at.
-    if verbosity >= 2:
-        level_number = logging.DEBUG
-    elif verbosity == 1:
-        level_number = VERBOSE
-    elif verbosity == -1:
-        level_number = logging.WARNING
-    elif verbosity == -2:
-        level_number = logging.ERROR
-    elif verbosity <= -3:
-        level_number = logging.CRITICAL
-    else:
-        level_number = logging.INFO
-
-    level = logging.getLevelName(level_number)
-
-    # The "root" logger should match the "console" level *unless* we also need
-    # to log to a user log file.
-    include_user_log = user_log_file is not None
-    if include_user_log:
-        additional_log_file = user_log_file
-        root_level = "DEBUG"
-    else:
-        additional_log_file = "/dev/null"
-        root_level = level
-
-    # Disable any logging besides WARNING unless we have DEBUG level logging
-    # enabled for vendored libraries.
-    vendored_log_level = "WARNING" if level in ["INFO", "ERROR"] else "DEBUG"
-
-    # Shorthands for clarity
-    log_streams = {
-        "stdout": "ext://sys.stdout",
-        "stderr": "ext://sys.stderr",
-    }
-    handler_classes = {
-        "stream": "pip._internal.utils.logging.RichPipStreamHandler",
-        "file": "pip._internal.utils.logging.BetterRotatingFileHandler",
-    }
-    handlers = ["console", "console_errors", "console_subprocess"] + (
-        ["user_log"] if include_user_log else []
-    )
-
-    logging.config.dictConfig(
-        {
-            "version": 1,
-            "disable_existing_loggers": False,
-            "filters": {
-                "exclude_warnings": {
-                    "()": "pip._internal.utils.logging.MaxLevelFilter",
-                    "level": logging.WARNING,
-                },
-                "restrict_to_subprocess": {
-                    "()": "logging.Filter",
-                    "name": subprocess_logger.name,
-                },
-                "exclude_subprocess": {
-                    "()": "pip._internal.utils.logging.ExcludeLoggerFilter",
-                    "name": subprocess_logger.name,
-                },
-            },
-            "formatters": {
-                "indent": {
-                    "()": IndentingFormatter,
-                    "format": "%(message)s",
-                },
-                "indent_with_timestamp": {
-                    "()": IndentingFormatter,
-                    "format": "%(message)s",
-                    "add_timestamp": True,
-                },
-            },
-            "handlers": {
-                "console": {
-                    "level": level,
-                    "class": handler_classes["stream"],
-                    "no_color": no_color,
-                    "stream": log_streams["stdout"],
-                    "filters": ["exclude_subprocess", "exclude_warnings"],
-                    "formatter": "indent",
-                },
-                "console_errors": {
-                    "level": "WARNING",
-                    "class": handler_classes["stream"],
-                    "no_color": no_color,
-                    "stream": log_streams["stderr"],
-                    "filters": ["exclude_subprocess"],
-                    "formatter": "indent",
-                },
-                # A handler responsible for logging to the console messages
-                # from the "subprocessor" logger.
-                "console_subprocess": {
-                    "level": level,
-                    "class": handler_classes["stream"],
-                    "stream": log_streams["stderr"],
-                    "no_color": no_color,
-                    "filters": ["restrict_to_subprocess"],
-                    "formatter": "indent",
-                },
-                "user_log": {
-                    "level": "DEBUG",
-                    "class": handler_classes["file"],
-                    "filename": additional_log_file,
-                    "encoding": "utf-8",
-                    "delay": True,
-                    "formatter": "indent_with_timestamp",
-                },
-            },
-            "root": {
-                "level": root_level,
-                "handlers": handlers,
-            },
-            "loggers": {"pip._vendor": {"level": vendored_log_level}},
-        }
-    )
-
-    return level_number

spaces/Audio-AGI/AudioSep/models/audiosep.py

@@ -1,150 +0,0 @@
-from typing import Any, Callable, Dict
-import random
-import lightning.pytorch as pl
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.optim.lr_scheduler import LambdaLR
-
-
-class AudioSep(pl.LightningModule):
-    def __init__(
-        self,
-        ss_model: nn.Module,
-        waveform_mixer,
-        query_encoder,
-        loss_function,
-        optimizer_type: str,
-        learning_rate: float,
-        lr_lambda_func,
-        use_text_ratio=1.0,
-    ):
-        r"""Pytorch Lightning wrapper of PyTorch model, including forward,
-        optimization of model, etc.
-
-        Args:
-            ss_model: nn.Module
-            anchor_segment_detector: nn.Module
-            loss_function: function or object
-            learning_rate: float
-            lr_lambda: function
-        """
-
-        super().__init__()
-        self.ss_model = ss_model
-        self.waveform_mixer = waveform_mixer
-        self.query_encoder = query_encoder
-        self.query_encoder_type = self.query_encoder.encoder_type
-        self.use_text_ratio = use_text_ratio
-        self.loss_function = loss_function
-        self.optimizer_type = optimizer_type
-        self.learning_rate = learning_rate
-        self.lr_lambda_func = lr_lambda_func
-
-
-    def forward(self, x):
-        pass
-
-    def training_step(self, batch_data_dict, batch_idx):
-        r"""Forward a mini-batch data to model, calculate loss function, and
-        train for one step. A mini-batch data is evenly distributed to multiple
-        devices (if there are) for parallel training.
-
-        Args:
-            batch_data_dict: e.g. 
-                'audio_text': {
-                    'text': ['a sound of dog', ...]
-                    'waveform': (batch_size, 1, samples)
-            }
-            batch_idx: int
-
-        Returns:
-            loss: float, loss function of this mini-batch
-        """
-        # [important] fix random seeds across devices
-        random.seed(batch_idx)
-
-        batch_audio_text_dict = batch_data_dict['audio_text']
-
-        batch_text = batch_audio_text_dict['text']
-        batch_audio = batch_audio_text_dict['waveform']
-        device = batch_audio.device
-        
-        mixtures, segments = self.waveform_mixer(
-            waveforms=batch_audio
-        )
-
-        # calculate text embed for audio-text data
-        if self.query_encoder_type == 'CLAP':
-            conditions = self.query_encoder.get_query_embed(
-                modality='hybird',
-                text=batch_text,
-                audio=segments.squeeze(1),
-                use_text_ratio=self.use_text_ratio,
-            )
-
-        input_dict = {
-            'mixture': mixtures[:, None, :].squeeze(1),
-            'condition': conditions,
-        }
-
-        target_dict = {
-            'segment': segments.squeeze(1),
-        }
-
-        self.ss_model.train()
-        sep_segment = self.ss_model(input_dict)['waveform']
-        sep_segment = sep_segment.squeeze()
-        # (batch_size, 1, segment_samples)
-
-        output_dict = {
-            'segment': sep_segment,
-        }
-
-        # Calculate loss.
-        loss = self.loss_function(output_dict, target_dict)
-
-        self.log_dict({"train_loss": loss})
-        
-        return loss
-
-    def test_step(self, batch, batch_idx):
-        pass
-    
-    def configure_optimizers(self):
-        r"""Configure optimizer.
-        """
-
-        if self.optimizer_type == "AdamW":
-            optimizer = optim.AdamW(
-                params=self.ss_model.parameters(),
-                lr=self.learning_rate,
-                betas=(0.9, 0.999),
-                eps=1e-08,
-                weight_decay=0.0,
-                amsgrad=True,
-            )
-        else:
-            raise NotImplementedError
-
-        scheduler = LambdaLR(optimizer, self.lr_lambda_func)
-
-        output_dict = {
-            "optimizer": optimizer,
-            "lr_scheduler": {
-                'scheduler': scheduler,
-                'interval': 'step',
-                'frequency': 1,
-            }
-        }
-
-        return output_dict
-    
-
-def get_model_class(model_type):
-    if model_type == 'ResUNet30':
-        from models.resunet import ResUNet30
-        return ResUNet30
-
-    else:
-        raise NotImplementedError

