diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Devathai Sonna Kavithai Tamil Full Movie Download BETTER.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Devathai Sonna Kavithai Tamil Full Movie Download BETTER.md
deleted file mode 100644
index 436dd98179310c33724ed3537b8bf0f75754eeb8..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Devathai Sonna Kavithai Tamil Full Movie Download BETTER.md	
+++ /dev/null
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-<br />
-<h1>How to Download Devathai Sonna Kavithai Tamil Full Movie Online</h1>
-<p>Devathai Sonna Kavithai is a 2014 Tamil romantic movie directed by Thesigan and starring newcomers in the lead roles. The movie is about a young man who falls in love with a girl who speaks to him through poetry. The movie was released on January 1, 2014 and received mixed reviews from critics and audiences.</p>
-<p>If you are looking for a way to download Devathai Sonna Kavithai Tamil full movie online, you have come to the right place. In this article, we will show you some of the best websites and platforms where you can watch or download this movie legally and safely. We will also give you some tips on how to optimize your search and avoid any malware or viruses.</p>
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-<h2>Best Websites to Download Devathai Sonna Kavithai Tamil Full Movie Online</h2>
-<p>There are many websites that offer Tamil movies for download or streaming, but not all of them are reliable or trustworthy. Some of them may contain harmful ads, pop-ups, or links that can infect your device with malware or viruses. Some of them may also violate the copyright laws and infringe on the rights of the movie makers and distributors.</p>
-<p>To avoid any such risks, we recommend you to use only the following websites that are legal and safe to download Devathai Sonna Kavithai Tamil full movie online:</p>
-<ul>
-<li><b>YouTube</b>: YouTube is one of the most popular and widely used platforms for watching and downloading videos online. You can find Devathai Sonna Kavithai Tamil full movie on YouTube by searching for its title or using the keyword "Devathai Sonna Kavithai Tamil Full Movie Download". You can also use filters such as duration, upload date, or quality to narrow down your results. You can watch the movie for free on YouTube or download it using a third-party app or website that allows YouTube video downloads.</li>
-<li><b>Dailymotion</b>: Dailymotion is another video-sharing platform that hosts a variety of content, including movies, TV shows, music, sports, and more. You can find Devathai Sonna Kavithai Tamil full movie on Dailymotion by searching for its title or using the keyword "Devathai Sonna Kavithai Tamil Full Movie Download". You can also use filters such as duration, upload date, or quality to narrow down your results. You can watch the movie for free on Dailymotion or download it using a third-party app or website that allows Dailymotion video downloads.</li>
-</ul>
-<h2>Tips to Optimize Your Search and Avoid Malware or Viruses</h2>
-<p>While using the above websites to download Devathai Sonna Kavithai Tamil full movie online, you should keep in mind some tips to optimize your search and avoid any malware or viruses:</p>
-<ul>
-<li><b>Use a VPN</b>: A VPN (Virtual Private Network) is a service that encrypts your internet traffic and hides your IP address and location from prying eyes. This can help you access geo-restricted content, bypass censorship, and protect your privacy and security online. You can use a VPN to access the above websites from anywhere in the world and enjoy Devathai Sonna Kavithai Tamil full movie without any hassle.</li>
-<li><b>Use an antivirus</b>: An antivirus is a software that detects and removes any malicious programs or files that may harm your device or data. You should always use an antivirus to scan your device before and after downloading any file from the internet. This can help you prevent any malware or viruses from infecting your device or stealing your information.</li>
-<li><b>Use a trusted source</b>: As mentioned earlier, not all websites that offer Tamil movies for download or streaming are reliable or trustworthy. You should always use a trusted source that has a good reputation and reviews from other users. You should also avoid clicking on any suspicious ads, pop-ups, or links that may redirect you to unwanted or harmful sites.</li>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Fifa 12 [CRACK ONLY] 100 WORKING Serial Key BETTER.md b/spaces/1gistliPinn/ChatGPT4/Examples/Fifa 12 [CRACK ONLY] 100 WORKING Serial Key BETTER.md
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index e909703bb775ec1a7d2db175c8e4555f92cdd49d..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Fifa 12 [CRACK ONLY] 100 WORKING Serial Key BETTER.md	
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diff --git a/spaces/1line/AutoGPT/ui/app.py b/spaces/1line/AutoGPT/ui/app.py
deleted file mode 100644
index d7dbd31e901969d090292215935bdbc3d9d75e37..0000000000000000000000000000000000000000
--- a/spaces/1line/AutoGPT/ui/app.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import gradio as gr
-import utils
-from api import AutoAPI, get_openai_api_key
-import os, shutil
-import json
-
-FILE_DIR = os.path.dirname(os.path.abspath(__file__))
-OUTPUT_DIR = os.path.join(os.path.dirname(FILE_DIR), "auto_gpt_workspace")
-if not os.path.exists(OUTPUT_DIR):
-    os.mkdir(OUTPUT_DIR)
-
-CSS = """
-#chatbot {font-family: monospace;}
-#files .generating {display: none;}
-#files .min {min-height: 0px;}
-"""
-
-with gr.Blocks(css=CSS) as app:
-    with gr.Column() as setup_pane:
-        gr.Markdown(f"""# Auto-GPT
-        1. Duplicate this Space: <a href="https://huggingface.co/spaces/{os.getenv('SPACE_ID')}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a> This will **NOT** work without duplication!
-        2. Enter your <a href="https://platform.openai.com/account/api-keys">OpenAI API Key</a> below.
-        """)
-        with gr.Row():
-            open_ai_key = gr.Textbox(
-                value=get_openai_api_key(),
-                label="OpenAI API Key",
-                type="password",
-            )
-        gr.Markdown(
-            "3. Fill the values below, then click 'Start'. There are example values you can load at the bottom of this page."
-        )
-        with gr.Row():
-            ai_name = gr.Textbox(label="AI Name", placeholder="e.g. Entrepreneur-GPT")
-            ai_role = gr.Textbox(
-                label="AI Role",
-                placeholder="e.g. an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth.",
-            )
-        top_5_goals = gr.Dataframe(
-            row_count=(5, "fixed"),
-            col_count=(1, "fixed"),
-            headers=["AI Goals - Enter up to 5"],
-            type="array"
-        )
-        start_btn = gr.Button("Start", variant="primary")
-        with open(os.path.join(FILE_DIR, "examples.json"), "r") as f:
-            example_values = json.load(f)
-        gr.Examples(
-            example_values,
-            [ai_name, ai_role, top_5_goals],
-        )
-    with gr.Column(visible=False) as main_pane:
-        with gr.Row():
-            with gr.Column(scale=2):
-                chatbot = gr.Chatbot(elem_id="chatbot")
-                with gr.Row():
-                    yes_btn = gr.Button("Yes", variant="primary", interactive=False)
-                    consecutive_yes = gr.Slider(
-                        1, 10, 1, step=1, label="Consecutive Yes", interactive=False
-                    )
-                custom_response = gr.Textbox(
-                    label="Custom Response",
-                    placeholder="Press 'Enter' to Submit.",
-                    interactive=False,
-                )
-            with gr.Column(scale=1):
-                gr.HTML(
-                    lambda: f"""
-                        Generated Files
-                        <pre><code style='overflow-x: auto'>{utils.format_directory(OUTPUT_DIR)}</pre></code>
-                """, every=3, elem_id="files"
-                )
-                download_btn = gr.Button("Download All Files")
-
-    chat_history = gr.State([[None, None]])
-    api = gr.State(None)
-
-    def start(open_ai_key, ai_name, ai_role, top_5_goals):
-        auto_api = AutoAPI(open_ai_key, ai_name, ai_role, top_5_goals)
-        return gr.Column.update(visible=False), gr.Column.update(visible=True), auto_api
-
-    def bot_response(chat, api):
-        messages = []
-        for message in api.get_chatbot_response():
-            messages.append(message)
-            chat[-1][1] = "\n".join(messages) + "..."
-            yield chat
-        chat[-1][1] = "\n".join(messages)
-        yield chat
-
-    def send_message(count, chat, api, message="Y"):
-        if message != "Y":
-            count = 1
-        for i in range(count):
-            chat.append([message, None])
-            yield chat, count - i
-            api.send_message(message)
-            for updated_chat in bot_response(chat, api):
-                yield updated_chat, count - i
-
-    def activate_inputs():
-        return {
-            yes_btn: gr.Button.update(interactive=True),
-            consecutive_yes: gr.Slider.update(interactive=True),
-            custom_response: gr.Textbox.update(interactive=True),
-        }
-
-    def deactivate_inputs():
-        return {
-            yes_btn: gr.Button.update(interactive=False),
-            consecutive_yes: gr.Slider.update(interactive=False),
-            custom_response: gr.Textbox.update(interactive=False),
-        }
-
-    start_btn.click(
-        start,
-        [open_ai_key, ai_name, ai_role, top_5_goals],
-        [setup_pane, main_pane, api],
-    ).then(bot_response, [chat_history, api], chatbot).then(
-        activate_inputs, None, [yes_btn, consecutive_yes, custom_response]
-    )
-
-    yes_btn.click(
-        deactivate_inputs, None, [yes_btn, consecutive_yes, custom_response]
-    ).then(
-        send_message, [consecutive_yes, chat_history, api], [chatbot, consecutive_yes]
-    ).then(
-        activate_inputs, None, [yes_btn, consecutive_yes, custom_response]
-    )
-    custom_response.submit(
-        deactivate_inputs, None, [yes_btn, consecutive_yes, custom_response]
-    ).then(
-        send_message,
-        [consecutive_yes, chat_history, api, custom_response],
-        [chatbot, consecutive_yes],
-    ).then(
-        activate_inputs, None, [yes_btn, consecutive_yes, custom_response]
-    )
-
-    def download_all_files():
-        shutil.make_archive("outputs", "zip", OUTPUT_DIR)
-
-    download_btn.click(download_all_files).then(None, _js=utils.DOWNLOAD_OUTPUTS_JS)
-
-app.queue(concurrency_count=20).launch(file_directories=[OUTPUT_DIR])
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Alias 2023 Free 30-Day Trial with 480p Video Option.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Alias 2023 Free 30-Day Trial with 480p Video Option.md
deleted file mode 100644
index 12d4c98b81e8340911b6f6e1ec4a9ee0e223b0df..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Alias 2023 Free 30-Day Trial with 480p Video Option.md	
+++ /dev/null
@@ -1,111 +0,0 @@
-
-<h1>How to Download Alias TV Series in 480p Resolution</h1>
-<p>If you are a fan of action, thriller, and science fiction genres, you might have heard of Alias, a popular TV series that ran from 2001 to 2006. The show starred Jennifer Garner as Sydney Bristow, a double agent for the CIA who poses as an operative for a criminal organization called SD-6. The series was created by J.J. Abrams, who also produced other hit shows like Lost, Fringe, and Westworld.</p>
-<p>In this article, we will tell you more about Alias TV series and why you should watch it. We will also explain what 480p resolution is and why you need it for downloading videos. Finally, we will show you how to download Alias TV series in 480p resolution using a free tool called YouTube-DL.</p>
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- <h2>What is Alias TV Series and Why You Should Watch It</h2>
-<h3>The Plot and Characters of Alias</h3>
-<p>The plot of Alias revolves around Sydney Bristow, who was recruited as a spy by a man who claimed to work for the CIA when she was a college student. She later discovered that she was actually working for SD-6, a rogue faction of the CIA that was part of a larger alliance of criminal organizations. She then decided to become a double agent for the real CIA and work to bring down SD-6 and its allies.</p>
-<p>Along the way, she faced many dangers and challenges, such as dealing with her estranged father Jack Bristow, who was also a double agent, her complicated relationship with her fellow agent Michael Vaughn, her best friend Francie Calfo, who was replaced by a look-alike assassin, and her mother Irina Derevko, who was a former KGB spy and a key figure in a global conspiracy involving an ancient prophet named Rambaldi.</p>
-<p>The show featured many twists and turns, cliffhangers, action sequences, gadgets, disguises, and exotic locations. It also had a stellar cast of supporting characters, such as Arvin Sloane, the leader of SD-6 who had a personal connection to Sydney; Marshall Flinkman, the quirky tech genius who helped Sydney on her missions; Marcus Dixon, Sydney's loyal partner at SD-6; Julian Sark, a ruthless mercenary who worked for various factions; Lauren Reed, Vaughn's wife who turned out to be a double agent; Nadia Santos, Sydney's half-sister who was also involved in the Rambaldi prophecy; Rachel Gibson, a young hacker who joined Sydney's team after being betrayed by her employer; Thomas Grace, a former Delta Force soldier who became Sydney's new partner; Kelly Peyton, a former friend of Rachel who became an enemy agent; and Renée Rienne, a mysterious freelance spy who had ties to Sydney's past.</h3>
- <h3>The Awards and Recognition of Alias</h3>
-<p>Alias was well received by critics and audiences alike. It won four Emmy Awards out of 11 nominations, including Outstanding Lead Actress in a Drama Series for Jennifer Garner in 2002. It also won a Golden Globe Award for Best Actress in a Television Series – Drama for Garner in 2002. The show was nominated for several other awards, such as Screen Actors Guild Awards, Teen Choice Awards, Saturn Awards, and People's Choice Awards.</p>
-<p>Alias was also included in several "best of" lists by various media outlets. For example, it was ranked number 36 on TV Guide's list of "50 Greatest TV Shows of All Time" in 2002. It was also ranked number seven on Entertainment Weekly's list of "The New Classics: TV" in 2008. The American Film Institute named it one of the top ten television programs of the year in <p>2003 and 2005. The show also influenced other spy-themed shows, such as Chuck, Nikita, and Covert Affairs.</p>
- <h2>What is 480p Resolution and Why You Need It</h2>
-<h3>The Definition and Features of 480p Resolution</h3>
-<p>480p resolution is a term that refers to the video quality of a digital display. It means that the video has 480 horizontal lines of pixels that are progressively scanned, meaning that each line is displayed in sequence. The "p" stands for progressive scan, as opposed to interlaced scan, which alternates between odd and even lines of pixels. Progressive scan produces a smoother and clearer image than interlaced scan.</p>
-<p>The aspect ratio of 480p resolution is usually 4:3, which means that the width of the screen is four times the height. However, some widescreen formats, such as 16:9, can also use 480p resolution. The pixel dimensions of 480p resolution are typically 640 x 480 for 4:3 aspect ratio and 854 x 480 for 16:9 aspect ratio.</p>
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-<p>Another benefit of 480p resolution is that it is compatible with most devices and platforms, such as TVs, computers, smartphones, tablets, DVD players, and game consoles. You can watch videos in 480p resolution on almost any screen without worrying about compatibility issues or format conversions.</p>
-<p>However, 480p resolution also has some drawbacks. One of them is that it has lower image quality than higher resolutions, especially when viewed on larger screens or from close distances. You might notice pixelation, blurriness, or distortion when watching videos in 480p resolution on a big screen or a high-definition display. You might also miss some details or colors that are present in higher resolutions.</p>
-<p>Another drawback of 480p resolution is that it might not be suitable for some types of videos, such as those that have fast motion, complex graphics, or high contrast. These videos might look choppy, blurry, or noisy when viewed in 480p resolution. You might also experience some lagging or buffering when streaming these videos in 480p resolution.</p>
- <h2>How to Download Alias TV Series in 480p Resolution Using YouTube-DL</h2>
-<h3>What is YouTube-DL and How to Install It</h3>
-<p>YouTube-DL is a free and open-source command-line tool that allows you to download videos from YouTube and other websites. You can use it to download videos in various formats and resolutions, including 480p. You can also use it to download audio files, subtitles, playlists, channels, and live streams.</p>
-<p>To install YouTube-DL on your device, you need to follow these steps:</p>
-<ul>
-<li>Download the latest version of YouTube-DL from its official website: https://youtube-dl.org/</li>
-<li>Extract the zip file and save the youtube-dl.exe file in a folder of your choice.</li>
-<li>Add the folder to your system's PATH environment variable so that you can run YouTube-DL from any directory.</li>
-<li>Open a command prompt window and type youtube-dl --version to check if YouTube-DL is installed correctly.</li>
-</ul>
- <h3>How to Find and Select the Video Quality from YouTube-DL</h3>
-<p>To find and select the video quality from YouTube-DL, you need to follow these steps:</p>
-<ul>
-<li>Copy the URL of the video that you want to download from YouTube or any other website.</li>
-<li>Open a command prompt window and type youtube-dl -F [URL] to list all the available formats and resolutions for the video.</li>
-<li>Look for the format code that corresponds to the video quality that you want to download. For example, if you want to download Alias TV series in 480p resolution with MP4 format, you might look for something like this: <code>18 mp4        640x360    medium , avc1.42001E, mp4a.40.2@96k (best)</code></li>
-<li>Note down the format code (in this case, 18) for later use.</li>
-</ul>
- <h3>How to Download the Video Using YouTube-DL</h3>
-<p>To download the video using YouTube-DL , you need to follow these steps:</p>
-<ul>
-<li>Open a command prompt window and type youtube-dl -f [format code] [URL] to download the video with the selected format and resolution. For example, if you want to download Alias TV series in 480p resolution with MP4 format, you might type something like this: <code>youtube-dl -f 18 https://www.youtube.com/watch?v=0hX-YLAjI_A</code></li>
-<li>Wait for the download to finish. You can check the progress and speed of the download on the command prompt window.</li>
-<li>Find the downloaded video file in the same folder where you saved the youtube-dl.exe file. You can rename or move the file as you wish.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Alias TV series in 480p resolution using YouTube-DL. We have also given you some information about Alias TV series and why you should watch it, as well as 480p resolution and why you need it. We hope that you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<h3>Q: Is YouTube-DL legal?</h3>
-<p>A: YouTube-DL is legal as long as you use it for personal and non-commercial purposes. However, downloading videos from YouTube or other websites might violate their terms of service or copyright laws, so you should always check the source and legality of the videos before downloading them.</p>
- <h3>Q: Can I use YouTube-DL to download videos from other websites besides YouTube?</h3>
-<p>A: Yes, YouTube-DL supports many other websites, such as Vimeo, Dailymotion, Facebook, Instagram, Twitter, and more. You can check the full list of supported websites here: https://ytdl-org.github.io/youtube-dl/supportedsites.html</p>
- <h3>Q: Can I use YouTube-DL to download videos in other resolutions besides 480p?</h3>
-<p>A: Yes, YouTube-DL can download videos in various resolutions, such as 240p, 360p, 720p, 1080p, or even 4K. You just need to find and select the appropriate format code from the list of available formats and resolutions for each video.</p>
- <h3>Q: Can I use YouTube-DL to download audio files or subtitles from videos?</h3>
-<p>A: Yes, YouTube-DL can download audio files or subtitles from videos. You can use the -x option to extract audio files from videos, or the --write-sub option to download subtitles from videos. You can also specify the format and language of the audio files or subtitles using other options. You can check the full list of options and examples here: https://github.com/ytdl-org/youtube-dl/blob/master/README.md#readme</p>
- <h3>Q: Can I use YouTube-DL to download playlists or channels from YouTube?</h3>
-<p>A: Yes, YouTube-DL can download playlists or channels from YouTube. You just need to copy and paste the URL of the playlist or channel instead of a single video. You can also use the --playlist-start and --playlist-end options to specify which videos from the playlist or channel you want to download.</p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Nicoo Free Fire Max APK and Enhance Your Gaming Experience.md b/spaces/1phancelerku/anime-remove-background/Download Nicoo Free Fire Max APK and Enhance Your Gaming Experience.md
deleted file mode 100644
index 90353327ea7d4e7e488b7b806790d4741cc9eab1..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Nicoo Free Fire Max APK and Enhance Your Gaming Experience.md	
+++ /dev/null
@@ -1,101 +0,0 @@
-
-<h1>Nicoo Free Fire Max APK Download 2023: How to Get Free Skins and More</h1>
-<p>If you are a fan of the popular FPS game Free Fire, you might have heard of Nicoo Free Fire Max, a third-party app that allows you to customize your avatars with various skins and accessories. But what is Nicoo Free Fire Max exactly, and how can you download and use it safely? In this article, we will answer these questions and more, so keep reading!</p>
- <h2>What is Nicoo Free Fire Max?</h2>
-<p>Nicoo Free Fire Max is an action app developed by Naviemu.inc that works as a skin injector for Free Fire. It lets you unlock and apply different skins for your characters, weapons, vehicles, parachutes, and more. You can also change the background and theme of the game, as well as the interface and sound effects. With Nicoo Free Fire Max, you can personalize your gaming experience and make it more fun and unique.</p>
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-<ul>
-<li>Free access to all skins in the game, including premium and exclusive ones.</li>
-<li>Easy to use interface with a simple tap-to-apply function.</li>
-<li>No need to root your device or modify the game files.</li>
-<li>Compatible with both Android and PC devices.</li>
-<li>Regular updates with new skins and features.</li>
-</ul>
- <h3>How to Download and Install Nicoo Free Fire Max APK</h3>
-<p>To download and install Nicoo Free Fire Max APK on your device, follow these steps:</p>
-<ol>
-<li>Go to the official website of Nicoo Free Fire Max or click on this link  .</li>
-<li>Select the latest version of the app and click on the download button.</li>
-<li>Wait for the download to finish and then locate the APK file on your device.</li>
-<li>Enable the installation from unknown sources on your device settings.</li>
-<li>Tap on the APK file and follow the instructions to install the app.</li>
-<li>Launch the app and grant it the necessary permissions.</li>
-<li>Open Free Fire from the app and enjoy the free skins!</li>
-</ol>
- <h2>Why Use Nicoo Free Fire Max?</h2>
-<p>Nicoo Free Fire Max is a great app for those who want to spice up their gameplay with different skins and accessories. But what are the benefits and risks of using it?</p>
-<p>nicoo app for free fire skins 2023<br />
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-nicoo app license and terms of service for free fire</p>
- <h3>Benefits of Using Nicoo Free Fire Max</h3>
-<p>Some of the benefits of using Nicoo Free Fire Max are:</p>
-<ul>
-<li>You can save money by not having to buy diamonds or coins to get skins in the game.</li>
-<li>You can impress your friends and enemies with your cool and stylish appearance.</li>
-<li>You can enhance your performance and confidence in the game with better skins.</li>
-<li>You can explore different combinations and styles with various skins.</li>
-</ul>
- <h3>Risks of Using Nicoo Free Fire Max</h3>
-<p>Some of the risks of using Nicoo Free Fire Max are:</p>
-<ul>
-<li>You might get banned from the game if you are detected by the anti-cheat system.</li>
-<li>You might expose your device to malware or viruses if you download from untrusted sources.</li>
-<li>You might lose your account data or personal information if you give them to fake or phishing websites.</li>
-<li>You might violate the terms and conditions of the game by using an unauthorized app.</li>
-</ul>
- <h2>Alternatives to Nicoo Free Fire Max</h2>
-<p>If you are not comfortable with using Nicoo Free Fire Max, or you want to try other apps that offer similar features, you can check out these alternatives:</p>
- <h3>Lulubox</h3>
-<p>Lulubox is another popular app that allows you to get free skins and mods for various games, including Free Fire. It also has a built-in game booster that can improve your device performance and battery life. You can download Lulubox from its official website   or from the Google Play Store .</p>
- <h3>Tool Skin</h3>
-<p>Tool Skin is a simple and lightweight app that lets you change the skins of your characters, weapons, backpacks, and more in Free Fire. It has a user-friendly interface and a large collection of skins to choose from. You can download Tool Skin from its official website  or from the Google Play Store .</p>
- <h2>Conclusion</h2>
-<p>Nicoo Free Fire Max is an app that can help you customize your Free Fire gameplay with various skins and accessories. It is easy to use and compatible with both Android and PC devices. However, it also comes with some risks, such as getting banned or infected by malware. Therefore, you should use it at your own discretion and with caution. Alternatively, you can try other apps like Lulubox or Tool Skin that offer similar features.</p>
- <h3>Summary of the article</h3>
-<p>In this article, we have discussed the following points:</p>
-<ul>
-<li>What is Nicoo Free Fire Max and what are its features?</li>
-<li>How to download and install Nicoo Free Fire Max APK on your device?</li>
-<li>Why use Nicoo Free Fire Max and what are the benefits and risks of using it?</li>
-<li>What are some alternatives to Nicoo Free Fire Max that you can try?</li>
-</ul>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Nicoo Free Fire Max:</p>
-<ol>
-<li><b>Is Nicoo Free Fire Max safe to use?</b><br>
-Nicoo Free Fire Max is not an official app from the developers of Free Fire, so it is not guaranteed to be safe or secure. You should only download it from trusted sources and scan it for viruses before installing it. You should also avoid giving your account details or personal information to any website or app that claims to be associated with Nicoo Free Fire Max.</li>
-<li><b>Is Nicoo Free Fire Max legal to use?</b><br>
-Nicoo Free Fire Max is not legal to use, as it violates the terms and conditions of Free Fire. Using it may result in your account being banned or suspended by the game authorities. You should only use it at your own risk and responsibility.</li>
-<li><b>Do other players see my skins when I use Nicoo Free Fire Max?</b><br>
-No, other players do not see your skins when you use Nicoo Free Fire Max. The skins are only visible to you on your device, as they are not part of the game data. Therefore, using Nicoo Free Fire Max does not give you any advantage or disadvantage over other players.</li>
