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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Be2works Full Version How to Repair Laptop Batteries with Ease.md

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-<h1>Download GX Developer 8.7 Full Crack</h1>
-<p>If you are looking for a reliable and powerful software to program and control Mitsubishi PLCs, you might want to try GX Developer. This software is widely used by engineers and technicians who work with Mitsubishi controllers in various industries. In this article, we will show you how to download GX Developer 8.7 full crack, which is the latest version of this software, and how to use it effectively.</p>
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- <h2>What is GX Developer?</h2>
-<p>GX Developer is the basic controller programming environment supporting Q Process, Q, L, FX Series and legacy controllers A and AnS Series. It is part of the MELSOFT series of software products developed by Mitsubishi Electric. It allows you to create, edit, debug and transfer programs for Mitsubishi PLCs using various programming languages, such as ladder logic, structured text, function block diagram, sequential function chart and MELSAP-L.</p>
- <h3>Features and benefits of GX Developer</h3>
-<p>Some of the features and benefits of GX Developer are:</p>
-<ul>
-<li>It supports multiple controllers and communication protocols, such as RS-232C, RS-422/485, Ethernet, USB and CC-Link.</li>
-<li>It has a user-friendly interface that allows you to easily access various functions and tools.</li>
-<li>It has a powerful editor that supports syntax highlighting, auto-completion, drag-and-drop, copy-and-paste and undo-redo operations.</li>
-<li>It has a built-in simulator that allows you to test your program without connecting to a real controller.</li>
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-<li>It has a data logging function that allows you to record and analyze the data from the controller.</li>
-<li>It has a password protection function that allows you to secure your program from unauthorized access or modification.</li>
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- <h3>System requirements for GX Developer</h3>
-<p>The minimum system requirements for GX Developer are:</p>
-<table>
-<tr><td>Operating system</td><td>Windows XP/Vista/7/8/10 (32-bit or 64-bit)</td></tr>
-<tr><td>CPU</td><td>Pentium III 800 MHz or higher</td></tr>
-<tr><td>Memory</td><td>256 MB or more</td></tr>
-<tr><td>HDD</td><td>500 MB or more of free space</td></tr>
-<tr><td>Display</td><td>XGA (1024 x 768) or higher resolution</td></tr>
-<tr><td>Others</td><td>CD-ROM drive, mouse, keyboard, printer (optional)</td></tr>
-</table>
- <h2>How to download GX Developer 8.7 full crack?</h2>
-<p>To download GX Developer 8.7 full crack, you need to follow these steps:</p>
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- <h3>Step 1: Visit the official website of Mitsubishi Electric</h3>
-<p>The first step is to visit the official website of Mitsubishi Electric at <a href="https://www.mitsubishielectric.com/app/fa/download/search.do?mode=manual&kisyu=%2Fmelsoft">https://www.mitsubishielectric.com/app/fa/download/search.do?mode=manual&kisyu=%2Fmelsoft</a>. This is where you can find all the software products related to Mitsubishi controllers.</p>
- <h3>Step 2: Select the software category and GX Developer product</h3>
-<p>The next step is to select the software category and GX Developer product from the list. You can use the search box or the filters to narrow down your options. For example, you can type "GX Developer" in the search box or select "MELSOFT" as the large category and "GX series" as the small category. Then, you will see a list of manuals for different versions of GX Developer. You need to select "GX Developer Version 8 Operating Manual" as your desired product.</p>
- <h3>Step 3: Download the installation file and the crack file</h3>
-<p>The third step is to download the installation file and the crack file for GX Developer 8.7 full crack. You can click on the "Download" button next to each file name to start downloading them. The installation file is named "GXDEV807E.zip" and has a size of about 1 GB. The crack file is named "GXDEV807E_Crack.zip" and has a size of about 10 MB.</p>
- <h3>Step 4: Install GX Developer and apply the crack</h3>
-<p>The final step is to install GX Developer and apply the crack to activate it. You need to follow these sub-steps:</p>
-<ol>
-<li>Extract both zip files using a tool like WinRAR or 7-Zip.</li>
-<li>Run the setup.exe file from the extracted folder of "GXDEV807E.zip". Follow the instructions on the screen to complete the installation process.</li>
-<li>Copy all the files from the extracted folder of "GXDEV807E_Crack.zip" and paste them into the installation folder of GX Developer. The default installation folder is "C:\Program Files (x86)\MELSOFT\GX Works2". Overwrite any existing files if prompted.</li>
-<li>Run GX Developer from your desktop shortcut or start menu. You should see a message saying "License registration completed" on the bottom right corner of the screen.</li>
-<li>Congratulations! You have successfully installed GX Developer 8.7 full crack on your computer.</li>
-</ol>
- <h2>How to use GX Developer 8.7?</h2>
-<p>To use GX Developer 8.7 effectively, you need to follow these steps:</p>
- <h3>Create a new project or open an existing one</h3>
-<p>The first step is to create a new project or open an existing one in GX Developer. You can do this by clicking on the "File" menu and selecting "New Project" or "Open Project". A project is a collection of files that contain your program, settings, comments and other information related to your controller. You need to specify a project name, a controller type, a communication method and other options when creating a new project.</p>
- <h3>Configure the controller settings and communication parameters</h3>
-<p>The next step is to configure the controller settings and communication parameters in GX Developer. You can do this by clicking on the "Project" menu and selecting "Parameter". A parameter is a value that determines how your controller operates or communicates with other devices. You need to set up parameters such as input/output assignments, device comments, memory allocation, network configuration, password protection and others according to your needs.</p>
- <h3>Write, edit and debug your program using various tools and languages</h3>
-<p>The third step is to write, edit and debug your program using various tools and languages in GX Developer. You can do this by clicking on the "Program" menu and selecting "Edit". An edit window will open where you can write your program using different programming languages such as ladder logic (LD), structured text (ST), function block diagram (FBD), sequential function chart (SFC) or MELSAP-L (ML). You can also use various tools such as syntax check, cross reference, comment input/output, device monitor/editor and others to help you write your program more efficiently.</p>
- <h3>Transfer your program to your controller and monitor its operation</h3>
-<p>The final step is to transfer your program to your controller and monitor its operation in GX Developer. You can do this by clicking on the "Online" menu and selecting "Transfer To PLC" or "Transfer From PLC". A transfer window will open where you can select which files you want to transfer between your computer and your controller. You can also click on monitor window or a test window will open where you can monitor or test the operation of your controller and your program. You can use various functions such as start/stop, force on/off, data change, data trace and others to control or observe your controller and your program.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, GX Developer is a powerful and reliable software that allows you to program and control Mitsubishi PLCs. It has many features and benefits that make it easy and convenient to use. It supports various programming languages and communication protocols. It also has a built-in simulator and a data logging function. To download GX Developer 8.7 full crack, you need to visit the official website of Mitsubishi Electric, select the software category and GX Developer product, download the installation file and the crack file, install GX Developer and apply the crack. To use GX Developer 8.7 effectively, you need to create or open a project, configure the controller settings and communication parameters, write, edit and debug your program using various tools and languages, transfer your program to your controller and monitor its operation.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about GX Developer 8.7 full crack:</p>
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-<li>A: No, it is not legal to download GX Developer 8.7 full crack. It is a violation of the software license agreement and intellectual property rights of Mitsubishi Electric. You should only download GX Developer from the official website of Mitsubishi Electric and pay for the license fee.</li>
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-<li>A: You can update GX Developer 8.7 by visiting the official website of Mitsubishi Electric and downloading the latest version of GX Developer or the update patch file. You should uninstall the previous version of GX Developer before installing the new one.</li>
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-<li>A: You can get technical support for GX Developer 8.7 by contacting Mitsubishi Electric or its authorized distributors or service providers in your region. You can also refer to the online help system or the user manual of GX Developer for more information.</li>
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-<h1>Boris FX Optics Review: A Powerful Plugin for Photo Editing</h1>
-<p>Boris FX Optics is a plugin that allows you to apply cinematic effects and filters to your photos in Photoshop, Lightroom, or as a standalone application. It offers over 160 filters and thousands of presets that can transform your images into stunning artworks.</p>
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- <p>If you want to rip an unprotected DVD or Blu-ray disc (or an ISO image or a DVD folder) into a video file format (such as AVI or MP4), you just need to click on the +DVD button at the top left corner of the interface.</p>
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- <p>Then choose your output format from the bottom row of icons (you can also select Blu-ray if you want to create a Blu-ray disc out of your source disc).</p>
- <p>You can also edit some settings such as title selection, language selection, subtitle selection, etc by clicking on the cogwheel icon next to each format icon.</p>
- <p>To start ripping process, click on Convert button at bottom right corner of interface. You can choose where save your ripped files or open them directly after ripping.</p>
- <p>If you want burn a video file format (such as AVI or MP4) into a DVD or Blu-ray disc (or an ISO image or a DVD folder), you just need to click on +Video button at top left corner of interface. Then add your video files from your computer. Then choose DVD or Blu-ray from bottom row of icons. custom DVD menus by clicking on Menu button next each format icon. You can choose from various templates, add your own background image, title, etc. To start burning process, click on Burn button at bottom right corner of interface. You can choose where save your burned files or open them directly after burning.</p>
- <h3 style="text-align: center;">How to edit videos and add subtitles</h3>
- <p>If you want edit your videos before converting them, you can use built-in video editor by clicking on scissors icon next each video file in list. You can perform various editing tasks such as trimming, cropping, rotating, flipping, joining, etc. You can also add transitions, effects, watermarks, etc by clicking on respective buttons at top right corner of editor window. To apply changes, click OK button at bottom right corner of editor window. To cancel changes, click Cancel button at bottom left corner of editor window.</p>
- <p>If you want add subtitles your videos before converting them, you can use built-in subtitle tool by clicking on SRT icon next each video file in list. You can either import external subtitle files (SSA/SRT/ASS) from your computer or search online subtitles by clicking on respective buttons at top right corner of subtitle window. You can also adjust some settings such as font size, color, position, etc by clicking on cogwheel icon at top left corner of subtitle window. To apply changes, click OK button at bottom right corner of subtitle window. To cancel changes, click Cancel button at bottom left corner of subtitle window.</p>
- <h3 style="text-align: center;">How to upload videos online</h3>
- <p>If you want upload your videos online after converting them, you can use built-in uploader by clicking on Upload button at top right corner of interface.</p>
- <p>You can choose from various websites such as YouTube, Facebook, Vimeo, Dailymotion, etc and enter your account details and video information.</p>
- <p>You can also adjust some settings such as video quality, privacy options, tags, etc by clicking on cogwheel icon next to each website icon.</p>
- <p>To start uploading process, click on Upload button at bottom right corner of interface. You can monitor the progress and status of your uploads and open them directly after uploading.</p>
- <h2 style="text-align: center;">Conclusion</h2>
- <p>Freemake Video Converter Gold 4.1.9.16 Portable Torrent is a powerful and versatile video converter that can handle any video format, device, or platform.</p>
- <p>It is also fast and efficient thanks to its integrated CUDA and DXVA technologies that optimize the conversion process and reduce CPU usage.</p>
- <p>It also has a gold pack feature that unlocks some exclusive options such as automatic black bar removal, custom DVD menus, backup function, advanced preset editor, etc.</p>
- <p>It is also easy to use and intuitive thanks to its simple and user-friendly interface and its built-in tools for editing, adding subtitles, and uploading videos online.</p>
- <p>If you want to try out Freemake Video Converter Gold 4.1.9.16 Portable Torrent for yourself, you can download it from various websites such as <a href="https://nsaneforums.com/topic/294666-freemake-video-converter-gold-41992-portable/">nsaneforums.com</a> or <a href="https://youngworldforum.forumfree.it/?t=72642240">youngworldforum.forumfree.it</a> using a torrent client such as uTorrent or BitTorrent.</p>
- <p>You don't need to install anything on your computer or register any account. You just need to run it from any USB drive or external hard drive and start converting your videos to any format, device, or platform you want.</p>
- <p>So what are you waiting for? Download Freemake Video Converter Gold 4.1.9.16 Portable Torrent today and enjoy the best video conversion experience ever!</p>
- <h2 style="text-align: center;">FAQs</h2>
- <ul>
-<li><strong>What are the system requirements for Freemake Video Converter Gold 4.1.9.16 Portable Torrent?</strong></li>
-</ul>
- <p>The system requirements for Freemake Video Converter Gold 4.1.9.16 Portable Torrent are:</p>
- <ul>
-<li>Windows Vista/7/8/10</li>
-<li>.NET Framework 4.0 or higher</li>
-<li>256 MB or more RAM</li>
-<li>50 MB free hard disc space</li>
-<li>Stable Internet connection for video download & YouTube upload</li>
-<li>DVD-ROM drive for burning DVD</li>
-<li>BD-ROM drive for burning Blu-ray</li>
-</ul>
- <ul>
-<li><strong>What is the difference between the free version and the gold pack?</strong></li>
-</ul>
- <p>The free version of Freemake Video Converter allows you to convert video files to various formats, devices, and platforms, as well as rip and burn DVDs and Blu-rays, edit videos, add subtitles, and upload videos online.</p>
- <p>The gold pack is a set of unique features that enhance the free version with some exclusive options such as automatic black bar removal, custom DVD menus, backup function, advanced preset editor, etc.</p>
- <ul>
-<li><strong>How to activate the gold pack features?</strong></li>
-</ul>
- <p>To activate the gold pack features, you need to purchase a license key from the official website <a href="http://www.freemake.com/packs/gold_freemake_video_converter/">http://www.freemake.com/packs/gold_freemake_video_converter/</a>.</p>
- <p>Then you need to enter the license key in the program by clicking on Help -> Get Gold Pack -> I already have a key -> Enter key -> OK.</p>
- <ul>
-<li><strong>How to update Freemake Video Converter Gold 4.1.9.16 Portable Torrent?</strong></li>
-</ul>
- <p>To update Freemake Video Converter Gold 4.1.9.16 Portable Torrent, you need to download the latest version from the official website <a href="http://www.freemake.com/free_video_converter/">http://www.freemake.com/free_video_converter/</a>.</p>
- <p>Then you need to overwrite the old executable file (FreemakeVideoConverterPortable.exe) with the new one in your portable folder.</p>
- <ul>
-<li><strong>How to contact the support team or report a problem?</strong></li>
-</ul>
- <p>To contact the support team or report a problem with Freemake Video Converter Gold 4.1.9.16 Portable Torrent, you can use the following methods:</p>
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-<li>Email: [email protected]</li>
-<li>Facebook: <a href="https://www.facebook.com/Freemake/">https://www.facebook.com/Freemake/</a></li>
-<li>Twitter: <a href="https://twitter.com/Freemakecom">@Freemakecom</a></li>
-<li>Contact form: <a href="http://support.freemake.com/hc/en-us/requests/new">http://support.freemake.com/hc/en-us/requests/new</a></li>
-</ul>
- </td>
- </tr>
- </table></p> 0a6ba089eb<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Bajrangi Bhaijaan 2015 720p DvdRip.md

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-
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spaces/1gistliPinn/ChatGPT4/Examples/Biologija Pries Egzamina Knyga Pdf 44.md

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-
-<h1>Biologija Pries Egzamina Knyga Pdf 44: A Review of the Best Biology Book for Exam Preparation</h1>
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spaces/1gistliPinn/ChatGPT4/Examples/Bongiovi Acoustics DPS Audio Enhancer 2.2.0.15 Activate HOT.md

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-<p>with bongiovi audio enhancer you can drastically improve the sound of any audio source, even your headphones or laptop speakers. the processing effect is exactly the same in all applications. buttons and sliders apply boost automatically to audio input, headphones, line-in, output and the master output. usually the strongest influence is just around the 80db mark, but boost values up to 90db are possible.</p>
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spaces/1line/AutoGPT/autogpt/json_utils/json_fix_llm.py