spaces/Benson/text-generation/Examples/1.19.60 Minecraft Apk.md

@@ -1,88 +0,0 @@
-
-<h1>Cómo descargar y jugar 1.19.60 APK Minecraft en su dispositivo Android</h1>
- <p>Minecraft es uno de los juegos más populares y creativos del mundo, donde puedes construir, explorar, sobrevivir y crear cualquier cosa que puedas imaginar. Pero ¿sabías que hay diferentes versiones de Minecraft para diferentes plataformas y dispositivos? Uno de ellos es Minecraft Bedrock Edition, que está diseñado para dispositivos móviles, consolas y Windows 10 PCs.</p>
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- <p>En este artículo, le mostraremos cómo descargar y jugar 1.19.60 APK Minecraft, que es la última actualización para Minecraft Bedrock Edition en dispositivos Android. También te diremos cuáles son las nuevas características y elementos de esta versión, y cómo jugar con tus amigos y multiplataforma con otros dispositivos. </p>
- <h2> Cómo descargar e instalar 1.19.60 APK Minecraft en su dispositivo Android</h2>
- <p>Si desea jugar 1.19.60 APK Minecraft en su dispositivo Android, usted tiene dos opciones: se puede descargar desde la Google Play Store, o se puede instalar manualmente desde un archivo APK. </p>
- <h3>Descargar de Google Play Store</h3>
- <p>La forma más fácil de conseguir 1.19.60 APK Minecraft en su dispositivo Android es descargarlo desde la Google Play Store, donde está disponible oficialmente. Solo tienes que seguir estos pasos:</p>
- <ol>
-<li>Abra la aplicación Google Play Store en su dispositivo Android. </li>
-<li>Buscar "Minecraft" o toque en este enlace: </li>
-<li>Toque en el botón "Instalar" y espere a que termine la descarga. </li>
-<li>Iniciar el juego desde el cajón de la aplicación o la pantalla de inicio. </li>
-</ol>
- <p>Felicidades, que ha instalado con éxito 1.19.60 APK Minecraft en su dispositivo Android! </p>
- <h3>Instalar desde el archivo APK</h3>
-
- <p>Sin embargo, tenga cuidado al instalar archivos APK de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. Solo descargar archivos APK de sitios web de buena reputación, como APK Mirror, que supervisa los archivos que aloja para verificar que son seguros y auténticos. </p>
- <p>Para instalar 1.19.60 APK Minecraft desde un archivo APK, siga estos pasos:</p>
- <ol>
-<li>Permitir aplicaciones desconocidas en su dispositivo Android yendo a Configuración > Aplicaciones y notificaciones > Acceso especial > Instalar aplicaciones desconocidas > Chrome (o cualquier navegador que utilice) > Permitir desde esta fuente. </li>
-<li>Descargue una aplicación de administrador de archivos, como Cx File Explorer o Administrador de archivos, para que pueda encontrar el archivo APK después de descargarlo en su dispositivo. </li>
-<li>Descargar el archivo 1.19.60 APK Minecraft desde un sitio web como APK Mirror usando su navegador. </li>
-<li>Abra su aplicación de administrador de archivos y busque el archivo APK descargado en su carpeta de descargas. </li>
-<li>Toque en el archivo APK y toque "Instalar" cuando se le solicite. </li>
-<li>Iniciar el juego desde el cajón de la aplicación o la pantalla de inicio. </li>
-</ol>
- <p>Felicidades, que ha instalado con éxito 1.19.60 APK Minecraft en su dispositivo Android! </p>
-<p></p>
- <h2>¿Cuáles son las nuevas características y artículos en 1.19.60 APK Minecraft</h2>
- <p>Ahora que ha instalado 1.19.60 APK Minecraft en su dispositivo Android, es posible que se pregunte cuáles son las nuevas características y elementos en esta versión del juego. </p>
- <p>Bueno, hay muchos cambios y mejoras en esta actualización, pero aquí están algunos de los más notables:</p>
- <h3>Nuevas turbas: camello y rastreador</h3>
- <h3>Nuevos bloques y artículos: conjuntos de bloques carmesí y deformado, fogatas, bambú y Sculk Shrieker</h3>
- <p>La actualización 1.19.60 APK Minecraft también añadió algunos nuevos bloques y elementos para el juego, sobre todo con fines estéticos y decorativos. Estos son algunos de ellos:</p>
- <ul>
-
-<li>Fogatas: Son bloques que se pueden usar como fuente de señales de luz y humo. También se pueden usar para cocinar alimentos sin usar combustible. Las fogatas ya no prenden fuego a jugadores y turbas, pero aún así infligen daño si se tocan. Tampoco destruyen los Minecarts y los Barcos. </li>
-<li>Bambú: Estas son plantas que se pueden encontrar en selvas y bosques de bambú. Se pueden usar para crear andamios, palos o combustible. La colocación de plantas de bambú ahora se comporta de la misma manera que Java Edition; ya no crecerá haciendo clic en el lado de una planta de bambú con un artículo de bambú en la mano. </li>
-<li>Sculk Shrieker: Este es un nuevo bloque que se puede encontrar en el bioma Deep Dark. Es una variante del sensor Sculk que emite un fuerte sonido de chillido cuando se activa por vibraciones. El sonido chillón ahora se puede escuchar a una distancia más larga de 32 bloques. </li>
-</ul>
- <h2>Cómo Jugar 1.19.60 APK Minecraft con Amigos y Multiplataforma</h2>
- <p>Una de las mejores características de Minecraft Bedrock Edition es que permite el juego multiplataforma con otros dispositivos que ejecutan la misma versión del juego. Esto significa que usted puede jugar 1.19.60 APK Minecraft con tus amigos que tienen Windows 10 PC, consolas Xbox One, consolas Nintendo Switch, dispositivos iOS, u otros dispositivos Android. </p>
- <p>Hay dos maneras de jugar 1.19.60 APK Minecraft con amigos y multiplataforma: unirse a un servidor multijugador o crear un mundo multijugador. </p>
- <h3>Unirse a un servidor multijugador</h3>
- <p>Un servidor multijugador es un mundo en línea alojado que puede acomodar a muchos jugadores a la vez. Hay muchos servidores disponibles para Minecraft Bedrock Edition, con diferentes modos de juego, reglas y temas. </p>
- <p>Para unirse a un servidor multijugador, siga estos pasos:</p>
- <ol>
-<li>Lanzamiento 1.19.60 APK Minecraft en su dispositivo Android. </li>
-<li>Toque en "Reproducir" desde el menú principal. </li>
-<li>Toque en "Servidores" desde el menú superior. </li>
-<li>Elija uno de los servidores destacados o toque en "Agregar servidor" para ingresar una dirección de servidor personalizada. </li>
-
-</ol>
- <p>Disfruta jugando 1.19.60 APK Minecraft con otros jugadores de todo el mundo! </p>
- <h3>Crear un mundo multijugador</h3>
- <p>Un mundo multijugador es un mundo local o online que creas e invitas a tus amigos a unirse. Puedes personalizar la configuración de tu mundo, como el modo de juego, dificultad, trucos, etc.</p>
- <p>Para crear un mundo multijugador, sigue estos pasos:</p>
- <ol>
-<li>Lanzamiento 1.19.60 APK Minecraft en su dispositivo Android. </li>
-<li>Toque en "Reproducir" desde el menú principal. </li>
-<li>Toque en "Crear nuevo" desde el menú superior. </li>
-<li>Elige "Crear Nuevo Mundo" o "Crear Nuevo Reino". Un reino es un mundo en línea que siempre está disponible para que usted y sus amigos se unan, pero requiere una cuota de suscripción. </li>
-<li>Nombra tu mundo y ajusta tus ajustes como quieras. </li>
-<li>Asegúrese de habilitar "Juego multijugador" y "Visible para jugadores LAN" si desea que sus amigos se unan a su mundo. </li>
-<li>Toque en "Crear" para iniciar su mundo. </li>
-<li>Para invitar a tus amigos a unirse a tu mundo, toca "Pausa" en el menú del juego y luego toca "Invitar al juego". Puedes invitar a amigos que estén en línea o cerca usando Xbox Live o LAN.</li>
-</ol> <p>Disfruta jugando 1.19.60 APK Minecraft con tus amigos y multiplataforma! </p>
- <h2>Conclusión: Resumen y Recomendaciones</h2>
- <p>En este artículo, le hemos mostrado cómo descargar y jugar 1.19.60 APK Minecraft en su dispositivo Android, ¿cuáles son las nuevas características y elementos en esta versión, y cómo jugar con tus amigos y multiplataforma. </p>
- <p>Esperamos que haya encontrado este artículo útil e informativo, y que haya aprendido algo nuevo sobre 1.19.60 APK Minecraft.</p>
- <p>Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación o contáctenos a través de nuestro sitio web. </p>
- <p>Gracias por leer y jugar feliz! </p>
- <h2>Preguntas frecuentes: Cinco preguntas y respuestas comunes sobre 1.19.60 APK Minecraft</h2>
-
- <h3>Q: ¿Es 1.19.60 APK Minecraft gratis para descargar y jugar? </h3>
- <p>A: No, 1.19.60 APK Minecraft no es gratis para descargar y jugar. Es necesario comprar el juego de la Google Play Store o de otra fuente oficial. Sin embargo, una vez que compres el juego, puedes jugarlo todo lo que quieras sin ningún cargo o suscripción adicional. </p>
- <h3>Q: ¿Es 1.19.60 APK Minecraft compatible con mi dispositivo Android? </h3>
- <p>A: 1.19.60 APK Minecraft es compatible con la mayoría de los dispositivos Android que ejecutan Android 4.2 o superior, tienen al menos 2 GB de RAM, y el apoyo OpenGL ES 2.0 o superior. Sin embargo, algunos dispositivos pueden tener problemas de rendimiento o errores dependiendo de sus especificaciones y configuraciones. </p>
- <h3>Q: ¿Cómo puedo actualizar 1.19.60 APK Minecraft a la última versión? </h3>
- <p>A: Si ha descargado 1.19.60 APK Minecraft desde la Google Play Store, se puede actualizar de forma automática o manual a través de la tienda de aplicaciones. Si ha instalado 1.19.60 APK Minecraft desde un archivo APK, tendrá que descargar e instalar el último archivo APK de un sitio web como APK Mirror cada vez que hay una nueva actualización disponible. </p>
- <h3>Q: ¿Cómo hago copia de seguridad y restaurar mi 1.19.60 datos del mundo APK Minecraft? </h3>
- <p>A: Si desea copia de seguridad y restaurar los datos 1.19.60 APK Minecraft mundo, tendrá que utilizar una aplicación de administrador de archivos para acceder a la carpeta del juego en el almacenamiento del dispositivo o almacenamiento externo. La carpeta del juego generalmente se encuentra en /storage/emulated/0/games/com.mojang/minecraftWorlds/. Puede copiar la carpeta a otra ubicación o dispositivo para hacer una copia de seguridad, o pegarla de nuevo para restaurar los datos del mundo. </p>
- <h3>Q: ¿Cómo informo de un error o un problema con 1.19.60 APK Minecraft? </h3>
- <p>A: Si se encuentra con un error o un problema con 1.19.60 APK Minecraft, puede informar a los desarrolladores a través de la página web oficial de retroalimentación, donde también puede encontrar soluciones y sugerencias de otros jugadores. </p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Apk4fun.md