-<li><b>Does Nicoo Free Fire Max work with Free Fire Max?</b><br>
-Yes, Nicoo Free Fire Max works with both Free Fire and Free Fire Max, as they are based on the same game engine. However, you may need to update the app regularly to match the latest version of the game.</li>
-<li><b>How can I contact the developers of Nicoo Free Fire Max?</b><br>
-You can contact the developers of Nicoo Free Fire Max by visiting their official website  or by sending them an email at support@naviemu.com. You can also follow them on their social media accounts   for updates and news.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/models/unet_1d_blocks.py b/spaces/1toTree/lora_test/ppdiffusers/models/unet_1d_blocks.py
deleted file mode 100644
index a895423756b7a19bb6c6f42327fb1d24fa623c50..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/models/unet_1d_blocks.py
+++ /dev/null
@@ -1,668 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import math
-
-import paddle
-import paddle.nn.functional as F
-from paddle import nn
-
-from .resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims
-
-
-class DownResnetBlock1D(nn.Layer):
-    def __init__(
-        self,
-        in_channels,
-        out_channels=None,
-        num_layers=1,
-        conv_shortcut=False,
-        temb_channels=32,
-        groups=32,
-        groups_out=None,
-        non_linearity=None,
-        time_embedding_norm="default",
-        output_scale_factor=1.0,
-        add_downsample=True,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-        self.time_embedding_norm = time_embedding_norm
-        self.add_downsample = add_downsample
-        self.output_scale_factor = output_scale_factor
-
-        if groups_out is None:
-            groups_out = groups
-
-        # there will always be at least one resnet
-        resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)]
-
-        for _ in range(num_layers):
-            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
-
-        self.resnets = nn.LayerList(resnets)
-
-        if non_linearity == "swish":
-            self.nonlinearity = lambda x: F.silu(x)
-        elif non_linearity == "mish":
-            self.nonlinearity = nn.Mish()
-        elif non_linearity == "silu":
-            self.nonlinearity = nn.Silu()
-        else:
-            self.nonlinearity = None
-
-        self.downsample = None
-        if add_downsample:
-            self.downsample = Downsample1D(out_channels, use_conv=True, padding=1)
-
-    def forward(self, hidden_states, temb=None):
-        output_states = ()
-
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for resnet in self.resnets[1:]:
-            hidden_states = resnet(hidden_states, temb)
-
-        output_states += (hidden_states,)
-
-        if self.nonlinearity is not None:
-            hidden_states = self.nonlinearity(hidden_states)
-
-        if self.downsample is not None:
-            hidden_states = self.downsample(hidden_states)
-
-        return hidden_states, output_states
-
-
-class UpResnetBlock1D(nn.Layer):
-    def __init__(
-        self,
-        in_channels,
-        out_channels=None,
-        num_layers=1,
-        temb_channels=32,
-        groups=32,
-        groups_out=None,
-        non_linearity=None,
-        time_embedding_norm="default",
-        output_scale_factor=1.0,
-        add_upsample=True,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.time_embedding_norm = time_embedding_norm
-        self.add_upsample = add_upsample
-        self.output_scale_factor = output_scale_factor
-
-        if groups_out is None:
-            groups_out = groups
-
-        # there will always be at least one resnet
-        resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)]
-
-        for _ in range(num_layers):
-            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
-
-        self.resnets = nn.LayerList(resnets)
-
-        if non_linearity == "swish":
-            self.nonlinearity = lambda x: F.silu(x)
-        elif non_linearity == "mish":
-            self.nonlinearity = nn.Mish()
-        elif non_linearity == "silu":
-            self.nonlinearity = nn.Silu()
-        else:
-            self.nonlinearity = None
-
-        self.upsample = None
-        if add_upsample:
-            self.upsample = Upsample1D(out_channels, use_conv_transpose=True)
-
-    def forward(self, hidden_states, res_hidden_states_tuple=None, temb=None):
-        if res_hidden_states_tuple is not None:
-            res_hidden_states = res_hidden_states_tuple[-1]
-            hidden_states = paddle.concat((hidden_states, res_hidden_states), axis=1)
-
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for resnet in self.resnets[1:]:
-            hidden_states = resnet(hidden_states, temb)
-
-        if self.nonlinearity is not None:
-            hidden_states = self.nonlinearity(hidden_states)
-
-        if self.upsample is not None:
-            hidden_states = self.upsample(hidden_states)
-
-        return hidden_states
-
-
-class ValueFunctionMidBlock1D(nn.Layer):
-    def __init__(self, in_channels, out_channels, embed_dim):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.embed_dim = embed_dim
-
-        self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim)
-        self.down1 = Downsample1D(out_channels // 2, use_conv=True)
-        self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim)
-        self.down2 = Downsample1D(out_channels // 4, use_conv=True)
-
-    def forward(self, x, temb=None):
-        x = self.res1(x, temb)
-        x = self.down1(x)
-        x = self.res2(x, temb)
-        x = self.down2(x)
-        return x
-
-
-class MidResTemporalBlock1D(nn.Layer):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        embed_dim,
-        num_layers: int = 1,
-        add_downsample: bool = False,
-        add_upsample: bool = False,
-        non_linearity=None,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.add_downsample = add_downsample
-
-        # there will always be at least one resnet
-        resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)]
-
-        for _ in range(num_layers):
-            resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim))
-
-        self.resnets = nn.LayerList(resnets)
-
-        if non_linearity == "swish":
-            self.nonlinearity = lambda x: F.silu(x)
-        elif non_linearity == "mish":
-            self.nonlinearity = nn.Mish()
-        elif non_linearity == "silu":
-            self.nonlinearity = nn.Silu()
-        else:
-            self.nonlinearity = None
-
-        self.upsample = None
-        if add_upsample:
-            self.upsample = Downsample1D(out_channels, use_conv=True)
-
-        self.downsample = None
-        if add_downsample:
-            self.downsample = Downsample1D(out_channels, use_conv=True)
-
-        if self.upsample and self.downsample:
-            raise ValueError("Block cannot downsample and upsample")
-
-    def forward(self, hidden_states, temb):
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for resnet in self.resnets[1:]:
-            hidden_states = resnet(hidden_states, temb)
-
-        if self.upsample:
-            hidden_states = self.upsample(hidden_states)
-        if self.downsample:
-            self.downsample = self.downsample(hidden_states)
-
-        return hidden_states
-
-
-class OutConv1DBlock(nn.Layer):
-    def __init__(self, num_groups_out, out_channels, embed_dim, act_fn):
-        super().__init__()
-        self.final_conv1d_1 = nn.Conv1D(embed_dim, embed_dim, 5, padding=2)
-        self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim)
-        if act_fn == "silu":
-            self.final_conv1d_act = nn.Silu()
-        if act_fn == "mish":
-            self.final_conv1d_act = nn.Mish()
-        self.final_conv1d_2 = nn.Conv1D(embed_dim, out_channels, 1)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.final_conv1d_1(hidden_states)
-        hidden_states = rearrange_dims(hidden_states)
-        hidden_states = self.final_conv1d_gn(hidden_states)
-        hidden_states = rearrange_dims(hidden_states)
-        hidden_states = self.final_conv1d_act(hidden_states)
-        hidden_states = self.final_conv1d_2(hidden_states)
-        return hidden_states
-
-
-class OutValueFunctionBlock(nn.Layer):
-    def __init__(self, fc_dim, embed_dim):
-        super().__init__()
-        self.final_block = nn.LayerList(
-            [
-                nn.Linear(fc_dim + embed_dim, fc_dim // 2),
-                nn.Mish(),
-                nn.Linear(fc_dim // 2, 1),
-            ]
-        )
-
-    def forward(self, hidden_states, temb):
-        hidden_states = hidden_states.reshape([hidden_states.shape[0], -1])
-        hidden_states = paddle.concat((hidden_states, temb), axis=-1)
-        for layer in self.final_block:
-            hidden_states = layer(hidden_states)
-
-        return hidden_states
-
-
-_kernels = {
-    "linear": [1 / 8, 3 / 8, 3 / 8, 1 / 8],
-    "cubic": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875],
-    "lanczos3": [
-        0.003689131001010537,
-        0.015056144446134567,
-        -0.03399861603975296,
-        -0.066637322306633,
-        0.13550527393817902,
-        0.44638532400131226,
-        0.44638532400131226,
-        0.13550527393817902,
-        -0.066637322306633,
-        -0.03399861603975296,
-        0.015056144446134567,
-        0.003689131001010537,
-    ],
-}
-
-
-class Downsample1d(nn.Layer):
-    def __init__(self, kernel="linear", pad_mode="reflect"):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = paddle.to_tensor(_kernels[kernel])
-        self.pad = kernel_1d.shape[0] // 2 - 1
-        self.register_buffer("kernel", kernel_1d)
-
-    def forward(self, hidden_states):
-        hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode, data_format="NCL")
-        weight = paddle.zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
-        indices = paddle.arange(hidden_states.shape[1])
-        weight[indices, indices] = self.kernel.cast(weight.dtype)
-        return F.conv1d(hidden_states, weight, stride=2)
-
-
-class Upsample1d(nn.Layer):
-    def __init__(self, kernel="linear", pad_mode="reflect"):
-        super().__init__()
-        self.pad_mode = pad_mode
-        kernel_1d = paddle.to_tensor(_kernels[kernel]) * 2
-        self.pad = kernel_1d.shape[0] // 2 - 1
-        self.register_buffer("kernel", kernel_1d)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode, data_format="NCL")
-        weight = paddle.zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
-        indices = paddle.arange(hidden_states.shape[1])
-        weight[indices, indices] = self.kernel.cast(weight.dtype)
-        return F.conv1d_transpose(hidden_states, weight, stride=2, padding=self.pad * 2 + 1)
-
-
-class SelfAttention1d(nn.Layer):
-    def __init__(self, in_channels, n_head=1, dropout_rate=0.0):
-        super().__init__()
-        self.channels = in_channels
-        self.group_norm = nn.GroupNorm(1, num_channels=in_channels)
-        self.num_heads = n_head
-
-        self.query = nn.Linear(self.channels, self.channels)
-        self.key = nn.Linear(self.channels, self.channels)
-        self.value = nn.Linear(self.channels, self.channels)
-
-        self.proj_attn = nn.Linear(self.channels, self.channels)
-
-        self.dropout = nn.Dropout(dropout_rate)
-
-    # (TODO junnyu) refactor self attention
-    def transpose_for_scores(self, projection: paddle.Tensor) -> paddle.Tensor:
-        new_projection_shape = projection.shape[:-1] + [self.num_heads, -1]
-        # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
-        new_projection = projection.reshape(new_projection_shape).transpose([0, 2, 1, 3])
-        return new_projection
-
-    def forward(self, hidden_states):
-        residual = hidden_states
-
-        hidden_states = self.group_norm(hidden_states)
-        hidden_states = hidden_states.transpose([0, 2, 1])
-
-        query_proj = self.query(hidden_states)
-        key_proj = self.key(hidden_states)
-        value_proj = self.value(hidden_states)
-
-        query_states = self.transpose_for_scores(query_proj)
-        key_states = self.transpose_for_scores(key_proj)
-        value_states = self.transpose_for_scores(value_proj)
-
-        scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1]))
-
-        attention_scores = paddle.matmul(query_states * scale, key_states * scale, transpose_y=True)
-        attention_probs = F.softmax(attention_scores, axis=-1)
-
-        # compute attention output
-        hidden_states = paddle.matmul(attention_probs, value_states)
-
-        hidden_states = hidden_states.transpose([0, 2, 1, 3])
-        new_hidden_states_shape = hidden_states.shape[:-2] + [
-            self.channels,
-        ]
-        hidden_states = hidden_states.reshape(new_hidden_states_shape)
-
-        # compute next hidden_states
-        hidden_states = self.proj_attn(hidden_states)
-        hidden_states = hidden_states.transpose([0, 2, 1])
-        hidden_states = self.dropout(hidden_states)
-        output = hidden_states + residual
-
-        return output
-
-
-class ResConvBlock(nn.Layer):
-    def __init__(self, in_channels, mid_channels, out_channels, is_last=False):
-        super().__init__()
-        self.is_last = is_last
-        self.has_conv_skip = in_channels != out_channels
-
-        if self.has_conv_skip:
-            self.conv_skip = nn.Conv1D(in_channels, out_channels, 1, bias_attr=False)
-
-        self.conv_1 = nn.Conv1D(in_channels, mid_channels, 5, padding=2)
-        self.group_norm_1 = nn.GroupNorm(1, mid_channels)
-        self.gelu_1 = nn.GELU()
-        self.conv_2 = nn.Conv1D(mid_channels, out_channels, 5, padding=2)
-
-        if not self.is_last:
-            self.group_norm_2 = nn.GroupNorm(1, out_channels)
-            self.gelu_2 = nn.GELU()
-
-    def forward(self, hidden_states):
-        residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states
-
-        hidden_states = self.conv_1(hidden_states)
-        hidden_states = self.group_norm_1(hidden_states)
-        hidden_states = self.gelu_1(hidden_states)
-        hidden_states = self.conv_2(hidden_states)
-
-        if not self.is_last:
-            hidden_states = self.group_norm_2(hidden_states)
-            hidden_states = self.gelu_2(hidden_states)
-
-        output = hidden_states + residual
-        return output
-
-
-class UNetMidBlock1D(nn.Layer):
-    def __init__(self, mid_channels, in_channels, out_channels=None):
-        super().__init__()
-
-        out_channels = in_channels if out_channels is None else out_channels
-
-        # there is always at least one resnet
-        self.down = Downsample1d("cubic")
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-        attentions = [
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(out_channels, out_channels // 32),
-        ]
-        self.up = Upsample1d(kernel="cubic")
-
-        self.attentions = nn.LayerList(attentions)
-        self.resnets = nn.LayerList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.down(hidden_states)
-        for attn, resnet in zip(self.attentions, self.resnets):
-            hidden_states = resnet(hidden_states)
-            hidden_states = attn(hidden_states)
-
-        hidden_states = self.up(hidden_states)
-
-        return hidden_states
-
-
-class AttnDownBlock1D(nn.Layer):
-    def __init__(self, out_channels, in_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        self.down = Downsample1d("cubic")
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-        attentions = [
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(out_channels, out_channels // 32),
-        ]
-
-        self.attentions = nn.LayerList(attentions)
-        self.resnets = nn.LayerList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.down(hidden_states)
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states)
-            hidden_states = attn(hidden_states)
-
-        return hidden_states, (hidden_states,)
-
-
-class DownBlock1D(nn.Layer):
-    def __init__(self, out_channels, in_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        self.down = Downsample1d("cubic")
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-
-        self.resnets = nn.LayerList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = self.down(hidden_states)
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        return hidden_states, (hidden_states,)
-
-
-class DownBlock1DNoSkip(nn.Layer):
-    def __init__(self, out_channels, in_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-
-        self.resnets = nn.LayerList(resnets)
-
-    def forward(self, hidden_states, temb=None):
-        hidden_states = paddle.concat([hidden_states, temb], axis=1)
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        return hidden_states, (hidden_states,)
-
-
-class AttnUpBlock1D(nn.Layer):
-    def __init__(self, in_channels, out_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = out_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-        attentions = [
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(mid_channels, mid_channels // 32),
-            SelfAttention1d(out_channels, out_channels // 32),
-        ]
-
-        self.attentions = nn.LayerList(attentions)
-        self.resnets = nn.LayerList(resnets)
-        self.up = Upsample1d(kernel="cubic")
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
-        res_hidden_states = res_hidden_states_tuple[-1]
-        hidden_states = paddle.concat([hidden_states, res_hidden_states], axis=1)
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states)
-            hidden_states = attn(hidden_states)
-
-        hidden_states = self.up(hidden_states)
-
-        return hidden_states
-
-
-class UpBlock1D(nn.Layer):
-    def __init__(self, in_channels, out_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = in_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels),
-        ]
-
-        self.resnets = nn.LayerList(resnets)
-        self.up = Upsample1d(kernel="cubic")
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
-        res_hidden_states = res_hidden_states_tuple[-1]
-        hidden_states = paddle.concat([hidden_states, res_hidden_states], axis=1)
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        hidden_states = self.up(hidden_states)
-
-        return hidden_states
-
-
-class UpBlock1DNoSkip(nn.Layer):
-    def __init__(self, in_channels, out_channels, mid_channels=None):
-        super().__init__()
-        mid_channels = in_channels if mid_channels is None else mid_channels
-
-        resnets = [
-            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, mid_channels),
-            ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),
-        ]
-
-        self.resnets = nn.LayerList(resnets)
-
-    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
-        res_hidden_states = res_hidden_states_tuple[-1]
-        hidden_states = paddle.concat([hidden_states, res_hidden_states], axis=1)
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states)
-
-        return hidden_states
-
-
-def get_down_block(down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample):
-    if down_block_type == "DownResnetBlock1D":
-        return DownResnetBlock1D(
-            in_channels=in_channels,
-            num_layers=num_layers,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-        )
-    elif down_block_type == "DownBlock1D":
-        return DownBlock1D(out_channels=out_channels, in_channels=in_channels)
-    elif down_block_type == "AttnDownBlock1D":
-        return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels)
-    elif down_block_type == "DownBlock1DNoSkip":
-        return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels)
-    raise ValueError(f"{down_block_type} does not exist.")
-
-
-def get_up_block(up_block_type, num_layers, in_channels, out_channels, temb_channels, add_upsample):
-    if up_block_type == "UpResnetBlock1D":
-        return UpResnetBlock1D(
-            in_channels=in_channels,
-            num_layers=num_layers,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-        )
-    elif up_block_type == "UpBlock1D":
-        return UpBlock1D(in_channels=in_channels, out_channels=out_channels)
-    elif up_block_type == "AttnUpBlock1D":
-        return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels)
-    elif up_block_type == "UpBlock1DNoSkip":
-        return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels)
-    raise ValueError(f"{up_block_type} does not exist.")
-
-
-def get_mid_block(mid_block_type, num_layers, in_channels, mid_channels, out_channels, embed_dim, add_downsample):
-    if mid_block_type == "MidResTemporalBlock1D":
-        return MidResTemporalBlock1D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            embed_dim=embed_dim,
-            add_downsample=add_downsample,
-        )
-    elif mid_block_type == "ValueFunctionMidBlock1D":
-        return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim)
-    elif mid_block_type == "UNetMidBlock1D":
-        return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels)
-    raise ValueError(f"{mid_block_type} does not exist.")
-
-
-def get_out_block(*, out_block_type, num_groups_out, embed_dim, out_channels, act_fn, fc_dim):
-    if out_block_type == "OutConv1DBlock":
-        return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn)
-    elif out_block_type == "ValueFunction":
-        return OutValueFunctionBlock(fc_dim, embed_dim)
-    return None
diff --git a/spaces/AIFILMS/StyleGANEX/scripts/inference.py b/spaces/AIFILMS/StyleGANEX/scripts/inference.py
deleted file mode 100644
index 9250d4b5b05d8a31527603d42823fd8b10234ce9..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/StyleGANEX/scripts/inference.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import os
-from argparse import Namespace
-
-from tqdm import tqdm
-import time
-import numpy as np
-import torch
-from PIL import Image
-from torch.utils.data import DataLoader
-import sys
-
-sys.path.append(".")
-sys.path.append("..")
-
-from configs import data_configs
-from datasets.inference_dataset import InferenceDataset
-from utils.common import tensor2im, log_input_image
-from options.test_options import TestOptions
-from models.psp import pSp
-
-
-def run():
-    test_opts = TestOptions().parse()
-
-    if test_opts.resize_factors is not None:
-        assert len(
-            test_opts.resize_factors.split(',')) == 1, "When running inference, provide a single downsampling factor!"
-        out_path_results = os.path.join(test_opts.exp_dir, 'inference_results',
-                                        'downsampling_{}'.format(test_opts.resize_factors))
-        out_path_coupled = os.path.join(test_opts.exp_dir, 'inference_coupled',
-                                        'downsampling_{}'.format(test_opts.resize_factors))
-    else:
-        out_path_results = os.path.join(test_opts.exp_dir, 'inference_results')
-        out_path_coupled = os.path.join(test_opts.exp_dir, 'inference_coupled')
-
-    os.makedirs(out_path_results, exist_ok=True)
-    os.makedirs(out_path_coupled, exist_ok=True)
-
-    # update test options with options used during training
-    ckpt = torch.load(test_opts.checkpoint_path, map_location='cpu')
-    opts = ckpt['opts']
-    opts.update(vars(test_opts))
-    if 'learn_in_w' not in opts:
-        opts['learn_in_w'] = False
-    if 'output_size' not in opts:
-        opts['output_size'] = 1024
-    opts = Namespace(**opts)
-
-    net = pSp(opts)
-    net.eval()
-    net.cuda()
-
-    print('Loading dataset for {}'.format(opts.dataset_type))
-    dataset_args = data_configs.DATASETS[opts.dataset_type]
-    transforms_dict = dataset_args['transforms'](opts).get_transforms()
-    dataset = InferenceDataset(root=opts.data_path,
-                               transform=transforms_dict['transform_inference'],
-                               opts=opts)
-    dataloader = DataLoader(dataset,
-                            batch_size=opts.test_batch_size,
-                            shuffle=False,
-                            num_workers=int(opts.test_workers),
-                            drop_last=True)
-
-    if opts.n_images is None:
-        opts.n_images = len(dataset)
-
-    global_i = 0
-    global_time = []
-    for input_batch in tqdm(dataloader):
-        if global_i >= opts.n_images:
-            break
-        with torch.no_grad():
-            input_cuda = input_batch.cuda().float()
-            tic = time.time()
-            result_batch = run_on_batch(input_cuda, net, opts)
-            toc = time.time()
-            global_time.append(toc - tic)
-
-        for i in range(opts.test_batch_size):
-            result = tensor2im(result_batch[i])
-            im_path = dataset.paths[global_i]
-
-            if opts.couple_outputs or global_i % 100 == 0:
-                input_im = log_input_image(input_batch[i], opts)
-                resize_amount = (256, 256) if opts.resize_outputs else (opts.output_size, opts.output_size)
-                if opts.resize_factors is not None:
-                    # for super resolution, save the original, down-sampled, and output
-                    source = Image.open(im_path)
-                    res = np.concatenate([np.array(source.resize(resize_amount)),
-                                          np.array(input_im.resize(resize_amount, resample=Image.NEAREST)),
-                                          np.array(result.resize(resize_amount))], axis=1)
-                else:
-                    # otherwise, save the original and output
-                    res = np.concatenate([np.array(input_im.resize(resize_amount)),
-                                          np.array(result.resize(resize_amount))], axis=1)
-                Image.fromarray(res).save(os.path.join(out_path_coupled, os.path.basename(im_path)))
-
-            im_save_path = os.path.join(out_path_results, os.path.basename(im_path))
-            Image.fromarray(np.array(result)).save(im_save_path)
-
-            global_i += 1
-
-    stats_path = os.path.join(opts.exp_dir, 'stats.txt')
-    result_str = 'Runtime {:.4f}+-{:.4f}'.format(np.mean(global_time), np.std(global_time))
-    print(result_str)
-
-    with open(stats_path, 'w') as f:
-        f.write(result_str)
-
-
-def run_on_batch(inputs, net, opts):
-    if opts.latent_mask is None:
-        result_batch = net(inputs, randomize_noise=False, resize=opts.resize_outputs)
-    else:
-        latent_mask = [int(l) for l in opts.latent_mask.split(",")]
-        result_batch = []
-        for image_idx, input_image in enumerate(inputs):
-            # get latent vector to inject into our input image
-            vec_to_inject = np.random.randn(1, 512).astype('float32')
-            _, latent_to_inject = net(torch.from_numpy(vec_to_inject).to("cuda"),
-                                      input_code=True,
-                                      return_latents=True)
-            # get output image with injected style vector
-            res = net(input_image.unsqueeze(0).to("cuda").float(),
-                      latent_mask=latent_mask,
-                      inject_latent=latent_to_inject,
-                      alpha=opts.mix_alpha,
-                      resize=opts.resize_outputs)
-            result_batch.append(res)
-        result_batch = torch.cat(result_batch, dim=0)
-    return result_batch
-
-
-if __name__ == '__main__':
-    run()
diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/README.md b/spaces/AIFILMS/audioldm-text-to-audio-generation/README.md
deleted file mode 100644
index 54ccb465bab6f54b115103a1f06a7259738980a7..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/audioldm-text-to-audio-generation/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: Audioldm Text To Audio Generation
-emoji: 🔊
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
-duplicated_from: haoheliu/audioldm-text-to-audio-generation
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-## Reference
-Part of the code from this repo is borrowed from the following repos. We would like to thank the authors of them for their contribution. 