@@ -1,220 +0,0 @@
-"""This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance
-of the ChatGPT API or LLM models."""
-from __future__ import annotations
-
-import contextlib
-import json
-from typing import Any, Dict
-
-from colorama import Fore
-from regex import regex
-
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_general import correct_json
-from autogpt.llm_utils import call_ai_function
-from autogpt.logs import logger
-from autogpt.speech import say_text
-
-JSON_SCHEMA = """
-{
-    "command": {
-        "name": "command name",
-        "args": {
-            "arg name": "value"
-        }
-    },
-    "thoughts":
-    {
-        "text": "thought",
-        "reasoning": "reasoning",
-        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-        "criticism": "constructive self-criticism",
-        "speak": "thoughts summary to say to user"
-    }
-}
-"""
-
-CFG = Config()
-
-
-def auto_fix_json(json_string: str, schema: str) -> str:
-    """Fix the given JSON string to make it parseable and fully compliant with
-        the provided schema using GPT-3.
-
-    Args:
-        json_string (str): The JSON string to fix.
-        schema (str): The schema to use to fix the JSON.
-    Returns:
-        str: The fixed JSON string.
-    """
-    # Try to fix the JSON using GPT:
-    function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
-    args = [f"'''{json_string}'''", f"'''{schema}'''"]
-    description_string = (
-        "This function takes a JSON string and ensures that it"
-        " is parseable and fully compliant with the provided schema. If an object"
-        " or field specified in the schema isn't contained within the correct JSON,"
-        " it is omitted. The function also escapes any double quotes within JSON"
-        " string values to ensure that they are valid. If the JSON string contains"
-        " any None or NaN values, they are replaced with null before being parsed."
-    )
-
-    # If it doesn't already start with a "`", add one:
-    if not json_string.startswith("`"):
-        json_string = "```json\n" + json_string + "\n```"
-    result_string = call_ai_function(
-        function_string, args, description_string, model=CFG.fast_llm_model
-    )
-    logger.debug("------------ JSON FIX ATTEMPT ---------------")
-    logger.debug(f"Original JSON: {json_string}")
-    logger.debug("-----------")
-    logger.debug(f"Fixed JSON: {result_string}")
-    logger.debug("----------- END OF FIX ATTEMPT ----------------")
-
-    try:
-        json.loads(result_string)  # just check the validity
-        return result_string
-    except json.JSONDecodeError:  # noqa: E722
-        # Get the call stack:
-        # import traceback
-        # call_stack = traceback.format_exc()
-        # print(f"Failed to fix JSON: '{json_string}' "+call_stack)
-        return "failed"
-
-
-def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]:
-    """Fix the given JSON string to make it parseable and fully compliant with two techniques.
-
-    Args:
-        json_string (str): The JSON string to fix.
-
-    Returns:
-        str: The fixed JSON string.
-    """
-
-    # Parse and print Assistant response
-    assistant_reply_json = fix_and_parse_json(assistant_reply)
-    if assistant_reply_json == {}:
-        assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
-            assistant_reply
-        )
-
-    if assistant_reply_json != {}:
-        return assistant_reply_json
-
-    logger.error(
-        "Error: The following AI output couldn't be converted to a JSON:\n",
-        assistant_reply,
-    )
-    if CFG.speak_mode:
-        say_text("I have received an invalid JSON response from the OpenAI API.")
-
-    return {}
-
-
-def fix_and_parse_json(
-    json_to_load: str, try_to_fix_with_gpt: bool = True
-) -> Dict[Any, Any]:
-    """Fix and parse JSON string
-
-    Args:
-        json_to_load (str): The JSON string.
-        try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
-            Defaults to True.
-
-    Returns:
-        str or dict[Any, Any]: The parsed JSON.
-    """
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = json_to_load.replace("\t", "")
-        return json.loads(json_to_load)
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = correct_json(json_to_load)
-        return json.loads(json_to_load)
-    # Let's do something manually:
-    # sometimes GPT responds with something BEFORE the braces:
-    # "I'm sorry, I don't understand. Please try again."
-    # {"text": "I'm sorry, I don't understand. Please try again.",
-    #  "confidence": 0.0}
-    # So let's try to find the first brace and then parse the rest
-    #  of the string
-    try:
-        brace_index = json_to_load.index("{")
-        maybe_fixed_json = json_to_load[brace_index:]
-        last_brace_index = maybe_fixed_json.rindex("}")
-        maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
-        return json.loads(maybe_fixed_json)
-    except (json.JSONDecodeError, ValueError) as e:
-        return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
-
-
-def try_ai_fix(
-    try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
-) -> Dict[Any, Any]:
-    """Try to fix the JSON with the AI
-
-    Args:
-        try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
-        exception (Exception): The exception that was raised.
-        json_to_load (str): The JSON string to load.
-
-    Raises:
-        exception: If try_to_fix_with_gpt is False.
-
-    Returns:
-        str or dict[Any, Any]: The JSON string or dictionary.
-    """
-    if not try_to_fix_with_gpt:
-        raise exception
-    if CFG.debug_mode:
-        logger.warn(
-            "Warning: Failed to parse AI output, attempting to fix."
-            "\n If you see this warning frequently, it's likely that"
-            " your prompt is confusing the AI. Try changing it up"
-            " slightly."
-        )
-    # Now try to fix this up using the ai_functions
-    ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA)
-
-    if ai_fixed_json != "failed":
-        return json.loads(ai_fixed_json)
-    # This allows the AI to react to the error message,
-    #   which usually results in it correcting its ways.
-    # logger.error("Failed to fix AI output, telling the AI.")
-    return {}
-
-
-def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
-    if CFG.speak_mode and CFG.debug_mode:
-        say_text(
-            "I have received an invalid JSON response from the OpenAI API. "
-            "Trying to fix it now."
-        )
-        logger.error("Attempting to fix JSON by finding outermost brackets\n")
-
-    try:
-        json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
-        json_match = json_pattern.search(json_string)
-
-        if json_match:
-            # Extract the valid JSON object from the string
-            json_string = json_match.group(0)
-            logger.typewriter_log(
-                title="Apparently json was fixed.", title_color=Fore.GREEN
-            )
-            if CFG.speak_mode and CFG.debug_mode:
-                say_text("Apparently json was fixed.")
-        else:
-            return {}
-
-    except (json.JSONDecodeError, ValueError):
-        if CFG.debug_mode:
-            logger.error(f"Error: Invalid JSON: {json_string}\n")
-        if CFG.speak_mode:
-            say_text("Didn't work. I will have to ignore this response then.")
-        logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
-        json_string = {}
-
-    return fix_and_parse_json(json_string)

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Challenge Your Friends and the World with Quiz Planet APK for Android.md

@@ -1,133 +0,0 @@
-
-<br>
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- <code>
-<table>
-<tr>
-<td>
-<h1>Quiz Planet APK Download: A Fun and Social Trivia Game</h1>
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-<h2>quiz planet apk download</h2><br /><p><b><b>DOWNLOAD</b> &#9881; <a href="https://urlin.us/2uT0u9">https://urlin.us/2uT0u9</a></b></p><br /><br />
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-<p>Quiz Planet gives you three power-ups that you can use during the game: skip, bomb, and freeze. Skip lets you skip a question if you don't know the answer or don't like it. Bomb eliminates two wrong answers from the four options. Freeze stops the timer for 10 seconds so you can think more carefully. You can use one power-up per question, but they are limited in number and cost coins or gems to buy more. Therefore, use them wisely and only when necessary.</p>
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-<p>Have you ever dreamed of making your imagination a reality with 3D models? Do you want to learn new skills and express your creativity in a fun and engaging way? Do you want to share your amazing creations with others and see what they have made? If you answered yes to any of these questions, then you should download 3D Bear app, a mobile augmented reality solution that allows you to create and experience your own virtual worlds. In this article, we will tell you what 3D Bear app is, why you should download it, how to download it, and how to use it.</p>
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spaces/1phancelerku/anime-remove-background/BGMI 2.2 APK and OBB Download Guide New Map Crossbow and More.md

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-<p>Before you download the update, make sure that your device meets the following requirements: - Android version: 5.1.1 or above - RAM: 2 GB or more - Storage space: 1 GB or more Also, make sure that you have a stable internet connection and enough battery power. It is recommended that you back up your game data before updating to avoid any loss or corruption.</p>
- <h3>Download link and instructions</h3>
-<p>To download the BGMI 2.2 update apk, you can use this link: [BGMI 2.2 Update Download Apk]. This is the official link from Krafton's website and it is safe and secure. Once you have downloaded the apk file, follow these instructions to install it on your device: - Locate the apk file in your device's file manager or downloads folder - Tap on the apk file to start the installation process - Allow the installation from unknown sources if prompted - Wait for the installation to complete - Launch the game and enjoy the update Note: If you have already installed BGMI on your device from Google Play Store or any other source, you don't need to uninstall it before installing the update. The update will overwrite the existing version without affecting your game data.</p>
- <h3>Troubleshooting tips</h3>
-<p>If you face any issues while downloading or installing the update, try these tips to solve them: - Check your internet connection and try again - Clear your device's cache and storage space - Restart your device and try again - Contact Krafton's customer support if the problem persists We hope that these tips will help you resolve any problems that you may encounter.</p>
- <h2>Conclusion</h2>
-<p>The BGMI 2.2 update is a massive update that brings a lot of new content and features to the game. You can explore a new map, use a new weapon, communicate with a new voice pack, play new modes, join new events, and get new skins, outfits, and accessories. You can also download the update easily by following our guide above. So what are you waiting for? Download the BGMI 2.2 update apk now and experience the thrill of Battlegrounds Mobile India like never before!</p>
- <h4>FAQs</h4> <p>Here are some of the frequently asked questions (FAQs) about the BGMI 2.2 update:</p>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is the BGMI 2.2 update available for iOS devices?</td>
-<td>No, the BGMI 2.2 update is currently available for Android devices only. Krafton has not announced any plans to release the update for iOS devices yet.</td>
-</tr>
-<tr>
-<td>How big is the BGMI 2.2 update apk file?</td>
-<td>The BGMI 2.2 update apk file is about 1 GB in size. However, you may need additional storage space to download and install the update on your device.</td>
-</tr>
-<tr>
-<td>Can I play with players from other regions or versions of the game?</td>
-<td>No, you can only play with players from India who have the same version of the game as you. You cannot play with players from other regions or versions of the game, such as PUBG Mobile or PUBG Mobile Lite.</td>
-</tr>
-<tr>
-<td>Will I lose my progress or rank if I update the game?</td>
-<td>No, you will not lose your progress or rank if you update the game. Your game data will be saved and transferred to the new version without any loss or corruption.</td>
-</tr>
-<tr>
-<td>How can I get more in-game currency and items in the game?</td>
-<td>You can get more in-game currency and items in the game by completing missions, participating in events, opening crates, spinning wheels, or purchasing them with real money. You can also use some promo codes or coupons to get some free rewards.</td>
-</tr>
-</table>
- <p>I hope that these FAQs will answer some of your queries and doubts about the BGMI 2.2 update. If you have any other questions, feel free to leave a comment below or contact Krafton's customer support.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Data Structure Using C By Udit Agarwal Pdf Free Extra Quality.md

@@ -1,72 +0,0 @@
-## Data Structure Using C By Udit Agarwal Pdf Free
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-# Data Structure Using C By Udit Agarwal: A Comprehensive Guide for Beginners
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-Data structures are the fundamental building blocks of any computer program. They are used to store, organize, and manipulate data efficiently. Data structures can be classified into two types: linear and nonlinear. Linear data structures store data in a sequential manner, such as arrays, stacks, queues, and linked lists. Nonlinear data structures store data in a hierarchical or networked manner, such as trees, graphs, and heaps.
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-Data Structure Using C By Udit Agarwal is a comprehensive book that covers all the topics related to data structures using C. The book starts with an introduction to C programming language, followed by chapters on arrays, strings, pointers, structures, unions, files, and dynamic memory allocation. The book then explains the concepts and implementation of various linear and nonlinear data structures using C. The book also includes numerous examples, exercises, and case studies to help the readers understand and apply the concepts.
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-## Linear Data Structures
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-Linear data structures are the simplest type of data structures that store data in a sequential manner. They have a linear relationship between the elements, meaning that each element has a unique predecessor and successor, except for the first and last element. Linear data structures can be implemented using arrays or linked lists. Some of the common linear data structures are:
-
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-- **Arrays:** An array is a collection of homogeneous elements that are stored in contiguous memory locations. Each element can be accessed by its index, which is a positive integer that represents its position in the array. Arrays have a fixed size and are easy to implement and use. However, arrays have some limitations, such as wasting memory space if not fully utilized, difficulty in inserting and deleting elements, and lack of flexibility in resizing.
-
-- **Stacks:** A stack is a collection of elements that follows the Last-In-First-Out (LIFO) principle. This means that the last element inserted into the stack is the first one to be removed from it. A stack has two basic operations: push and pop. Push adds an element to the top of the stack, while pop removes and returns the top element from the stack. A stack can be used to implement recursion, reverse a string, check for balanced parentheses, and evaluate postfix expressions.
-
-- **Queues:** A queue is a collection of elements that follows the First-In-First-Out (FIFO) principle. This means that the first element inserted into the queue is the first one to be removed from it. A queue has two basic operations: enqueue and dequeue. Enqueue adds an element to the rear of the queue, while dequeue removes and returns the front element from the queue. A queue can be used to implement scheduling, buffering, simulation, and breadth-first search.
-
-- **Linked Lists:** A linked list is a collection of heterogeneous elements that are stored in non-contiguous memory locations. Each element is called a node, which contains some data and a pointer to the next node. The first node is called the head, and the last node is called the tail. A linked list has no fixed size and can grow or shrink dynamically. A linked list can overcome some of the limitations of arrays, such as wasting memory space, difficulty in inserting and deleting elements, and lack of flexibility in resizing. However, linked lists have some drawbacks, such as increased complexity in implementation and access time, difficulty in random access, and extra memory space for pointers.
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spaces/1phancelerku/anime-remove-background/Download LOST in BLUE and Explore a Mysterious Island on Android.md