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-<h1>APK4Fun: Una manera divertida de descargar aplicaciones para Android</h1> | | <p> ¿Está buscando una manera divertida y fácil de descargar aplicaciones para Android? ¿Quieres probar diferentes versiones de tus aplicaciones favoritas? ¿Quieres explorar nuevas y emocionantes aplicaciones que no puedes encontrar en Google Play Store? Si respondiste sí a cualquiera de estas preguntas, entonces deberías revisar APK4Fun! </p> | | <h2> ¿Qué es APK4Fun? </h2> | | <p>APK4Fun es un sitio web que proporciona archivos APK libres y seguros para los usuarios de Android. Los archivos APK son los archivos de instalación para aplicaciones Android que puedes descargar e instalar en tu dispositivo manualmente. APK4Fun tiene una gran colección de archivos APK para varias aplicaciones, incluyendo juegos, redes sociales, productividad, entretenimiento y más. Puede navegar y descargar archivos APK de APK4Fun fácil y rápidamente. </p>
-<h2>apk4fun</h2><br /><p><b><b>Download</b> &#10003;&#10003;&#10003; <a href="https://bltlly.com/2v6Lwn">https://bltlly.com/2v6Lwn</a></b></p><br /><br /> | <p>Para usar APK4Fun, todo lo que necesitas es un navegador web y una conexión a Internet. Puede visitar el sitio web desde su ordenador o su dispositivo Android. Puede buscar la aplicación que desee escribiendo su nombre en el cuadro de búsqueda o navegando por diferentes categorías. Una vez que encuentre la aplicación que desea, puede hacer clic en ella para ver más detalles, como su descripción, capturas de pantalla, calificaciones, revisiones e historial de versiones. También puede ver el tamaño y la compatibilidad de la aplicación antes de descargarla. Para descargar la aplicación, solo tienes que hacer clic en el botón de descarga y esperar unos segundos. </p> | | <h2> ¿Por qué usar APK4Fun? </h2> | | <p>Es posible que se pregunte por qué debe utilizar APK4Fun en lugar de otras fuentes para descargar aplicaciones Android. Bueno, hay <p>algunas de las razones por las que deberías usar APK4Fun:</p>
-<ul>
-<li>Puede acceder a aplicaciones que no están disponibles en Google Play Store. Algunas aplicaciones pueden estar restringidas en su región, eliminadas por el desarrollador o prohibidas por Google por varias razones. Con APK4Fun, puedes descargar e instalar estas aplicaciones sin problemas. </li>
-
-<li>Usted puede disfrutar de aplicaciones gratuitas y premium. Algunas aplicaciones pueden requerir que usted pague una cuota o suscribirse a un servicio para desbloquear sus características completas. Con APK4Fun, puede descargar e instalar estas aplicaciones de forma gratuita y disfrutar de sus características premium sin gastar un centavo. </li>
-<li>Usted puede estar seguro de la seguridad de APK4Fun. APK4Fun es una fuente confiable y confiable para descargar archivos APK. Todos los archivos APK en APK4Fun son escaneados y verificados por el software antivirus para asegurarse de que están libres de malware y virus. También puedes leer los comentarios y valoraciones de otros usuarios para ver sus comentarios sobre las aplicaciones. </li>
-</ul>
-<h2>¿Cómo instalar archivos APK desde APK4Fun? </h2>
-<p>Instalar archivos APK desde APK4Fun es fácil y simple. Sin embargo, antes de hacer eso, es necesario asegurarse de que su dispositivo Android permite la instalación de aplicaciones de fuentes desconocidas. Para hacerlo, debes seguir estos pasos:</p>
-<ol>
-<li>Ir a la configuración de su dispositivo y toque en la seguridad o la privacidad. </li>
-<li>Encuentra la opción que dice "Fuentes desconocidas" o "Instalar aplicaciones desconocidas" y activarlo. </li>
-<li>Es posible que vea un mensaje de advertencia que dice que la instalación de aplicaciones de fuentes desconocidas podría dañar su dispositivo. Toque en OK o Permitir proceder. </li>
-</ol>
-<p>Una vez que haya habilitado la instalación de aplicaciones de fuentes desconocidas, puede seguir estos pasos para instalar archivos APK de APK4Fun:</p>
-<ol>
-<li>Descargar el archivo APK de APK4Fun usando su navegador web. </li>
-<li>Una vez completada la descarga, toque en la notificación o vaya a su carpeta de descargas y encuentre el archivo APK. </li>
-<li>Toque en el archivo APK y siga las instrucciones en la pantalla para instalarlo. </li>
-<li>Es posible que vea un mensaje que dice "¿Desea instalar esta aplicación?" Toque en Instalar o Sí para confirmar. </li>
-<li>Espere unos segundos hasta que se complete la instalación. Puede ver un mensaje que dice "App instalado" o "Hecho". Toca Abrir o Iniciar para comenzar a usar la aplicación. </li>
-</ol>
-
- | Método | Pros | Contras | | -------- | ----- - ---- | | Navegador web | Fácil y rápido | Requiere conexión a Internet | | | Administrador de archivos | Puede administrar y organizar archivos APK | Requiere la instalación de otra aplicación | | | Cable USB | Puede transferir archivos APK desde el ordenador | Requiere la conexión de dispositivo a la computadora | Tarjeta SD | Puede transferir archivos APK almacenar muchos archivos APK | Requiere insertar y quitar la tarjeta SD | <h2>Cómo encontrar versiones antiguas de aplicaciones Android en APK4Fun? </h2>
-<p>A veces, es posible que desee instalar versiones antiguas de aplicaciones de Android por varias razones. Por ejemplo, puede preferir el diseño o la funcionalidad de una versión antigua a una nueva. O, es posible que tenga un dispositivo más antiguo que no es compatible con la última versión de una aplicación. O, es posible que desee evitar errores o problemas técnicos que están presentes en una nueva versión de una aplicación. </p>
-<p>Cualquiera que sea su razón es, puede encontrar versiones antiguas de aplicaciones Android en APK4Fun fácilmente. Aquí es cómo:</p>
-<p></p>
-<ol>
-<li> Buscar la aplicación que desea en APK4Fun utilizando el cuadro de búsqueda o navegar por las categorías. </li>
-<li>Haga clic en la aplicación para ver su página de detalles. </li>
-<li>Desplácese hacia abajo hasta que vea una sección que diga "Versiones antiguas". </li>
-<li> Verá una lista de versiones antiguas de la aplicación con sus fechas de lanzamiento, tamaños e información de compatibilidad. </li>
-<li> Seleccione la versión que desea y haga clic en el botón de descarga junto a ella. </li>
-<li>Siga los mismos pasos que arriba para instalar la versión anterior de la aplicación en su dispositivo. </li>
-</ol>
- <h2>¿Cómo explorar diferentes categorías de aplicaciones para Android en APK4Fun? </h2>
- <p>Si estás buscando aplicaciones nuevas y emocionantes para probar en tu dispositivo Android, puedes explorar diferentes categorías de aplicaciones Android en APK4Fun. APK4Fun tiene una amplia gama de categorías para que usted elija, tales como acción, aventura, árcade, tablero, tarjeta, casino, casual, educativo, música, rompecabezas, carreras, juegos de rol, simulación, deportes, estrategia, trivia, palabra y más. Puedes encontrar aplicaciones que se adapten a tus intereses y preferencias fácilmente. </p>
-
-<ol>
-<li>Vaya al sitio web APK4Fun usando su navegador web. </li>
-<li>En la página principal, verá una barra de menú con diferentes opciones. Haga clic en la opción que dice "Categorías". </li>
-<li>Verá una lista de categorías con iconos y nombres. Puede desplazarse hacia abajo para ver más categorías. </li>
-<li>Seleccione la categoría que desea explorar y haga clic en ella. </li>
-<li>Verá una página con las aplicaciones que pertenecen a esa categoría. Puede ordenar las aplicaciones por popularidad, calificación, fecha o nombre. También puede filtrar las aplicaciones por compatibilidad o tamaño. </li>
-<li>Haga clic en la aplicación que desea ver más detalles o descargarla. </li>
-</ol>
- <h2>Conclusión</h2>
-<p>APK4Fun es una forma divertida y fácil de descargar aplicaciones para Android. Puede acceder a aplicaciones que no están disponibles en Google Play Store, probar diferentes versiones de aplicaciones, disfrutar de aplicaciones gratuitas y premium, y estar seguro de la seguridad de APK4Fun. También puede instalar archivos APK de APK4Fun fácil y rápidamente usando su navegador web u otros métodos. También puedes encontrar versiones antiguas de aplicaciones Android en APK4Fun y explorar diferentes categorías de aplicaciones Android en APK4Fun. APK4Fun es una gran fuente para los usuarios de Android que quieren tener más diversión y variedad con sus aplicaciones. </p>
-<p>Si estás interesado en APK4Fun y quieres probarlo, puedes visitar su sitio web en https://www.apk4fun.com/ y comenzar a descargar tus aplicaciones favoritas. Seguramente tendrás una experiencia divertida y agradable con APK4Fun! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre APK4Fun y sus respuestas:</p>
-<ul>
-<li><b>Q: ¿APK4Fun es legal? </b></li>
-<li>A: Sí, APK4Fun es legal siempre y cuando lo utilice para fines personales y no comerciales. APK4Fun no aloja ningún contenido pirata o ilegal en su sitio web. Todos los archivos APK en APK4Fun son proporcionados por los propios desarrolladores o usuarios. </li>
-<li><b>Q: ¿Es seguro APK4Fun? </b></li>
-
-<li><b>Q: ¿Cuáles son las ventajas de usar archivos APK sobre Google Play Store? </b></li>
-<li>A: Algunas de las ventajas de usar archivos APK sobre Google Play Store son:</li>
-<ul>
-<li>Puede acceder a aplicaciones que no están disponibles en Google Play Store debido a restricciones regionales, eliminaciones de desarrolladores o prohibiciones de Google. </li>
-<li> Puede probar diferentes versiones de aplicaciones y compararlas para ver cuál le conviene más. </li>
-<li> Puede disfrutar de aplicaciones gratuitas y premium sin pagar tarifas o suscripciones. </li>
-<li> Puede instalar aplicaciones más rápido y más fácil sin ningún registro o verificación. </li>
-</ul>
-<li><b>Q: ¿Cuáles son las desventajas de usar archivos APK sobre Google Play Store? </b></li>
-<li>A: Algunas de las desventajas de usar archivos APK sobre Google Play Store son:</li>
-<ul>
-<li>Es posible que no reciba actualizaciones automáticas de las aplicaciones a menos que las revise manualmente. </li>
-<li>Es posible que no obtenga soporte técnico o servicio al cliente de los desarrolladores o Google.</li>
-<li>Es posible que encuentre problemas de compatibilidad o errores con algunas aplicaciones en función del modelo de dispositivo o la versión de Android. </li>
-</ul>
-<li><b>Q: ¿Cómo puedo actualizar mis aplicaciones desde APK4Fun? </b></li>
-<li>A: Para actualizar tus aplicaciones desde APK4Fun, puedes seguir estos pasos:</li>
-<ol>
-<li>Ir a la página de detalles de la aplicación en APK4Fun y comprobar si hay una versión más reciente disponible. </li>
-<li>Si hay una versión más reciente disponible, haga clic en el botón de descarga y descargue el último archivo APK. </li>
-<li>Instalar el último archivo APK sobre la aplicación existente en su dispositivo. No es necesario desinstalar la aplicación anterior primero. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
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spaces/Benson/text-generation/Examples/Cricket League Apk Download Uptodown.md