-
-> https://github.com/LAION-AI/CLAP
-> https://github.com/CompVis/stable-diffusion
-> https://github.com/v-iashin/SpecVQGAN 
-> https://github.com/toshas/torch-fidelity
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/txt_processors/__init__.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/txt_processors/__init__.py
deleted file mode 100644
index 7815fc6d95bd38518a6213df09d2a020b77106f8..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/txt_processors/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import en, zh, zh_aishell_no_tone_sing
\ No newline at end of file
diff --git a/spaces/AIZero2Hero4Health/8-NLPSimilarityHeatmapCluster-SL/app.py b/spaces/AIZero2Hero4Health/8-NLPSimilarityHeatmapCluster-SL/app.py
deleted file mode 100644
index 8b53f979d9f3ac86b100b5f19647e5ac4a7fa8ea..0000000000000000000000000000000000000000
--- a/spaces/AIZero2Hero4Health/8-NLPSimilarityHeatmapCluster-SL/app.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import streamlit as st
-import nltk
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-from scipy.spatial.distance import cosine
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-import tensorflow as tf
-import tensorflow_hub as hub
-
-
-def cluster_examples(messages, embed, nc=3):
-    km = KMeans(
-    n_clusters=nc, init='random',
-    n_init=10, max_iter=300, 
-    tol=1e-04, random_state=0
-    )
-    km = km.fit_predict(embed)
-    for n in range(nc):
-        idxs = [i for i in range(len(km)) if km[i] == n]
-        ms = [messages[i] for i in idxs]
-        st.markdown ("CLUSTER : %d"%n)
-        for m in ms:
-            st.markdown (m)
-
-
-def plot_heatmap(labels, heatmap, rotation=90):
-  sns.set(font_scale=1.2)
-  fig, ax = plt.subplots()
-  g = sns.heatmap(
-      heatmap,
-      xticklabels=labels,
-      yticklabels=labels,
-      vmin=-1,
-      vmax=1,
-      cmap="coolwarm")
-  g.set_xticklabels(labels, rotation=rotation)
-  g.set_title("Textual Similarity")
-
-  st.pyplot(fig)
-  #plt.show()
-
-#st.header("Sentence Similarity Demo")
-
-# Streamlit text boxes
-text = st.text_area('Enter sentences:', value="Self confidence in outcomes helps us win and to make us successful.\nShe has a seriously impressive intellect and mind.\nStimulating and deep conversation helps us develop and grow.\nFrom basic quantum particles we get aerodynamics, friction, surface tension, weather, electromagnetism.\nIf she actively engages and comments positively, her anger disappears adapting into win-win's favor.\nI love interesting topics of conversation and the understanding and exploration of thoughts.\nThere is the ability to manipulate things the way you want in your mind to go how you want when you are self confident, that we don’t understand yet.")
-
-nc = st.slider('Select a number of clusters:', min_value=1, max_value=15, value=3)
-
-model_type = st.radio("Choose model:", ('Sentence Transformer', 'Universal Sentence Encoder'), index=0)
-
-# Model setup
-if model_type == "Sentence Transformer":
-    model = SentenceTransformer('paraphrase-distilroberta-base-v1')
-elif model_type == "Universal Sentence Encoder":
-    model_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
-    model = hub.load(model_url)
-
-nltk.download('punkt')
-
-# Run model
-if text:
-    sentences = nltk.tokenize.sent_tokenize(text)
-    if model_type == "Sentence Transformer":
-        embed = model.encode(sentences)
-    elif model_type == "Universal Sentence Encoder":
-        embed = model(sentences).numpy()
-    sim = np.zeros([len(embed), len(embed)])
-    for i,em in enumerate(embed):
-        for j,ea in enumerate(embed):
-            sim[i][j] = 1.0-cosine(em,ea)
-    st.subheader("Similarity Heatmap")
-    plot_heatmap(sentences, sim)
-    st.subheader("Results from K-Means Clustering")
-    cluster_examples(sentences, embed, nc)
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-150e_deepfashion2_short_sleeved_dress_256x192/td_hm_res50_4xb64-150e_deepfashion2_short_sleeved_dress_256x192.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-150e_deepfashion2_short_sleeved_dress_256x192/td_hm_res50_4xb64-150e_deepfashion2_short_sleeved_dress_256x192.py
deleted file mode 100644
index c9da2a8fe992607a34f4afd307745a7d822b3cb8..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-150e_deepfashion2_short_sleeved_dress_256x192/td_hm_res50_4xb64-150e_deepfashion2_short_sleeved_dress_256x192.py
+++ /dev/null
@@ -1,2861 +0,0 @@
-default_scope = 'mmpose'
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=50),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(
-        type='CheckpointHook', interval=10, save_best='PCK', rule='greater'),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    visualization=dict(type='PoseVisualizationHook', enable=False))
-custom_hooks = [dict(type='SyncBuffersHook')]
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'))
-vis_backends = [dict(type='LocalVisBackend')]
-visualizer = dict(
-    type='PoseLocalVisualizer',
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')],
-    name='visualizer')
-log_processor = dict(
-    type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
-log_level = 'INFO'
-load_from = None
-resume = False
-backend_args = dict(backend='local')
-train_cfg = dict(by_epoch=True, max_epochs=150, val_interval=10)
-val_cfg = dict()
-test_cfg = dict()
-colors = dict(
-    sss=[255, 128, 0],
-    lss=[255, 0, 128],
-    sso=[128, 0, 255],
-    lso=[0, 128, 255],
-    vest=[0, 128, 128],
-    sling=[0, 0, 128],
-    shorts=[128, 128, 128],
-    trousers=[128, 0, 128],
-    skirt=[64, 128, 128],
-    ssd=[64, 64, 128],
-    lsd=[128, 64, 0],
-    vd=[128, 64, 255],
-    sd=[128, 64, 0])
-dataset_info = dict(
-    dataset_name='deepfashion2',
-    paper_info=dict(
-        author=
-        'Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and Ping Luo',
-        title=
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-        container=
-        'Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)',
-        year='2019',
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-            swap='lso_kpt35'),
-        127:
-        dict(
-            name='lso_kpt39',
-            id=127,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt36'),
-        128:
-        dict(name='vest_kpt1', id=128, color=[0, 128, 128], type='', swap=''),
-        129:
-        dict(
-            name='vest_kpt2',
-            id=129,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt6'),
-        130:
-        dict(
-            name='vest_kpt3',
-            id=130,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt5'),
-        131:
-        dict(name='vest_kpt4', id=131, color=[0, 128, 128], type='', swap=''),
-        132:
-        dict(
-            name='vest_kpt5',
-            id=132,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt3'),
-        133:
-        dict(
-            name='vest_kpt6',
-            id=133,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt2'),
-        134:
-        dict(
-            name='vest_kpt7',
-            id=134,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt15'),
-        135:
-        dict(
-            name='vest_kpt8',
-            id=135,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt14'),
-        136:
-        dict(
-            name='vest_kpt9',
-            id=136,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt13'),
-        137:
-        dict(
-            name='vest_kpt10',
-            id=137,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt12'),
-        138:
-        dict(name='vest_kpt11', id=138, color=[0, 128, 128], type='', swap=''),
-        139:
-        dict(
-            name='vest_kpt12',
-            id=139,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt10'),
-        140:
-        dict(name='vest_kpt13', id=140, color=[0, 128, 128], type='', swap=''),
-        141:
-        dict(
-            name='vest_kpt14',
-            id=141,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt8'),
-        142:
-        dict(
-            name='vest_kpt15',
-            id=142,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt7'),
-        143:
-        dict(name='sling_kpt1', id=143, color=[0, 0, 128], type='', swap=''),
-        144:
-        dict(
-            name='sling_kpt2',
-            id=144,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt6'),
-        145:
-        dict(
-            name='sling_kpt3',
-            id=145,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt5'),
-        146:
-        dict(name='sling_kpt4', id=146, color=[0, 0, 128], type='', swap=''),
-        147:
-        dict(
-            name='sling_kpt5',
-            id=147,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt3'),
-        148:
-        dict(
-            name='sling_kpt6',
-            id=148,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt2'),
-        149:
-        dict(
-            name='sling_kpt7',
-            id=149,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt15'),
-        150:
-        dict(
-            name='sling_kpt8',
-            id=150,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt14'),
-        151:
-        dict(
-            name='sling_kpt9',
-            id=151,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt13'),
-        152:
-        dict(
-            name='sling_kpt10',
-            id=152,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt12'),
-        153:
-        dict(name='sling_kpt11', id=153, color=[0, 0, 128], type='', swap=''),
-        154:
-        dict(
-            name='sling_kpt12',
-            id=154,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt10'),
-        155:
-        dict(
-            name='sling_kpt13',
-            id=155,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt9'),
-        156:
-        dict(
-            name='sling_kpt14',
-            id=156,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt8'),
-        157:
-        dict(
-            name='sling_kpt15',
-            id=157,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt7'),
-        158:
-        dict(
-            name='shorts_kpt1',
-            id=158,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt3'),
-        159:
-        dict(
-            name='shorts_kpt2',
-            id=159,
-            color=[128, 128, 128],
-            type='',
-            swap=''),
-        160:
-        dict(
-            name='shorts_kpt3',
-            id=160,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt1'),
-        161:
-        dict(
-            name='shorts_kpt4',
-            id=161,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt10'),
-        162:
-        dict(
-            name='shorts_kpt5',
-            id=162,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt9'),
-        163:
-        dict(
-            name='shorts_kpt6',
-            id=163,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt8'),
-        164:
-        dict(
-            name='shorts_kpt7',
-            id=164,
-            color=[128, 128, 128],
-            type='',
-            swap=''),
-        165:
-        dict(
-            name='shorts_kpt8',
-            id=165,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt6'),
-        166:
-        dict(
-            name='shorts_kpt9',
-            id=166,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt5'),
-        167:
-        dict(
-            name='shorts_kpt10',
-            id=167,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt4'),
-        168:
-        dict(
-            name='trousers_kpt1',
-            id=168,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt3'),
-        169:
-        dict(
-            name='trousers_kpt2',
-            id=169,
-            color=[128, 0, 128],
-            type='',
-            swap=''),
-        170:
-        dict(
-            name='trousers_kpt3',
-            id=170,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt1'),
-        171:
-        dict(
-            name='trousers_kpt4',
-            id=171,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt14'),
-        172:
-        dict(
-            name='trousers_kpt5',
-            id=172,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt13'),
-        173:
-        dict(
-            name='trousers_kpt6',
-            id=173,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt12'),
-        174:
-        dict(
-            name='trousers_kpt7',
-            id=174,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt11'),
-        175:
-        dict(
-            name='trousers_kpt8',
-            id=175,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt10'),
-        176:
-        dict(
-            name='trousers_kpt9',
-            id=176,
-            color=[128, 0, 128],
-            type='',
-            swap=''),
-        177:
-        dict(
-            name='trousers_kpt10',
-            id=177,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt8'),
-        178:
-        dict(
-            name='trousers_kpt11',
-            id=178,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt7'),
-        179:
-        dict(
-            name='trousers_kpt12',
-            id=179,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt6'),
-        180:
-        dict(
-            name='trousers_kpt13',
-            id=180,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt5'),
-        181:
-        dict(
-            name='trousers_kpt14',
-            id=181,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt4'),
-        182:
-        dict(
-            name='skirt_kpt1',
-            id=182,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt3'),
-        183:
-        dict(
-            name='skirt_kpt2', id=183, color=[64, 128, 128], type='', swap=''),
-        184:
-        dict(
-            name='skirt_kpt3',
-            id=184,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt1'),
-        185:
-        dict(
-            name='skirt_kpt4',
-            id=185,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt8'),
-        186:
-        dict(
-            name='skirt_kpt5',
-            id=186,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt7'),
-        187:
-        dict(
-            name='skirt_kpt6', id=187, color=[64, 128, 128], type='', swap=''),
-        188:
-        dict(
-            name='skirt_kpt7',
-            id=188,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt5'),
-        189:
-        dict(
-            name='skirt_kpt8',
-            id=189,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt4'),
-        190:
-        dict(name='ssd_kpt1', id=190, color=[64, 64, 128], type='', swap=''),
-        191:
-        dict(
-            name='ssd_kpt2',
-            id=191,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt6'),
-        192:
-        dict(
-            name='ssd_kpt3',
-            id=192,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt5'),
-        193:
-        dict(name='ssd_kpt4', id=193, color=[64, 64, 128], type='', swap=''),
-        194:
-        dict(
-            name='ssd_kpt5',
-            id=194,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt3'),
-        195:
-        dict(
-            name='ssd_kpt6',
-            id=195,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt2'),
-        196:
-        dict(
-            name='ssd_kpt7',
-            id=196,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt29'),
-        197:
-        dict(
-            name='ssd_kpt8',
-            id=197,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt28'),
-        198:
-        dict(
-            name='ssd_kpt9',
-            id=198,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt27'),
-        199:
-        dict(
-            name='ssd_kpt10',
-            id=199,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt26'),
-        200:
-        dict(
-            name='ssd_kpt11',
-            id=200,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt25'),
-        201:
-        dict(
-            name='ssd_kpt12',
-            id=201,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt24'),
-        202:
-        dict(
-            name='ssd_kpt13',
-            id=202,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt23'),
-        203:
-        dict(
-            name='ssd_kpt14',
-            id=203,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt22'),
-        204:
-        dict(
-            name='ssd_kpt15',
-            id=204,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt21'),
-        205:
-        dict(
-            name='ssd_kpt16',
-            id=205,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt20'),
-        206:
-        dict(
-            name='ssd_kpt17',
-            id=206,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt19'),
-        207:
-        dict(name='ssd_kpt18', id=207, color=[64, 64, 128], type='', swap=''),
-        208:
-        dict(
-            name='ssd_kpt19',
-            id=208,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt17'),
-        209:
-        dict(
-            name='ssd_kpt20',
-            id=209,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt16'),
-        210:
-        dict(
-            name='ssd_kpt21',
-            id=210,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt15'),
-        211:
-        dict(
-            name='ssd_kpt22',
-            id=211,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt14'),
-        212:
-        dict(
-            name='ssd_kpt23',
-            id=212,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt13'),
-        213:
-        dict(
-            name='ssd_kpt24',
-            id=213,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt12'),
-        214:
-        dict(
-            name='ssd_kpt25',
-            id=214,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt11'),
-        215:
-        dict(
-            name='ssd_kpt26',
-            id=215,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt10'),
-        216:
-        dict(
-            name='ssd_kpt27',
-            id=216,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt9'),
-        217:
-        dict(
-            name='ssd_kpt28',
-            id=217,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt8'),
-        218:
-        dict(
-            name='ssd_kpt29',
-            id=218,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt7'),
-        219:
-        dict(name='lsd_kpt1', id=219, color=[128, 64, 0], type='', swap=''),
-        220:
-        dict(
-            name='lsd_kpt2',
-            id=220,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt6'),
-        221:
-        dict(
-            name='lsd_kpt3',
-            id=221,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt5'),
-        222:
-        dict(name='lsd_kpt4', id=222, color=[128, 64, 0], type='', swap=''),
-        223:
-        dict(
-            name='lsd_kpt5',
-            id=223,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt3'),
-        224:
-        dict(
-            name='lsd_kpt6',
-            id=224,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt2'),
-        225:
-        dict(
-            name='lsd_kpt7',
-            id=225,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt37'),
-        226:
-        dict(
-            name='lsd_kpt8',
-            id=226,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt36'),
-        227:
-        dict(
-            name='lsd_kpt9',
-            id=227,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt35'),
-        228:
-        dict(
-            name='lsd_kpt10',
-            id=228,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt34'),
-        229:
-        dict(
-            name='lsd_kpt11',
-            id=229,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt33'),
-        230:
-        dict(
-            name='lsd_kpt12',
-            id=230,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt32'),
-        231:
-        dict(
-            name='lsd_kpt13',
-            id=231,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt31'),
-        232:
-        dict(
-            name='lsd_kpt14',
-            id=232,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt30'),
-        233:
-        dict(
-            name='lsd_kpt15',
-            id=233,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt29'),
-        234:
-        dict(
-            name='lsd_kpt16',
-            id=234,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt28'),
-        235:
-        dict(
-            name='lsd_kpt17',
-            id=235,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt27'),
-        236:
-        dict(
-            name='lsd_kpt18',
-            id=236,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt26'),
-        237:
-        dict(
-            name='lsd_kpt19',
-            id=237,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt25'),
-        238:
-        dict(
-            name='lsd_kpt20',
-            id=238,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt24'),
-        239:
-        dict(
-            name='lsd_kpt21',
-            id=239,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt23'),
-        240:
-        dict(name='lsd_kpt22', id=240, color=[128, 64, 0], type='', swap=''),
-        241:
-        dict(
-            name='lsd_kpt23',
-            id=241,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt21'),
-        242:
-        dict(
-            name='lsd_kpt24',
-            id=242,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt20'),
-        243:
-        dict(
-            name='lsd_kpt25',
-            id=243,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt19'),
-        244:
-        dict(
-            name='lsd_kpt26',
-            id=244,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt18'),
-        245:
-        dict(
-            name='lsd_kpt27',
-            id=245,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt17'),
-        246:
-        dict(
-            name='lsd_kpt28',
-            id=246,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt16'),
-        247:
-        dict(
-            name='lsd_kpt29',
-            id=247,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt15'),
-        248:
-        dict(
-            name='lsd_kpt30',
-            id=248,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt14'),
-        249:
-        dict(
-            name='lsd_kpt31',
-            id=249,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt13'),
-        250:
-        dict(
-            name='lsd_kpt32',
-            id=250,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt12'),
-        251:
-        dict(
-            name='lsd_kpt33',
-            id=251,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt11'),
-        252:
-        dict(
-            name='lsd_kpt34',
-            id=252,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt10'),
-        253:
-        dict(
-            name='lsd_kpt35',
-            id=253,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt9'),
-        254:
-        dict(
-            name='lsd_kpt36',
-            id=254,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt8'),
-        255:
-        dict(
-            name='lsd_kpt37',
-            id=255,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt7'),
-        256:
-        dict(name='vd_kpt1', id=256, color=[128, 64, 255], type='', swap=''),
-        257:
-        dict(
-            name='vd_kpt2',
-            id=257,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt6'),
-        258:
-        dict(
-            name='vd_kpt3',
-            id=258,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt5'),
-        259:
-        dict(name='vd_kpt4', id=259, color=[128, 64, 255], type='', swap=''),
-        260:
-        dict(
-            name='vd_kpt5',
-            id=260,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt3'),
-        261:
-        dict(
-            name='vd_kpt6',
-            id=261,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt2'),
-        262:
-        dict(
-            name='vd_kpt7',
-            id=262,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt19'),
-        263:
-        dict(
-            name='vd_kpt8',
-            id=263,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt18'),
-        264:
-        dict(
-            name='vd_kpt9',
-            id=264,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt17'),
-        265:
-        dict(
-            name='vd_kpt10',
-            id=265,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt16'),
-        266:
-        dict(
-            name='vd_kpt11',
-            id=266,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt15'),
-        267:
-        dict(
-            name='vd_kpt12',
-            id=267,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt14'),
-        268:
-        dict(name='vd_kpt13', id=268, color=[128, 64, 255], type='', swap=''),
-        269:
-        dict(
-            name='vd_kpt14',
-            id=269,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt12'),
-        270:
-        dict(
-            name='vd_kpt15',
-            id=270,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt11'),
-        271:
-        dict(
-            name='vd_kpt16',
-            id=271,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt10'),
-        272:
-        dict(
-            name='vd_kpt17',
-            id=272,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt9'),
-        273:
-        dict(
-            name='vd_kpt18',
-            id=273,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt8'),
-        274:
-        dict(
-            name='vd_kpt19',
-            id=274,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt7'),
-        275:
-        dict(name='sd_kpt1', id=275, color=[128, 64, 0], type='', swap=''),
-        276:
-        dict(
-            name='sd_kpt2',
-            id=276,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt6'),
-        277:
-        dict(
-            name='sd_kpt3',
-            id=277,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt5'),
-        278:
-        dict(name='sd_kpt4', id=278, color=[128, 64, 0], type='', swap=''),
-        279:
-        dict(
-            name='sd_kpt5',
-            id=279,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt3'),
-        280:
-        dict(
-            name='sd_kpt6',
-            id=280,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt2'),
-        281:
-        dict(
-            name='sd_kpt7',
-            id=281,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt19'),
-        282:
-        dict(
-            name='sd_kpt8',
-            id=282,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt18'),
-        283:
-        dict(
-            name='sd_kpt9',
-            id=283,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt17'),
-        284:
-        dict(
-            name='sd_kpt10',
-            id=284,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt16'),
-        285:
-        dict(
-            name='sd_kpt11',
-            id=285,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt15'),
-        286:
-        dict(
-            name='sd_kpt12',
-            id=286,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt14'),
-        287:
-        dict(name='sd_kpt13', id=287, color=[128, 64, 0], type='', swap=''),
-        288:
-        dict(
-            name='sd_kpt14',
-            id=288,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt12'),
-        289:
-        dict(
-            name='sd_kpt15',
-            id=289,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt11'),
-        290:
-        dict(
-            name='sd_kpt16',
-            id=290,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt10'),
-        291:
-        dict(
-            name='sd_kpt17',
-            id=291,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt9'),
-        292:
-        dict(
-            name='sd_kpt18',
-            id=292,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt8'),
-        293:
-        dict(
-            name='sd_kpt19',
-            id=293,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt7')
-    }),
-    skeleton_info=dict({
-        0:
-        dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]),
-        1:
-        dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]),
-        2:
-        dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]),
-        3:
-        dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]),
-        4:
-        dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]),
-        5:
-        dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]),
-        6:
-        dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]),
-        7:
-        dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]),
-        8:
-        dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]),
-        9:
-        dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]),
-        10:
-        dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]),
-        11:
-        dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]),
-        12:
-        dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]),
-        13:
-        dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]),
-        14:
-        dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]),
-        15:
-        dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]),
-        16:
-        dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]),
-        17:
-        dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]),
-        18:
-        dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]),
-        19:
-        dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]),
-        20:
-        dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]),
-        21:
-        dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]),
-        22:
-        dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]),
-        23:
-        dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]),
-        24:
-        dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]),
-        25:
-        dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]),
-        26:
-        dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]),
-        27:
-        dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]),
-        28:
-        dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]),
-        29:
-        dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]),
-        30:
-        dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]),
-        31:
-        dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]),
-        32:
-        dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]),
-        33:
-        dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]),
-        34:
-        dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]),
-        35:
-        dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]),
-        36:
-        dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]),
-        37:
-        dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]),
-        38:
-        dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]),
-        39:
-        dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]),
-        40:
-        dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]),
-        41:
-        dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]),
-        42:
-        dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]),
-        43:
-        dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]),
-        44:
-        dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]),
-        45:
-        dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]),
-        46:
-        dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]),
-        47:
-        dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]),
-        48:
-        dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]),
-        49:
-        dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]),
-        50:
-        dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]),
-        51:
-        dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]),
-        52:
-        dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]),
-        53:
-        dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]),
-        54:
-        dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]),
-        55:
-        dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]),
-        56:
-        dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]),
-        57:
-        dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]),
-        58:
-        dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]),
-        59:
-        dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]),
-        60:
-        dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]),
-        61:
-        dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]),
-        62:
-        dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]),
-        63:
-        dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]),
-        64:
-        dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]),
-        65:
-        dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]),
-        66:
-        dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]),
-        67:
-        dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]),
-        68:
-        dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]),
-        69:
-        dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]),
-        70:
-        dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]),
-        71:
-        dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]),
-        72:
-        dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]),
-        73:
-        dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]),
-        74:
-        dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]),
-        75:
-        dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]),
-        76:
-        dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]),
-        77:
-        dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]),
-        78:
-        dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]),
-        79:
-        dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]),
-        80:
-        dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]),
-        81:
-        dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]),
-        82:
-        dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]),
-        83:
-        dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]),
-        84:
-        dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]),
-        85:
-        dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]),
-        86:
-        dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]),
-        87:
-        dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]),
-        88:
-        dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]),
-        89:
-        dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]),
-        90:
-        dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]),
-        91:
-        dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]),
-        92:
-        dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]),
-        93:
-        dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]),
-        94:
-        dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]),
-        95:
-        dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]),
-        96:
-        dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]),
-        97:
-        dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]),
-        98:
-        dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]),
-        99:
-        dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]),
-        100:
-        dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]),
-        101:
-        dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]),
-        102:
-        dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]),
-        103:
-        dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]),
-        104:
-        dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]),
-        105:
-        dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]),
-        106:
-        dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]),
-        107:
-        dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]),
-        108:
-        dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]),
-        109:
-        dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]),
-        110:
-        dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]),
-        111:
-        dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]),
-        112:
-        dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]),
-        113:
-        dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]),
-        114:
-        dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]),
-        115:
-        dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]),
-        116:
-        dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]),
-        117:
-        dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]),
-        118:
-        dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]),
-        119:
-        dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]),
-        120:
-        dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]),
-        121:
-        dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]),
-        122:
-        dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]),
-        123:
-        dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]),
-        124:
-        dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]),
-        125:
-        dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]),
-        126:
-        dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]),
-        127:
-        dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]),
-        128:
-        dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]),
-        129:
-        dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]),
-        130:
-        dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]),
-        131:
-        dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]),
-        132:
-        dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]),
-        133:
-        dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]),
-        134:
-        dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]),
-        135:
-        dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]),
-        136:
-        dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]),
-        137:
-        dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]),
-        138:
-        dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]),
-        139:
-        dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]),
-        140:
-        dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]),
-        141:
-        dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]),
-        142:
-        dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]),
-        143:
-        dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]),
-        144:
-        dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]),
-        145:
-        dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]),
-        146:
-        dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]),
-        147:
-        dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]),
-        148:
-        dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]),
-        149:
-        dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]),
-        150:
-        dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]),
-        151:
-        dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]),
-        152:
-        dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]),
-        153:
-        dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]),
-        154:
-        dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]),
-        155:
-        dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]),
-        156:
-        dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]),
-        157:
-        dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]),
-        158:
-        dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]),
-        159:
-        dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]),
-        160:
-        dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]),
-        161:
-        dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]),
-        162:
-        dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]),
-        163:
-        dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]),
-        164:
-        dict(
-            link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128,
-                                                                128]),
-        165:
-        dict(
-            link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128,
-                                                                128]),
-        166:
-        dict(
-            link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128,
-                                                                128]),
-        167:
-        dict(
-            link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128,
-                                                                128]),
-        168:
-        dict(
-            link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128,
-                                                                128]),
-        169:
-        dict(
-            link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128,
-                                                                128]),
-        170:
-        dict(
-            link=('shorts_kpt9', 'shorts_kpt10'),
-            id=170,
-            color=[128, 128, 128]),
-        171:
-        dict(
-            link=('shorts_kpt10', 'shorts_kpt3'),
-            id=171,
-            color=[128, 128, 128]),
-        172:
-        dict(
-            link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128,
-                                                                128]),
-        173:
-        dict(
-            link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128,
-                                                                128]),
-        174:
-        dict(
-            link=('trousers_kpt1', 'trousers_kpt4'),
-            id=174,
-            color=[128, 0, 128]),
-        175:
-        dict(
-            link=('trousers_kpt4', 'trousers_kpt5'),
-            id=175,
-            color=[128, 0, 128]),
-        176:
-        dict(
-            link=('trousers_kpt5', 'trousers_kpt6'),
-            id=176,
-            color=[128, 0, 128]),
-        177:
-        dict(
-            link=('trousers_kpt6', 'trousers_kpt7'),
-            id=177,
-            color=[128, 0, 128]),
-        178:
-        dict(
-            link=('trousers_kpt7', 'trousers_kpt8'),
-            id=178,
-            color=[128, 0, 128]),
-        179:
-        dict(
-            link=('trousers_kpt8', 'trousers_kpt9'),
-            id=179,
-            color=[128, 0, 128]),
-        180:
-        dict(
-            link=('trousers_kpt9', 'trousers_kpt10'),
-            id=180,
-            color=[128, 0, 128]),
-        181:
-        dict(
-            link=('trousers_kpt10', 'trousers_kpt11'),
-            id=181,
-            color=[128, 0, 128]),
-        182:
-        dict(
-            link=('trousers_kpt11', 'trousers_kpt12'),
-            id=182,
-            color=[128, 0, 128]),
-        183:
-        dict(
-            link=('trousers_kpt12', 'trousers_kpt13'),
-            id=183,
-            color=[128, 0, 128]),
-        184:
-        dict(
-            link=('trousers_kpt13', 'trousers_kpt14'),
-            id=184,
-            color=[128, 0, 128]),
-        185:
-        dict(
-            link=('trousers_kpt14', 'trousers_kpt3'),
-            id=185,
-            color=[128, 0, 128]),
-        186:
-        dict(
-            link=('trousers_kpt3', 'trousers_kpt2'),
-            id=186,
-            color=[128, 0, 128]),
-        187:
-        dict(
-            link=('trousers_kpt2', 'trousers_kpt1'),
-            id=187,
-            color=[128, 0, 128]),
-        188:
-        dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]),
-        189:
-        dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]),
-        190:
-        dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]),
-        191:
-        dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]),
-        192:
-        dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]),
-        193:
-        dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]),
-        194:
-        dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]),
-        195:
-        dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]),
-        196:
-        dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]),
-        197:
-        dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]),
-        198:
-        dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]),
-        199:
-        dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]),
-        200:
-        dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]),
-        201:
-        dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]),
-        202:
-        dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]),
-        203:
-        dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]),
-        204:
-        dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]),
-        205:
-        dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]),
-        206:
-        dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]),
-        207:
-        dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]),
-        208:
-        dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]),
-        209:
-        dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]),
-        210:
-        dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]),
-        211:
-        dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]),
-        212:
-        dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]),
-        213:
-        dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]),
-        214:
-        dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]),
-        215:
-        dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]),
-        216:
-        dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]),
-        217:
-        dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]),
-        218:
-        dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]),
-        219:
-        dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]),
-        220:
-        dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]),
-        221:
-        dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]),
-        222:
-        dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]),
-        223:
-        dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]),
-        224:
-        dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]),
-        225:
-        dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]),
-        226:
-        dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]),
-        227:
-        dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]),
-        228:
-        dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]),
-        229:
-        dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]),
-        230:
-        dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]),
-        231:
-        dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]),
-        232:
-        dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]),
-        233:
-        dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]),
-        234:
-        dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]),
-        235:
-        dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]),
-        236:
-        dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]),
-        237:
-        dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]),
-        238:
-        dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]),
-        239:
-        dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]),
-        240:
-        dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]),
-        241:
-        dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]),
-        242:
-        dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]),
-        243:
-        dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]),
-        244:
-        dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]),
-        245:
-        dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]),
-        246:
-        dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]),
-        247:
-        dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]),
-        248:
-        dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]),
-        249:
-        dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]),
-        250:
-        dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]),
-        251:
-        dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]),
-        252:
-        dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]),
-        253:
-        dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]),
-        254:
-        dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]),
-        255:
-        dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]),
-        256:
-        dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]),
-        257:
-        dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]),
-        258:
-        dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]),
-        259:
-        dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]),
-        260:
-        dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]),
-        261:
-        dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]),
-        262:
-        dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]),
-        263:
-        dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]),
-        264:
-        dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]),
-        265:
-        dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]),
-        266:
-        dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]),
-        267:
-        dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]),
-        268:
-        dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]),
-        269:
-        dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]),
-        270:
-        dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]),
-        271:
-        dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]),
-        272:
-        dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]),
-        273:
-        dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]),
-        274:
-        dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]),
-        275:
-        dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]),
-        276:
-        dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]),
-        277:
-        dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]),
-        278:
-        dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]),
-        279:
-        dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]),
-        280:
-        dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]),
-        281:
-        dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]),
-        282:
-        dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]),
-        283:
-        dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]),
-        284:
-        dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]),
-        285:
-        dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]),
-        286:
-        dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]),
-        287:
-        dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]),
-        288:
-        dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]),
-        289:
-        dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]),
-        290:
-        dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]),
-        291:
-        dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]),
-        292:
-        dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]),
-        293:
-        dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]),
-        294:
-        dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]),
-        295:
-        dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]),
-        296:
-        dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]),
-        297:
-        dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]),
-        298:
-        dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]),
-        299:
-        dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]),
-        300:
-        dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]),
-        301:
-        dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]),
-        302:
-        dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]),
-        303:
-        dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0])
-    }),
-    joint_weights=[
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
-    ],
-    sigmas=[])
-param_scheduler = [
-    dict(
-        type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False),
-    dict(
-        type='MultiStepLR',
-        begin=0,
-        end=150,
-        milestones=[100, 130],
-        gamma=0.1,
-        by_epoch=True)
-]
-optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005))
-auto_scale_lr = dict(base_batch_size=512)
-dataset_type = 'DeepFashion2Dataset'
-data_mode = 'topdown'
-data_root = 'data/deepfashion2/'
-codec = dict(
-    type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
-train_pipeline = [
-    dict(type='LoadImage'),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='RandomFlip', direction='horizontal'),
-    dict(
-        type='RandomBBoxTransform',
-        shift_prob=0,
-        rotate_factor=60,
-        scale_factor=(0.75, 1.25)),
-    dict(type='TopdownAffine', input_size=(192, 256)),
-    dict(
-        type='GenerateTarget',
-        encoder=dict(
-            type='MSRAHeatmap',
-            input_size=(192, 256),
-            heatmap_size=(48, 64),
-            sigma=2)),
-    dict(type='PackPoseInputs')
-]
-val_pipeline = [
-    dict(type='LoadImage', backend_args=dict(backend='local')),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='TopdownAffine', input_size=(192, 256)),
-    dict(type='PackPoseInputs')
-]
-train_dataloader = dict(
-    batch_size=64,
-    num_workers=6,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='train/deepfashion2_short_sleeved_dress.json',
-        data_prefix=dict(img='train/image/'),
-        pipeline=[
-            dict(type='LoadImage'),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='RandomFlip', direction='horizontal'),
-            dict(
-                type='RandomBBoxTransform',
-                shift_prob=0,
-                rotate_factor=60,
-                scale_factor=(0.75, 1.25)),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(
-                type='GenerateTarget',
-                encoder=dict(
-                    type='MSRAHeatmap',
-                    input_size=(192, 256),
-                    heatmap_size=(48, 64),
-                    sigma=2)),
-            dict(type='PackPoseInputs')
-        ]))
-val_dataloader = dict(
-    batch_size=32,
-    num_workers=6,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='validation/deepfashion2_short_sleeved_dress.json',
-        data_prefix=dict(img='validation/image/'),
-        test_mode=True,
-        pipeline=[
-            dict(type='LoadImage', backend_args=dict(backend='local')),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(type='PackPoseInputs')
-        ]))
-test_dataloader = dict(
-    batch_size=32,
-    num_workers=6,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='validation/deepfashion2_short_sleeved_dress.json',
-        data_prefix=dict(img='validation/image/'),
-        test_mode=True,
-        pipeline=[
-            dict(type='LoadImage', backend_args=dict(backend='local')),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(type='PackPoseInputs')
-        ]))
-channel_cfg = dict(
-    num_output_channels=294,
-    dataset_joints=294,
-    dataset_channel=[[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-        38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-        56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
-        74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-        92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
-        108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
-        122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
-        136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
-        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
-        178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
-        192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
-        206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-        220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
-        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
-        248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
-        262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
-        276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-        290, 291, 292, 293
-    ]],
-    inference_channel=[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-        38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-        56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
-        74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-        92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
-        108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
-        122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
-        136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
-        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
-        178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
-        192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
-        206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-        220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
-        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
-        248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
-        262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
-        276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-        290, 291, 292, 293
-    ])
-model = dict(
-    type='TopdownPoseEstimator',
-    data_preprocessor=dict(
-        type='PoseDataPreprocessor',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        bgr_to_rgb=True),
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
-    head=dict(
-        type='HeatmapHead',
-        in_channels=2048,
-        out_channels=294,
-        loss=dict(type='KeypointMSELoss', use_target_weight=True),
-        decoder=dict(
-            type='MSRAHeatmap',
-            input_size=(192, 256),
-            heatmap_size=(48, 64),
-            sigma=2)),
-    test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True))
-val_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE')
-]
-test_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE')
-]
-launcher = 'pytorch'
-work_dir = './work_dirs/td_hm_res50_4xb64-150e_deepfashion2_short_sleeved_dress_256x192'
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnet34_cifar.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnet34_cifar.py
deleted file mode 100644
index 55d033bc30bcbde7aef8e57ad950f59c248ad74b..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnet34_cifar.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(
-        type='ResNet_CIFAR',
-        depth=34,
-        num_stages=4,
-        out_indices=(3, ),
-        style='pytorch'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=10,
-        in_channels=512,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-    ))
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/LayoutWritable.js b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/LayoutWritable.js
deleted file mode 100644
index 2f4da9393ed0595b45c252e31c70cd0c2c446d6f..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/LayoutWritable.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import { writable } from "svelte/store";
-
-export const isloading_writable = writable(false);
-export const is_init_writable = writable(false);
-export const cancel_writable = writable(false);
-export const refresh_chats_writable = writable([]);
-export const refresh_chats_writable_empty = writable(false);
-export const curr_model_writable = writable(0);
-export const curr_model_writable_string = writable("");
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/ymlachievements.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/ymlachievements.d.ts
deleted file mode 100644
index b993b156a3131302c71c241d51a95d414bb64c88..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/ymlachievements.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import Achievements from './logic/achievements/ymlachievements/Achievements';
-export default Achievements;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/holygrail/methods/CreatExpandContainer.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/holygrail/methods/CreatExpandContainer.js
deleted file mode 100644
index 702ea005792329787a498422b7aa05d0a7f16c07..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/holygrail/methods/CreatExpandContainer.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import Sizer from '../../sizer/Sizer.js';
-
-var CreatExpandContainer = function (scene, orientation) {
-    var container = new Sizer(scene, {
-        orientation: orientation
-    })
-    scene.add.existing(container);
-    return container;
-}
-
-export default CreatExpandContainer;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateCanvas.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateCanvas.js
deleted file mode 100644
index 9425db8b74ee279d76002603c74e663d648c8d93..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateCanvas.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import Canvas from '../../canvas/Canvas.js';
-import SetTextureProperties from './utils/SetTextureProperties.js';
-
-
-var CreateCanvas = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    var width = data.width || 1;
-    var height = data.height || 1;
-    var gameObject = new Canvas(scene, 0, 0, width, height);
-
-    if (data.fill !== undefined) {
-        gameObject.fill(data.fill);
-    }
-
-    SetTextureProperties(gameObject, data);
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateCanvas;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNinePatch2.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNinePatch2.js
deleted file mode 100644
index b4477b0e8ca3600651282068630055ac6ea3aa09..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNinePatch2.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import NinePatch from '../../ninepatch2/NinePatch.js';
-
-var CreateNinePatch2 = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    var gameObject = new NinePatch(scene, data);
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-export default CreateNinePatch2;
\ No newline at end of file
diff --git a/spaces/Agusbs98/automatic-ecg-diagnosis/nets/nets.py b/spaces/Agusbs98/automatic-ecg-diagnosis/nets/nets.py
deleted file mode 100644
index 052695901923d58df8b865810e38ec1fd8edd913..0000000000000000000000000000000000000000
--- a/spaces/Agusbs98/automatic-ecg-diagnosis/nets/nets.py
+++ /dev/null
@@ -1,73 +0,0 @@
-
-import os, sys
-from libs import *
-from .layers import *
-from .modules import *
-from .bblocks import *
-from .backbones import *
-
-class LightX3ECG(nn.Module):
-    def __init__(self, 
-        base_channels = 64, 
-        num_classes = 1, 
-    ):
-        super(LightX3ECG, self).__init__()
-        self.backbone_0 = LightSEResNet18(base_channels)
-        self.backbone_1 = LightSEResNet18(base_channels)
-        self.backbone_2 = LightSEResNet18(base_channels)
-        self.lw_attention = nn.Sequential(
-            nn.Linear(
-                base_channels*24, base_channels*8, 
-            ), 
-            nn.BatchNorm1d(base_channels*8), 
-            nn.ReLU(), 
-            nn.Dropout(0.3), 
-            nn.Linear(
-                base_channels*8, 3, 
-            ), 
-        )
-
-        self.classifier = nn.Sequential(
-            nn.Dropout(0.2), 
-            nn.Linear(
-                base_channels*8, num_classes, 
-            ), 
-        )
-
-    def forward(self, 
-        input, 
-        return_attention_scores = False, 
-    ):
-        features_0 = self.backbone_0(input[:, 0, :].unsqueeze(1)).squeeze(2)
-        features_1 = self.backbone_1(input[:, 1, :].unsqueeze(1)).squeeze(2)
-        features_2 = self.backbone_2(input[:, 2, :].unsqueeze(1)).squeeze(2)
-        attention_scores = torch.sigmoid(
-            self.lw_attention(
-                torch.cat(
-                [
-                    features_0, 
-                    features_1, 
-                    features_2, 
-                ], 
-                dim = 1, 
-                )
-            )
-        )
-        merged_features = torch.sum(
-            torch.stack(
-            [
-                features_0, 
-                features_1, 
-                features_2, 
-            ], 
-            dim = 1, 
-            )*attention_scores.unsqueeze(-1), 
-            dim = 1, 
-        )
-
-        output = self.classifier(merged_features)
-
-        if not return_attention_scores:
-            return output
-        else:
-            return output, attention_scores
\ No newline at end of file
diff --git a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/README.md b/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/README.md
deleted file mode 100644
index 1b24e6efdb04cb1460e4fe3257d2303677c5a0e1..0000000000000000000000000000000000000000
--- a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multilingual Anime TTS
-emoji: 🎙🐴
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.7
-app_file: app.py
-pinned: false
-duplicated_from: Plachta/VITS-Umamusume-voice-synthesizer
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AlphaDragon/Voice-Clone/app.py b/spaces/AlphaDragon/Voice-Clone/app.py
deleted file mode 100644
index ca085e087f220d46b95e5455ee6da92bf72ce764..0000000000000000000000000000000000000000
--- a/spaces/AlphaDragon/Voice-Clone/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import gradio as gr
-from TTS.api import TTS
-
-# Init TTS
-tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
-zh_tts = TTS(model_name="tts_models/zh-CN/baker/tacotron2-DDC-GST", progress_bar=False, gpu=False)
-de_tts = TTS(model_name="tts_models/de/thorsten/vits", gpu=False)
-es_tts = TTS(model_name="tts_models/es/mai/tacotron2-DDC", progress_bar=False, gpu=False)
-
-def text_to_speech(text: str, speaker_wav, speaker_wav_file, language: str):
-    if speaker_wav_file and not speaker_wav:
-        speaker_wav = speaker_wav_file
-    file_path = "output.wav"
-    if language == "zh-CN":
-        # if speaker_wav is not None:
-        #     zh_tts.tts_to_file(text, speaker_wav=speaker_wav, file_path=file_path)
-        # else:
-        zh_tts.tts_to_file(text, file_path=file_path)
-    elif language == "de":
-        # if speaker_wav is not None:
-        #     de_tts.tts_to_file(text, speaker_wav=speaker_wav, file_path=file_path)
-        # else:
-        de_tts.tts_to_file(text, file_path=file_path)
-    elif language == "es":
-        # if speaker_wav is not None:
-        #     es_tts.tts_to_file(text, speaker_wav=speaker_wav, file_path=file_path)
-        # else:
-        es_tts.tts_to_file(text, file_path=file_path)
-    else:
-        if speaker_wav is not None:
-            tts.tts_to_file(text, speaker_wav=speaker_wav, language=language, file_path=file_path)
-        else:
-            tts.tts_to_file(text, speaker=tts.speakers[0], language=language, file_path=file_path)
-    return file_path
-
-
-
-title = "Voice-Cloning-Demo"
-
-def toggle(choice):
-    if choice == "mic":
-        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
-    else:
-        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
-
-def handle_language_change(choice):
-    if choice == "zh-CN" or choice == "de" or choice == "es":
-        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-    else:
-        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-warming_text = """Please note that Chinese, German, and Spanish are currently not supported for voice cloning."""
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(label="Input the text", value="", max_lines=3)
-            lan_input = gr.Radio(label="Language", choices=["en", "fr-fr", "pt-br", "zh-CN", "de", "es"], value="en")
-            gr.Markdown(warming_text)
-            radio = gr.Radio(["mic", "file"], value="mic",
-                             label="How would you like to upload your audio?")
-            audio_input_mic = gr.Audio(label="Voice to clone", source="microphone", type="filepath", visible=True)
-            audio_input_file = gr.Audio(label="Voice to clone", type="filepath", visible=False)
-
-            with gr.Row():
-                with gr.Column():
-                    btn_clear = gr.Button("Clear")
-                with gr.Column():
-                    btn = gr.Button("Submit", variant="primary")
-        with gr.Column():
-            audio_output = gr.Audio(label="Output")
-
-    # gr.Examples(examples, fn=inference, inputs=[audio_file, text_input],
-    #                   outputs=audio_output, cache_examples=True)
-    btn.click(text_to_speech, inputs=[text_input, audio_input_mic,
-              audio_input_file, lan_input], outputs=audio_output)
-    radio.change(toggle, radio, [audio_input_mic, audio_input_file])
-    lan_input.change(handle_language_change, lan_input, [radio, audio_input_mic, audio_input_file])
-
-demo.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Amrrs/pdf-table-extractor/README.md b/spaces/Amrrs/pdf-table-extractor/README.md
deleted file mode 100644
index 46660a936b00d80857d8c27fef7c7150590e24f2..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/pdf-table-extractor/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Pdf Table Extractor
-emoji: 📄
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_vae.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_vae.py
deleted file mode 100644
index 0abb2f056e3cf882755be13343d76b1c98c1e1f7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_models_vae.py
+++ /dev/null
@@ -1,600 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-import torch
-from parameterized import parameterized
-
-from diffusers import AsymmetricAutoencoderKL, AutoencoderKL
-from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import enable_full_determinism
-
-from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
-
-
-enable_full_determinism()
-
-
-class AutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = AutoencoderKL
-    main_input_name = "sample"
-    base_precision = 1e-2
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_channels = 3
-        sizes = (32, 32)
-
-        image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-
-        return {"sample": image}
-
-    @property
-    def input_shape(self):
-        return (3, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (3, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "block_out_channels": [32, 64],
-            "in_channels": 3,
-            "out_channels": 3,
-            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            "latent_channels": 4,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    def test_forward_signature(self):
-        pass
-
-    def test_training(self):
-        pass
-
-    @unittest.skipIf(torch_device == "mps", "Gradient checkpointing skipped on MPS")
-    def test_gradient_checkpointing(self):
-        # enable deterministic behavior for gradient checkpointing
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        assert not model.is_gradient_checkpointing and model.training
-
-        out = model(**inputs_dict).sample
-        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
-        # we won't calculate the loss and rather backprop on out.sum()
-        model.zero_grad()
-
-        labels = torch.randn_like(out)
-        loss = (out - labels).mean()
-        loss.backward()
-
-        # re-instantiate the model now enabling gradient checkpointing
-        model_2 = self.model_class(**init_dict)
-        # clone model
-        model_2.load_state_dict(model.state_dict())
-        model_2.to(torch_device)
-        model_2.enable_gradient_checkpointing()
-
-        assert model_2.is_gradient_checkpointing and model_2.training
-
-        out_2 = model_2(**inputs_dict).sample
-        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
-        # we won't calculate the loss and rather backprop on out.sum()
-        model_2.zero_grad()
-        loss_2 = (out_2 - labels).mean()
-        loss_2.backward()
-
-        # compare the output and parameters gradients
-        self.assertTrue((loss - loss_2).abs() < 1e-5)
-        named_params = dict(model.named_parameters())
-        named_params_2 = dict(model_2.named_parameters())
-        for name, param in named_params.items():
-            self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
-
-    def test_from_pretrained_hub(self):
-        model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
-        self.assertIsNotNone(model)
-        self.assertEqual(len(loading_info["missing_keys"]), 0)
-
-        model.to(torch_device)
-        image = model(**self.dummy_input)
-
-        assert image is not None, "Make sure output is not None"
-
-    def test_output_pretrained(self):
-        model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
-        model = model.to(torch_device)
-        model.eval()
-
-        if torch_device == "mps":
-            generator = torch.manual_seed(0)
-        else:
-            generator = torch.Generator(device=torch_device).manual_seed(0)
-
-        image = torch.randn(
-            1,
-            model.config.in_channels,
-            model.config.sample_size,
-            model.config.sample_size,
-            generator=torch.manual_seed(0),
-        )
-        image = image.to(torch_device)
-        with torch.no_grad():
-            output = model(image, sample_posterior=True, generator=generator).sample
-
-        output_slice = output[0, -1, -3:, -3:].flatten().cpu()
-
-        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
-        # the expected output slices are not the same for CPU and GPU.