@@ -1,170 +0,0 @@
-
-<h1>How to Download Lost in Blue for Android</h1>
-<p>Do you love survival games that challenge you to use your wits and skills to stay alive on a mysterious island? If so, you might want to check out <strong>Lost in Blue</strong>, a survival sandbox game developed by Volcano Force for Android devices. In this game, you will have to collect resources, craft items, build facilities, and fight enemies as you try to find a way back home. You can also team up with other players from around the world and form camps to cooperate and compete with each other. In this article, we will show you how to download Lost in Blue for Android and give you some tips on how to start playing.</p>
- <h2>What is Lost in Blue?</h2>
-<p>Lost in Blue is a survival sandbox game that was released globally in June 2023. The game is inspired by the popular Lost in Blue series of video games that were originally released for Nintendo DS. The game has received positive reviews from critics and players alike for its stunning graphics, immersive gameplay, and diverse content.</p>
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-<p>The game's story begins with a plane crash that leaves you stranded on a strange island. You will have to explore various natural environments like beaches, rainforests, volcanoes, glaciers, etc. and overcome different obstacles such as mutant zombies, militias, wild creatures, etc. You will also have to craft weapons and tools, build facilities and houses, and grow food to survive. You can choose from four different professions that affect your skills and abilities: hunter, gatherer, engineer, or doctor. You can also join or create camps with other players and cooperate or compete with them.</p>
- <h3>Why should you play Lost in Blue?</h3>
-<p>There are many reasons why you should download and play Lost in Blue if you are a fan of survival games. Here are some of them:</p>
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-<ul>
-<li>The game has <strong>amazing graphics</strong> that make you feel like you are really on an island. The game uses realistic lighting effects, dynamic weather changes, and detailed textures that create a stunning visual experience.</li>
-<li>The game has <strong>engaging gameplay</strong> that keeps you hooked for hours. The game offers a lot of activities and challenges that test your survival skills. You can explore different locations, collect resources, craft items, build facilities, and fight enemies. You can also trade, chat, and cooperate with other players in real-time.</li>
-<li>The game has <strong>diverse content</strong> that offers a lot of variety and replay value. The game has over 100 types of resources, over 200 types of items, over 50 types of facilities, and over 40 types of enemies. The game also has different modes, such as story mode, survival mode, and multiplayer mode.</li>
-</ul>
- <h2>How to download Lost in Blue for Android?</h2>
-<p>If you are interested in playing Lost in Blue, you will need to download and install the game on your Android device. The game is available for free on the Google Play Store, but it also has some in-app purchases that can enhance your gaming experience. Here is a step-by-step guide on how to download Lost in Blue for Android:</p>
- <h3>Requirements for downloading Lost in Blue</h3>
-<p>Before you download Lost in Blue, you should make sure that your device meets the minimum and recommended specifications for running the game smoothly. According to the game's official website, these are the requirements for downloading Lost in Blue:</p>
-<table>
-<tr>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>Android 5.0 or higher</td>
-<td>Android 8.0 or higher</td>
-</tr>
-<tr>
-<td>2 GB of RAM</td>
-<td>4 GB of RAM or more</td>
-</tr>
-<tr>
-<td>2 GB of free storage space</td>
-<td>4 GB of free storage space or more</td>
-</tr>
-<tr>
-<td>Internet connection</td>
-<td>Wi-Fi or 4G connection</td>
-</tr>
-</table>
- <h3>Steps for downloading Lost in Blue</h3>
-<p>Once you have checked that your device meets the requirements, you can follow these steps to download Lost in Blue:</p>
-<ol>
-<li>Open the Google Play Store app on your device and search for "Lost in Blue" or use this link to go directly to the game's page.</li>
-<li>Tap on the "Install" button and wait for the game to download and install on your device. The game's size is about 1.5 GB, so it may take some time depending on your internet speed.</li>
-<li>After the installation is complete, tap on the "Open" button or find the game's icon on your home screen and launch the game.</li>
-<li>The game will ask you to grant some permissions, such as access to your storage, location, microphone, etc. You can choose to allow or deny these permissions, but some of them may be necessary for the game to function properly.</li>
-<li>The game will also ask you to log in with your Google account or create a new account with your email address. You can choose either option, but logging in with your Google account will allow you to sync your progress across different devices and access some features like leaderboards and achievements.</li>
-<li>The game will then load some data and resources and take you to the main menu. You can now start playing Lost in Blue!</li>
-</ol>
- <h3>Tips for downloading Lost in Blue</h3>
-<p>To avoid any issues or errors while downloading Lost in Blue, here are some tips that you should keep in mind:</p>
-<ul>
-<li>Make sure that you have enough storage space on your device before downloading the game. You can check your available space by going to Settings > Storage on your device.</li>
-<li>Make sure that you have a stable internet connection while downloading the game. You can use Wi-Fi or 4G if possible, as they are faster and more reliable than 3G or other networks.</li>
-<li>Make sure that your device's battery is sufficiently charged before downloading the game. You can plug in your device to a power source or enable battery saver mode if needed.</li>
-<li>If you encounter any problems while downloading the game, such as slow speed, interrupted download, or corrupted file, you can try clearing the cache and data of the Google Play Store app by going to Settings > Apps > Google Play Store > Storage > Clear Cache/Clear Data. You can also try restarting your device or using a different network.</li>
-<li>If you have any questions or feedback about the game, you can contact the game's customer service by tapping on the "Help" button on the main menu or sending an email to [email protected].</li>
-</ul>
- <h2>How to start playing Lost in Blue?</h2> in various events and activities. However, joining a camp also means that you will have to follow the camp's rules and share your resources with other members. You can also leave or change your camp at any time, but you will lose some of your progress and items.</p>
- <p>To join a camp, follow these steps:</p>
-<ol>
-<li>After choosing your profession, you will see a screen where you can join a camp. You can tap on the "Join Camp" button to see a list of available camps that you can join.</li>
-<li>You can also tap on the "Create Camp" button to create your own camp. You will need to enter a camp name, a camp slogan, and a camp flag. You will also need to pay some gold coins as a creation fee.</li>
-<li>Whether you join or create a camp, you will see a screen where you can see your camp's information, such as its name, slogan, flag, members, level, rank, etc. You can also see your camp's base on the map and visit it by tapping on the "Go" button.</li>
-<li>You have successfully joined a camp!</li>
-</ol>
- <h2>How to survive in Lost in Blue?</h2>
-<p>Now that you have completed the initial steps, you are ready to start your survival adventure in Lost in Blue. However, surviving on the island is not easy, as you will face many dangers and difficulties. You will need to gather resources, craft items, build facilities, and fight enemies to stay alive. Here are some basic tips and tricks on how to survive in Lost in Blue:</p>
- <h3>Gathering resources</h3>
-<p>One of the most important aspects of survival is gathering resources. Resources are materials that you can collect from the environment and use for crafting or building. There are many types of resources in Lost in Blue, such as wood, stone, metal, fiber, food, water, etc. You can find them in different locations, such as forests, beaches, caves, etc.</p>
-<p>To gather resources, follow these steps:</p>
-<ol>
-<li>Find a resource node that you want to collect. You can see the name and icon of the resource on the top of the screen when you approach it.</li>
-<li>Tap on the resource node to start gathering it. You will see a progress bar that shows how much of the resource you have collected.</li>
-<li>If you have a tool that matches the resource type, such as an axe for wood or a pickaxe for stone, you can use it to gather faster and get more resources. You can equip a tool by tapping on its icon on the bottom right corner of the screen.</li>
-<li>When you have collected enough of the resource, it will be added to your inventory. You can check your inventory by tapping on the backpack icon on the top right corner of the screen.</li>
-<li>You have successfully gathered resources!</li>
-</ol>
- <h3>Crafting items</h3>
-<p>Another important aspect of survival is crafting items. Items are objects that you can create from the resources you gathered and use for various purposes. There are many types of items in Lost in Blue, such as weapons, tools, equipment, consumables, etc. You can use them to improve your survival chances, such as by increasing your attack power, defense, speed, health, etc.</p>
- <p>To craft items, follow these steps:</p>
-<ol>
-<li>Open your inventory by tapping on the backpack icon on the top right corner of the screen.</li>
-<li>Tap on the "Craft" button on the bottom of the screen to see the list of items that you can craft.</li>
-<li>Tap on the item that you want to craft to see its details, such as its name, description, icon, and required resources.</li>
-<li>If you have enough resources to craft the item, you can tap on the "Craft" button below the item details. You will see a progress bar that shows how long it takes to craft the item.</li>
-<li>When the crafting is complete, the item will be added to your inventory. You can equip or use the item by tapping on its icon in your inventory.</li>
-<li>You have successfully crafted items!</li>
-</ol>
- <h3>Building facilities</h3>
-<p>Another important aspect of survival is building facilities. Facilities are structures that you can build from the resources you gathered and use for various purposes. There are many types of facilities in Lost in Blue, such as houses, workbenches, towers, fences, etc. You can use them to enhance your camp, such as by providing shelter, storage, production, defense, etc.</p>
- <p>To build facilities, follow these steps:</p>
-<ol>
-<li>Go to your camp's base by tapping on the "Camp" button on the top left corner of the screen and then tapping on the "Go" button next to your camp's name.</li>
-<li>Tap on the "Build" button on the bottom of the screen to see the list of facilities that you can build.</li>
-<li>Tap on the facility that you want to build to see its details, such as its name, description, icon, and required resources.</li>
-<li>If you have enough resources to build the facility, you can tap on the "Build" button below the facility details. You will see a blueprint of the facility that you can drag and rotate on the ground.</li>
-<li>When you have found a suitable spot for the facility, you can tap on the "Confirm" button to start building it. You will see a progress bar that shows how long it takes to build the facility.</li>
-<li>When the building is complete, the facility will be ready to use. You can interact with it by tapping on it and choosing an option from the menu.</li>
-<li>You have successfully built facilities!</li>
-</ol>
- <h3>Fighting enemies</h3>
-<p>The final aspect of survival that we will cover is fighting enemies. Enemies are hostile creatures or players that will try to attack you and harm you. There are many types of enemies in Lost in Blue, such as zombies, wild animals, militias, other players, etc. You will need to fight them to protect yourself and your camp, as well as to get loot and rewards from them.</p>
- <p>To fight enemies, follow these steps:</p>
-<ol>
-<li>Find an enemy that you want to fight. You can see the name and health bar of the enemy on the top of the screen when you target it.</li>
-<li>Tap on the enemy to start attacking it. You will use your equipped weapon or tool to deal damage to the enemy. You can also use the virtual joystick and buttons to move around and dodge the enemy's attacks.</li>
-<li>If you have any items or skills that can help you in combat, such as consumables, traps, or abilities, you can use them by tapping on their icons on the bottom right corner of the screen.</li>
-<li>When you have reduced the enemy's health to zero, you will defeat it. You can loot the enemy's corpse by tapping on it and choosing an option from the menu. You can get various items and resources from the enemy, such as meat, leather, metal, etc.</li>
-<li>You have successfully fought enemies!</li>
-</ol>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Lost in Blue for Android and how to start playing the game. We have also given you some basic tips and tricks on how to survive on the island. We hope that you have found this article helpful and informative. If you are looking for a survival sandbox game that offers a lot of fun and challenge, you should definitely give Lost in Blue a try. You can download the game for free from the Google Play Store and start your adventure today!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Lost in Blue and their answers:</p>
-<ul>
-<li><strong>Q: How can I play with my friends in Lost in Blue?</strong></li>
-<li>A: You can play with your friends in Lost in Blue by joining or creating a camp with them. You can invite your friends to your camp by tapping on the "Camp" button on the top left corner of the screen and then tapping on the "Invite" button next to your friend's name. You can also join your friend's camp by tapping on the "Join Camp" button on the main menu and then searching for your friend's camp name.</li>
-<li><strong>Q: How can I save my progress in Lost in Blue?</strong></li>
-<li>A: You can save your progress in Lost in Blue by logging in with your Google account or creating a new account with your email address. The game will automatically sync your progress across different devices and platforms. You can also manually save your progress by tapping on the "Settings" button on the top right corner of the screen and then tapping on the "Save" button.</li>
-<li><strong>Q: How can I change my profession in Lost in Blue?</strong></li>
-<li>A: You can change your profession in Lost in Blue by tapping on the "Profession" button on the top right corner of the screen and then tapping on the "Change" button below your current profession. You will need to pay some gold coins as a change fee. You can also change your profession for free once every 24 hours.</li>
-<li><strong>Q: How can I get more gold coins in Lost in Blue?</strong></li>
-<li>A: You can get more gold coins in Lost in Blue by completing various tasks and achievements, participating in events and activities, trading with other players, or purchasing them with real money.</li>
-<li><strong>Q: How can I contact the game's customer service?</strong></li>
-<li>A: You can contact the game's customer service by tapping on the "Help" button on the main menu or sending an email to [email protected].</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/2023Liu2023/bingo/tests/parse.ts

@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
-  const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
-  const headers = parseHeadersFromCurl(content)
-  console.log(headers)
-
-  const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
-  const cmdHeaders = parseHeadersFromCurl(cmdContent)
-  console.log(cmdHeaders)
-})()

spaces/2ndelement/voicevox/build_util/check_release_build.py

@@ -1,70 +0,0 @@
-"""
-ビルド結果をテストする
-"""
-import argparse
-import json
-import time
-from io import BytesIO
-from pathlib import Path
-from subprocess import Popen
-from urllib.parse import urlencode
-from urllib.request import Request, urlopen
-
-import soundfile
-
-base_url = "http://127.0.0.1:50021/"
-
-
-def test_release_build(dist_dir: Path, skip_run_process: bool) -> None:
-    run_file = dist_dir / "run"
-    if not run_file.exists():
-        run_file = dist_dir / "run.exe"
-
-    # 起動
-    process = None
-    if not skip_run_process:
-        process = Popen([run_file.absolute()], cwd=dist_dir)
-        time.sleep(60)  # 待機
-
-    # バージョン取得テスト
-    req = Request(base_url + "version")
-    with urlopen(req) as res:
-        assert len(res.read()) > 0
-
-    # テキスト -> クエリ
-    text = "こんにちは、音声合成の世界へようこそ"
-    req = Request(
-        base_url + "audio_query?" + urlencode({"speaker": "1", "text": text}),
-        method="POST",
-    )
-    with urlopen(req) as res:
-        query = json.loads(res.read().decode("utf-8"))
-
-    # クエリ -> 音声
-    req = Request(base_url + "synthesis?speaker=1", method="POST")
-    req.add_header("Content-Type", "application/json")
-    req.data = json.dumps(query).encode("utf-8")
-    with urlopen(req) as res:
-        wave = res.read()
-    soundfile.read(BytesIO(wave))
-
-    # エンジンマニフェスト
-    req = Request(base_url + "engine_manifest", method="GET")
-    with urlopen(req) as res:
-        manifest = json.loads(res.read().decode("utf-8"))
-        assert "uuid" in manifest
-
-    if not skip_run_process:
-        # プロセスが稼働中であることを確認
-        assert process.poll() is None
-
-        # 停止
-        process.terminate()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--dist_dir", type=Path, default=Path("dist/"))
-    parser.add_argument("--skip_run_process", action="store_true")
-    args = parser.parse_args()
-    test_release_build(dist_dir=args.dist_dir, skip_run_process=args.skip_run_process)

spaces/7hao/bingo/src/lib/utils.ts

@@ -1,138 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function random (start: number, end: number) {
-  return start + Math.ceil(Math.random() * (end - start))
-}
-
-export function randomIP() {
-  return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}`
-}
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-
-  return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch(e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
-  const date = new Date(input)
-  return date.toLocaleDateString('en-US', {
-    month: 'long',
-    day: 'numeric',
-    year: 'numeric'
-  })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-export const DEFAULT_IP = process.env.BING_IP || randomIP()
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>) {
-  let {
-    BING_COOKIE = process.env.BING_COOKIE,
-    BING_UA = process.env.BING_UA,
-    BING_IP = process.env.BING_IP,
-    BING_HEADER = process.env.BING_HEADER,
-  } = cookies
-
-  if (BING_HEADER) {
-    return extraHeadersFromCookie({
-      BING_HEADER,
-      ...cookies,
-    })
-  }
-
-  const ua = parseUA(BING_UA)
-
-  if (!BING_COOKIE) {
-    BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || 'xxx' // hf 暂时不用 Cookie 也可以正常使用
-  }
-
-  const parsedCookie = parseCookie(BING_COOKIE, '_U')
-  if (!parsedCookie) {
-    throw new Error('Invalid Cookie')
-  }
-  return {
-    'x-forwarded-for': BING_IP || DEFAULT_IP,
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    cookie: `_U=${parsedCookie}` || '',
-  }
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}

spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_33966KB.py

@@ -1,126 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle

spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_33966KB.py

@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_33966KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 16)
-        self.stg1_high_band_net = BaseASPPNet(2, 16)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(8, 16)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(16, 32)
-
-        self.out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/AI-Hobbyist/Hoyo-RVC/infer_batch_rvc.py

@@ -1,217 +0,0 @@
-"""
-v1
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
-v2
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
-"""
-import os, sys, pdb, torch
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import argparse
-import glob
-import sys
-import torch
-import tqdm as tq
-from multiprocessing import cpu_count
-
-
-class Config:
-    def __init__(self, device, is_half):
-        self.device = device
-        self.is_half = is_half
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("16系/10系显卡和P40强制单精度")
-                self.is_half = False
-                for config_file in ["32k.json", "40k.json", "48k.json"]:
-                    with open(f"configs/{config_file}", "r") as f:
-                        strr = f.read().replace("true", "false")
-                    with open(f"configs/{config_file}", "w") as f:
-                        f.write(strr)
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("没有发现支持的N卡, 使用MPS进行推理")
-            self.device = "mps"
-        else:
-            print("没有发现支持的N卡, 使用CPU进行推理")
-            self.device = "cpu"
-            self.is_half = True
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
-
-
-f0up_key = sys.argv[1]
-input_path = sys.argv[2]
-index_path = sys.argv[3]
-f0method = sys.argv[4]  # harvest or pm
-opt_path = sys.argv[5]
-model_path = sys.argv[6]
-index_rate = float(sys.argv[7])
-device = sys.argv[8]
-is_half = bool(sys.argv[9])
-filter_radius = int(sys.argv[10])
-resample_sr = int(sys.argv[11])
-rms_mix_rate = float(sys.argv[12])
-protect = float(sys.argv[13])
-print(sys.argv)
-config = Config(device, is_half)
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from vc_infer_pipeline import VC
-from infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from my_utils import load_audio
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-
-hubert_model = None
-
-
-def load_hubert():
-    global hubert_model
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(device)
-    if is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-
-def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
-    global tgt_sr, net_g, vc, hubert_model, version
-    if input_audio is None:
-        return "You need to upload an audio", None
-    f0_up_key = int(f0_up_key)
-    audio = load_audio(input_audio, 16000)
-    times = [0, 0, 0]
-    if hubert_model == None:
-        load_hubert()
-    if_f0 = cpt.get("f0", 1)
-    # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
-    audio_opt = vc.pipeline(
-        hubert_model,
-        net_g,
-        sid,
-        audio,
-        input_audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=f0_file,
-    )
-    print(times)
-    return audio_opt
-
-
-def get_vc(model_path):
-    global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
-    print("loading pth %s" % model_path)
-    cpt = torch.load(model_path, map_location="cpu")
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif version == "v2":
-        if if_f0 == 1:  #
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-    del net_g.enc_q
-    print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净，真奇葩
-    net_g.eval().to(device)
-    if is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-    vc = VC(tgt_sr, config)
-    n_spk = cpt["config"][-3]
-    # return {"visible": True,"maximum": n_spk, "__type__": "update"}
-
-
-get_vc(model_path)
-audios = os.listdir(input_path)
-for file in tq.tqdm(audios):
-    if file.endswith(".wav"):
-        file_path = input_path + "/" + file
-        wav_opt = vc_single(
-            0, file_path, f0up_key, None, f0method, index_path, index_rate
-        )
-        out_path = opt_path + "/" + file
-        wavfile.write(out_path, tgt_sr, wav_opt)