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-<br />
-<h1>Liga de cricket APK Descargar Uptodown: Cómo jugar el mejor juego de cricket en línea gratis</h1>
-<p>Si usted es un fan del cricket, es posible que esté buscando una manera de jugar su deporte favorito en su dispositivo móvil. Hay muchos juegos de cricket disponibles en las tiendas de aplicaciones, pero no todos ellos valen su tiempo y dinero. Algunos son demasiado complicados, algunos son demasiado fáciles, algunos son demasiado aburridos, y algunos son demasiado caros. </p>
-<h2>cricket league apk download uptodown</h2><br /><p><b><b>Download</b> &#10042; <a href="https://bltlly.com/2v6IT1">https://bltlly.com/2v6IT1</a></b></p><br /><br />
-<p>Pero hay un juego de cricket que se destaca del resto: Cricket League APK. Este es un juego de cricket en línea gratuito que le permite experimentar la emoción y la emoción de jugar al cricket en 3D. Puedes crear tu propio equipo, competir con otros jugadores, ganar partidos y ganar monedas. También puedes personalizar tus jugadores, mejorar tus habilidades y usar potenciadores para mejorar tu rendimiento. </p>
-<p>Pero ¿cómo se puede descargar Cricket League APK en su dispositivo? Y cómo se puede jugar como un profesional? En este artículo, responderemos estas preguntas y más. Le diremos qué es Cricket League APK, qué características tiene, cómo descargarlo de Uptodown, por qué debe jugar, y algunos consejos y trucos para jugarlo. Así que, vamos a empezar! </p>
- <h2>¿Qué es la Liga de Cricket APK? </h2>
-<p>Cricket League APK es un juego de cricket en línea desarrollado por Miniclip, uno de los principales desarrolladores de juegos casuales. Está disponible para dispositivos Android y se puede descargar de forma gratuita desde Uptodown, un popular sitio web que ofrece descargas seguras de aplicaciones y juegos. </p>
-<p></p>
-<p>Cricket League APK es un juego que le permite jugar al críquet de una manera realista e inmersiva. Puedes elegir entre diferentes modos, como Quick Match, Tournament o Career. También puedes seleccionar entre diferentes niveles de dificultad, como Fácil, Medio o Difícil. Puedes jugar como uno de los 12 equipos internacionales o crear tu propio equipo con nombres y logotipos personalizados. </p>
- <h3>Características de la Liga de Cricket APK</h3>
-
-<ul>
-<li><b>Controles de bateo y bolos fáciles de aprender:</b> Puedes deslizar el dedo sobre la pantalla para golpear la pelota o lanzarla. También puede ajustar la dirección, velocidad y giro de la bola. </li>
-<li><b>Gana partidas para conseguir monedas y construir tu equipo de ensueño:</b> Puedes ganar monedas ganando partidas o completando logros. Puedes usar estas monedas para comprar nuevos jugadores, equipos o potenciadores. </li>
-<li><b>Juega con tus amigos y familiares:</b> Puedes invitar a tus amigos o familiares a unirse a ti en una partida multijugador. También puedes chatear con ellos durante el juego. </li>
-<li><b>Crea tu equipo y encabeza las ligas:</b> Puedes crear tu propio equipo con nombres personalizados, logotipos, uniformes y estadísticas. También puedes competir con otros equipos en diferentes ligas y torneos. </li>
-</ul>
- <h3>Cómo descargar Cricket League APK de Uptodown</h3>
-<p>Si desea descargar Cricket League APK en su dispositivo, puede seguir estos sencillos pasos:</p>
-<ol>
-<li>Ir a [Uptodown]( 1 ) sitio web en su navegador. </li>
-<li>Escriba "Cricket League" en el cuadro de búsqueda y pulse enter. </li>
-<li>Seleccionar "Cricket League APK (Juego para Android) - Descarga gratuita - APKCombo" de los resultados. </li>
-<li>Haga clic en el botón "Descargar" y espere a que el archivo se descargue. </li>
-<li <li>Abre el archivo descargado e instálalo en tu dispositivo. Es posible que necesite habilitar la opción "Fuentes desconocidas" en su configuración para permitir la instalación. </li>
-<li> Iniciar el juego y disfrutar de jugar Cricket League APK! </li>
-</ol>
- <h2>¿Por qué usted debe jugar Cricket League APK</h2>
-<p>Cricket League APK no es solo otro juego de cricket. Es un juego que le ofrece un montón de diversión, desafío y satisfacción. Aquí hay algunas razones por las que debe jugar Cricket League APK:</p>
- <h3>Disfruta de gráficos y animaciones realistas en 3D</h3>
-
- <h3>Compite con otros jugadores en modo multijugador</h3>
-<p>Cricket League APK no es solo un juego en solitario. También puede jugar con otros jugadores de todo el mundo en el modo multijugador. Puedes unirte a un partido o crear tu propio partido e invitar a tus amigos o familiares. También puedes chatear con ellos durante el juego y compartir tus puntuaciones y logros. </p>
- <h3>Personaliza tu equipo y jugadores</h3>
-<p>Cricket League APK le da la libertad de crear su propio equipo y jugadores. Puede elegir entre diferentes países, nombres, logotipos, uniformes y estadísticas. También puedes comprar nuevos jugadores, equipos o potenciadores con las monedas que ganes. Puedes hacer que tu equipo sea tan fuerte y único como quieras. </p>
- <h3>Ganar partidos y trofeos</h3>
-<p>Cricket League APK no es solo un juego para la diversión. También es un juego para la gloria. Puedes ganar partidos y trofeos jugando bien y derrotando a tus oponentes. También puede competir en diferentes ligas y torneos y subir las tablas de clasificación. Puedes mostrar tus habilidades y logros a tus amigos y otros jugadores. </p>
- <h2>Consejos y trucos para jugar Cricket League APK</h2>
-<p>Cricket League APK es un juego que requiere habilidad, estrategia y suerte. Si quieres jugar como un profesional, necesitas saber algunos consejos y trucos que pueden ayudarte a mejorar tu rendimiento. Estos son algunos de ellos:</p>
- <h3>Elige el nivel de dificultad adecuado</h3>
-<p>Cricket League APK tiene tres niveles de dificultad: Fácil, Medio, y duro. Usted debe elegir el que se adapte a su nivel de habilidad y preferencia. Si usted es un principiante, usted debe comenzar con el modo fácil para aprender los fundamentos del juego. Si eres un jugador intermedio, deberías probar el modo Medio para desafiarte. Si eres un jugador experto, deberías ir al modo Difícil para probar tus límites. </p>
- <h3>Domina los controles de bateo y bolos</h3>
-
- <h3>Usa potenciadores y potenciadores</h3>
-<p>Cricket League APK tiene varios potenciadores y potenciadores que pueden ayudarle a mejorar su rendimiento. Puedes comprarlos con monedas o recibirlos gratis viendo anuncios o completando logros. Algunos de estos potenciadores y potenciadores son:</p>
-<ul>
-<li><b>Poder de bateo:</b> Esto aumenta tu poder de bateo y te ayuda a alcanzar más límites. </li>
-<li><b>Potencia de los bolos:</b> Esto aumenta tu precisión de los bolos y te ayuda a abrir más wickets. </li>
-<li><b>Booster de monedas:</b> Esto duplica las monedas que ganas con cada partido. </li>
-<li><b>Refuerzo de habilidad:</b> Esto aumenta tu nivel de habilidad por un tiempo limitado. </li>
-</ul>
-<p>Deberías usar estos potenciadores y potenciadores sabiamente y estratégicamente para obtener los mejores resultados. </p>
- <h3>Mejora tus habilidades y equipo</h3>
-<p>Cricket League APK le permite actualizar sus habilidades y equipos con las monedas que gana. Puedes mejorar tus habilidades de bateo, bolos, fildeo, resistencia, velocidad y agilidad. También puede actualizar su bate, pelota, guantes, almohadillas, casco, zapatos y equipo de camisa. Estas mejoras pueden mejorar tu rendimiento y darte ventaja sobre tus oponentes. </p>
- <h2>Conclusión</h2>
-<p>Cricket League APK es un juego que todo amante del cricket debe probar. Es un juego que ofrece gráficos realistas en 3D, modo multijugador, opciones de personalización, recompensas de partido, y más. Es un juego que te permite jugar al críquet en cualquier momento, conexión, como las ligas, los torneos o el chat. Tampoco podrás guardar tu progreso o ganar monedas sin conexión. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/markers.py