-        if torch_device == "mps":
-            expected_output_slice = torch.tensor(
-                [
-                    -4.0078e-01,
-                    -3.8323e-04,
-                    -1.2681e-01,
-                    -1.1462e-01,
-                    2.0095e-01,
-                    1.0893e-01,
-                    -8.8247e-02,
-                    -3.0361e-01,
-                    -9.8644e-03,
-                ]
-            )
-        elif torch_device == "cpu":
-            expected_output_slice = torch.tensor(
-                [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
-            )
-        else:
-            expected_output_slice = torch.tensor(
-                [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
-            )
-
-        self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
-
-
-class AsymmetricAutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = AsymmetricAutoencoderKL
-    main_input_name = "sample"
-    base_precision = 1e-2
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_channels = 3
-        sizes = (32, 32)
-
-        image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-        mask = torch.ones((batch_size, 1) + sizes).to(torch_device)
-
-        return {"sample": image, "mask": mask}
-
-    @property
-    def input_shape(self):
-        return (3, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (3, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "in_channels": 3,
-            "out_channels": 3,
-            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            "down_block_out_channels": [32, 64],
-            "layers_per_down_block": 1,
-            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            "up_block_out_channels": [32, 64],
-            "layers_per_up_block": 1,
-            "act_fn": "silu",
-            "latent_channels": 4,
-            "norm_num_groups": 32,
-            "sample_size": 32,
-            "scaling_factor": 0.18215,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    def test_forward_signature(self):
-        pass
-
-    def test_forward_with_norm_groups(self):
-        pass
-
-
-@slow
-class AutoencoderKLIntegrationTests(unittest.TestCase):
-    def get_file_format(self, seed, shape):
-        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
-
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
-        dtype = torch.float16 if fp16 else torch.float32
-        image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
-        return image
-
-    def get_sd_vae_model(self, model_id="CompVis/stable-diffusion-v1-4", fp16=False):
-        revision = "fp16" if fp16 else None
-        torch_dtype = torch.float16 if fp16 else torch.float32
-
-        model = AutoencoderKL.from_pretrained(
-            model_id,
-            subfolder="vae",
-            torch_dtype=torch_dtype,
-            revision=revision,
-        )
-        model.to(torch_device)
-
-        return model
-
-    def get_generator(self, seed=0):
-        if torch_device == "mps":
-            return torch.manual_seed(seed)
-        return torch.Generator(device=torch_device).manual_seed(seed)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
-            [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
-            # fmt: on
-        ]
-    )
-    def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):
-        model = self.get_sd_vae_model()
-        image = self.get_sd_image(seed)
-        generator = self.get_generator(seed)
-
-        with torch.no_grad():
-            sample = model(image, generator=generator, sample_posterior=True).sample
-
-        assert sample.shape == image.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
-            [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_stable_diffusion_fp16(self, seed, expected_slice):
-        model = self.get_sd_vae_model(fp16=True)
-        image = self.get_sd_image(seed, fp16=True)
-        generator = self.get_generator(seed)
-
-        with torch.no_grad():
-            sample = model(image, generator=generator, sample_posterior=True).sample
-
-        assert sample.shape == image.shape
-
-        output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=1e-2)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
-            [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
-            # fmt: on
-        ]
-    )
-    def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):
-        model = self.get_sd_vae_model()
-        image = self.get_sd_image(seed)
-
-        with torch.no_grad():
-            sample = model(image).sample
-
-        assert sample.shape == image.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
-            [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_stable_diffusion_decode(self, seed, expected_slice):
-        model = self.get_sd_vae_model()
-        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
-
-        with torch.no_grad():
-            sample = model.decode(encoding).sample
-
-        assert list(sample.shape) == [3, 3, 512, 512]
-
-        output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
-            [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_stable_diffusion_decode_fp16(self, seed, expected_slice):
-        model = self.get_sd_vae_model(fp16=True)
-        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)
-
-        with torch.no_grad():
-            sample = model.decode(encoding).sample
-
-        assert list(sample.shape) == [3, 3, 512, 512]
-
-        output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
-
-    @parameterized.expand([(13,), (16,), (27,)])
-    @require_torch_gpu
-    @unittest.skipIf(not is_xformers_available(), reason="xformers is not required when using PyTorch 2.0.")
-    def test_stable_diffusion_decode_xformers_vs_2_0_fp16(self, seed):
-        model = self.get_sd_vae_model(fp16=True)
-        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)
-
-        with torch.no_grad():
-            sample = model.decode(encoding).sample
-
-        model.enable_xformers_memory_efficient_attention()
-        with torch.no_grad():
-            sample_2 = model.decode(encoding).sample
-
-        assert list(sample.shape) == [3, 3, 512, 512]
-
-        assert torch_all_close(sample, sample_2, atol=1e-1)
-
-    @parameterized.expand([(13,), (16,), (37,)])
-    @require_torch_gpu
-    @unittest.skipIf(not is_xformers_available(), reason="xformers is not required when using PyTorch 2.0.")
-    def test_stable_diffusion_decode_xformers_vs_2_0(self, seed):
-        model = self.get_sd_vae_model()
-        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
-
-        with torch.no_grad():
-            sample = model.decode(encoding).sample
-
-        model.enable_xformers_memory_efficient_attention()
-        with torch.no_grad():
-            sample_2 = model.decode(encoding).sample
-
-        assert list(sample.shape) == [3, 3, 512, 512]
-
-        assert torch_all_close(sample, sample_2, atol=1e-2)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
-            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
-            # fmt: on
-        ]
-    )
-    def test_stable_diffusion_encode_sample(self, seed, expected_slice):
-        model = self.get_sd_vae_model()
-        image = self.get_sd_image(seed)
-        generator = self.get_generator(seed)
-
-        with torch.no_grad():
-            dist = model.encode(image).latent_dist
-            sample = dist.sample(generator=generator)
-
-        assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
-
-        output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        tolerance = 3e-3 if torch_device != "mps" else 1e-2
-        assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)
-
-    def test_stable_diffusion_model_local(self):
-        model_id = "stabilityai/sd-vae-ft-mse"
-        model_1 = AutoencoderKL.from_pretrained(model_id).to(torch_device)
-
-        url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"
-        model_2 = AutoencoderKL.from_single_file(url).to(torch_device)
-        image = self.get_sd_image(33)
-
-        with torch.no_grad():
-            sample_1 = model_1(image).sample
-            sample_2 = model_2(image).sample
-
-        assert sample_1.shape == sample_2.shape
-
-        output_slice_1 = sample_1[-1, -2:, -2:, :2].flatten().float().cpu()
-        output_slice_2 = sample_2[-1, -2:, -2:, :2].flatten().float().cpu()
-
-        assert torch_all_close(output_slice_1, output_slice_2, atol=3e-3)
-
-
-@slow
-class AsymmetricAutoencoderKLIntegrationTests(unittest.TestCase):
-    def get_file_format(self, seed, shape):
-        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
-
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
-        dtype = torch.float16 if fp16 else torch.float32
-        image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
-        return image
-
-    def get_sd_vae_model(self, model_id="cross-attention/asymmetric-autoencoder-kl-x-1-5", fp16=False):
-        revision = "main"
-        torch_dtype = torch.float32
-
-        model = AsymmetricAutoencoderKL.from_pretrained(
-            model_id,
-            torch_dtype=torch_dtype,
-            revision=revision,
-        )
-        model.to(torch_device).eval()
-
-        return model
-
-    def get_generator(self, seed=0):
-        if torch_device == "mps":
-            return torch.manual_seed(seed)
-        return torch.Generator(device=torch_device).manual_seed(seed)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, [-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078], [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824]],
-            [47, [0.4400, 0.0543, 0.2873, 0.2946, 0.0553, 0.0839, -0.1585, 0.2529], [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089]],
-            # fmt: on
-        ]
-    )
-    def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):
-        model = self.get_sd_vae_model()
-        image = self.get_sd_image(seed)
-        generator = self.get_generator(seed)
-
-        with torch.no_grad():
-            sample = model(image, generator=generator, sample_posterior=True).sample
-
-        assert sample.shape == image.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, [-0.0340, 0.2870, 0.1698, -0.0105, -0.3448, 0.3529, -0.1321, 0.1097], [-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078]],
-            [47, [0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531], [0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531]],
-            # fmt: on
-        ]
-    )
-    def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):
-        model = self.get_sd_vae_model()
-        image = self.get_sd_image(seed)
-
-        with torch.no_grad():
-            sample = model(image).sample
-
-        assert sample.shape == image.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [13, [-0.0521, -0.2939,  0.1540, -0.1855, -0.5936, -0.3138, -0.4579, -0.2275]],
-            [37, [-0.1820, -0.4345, -0.0455, -0.2923, -0.8035, -0.5089, -0.4795, -0.3106]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_stable_diffusion_decode(self, seed, expected_slice):
-        model = self.get_sd_vae_model()
-        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
-
-        with torch.no_grad():
-            sample = model.decode(encoding).sample
-
-        assert list(sample.shape) == [3, 3, 512, 512]
-
-        output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=2e-3)
-
-    @parameterized.expand([(13,), (16,), (37,)])
-    @require_torch_gpu
-    @unittest.skipIf(not is_xformers_available(), reason="xformers is not required when using PyTorch 2.0.")
-    def test_stable_diffusion_decode_xformers_vs_2_0(self, seed):
-        model = self.get_sd_vae_model()
-        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
-
-        with torch.no_grad():
-            sample = model.decode(encoding).sample
-
-        model.enable_xformers_memory_efficient_attention()
-        with torch.no_grad():
-            sample_2 = model.decode(encoding).sample
-
-        assert list(sample.shape) == [3, 3, 512, 512]
-
-        assert torch_all_close(sample, sample_2, atol=5e-2)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
-            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
-            # fmt: on
-        ]
-    )
-    def test_stable_diffusion_encode_sample(self, seed, expected_slice):
-        model = self.get_sd_vae_model()
-        image = self.get_sd_image(seed)
-        generator = self.get_generator(seed)
-
-        with torch.no_grad():
-            dist = model.encode(image).latent_dist
-            sample = dist.sample(generator=generator)
-
-        assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
-
-        output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        tolerance = 3e-3 if torch_device != "mps" else 1e-2
-        assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py
deleted file mode 100644
index 4e00a059f8d2e58d23d6b77764456be351bd3115..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py
+++ /dev/null
@@ -1,15 +0,0 @@
-_base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
-model = dict(
-    type='GFL',
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_r50_fpn_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_r50_fpn_2x_coco.py
deleted file mode 100644
index 927915fa8c63d380cc4bd62a580ffaad8b1ce386..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_r50_fpn_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './retinanet_r50_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/AndySAnker/DeepStruc/predict.py b/spaces/AndySAnker/DeepStruc/predict.py
deleted file mode 100644
index 3ad08905adeec57368e47045a0c28ae1ecb7bd28..0000000000000000000000000000000000000000
--- a/spaces/AndySAnker/DeepStruc/predict.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import sys, argparse
-import streamlit as st
-from tools.module import Net
-import torch, random, time
-import numpy as np
-import pytorch_lightning as pl
-from tools.utils import get_data, format_predictions, plot_ls, get_model, save_predictions
-
-def main(args):
-    time_start = time.time()
-    data, data_name, project_name = get_data(args)
-    model_path, model_arch = get_model(args.model)
-    
-    Net(model_arch=model_arch)
-    DeepStruc = Net.load_from_checkpoint(model_path,model_arch=model_arch)
-    #start_time = time.time()
-    xyz_pred, latent_space, kl, mu, sigma = DeepStruc(data, mode='prior', sigma_scale=args.sigma)
-    #st.write("one prediction: " , time.time() - start_time)
-    #start_time = time.time()
-    #for i in range(1000):
-    #    xyz_pred, latent_space, kl, mu, sigma = DeepStruc(data, mode='prior', sigma_scale=args.sigma)
-    #st.write("thousand predictions: " , time.time() - start_time)
-
-    samling_pairs = format_predictions(latent_space, data_name, mu, sigma, args.sigma)
-    
-    df, mk_dir, index_highlight = samling_pairs, project_name, args.index_plot
-
-    these_cords = save_predictions(xyz_pred, samling_pairs, project_name, model_arch, args)
-    
-    return df, index_highlight, these_cords
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/deform_conv.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/deform_conv.py
deleted file mode 100644
index a3f8c75ee774823eea334e3b3732af6a18f55038..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/deform_conv.py
+++ /dev/null
@@ -1,405 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext('_ext', [
-    'deform_conv_forward', 'deform_conv_backward_input',
-    'deform_conv_backward_parameters'
-])
-
-
-class DeformConv2dFunction(Function):
-
-    @staticmethod
-    def symbolic(g,
-                 input,
-                 offset,
-                 weight,
-                 stride,
-                 padding,
-                 dilation,
-                 groups,
-                 deform_groups,
-                 bias=False,
-                 im2col_step=32):
-        return g.op(
-            'mmcv::MMCVDeformConv2d',
-            input,
-            offset,
-            weight,
-            stride_i=stride,
-            padding_i=padding,
-            dilation_i=dilation,
-            groups_i=groups,
-            deform_groups_i=deform_groups,
-            bias_i=bias,
-            im2col_step_i=im2col_step)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                offset,
-                weight,
-                stride=1,
-                padding=0,
-                dilation=1,
-                groups=1,
-                deform_groups=1,
-                bias=False,
-                im2col_step=32):
-        if input is not None and input.dim() != 4:
-            raise ValueError(
-                f'Expected 4D tensor as input, got {input.dim()}D tensor \
-                  instead.')
-        assert bias is False, 'Only support bias is False.'
-        ctx.stride = _pair(stride)
-        ctx.padding = _pair(padding)
-        ctx.dilation = _pair(dilation)
-        ctx.groups = groups
-        ctx.deform_groups = deform_groups
-        ctx.im2col_step = im2col_step
-
-        # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
-        # amp won't cast the type of model (float32), but "offset" is cast
-        # to float16 by nn.Conv2d automatically, leading to the type
-        # mismatch with input (when it is float32) or weight.
-        # The flag for whether to use fp16 or amp is the type of "offset",
-        # we cast weight and input to temporarily support fp16 and amp
-        # whatever the pytorch version is.
-        input = input.type_as(offset)
-        weight = weight.type_as(input)
-        ctx.save_for_backward(input, offset, weight)
-
-        output = input.new_empty(
-            DeformConv2dFunction._output_size(ctx, input, weight))
-
-        ctx.bufs_ = [input.new_empty(0), input.new_empty(0)]  # columns, ones
-
-        cur_im2col_step = min(ctx.im2col_step, input.size(0))
-        assert (input.size(0) %
-                cur_im2col_step) == 0, 'im2col step must divide batchsize'
-        ext_module.deform_conv_forward(
-            input,
-            weight,
-            offset,
-            output,
-            ctx.bufs_[0],
-            ctx.bufs_[1],
-            kW=weight.size(3),
-            kH=weight.size(2),
-            dW=ctx.stride[1],
-            dH=ctx.stride[0],
-            padW=ctx.padding[1],
-            padH=ctx.padding[0],
-            dilationW=ctx.dilation[1],
-            dilationH=ctx.dilation[0],
-            group=ctx.groups,
-            deformable_group=ctx.deform_groups,
-            im2col_step=cur_im2col_step)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input, offset, weight = ctx.saved_tensors
-
-        grad_input = grad_offset = grad_weight = None
-
-        cur_im2col_step = min(ctx.im2col_step, input.size(0))
-        assert (input.size(0) % cur_im2col_step
-                ) == 0, 'batch size must be divisible by im2col_step'
-
-        grad_output = grad_output.contiguous()
-        if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-            grad_input = torch.zeros_like(input)
-            grad_offset = torch.zeros_like(offset)
-            ext_module.deform_conv_backward_input(
-                input,
-                offset,
-                grad_output,
-                grad_input,
-                grad_offset,
-                weight,
-                ctx.bufs_[0],
-                kW=weight.size(3),
-                kH=weight.size(2),
-                dW=ctx.stride[1],
-                dH=ctx.stride[0],
-                padW=ctx.padding[1],
-                padH=ctx.padding[0],
-                dilationW=ctx.dilation[1],
-                dilationH=ctx.dilation[0],
-                group=ctx.groups,
-                deformable_group=ctx.deform_groups,
-                im2col_step=cur_im2col_step)
-
-        if ctx.needs_input_grad[2]:
-            grad_weight = torch.zeros_like(weight)
-            ext_module.deform_conv_backward_parameters(
-                input,
-                offset,
-                grad_output,
-                grad_weight,
-                ctx.bufs_[0],
-                ctx.bufs_[1],
-                kW=weight.size(3),
-                kH=weight.size(2),
-                dW=ctx.stride[1],
-                dH=ctx.stride[0],
-                padW=ctx.padding[1],
-                padH=ctx.padding[0],
-                dilationW=ctx.dilation[1],
-                dilationH=ctx.dilation[0],
-                group=ctx.groups,
-                deformable_group=ctx.deform_groups,
-                scale=1,
-                im2col_step=cur_im2col_step)
-
-        return grad_input, grad_offset, grad_weight, \
-            None, None, None, None, None, None, None
-
-    @staticmethod
-    def _output_size(ctx, input, weight):
-        channels = weight.size(0)
-        output_size = (input.size(0), channels)
-        for d in range(input.dim() - 2):
-            in_size = input.size(d + 2)
-            pad = ctx.padding[d]
-            kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
-            stride_ = ctx.stride[d]
-            output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
-        if not all(map(lambda s: s > 0, output_size)):
-            raise ValueError(
-                'convolution input is too small (output would be ' +
-                'x'.join(map(str, output_size)) + ')')
-        return output_size
-
-
-deform_conv2d = DeformConv2dFunction.apply
-
-
-class DeformConv2d(nn.Module):
-    r"""Deformable 2D convolution.
-
-    Applies a deformable 2D convolution over an input signal composed of
-    several input planes. DeformConv2d was described in the paper
-    `Deformable Convolutional Networks
-    <https://arxiv.org/pdf/1703.06211.pdf>`_
-
-    Note:
-        The argument ``im2col_step`` was added in version 1.3.17, which means
-        number of samples processed by the ``im2col_cuda_kernel`` per call.
-        It enables users to define ``batch_size`` and ``im2col_step`` more
-        flexibly and solved `issue mmcv#1440
-        <https://github.com/open-mmlab/mmcv/issues/1440>`_.
-
-    Args:
-        in_channels (int): Number of channels in the input image.
-        out_channels (int): Number of channels produced by the convolution.
-        kernel_size(int, tuple): Size of the convolving kernel.
-        stride(int, tuple): Stride of the convolution. Default: 1.
-        padding (int or tuple): Zero-padding added to both sides of the input.
-            Default: 0.
-        dilation (int or tuple): Spacing between kernel elements. Default: 1.
-        groups (int): Number of blocked connections from input.
-            channels to output channels. Default: 1.
-        deform_groups (int): Number of deformable group partitions.
-        bias (bool): If True, adds a learnable bias to the output.
-            Default: False.
-        im2col_step (int): Number of samples processed by im2col_cuda_kernel
-            per call. It will work when ``batch_size`` > ``im2col_step``, but
-            ``batch_size`` must be divisible by ``im2col_step``. Default: 32.
-            `New in version 1.3.17.`
-    """
-
-    @deprecated_api_warning({'deformable_groups': 'deform_groups'},
-                            cls_name='DeformConv2d')
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 kernel_size: Union[int, Tuple[int, ...]],
-                 stride: Union[int, Tuple[int, ...]] = 1,
-                 padding: Union[int, Tuple[int, ...]] = 0,
-                 dilation: Union[int, Tuple[int, ...]] = 1,
-                 groups: int = 1,
-                 deform_groups: int = 1,
-                 bias: bool = False,
-                 im2col_step: int = 32) -> None:
-        super(DeformConv2d, self).__init__()
-
-        assert not bias, \
-            f'bias={bias} is not supported in DeformConv2d.'
-        assert in_channels % groups == 0, \
-            f'in_channels {in_channels} cannot be divisible by groups {groups}'
-        assert out_channels % groups == 0, \
-            f'out_channels {out_channels} cannot be divisible by groups \
-              {groups}'
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = _pair(kernel_size)
-        self.stride = _pair(stride)
-        self.padding = _pair(padding)
-        self.dilation = _pair(dilation)
-        self.groups = groups
-        self.deform_groups = deform_groups
-        self.im2col_step = im2col_step
-        # enable compatibility with nn.Conv2d
-        self.transposed = False
-        self.output_padding = _single(0)
-
-        # only weight, no bias
-        self.weight = nn.Parameter(
-            torch.Tensor(out_channels, in_channels // self.groups,
-                         *self.kernel_size))
-
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        # switch the initialization of `self.weight` to the standard kaiming
-        # method described in `Delving deep into rectifiers: Surpassing
-        # human-level performance on ImageNet classification` - He, K. et al.
-        # (2015), using a uniform distribution
-        nn.init.kaiming_uniform_(self.weight, nonlinearity='relu')
-
-    def forward(self, x: Tensor, offset: Tensor) -> Tensor:
-        """Deformable Convolutional forward function.
-
-        Args:
-            x (Tensor): Input feature, shape (B, C_in, H_in, W_in)
-            offset (Tensor): Offset for deformable convolution, shape
-                (B, deform_groups*kernel_size[0]*kernel_size[1]*2,
-                H_out, W_out), H_out, W_out are equal to the output's.
-
-                An offset is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.
-                The spatial arrangement is like:
-
-                .. code:: text
-
-                    (x0, y0) (x1, y1) (x2, y2)
-                    (x3, y3) (x4, y4) (x5, y5)
-                    (x6, y6) (x7, y7) (x8, y8)
-
-        Returns:
-            Tensor: Output of the layer.
-        """
-        # To fix an assert error in deform_conv_cuda.cpp:128
-        # input image is smaller than kernel
-        input_pad = (x.size(2) < self.kernel_size[0]) or (x.size(3) <
-                                                          self.kernel_size[1])
-        if input_pad:
-            pad_h = max(self.kernel_size[0] - x.size(2), 0)
-            pad_w = max(self.kernel_size[1] - x.size(3), 0)
-            x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
-            offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0)
-            offset = offset.contiguous()
-        out = deform_conv2d(x, offset, self.weight, self.stride, self.padding,
-                            self.dilation, self.groups, self.deform_groups,
-                            False, self.im2col_step)
-        if input_pad:
-            out = out[:, :, :out.size(2) - pad_h, :out.size(3) -
-                      pad_w].contiguous()
-        return out
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(in_channels={self.in_channels},\n'
-        s += f'out_channels={self.out_channels},\n'
-        s += f'kernel_size={self.kernel_size},\n'
-        s += f'stride={self.stride},\n'
-        s += f'padding={self.padding},\n'
-        s += f'dilation={self.dilation},\n'
-        s += f'groups={self.groups},\n'
-        s += f'deform_groups={self.deform_groups},\n'
-        # bias is not supported in DeformConv2d.
-        s += 'bias=False)'
-        return s
-
-
-@CONV_LAYERS.register_module('DCN')
-class DeformConv2dPack(DeformConv2d):
-    """A Deformable Conv Encapsulation that acts as normal Conv layers.
-
-    The offset tensor is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.
-    The spatial arrangement is like:
-
-    .. code:: text
-
-        (x0, y0) (x1, y1) (x2, y2)
-        (x3, y3) (x4, y4) (x5, y5)
-        (x6, y6) (x7, y7) (x8, y8)
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int or tuple[int]): Same as nn.Conv2d.