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/hifigan/hifigan.py

@@ -1,365 +0,0 @@
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-from modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork
-from modules.parallel_wavegan.models.source import SourceModuleHnNSF
-import numpy as np
-
-LRELU_SLOPE = 0.1
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Conv1d1x1(Conv1d):
-    """1x1 Conv1d with customized initialization."""
-
-    def __init__(self, in_channels, out_channels, bias):
-        """Initialize 1x1 Conv1d module."""
-        super(Conv1d1x1, self).__init__(in_channels, out_channels,
-                                        kernel_size=1, padding=0,
-                                        dilation=1, bias=bias)
-
-
-class HifiGanGenerator(torch.nn.Module):
-    def __init__(self, h, c_out=1):
-        super(HifiGanGenerator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h['resblock_kernel_sizes'])
-        self.num_upsamples = len(h['upsample_rates'])
-
-        if h['use_pitch_embed']:
-            self.harmonic_num = 8
-            self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates']))
-            self.m_source = SourceModuleHnNSF(
-                sampling_rate=h['audio_sample_rate'],
-                harmonic_num=self.harmonic_num)
-            self.noise_convs = nn.ModuleList()
-        self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3))
-        resblock = ResBlock1 if h['resblock'] == '1' else ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h['upsample_rates'], h['upsample_kernel_sizes'])):
-            c_cur = h['upsample_initial_channel'] // (2 ** (i + 1))
-            self.ups.append(weight_norm(
-                ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2)))
-            if h['use_pitch_embed']:
-                if i + 1 < len(h['upsample_rates']):
-                    stride_f0 = np.prod(h['upsample_rates'][i + 1:])
-                    self.noise_convs.append(Conv1d(
-                        1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
-                else:
-                    self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h['upsample_initial_channel'] // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h['resblock_kernel_sizes'], h['resblock_dilation_sizes'])):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x, f0=None):
-        if f0 is not None:
-            # harmonic-source signal, noise-source signal, uv flag
-            f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)
-            har_source, noi_source, uv = self.m_source(f0)
-            har_source = har_source.transpose(1, 2)
-
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            x = self.ups[i](x)
-            if f0 is not None:
-                x_source = self.noise_convs[i](har_source)
-                x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1):
-        super(DiscriminatorP, self).__init__()
-        self.use_cond = use_cond
-        if use_cond:
-            from utils.hparams import hparams
-            t = hparams['hop_size']
-            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
-            c_in = 2
-
-        self.period = period
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(c_in, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x, mel):
-        fmap = []
-        if self.use_cond:
-            x_mel = self.cond_net(mel)
-            x = torch.cat([x_mel, x], 1)
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_cond=False, c_in=1):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorP(2, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(3, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(5, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(7, use_cond=use_cond, c_in=c_in),
-            DiscriminatorP(11, use_cond=use_cond, c_in=c_in),
-        ])
-
-    def forward(self, y, y_hat, mel=None):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y, mel)
-            y_d_g, fmap_g = d(y_hat, mel)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False, use_cond=False, upsample_rates=None, c_in=1):
-        super(DiscriminatorS, self).__init__()
-        self.use_cond = use_cond
-        if use_cond:
-            t = np.prod(upsample_rates)
-            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
-            c_in = 2
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(c_in, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x, mel):
-        if self.use_cond:
-            x_mel = self.cond_net(mel)
-            x = torch.cat([x_mel, x], 1)
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self, use_cond=False, c_in=1):
-        super(MultiScaleDiscriminator, self).__init__()
-        from utils.hparams import hparams
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True, use_cond=use_cond,
-                           upsample_rates=[4, 4, hparams['hop_size'] // 16],
-                           c_in=c_in),
-            DiscriminatorS(use_cond=use_cond,
-                           upsample_rates=[4, 4, hparams['hop_size'] // 32],
-                           c_in=c_in),
-            DiscriminatorS(use_cond=use_cond,
-                           upsample_rates=[4, 4, hparams['hop_size'] // 64],
-                           c_in=c_in),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=1),
-            AvgPool1d(4, 2, padding=1)
-        ])
-
-    def forward(self, y, y_hat, mel=None):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i - 1](y)
-                y_hat = self.meanpools[i - 1](y_hat)
-            y_d_r, fmap_r = d(y, mel)
-            y_d_g, fmap_g = d(y_hat, mel)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    r_losses = 0
-    g_losses = 0
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg ** 2)
-        r_losses += r_loss
-        g_losses += g_loss
-    r_losses = r_losses / len(disc_real_outputs)
-    g_losses = g_losses / len(disc_real_outputs)
-    return r_losses, g_losses
-
-
-def cond_discriminator_loss(outputs):
-    loss = 0
-    for dg in outputs:
-        g_loss = torch.mean(dg ** 2)
-        loss += g_loss
-    loss = loss / len(outputs)
-    return loss
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        loss += l
-    loss = loss / len(disc_outputs)
-    return loss

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/classifier.py

@@ -1,267 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from torch.nn import functional as F
-from torch.optim import AdamW
-from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
-
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {
-    'class_label': EncoderUNetModel,
-    'segmentation': UNetModel
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class NoisyLatentImageClassifier(pl.LightningModule):
-
-    def __init__(self,
-                 diffusion_path,
-                 num_classes,
-                 ckpt_path=None,
-                 pool='attention',
-                 label_key=None,
-                 diffusion_ckpt_path=None,
-                 scheduler_config=None,
-                 weight_decay=1.e-2,
-                 log_steps=10,
-                 monitor='val/loss',
-                 *args,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.num_classes = num_classes
-        # get latest config of diffusion model
-        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
-        self.diffusion_config = OmegaConf.load(diffusion_config).model
-        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
-        self.load_diffusion()
-
-        self.monitor = monitor
-        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
-        self.log_steps = log_steps
-
-        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
-            else self.diffusion_model.cond_stage_key
-
-        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
-
-        if self.label_key not in __models__:
-            raise NotImplementedError()
-
-        self.load_classifier(ckpt_path, pool)
-
-        self.scheduler_config = scheduler_config
-        self.use_scheduler = self.scheduler_config is not None
-        self.weight_decay = weight_decay
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def load_diffusion(self):
-        model = instantiate_from_config(self.diffusion_config)
-        self.diffusion_model = model.eval()
-        self.diffusion_model.train = disabled_train
-        for param in self.diffusion_model.parameters():
-            param.requires_grad = False
-
-    def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
-        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
-        model_config.out_channels = self.num_classes
-        if self.label_key == 'class_label':
-            model_config.pool = pool
-
-        self.model = __models__[self.label_key](**model_config)
-        if ckpt_path is not None:
-            print('#####################################################################')
-            print(f'load from ckpt "{ckpt_path}"')
-            print('#####################################################################')
-            self.init_from_ckpt(ckpt_path)
-
-    @torch.no_grad()
-    def get_x_noisy(self, x, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x))
-        continuous_sqrt_alpha_cumprod = None
-        if self.diffusion_model.use_continuous_noise:
-            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
-            # todo: make sure t+1 is correct here
-
-        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
-                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
-
-    def forward(self, x_noisy, t, *args, **kwargs):
-        return self.model(x_noisy, t)
-
-    @torch.no_grad()
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    @torch.no_grad()
-    def get_conditioning(self, batch, k=None):
-        if k is None:
-            k = self.label_key
-        assert k is not None, 'Needs to provide label key'
-
-        targets = batch[k].to(self.device)
-
-        if self.label_key == 'segmentation':
-            targets = rearrange(targets, 'b h w c -> b c h w')
-            for down in range(self.numd):
-                h, w = targets.shape[-2:]
-                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
-
-            # targets = rearrange(targets,'b c h w -> b h w c')
-
-        return targets
-
-    def compute_top_k(self, logits, labels, k, reduction="mean"):
-        _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == "mean":
-            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-        elif reduction == "none":
-            return (top_ks == labels[:, None]).float().sum(dim=-1)
-
-    def on_train_epoch_start(self):
-        # save some memory
-        self.diffusion_model.model.to('cpu')
-
-    @torch.no_grad()
-    def write_logs(self, loss, logits, targets):
-        log_prefix = 'train' if self.training else 'val'
-        log = {}
-        log[f"{log_prefix}/loss"] = loss.mean()
-        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
-            logits, targets, k=1, reduction="mean"
-        )
-        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
-            logits, targets, k=5, reduction="mean"
-        )
-
-        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
-        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
-        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
-        lr = self.optimizers().param_groups[0]['lr']
-        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
-
-    def shared_step(self, batch, t=None):
-        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
-        targets = self.get_conditioning(batch)
-        if targets.dim() == 4:
-            targets = targets.argmax(dim=1)
-        if t is None:
-            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
-        else:
-            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
-        x_noisy = self.get_x_noisy(x, t)
-        logits = self(x_noisy, t)
-
-        loss = F.cross_entropy(logits, targets, reduction='none')
-
-        self.write_logs(loss.detach(), logits.detach(), targets.detach())
-
-        loss = loss.mean()
-        return loss, logits, x_noisy, targets
-
-    def training_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-        return loss
-
-    def reset_noise_accs(self):
-        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
-                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
-
-    def on_validation_start(self):
-        self.reset_noise_accs()
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-
-        for t in self.noisy_acc:
-            _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
-            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
-
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
-
-        if self.use_scheduler:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [optimizer], scheduler
-
-        return optimizer
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, *args, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.diffusion_model.first_stage_key)
-        log['inputs'] = x
-
-        y = self.get_conditioning(batch)
-
-        if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-            log['labels'] = y
-
-        if ismap(y):
-            log['labels'] = self.diffusion_model.to_rgb(y)
-
-            for step in range(self.log_steps):
-                current_time = step * self.log_time_interval
-
-                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
-
-                log[f'inputs@t{current_time}'] = x_noisy
-
-                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
-                pred = rearrange(pred, 'b h w c -> b c h w')
-
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
-
-        for key in log:
-            log[key] = log[key][:N]
-
-        return log

spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/commons/meters.py

@@ -1,42 +0,0 @@
-import time
-import torch
-
-
-class AvgrageMeter(object):
-
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.avg = 0
-        self.sum = 0
-        self.cnt = 0
-
-    def update(self, val, n=1):
-        self.sum += val * n
-        self.cnt += n
-        self.avg = self.sum / self.cnt
-
-
-class Timer:
-    timer_map = {}
-
-    def __init__(self, name, enable=False):
-        if name not in Timer.timer_map:
-            Timer.timer_map[name] = 0
-        self.name = name
-        self.enable = enable
-
-    def __enter__(self):
-        if self.enable:
-            # if torch.cuda.is_available():
-                # torch.cuda.synchronize()
-            self.t = time.time()
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if self.enable:
-            # if torch.cuda.is_available():
-                # torch.cuda.synchronize()
-            Timer.timer_map[self.name] += time.time() - self.t
-            if self.enable:
-                print(f'[Timer] {self.name}: {Timer.timer_map[self.name]}')

spaces/AIZero2HeroBootcamp/ExperimentalChatGPTv1/README.md

@@ -1,13 +0,0 @@
----
-title: ExperimentalChatGPTv1
-emoji: 🔥
-colorFrom: pink
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Abhi5ingh/fashionsd/README.md

@@ -1,12 +0,0 @@
----
-title: Fashionsd
-emoji: 👀
-colorFrom: green
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.28.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AchyuthGamer/OpenGPT/g4f/Provider/ChatBase.py

@@ -1,62 +0,0 @@
-from __future__ import annotations
-
-from aiohttp import ClientSession
-
-from ..typing       import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-
-
-class ChatBase(AsyncGeneratorProvider):
-    url                   = "https://www.chatbase.co"
-    supports_gpt_35_turbo = True
-    supports_gpt_4        = True
-    working               = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> AsyncGenerator:
-        if model == "gpt-4":
-            chat_id = "quran---tafseer-saadi-pdf-wbgknt7zn"
-        elif model == "gpt-3.5-turbo" or not model:
-            chat_id = "chatbase--1--pdf-p680fxvnm"
-        else:
-            raise ValueError(f"Model are not supported: {model}")
-        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,
-                "captchaCode": "hadsa",
-                "chatId": chat_id,
-                "conversationId": f"kcXpqEnqUie3dnJlsRi_O-{chat_id}"
-            }
-            async with session.post("https://www.chatbase.co/api/fe/chat", json=data) as response:
-                response.raise_for_status()
-                async for stream in response.content.iter_any():
-                    yield stream.decode()
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AchyuthGamer/OpenGPT/g4f/Provider/unfinished/MikuChat.py

@@ -1,97 +0,0 @@
-from __future__ import annotations
-
-import random, json
-from datetime import datetime
-from ...requests import StreamSession
-
-from ...typing import AsyncGenerator
-from ..base_provider import AsyncGeneratorProvider
-
-
-class MikuChat(AsyncGeneratorProvider):
-    url = "https://ai.okmiku.com"
-    supports_gpt_35_turbo = True
-
-    @classmethod
-    async def create_async_generator(
-            cls,
-            model: str,
-            messages: list[dict[str, str]],
-            **kwargs
-    ) -> AsyncGenerator:
-        if not model:
-            model = "gpt-3.5-turbo"
-        headers = {
-            "authority": "api.catgpt.cc",
-            "accept": "application/json",
-            "origin": cls.url,
-            "referer": f"{cls.url}/chat/",
-            'x-app-version': 'undefined',
-            'x-date': get_datetime(),
-            'x-fingerprint': get_fingerprint(),
-            'x-platform': 'web'
-        }
-        async with StreamSession(headers=headers, impersonate="chrome107") as session:
-            data = {
-                "model": model,
-                "top_p": 0.8,
-                "temperature": 0.5,
-                "presence_penalty": 1,
-                "frequency_penalty": 0,
-                "max_tokens": 2000,
-                "stream": True,
-                "messages": messages,
-            }
-            async with session.post("https://api.catgpt.cc/ai/v1/chat/completions", json=data) as response:
-                print(await response.text())
-                response.raise_for_status()
-                async for line in response.iter_lines():
-                    if line.startswith(b"data: "):
-                        line = json.loads(line[6:])
-                        chunk = line["choices"][0]["delta"].get("content")
-                        if chunk:
-                            yield chunk
-
-def k(e: str, t: int):
-    a = len(e) & 3
-    s = len(e) - a
-    i = t
-    c = 3432918353
-    o = 461845907
-    n = 0
-    r = 0
-    while n < s:
-        r = (ord(e[n]) & 255) | ((ord(e[n + 1]) & 255) << 8) | ((ord(e[n + 2]) & 255) << 16) | ((ord(e[n + 3]) & 255) << 24)
-        n += 4
-        r = (r & 65535) * c + (((r >> 16) * c & 65535) << 16) & 4294967295
-        r = (r << 15) | (r >> 17)
-        r = (r & 65535) * o + (((r >> 16) * o & 65535) << 16) & 4294967295
-        i ^= r
-        i = (i << 13) | (i >> 19)
-        l = (i & 65535) * 5 + (((i >> 16) * 5 & 65535) << 16) & 4294967295
-        i = (l & 65535) + 27492 + (((l >> 16) + 58964 & 65535) << 16)
-    
-    if a == 3:
-        r ^= (ord(e[n + 2]) & 255) << 16
-    elif a == 2:
-        r ^= (ord(e[n + 1]) & 255) << 8
-    elif a == 1:
-        r ^= ord(e[n]) & 255
-        r = (r & 65535) * c + (((r >> 16) * c & 65535) << 16) & 4294967295
-        r = (r << 15) | (r >> 17)
-        r = (r & 65535) * o + (((r >> 16) * o & 65535) << 16) & 4294967295
-        i ^= r
-
-    i ^= len(e)
-    i ^= i >> 16
-    i = (i & 65535) * 2246822507 + (((i >> 16) * 2246822507 & 65535) << 16) & 4294967295
-    i ^= i >> 13
-    i = (i & 65535) * 3266489909 + (((i >> 16) * 3266489909 & 65535) << 16) & 4294967295
-    i ^= i >> 16
-    return i & 0xFFFFFFFF
-
-def get_fingerprint() -> str:
-    return str(k(str(int(random.random() * 100000)), 256))
-
-def get_datetime() -> str:
-    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