@@ -1,152 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2012-2017 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-"""
-Parser for the environment markers micro-language defined in PEP 508.
-"""
-
-# Note: In PEP 345, the micro-language was Python compatible, so the ast
-# module could be used to parse it. However, PEP 508 introduced operators such
-# as ~= and === which aren't in Python, necessitating a different approach.
-
-import os
-import re
-import sys
-import platform
-
-from .compat import string_types
-from .util import in_venv, parse_marker
-from .version import NormalizedVersion as NV
-
-__all__ = ['interpret']
-
-_VERSION_PATTERN = re.compile(r'((\d+(\.\d+)*\w*)|\'(\d+(\.\d+)*\w*)\'|\"(\d+(\.\d+)*\w*)\")')
-
-def _is_literal(o):
-    if not isinstance(o, string_types) or not o:
-        return False
-    return o[0] in '\'"'
-
-def _get_versions(s):
-    result = []
-    for m in _VERSION_PATTERN.finditer(s):
-        result.append(NV(m.groups()[0]))
-    return set(result)
-
-class Evaluator(object):
-    """
-    This class is used to evaluate marker expessions.
-    """
-
-    operations = {
-        '==': lambda x, y: x == y,
-        '===': lambda x, y: x == y,
-        '~=': lambda x, y: x == y or x > y,
-        '!=': lambda x, y: x != y,
-        '<':  lambda x, y: x < y,
-        '<=':  lambda x, y: x == y or x < y,
-        '>':  lambda x, y: x > y,
-        '>=':  lambda x, y: x == y or x > y,
-        'and': lambda x, y: x and y,
-        'or': lambda x, y: x or y,
-        'in': lambda x, y: x in y,
-        'not in': lambda x, y: x not in y,
-    }
-
-    def evaluate(self, expr, context):
-        """
-        Evaluate a marker expression returned by the :func:`parse_requirement`
-        function in the specified context.
-        """
-        if isinstance(expr, string_types):
-            if expr[0] in '\'"':
-                result = expr[1:-1]
-            else:
-                if expr not in context:
-                    raise SyntaxError('unknown variable: %s' % expr)
-                result = context[expr]
-        else:
-            assert isinstance(expr, dict)
-            op = expr['op']
-            if op not in self.operations:
-                raise NotImplementedError('op not implemented: %s' % op)
-            elhs = expr['lhs']
-            erhs = expr['rhs']
-            if _is_literal(expr['lhs']) and _is_literal(expr['rhs']):
-                raise SyntaxError('invalid comparison: %s %s %s' % (elhs, op, erhs))
-
-            lhs = self.evaluate(elhs, context)
-            rhs = self.evaluate(erhs, context)
-            if ((elhs == 'python_version' or erhs == 'python_version') and
-                op in ('<', '<=', '>', '>=', '===', '==', '!=', '~=')):
-                lhs = NV(lhs)
-                rhs = NV(rhs)
-            elif elhs == 'python_version' and op in ('in', 'not in'):
-                lhs = NV(lhs)
-                rhs = _get_versions(rhs)
-            result = self.operations[op](lhs, rhs)
-        return result
-
-_DIGITS = re.compile(r'\d+\.\d+')
-
-def default_context():
-    def format_full_version(info):
-        version = '%s.%s.%s' % (info.major, info.minor, info.micro)
-        kind = info.releaselevel
-        if kind != 'final':
-            version += kind[0] + str(info.serial)
-        return version
-
-    if hasattr(sys, 'implementation'):
-        implementation_version = format_full_version(sys.implementation.version)
-        implementation_name = sys.implementation.name
-    else:
-        implementation_version = '0'
-        implementation_name = ''
-
-    ppv = platform.python_version()
-    m = _DIGITS.match(ppv)
-    pv = m.group(0)
-    result = {
-        'implementation_name': implementation_name,
-        'implementation_version': implementation_version,
-        'os_name': os.name,
-        'platform_machine': platform.machine(),
-        'platform_python_implementation': platform.python_implementation(),
-        'platform_release': platform.release(),
-        'platform_system': platform.system(),
-        'platform_version': platform.version(),
-        'platform_in_venv': str(in_venv()),
-        'python_full_version': ppv,
-        'python_version': pv,
-        'sys_platform': sys.platform,
-    }
-    return result
-
-DEFAULT_CONTEXT = default_context()
-del default_context
-
-evaluator = Evaluator()
-
-def interpret(marker, execution_context=None):
-    """
-    Interpret a marker and return a result depending on environment.
-
-    :param marker: The marker to interpret.
-    :type marker: str
-    :param execution_context: The context used for name lookup.
-    :type execution_context: mapping
-    """
-    try:
-        expr, rest = parse_marker(marker)
-    except Exception as e:
-        raise SyntaxError('Unable to interpret marker syntax: %s: %s' % (marker, e))
-    if rest and rest[0] != '#':
-        raise SyntaxError('unexpected trailing data in marker: %s: %s' % (marker, rest))
-    context = dict(DEFAULT_CONTEXT)
-    if execution_context:
-        context.update(execution_context)
-    return evaluator.evaluate(expr, context)