-        stride (int or tuple[int]): Same as nn.Conv2d.
-        padding (int or tuple[int]): Same as nn.Conv2d.
-        dilation (int or tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        bias (bool or str): If specified as `auto`, it will be decided by the
-            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
-            False.
-    """
-
-    _version = 2
-
-    def __init__(self, *args, **kwargs):
-        super(DeformConv2dPack, self).__init__(*args, **kwargs)
-        self.conv_offset = nn.Conv2d(
-            self.in_channels,
-            self.deform_groups * 2 * self.kernel_size[0] * self.kernel_size[1],
-            kernel_size=self.kernel_size,
-            stride=_pair(self.stride),
-            padding=_pair(self.padding),
-            dilation=_pair(self.dilation),
-            bias=True)
-        self.init_offset()
-
-    def init_offset(self):
-        self.conv_offset.weight.data.zero_()
-        self.conv_offset.bias.data.zero_()
-
-    def forward(self, x):
-        offset = self.conv_offset(x)
-        return deform_conv2d(x, offset, self.weight, self.stride, self.padding,
-                             self.dilation, self.groups, self.deform_groups,
-                             False, self.im2col_step)
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        version = local_metadata.get('version', None)
-
-        if version is None or version < 2:
-            # the key is different in early versions
-            # In version < 2, DeformConvPack loads previous benchmark models.
-            if (prefix + 'conv_offset.weight' not in state_dict
-                    and prefix[:-1] + '_offset.weight' in state_dict):
-                state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
-                    prefix[:-1] + '_offset.weight')
-            if (prefix + 'conv_offset.bias' not in state_dict
-                    and prefix[:-1] + '_offset.bias' in state_dict):
-                state_dict[prefix +
-                           'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
-                                                                '_offset.bias')
-
-        if version is not None and version > 1:
-            print_log(
-                f'DeformConv2dPack {prefix.rstrip(".")} is upgraded to '
-                'version 2.',
-                logger='root')
-
-        super()._load_from_state_dict(state_dict, prefix, local_metadata,
-                                      strict, missing_keys, unexpected_keys,
-                                      error_msgs)
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/util.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/util.py
deleted file mode 100644
index 90831643d19cc1b9b0940df3d4fd4d846ba74a05..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/util.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import numpy as np
-import cv2
-import os
-
-
-annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
-
-
-def HWC3(x):
-    assert x.dtype == np.uint8
-    if x.ndim == 2:
-        x = x[:, :, None]
-    assert x.ndim == 3
-    H, W, C = x.shape
-    assert C == 1 or C == 3 or C == 4
-    if C == 3:
-        return x
-    if C == 1:
-        return np.concatenate([x, x, x], axis=2)
-    if C == 4:
-        color = x[:, :, 0:3].astype(np.float32)
-        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
-        y = color * alpha + 255.0 * (1.0 - alpha)
-        y = y.clip(0, 255).astype(np.uint8)
-        return y
-
-
-def resize_image(input_image, resolution):
-    H, W, C = input_image.shape
-    H = float(H)
-    W = float(W)
-    k = float(resolution) / min(H, W)
-    H *= k
-    W *= k
-    H = int(np.round(H / 64.0)) * 64
-    W = int(np.round(W / 64.0)) * 64
-    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
-    return img
diff --git a/spaces/Apex-X/nono/roop/processors/frame/core.py b/spaces/Apex-X/nono/roop/processors/frame/core.py
deleted file mode 100644
index 498169d34a00e0a2547940380afd69967a2eca8c..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/nono/roop/processors/frame/core.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import os
-import sys
-import importlib
-import psutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from queue import Queue
-from types import ModuleType
-from typing import Any, List, Callable
-from tqdm import tqdm
-
-import roop
-
-FRAME_PROCESSORS_MODULES: List[ModuleType] = []
-FRAME_PROCESSORS_INTERFACE = [
-    'pre_check',
-    'pre_start',
-    'process_frame',
-    'process_frames',
-    'process_image',
-    'process_video',
-    'post_process'
-]
-
-
-def load_frame_processor_module(frame_processor: str) -> Any:
-    try:
-        frame_processor_module = importlib.import_module(f'roop.processors.frame.{frame_processor}')
-        for method_name in FRAME_PROCESSORS_INTERFACE:
-            if not hasattr(frame_processor_module, method_name):
-                raise NotImplementedError
-    except ModuleNotFoundError:
-        sys.exit(f'Frame processor {frame_processor} not found.')
-    except NotImplementedError:
-        sys.exit(f'Frame processor {frame_processor} not implemented correctly.')
-    return frame_processor_module
-
-
-def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
-    global FRAME_PROCESSORS_MODULES
-
-    if not FRAME_PROCESSORS_MODULES:
-        for frame_processor in frame_processors:
-            frame_processor_module = load_frame_processor_module(frame_processor)
-            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
-    return FRAME_PROCESSORS_MODULES
-
-
-def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], update: Callable[[], None]) -> None:
-    with ThreadPoolExecutor(max_workers=roop.globals.execution_threads) as executor:
-        futures = []
-        queue = create_queue(temp_frame_paths)
-        queue_per_future = max(len(temp_frame_paths) // roop.globals.execution_threads, 1)
-        while not queue.empty():
-            future = executor.submit(process_frames, source_path, pick_queue(queue, queue_per_future), update)
-            futures.append(future)
-        for future in as_completed(futures):
-            future.result()
-
-
-def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
-    queue: Queue[str] = Queue()
-    for frame_path in temp_frame_paths:
-        queue.put(frame_path)
-    return queue
-
-
-def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
-    queues = []
-    for _ in range(queue_per_future):
-        if not queue.empty():
-            queues.append(queue.get())
-    return queues
-
-
-def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
-    progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
-    total = len(frame_paths)
-    with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
-        multi_process_frame(source_path, frame_paths, process_frames, lambda: update_progress(progress))
-
-
-def update_progress(progress: Any = None) -> None:
-    process = psutil.Process(os.getpid())
-    memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
-    progress.set_postfix({
-        'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
-        'execution_providers': roop.globals.execution_providers,
-        'execution_threads': roop.globals.execution_threads
-    })
-    progress.refresh()
-    progress.update(1)
diff --git a/spaces/Arvi/feedback_generator/app.py b/spaces/Arvi/feedback_generator/app.py
deleted file mode 100644
index 0c14c7a46900b4eeae6b2dc22ab6795badf01397..0000000000000000000000000000000000000000
--- a/spaces/Arvi/feedback_generator/app.py
+++ /dev/null
@@ -1,407 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Untitled19.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/123iPxfG1KBLCe4t3m41RIziyYLSOxg30
-"""
-
-
-import gradio as gr
-import pandas as pd
-import numpy as np
-
-df=pd.read_csv(r'final_processed.csv')
-
-def assign_weights(Name,col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12,col13,col14,col15):
-    import gradio as gr
-    import pandas as pd
-    import numpy as np
-    df=pd.read_csv(r'final_processed.csv')
-    df.drop(['Unnamed: 0'], axis=1,inplace=True)
-    from sklearn import preprocessing
-    label_encoder = preprocessing.LabelEncoder()
-
-
-    y={'academic time':col2,'task dedication':col3,'physical activity':col4,'favourite sport':col5,'family time':col6,'poor sleep':col7,'fitness':col8,
-       'loss of concentration':col9,'eating habits':col10,'free time':col11,'motivation':col12,'social media':col13,'social media on academics':col14,'performance':col15}
-    df=df.append(y,ignore_index=True)
-
-
-    df['academic time']= label_encoder.fit_transform(df['academic time'])
-    df['task dedication']= label_encoder.fit_transform(df['task dedication'])
-    df['physical activity']= label_encoder.fit_transform(df['physical activity'])
-    df['favorite sport']= label_encoder.fit_transform(df['favorite sport'])
-    df['family time']= label_encoder.fit_transform(df['family time'])
-    df['poor sleep']= label_encoder.fit_transform(df['poor sleep'])
-    df['fitness']= label_encoder.fit_transform(df['fitness'])
-    df['loss of concentration']= label_encoder.fit_transform(df['loss of concentration'])
-    df['eating habits']= label_encoder.fit_transform(df['eating habits'])
-    df['free time']= label_encoder.fit_transform(df['free time'])
-    df['motivation']= label_encoder.fit_transform(df['motivation'])
-    df['social media']= label_encoder.fit_transform(df['social media'])
-    df['socail media on academics']= label_encoder.fit_transform(df['socail media on academics'])
-    df['performance']= label_encoder.fit_transform(df['performance'])
-
-    df.loc[df['academic time'] == 4, 'weight_academic'] =0.45
-    df.loc[df['academic time'] == 1, 'weight_academic'] =0.15
-    df.loc[df['academic time'] == 0, 'weight_academic'] =0.05
-    df.loc[df['academic time'] == 2, 'weight_academic'] =0.35
-    df.loc[df['academic time'] == 3, 'weight_academic'] =0.00
-
-    df.loc[df['task dedication'] == 0, 'weight_task'] =0.00
-    df.loc[df['task dedication'] == 1, 'weight_task'] =0.05
-    df.loc[df['task dedication'] == 2, 'weight_task'] =0.20
-    df.loc[df['task dedication'] == 3, 'weight_task'] =0.25
-    df.loc[df['task dedication'] == 4, 'weight_task'] =0.50
-
-    df.loc[df['physical activity'] == 0, 'weight_physic'] =0.00
-    df.loc[df['physical activity'] == 1, 'weight_physic'] =1.00
-
-    df.loc[df['favorite sport'] == 0, 'weight_play'] =0.20
-    df.loc[df['favorite sport'] == 1, 'weight_play'] =0.20
-    df.loc[df['favorite sport'] == 2, 'weight_play'] =0.20
-    df.loc[df['favorite sport'] == 3, 'weight_play'] =0.20
-    df.loc[df['favorite sport'] == 4, 'weight_play'] =0.00
-    df.loc[df['favorite sport'] == 5, 'weight_play'] =0.20
-
-    df.loc[df['family time'] == 3, 'weight_familytime'] =0.40
-    df.loc[df['family time'] == 2, 'weight_familytime'] =0.10
-    df.loc[df['family time'] == 1, 'weight_familytime'] =0.00
-    df.loc[df['family time'] == 0, 'weight_familytime'] =0.40
-    df.loc[df['family time'] == 4, 'weight_familytime'] =0.10
-
-    df.loc[df['poor sleep'] == 4, 'weight_sleep'] =0.00
-    df.loc[df['poor sleep'] == 3, 'weight_sleep'] =0.05
-    df.loc[df['poor sleep'] == 0, 'weight_sleep'] =0.00
-    df.loc[df['poor sleep'] == 2, 'weight_sleep'] =0.40
-    df.loc[df['poor sleep'] == 1, 'weight_sleep'] =0.55
-
-    df.loc[df['loss of concentration'] == 4, 'weight_conc'] =0.20
-    df.loc[df['loss of concentration'] == 0, 'weight_conc'] =0.05
-    df.loc[df['loss of concentration'] == 1, 'weight_conc'] =0.00
-    df.loc[df['loss of concentration'] == 3, 'weight_conc'] =0.75
-    df.loc[df['loss of concentration'] == 2, 'weight_conc'] =0.05
-
-    df.loc[df['eating habits'] == 4, 'weight_eating'] =0.20
-    df.loc[df['eating habits'] == 0, 'weight_eating'] =0.05
-    df.loc[df['eating habits'] == 1, 'weight_eating'] =0.00
-    df.loc[df['eating habits'] == 3, 'weight_eating'] =0.75
-    df.loc[df['eating habits'] == 2, 'weight_eating'] =0.05
-
-    df.loc[df['fitness'] == 2, 'weight_fit'] =0.60
-    df.loc[df['fitness'] == 0, 'weight_fit'] =0.10
-    df.loc[df['fitness'] == 1, 'weight_fit'] =0.30
-    df.loc[df['fitness'] == 3, 'weight_fit'] =0.00
-
-    df.loc[df['free time'] == 3, 'weight_time'] =0.50
-    df.loc[df['free time'] == 2, 'weight_time'] =0.10
-    df.loc[df['free time'] == 1, 'weight_time'] =0.20
-    df.loc[df['free time'] == 0, 'weight_time'] =0.20
-
-    df.loc[df['motivation'] == 3, 'weight_motivation'] =0.30
-    df.loc[df['motivation'] == 2, 'weight_motivation'] =0.25
-    df.loc[df['motivation'] == 1, 'weight_motivation'] =0.25
-    df.loc[df['motivation'] == 0, 'weight_motivation'] =0.20
-
-    df.loc[df['social media'] == 3, 'weight_media'] =0.00
-    df.loc[df['social media'] == 2, 'weight_media'] =0.65
-    df.loc[df['social media'] == 1, 'weight_media'] =0.10
-    df.loc[df['social media'] == 0, 'weight_media'] =0.25
-
-
-    df.loc[df['socail media on academics'] == 0, 'weight_media_academics'] =0.00
-    df.loc[df['socail media on academics'] == 1, 'weight_media_academics'] =1.00
-
-    df.loc[df['performance'] == 4, 'weight_performance']=0.55
-    df.loc[df['performance'] == 3, 'weight_performance']=0.00
-    df.loc[df['performance'] == 2, 'weight_performance']=0.30
-    df.loc[df['performance'] == 1, 'weight_performance']=0.10
-    df.loc[df['performance'] == 0, 'weight_performance']=0.05
-
-    df['total']=df.iloc[:,14:].sum(axis=1)
-
-
-    df.loc[(df['weight_academic']<0.35) | (df['weight_task']<0.25),'academic value']=0
-    df.loc[(df['weight_academic']>=0.35) & (df['weight_task']>=0.25),'academic value']=1
-    df.inplace=1
-
-    df.loc[(df['weight_academic']<0.35) | (df['weight_time']<0.20),'time value']=0
-    df.loc[(df['weight_academic']>=0.35) & (df['weight_time']>=0.20),'time value']=1
-    df.inplace=1
-
-    df.loc[((df['weight_academic']<=0.35) & (df['weight_conc']>=0.20)) | ((df['weight_academic']>=0.35) & (df['weight_conc']>=0.20)),'productive value']=1
-    df.loc[((df['weight_academic']>=0.35) & (df['weight_conc']<0.20)) | ((df['weight_academic']<0.35) & (df['weight_conc']<0.20)),'productive value']=0
-    df.inplace=1
-
-    df.loc[(df['weight_physic']==1) & (df['weight_play']==0.2) & (df['weight_fit']>=0.3) & (df['weight_eating']>=0.20),'fitness_value']=1
-    df.loc[(df['weight_physic']!=1) | (df['weight_play']!=0.2) | (df['weight_fit']<0.3) | (df['weight_eating']<0.20),'fitness_value']=0
-    df.inplace=1
-
-
-    df.loc[(df['weight_sleep']>=0.40) & (df['weight_conc']>=0.20) ,'sleep value']=1
-    df.loc[(df['weight_sleep']<0.40) | (df['weight_conc']<0.20),'sleep value']=0
-    df.inplace=1
-
-    df.loc[(df['weight_familytime']==0.40) & (df['weight_motivation']==0.25) ,'motivation value']=1
-    df.loc[(df['weight_familytime']!=0.40) | (df['weight_motivation']!=0.25),'motivation value']=0
-    df.inplace=1
-
-    df.loc[(df['weight_performance']>=0.30) ,'performance_value']=1
-    df.loc[(df['weight_performance']<0.30),'performance_value']=0
-    df.inplace=1
-
-    df.loc[(df['weight_media']>=0.25) & (df['weight_media_academics']==0.00) ,'media_value']=1
-    df.loc[(df['weight_media']<0.25) | (df['weight_media_academics']!=0.00),'media_value']=0
-    df.inplace=1
-
-    df.loc[df['total']>=4.0,'overall']=1
-    df.loc[df['total']<4.0,'overall']=0
-    df.inplace=1
-
-
-    X = df[['academic time',
-    'task dedication',
-    'physical activity',
-    'favorite sport',
-    'family time',
-    'poor sleep',
-    'fitness',
-    'loss of concentration',
-    'eating habits',
-    'free time',
-    'motivation',
-    'social media',
-    'socail media on academics',
-    'performance',
-    'weight_academic',
-    'weight_task',
-    'weight_physic',
-    'weight_play',
-    'weight_familytime',
-    'weight_sleep',
-    'weight_conc',
-    'weight_eating',
-    'weight_fit',
-    'weight_time',
-    'weight_motivation',
-    'weight_media',
-    'weight_media_academics',
-    'weight_performance',
-    'total'
-    ]]
-    y1 = df['academic value']
-    y2=df['time value']
-    y3=df['productive value']
-    y4=df['fitness_value']
-    y5=df['sleep value']
-    y6=df['motivation value']
-    y7=df['performance_value']
-    y8=df['media_value']
-    y9=df['overall']
-    from sklearn.model_selection import train_test_split
-    X_train,X_test,y1_train,y1_test = train_test_split(X,y1,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y2_train,y2_test = train_test_split(X,y2,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y3_train,y3_test = train_test_split(X,y3,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y4_train,y4_test = train_test_split(X,y4,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y5_train,y5_test = train_test_split(X,y5,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y6_train,y6_test = train_test_split(X,y6,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y7_train,y7_test = train_test_split(X,y7,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y8_train,y8_test = train_test_split(X,y8,test_size=0.3,random_state = 0,shuffle = True)
-    X_train,X_test,y9_train,y9_test = train_test_split(X,y9,test_size=0.3,random_state = 0,shuffle = True)
-    from sklearn.ensemble import RandomForestClassifier as rfc
-    import xgboost as xgb
-    rfc1 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc2 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc3 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc4 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc5 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc6 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc7 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc8 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc9 = xgb.XGBClassifier(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
-                max_depth = 5, alpha = 10, n_estimators = 10)
-    rfc1.fit(X_train,y1_train)
-    rfc2.fit(X_train,y2_train)
-    rfc3.fit(X_train,y3_train)
-    rfc4.fit(X_train,y4_train)
-    rfc5.fit(X_train,y5_train)
-    rfc6.fit(X_train,y6_train)
-    rfc7.fit(X_train,y7_train)
-    rfc8.fit(X_train,y8_train)
-    rfc9.fit(X_train,y9_train)
-    import random
-
-    z=df.tail(1)
-
-
-
-
-    if z['academic value'].eq(1).all():
-      a=['You are in the right track just try to stick on to your schedule','HARRRRRDDDD WORK always payys off you seem to be going in the right track',
-         'The way is classiscal!! a tip for you is to listen to some classical music before studying ','You are driven by your own intrest keep riding',
-         'Your study time is great ,now its to take a short break',
-         'WOWWW you are just a just synonym of hard work and dedication ' ]
-      res1="feedback on youe study schedule --> " +random.choice(a)
-    if z['academic value'].eq(0).all():
-       b=['If you know your “WHY”, finding your “HOW" will not be difficult.you just need to start working','Focusing is about saying no.just learn to say no to things which distracts you .u just need to put a little more focus on your studytime',
-          'Be the early bird that gets the first worm.set your body clock and start working','listen to directions,follow through assignments,learn for yourself.you just need to enjoy the process',
-          'measure for progress not the time you are working ,try to put in more studytime','postponment will postpone you,finish your daily tasks when you have the time',
-          'you are just off track,there is still time and sure that you will reach great heights ','you surely have the talent its now in your hands to make wonders!!!! talent without hardwork?? what do you think ','enroll yourself to a personalized learning environament which gives you a controll and education experience ']
-       res1="feedback on youe study schedule --> "+random.choice(b)
-
-
-    if z['time value'].eq(1).all():
-      c=['there is a saying give me 6 hours to chop a tree and i will spend the 1st hr sharpening the axe, the fact here is you have sharpenend your axe','your timimg is great you are managing time well'
-      'its seems you hsve been studying long take a quick break and come back ','you are enjoying your time keep putting the same efforts you put','keep managing the time like the way you are doing now,this attribute will take care of the rest'
-      ,'you seem to stay organized and on track with your procative planning and systematic scheduling ']
-      res2="Feedback on how you manage time --> "+random.choice(c)
-    if z['time value'].eq(0).all():
-      d=['you have to start spending time on academics and show some interest in succeeding,you are the pilot who should stop time from flying and bring it on your control','start working and stick to a time table and set your body clock','try to be more organized and start spending quality time towards studies'
-      'start learning to manage time and priortize on your academics','spend more time on your weak areas ,try to strech out for long hours','the biggest obstracle stopping you from winning is time management,prepare a timetable and stick to it',
-      'play while you play and work while you work dont try to mix up things','dont try to procastinate finish your day to day jobs when and where you get time']
-      res2="Feedback on how you manage time --> "+random.choice(d)
-
-    if z['productive value'].eq(1).all():
-      e=['you are smart,productive and have a good way of preparation in your studies','Be more proactive and try to participate in class,you are effiecient and can reach heights with your effectiveness','you have the ability to study things smartly and quickly,pick areas which are more brain-storming',
-         'you have the ability to intepret things and your mind is sharp and you are a good listener','you are the master-mind,you are the person who shouldnt miss out in enrolling to IIts,NITs or whatever','you are productive person if u feel you are not delivering your 100% its not because because you arent studying,its something else']
-      res3="Feedback on your productivity --> "+random.choice(e)
-    if z['productive value'].eq(0).all():
-      f=['Try to stick on to an approach which is convinient to you ,have a clear mind before you start working','start solving more,puzzles and a daily sudoko is a good start, you just need to be on your toes and tune your mind to solve various activities ','think!think!think analyse where you lack and start building strategies to improve yourself'
-      'class participation its high time you start taking decisions and choose to be proactive','connect everything with what you are learining so that it will stick in your mind and helps you to recollect when and where you require','enjoy the process of learning dont be monotonous and a bookworm tame your mind to face your challenges','actively consult your instructor to enrich yourself with lot ways to improve your productivity',
-      'rather than a brute-force approach try to think more of an optimal solution to a problem','gather a lot of resoruces and try to sit in your desk ,take mobile breaks(short one), an online chess game might be an eye opener for your next session ']
-      res3="Feedback on your productivity --> "+random.choice(f)
-
-    if z['fitness_value'].eq(1).all():
-      g=['fitness is your key ,if your body is strong your mind is stronger. Maintaining a good fitness is really important for your health as well as it empowers your learining ',' I can see you have spent time in maintaing your body. Keep winning more golds ','you have choosen to step out of your comfort zone and by trying to put some gains,this will surely be a stepping stone in other important sectors','your fitness level is reasonably good indicating that you are sticking to a schedule kind of person which is really good',
-      'you are in a good shape which is a key for self_confidence and gives you a lot of motivation','you are a sportive person ,this will really help you to socialize and gives you a lot of energy to start new things ','you are an open-minded person ,this is really the best character one could ask for,half the problems are over if one is listening and able to make good decisions ']
-      res4="Feedback on your fitness --> "+random.choice(g)
-    if z['fitness_value'].eq(0).all():
-      h=['A weak body is a liability, you guys being the future generation should definetly be fit and healthy to lead the society at its best','your body should always get the first priority and should be taken care properly',
-         'Any physical activity will make you disipline and gives you self confidence. Join your school team today ','out of all a hungry stomach isnt fit for a brisk study session ,being physically fit lets you do more activity even improve your academics ',
-         'engage yourself in any physical activity for 20 mins as it can improve your concentration and helps your focus in learning ','out of your busy schedule try devoting just 15 mins get down do some pushups or squats or a brisk jog will do good ']
-      res4="Feedback on your fitness --> "+random.choice(h)
-
-    if z['sleep value'].eq(1).all():
-      i=['Good that you have a proper sleep, just stick to it and try finishing all your work in the day time and get enough rest','Its pretty impressive that you are giving enough importance to your sleep, shows that you have good time management skills and a sweet dream','getting a good sleep even during your stressed timetables shows that you stay at the moment',
-        'a good fitness routine followed by a good-sleep is a good sunday schedule and a good starter for a hectic next week which i hope you would have experienced many times','its good that you have a good sleep everynight this is big boost for a bright tomorrow']
-      res5="Feedback on your sleep time --> "+random.choice(i)
-    if z['sleep value'].eq(0).all():
-
-      j=['The time we sleep is only when we rest our mind, eyes and the whole body which is really crucial for a stduent',' Try not using any devices an hour before you sleep, have a good sleep cycle for atleast 6 to 7 hrs a day','Get enough rest, dont stress your body too much.',
-         'Prioritize your sleep, dont have caffinated drinks late in the evening and getting good sleep will make you feel fresh and enegrytic all day long ',
-         'a 7 - hour refresh will set your body clock for the rest of your day so please ensure that you get adequate rest','if you are sleep deprieved make sure you exhaust all your energy during the day and make sure you get a pleasant and peaceful sleep',
-         'tests prove that sleep deprivation is a result for low academic performance make sure you dont fall under that','Please ensure that the extra miles which you are putting doesnt affect your sleep']
-
-      res5="Feedback on your sleep time --> "+random.choice(j)
-
-    if z['motivation value'].eq(1).all():
-       k=['you are fairly motivated ,Motivation drives everyone to work better to achive something,it lits a light inside you ','you should be really proud that you have good motivation at a really young age,use it in areas where you feel a bit off',
-          'None of the greatest achievers couldnt have done it without motivation and self motivation is really powerfull tool to success ,you are one among them Keep going!',
-          'a good level of motivation gives you high spirits and a good attitude,your attitude builds YOU']
-
-       res6="motivation factor --> "+random.choice(k)
-    if z['motivation value'].eq(0).all():
-
-       l=['Nobody in the world is born with motivation,in this modern era you cant expect external motivation,you better be your own motivation','messi took eighteen years to be the G.O.A.T ignoring all demotivation and insults its finally your time',
-          'change your scenery sitting in a desk all-day makes you dull ,to renew interest,a new setting can be just what some students need to stay motivated to learn',
-          'lay-out clear objectives before you start learning so that there is no confussion','Make your goals high but attainable dont be afraid to push yourself to get more out of them ',
-          'Spend some quality time with your family listen to their experiences and try to dollow their footsteps']
-
-
-       res6="motivation factor --> "+random.choice(l)
-
-    if z['performance_value'].eq(1).all():
-       m=['Good job you!! Your hardwork and efforts paid off, you have nothing to worry about ,you are academically strong','To be honest that grades made me a little jealous. I can see the work you are putting towards academics',
-          'Give a big hit on boards make your parents and teachers proud, trust me that is super satisfying','academic performance gives you a lot of boost to you take that put in all other aspects which will give you overall developement',
-          'the most satisfying thing is scoring high its great that you are easily doing it','you are almost sorted out you now just have to take care of the bits and pieces']
-
-       res7="Feedback on your performance --> "+random.choice(m)
-
-    if z['performance_value'].eq(0).all():
-       n=['Its never late to begin. Divide your work, note important things mentioned in class spend more time in studies','Dont be ashamed to ask doubts we dont mind others judging. So we start from physics today? jk',
-          'Start studying with your friends, seek help from teachers,Remember the hardwork you put never fails you','analyse where you are making errors if you find that you are making mistakes while writing try practicing the sample papers it will help you to an extent'
-          ,'you are almost there!!take short notes of the theoritical concepts so that it will be easy for reference','dont worry about where you are standing at the moment ,back yourself ,start it from scratch']
-
-       res7="Feedback on your performance --> "+random.choice(n)
-
-    if z['media_value'].eq(1).all():
-       o=[' In the world of people being addicted to social media today, its happy to see someone like you','Its good that you are not scrolling too much','Having a good social profile is important and you having a limit is really impressive'
-       ,'Having the self control on yourself is really great but ensure that dont overdo on anything else','you are self-conscious which is really a great character to acquire']
-
-       res8="Feedback on your social media time --> "+random.choice(o)
-
-    if z['media_value'].eq(0).all():
-      p=['Its really common for this generation people to get addicted to social media. All you have to do is keep track of the time, dont over do stuffs and you dont have to post a story everyday.',
-         'Nothing wrong becoming a social idle, but right now concentrate in your studies','socially active is essential but over - scrolling  will trap you in the matrix which you are unaware of',
-         'stay in your limits socially active for more than a hour during high school is ill advised','knowing that its impacting you and using social media again !! what is that??']