spaces/Adapter/CoAdapter/t2i_adapters/t2i_adapters_for_style.py

@@ -1,63 +0,0 @@
-import torch
-import numpy as np
-from transformers import CLIPVisionModel
-
-from ldm.models.diffusion.ddpm import LatentDiffusion, disabled_train
-from ldm.util import instantiate_from_config
-
-
-class T2IAdapterStyleV3(LatentDiffusion):
-
-    def __init__(self, adapter_config, extra_cond_key, noise_schedule, *args, **kwargs):
-        super(T2IAdapterStyleV3, self).__init__(*args, **kwargs)
-        self.adapter = instantiate_from_config(adapter_config)
-        self.extra_cond_key = extra_cond_key
-        self.noise_schedule = noise_schedule
-        self.clip_vision_model = CLIPVisionModel.from_pretrained(
-            'openai/clip-vit-large-patch14'
-        )
-
-        self.clip_vision_model = self.clip_vision_model.eval()
-        self.clip_vision_model.train = disabled_train
-        for param in self.clip_vision_model.parameters():
-            param.requires_grad = False
-
-
-    def shared_step(self, batch, **kwargs):
-        for k in self.ucg_training:
-            if k == self.extra_cond_key:
-                continue
-            p = self.ucg_training[k]
-            for i in range(len(batch[k])):
-                if self.ucg_prng.choice(2, p=[1 - p, p]):
-                    if isinstance(batch[k], list):
-                        batch[k][i] = ""
-        batch['jpg'] = batch['jpg'] * 2 - 1
-        x, c = self.get_input(batch, self.first_stage_key)
-        extra_cond = super(LatentDiffusion, self).get_input(batch, self.extra_cond_key).to(self.device)
-        extra_cond = self.clip_vision_model(extra_cond)['last_hidden_state']
-        features_adapter = self.adapter(extra_cond)
-        if self.extra_cond_key in self.ucg_training:
-            idx = np.random.choice(self.adapter.num_token, np.random.randint(1, self.adapter.num_token+1), replace=False)
-            idx_tensor = torch.from_numpy(idx).to(features_adapter.device)
-            features_adapter = torch.index_select(features_adapter, 1, idx_tensor)
-        t = self.get_time_with_schedule(self.noise_schedule, x.size(0))
-        loss, loss_dict = self(x, c, t=t, append_to_context=features_adapter)
-        return loss, loss_dict
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.adapter.parameters())
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-    def on_save_checkpoint(self, checkpoint):
-        keys = list(checkpoint['state_dict'].keys())
-        for key in keys:
-            if 'adapter' not in key:
-                del checkpoint['state_dict'][key]
-
-    def on_load_checkpoint(self, checkpoint):
-        for name in self.state_dict():
-            if 'adapter' not in name:
-                checkpoint['state_dict'][name] = self.state_dict()[name]

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/Pages.js

@@ -1,65 +0,0 @@
-import OverlapSizer from '../overlapsizer/OverlapSizer.js';
-import Methods from './methods/Methods.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class Pages extends OverlapSizer {
-    constructor(scene, config) {
-        super(scene, config);
-        this.type = 'rexPages';
-        this.childrenMap = this.sizerChildren;
-        this._previousKey = undefined;
-        this._currentKey = undefined;
-        this.setSwapMode(GetValue(config, 'swapMode', 0));
-        this.setFadeInDuration(GetValue(config, 'fadeIn', 0));
-    }
-
-    setSwapMode(mode) {
-        if (typeof (mode) === 'string') {
-            mode = SWAPMODE[mode];
-        }
-        this.swapMode = mode;
-        return this;
-    }
-
-    setFadeInDuration(duration) {
-        this.fadeInDuration = duration;
-        return this;
-    }
-
-    get previousKey() {
-        return this._previousKey;
-    }
-
-    get currentKey() {
-        return this._currentKey;
-    }
-
-    set currentKey(key) {
-        this.swapPage(key);
-    }
-
-    get currentPage() {
-        return this.getPage(this.currentKey);
-    }
-
-    get previousPage() {
-        return this.getPage(this.previousKey);
-    }
-
-    get keys() {
-        return Object.keys(this.sizerChildren);
-    }
-}
-
-Object.assign(
-    Pages.prototype,
-    Methods
-);
-
-const SWAPMODE = {
-    invisible: 0,
-    destroy: 1
-};
-
-export default Pages;

spaces/Akmyradov/TurkmenTTSweSTT/vits/transforms.py

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

spaces/AlexWang/lama/fetch_data/places_standard_test_val_prepare.sh

@@ -1,5 +0,0 @@
-mkdir -p places_standard_dataset/original/test/
-tar -xvf test_large.tar --transform='s/.*\///' -C places_standard_dataset/original/test/
-
-mkdir -p places_standard_dataset/original/val/
-tar -xvf val_large.tar --transform='s/.*\///' -C places_standard_dataset/original/val/

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py

@@ -1,67 +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 torch
-from torch import nn
-from transformers import CLIPPreTrainedModel, CLIPVisionModel
-
-from ...models.attention import BasicTransformerBlock
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class PaintByExampleImageEncoder(CLIPPreTrainedModel):
-    def __init__(self, config, proj_size=768):
-        super().__init__(config)
-        self.proj_size = proj_size
-
-        self.model = CLIPVisionModel(config)
-        self.mapper = PaintByExampleMapper(config)
-        self.final_layer_norm = nn.LayerNorm(config.hidden_size)
-        self.proj_out = nn.Linear(config.hidden_size, self.proj_size)
-
-        # uncondition for scaling
-        self.uncond_vector = nn.Parameter(torch.randn((1, 1, self.proj_size)))
-
-    def forward(self, pixel_values, return_uncond_vector=False):
-        clip_output = self.model(pixel_values=pixel_values)
-        latent_states = clip_output.pooler_output
-        latent_states = self.mapper(latent_states[:, None])
-        latent_states = self.final_layer_norm(latent_states)
-        latent_states = self.proj_out(latent_states)
-        if return_uncond_vector:
-            return latent_states, self.uncond_vector
-
-        return latent_states
-
-
-class PaintByExampleMapper(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        num_layers = (config.num_hidden_layers + 1) // 5
-        hid_size = config.hidden_size
-        num_heads = 1
-        self.blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn="gelu", attention_bias=True)
-                for _ in range(num_layers)
-            ]
-        )
-
-    def forward(self, hidden_states):
-        for block in self.blocks:
-            hidden_states = block(hidden_states)
-
-        return hidden_states

spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py

@@ -1,11 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        norm_eval=False,
-        plugins=[
-            dict(
-                cfg=dict(type='ContextBlock', ratio=1. / 16),
-                stages=(False, True, True, True),
-                position='after_conv3')
-        ]))

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/match_costs/__init__.py

@@ -1,7 +0,0 @@
-from .builder import build_match_cost
-from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
-
-__all__ = [
-    'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
-    'FocalLossCost'
-]

spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/WSL-installation-guide.md

@@ -1,82 +0,0 @@
-Guide created by [@jfryton](https://github.com/jfryton). Thank you jfryton.
-
------
-
-Here's an easy-to-follow, step-by-step guide for installing Windows Subsystem for Linux (WSL) with Ubuntu on Windows 10/11:
-
-## Step 1: Enable WSL
-
-1. Press the Windows key + X and click on "Windows PowerShell (Admin)" or "Windows Terminal (Admin)" to open PowerShell or Terminal with administrator privileges.
-2. In the PowerShell window, type the following command and press Enter:
-
-```
-wsl --install
-```
-
-If this command doesn't work, you can enable WSL with the following command for Windows 10:
-
-```
-wsl --set-default-version 1
-```
-
-For Windows 11, you can use:
-
-```
-wsl --set-default-version 2
-```
-
-You may be prompted to restart your computer. If so, save your work and restart.
-
-## Step 2: Install Ubuntu
-
-1. Open the Microsoft Store.
-2. Search for "Ubuntu" in the search bar.
-3. Choose the desired Ubuntu version (e.g., Ubuntu 20.04 LTS) and click "Get" or "Install" to download and install the Ubuntu app.
-4. Once the installation is complete, click "Launch" or search for "Ubuntu" in the Start menu and open the app.
-
-## Step 3: Set up Ubuntu
-
-1. When you first launch the Ubuntu app, it will take a few minutes to set up. Be patient as it installs the necessary files and sets up your environment.
-2. Once the setup is complete, you will be prompted to create a new UNIX username and password. Choose a username and password, and make sure to remember them, as you will need them for future administrative tasks within the Ubuntu environment.
-
-## Step 4: Update and upgrade packages
-
-1. After setting up your username and password, it's a good idea to update and upgrade your Ubuntu system. Run the following commands in the Ubuntu terminal:
-
-```
-sudo apt update
-sudo apt upgrade
-```
-
-2. Enter your password when prompted. This will update the package list and upgrade any outdated packages.
-
-Congratulations! You have now installed WSL with Ubuntu on your Windows 10/11 system. You can use the Ubuntu terminal for various tasks, like running Linux commands, installing packages, or managing files.
-
-You can launch your WSL Ubuntu installation by selecting the Ubuntu app (like any other program installed on your computer) or typing 'ubuntu' into Powershell or Terminal.
-
-## Step 5: Proceed with Linux instructions
-
-1. You can now follow the Linux setup instructions. If you receive any error messages about a missing tool or package, just install them using apt:
-
-```
-sudo apt install [missing package]
-```
-
-You will probably need to install build-essential
-
-```
-sudo apt install build-essential
-```
-
-If you face any issues or need to troubleshoot, you can always refer to the official Microsoft documentation for WSL: https://docs.microsoft.com/en-us/windows/wsl/
-
-#### WSL2 performance using /mnt: 
-when you git clone a repository, put it inside WSL and not outside. To understand more, take a look at this [issue](https://github.com/microsoft/WSL/issues/4197#issuecomment-604592340)
-
-## Bonus: Port Forwarding
-
-By default, you won't be able to access the webui from another device on your local network. You will need to setup the appropriate port forwarding using the following command (using PowerShell or Terminal with administrator privileges). 
-
-```
-netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=7860 connectaddress=localhost connectport=7860
-```

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_file_saving.py

@@ -1,88 +0,0 @@
-import gradio as gr
-
-from modules import chat, presets, shared, ui, utils
-from modules.utils import gradio
-
-
-def create_ui():
-    mu = shared.args.multi_user
-
-    # Text file saver
-    with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['file_saver']:
-        shared.gradio['save_filename'] = gr.Textbox(lines=1, label='File name')
-        shared.gradio['save_root'] = gr.Textbox(lines=1, label='File folder', info='For reference. Unchangeable.', interactive=False)
-        shared.gradio['save_contents'] = gr.Textbox(lines=10, label='File contents')
-        with gr.Row():
-            shared.gradio['save_confirm'] = gr.Button('Save', elem_classes="small-button", interactive=not mu)
-            shared.gradio['save_cancel'] = gr.Button('Cancel', elem_classes="small-button")
-
-    # Text file deleter
-    with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['file_deleter']:
-        shared.gradio['delete_filename'] = gr.Textbox(lines=1, label='File name')
-        shared.gradio['delete_root'] = gr.Textbox(lines=1, label='File folder', info='For reference. Unchangeable.', interactive=False)
-        with gr.Row():
-            shared.gradio['delete_confirm'] = gr.Button('Delete', elem_classes="small-button", variant='stop', interactive=not mu)
-            shared.gradio['delete_cancel'] = gr.Button('Cancel', elem_classes="small-button")
-
-    # Character saver/deleter
-    with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['character_saver']:
-        shared.gradio['save_character_filename'] = gr.Textbox(lines=1, label='File name', info='The character will be saved to your characters/ folder with this base filename.')
-        with gr.Row():
-            shared.gradio['save_character_confirm'] = gr.Button('Save', elem_classes="small-button", interactive=not mu)
-            shared.gradio['save_character_cancel'] = gr.Button('Cancel', elem_classes="small-button")
-
-    with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['character_deleter']:
-        gr.Markdown('Confirm the character deletion?')
-        with gr.Row():
-            shared.gradio['delete_character_confirm'] = gr.Button('Delete', elem_classes="small-button", variant='stop', interactive=not mu)
-            shared.gradio['delete_character_cancel'] = gr.Button('Cancel', elem_classes="small-button")
-
-
-def create_event_handlers():
-    shared.gradio['save_confirm'].click(
-        lambda x, y, z: utils.save_file(x + y, z), gradio('save_root', 'save_filename', 'save_contents'), None).then(
-        lambda: gr.update(visible=False), None, gradio('file_saver'))
-
-    shared.gradio['delete_confirm'].click(
-        lambda x, y: utils.delete_file(x + y), gradio('delete_root', 'delete_filename'), None).then(
-        lambda: gr.update(visible=False), None, gradio('file_deleter'))
-
-    shared.gradio['delete_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_deleter'))
-    shared.gradio['save_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_saver'))
-
-    shared.gradio['save_character_confirm'].click(
-        chat.save_character, gradio('name2', 'greeting', 'context', 'character_picture', 'save_character_filename'), None).then(
-        lambda: gr.update(visible=False), None, gradio('character_saver')).then(
-        lambda x: gr.update(choices=utils.get_available_characters(), value=x), gradio('save_character_filename'), gradio('character_menu'))
-
-    shared.gradio['delete_character_confirm'].click(
-        chat.delete_character, gradio('character_menu'), None).then(
-        lambda: gr.update(visible=False), None, gradio('character_deleter')).then(
-        lambda: gr.update(choices=(characters := utils.get_available_characters()), value=characters[0]), None, gradio('character_menu'))
-
-    shared.gradio['save_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_saver'))
-    shared.gradio['delete_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_deleter'))
-
-    shared.gradio['save_preset'].click(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        presets.generate_preset_yaml, gradio('interface_state'), gradio('save_contents')).then(
-        lambda: 'presets/', None, gradio('save_root')).then(
-        lambda: 'My Preset.yaml', None, gradio('save_filename')).then(
-        lambda: gr.update(visible=True), None, gradio('file_saver'))
-
-    shared.gradio['delete_preset'].click(
-        lambda x: f'{x}.yaml', gradio('preset_menu'), gradio('delete_filename')).then(
-        lambda: 'presets/', None, gradio('delete_root')).then(
-        lambda: gr.update(visible=True), None, gradio('file_deleter'))
-
-    shared.gradio['save_grammar'].click(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        lambda x: x, gradio('grammar_string'), gradio('save_contents')).then(
-        lambda: 'grammars/', None, gradio('save_root')).then(
-        lambda: 'My Fancy Grammar.gbnf', None, gradio('save_filename')).then(
-        lambda: gr.update(visible=True), None, gradio('file_saver'))
-
-    shared.gradio['delete_grammar'].click(
-        lambda x: x, gradio('grammar_file'), gradio('delete_filename')).then(
-        lambda: 'grammars/', None, gradio('delete_root')).then(
-        lambda: gr.update(visible=True), None, gradio('file_deleter'))