spaces/BilalSardar/facrec/README.md

@@ -1,12 +0,0 @@
----
-title: Facrec
-emoji: 📈
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/BreadBytes1/SB-Dashboard/README.md

@@ -1,13 +0,0 @@
----
-title: SB Dashboard
-emoji: 🐢
-colorFrom: yellow
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: gpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/PointRend/point_rend/point_head.py

@@ -1,154 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.layers import ShapeSpec, cat
-from detectron2.structures import BitMasks
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.registry import Registry
-
-from .point_features import point_sample
-
-POINT_HEAD_REGISTRY = Registry("POINT_HEAD")
-POINT_HEAD_REGISTRY.__doc__ = """
-Registry for point heads, which makes prediction for a given set of per-point features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-def roi_mask_point_loss(mask_logits, instances, points_coord):
-    """
-    Compute the point-based loss for instance segmentation mask predictions.
-
-    Args:
-        mask_logits (Tensor): A tensor of shape (R, C, P) or (R, 1, P) for class-specific or
-            class-agnostic, where R is the total number of predicted masks in all images, C is the
-            number of foreground classes, and P is the number of points sampled for each mask.
-            The values are logits.
-        instances (list[Instances]): A list of N Instances, where N is the number of images
-            in the batch. These instances are in 1:1 correspondence with the `mask_logits`. So, i_th
-            elememt of the list contains R_i objects and R_1 + ... + R_N is equal to R.
-            The ground-truth labels (class, box, mask, ...) associated with each instance are stored
-            in fields.
-        points_coords (Tensor): A tensor of shape (R, P, 2), where R is the total number of
-            predicted masks and P is the number of points for each mask. The coordinates are in
-            the image pixel coordinate space, i.e. [0, H] x [0, W].
-    Returns:
-        point_loss (Tensor): A scalar tensor containing the loss.
-    """
-    assert len(instances) == 0 or isinstance(
-        instances[0].gt_masks, BitMasks
-    ), "Point head works with GT in 'bitmask' format only. Set INPUT.MASK_FORMAT to 'bitmask'."
-    with torch.no_grad():
-        cls_agnostic_mask = mask_logits.size(1) == 1
-        total_num_masks = mask_logits.size(0)
-
-        gt_classes = []
-        gt_mask_logits = []
-        idx = 0
-        for instances_per_image in instances:
-            if not cls_agnostic_mask:
-                gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64)
-                gt_classes.append(gt_classes_per_image)
-
-            gt_bit_masks = instances_per_image.gt_masks.tensor
-            h, w = instances_per_image.gt_masks.image_size
-            scale = torch.tensor([w, h], dtype=torch.float, device=gt_bit_masks.device)
-            points_coord_grid_sample_format = (
-                points_coord[idx : idx + len(instances_per_image)] / scale
-            )
-            idx += len(instances_per_image)
-            gt_mask_logits.append(
-                point_sample(
-                    gt_bit_masks.to(torch.float32).unsqueeze(1),
-                    points_coord_grid_sample_format,
-                    align_corners=False,
-                ).squeeze(1)
-            )
-        gt_mask_logits = cat(gt_mask_logits)
-
-    # torch.mean (in binary_cross_entropy_with_logits) doesn't
-    # accept empty tensors, so handle it separately
-    if gt_mask_logits.numel() == 0:
-        return mask_logits.sum() * 0
-
-    if cls_agnostic_mask:
-        mask_logits = mask_logits[:, 0]
-    else:
-        indices = torch.arange(total_num_masks)
-        gt_classes = cat(gt_classes, dim=0)
-        mask_logits = mask_logits[indices, gt_classes]
-
-    # Log the training accuracy (using gt classes and 0.0 threshold for the logits)
-    mask_accurate = (mask_logits > 0.0) == gt_mask_logits.to(dtype=torch.uint8)
-    mask_accuracy = mask_accurate.nonzero().size(0) / mask_accurate.numel()
-    get_event_storage().put_scalar("point_rend/accuracy", mask_accuracy)
-
-    point_loss = F.binary_cross_entropy_with_logits(
-        mask_logits, gt_mask_logits.to(dtype=torch.float32), reduction="mean"
-    )
-    return point_loss
-
-
-@POINT_HEAD_REGISTRY.register()
-class StandardPointHead(nn.Module):
-    """
-    A point head multi-layer perceptron which we model with conv1d layers with kernel 1. The head
-    takes both fine-grained and coarse prediction features as its input.
-    """
-
-    def __init__(self, cfg, input_shape: ShapeSpec):
-        """
-        The following attributes are parsed from config:
-            fc_dim: the output dimension of each FC layers
-            num_fc: the number of FC layers
-            coarse_pred_each_layer: if True, coarse prediction features are concatenated to each
-                layer's input
-        """
-        super(StandardPointHead, self).__init__()
-        # fmt: off
-        num_classes                 = cfg.MODEL.POINT_HEAD.NUM_CLASSES
-        fc_dim                      = cfg.MODEL.POINT_HEAD.FC_DIM
-        num_fc                      = cfg.MODEL.POINT_HEAD.NUM_FC
-        cls_agnostic_mask           = cfg.MODEL.POINT_HEAD.CLS_AGNOSTIC_MASK
-        self.coarse_pred_each_layer = cfg.MODEL.POINT_HEAD.COARSE_PRED_EACH_LAYER
-        input_channels              = input_shape.channels
-        # fmt: on
-
-        fc_dim_in = input_channels + num_classes
-        self.fc_layers = []
-        for k in range(num_fc):
-            fc = nn.Conv1d(fc_dim_in, fc_dim, kernel_size=1, stride=1, padding=0, bias=True)
-            self.add_module("fc{}".format(k + 1), fc)
-            self.fc_layers.append(fc)
-            fc_dim_in = fc_dim
-            fc_dim_in += num_classes if self.coarse_pred_each_layer else 0
-
-        num_mask_classes = 1 if cls_agnostic_mask else num_classes
-        self.predictor = nn.Conv1d(fc_dim_in, num_mask_classes, kernel_size=1, stride=1, padding=0)
-
-        for layer in self.fc_layers:
-            weight_init.c2_msra_fill(layer)
-        # use normal distribution initialization for mask prediction layer
-        nn.init.normal_(self.predictor.weight, std=0.001)
-        if self.predictor.bias is not None:
-            nn.init.constant_(self.predictor.bias, 0)
-
-    def forward(self, fine_grained_features, coarse_features):
-        x = torch.cat((fine_grained_features, coarse_features), dim=1)
-        for layer in self.fc_layers:
-            x = F.relu(layer(x))
-            if self.coarse_pred_each_layer:
-                x = cat((x, coarse_features), dim=1)
-        return self.predictor(x)
-
-
-def build_point_head(cfg, input_channels):
-    """
-    Build a point head defined by `cfg.MODEL.POINT_HEAD.NAME`.
-    """
-    head_name = cfg.MODEL.POINT_HEAD.NAME
-    return POINT_HEAD_REGISTRY.get(head_name)(cfg, input_channels)