-
-      res8="Feedback on your social media time --> "+random.choice(p)
-
-
-    if z['overall'].eq(1).all():
-       q=['OMG!! Im thinking of getting a piece of advise from you you are almost there good that you equally participate in everything','You are an explorer and can learn new things easily,you are about to win the race',
-          'Your works are impressing everyone right from your teacher,friends and your parents, You are active,brisk and have good potential to improve your performance',
-          'You are doing great ,you  are ready for new challenges and failures doesnt bother you ','You are multi tasker and ensure that you dont sink with over-confidence','Dont put yourself in any kind of pressure, eventhough you feel stressed time will answer to it and you will pass with flying colours'
-          'You are growing with confidence, take it to learn new things,choose your core and find your destiny']
-
-       res9=random.choice(q)
-
-    if z['overall'].eq(0).all():
-
-      r=['Its all good everyone goes out of form,the comeback is always on start putting consistent efforts','Put in the time, hardwork and you can already see it coming,you are just a few steps dowm','When we hit out lowest point we are open to the greatest change  you are going to bring the best out of it. And yes that was said by Avatar Roku'
-      ,'Choose the right person whom you feel will take you through all the obstracles you need make things more clear','The best view comes after the hardest climb you can climb the moutain ahead of you','You just need to reboot and have a good set-up ,stay optimistic and everything will take care of itself if you take one step at a time',
-      'You are nearing the pinacle of your true potential,just few changes hear and there you will be on your prime']
-
-      res9=random.choice(r)
-
-
-
-
-
-
-
-
-    return "hi " + str (Name) + " this is a predictive model these are some wild guesses so just take the points which you feel may work in your case \nalso if u feel the feeadbacks are harsh please flag your opinion \ntake your time to read this and hope u like it 😊\n\n\n"+ res1+"  ,\n " + res2 +"  ,\n " + res3 +"  ,\n " + res4 +"  ,\n " + res5 +"  ,\n " + res6 +"  ,\n " + res7 +"  ,\n " + res8 +"  ,\n\n\n " + res9
-
-list(df.columns)
-
-df.isna().sum()
-
-demo = gr.Interface(
-    fn=assign_weights,
-    inputs=[
-        "text",
-        gr.Dropdown(['Science','Commerce'], label="Choose your stream"),
-        gr.Radio(["<5", "5 - 12", "13 - 20", "20 - 30",">30"],label='On an average, how many hours a week do you spend on academics?'),
-        gr.Radio(["0 - 20%", "20 - 40%", "40 - 60%", "60 - 80%","80 -100%"],label='How willing are you to work on a particular task ?'),
-        gr.Radio(["Yes", "No", ],label='Do you take up  any physical activity at regular intervals(at least 3 hours a week) ?'),
-        gr.Radio(["Football", "Cricket", "Basketball", "Tennis" , "Chess" ,"Other","Not interested in sports"],label='Choose your favourite sport you follow or play'),
-        gr.Radio(["Never", "Occasionally", "Sometimes", "Often" , "Always"],label='How often do you spend time with your friends and family?'),
-        gr.Radio(["Always", "Very often", "Sometimes", "Rarely" ,"Never"],label='Has poor sleep troubled you in the last month?'),
-        gr.Radio(["Perfect", "Good", "Average", "Poor"],label='What is your current level of fitness?'),
-        gr.Radio(["Never", "Once in a while", "About half the time", "Most of the time","Always"],label='Do you feel kinda losing concentration during classes and other activities'),
-        gr.Radio(["Never", "Once in a while", "About half the time", "Most of the time","Always"],label='is there a change in your eating habits(either under eating or overeating'),
-        gr.Radio(["< 2", "2 - 5", "5 - 8", "> 8"],label='How many hours of free time do you have after school?'),
-        gr.Radio(["Asking a lot of questions to the teacher", "Completing various assignments", "Sports and other extracurricular activities", "Other"],label='What motivates you to learn more?'),
-        gr.Radio(["<30 mins", "30 - 60", "60 - 120", ">120 mins"],label='How long you spend your time on social media on a daily basis? '),
-        gr.Radio(["Yes", "No"],label='Do you feel that spending time on social media has been a reason for the deterioration in your academic performance?'),
-        gr.Radio(["<30%", "30% - 50%", "50% - 70%", "70% - 90%",">90%"],label='How much you score in your academics'),
-    ],
-    outputs=['text'],
-
-    title="Performance predictor and feedback generator",
-    description="Here's a sample performance calculator. Enjoy!"
-
-  )
-demo.launch(inline=False)
-
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/grit/data/datasets/object365.py b/spaces/Awiny/Image2Paragraph/models/grit_src/grit/data/datasets/object365.py
deleted file mode 100644
index 8b8cc19da23d8397284b50588ee46e750b5b7552..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/grit/data/datasets/object365.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import logging
-import os
-from fvcore.common.timer import Timer
-from detectron2.structures import BoxMode
-from fvcore.common.file_io import PathManager
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from lvis import LVIS
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["load_o365_json", "register_o365_instances"]
-
-
-def register_o365_instances(name, metadata, json_file, image_root):
-    DatasetCatalog.register(name, lambda: load_o365_json(
-        json_file, image_root, name))
-    MetadataCatalog.get(name).set(
-        json_file=json_file, image_root=image_root,
-        evaluator_type="lvis", **metadata
-    )
-
-
-def get_o365_meta():
-    categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}]
-    o365_categories = sorted(categories, key=lambda x: x["id"])
-    thing_classes = [k["name"] for k in o365_categories]
-    meta = {"thing_classes": thing_classes}
-    return meta
-
-
-def load_o365_json(json_file, image_root, dataset_name=None):
-    '''
-    Load Object365 class name text for object description for GRiT
-    '''
-
-    json_file = PathManager.get_local_path(json_file)
-
-    timer = Timer()
-    lvis_api = LVIS(json_file)
-    if timer.seconds() > 1:
-        logger.info("Loading {} takes {:.2f} seconds.".format(
-            json_file, timer.seconds()))
-
-    class_names = {}
-    sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id'])
-    for x in sort_cat:
-        if '/' in x['name']:
-            text = ''
-            for xx in x['name'].split('/'):
-                text += xx
-                text += ' '
-            text = text[:-1]
-        else:
-            text = x['name']
-        class_names[x['id']] = text
-
-    img_ids = sorted(lvis_api.imgs.keys())
-    imgs = lvis_api.load_imgs(img_ids)
-    anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
-
-    ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
-    assert len(set(ann_ids)) == len(ann_ids), \
-        "Annotation ids in '{}' are not unique".format(json_file)
-
-    imgs_anns = list(zip(imgs, anns))
-    logger.info("Loaded {} images in the LVIS v1 format from {}".format(
-        len(imgs_anns), json_file))
-
-    dataset_dicts = []
-
-    for (img_dict, anno_dict_list) in imgs_anns:
-        record = {}
-        if "file_name" in img_dict:
-            file_name = img_dict["file_name"]
-            record["file_name"] = os.path.join(image_root, file_name)
-
-        record["height"] = int(img_dict["height"])
-        record["width"] = int(img_dict["width"])
-        image_id = record["image_id"] = img_dict["id"]
-
-        objs = []
-        for anno in anno_dict_list:
-            assert anno["image_id"] == image_id
-            if anno.get('iscrowd', 0) > 0:
-                continue
-            obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
-            obj["category_id"] = 0
-            obj["object_description"] = class_names[anno['category_id']]
-
-            objs.append(obj)
-        record["annotations"] = objs
-        if len(record["annotations"]) == 0:
-            continue
-        record["task"] = "ObjectDet"
-        dataset_dicts.append(record)
-
-    return dataset_dicts
-
-
-_CUSTOM_SPLITS_LVIS = {
-    "object365_train": ("object365/images/train/", "object365/annotations/train_v1.json"),
-}
-
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
-    register_o365_instances(
-        key,
-        get_o365_meta(),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )
\ No newline at end of file
diff --git a/spaces/Bart92/RVC_HF/infer/modules/ipex/attention.py b/spaces/Bart92/RVC_HF/infer/modules/ipex/attention.py
deleted file mode 100644
index 0eed59630d76a56e3fd96aa5bb6518b0c61e81bb..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/infer/modules/ipex/attention.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import torch
-import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-
-# pylint: disable=protected-access, missing-function-docstring, line-too-long
-
-original_torch_bmm = torch.bmm
-def torch_bmm(input, mat2, *, out=None):
-    if input.dtype != mat2.dtype:
-        mat2 = mat2.to(input.dtype)
-
-    #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
-    batch_size_attention, input_tokens, mat2_shape = input.shape[0], input.shape[1], mat2.shape[2]
-    block_multiply = 2.4 if input.dtype == torch.float32 else 1.2
-    block_size = (batch_size_attention * input_tokens * mat2_shape) / 1024 * block_multiply #MB
-    split_slice_size = batch_size_attention
-    if block_size >= 4000:
-        do_split = True
-        #Find something divisible with the input_tokens
-        while ((split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply) > 4000:
-            split_slice_size = split_slice_size // 2
-            if split_slice_size <= 1:
-                split_slice_size = 1
-                break
-    else:
-        do_split = False
-
-    split_block_size = (split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply #MB
-    split_2_slice_size = input_tokens
-    if split_block_size >= 4000:
-        do_split_2 = True
-        #Find something divisible with the input_tokens
-        while ((split_slice_size * split_2_slice_size * mat2_shape) / 1024 * block_multiply) > 4000:
-            split_2_slice_size = split_2_slice_size // 2
-            if split_2_slice_size <= 1:
-                split_2_slice_size = 1
-                break
-    else:
-        do_split_2 = False
-
-    if do_split:
-        hidden_states = torch.zeros(input.shape[0], input.shape[1], mat2.shape[2], device=input.device, dtype=input.dtype)
-        for i in range(batch_size_attention // split_slice_size):
-            start_idx = i * split_slice_size
-            end_idx = (i + 1) * split_slice_size
-            if do_split_2:
-                for i2 in range(input_tokens // split_2_slice_size): # pylint: disable=invalid-name
-                    start_idx_2 = i2 * split_2_slice_size
-                    end_idx_2 = (i2 + 1) * split_2_slice_size
-                    hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm(
-                        input[start_idx:end_idx, start_idx_2:end_idx_2],
-                        mat2[start_idx:end_idx, start_idx_2:end_idx_2],
-                        out=out
-                    )
-            else:
-                hidden_states[start_idx:end_idx] = original_torch_bmm(
-                    input[start_idx:end_idx],
-                    mat2[start_idx:end_idx],
-                    out=out
-                )
-    else:
-        return original_torch_bmm(input, mat2, out=out)
-    return hidden_states
-
-original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
-def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
-    #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
-    shape_one, batch_size_attention, query_tokens, shape_four = query.shape
-    block_multiply = 2.4 if query.dtype == torch.float32 else 1.2
-    block_size = (shape_one * batch_size_attention * query_tokens * shape_four) / 1024 * block_multiply #MB
-    split_slice_size = batch_size_attention
-    if block_size >= 4000:
-        do_split = True
-        #Find something divisible with the shape_one
-        while ((shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply) > 4000:
-            split_slice_size = split_slice_size // 2
-            if split_slice_size <= 1:
-                split_slice_size = 1
-                break
-    else:
-        do_split = False
-
-    split_block_size = (shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply #MB
-    split_2_slice_size = query_tokens
-    if split_block_size >= 4000:
-        do_split_2 = True
-        #Find something divisible with the batch_size_attention
-        while ((shape_one * split_slice_size * split_2_slice_size * shape_four) / 1024 * block_multiply) > 4000:
-            split_2_slice_size = split_2_slice_size // 2
-            if split_2_slice_size <= 1:
-                split_2_slice_size = 1
-                break
-    else:
-        do_split_2 = False
-
-    if do_split:
-        hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype)
-        for i in range(batch_size_attention // split_slice_size):
-            start_idx = i * split_slice_size
-            end_idx = (i + 1) * split_slice_size
-            if do_split_2:
-                for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
-                    start_idx_2 = i2 * split_2_slice_size
-                    end_idx_2 = (i2 + 1) * split_2_slice_size
-                    hidden_states[:, start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention(
-                        query[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                        key[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                        value[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                        attn_mask=attn_mask[:, start_idx:end_idx, start_idx_2:end_idx_2] if attn_mask is not None else attn_mask,
-                        dropout_p=dropout_p, is_causal=is_causal
-                    )
-            else:
-                hidden_states[:, start_idx:end_idx] = original_scaled_dot_product_attention(
-                    query[:, start_idx:end_idx],
-                    key[:, start_idx:end_idx],
-                    value[:, start_idx:end_idx],
-                    attn_mask=attn_mask[:, start_idx:end_idx] if attn_mask is not None else attn_mask,
-                    dropout_p=dropout_p, is_causal=is_causal
-                )
-    else:
-        return original_scaled_dot_product_attention(
-            query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal
-        )
-    return hidden_states
-
-def attention_init():
-    #ARC GPUs can't allocate more than 4GB to a single block:
-    torch.bmm = torch_bmm
-    torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
\ No newline at end of file
diff --git a/spaces/Benebene/Chat-question-answering/interface.py b/spaces/Benebene/Chat-question-answering/interface.py
deleted file mode 100644
index 344a48c4ac3e246f86fb767944632a39e1b2c7c1..0000000000000000000000000000000000000000
--- a/spaces/Benebene/Chat-question-answering/interface.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import gradio as gr
-from utils import Stuff
-
-
-def launch_gradio(s: Stuff):
-    with gr.Blocks() as demo:
-        question = gr.Textbox(label = 'Type your question about astronomy here :')
-        output = gr.Textbox(label = 'The answer is...')