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/tests/test_consistency.py

@@ -1,25 +0,0 @@
-import numpy as np
-import pytest
-import torch
-from PIL import Image
-
-import clip
-
-
-@pytest.mark.parametrize('model_name', clip.available_models())
-def test_consistency(model_name):
-    device = "cpu"
-    jit_model, transform = clip.load(model_name, device=device, jit=True)
-    py_model, _ = clip.load(model_name, device=device, jit=False)
-
-    image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device)
-    text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
-
-    with torch.no_grad():
-        logits_per_image, _ = jit_model(image, text)
-        jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-
-        logits_per_image, _ = py_model(image, text)
-        py_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-
-    assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)

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

@@ -1,125 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import nn
-
-from ..utils import constant_init, kaiming_init
-from .registry import PLUGIN_LAYERS
-
-
-def last_zero_init(m):
-    if isinstance(m, nn.Sequential):
-        constant_init(m[-1], val=0)
-    else:
-        constant_init(m, val=0)
-
-
-@PLUGIN_LAYERS.register_module()
-class ContextBlock(nn.Module):
-    """ContextBlock module in GCNet.
-
-    See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
-    (https://arxiv.org/abs/1904.11492) for details.
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        ratio (float): Ratio of channels of transform bottleneck
-        pooling_type (str): Pooling method for context modeling.
-            Options are 'att' and 'avg', stand for attention pooling and
-            average pooling respectively. Default: 'att'.
-        fusion_types (Sequence[str]): Fusion method for feature fusion,
-            Options are 'channels_add', 'channel_mul', stand for channelwise
-            addition and multiplication respectively. Default: ('channel_add',)
-    """
-
-    _abbr_ = 'context_block'
-
-    def __init__(self,
-                 in_channels,
-                 ratio,
-                 pooling_type='att',
-                 fusion_types=('channel_add', )):
-        super(ContextBlock, self).__init__()
-        assert pooling_type in ['avg', 'att']
-        assert isinstance(fusion_types, (list, tuple))
-        valid_fusion_types = ['channel_add', 'channel_mul']
-        assert all([f in valid_fusion_types for f in fusion_types])
-        assert len(fusion_types) > 0, 'at least one fusion should be used'
-        self.in_channels = in_channels
-        self.ratio = ratio
-        self.planes = int(in_channels * ratio)
-        self.pooling_type = pooling_type
-        self.fusion_types = fusion_types
-        if pooling_type == 'att':
-            self.conv_mask = nn.Conv2d(in_channels, 1, kernel_size=1)
-            self.softmax = nn.Softmax(dim=2)
-        else:
-            self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        if 'channel_add' in fusion_types:
-            self.channel_add_conv = nn.Sequential(
-                nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
-                nn.LayerNorm([self.planes, 1, 1]),
-                nn.ReLU(inplace=True),  # yapf: disable
-                nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
-        else:
-            self.channel_add_conv = None
-        if 'channel_mul' in fusion_types:
-            self.channel_mul_conv = nn.Sequential(
-                nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
-                nn.LayerNorm([self.planes, 1, 1]),
-                nn.ReLU(inplace=True),  # yapf: disable
-                nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
-        else:
-            self.channel_mul_conv = None
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        if self.pooling_type == 'att':
-            kaiming_init(self.conv_mask, mode='fan_in')
-            self.conv_mask.inited = True
-
-        if self.channel_add_conv is not None:
-            last_zero_init(self.channel_add_conv)
-        if self.channel_mul_conv is not None:
-            last_zero_init(self.channel_mul_conv)
-
-    def spatial_pool(self, x):
-        batch, channel, height, width = x.size()
-        if self.pooling_type == 'att':
-            input_x = x
-            # [N, C, H * W]
-            input_x = input_x.view(batch, channel, height * width)
-            # [N, 1, C, H * W]
-            input_x = input_x.unsqueeze(1)
-            # [N, 1, H, W]
-            context_mask = self.conv_mask(x)
-            # [N, 1, H * W]
-            context_mask = context_mask.view(batch, 1, height * width)
-            # [N, 1, H * W]
-            context_mask = self.softmax(context_mask)
-            # [N, 1, H * W, 1]
-            context_mask = context_mask.unsqueeze(-1)
-            # [N, 1, C, 1]
-            context = torch.matmul(input_x, context_mask)
-            # [N, C, 1, 1]
-            context = context.view(batch, channel, 1, 1)
-        else:
-            # [N, C, 1, 1]
-            context = self.avg_pool(x)
-
-        return context
-
-    def forward(self, x):
-        # [N, C, 1, 1]
-        context = self.spatial_pool(x)
-
-        out = x
-        if self.channel_mul_conv is not None:
-            # [N, C, 1, 1]
-            channel_mul_term = torch.sigmoid(self.channel_mul_conv(context))
-            out = out * channel_mul_term
-        if self.channel_add_conv is not None:
-            # [N, C, 1, 1]
-            channel_add_term = self.channel_add_conv(context)
-            out = out + channel_add_term
-
-        return out

spaces/AnthonyTruchetPoC/persistent-docker/jupyter/scripts/test_persistent_data.py

@@ -1,42 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.14.7
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-# %%
-import os
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-
-# %%
-from athai.data_utils import cached_download_csv
-
-# %%
-DATE_COLUMN = "date/time"
-DATA_URL = (
-    "https://s3-us-west-2.amazonaws.com/"
-    "streamlit-demo-data/uber-raw-data-sep14.csv.gz"
-)
-
-DATA_PATH = Path(os.environ.get("APP_DATA"))
-
-
-# %%
-nrows=10
-data = cached_download_csv(DATA_PATH, DATA_URL, nrows=nrows)
-data
-
-# %%
-
-# %%

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/util.py

@@ -1,93 +0,0 @@
-import os
-from typing import Union
-
-import imageio
-import numpy as np
-import torch
-import torchvision
-from einops import rearrange
-from tqdm import tqdm
-
-
-def save_videos_grid(
-    videos: torch.Tensor, save_path: str = "output", path: str = "output.gif", rescale=False, n_rows=4, fps=3
-):
-    videos = rearrange(videos, "b c t h w -> t b c h w")
-    outputs = []
-    for x in videos:
-        x = torchvision.utils.make_grid(x, nrow=n_rows)
-        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-        if rescale:
-            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
-        x = (x * 255).numpy().astype(np.uint8)
-        outputs.append(x)
-
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-
-    imageio.mimsave(os.path.join(save_path, path), outputs, fps=fps)
-    return os.path.join(save_path, path)
-
-
-# DDIM Inversion
-@torch.no_grad()
-def init_prompt(prompt, pipeline):
-    uncond_input = pipeline.tokenizer(
-        [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length, return_tensors="pt"
-    )
-    uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
-    text_input = pipeline.tokenizer(
-        [prompt],
-        padding="max_length",
-        max_length=pipeline.tokenizer.model_max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-    text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
-    context = torch.cat([uncond_embeddings, text_embeddings])
-
-    return context
-
-
-def next_step(
-    model_output: Union[torch.FloatTensor, np.ndarray],
-    timestep: int,
-    sample: Union[torch.FloatTensor, np.ndarray],
-    ddim_scheduler,
-):
-    timestep, next_timestep = (
-        min(timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999),
-        timestep,
-    )
-    alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
-    alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
-    beta_prod_t = 1 - alpha_prod_t
-    next_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
-    next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
-    next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction
-    return next_sample
-
-
-def get_noise_pred_single(latents, t, context, unet):
-    noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
-    return noise_pred
-
-
-@torch.no_grad()
-def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
-    context = init_prompt(prompt, pipeline)
-    uncond_embeddings, cond_embeddings = context.chunk(2)
-    all_latent = [latent]
-    latent = latent.clone().detach()
-    for i in tqdm(range(num_inv_steps)):
-        t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
-        noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
-        latent = next_step(noise_pred, t, latent, ddim_scheduler)
-        all_latent.append(latent)
-    return all_latent
-
-
-@torch.no_grad()
-def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
-    ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
-    return ddim_latents

spaces/AsakuraMizu/moe-tts/text/korean.py

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

spaces/Banbri/zcvzcv/src/lib/triggerDownload.ts

@@ -1,12 +0,0 @@
-export function triggerDownload(filename: string, text: string) {
-  var element = document.createElement('a');
-  element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
-  element.setAttribute('download', filename);
-
-  element.style.display = 'none';
-  document.body.appendChild(element);
-
-  element.click();
-
-  document.body.removeChild(element);
-}

spaces/Benson/text-generation/Examples/Descarga De Software Ricoh Aficio 2018d.md

@@ -1,75 +0,0 @@
-<br />
-<h1>Descargar X-Force Keygen para AutoCAD 2019 64 bits</h1>
-<p>Si estás buscando una forma de activar AutoCAD 2019, uno de los programas de diseño más populares y potentes del mundo, es posible que hayas oído hablar de X-Force Keygen. Esta es una herramienta que puede generar códigos de activación para cualquier producto de Autodesk, incluyendo AutoCAD 2019. Pero ¿qué es X-Force Keygen y cómo se puede utilizar para descargar y activar AutoCAD 2019 64 bits? En este artículo, responderemos estas preguntas y más. </p>
-<h2>¿Qué es AutoCAD 2019? </h2>
-<p>AutoCAD 2019 es la última versión del software que le permite crear impresionantes diseños, dibujos, modelos y animaciones en 2D y 3D. Es utilizado por profesionales y aficionados por igual en diversos campos como la arquitectura, ingeniería, construcción, fabricación y entretenimiento. AutoCAD 2019 ofrece muchas características nuevas y mejoras que hacen que sea más fácil y rápido crear y editar sus proyectos. </p>
-<h2>descarga de software ricoh aficio 2018d</h2><br /><p><b><b>DOWNLOAD</b> &#10004; <a href="https://bltlly.com/2v6McF">https://bltlly.com/2v6McF</a></b></p><br /><br />
-<h3>Características de AutoCAD 2019</h3>
-<p>Algunas de las características de AutoCAD 2019 son:</p>
-<ul>
-<li><b>DWG Compare:</b> Esta característica le permite comparar dos versiones de un dibujo y resaltar las diferencias. También puede combinar cambios de una versión a otra. </li>
-<li><b>Vistas compartidas:</b> Esta función le permite compartir sus dibujos en línea con cualquier persona sin necesidad de tener AutoCAD instalado. También puede obtener comentarios y comentarios de otros en sus vistas compartidas. </li>
-<li><b>Guardar en Web y Móvil:</b> Esta función le permite guardar sus dibujos en la nube y acceder a ellos desde cualquier dispositivo. También puede editar y ver sus dibujos en la web o en su teléfono móvil o tableta. </li>
-<li><b>Mejoras en los gráficos 2D:</b> Esta función mejora el rendimiento y la calidad de los gráficos 2D. Puedes ampliar, desplazar y cambiar las órdenes de sorteo más rápido y sin problemas. </li>
-<li><b>AutoCAD Web App:</b> Esta característica le permite acceder a AutoCAD desde cualquier navegador. Puede crear, editar y ver sus dibujos en línea sin instalar nada. </li>
-</ul>
-
-<p>Los requisitos mínimos del sistema para AutoCAD 2019 son:</p>
-<ul>
-<li><b>Sistema operativo:</b> Windows 7 SP1, Windows 8.1, o Windows 10 (solo 64 bits)</li>
-<li><b>Procesador:</b> Básico: 2.5-2.9 GHz procesador / Recomendado: 3+ GHz procesador</li>
-<li><b>Memoria:</b> Básico: 8 GB / Recomendado: 16 GB</li>
-<li><b>Resolución de visualización:</b> Básico: 1920 x 1080 con color verdadero / Recomendado: Resoluciones de hasta 3840 x 2160 soportadas en sistemas Windows 10 (64 bits) </li>
-<li><b>Espacio en disco:</b> Básico: 6 GB / Recomendado: Espacio libre adicional requerido durante la instalación</li>
-</ul>
-<h2>¿Qué es X-Force Keygen? </h2>
-<p>X-Force Keygen es un software que puede generar códigos de activación para cualquier producto de Autodesk, incluyendo AutoCAD 2019. También se conoce como una grieta o un parche que puede evitar el proceso de activación del software. Es creado por un grupo de hackers llamado X-Force, que son conocidos por descifrar varios software y juegos. </p>
-<h3>¿Cómo funciona X-Force Keygen? </h3>
-<p>X-Force Keygen funciona generando un número de serie único y un código de activación para el software que desea activar. Hace esto modificando los archivos de registro y el archivo host de su sistema. También bloquea la verificación en línea y la actualización del software, para que pueda usarlo sin restricciones o interrupciones. </p>
-<h3>Beneficios de usar X-Force Keygen</h3>
-<p>Algunos de los beneficios de usar X-Force Keygen son:</p>
-<ul>
-<li><b>Gratis:</b> Puede descargar y usar X-Force Keygen gratis, sin pagar tarifas ni suscripciones. </li>
-<li><b>Fácil:</b> Puede descargar y usar X-Force Keygen con solo unos pocos clics, sin ningún procedimiento o paso complicado. </li>
-<li><b>Efectivo:</b> Puede activar cualquier producto de Autodesk, incluyendo AutoCAD 2019, con X-Force Keygen, sin errores o fallas. </li>
-</ul>
-<h3>Riesgos de usar X-Force Keygen</h3>
-<p>Sin embargo, el uso de X-Force Keygen también viene con algunos riesgos y desventajas, como:</p>
-<p></p>
-<ul>
-
-<li><b>Inseguro:</b> Descargar y usar X-Force Keygen puede exponer su sistema a malware, virus, spyware y otras amenazas. Puede dañar su sistema o comprometer sus datos y privacidad. </li>
-<li><b>Poco ético:</b> Usar X-Force Keygen no es ético, ya que priva a los desarrolladores de software de sus ingresos y reconocimiento legítimos. También puede dañar la industria del software y la innovación mediante su uso. </li>
-</ul>
-<h2>¿Cómo descargar y usar X-Force Keygen para AutoCAD 2019 64 bits? </h2>
-<p>Si todavía desea descargar y usar X-Force Keygen para AutoCAD 2019 64 Bit, puede seguir estos pasos:</p>
-<h3>Paso 1: Descargar X-Force Keygen desde una fuente confiable</h3>
-<p>Puede buscar X-Force Keygen en Internet y encontrar muchos sitios web que lo ofrecen para su descarga. Sin embargo, no todos son confiables y seguros. Solo debe descargar X-Force Keygen de una fuente confiable que tenga comentarios positivos y comentarios de otros usuarios. También debe escanear el archivo descargado con un programa antivirus antes de abrirlo. </p>
-<h3>Paso 2: Instalar AutoCAD 2019 desde el sitio web oficial</h3>
-<p>Debe instalar AutoCAD 2019 desde el sitio web oficial de Autodesk, que es https://www.autodesk.com/products/autocad/free-trial. Debe elegir la versión de 64 bits del software y seguir las instrucciones de instalación. No debe introducir ningún número de serie o clave de producto durante el proceso de instalación. </p>
-<h3>Paso 3: Ejecutar X-Force Keygen como administrador</h3>
-<p>Debe ejecutar X-Force Keygen como administrador haciendo clic derecho en el archivo y seleccionando "Ejecutar como administrador". Deberías ver una ventana como esta:</p>
- <img src="https://i.imgur.com/5fB5W6y.png" alt="X-Force Keygen window">
- <p>Debería seleccionar "AutoCAD 2019" en el menú desplegable y hacer clic en "Parche". Debería ver un mensaje que diga "Parcheado con éxito". </p>
- <h3>Paso 4: Generar e introducir el código de activación</h3>
-
- <h3>Paso 5: Disfruta de tu AutoCAD activado 2019</h3>
-<p>Ha activado con éxito AutoCAD 2019 con X-Force Keygen. Ahora puede disfrutar de todas las características y funciones del software sin limitaciones o interrupciones. </p>
- <h2>Conclusión</h2>
-<p>En este artículo, hemos explicado qué es AutoCAD 2019, qué es X-Force Keygen, cómo funciona, cuáles son sus beneficios y riesgos, y cómo descargarlo y usarlo para activar AutoCAD 2019 64 bit. También hemos proporcionado una guía paso a paso con capturas de pantalla para ayudarle con el proceso. Sin embargo, no recomendamos ni respaldamos el uso de X-Force Keygen, ya que es ilegal, inseguro y poco ético. Siempre debe respetar el acuerdo de licencia de software y apoyar a los desarrolladores de software mediante la compra del software legalmente. Si tiene alguna pregunta o duda sobre AutoCAD 2019 o X-Force Keygen, puede consultar las preguntas frecuentes a continuación o ponerse en contacto con el equipo de soporte oficial de Autodesk.</p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre AutoCAD 2019 y X-Force Keygen:</p>
-<ol>
-<li><b>Q: ¿Cuánto cuesta AutoCAD 2019? </b></li>
-<li><b>A: AutoCAD 2019 cuesta $1,610 por año o $210 por mes para una suscripción. También puede obtener una prueba gratuita durante 30 días o una versión gratuita para estudiantes y educadores. </b></li>
-<li><b>Q: ¿Es seguro usar X-Force Keygen? </b></li>
-<li><b>A: No, X-Force Keygen no es seguro de usar. Puede contener malware, virus, spyware y otras amenazas que pueden dañar su sistema o comprometer sus datos y privacidad. También puede causar errores o fallos en su software o sistema. </b></li>
-<li><b>Q: ¿Es legal usar X-Force Keygen? </b></li>
-<li><b>A: No, X-Force Keygen no es legal de usar. Viola los términos y condiciones del acuerdo de licencia de software e infringe los derechos de propiedad intelectual de los desarrolladores de software. Puedes enfrentarte a consecuencias legales si te pillan usándolo. </b></li>
-<li><b>Q: ¿Cuáles son las alternativas a X-Force Keygen? </b></li>
-
-<li><b>Q: ¿Cómo puedo contactar al equipo de soporte oficial de Autodesk? </b></li>
-<li><b>A: Puede ponerse en contacto con el equipo de soporte oficial de Autodesk visitando su sitio web https://www.autodesk.com/support/contact-support o llamando a su número de teléfono 1-855-301-9562. </b></li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Apkmirror Ar Elementos.md