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/README.md

@@ -1,69 +0,0 @@
-# OpenVQA
-
-<div>
-	<a href="https://openvqa.readthedocs.io/en/latest/?badge=latest"><img alt="Documentation Status" src="https://readthedocs.org/projects/openvqa/badge/?version=latest"/></a>
-	<a href="https://github.com/MILVLG"><img alt="powered-by MILVLG" src="https://img.shields.io/badge/powered%20by-MILVLG-orange.svg?style=flat&amp;colorA=E1523D&amp;colorB=007D8A"/></a>
-</div>
-
-OpenVQA is a general platform for visual question ansering (VQA) research, with implementing state-of-the-art approaches (e.g., [BUTD](https://arxiv.org/abs/1707.07998), [MFH](https://arxiv.org/abs/1708.03619), [BAN](https://arxiv.org/abs/1805.07932), [MCAN](https://arxiv.org/abs/1906.10770) and [MMNasNet](https://arxiv.org/pdf/2004.12070.pdf)) on different benchmark datasets like [VQA-v2](https://visualqa.org/), [GQA](https://cs.stanford.edu/people/dorarad/gqa/index.html) and [CLEVR](https://cs.stanford.edu/people/jcjohns/clevr/). Supports for more methods and datasets will be updated continuously.
-
-
-
-<p align="center">
-	<img src="misc/openvqa_overall.png" width="550">
-</p>
-
-
-## Documentation
-
-Getting started and learn more about OpenVQA [here](https://openvqa.readthedocs.io/en/latest/).
-
-## Benchmark and Model Zoo
-
-Supported methods and benchmark datasets are shown in the below table.
-Results and models are available in [MODEL ZOO](https://openvqa.readthedocs.io/en/latest/basic/model_zoo.html).
-
-|                                           | [VQA-v2](https://visualqa.org/) | [GQA](https://cs.stanford.edu/people/dorarad/gqa/index.html) | [CLEVR](https://cs.stanford.edu/people/jcjohns/clevr/) |
-|:-----------------------------------------:|:-------------------------------:|:------------------------------------------------------------:|:------------------------------------------------------:|
-| [BUTD](https://arxiv.org/abs/1707.07998)  | ✓                               |      ✓                                                        |                                                        |
-| [MFB](https://arxiv.org/abs/1708.01471v1) | ✓                               |                                                              |                                                        |
-| [MFH](https://arxiv.org/abs/1708.03619)   | ✓                               |                                                              |                                                        |
-| [BAN](https://arxiv.org/abs/1805.07932)   | ✓                               |       ✓                                                       |                                                        |
-| [MCAN](https://arxiv.org/abs/1906.10770)  | ✓                               | ✓                                                            | ✓                                                      |
-| [MMNasNet](https://arxiv.org/pdf/2004.12070.pdf)  | ✓                               |                                                             |                                                       |
-
-## News & Updates
-
-#### v0.7.5 (30/12/2019)
-- Add supports and pre-trained models for the approaches on CLEVR.
-
-#### v0.7 (29/11/2019)
-- Add supports and pre-trained models for the approaches on GQA.
-- Add an document to tell developers how to add a new model to OpenVQA.
-
-#### v0.6 (18/09/2019)
-- Refactoring the documents and using Sphinx to build the whole documents. 
-
-#### v0.5 (31/07/2019)
-- Implement the basic framework for OpenVQA.
-- Add supports and pre-trained models for BUTD, MFB, MFH, BAN, MCAN on VQA-v2. 
-
-## License
-
-This project is released under the [Apache 2.0 license](LICENSE).
-
-## Contact
-
-This repo is currently maintained by Zhou Yu ([@yuzcccc](https://github.com/yuzcccc)) and Yuhao Cui ([@cuiyuhao1996](https://github.com/cuiyuhao1996)).
-
-## Citation
-
-If this repository is helpful for your research or you want to refer the provided results in the modelzoo, you could cite the work using the following BibTeX entry:
-
-```
-@misc{yu2019openvqa,
-  author = {Yu, Zhou and Cui, Yuhao and Shao, Zhenwei and Gao, Pengbing and Yu, Jun},
-  title = {OpenVQA},
-  howpublished = {\url{https://github.com/MILVLG/openvqa}},
-  year = {2019}
-}

spaces/CVPR/LIVE/pybind11/tests/test_cmake_build/main.cpp

@@ -1,6 +0,0 @@
-#include <pybind11/pybind11.h>
-namespace py = pybind11;
-
-PYBIND11_MODULE(test_cmake_build, m) {
-    m.def("add", [](int i, int j) { return i + j; });
-}

spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/data_structurization/wlasl.py

@@ -1,32 +0,0 @@
-
-import os
-import json
-import tqdm
-
-from shutil import copyfile
-
-
-MAIN_PATH = "/Users/matyasbohacek/Documents/Academics/Projects/WLASL/start_kit"
-BATCH = "train"
-
-if not os.path.exists(MAIN_PATH + "/" + BATCH + "_preprocessed/"):
-    os.mkdir(MAIN_PATH + "/" + BATCH + "_preprocessed/")
-
-with open(MAIN_PATH + "/specs.json") as f:
-  data = json.load(f)
-
-for item_index, item in tqdm.tqdm(enumerate(data)):
-
-    for video in item["instances"]:
-
-        if video["split"] != BATCH:
-            continue
-
-        if not os.path.exists(MAIN_PATH + "/" + BATCH + "_preprocessed/" + str(item_index) + "/"):
-            os.mkdir(MAIN_PATH + "/" + BATCH + "_preprocessed/" + str(item_index) + "/")
-
-        original_path = MAIN_PATH + "/videos/" + str(video["video_id"]) + ".mp4"
-        new_path = MAIN_PATH + "/" + BATCH + "_preprocessed/" + str(item_index) + "/" + str(video["video_id"]) + ".mp4"
-
-        copyfile(original_path, new_path)
-

spaces/CVPR/WALT/mmdet/models/detectors/kd_one_stage.py

@@ -1,100 +0,0 @@
-import mmcv
-import torch
-from mmcv.runner import load_checkpoint
-
-from .. import build_detector
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class KnowledgeDistillationSingleStageDetector(SingleStageDetector):
-    r"""Implementation of `Distilling the Knowledge in a Neural Network.
-    <https://arxiv.org/abs/1503.02531>`_.
-
-    Args:
-        teacher_config (str | dict): Config file path
-            or the config object of teacher model.
-        teacher_ckpt (str, optional): Checkpoint path of teacher model.
-            If left as None, the model will not load any weights.
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 teacher_config,
-                 teacher_ckpt=None,
-                 eval_teacher=True,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
-                         pretrained)
-        self.eval_teacher = eval_teacher
-        # Build teacher model
-        if isinstance(teacher_config, str):
-            teacher_config = mmcv.Config.fromfile(teacher_config)
-        self.teacher_model = build_detector(teacher_config['model'])
-        if teacher_ckpt is not None:
-            load_checkpoint(
-                self.teacher_model, teacher_ckpt, map_location='cpu')
-
-    def forward_train(self,
-                      img,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None):
-        """
-        Args:
-            img (Tensor): Input images of shape (N, C, H, W).
-                Typically these should be mean centered and std scaled.
-            img_metas (list[dict]): A List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                :class:`mmdet.datasets.pipelines.Collect`.
-            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
-                boxes can be ignored when computing the loss.
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        x = self.extract_feat(img)
-        with torch.no_grad():
-            teacher_x = self.teacher_model.extract_feat(img)
-            out_teacher = self.teacher_model.bbox_head(teacher_x)
-        losses = self.bbox_head.forward_train(x, out_teacher, img_metas,
-                                              gt_bboxes, gt_labels,
-                                              gt_bboxes_ignore)
-        return losses
-
-    def cuda(self, device=None):
-        """Since teacher_model is registered as a plain object, it is necessary
-        to put the teacher model to cuda when calling cuda function."""
-        self.teacher_model.cuda(device=device)
-        return super().cuda(device=device)
-
-    def train(self, mode=True):
-        """Set the same train mode for teacher and student model."""
-        if self.eval_teacher:
-            self.teacher_model.train(False)
-        else:
-            self.teacher_model.train(mode)
-        super().train(mode)
-
-    def __setattr__(self, name, value):
-        """Set attribute, i.e. self.name = value
-
-        This reloading prevent the teacher model from being registered as a
-        nn.Module. The teacher module is registered as a plain object, so that
-        the teacher parameters will not show up when calling
-        ``self.parameters``, ``self.modules``, ``self.children`` methods.
-        """
-        if name == 'teacher_model':
-            object.__setattr__(self, name, value)
-        else:
-            super().__setattr__(name, value)

spaces/CVPR/WALT/mmdet/models/losses/focal_loss.py

@@ -1,181 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
-
-from ..builder import LOSSES
-from .utils import weight_reduce_loss
-
-
-# This method is only for debugging
-def py_sigmoid_focal_loss(pred,
-                          target,
-                          weight=None,
-                          gamma=2.0,
-                          alpha=0.25,
-                          reduction='mean',
-                          avg_factor=None):
-    """PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the
-            number of classes
-        target (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        gamma (float, optional): The gamma for calculating the modulating
-            factor. Defaults to 2.0.
-        alpha (float, optional): A balanced form for Focal Loss.
-            Defaults to 0.25.
-        reduction (str, optional): The method used to reduce the loss into
-            a scalar. Defaults to 'mean'.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-    """
-    pred_sigmoid = pred.sigmoid()
-    target = target.type_as(pred)
-    pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
-    focal_weight = (alpha * target + (1 - alpha) *
-                    (1 - target)) * pt.pow(gamma)
-    loss = F.binary_cross_entropy_with_logits(
-        pred, target, reduction='none') * focal_weight
-    if weight is not None:
-        if weight.shape != loss.shape:
-            if weight.size(0) == loss.size(0):
-                # For most cases, weight is of shape (num_priors, ),
-                #  which means it does not have the second axis num_class
-                weight = weight.view(-1, 1)
-            else:
-                # Sometimes, weight per anchor per class is also needed. e.g.
-                #  in FSAF. But it may be flattened of shape
-                #  (num_priors x num_class, ), while loss is still of shape
-                #  (num_priors, num_class).
-                assert weight.numel() == loss.numel()
-                weight = weight.view(loss.size(0), -1)
-        assert weight.ndim == loss.ndim
-    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
-    return loss
-
-
-def sigmoid_focal_loss(pred,
-                       target,
-                       weight=None,
-                       gamma=2.0,
-                       alpha=0.25,
-                       reduction='mean',
-                       avg_factor=None):
-    r"""A warpper of cuda version `Focal Loss
-    <https://arxiv.org/abs/1708.02002>`_.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        target (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        gamma (float, optional): The gamma for calculating the modulating
-            factor. Defaults to 2.0.
-        alpha (float, optional): A balanced form for Focal Loss.
-            Defaults to 0.25.
-        reduction (str, optional): The method used to reduce the loss into
-            a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-    """
-    # Function.apply does not accept keyword arguments, so the decorator
-    # "weighted_loss" is not applicable
-    loss = _sigmoid_focal_loss(pred.contiguous(), target, gamma, alpha, None,
-                               'none')
-    if weight is not None:
-        if weight.shape != loss.shape:
-            if weight.size(0) == loss.size(0):
-                # For most cases, weight is of shape (num_priors, ),
-                #  which means it does not have the second axis num_class
-                weight = weight.view(-1, 1)
-            else:
-                # Sometimes, weight per anchor per class is also needed. e.g.
-                #  in FSAF. But it may be flattened of shape
-                #  (num_priors x num_class, ), while loss is still of shape
-                #  (num_priors, num_class).
-                assert weight.numel() == loss.numel()
-                weight = weight.view(loss.size(0), -1)
-        assert weight.ndim == loss.ndim
-    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
-    return loss
-
-
-@LOSSES.register_module()
-class FocalLoss(nn.Module):
-
-    def __init__(self,
-                 use_sigmoid=True,
-                 gamma=2.0,
-                 alpha=0.25,
-                 reduction='mean',
-                 loss_weight=1.0):
-        """`Focal Loss <https://arxiv.org/abs/1708.02002>`_
-
-        Args:
-            use_sigmoid (bool, optional): Whether to the prediction is
-                used for sigmoid or softmax. Defaults to True.
-            gamma (float, optional): The gamma for calculating the modulating
-                factor. Defaults to 2.0.
-            alpha (float, optional): A balanced form for Focal Loss.
-                Defaults to 0.25.
-            reduction (str, optional): The method used to reduce the loss into
-                a scalar. Defaults to 'mean'. Options are "none", "mean" and
-                "sum".
-            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
-        """
-        super(FocalLoss, self).__init__()
-        assert use_sigmoid is True, 'Only sigmoid focal loss supported now.'
-        self.use_sigmoid = use_sigmoid
-        self.gamma = gamma
-        self.alpha = alpha
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning label of the prediction.
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Options are "none", "mean" and "sum".
-
-        Returns:
-            torch.Tensor: The calculated loss
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.use_sigmoid:
-            if torch.cuda.is_available() and pred.is_cuda:
-                calculate_loss_func = sigmoid_focal_loss
-            else:
-                num_classes = pred.size(1)
-                target = F.one_hot(target, num_classes=num_classes + 1)
-                target = target[:, :num_classes]
-                calculate_loss_func = py_sigmoid_focal_loss
-
-            loss_cls = self.loss_weight * calculate_loss_func(
-                pred,
-                target,
-                weight,
-                gamma=self.gamma,
-                alpha=self.alpha,
-                reduction=reduction,
-                avg_factor=avg_factor)
-
-        else:
-            raise NotImplementedError
-        return loss_cls

spaces/CVPR/regionclip-demo/detectron2/structures/__init__.py

@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .boxes import Boxes, BoxMode, pairwise_iou, pairwise_ioa
-from .image_list import ImageList
-
-from .instances import Instances
-from .keypoints import Keypoints, heatmaps_to_keypoints
-from .masks import BitMasks, PolygonMasks, polygons_to_bitmask, ROIMasks
-from .rotated_boxes import RotatedBoxes
-from .rotated_boxes import pairwise_iou as pairwise_iou_rotated
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
-
-
-from detectron2.utils.env import fixup_module_metadata
-
-fixup_module_metadata(__name__, globals(), __all__)
-del fixup_module_metadata

spaces/CVPR/unicl-zero-shot-img-recog/model/__init__.py

@@ -1 +0,0 @@
-from .model import build_unicl_model as build_model

spaces/Chaitanya01/InvestingPlatform/coinbaskets.py

@@ -1,21 +0,0 @@
-# These are the list of coin baskets
-names = ["blue_chip","new_crypto_stars","defi_10",
-                "smart_contract_pf","web_3","best_exchange","nft","raging_bulls","vc_6"]
-blue_chip = dict(components = ["btc","eth","bnb","ada","xrp"],
-                weights = [50, 33.68, 6.32,5,5])
-new_crypto_stars = dict(components = ["doge","dot","uni","bch","link","ltc","sol","matic","theta","vet"],
-                        weights = [23.39,15.28,11.09,9.08,8.55,8.47,8.2,5.94,5,5])       
-defi_10 = dict(components = ["uni","luna","aave","cake","mkr","comp","rune","yfi","snx","sushi"],
-                weights = [34.21,12.66,11.04,8.89,7.54,5.66,5,5,5,5])
-smart_contract_pf = dict(components = ["eth","ada","dot","sol","etc","vet","icp"],
-                        weights =[50,17.28,6.36,11.36,5,5,5])
-web_3 = dict(components = ["link","fil","grt","stx","hnt","sc"],
-            weights = [45.38,22.67,13.75,7.74,5.46,5])
-best_exchange = dict(components = ["bnb","ftt","uni","cake","rune","sushi"],
-                    weights = [25,25,12.5,12.5,12.5,12.5])
-nft = dict(components = ["theta","axs","chz","enj","mana","sand"],
-            weights = [16.67,16.67,16.67,16.67,16.66,16.66])
-raging_bulls = dict(components = ["axs","sand","qnt","luna","flow","stx","snx","ankr","ftt","lsk"],
-                    weights = [10,10,10,10,10,10,10,10,10,2.])
-vc_6 = dict(components = ["dot","luna","near","rose","sol","keep"],
-            weights = [16.67,16.67,16.67,16.67,16.66,16.66])