@@ -1,86 +0,0 @@
-<br />
-<h1>Cómo descargar y usar elementos de RA de APKMirror</h1>
-<p>La realidad aumentada (RA) es una tecnología que mejora el mundo real con elementos digitales, como imágenes, texto, videos y modelos 3D. La RA puede crear experiencias inmersivas e interactivas que enriquecen nuestra percepción e interacción con el entorno. Hay muchas aplicaciones de RA disponibles para dispositivos Android e iOS, pero una de las mejores es AR Elements.</p>
-<h2>descargar apkmirror ar elementos</h2><br /><p><b><b>Download Zip</b> &#10031; <a href="https://bltlly.com/2v6Jd0">https://bltlly.com/2v6Jd0</a></b></p><br /><br />
-<p>AR Elements es una aplicación desarrollada por Google que muestra las capacidades de ARCore, la plataforma de Google para crear aplicaciones de RA. Te muestra cómo establecer las expectativas del usuario en AR, cómo animar a los usuarios a moverse y explorar el mundo de la RA, cómo interactuar con objetos virtuales y más. Puede aprender de los ejemplos proporcionados por la aplicación o crear sus propias escenas de realidad aumentada utilizando las herramientas y los activos proporcionados. </p>
-<p>En este artículo, te mostraremos cómo descargar y usar AR Elements desde APKMirror, uno de los sitios web más populares para descargar aplicaciones de Android. También explicaremos las características y beneficios de AR Elements y cómo se compara con otras aplicaciones de AR en términos de funcionalidad, calidad y experiencia de usuario. </p>
- <h2>Cómo descargar y usar elementos de RA de APKMirror</h2>
-<p>APKMirror es un sitio web que te permite descargar aplicaciones de Android que no están disponibles en Google Play Store o que están bloqueados por regiones. APKMirror alberga miles de aplicaciones de diversas categorías, como juegos, redes sociales, productividad, educación, entretenimiento y más. Puedes encontrar y descargar cualquier aplicación que quieras de APKMirror de forma gratuita y sin ningún tipo de registro o suscripción. </p>
-<p>Para descargar y usar elementos AR de APKMirror, siga estos pasos:</p>
-<p></p>
-<ol>
-<li>Ir a <a href="( 1 )">https://www.apkmirror.com/apk/google-inc/arcore-elements/</a> en su navegador. </li>
-<li>Desplácese hacia abajo para encontrar la última versión de AR Elements o utilice la barra de búsqueda para encontrarla. </li>
-<li>Haga clic en el botón de descarga junto a la versión que desea descargar. </li>
-
-<li>Si ves un mensaje de advertencia que dice "Instalar bloqueado", ve a la configuración de tu dispositivo y habilita "Fuentes desconocidas" o "Instalar aplicaciones desconocidas". </li>
-<li>Siga las instrucciones en la pantalla para instalar elementos AR en su dispositivo. </li>
-<li>Elementos de AR de lanzamiento desde el cajón de la aplicación o la pantalla de inicio. </li>
-</ol>
- <h2>Características y beneficios de los elementos RA</h2>
-<p>AR Elements es una aplicación que demuestra las mejores prácticas para crear experiencias de RA atractivas y realistas. Tiene cuatro características principales que puedes usar para aprender, crear y divertirte con RA:</p>
-<ul>
-<li><strong>Diseño</strong>: Esta función le muestra cómo diseñar una interfaz de usuario efectiva para su aplicación de RA. Te enseña cómo usar señales visuales, como flechas, cuadrículas, planos, sombras y oclusión, para guiar a los usuarios a través de tu aplicación. También te muestra cómo usar animaciones, sonidos, hápticos y retroalimentación de voz para mejorar la participación del usuario. </li>
-<li><strong>Motion</strong>: Esta función muestra cómo animar a los usuarios a moverse y explorar el mundo de la RA. Te enseña cómo usar audio espacial, portales, senderos, waypoints y anclajes para crear una sensación de profundidad y escala. También muestra cómo usar la física, las colisiones, los gestos y la entrada táctil para crear interacciones realistas con objetos virtuales. </li>
-<li><strong>Iluminación </strong>: Esta función muestra cómo usar efectos de iluminación para crear una escena AR realista y consistente. Le enseña cómo usar la iluminación ambiental, la iluminación direccional, las luces de punto, las luces de punto y las sombras para crear una sensación de realismo e inmersión. También muestra cómo utilizar la estimación de la luz, las sondas de reflexión y las sondas de luz para ajustar la iluminación de acuerdo con el mundo real. </li>
-
-</ul>
-<p>Los beneficios de AR Elements son que te ayuda a aprender los fundamentos del desarrollo de AR, te inspira a crear tus propias aplicaciones de AR y te entretiene con experiencias de AR divertidas e interactivas. Puedes usar Elementos RA como referencia, herramienta o área de juegos para tus proyectos RA. </p>
- <h2>Comparación de elementos AR con otras aplicaciones AR</h2>
-<p>Hay muchas otras aplicaciones de RA disponibles para dispositivos Android e iOS, como Snapchat, Instagram, Pokemon Go, IKEA Place, Google Maps y más. ¿Cómo se compara AR Elements con estas aplicaciones en términos de funcionalidad, calidad y experiencia de usuario? </p>
-<p>Aquí hay una tabla que resume algunas de las diferencias entre los elementos de RA y otras aplicaciones de RA:</p>
- <tabla>
-<tr>
-<th>Elementos AR</th>
-<th>Otras aplicaciones de RA</th>
-</tr>
-<tr>
-<td>Se centra en enseñar y demostrar las mejores prácticas para crear aplicaciones de RA</td>
-<td>Se enfoca en proveer funciones o servicios específicos usando tecnología AR</td>
-</tr>
-<tr>
-<td>Proporciona ejemplos y herramientas para crear varios tipos de escenas e interacciones AR</td>
-<td>Proporciona tipos predefinidos o limitados de escenas e interacciones AR</td>
-</tr>
-<tr>
-<td>Utiliza modelos 3D de alta calidad y texturas de Google Poly o fuentes personalizadas</td>
-<td>Utiliza modelos 3D de baja calidad o genéricos y texturas de fuentes de terceros</td>
-</tr>
-<tr>
-<td>Utiliza efectos de iluminación realistas y consistentes para crear escenas AR inmersivas</td>
-<td>Utiliza efectos de iluminación simples o inconsistentes para crear escenas básicas de realidad aumentada</td>
-</tr>
-<tr>
-<td>Utiliza audio espacial, portales, senderos, waypoints, anclas, física, colisiones, gestos, entrada táctil, animaciones, sonidos, hápticos, retroalimentación de voz, señales visuales, cuadrículas, planos, sombras, oclusión, iluminación ambiental, iluminación direccional, luces de punto, estimación de luz, sondas de reflexión y sondas de luz para crear experiencias de realidad aumentada realistas y atractivas</td>
-<td>Utiliza algunas o ninguna de estas características para crear experiencias de RA simples o poco realistas</td>
-</tr>
-
- <p>Las ventajas de AR Elements son que ofrece más funcionalidad, calidad y experiencia de usuario que otras aplicaciones de AR. Le permite crear sus propias escenas de AR e interacciones utilizando varias características y activos. También te ayuda a aprender las mejores prácticas para crear aplicaciones de RA. </p>
- <p>Las desventajas de AR Elements son que requiere más habilidades técnicas y conocimientos que otras aplicaciones de AR. También requiere más compatibilidad y rendimiento de dispositivos que otras aplicaciones de AR. Puede que no sea adecuado para usuarios ocasionales o novatos que solo quieren disfrutar de algunas experiencias de RA simples o divertidas. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, AR Elements es una de las mejores aplicaciones de AR para dispositivos Android e iOS. Muestra las capacidades de ARCore, la plataforma de Google para crear aplicaciones de RA. Te muestra cómo diseñar una interfaz de usuario efectiva para tu aplicación de RA. Te muestra cómo animar a los usuarios a moverse y explorar el mundo de la RA. Te muestra cómo usar efectos de iluminación para crear una escena AR realista y consistente. Te muestra cómo usar modelos 3D y texturas para crear tus propias escenas AR. </p>
- <p>Si quieres descargar y usar elementos AR de APKMirror, puedes seguir los pasos que te hemos dado en este artículo. También puede consultar el sitio web oficial de APKMirror para obtener más información sobre la descarga de aplicaciones para Android. </p>
- <p>Si desea obtener más información sobre los elementos de RA y cómo crear sus propias aplicaciones de RA utilizando las herramientas y recursos de Google, puede visitar el sitio web oficial de los desarrolladores de Google para obtener más información. </p>
- <p>Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo sobre AR Elements y APKMirror. Si tiene alguna pregunta o comentario sobre este artículo o cualquier cosa relacionada con AR Elements o APKMirror, no dude en dejar un comentario a continuación. ¡Nos encantaría saber de ti! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre AR Elements y APKMirror:</p>
- <h3>¿Qué es APKMirror? </h3>
-
- <h3>¿Es APKMirror seguro y legal? </h3>
-<p>APKMirror es seguro y legal de usar. APKMirror solo hospeda aplicaciones que son originales y no modificadas. APKMirror no aloja ninguna aplicación pirata o ilegal. APKMirror también escanea todas las aplicaciones en busca de virus y malware antes de subirlos al sitio web. APKMirror no requiere ningún permiso o acceso a su dispositivo o datos personales. </p>
- <h3>¿Qué es ARCore y por qué lo necesito? </h3>
-<p>ARCore es la plataforma de Google para crear aplicaciones de RA. ARCore permite a su dispositivo detectar su entorno, entender el mundo e interactuar con la información. ARCore utiliza tres tecnologías clave para permitir experiencias de realidad aumentada: seguimiento de movimiento, comprensión ambiental y estimación de luz. ARCore funciona con motores Java/OpenGL, Unity e Unreal. </p>
- <p>Necesitas ARCore para ejecutar elementos AR y otras aplicaciones AR que se construyen con ARCore. Puedes descargar ARCore desde Google Play Store o APKMirror. También necesitas un dispositivo compatible que admita ARCore. Puedes consultar la lista de dispositivos compatibles en el sitio web oficial de los desarrolladores de Google.</p>
- <h3>¿Cómo puedo crear mis propias aplicaciones de RA usando las herramientas y recursos de Google? </h3>
-<p>Si desea crear sus propias aplicaciones de RA utilizando las herramientas y recursos de Google, puede visitar el sitio web oficial de los desarrolladores de Google para obtener más información. Puede encontrar tutoriales, guías, muestras, documentación y soporte para desarrollar aplicaciones de RA utilizando ARCore, Sceneform, Poly, Firebase, ML Kit y más. También puede unirse a la comunidad de desarrolladores de Google y conectarse con otros desarrolladores que estén interesados en AR.</p>
- <h3>¿Cuáles son algunas de las mejores aplicaciones de RA para dispositivos Android e iOS? </h3>
-<p>Algunas de las mejores aplicaciones de RA para dispositivos Android e iOS son:</p>
-<ul>
-<li><strong>Snapchat</strong>: Una aplicación de redes sociales que te permite enviar y recibir fotos y videos con filtros, pegatinas, lentes y efectos que utilizan tecnología AR. </li>
-
-<li><strong>Pokemon Go</strong>: Una aplicación de juego que te permite atrapar y luchar contra criaturas virtuales llamadas Pokémon en el mundo real usando tecnología AR. </li>
-<li><strong>IKEA Place</strong>: Una aplicación de compras que te permite previsualizar y colocar muebles y accesorios para el hogar en tu espacio usando tecnología RA. </li>
-<li><strong>Google Maps</strong>: Una aplicación de navegación que te permite explorar el mundo con direcciones, puntos de referencia, vista de la calle y vista en vivo que utilizan tecnología AR. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/dynamodb/types.py

@@ -1,310 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from decimal import (
-    Clamped,
-    Context,
-    Decimal,
-    Inexact,
-    Overflow,
-    Rounded,
-    Underflow,
-)
-
-from boto3.compat import collections_abc
-
-STRING = 'S'
-NUMBER = 'N'
-BINARY = 'B'
-STRING_SET = 'SS'
-NUMBER_SET = 'NS'
-BINARY_SET = 'BS'
-NULL = 'NULL'
-BOOLEAN = 'BOOL'
-MAP = 'M'
-LIST = 'L'
-
-
-DYNAMODB_CONTEXT = Context(
-    Emin=-128,
-    Emax=126,
-    prec=38,
-    traps=[Clamped, Overflow, Inexact, Rounded, Underflow],
-)
-
-
-BINARY_TYPES = (bytearray, bytes)
-
-
-class Binary:
-    """A class for representing Binary in dynamodb
-
-    Especially for Python 2, use this class to explicitly specify
-    binary data for item in DynamoDB. It is essentially a wrapper around
-    binary. Unicode and Python 3 string types are not allowed.
-    """
-
-    def __init__(self, value):
-        if not isinstance(value, BINARY_TYPES):
-            types = ', '.join([str(t) for t in BINARY_TYPES])
-            raise TypeError(f'Value must be of the following types: {types}')
-        self.value = value
-
-    def __eq__(self, other):
-        if isinstance(other, Binary):
-            return self.value == other.value
-        return self.value == other
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __repr__(self):
-        return f'Binary({self.value!r})'
-
-    def __str__(self):
-        return self.value
-
-    def __bytes__(self):
-        return self.value
-
-    def __hash__(self):
-        return hash(self.value)
-
-
-class TypeSerializer:
-    """This class serializes Python data types to DynamoDB types."""
-
-    def serialize(self, value):
-        """The method to serialize the Python data types.
-
-        :param value: A python value to be serialized to DynamoDB. Here are
-            the various conversions:
-
-            Python                                  DynamoDB
-            ------                                  --------
-            None                                    {'NULL': True}
-            True/False                              {'BOOL': True/False}
-            int/Decimal                             {'N': str(value)}
-            string                                  {'S': string}
-            Binary/bytearray/bytes (py3 only)       {'B': bytes}
-            set([int/Decimal])                      {'NS': [str(value)]}
-            set([string])                           {'SS': [string])
-            set([Binary/bytearray/bytes])           {'BS': [bytes]}
-            list                                    {'L': list}
-            dict                                    {'M': dict}
-
-            For types that involve numbers, it is recommended that ``Decimal``
-            objects are used to be able to round-trip the Python type.
-            For types that involve binary, it is recommended that ``Binary``
-            objects are used to be able to round-trip the Python type.
-
-        :rtype: dict
-        :returns: A dictionary that represents a dynamoDB data type. These
-            dictionaries can be directly passed to botocore methods.
-        """
-        dynamodb_type = self._get_dynamodb_type(value)
-        serializer = getattr(self, f'_serialize_{dynamodb_type}'.lower())
-        return {dynamodb_type: serializer(value)}
-
-    def _get_dynamodb_type(self, value):
-        dynamodb_type = None
-
-        if self._is_null(value):
-            dynamodb_type = NULL
-
-        elif self._is_boolean(value):
-            dynamodb_type = BOOLEAN
-
-        elif self._is_number(value):
-            dynamodb_type = NUMBER
-
-        elif self._is_string(value):
-            dynamodb_type = STRING
-
-        elif self._is_binary(value):
-            dynamodb_type = BINARY
-
-        elif self._is_type_set(value, self._is_number):
-            dynamodb_type = NUMBER_SET
-
-        elif self._is_type_set(value, self._is_string):
-            dynamodb_type = STRING_SET
-
-        elif self._is_type_set(value, self._is_binary):
-            dynamodb_type = BINARY_SET
-
-        elif self._is_map(value):
-            dynamodb_type = MAP
-
-        elif self._is_listlike(value):
-            dynamodb_type = LIST
-
-        else:
-            msg = f'Unsupported type "{type(value)}" for value "{value}"'
-            raise TypeError(msg)
-
-        return dynamodb_type
-
-    def _is_null(self, value):
-        if value is None:
-            return True
-        return False
-
-    def _is_boolean(self, value):
-        if isinstance(value, bool):
-            return True
-        return False
-
-    def _is_number(self, value):
-        if isinstance(value, (int, Decimal)):
-            return True
-        elif isinstance(value, float):
-            raise TypeError(
-                'Float types are not supported. Use Decimal types instead.'
-            )
-        return False
-
-    def _is_string(self, value):
-        if isinstance(value, str):
-            return True
-        return False
-
-    def _is_binary(self, value):
-        if isinstance(value, (Binary, bytearray, bytes)):
-            return True
-        return False
-
-    def _is_set(self, value):
-        if isinstance(value, collections_abc.Set):
-            return True
-        return False
-
-    def _is_type_set(self, value, type_validator):
-        if self._is_set(value):
-            if False not in map(type_validator, value):
-                return True
-        return False
-
-    def _is_map(self, value):
-        if isinstance(value, collections_abc.Mapping):
-            return True
-        return False
-
-    def _is_listlike(self, value):
-        if isinstance(value, (list, tuple)):
-            return True
-        return False
-
-    def _serialize_null(self, value):
-        return True
-
-    def _serialize_bool(self, value):
-        return value
-
-    def _serialize_n(self, value):
-        number = str(DYNAMODB_CONTEXT.create_decimal(value))
-        if number in ['Infinity', 'NaN']:
-            raise TypeError('Infinity and NaN not supported')
-        return number
-
-    def _serialize_s(self, value):
-        return value
-
-    def _serialize_b(self, value):
-        if isinstance(value, Binary):
-            value = value.value
-        return value
-
-    def _serialize_ss(self, value):
-        return [self._serialize_s(s) for s in value]
-
-    def _serialize_ns(self, value):
-        return [self._serialize_n(n) for n in value]
-
-    def _serialize_bs(self, value):
-        return [self._serialize_b(b) for b in value]
-
-    def _serialize_l(self, value):
-        return [self.serialize(v) for v in value]
-
-    def _serialize_m(self, value):
-        return {k: self.serialize(v) for k, v in value.items()}
-
-
-class TypeDeserializer:
-    """This class deserializes DynamoDB types to Python types."""
-
-    def deserialize(self, value):
-        """The method to deserialize the DynamoDB data types.
-
-        :param value: A DynamoDB value to be deserialized to a pythonic value.
-            Here are the various conversions:
-
-            DynamoDB                                Python
-            --------                                ------
-            {'NULL': True}                          None
-            {'BOOL': True/False}                    True/False
-            {'N': str(value)}                       Decimal(str(value))
-            {'S': string}                           string
-            {'B': bytes}                            Binary(bytes)
-            {'NS': [str(value)]}                    set([Decimal(str(value))])
-            {'SS': [string]}                        set([string])
-            {'BS': [bytes]}                         set([bytes])
-            {'L': list}                             list
-            {'M': dict}                             dict
-
-        :returns: The pythonic value of the DynamoDB type.
-        """
-
-        if not value:
-            raise TypeError(
-                'Value must be a nonempty dictionary whose key '
-                'is a valid dynamodb type.'
-            )
-        dynamodb_type = list(value.keys())[0]
-        try:
-            deserializer = getattr(
-                self, f'_deserialize_{dynamodb_type}'.lower()
-            )
-        except AttributeError:
-            raise TypeError(f'Dynamodb type {dynamodb_type} is not supported')
-        return deserializer(value[dynamodb_type])
-
-    def _deserialize_null(self, value):
-        return None
-
-    def _deserialize_bool(self, value):
-        return value
-
-    def _deserialize_n(self, value):
-        return DYNAMODB_CONTEXT.create_decimal(value)
-
-    def _deserialize_s(self, value):
-        return value
-
-    def _deserialize_b(self, value):
-        return Binary(value)
-
-    def _deserialize_ns(self, value):
-        return set(map(self._deserialize_n, value))
-
-    def _deserialize_ss(self, value):
-        return set(map(self._deserialize_s, value))
-
-    def _deserialize_bs(self, value):
-        return set(map(self._deserialize_b, value))
-
-    def _deserialize_l(self, value):
-        return [self.deserialize(v) for v in value]
-
-    def _deserialize_m(self, value):
-        return {k: self.deserialize(v) for k, v in value.items()}

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/request.py

@@ -1,170 +0,0 @@
-from __future__ import absolute_import
-
-from .filepost import encode_multipart_formdata
-from .packages.six.moves.urllib.parse import urlencode
-
-__all__ = ["RequestMethods"]
-
-
-class RequestMethods(object):
-    """
-    Convenience mixin for classes who implement a :meth:`urlopen` method, such
-    as :class:`urllib3.HTTPConnectionPool` and
-    :class:`urllib3.PoolManager`.
-
-    Provides behavior for making common types of HTTP request methods and
-    decides which type of request field encoding to use.
-
-    Specifically,
-
-    :meth:`.request_encode_url` is for sending requests whose fields are
-    encoded in the URL (such as GET, HEAD, DELETE).
-
-    :meth:`.request_encode_body` is for sending requests whose fields are
-    encoded in the *body* of the request using multipart or www-form-urlencoded
-    (such as for POST, PUT, PATCH).
-
-    :meth:`.request` is for making any kind of request, it will look up the
-    appropriate encoding format and use one of the above two methods to make
-    the request.
-
-    Initializer parameters:
-
-    :param headers:
-        Headers to include with all requests, unless other headers are given
-        explicitly.
-    """
-
-    _encode_url_methods = {"DELETE", "GET", "HEAD", "OPTIONS"}
-
-    def __init__(self, headers=None):
-        self.headers = headers or {}
-
-    def urlopen(
-        self,
-        method,
-        url,
-        body=None,
-        headers=None,
-        encode_multipart=True,
-        multipart_boundary=None,
-        **kw
-    ):  # Abstract
-        raise NotImplementedError(
-            "Classes extending RequestMethods must implement "
-            "their own ``urlopen`` method."
-        )
-
-    def request(self, method, url, fields=None, headers=None, **urlopen_kw):
-        """
-        Make a request using :meth:`urlopen` with the appropriate encoding of
-        ``fields`` based on the ``method`` used.
-
-        This is a convenience method that requires the least amount of manual
-        effort. It can be used in most situations, while still having the
-        option to drop down to more specific methods when necessary, such as
-        :meth:`request_encode_url`, :meth:`request_encode_body`,
-        or even the lowest level :meth:`urlopen`.
-        """
-        method = method.upper()
-
-        urlopen_kw["request_url"] = url
-
-        if method in self._encode_url_methods:
-            return self.request_encode_url(
-                method, url, fields=fields, headers=headers, **urlopen_kw
-            )
-        else:
-            return self.request_encode_body(
-                method, url, fields=fields, headers=headers, **urlopen_kw
-            )
-
-    def request_encode_url(self, method, url, fields=None, headers=None, **urlopen_kw):
-        """
-        Make a request using :meth:`urlopen` with the ``fields`` encoded in
-        the url. This is useful for request methods like GET, HEAD, DELETE, etc.
-        """
-        if headers is None:
-            headers = self.headers
-
-        extra_kw = {"headers": headers}
-        extra_kw.update(urlopen_kw)
-
-        if fields:
-            url += "?" + urlencode(fields)
-
-        return self.urlopen(method, url, **extra_kw)
-
-    def request_encode_body(
-        self,
-        method,
-        url,
-        fields=None,
-        headers=None,
-        encode_multipart=True,
-        multipart_boundary=None,
-        **urlopen_kw
-    ):
-        """
-        Make a request using :meth:`urlopen` with the ``fields`` encoded in
-        the body. This is useful for request methods like POST, PUT, PATCH, etc.
-
-        When ``encode_multipart=True`` (default), then
-        :func:`urllib3.encode_multipart_formdata` is used to encode
-        the payload with the appropriate content type. Otherwise
-        :func:`urllib.parse.urlencode` is used with the
-        'application/x-www-form-urlencoded' content type.
-
-        Multipart encoding must be used when posting files, and it's reasonably
-        safe to use it in other times too. However, it may break request
-        signing, such as with OAuth.
-
-        Supports an optional ``fields`` parameter of key/value strings AND
-        key/filetuple. A filetuple is a (filename, data, MIME type) tuple where
-        the MIME type is optional. For example::
-
-            fields = {
-                'foo': 'bar',
-                'fakefile': ('foofile.txt', 'contents of foofile'),
-                'realfile': ('barfile.txt', open('realfile').read()),
-                'typedfile': ('bazfile.bin', open('bazfile').read(),
-                              'image/jpeg'),
-                'nonamefile': 'contents of nonamefile field',
-            }
-
-        When uploading a file, providing a filename (the first parameter of the
-        tuple) is optional but recommended to best mimic behavior of browsers.
-
-        Note that if ``headers`` are supplied, the 'Content-Type' header will
-        be overwritten because it depends on the dynamic random boundary string
-        which is used to compose the body of the request. The random boundary
-        string can be explicitly set with the ``multipart_boundary`` parameter.
-        """
-        if headers is None:
-            headers = self.headers
-
-        extra_kw = {"headers": {}}
-
-        if fields:
-            if "body" in urlopen_kw:
-                raise TypeError(
-                    "request got values for both 'fields' and 'body', can only specify one."
-                )
-
-            if encode_multipart:
-                body, content_type = encode_multipart_formdata(
-                    fields, boundary=multipart_boundary
-                )
-            else:
-                body, content_type = (
-                    urlencode(fields),
-                    "application/x-www-form-urlencoded",
-                )
-
-            extra_kw["body"] = body
-            extra_kw["headers"] = {"Content-Type": content_type}
-
-        extra_kw["headers"].update(headers)
-        extra_kw.update(urlopen_kw)
-
-        return self.urlopen(method, url, **extra_kw)

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/py34compat.py

@@ -1,13 +0,0 @@
-import importlib
-
-try:
-    import importlib.util
-except ImportError:
-    pass
-
-
-try:
-    module_from_spec = importlib.util.module_from_spec
-except AttributeError:
-    def module_from_spec(spec):
-        return spec.loader.load_module(spec.name)

spaces/BlackCub/ChatGPT4/app.py

@@ -1,191 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Inferenec function
-def predict(openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {openai_gpt4_key}"  #Users will provide their own OPENAI_API_KEY 
-    }
-    print(f"system message is ^^ {system_msg}")
-    if system_msg.strip() == '':
-        initial_message = [{"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = []
-    else:
-        initial_message= [{"role": "system", "content": system_msg},
-                   {"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = [{"role": "system", "content": system_msg},]
-        
-    if chat_counter == 0 :
-        payload = {
-        "model": "gpt-4",
-        "messages": initial_message , 
-        "temperature" : 1.0,
-        "top_p":1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-        print(f"chat_counter - {chat_counter}")
-    else: #if chat_counter != 0 :
-        messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
-        for data in chatbot:
-          user = {}
-          user["role"] = "user" 
-          user["content"] = data[0] 
-          assistant = {}
-          assistant["role"] = "assistant" 
-          assistant["content"] = data[1]
-          messages.append(user)
-          messages.append(assistant)
-        temp = {}
-        temp["role"] = "user" 
-        temp["content"] = inputs
-        messages.append(temp)
-        #messages
-        payload = {
-        "model": "gpt-4",
-        "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,}
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"Logging : payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    print(f"Logging : response code - {response}")
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter, response  # resembles {chatbot: chat, state: history}  
-                   
-#Resetting to blank
-def reset_textbox():
-    return gr.update(value='')
-
-#to set a component as visible=False
-def set_visible_false():
-    return gr.update(visible=False)
-
-#to set a component as visible=True
-def set_visible_true():
-    return gr.update(visible=True)
-
-title = """<h1 align="center">🔥GPT4 using Chat-Completions API & 🚀Gradio-Streaming</h1>"""
-#display message for themes feature
-theme_addon_msg = """<center>🌟 This Demo also introduces you to Gradio Themes. Discover more on Gradio website using our <a href="https://gradio.app/theming-guide/" target="_blank">Themeing-Guide🎨</a>! You can develop from scratch, modify an existing Gradio theme, and share your themes with community by uploading them to huggingface-hub easily using <code>theme.push_to_hub()</code>.</center>
-""" 
-
-#Using info to add additional information about System message in GPT4
-system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. 
-System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
-
-#Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
-                      text_size=gr.themes.sizes.text_lg)                
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
-                      theme=theme) as demo:
-    gr.HTML(title)
-    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you access to GPT4 API with System Messages. Please note that you would be needing an OPENAI API key for GPT4 access🙌</h1>""")
-    gr.HTML(theme_addon_msg)
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-
-    with gr.Column(elem_id = "col_container"):
-        #Users need to provide their own GPT4 API key, it is no longer provided by Huggingface 
-        with gr.Row():
-            openai_gpt4_key = gr.Textbox(label="OpenAI GPT4 Key", value="", type="password", placeholder="sk..", info = "You have to provide your own GPT4 keys for this app to function properly",)
-            with gr.Accordion(label="System message:", open=False):
-                system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="",placeholder="Type here..")
-                accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
-                          
-        chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot")
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
-        state = gr.State([]) 
-        with gr.Row():
-            with gr.Column(scale=7):
-                b1 = gr.Button().style(full_width=True)
-            with gr.Column(scale=3):
-                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
-    
-        #top_p, temperature
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
-
-    #Event handling
-    inputs.submit( predict, [openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    
-    inputs.submit(set_visible_false, [], [system_msg])
-    b1.click(set_visible_false, [], [system_msg])
-    inputs.submit(set_visible_true, [], [accordion_msg])
-    b1.click(set_visible_true, [], [accordion_msg])
-    
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-
-    #Examples 
-    with gr.Accordion(label="Examples for System message:", open=False):
-        gr.Examples(
-                examples = [["""You are an AI programming assistant.
-        
-                - Follow the user's requirements carefully and to the letter.
-                - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail.
-                - Then output the code in a single code block.
-                - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""],
-                ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."],
-                ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."],
-                ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."],
-                ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."],
-                ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."],
-                ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."],
-                ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."],
-                ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."],
-                ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."],
-                ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."],
-                ["You are a helpful assistant that provides detailed and accurate information."],
-                ["You are an assistant that speaks like Shakespeare."],
-                ["You are a friendly assistant who uses casual language and humor."],
-                ["You are a financial advisor who gives expert advice on investments and budgeting."],
-                ["You are a health and fitness expert who provides advice on nutrition and exercise."],
-                ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."],
-                ["You are a movie critic who shares insightful opinions on films and their themes."],
-                ["You are a history enthusiast who loves to discuss historical events and figures."],
-                ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."],
-                ["You are an AI poet who can compose creative and evocative poems on any given topic."],],
-                inputs = system_msg,)
-        
-demo.queue(max_size=99, concurrency_count=20).launch(debug=True)