-        button = gr.Button('Enter')
-        button.click(fn = s.get_answer, inputs = question, outputs=output)
-        
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk Mod Fox App.md b/spaces/Benson/text-generation/Examples/Descargar Apk Mod Fox App.md
deleted file mode 100644
index 83db28c1a62bf3c096b8e1231ed2ba1d4cefde9c..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Apk Mod Fox App.md	
+++ /dev/null
@@ -1,57 +0,0 @@
-
-<h1>Descargar APK Mod Fox App: Cómo obtener la mejor experiencia de navegador en Android</h1>
-<p>Si está buscando una aplicación de navegador rápida, segura y personalizable para su dispositivo Android, es posible que desee probar la aplicación APK Mod Fox. Esta es una versión modificada del popular navegador Firefox, que ofrece muchas características y beneficios que no están disponibles en la aplicación original. En este artículo, le mostraremos lo que es APK Mod Fox App, cómo descargar e instalar, y cómo usarlo para obtener la mejor experiencia de navegador en Android.</p>
-<h2>¿Qué es la aplicación APK Mod Fox? </h2>
-<p>APK Mod Fox App es una versión modificada de la aplicación Firefox Browser, que es uno de los navegadores web más populares y de confianza en el mundo. Firefox Browser es conocido por su velocidad, privacidad y opciones de personalización, pero también tiene algunas limitaciones y desventajas que algunos usuarios pueden encontrar molesto o inconveniente. Por ejemplo, tiene anuncios, rastreadores, ventanas emergentes y otros elementos no deseados que pueden afectar su experiencia de navegación. También consume mucha batería y memoria, lo que puede ralentizar el dispositivo. </p>
-<h2>descargar apk mod fox app</h2><br /><p><b><b>Download Zip</b> ->->->-> <a href="https://bltlly.com/2v6KZv">https://bltlly.com/2v6KZv</a></b></p><br /><br />
-<p>Ahí es donde APK Mod Fox App entra en juego. Esta es una versión modificada de la aplicación Firefox Browser que elimina todos los anuncios, rastreadores, ventanas emergentes y otros elementos no deseados de la aplicación. También optimiza el rendimiento de la aplicación y reduce su consumo de batería y memoria. También agrega algunas características y mejoras adicionales que no están disponibles en la aplicación original, como el modo oscuro, el modo nocturno, el modo de incógnito, el bloqueador de anuncios, la VPN, el administrador de descargas y más. Con la aplicación APK Mod Fox, puedes disfrutar de una experiencia de navegador más rápida, fluida y privada en tu dispositivo Android. </p>
-<h3>Los beneficios de usar APK Mod Fox App</h3>
-<p>Algunos de los beneficios de usar la aplicación APK Mod Fox son:</p>
-<ul>
-<li>Puedes navegar por la web sin anuncios, rastreadores, ventanas emergentes u otros elementos molestos que puedan distraerte o comprometer tu privacidad. </li>
-
-<li>Puede acceder a varias características y herramientas que pueden mejorar su experiencia de navegación, como el modo oscuro, el modo nocturno, el modo de incógnito, el bloqueador de anuncios, la VPN, el administrador de descargas y más. </li>
-<li> Puede ahorrar batería y memoria mediante el uso de una aplicación de navegador ligera y optimizada que no consume demasiados recursos de su dispositivo. </li>
-<li> Puede disfrutar de un rendimiento del navegador rápido y suave que puede cargar páginas web de forma rápida y sin problemas. </li>
-</ul>
-<h3>Los inconvenientes de usar APK Mod Fox App</h3>
-<p>Algunos de los inconvenientes de usar APK Mod Fox App son:</p>
-<ul>
-<li>Es posible que encuentre algunos problemas de compatibilidad o errores con algunos sitios web o aplicaciones que no están diseñados para la aplicación modded. </li>
-<li>Es posible que no reciba actualizaciones regulares o soporte de los desarrolladores oficiales de la aplicación Firefox Browser. </li>
-<li>Es posible que exponga su dispositivo a posibles riesgos de seguridad o malware si descarga la aplicación modificada desde una fuente no confiable o si habilita fuentes desconocidas en su dispositivo. </li>
-</ul>
-<h2>¿Cómo descargar e instalar la aplicación APK Mod Fox? </h2>
-<p>Si desea descargar e instalar la aplicación APK Mod Fox en su dispositivo Android, debe seguir estos pasos:</p>
-<h3>Paso 1: Encontrar una fuente confiable para la aplicación modded</h3>
-<p>El primer paso es encontrar una fuente confiable para la aplicación modded. No se puede descargar APK Mod Fox App desde la Google Play Store, ya que no es una aplicación oficial. Es necesario encontrar un sitio web de terceros o plataforma que ofrece la aplicación modded para su descarga gratuita. Sin embargo, debe tener cuidado y hacer algunas investigaciones antes de descargar la aplicación modificada desde cualquier fuente. Usted necesita para asegurarse de que la fuente es confiable y de buena reputación, y que la aplicación modded es seguro y libre de virus. Puede comprobar las revisiones, calificaciones, comentarios y comentarios de otros usuarios que han descargado la aplicación modificada desde la misma fuente. También puede usar un escáner de malware o una aplicación antivirus para escanear la aplicación modificada antes de instalarla en su dispositivo. </p>
-
-<p>El segundo paso es habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de Google Play Store. De forma predeterminada, esta configuración está desactivada en la mayoría de los dispositivos Android, ya que puede exponer su dispositivo a posibles riesgos de seguridad o malware. Sin embargo, si desea instalar APK Mod Fox App, es necesario habilitar esta configuración temporalmente. Para hacer esto, es necesario ir a la configuración de su dispositivo, a continuación, toque en la seguridad o la privacidad, a continuación, busque la opción que dice fuentes desconocidas o instalar aplicaciones desconocidas. Luego, cambie el interruptor o marque la casilla para habilitar esta opción. También es posible que necesite conceder permiso para la fuente o aplicación específica que desea instalar. </p>
-<h3>Paso 3: Descargar e instalar el archivo APK</h3>
-<p>El tercer paso es descargar e instalar el archivo APK de la aplicación APK Mod Fox en su dispositivo. Para hacer esto, debe abrir la aplicación del navegador en su dispositivo, luego ir al sitio web o plataforma donde encontró la aplicación modificada. Luego, busque el botón de descarga o enlace para la aplicación modded, y toque en él. Es posible que vea una ventana emergente o una notificación que le pida que confirme la descarga o instalación de la aplicación modificada. Toque en Aceptar o Instalar para continuar. Espere a que se complete el proceso de descarga e instalación, que puede tardar unos minutos dependiendo de la velocidad de Internet y el rendimiento del dispositivo. </p>
-<h2>¿Cómo usar la aplicación APK Mod Fox? </h2>
-<p>Una vez que haya descargado e instalado la aplicación APK Mod Fox en su dispositivo, puede comenzar a usarla para navegar por la web en su dispositivo Android. Aquí hay algunos consejos sobre cómo utilizar APK Mod Fox App:</p>
-<p></p>
-<h3>Personaliza la configuración y las preferencias de tu navegador</h3>
-
-<h3>Navegar por la web con mayor privacidad y seguridad</h3>
-<p>Otra ventaja de usar APK Mod Fox App es que se puede navegar por la web con mayor privacidad y seguridad. La aplicación modificada elimina todos los anuncios, rastreadores, ventanas emergentes y otros elementos no deseados de las páginas web que visita. También protege su actividad en línea y los datos de hackers, ISP, anunciantes y otros terceros que podrían tratar de espiar a usted o robar su información. También puede usar funciones como el modo incógnito, VPN y bloqueador de anuncios para aumentar aún más su privacidad y seguridad mientras navega por la web. </p>
-<h3>Disfrute del rendimiento rápido y suave de la aplicación</h3>
-<p>Una tercera ventaja de usar APK Mod Fox App es que se puede disfrutar del rendimiento rápido y suave de la aplicación. La aplicación modded optimiza el rendimiento de la aplicación y reduce su consumo de batería y memoria. También mejora la velocidad y suavidad de la aplicación mediante la carga de páginas web de forma rápida y sin problemas. También puedes usar funciones como gestor de descargas, VPN y bloqueador de anuncios para aumentar la velocidad de navegación y evitar interrupciones o ralentizaciones. </p>
-<h2>Conclusión</h2>
-<p>APK Mod Fox App es una versión modificada de la aplicación del navegador Firefox que ofrece muchos beneficios y características que no están disponibles en la aplicación original. Es una aplicación de navegador rápida, segura y personalizable que puede mejorar su experiencia de navegación en Android. Sin embargo, también tiene algunos inconvenientes y riesgos que debe tener en cuenta antes de descargarlo e instalarlo en su dispositivo. Necesitas encontrar una fuente confiable para la aplicación modded, habilitar fuentes desconocidas en tu dispositivo y escanear la aplicación modded en busca de malware o virus. También debe tener cuidado con la compatibilidad y las actualizaciones de la aplicación modded. </p>
-<h4>Resumen de los puntos principales</h4>
-<p>En este artículo, te hemos mostrado:</p>
-<ul>
-<li> ¿Qué es APK Mod Fox App y cómo se diferencia de la aplicación original del navegador Firefox. </li>
-<li>Los beneficios y desventajas de usar APK Mod Fox App.</li>
-
-<li>Cómo utilizar APK Mod Fox App para obtener la mejor experiencia de navegador en Android.</li>
-</ul>
-<h4>Llamada a la acción para los lectores</h4>
-<p>Si usted está interesado en probar APK Mod Fox App, puede seguir los pasos que hemos proporcionado en este artículo para descargar e instalar en su dispositivo. Sin embargo, también debe hacer su propia investigación y comprobar las revisiones y calificaciones de la aplicación modded antes de descargarlo. También debe realizar una copia de seguridad de sus datos y dispositivo antes de instalar la aplicación modded, en caso de que algo salga mal o desee desinstalarlo más tarde. También debe tener cuidado con la seguridad y la privacidad de su actividad en línea y los datos durante el uso de la aplicación modded. </p>
-<p>Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer! </p>
-
-podría querer probar estas aplicaciones de navegador para Android: - Brave Browser: Esta es una aplicación de navegador rápido, seguro y privado que bloquea los anuncios y rastreadores por defecto. También le recompensa con criptomoneda para navegar por la web. - Opera Browser: Esta es una aplicación de navegador rápida, ligera y personalizable que ofrece funciones como bloqueador de anuncios, VPN, ahorro de datos, modo nocturno y más. - Chrome Browser: Esta es una aplicación de navegador popular y confiable que ofrece características como sincronización, búsqueda por voz, modo de incógnito, modo oscuro y más. </p> 64aa2da5cf<br />
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@@ -1,89 +0,0 @@
-
-<h1>Descargar Racing Limits Mod APK: Una guía para los entusiastas de las carreras</h1>
-<p>Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de <strong>Racing Limits</strong>, un popular juego de carreras estilo árcade que te permite competir en la ciudad y el tráfico de carreteras. Este juego ofrece física de conducción realista, vehículos de alto detalle, afinaciones y mejoras, gráficos realistas y cinco modos de carreras agradables. Sin embargo, si quieres disfrutar del juego al máximo, es posible que desee descargar el <strong>Racing Limits mod APK</strong>, que le da dinero ilimitado y acceso a todas las características del juego. En este artículo, le diremos qué es Racing Limits, cuáles son sus características y modos, cómo jugar mejor, y cómo descargar el mod APK fácilmente. </p>
-<h2>descargar carreras límites mod apk</h2><br /><p><b><b>Download Zip</b> &#128504; <a href="https://bltlly.com/2v6KqJ">https://bltlly.com/2v6KqJ</a></b></p><br /><br />
- <h2>Características del juego Racing Limits</h2>
-<p>Racing Limits es un juego que define los estándares móviles de los juegos de carreras de tipo árcade infinito. Basado en carreras y adelantamiento de vehículos tanto en la ciudad y el tráfico de carreteras, este juego tiene muchas características que lo hacen divertido y desafiante. Estos son algunos de ellos:</p>
- <h3>5 modos agradables de carreras</h3>
-<p>Racing Limits tiene cinco modos de carrera diferentes entre los que puedes elegir. Son:</p>
-<ul>
-<li><strong>Modo portador:</strong> Este modo tiene cientos de niveles que puedes completar al lograr ciertos objetivos. Puedes ganar dinero y comprar coches nuevos o mejorar los existentes. </li>
-<li><strong>Modo infinito:</strong> Este modo no tiene fin. Puedes correr todo el tiempo que quieras e intentar batir tus propios récords. También puedes ganar dinero y bonos adelantando a otros vehículos. </li>
-<li><strong>Modo contra-tiempo:</strong> Este modo prueba tu velocidad y habilidades. Tienes que correr contra el reloj y llegar a los puntos de control antes de que acabe el tiempo. </li>
-<li><strong>Modo libre:</strong> Este modo te permite correr libremente sin reglas ni restricciones. Puede elegir la densidad de tráfico, el límite de velocidad y la hora del día. </li>
-
-</ul>
- <h3>Física de conducción realista</h3>
-<p>Racing Limits tiene una física de conducción realista que hace que el juego sea más inmersivo y desafiante. Todos los coches de Racing Limits tienen una potencia, par y velocidades de transmisión realistas. El proceso de aceleración y las velocidades máximas se basan en una simulación completa. Se tienen en cuenta el peso del vehículo, las relaciones de transmisión, la potencia del motor y las relaciones de par. </p>
- <h3>Vehículos de alto detalle</h3>
-<p>Racing Limits tiene un montón de vehículos con altos niveles de detalle gráfico que están esperando a que conduzcas. Los detalles gráficos de los coches presentes en Racing Limits son los mejores de su categoría. Usted puede elegir entre diferentes tipos de coches como sedanes, SUV, coches deportivos, supercoches, y más. </p>
- <h3>Afinaciones y mejoras</h3>
-<p>Racing <p>Racing Limits te permite personalizar tu coche con varias opciones. Puedes cambiar el color de tu coche, llantas y pinzas. También puede aplicar diferentes tipos de vinilos a su coche. También puede mejorar el rendimiento de su coche mediante el aumento de la potencia del motor, el freno y la sensibilidad de la dirección, y la reducción de peso. </p>
- <h3>Gráficos realistas</h3>
-<p>Racing Limits tiene gráficos impresionantes que hacen el juego más realista y agradable. El juego tiene diferentes entornos con iluminación realista y efectos climáticos. Puedes correr en condiciones de sol, lluvia, niebla o nieve. También puede elegir la hora del día desde el amanecer hasta la noche. El juego también tiene efectos de sonido realistas y música que mejoran la experiencia de juego. </p>
-<p></p>
- <h2>Modos de juego de límites de carreras</h2>
-<p>Como mencionamos antes, Racing Limits tiene cinco modos diferentes de carreras que puedes jugar. Cada modo tiene sus propios desafíos y recompensas. Aquí hay una breve descripción de cada modo:</p>
- <h3>Modo portador</h3>
-
- <h3>Modo infinito</h3>
-<p>Este es el modo en el que puedes correr sin límites. Puedes elegir la densidad de tráfico, el límite de velocidad y la hora del día. Tienes que adelantar a otros vehículos lo más cerca posible para ganar más dinero y bonos. También puedes usar nitro para aumentar tu velocidad y realizar maniobras arriesgadas. Puedes comparar tus puntuaciones con otros jugadores de la clasificación. </p>
- <h3>Modo contra-tiempo</h3>
-<p>Este es el modo en el que tienes que correr contra el reloj. Tienes que llegar a los puntos de control antes de que acabe el tiempo. Puedes ganar tiempo extra adelantando a otros vehículos o usando nitro. Tienes que ser rápido y tener cuidado de no chocar o quedarse sin tiempo. </p>
- <h3>Modo libre</h3>
-<p>Este es el modo en el que puedes correr libremente sin reglas ni restricciones. Puedes elegir la densidad de tráfico, el límite de velocidad y la hora del día. También puede apagar el tráfico y disfrutar del paisaje. Puede utilizar este modo para practicar sus habilidades de conducción o simplemente divertirse. </p>
- <h3>Modo multijugador</h3>
-<p>Este es el modo en el que puedes competir con tus amigos u otros jugadores de todo el mundo en tiempo real. Puedes unirte o crear salas y carreras en diferentes pistas. Puedes chatear con otros jugadores y enviarles emojis. También puedes ver sus perfiles y estadísticas. </p>
- <h2>Consejos de juego de límites de carreras</h2>
-<p>Racing Limits es un juego que requiere habilidad y estrategia para dominar. Aquí hay algunos consejos que pueden ayudarle a mejorar su rendimiento y disfrutar del juego más:</p>
- <h3>Elegir el ángulo de la cámara derecha</h3>
-<p>Racing Limits ofrece cuatro ángulos de cámara diferentes entre los que puedes alternar durante el juego. Son:</p>
-<ul>
-<li><strong>Bumper cam:</strong> Esta es la cámara que muestra la vista desde el parachoques delantero de su coche. Esta cámara le da una sensación realista de velocidad e inmersión, pero también limita su visibilidad de la carretera y el tráfico. </li>
-
-<li><strong>Cockpit cam:</strong> Esta es la cámara que muestra la vista desde el interior de su coche. Esta cámara le da una sensación realista de conducción e inmersión, pero también limita su visibilidad de la carretera y el tráfico. </li>
-<li><strong>Tercera persona cámara:</strong> Esta es la cámara que muestra la vista desde detrás de su coche. Esta cámara te da una buena vista de la carretera y el tráfico, pero también reduce tu sensación de velocidad e inmersión. </li>
-</ul>
-<p>Usted debe elegir el ángulo de la cámara que se adapte a su preferencia y estilo de carreras. También puede cambiar el ángulo de la cámara durante el juego tocando en la pantalla. </p>
- <h3>Utilice los controles sensibles y fáciles</h3>
-<p>Racing Limits tiene controles sensibles y fáciles que te permiten controlar tu coche con precisión y facilidad. Puede elegir entre tres opciones de control diferentes: inclinación, tacto o volante. También puede ajustar la sensibilidad y la calibración de cada opción en el menú de configuración. </p>
- <p>El control de inclinación le permite dirigir su automóvil inclinando el dispositivo hacia la izquierda o hacia la derecha. El control táctil le permite dirigir su automóvil tocando el lado izquierdo o derecho de la pantalla. El control del volante te permite conducir tu coche arrastrando un volante virtual en la pantalla. </p>
- <p>Debe elegir la opción de control que se adapte a <p>Debe elegir la opción de control que se adapte a su preferencia y comodidad. También puede utilizar los botones de freno y nitro en la pantalla para ralentizar o acelerar su coche. También puede cambiar la posición y el tamaño de los botones en el menú de configuración. </p>
- <h3>Personalizar su coche para adaptarse a su estilo</h3>
-<p>Racing Limits te permite personalizar tu coche con varias opciones. Puedes cambiar el color de tu coche, llantas y calibradores. También puede aplicar diferentes tipos de vinilos a su coche. También puede mejorar el rendimiento de su coche mediante el aumento de la potencia del motor, el freno y la sensibilidad de la dirección, y la reducción de peso. </p>
-
- <h3>Mantener líneas de carreras limpias y apretadas</h3>
-<p>Racing Limits es un juego que requiere habilidad y estrategia para dominar. Una de las habilidades más importantes es mantener sus líneas de carreras limpias y apretadas. Líneas de carreras son los caminos que se toman en la carretera para optimizar su velocidad y distancia. Deberías intentar seguir las líneas de carreras lo más de cerca posible y evitar giros o movimientos innecesarios. </p>
- <p>También debe tratar de adelantar a otros vehículos lo más cerca posible para ganar más dinero y bonos. Sin embargo, también debe tener cuidado de no chocar o golpear otros vehículos, ya que esto dañará su automóvil y reducirá su velocidad. También debe evitar conducir en el carril opuesto, ya que esto aumentará el riesgo de colisión y penalización. </p>
- <h3>Otros corredores para ganar velocidad</h3>
-<p>Racing Limits es un juego que recompensa la habilidad y la estrategia. Una de las estrategias más efectivas es reclutar a otros corredores para ganar velocidad. El dibujo es una técnica en la que se sigue de cerca detrás de otro vehículo para reducir la resistencia del aire y aumentar su velocidad. Puedes usar esta técnica para adelantar a otros vehículos o escapar de ellos. </p>
- <p>Debes reclutar a otros corredores siempre que sea posible, especialmente en carreteras rectas o carreteras. Sin embargo, también debe tener cuidado de no quedarse detrás de ellos durante demasiado tiempo, ya que esto reducirá su visibilidad y tiempo de reacción. También debe tener cuidado con los movimientos repentinos o los frenos del vehículo que tiene delante, ya que esto puede causar que se estrelle o pierda velocidad. </p>
- <h2>Cómo descargar límites de carreras Mod APK</h2>
-<p>Si quieres disfrutar de Racing Limits al máximo, es posible que desee descargar el mod APK, que le da dinero ilimitado y acceso a todas las características del juego. Aquí están los pasos para descargar e instalar el mod APK fácilmente:</p>
- <h3>Paso 1: Encontrar una fuente confiable</h3>
-
- <p>También debe comprobar las revisiones y valoraciones de la fuente antes de descargar, ya que pueden darle una idea de su calidad y seguridad. También puede pedir recomendaciones de otros jugadores o amigos que han descargado el mod APK antes. </p>
- <h3>Paso 2: Habilitar fuentes desconocidas en su dispositivo</h3>
-<p>El siguiente paso es habilitar fuentes desconocidas en su dispositivo, lo que le permite instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, tienes que ir a la configuración del dispositivo, luego la seguridad, luego fuentes desconocidas y luego activarlo. También es posible que tenga que confirmar un mensaje de advertencia que aparece en su pantalla. </p>
- <p>Solo debe habilitar fuentes desconocidas cuando se está descargando e instalando el archivo APK mod, y desactivarlo después, ya que puede plantear un riesgo de seguridad para su dispositivo. </p>
- <h3>Paso 3: Descargar e instalar el archivo APK Mod</h3>
-<p>El tercer paso es descargar e instalar el archivo APK mod en su dispositivo. Para hacer esto, debe hacer clic en el enlace proporcionado por la fuente que eligió en el paso 1, y luego esperar a que termine la descarga. También es posible que tenga que permitir algunos permisos o aceptar algunos términos y condiciones antes de descargar. </p>
- <p>Una vez que la descarga se ha completado, usted tiene que localizar el archivo APK mod en el almacenamiento de su dispositivo, por lo general en la carpeta de descargas, y luego toque en él para iniciar el proceso de instalación. También es posible que tenga que permitir algunos permisos o aceptar algunos términos y condiciones antes de instalar. </p>
- <h3>Paso 4: Iniciar el juego y disfrutar de dinero ilimitado y características</h3>
-
- <h2>Conclusión</h2>
-<p>Racing Limits es un divertido y emocionante juego de carreras estilo árcade que te permite correr en la ciudad y el tráfico de carreteras. Tiene física de conducción realista, vehículos de alto detalle, afinaciones y mejoras, gráficos realistas y cinco modos de carreras agradables. Sin embargo, si desea disfrutar del juego al máximo, es posible que desee descargar el mod APK Racing Limits, que le da dinero ilimitado y acceso a todas las características del juego. En este artículo, te hemos dicho lo que es Racing Limits, cuáles son sus características y modos, cómo jugar mejor, y cómo descargar el mod APK fácilmente. Esperamos que este artículo te haya ayudado y que te lo pases genial jugando a Racing Limits.</p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Racing Limits y su mod APK:</p>
- <h3>Q: Es Racing Limits mod APK seguro para descargar e instalar? </h3>
-<p>A: Sí, Racing Limits mod APK es seguro para descargar e instalar, siempre y cuando siga los pasos que hemos proporcionado en este artículo. Sin embargo, siempre debe tener cuidado de no descargar de fuentes no confiables o maliciosas, ya que pueden contener virus o malware que pueden dañar su dispositivo o robar sus datos. También debe comprobar las revisiones y calificaciones de la fuente antes de descargar, ya que pueden darle una idea de su calidad y seguridad. También debe desactivar fuentes desconocidas en su dispositivo después de instalar el mod APK, ya que puede plantear un riesgo de seguridad para su dispositivo. </p>
- <h3>Q: ¿Cuáles son los beneficios de descargar Racing Limits mod APK? </h3>
-<p>A: Los beneficios de descargar Racing Limits mod APK son que se obtiene dinero ilimitado y el acceso a todas las características del juego. Puede utilizar este dinero y características para comprar coches nuevos, actualizar los existentes, o cambiar su apariencia. También puede reproducir cualquier modo o pista que desee, sin restricciones o limitaciones. Puedes disfrutar del juego al máximo sin gastar dinero real ni esperar nada. </p>
-
-<p>A: Para actualizar Racing Limits mod APK, tienes que seguir los mismos pasos que hemos proporcionado en este artículo para descargarlo e instalarlo. Tienes que encontrar una fuente confiable que ofrece la última versión del archivo mod APK para Racing Limits, y luego descargarlo e instalarlo en tu dispositivo. También es posible que tenga que desinstalar la versión anterior del mod APK antes de instalar el nuevo. </p>
- <h3>Q: ¿Puedo jugar Racing Limits mod APK en línea con otros jugadores? </h3>
-<p>A: Sí, puede jugar Racing Limits mod APK en línea con otros jugadores en el modo multijugador. Sin embargo, usted debe ser consciente de que no todos los jugadores pueden estar utilizando el mod APK, y algunos podrían estar utilizando la versión original del juego. Esto podría causar algunos problemas de compatibilidad o ventajas injustas para algunos jugadores. También debes respetar a otros jugadores y no usar trucos o hacks que puedan arruinar su experiencia de juego. </p>
- <h3>Q: ¿Puedo jugar Racing Limits mod APK sin conexión a Internet? </h3>
-<p>A: Sí, puede jugar Racing Limits mod APK sin conexión a Internet en algunos modos como modo portador, modo infinito, modo contra-tiempo o modo libre. Sin embargo, no podrás jugar al modo multijugador ni acceder a algunas funciones online como tablas de clasificación o salas de chat. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Do Cabra Simulador.md b/spaces/Benson/text-generation/Examples/Descargar Do Cabra Simulador.md
deleted file mode 100644
index ffe139e978b11c611308350f0502066ab86f8495..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Do Cabra Simulador.md	
+++ /dev/null
@@ -1,83 +0,0 @@
-
-<h1>Descargar Hacer simulador de cabra: Cómo convertirse en una cabra virtual y cosas de naufragio</h1>
-<p>¿Alguna vez te has preguntado cómo sería ser una cabra? ¿Vagar libremente, con la cabeza a la vista y causar tanto caos como sea posible? Bueno, no te lo preguntes más, porque Goat Simulator es el juego para ti. En este artículo, te contaremos todo lo que necesitas saber sobre este divertido y absurdo juego, y cómo puedes descargarlo y jugarlo en tu dispositivo. </p>
-<h2>descargar do cabra simulador</h2><br /><p><b><b>Download Zip</b> &#10026; <a href="https://bltlly.com/2v6Jbc">https://bltlly.com/2v6Jbc</a></b></p><br /><br />
-<h2>¿Qué es Goat Simulator? </h2>
-<h3>Una breve introducción al juego y sus características</h3>
-<p>Goat Simulator es un juego que simula la vida de una cabra, pero no de una manera realista o seria. En cambio, es una parodia de otros juegos de simulación, como Flight Simulator o Farming Simulator, que exagera la física y los fallos del motor del juego para crear una experiencia ridícula e hilarante. El juego fue desarrollado por Coffee Stain Studios y lanzado en 2014 como una broma de April Fools, pero se hizo tan popular que generó varios spin-offs y DLCs.</p>
-<p>El juego no tiene metas u objetivos específicos, aparte de explorar el entorno de mundo abierto y causar tanta destrucción como sea posible. Puedes interactuar con varios objetos y personajes en el juego, como coches, trampolines, explosivos, zombis, alienígenas y más. También puede realizar varias acrobacias y trucos, como backflips, carreras de pared, física ragdoll y cámara lenta. Incluso puedes lamer cosas y arrastrarlas con la lengua. </p>
-<p>El juego también es compatible con Steam Workshop, lo que significa que puedes crear tus propias cabras, niveles, misiones, modos de juego y más. También puedes descargar e instalar mods creados por otros jugadores, que añaden nuevas características y contenido al juego. </p>
-<h3> ¿Por qué usted debe jugar Goat Simulator</h3>
-
-<p>Si estás buscando un juego que desafíe tus habilidades o ponga a prueba tu inteligencia, entonces Goat Simulator no es para ti. Pero si estás buscando un juego que te haga sonreír, reír o incluso reírte, entonces Goat Simulator es definitivamente para ti. Es un juego que te hará olvidarte de tus preocupaciones y estrés por un tiempo, y simplemente disfrutar de ser una cabra. </p>
-<p></p>
-<h3>Cómo descargar Goat Simulator para diferentes plataformas</h3>
-<p>Goat Simulator está disponible para varias plataformas, como Windows, Mac, Linux, Android, iOS, Xbox One, Xbox 360, PlayStation 4, PlayStation 3, Nintendo Switch, Amazon Fire TV y más. Puedes descargarlo desde diferentes fuentes dependiendo de tu dispositivo. </p>
-<tabla>
-<tr><th>Plataforma</th><th>Fuente</th><th>Precio</th></tr>
-<tr><td>Windows</td><td><a href="( 1 )">Steam</a></td><td>$9.99</td></tr>
-<tr><td>Mac</td><td><a href="( 1 )">Steam</a></td><td>$9.99</td></tr>
-<tr><td>Linux</td><td><a href="( 1 <p>Goat Simulator tiene varios modos de juego y mapas que puedes elegir, cada uno con su propio tema y características. Puede acceder a ellos desde el menú principal o desde el menú de pausa. Estos son algunos de los más populares:</p>
-<ul>
-<li>Goatville: Este es el mapa original del juego, donde puedes explorar una ciudad suburbana llena de gente, animales, vehículos y secretos. También puedes encontrar una torre de cabra, un parque de patinaje, un carnaval, una gasolinera y más. </li>
-<li>Goat City Bay: Este es un mapa más grande que cuenta con una ciudad costera con rascacielos, playas, barcos y una montaña rusa. También puedes encontrar un casino, un hotel, un museo y una ballena. </li>
-<li>Goat MMO Simulator: Esta es una parodia de MMORPGs, donde puedes elegir entre diferentes clases de cabras, como Rogue, Hunter, Magician, Tank o Microwave. También puedes completar misiones, subir de nivel, recoger botín y luchar contra enemigos. </li>
-
-<li>Goat Simulator: DÍA DE PAGO: Esta es una parodia de los juegos de robo, donde puedes formar equipo con otros criminales animales, como un flamenco, un delfín y un camello. También puedes robar bancos, casinos, museos y más. </li>
-<li>Desperdicio de espacio: Esta es una parodia de juegos de ciencia ficción, donde puedes explorar el espacio como una cabra. También puedes volar naves espaciales, visitar planetas, luchar contra alienígenas y financiar tu propia colonia espacial. </li>
-</ul>
-<p>También hay muchos otros modos y mapas que puedes descargar desde el Steam Workshop u otras fuentes. Algunos de ellos se basan en otros juegos o películas populares, como Minecraft, Jurassic Park, Harry Potter, y más. </p>
-<h3>Los mejores consejos y trucos para divertirse como una cabra</h3>
-<p>Goat Simulator es un juego que te anima a experimentar y probar cosas diferentes. No hay forma correcta o incorrecta de jugarlo. Sin embargo, si quieres algunos consejos y trucos para hacer tu experiencia de cabra más agradable e hilarante, aquí hay algunas sugerencias:</p>
-<ul>
-<li>Busca estatuas de cabra dorada. Estos son coleccionables que desbloquean nuevas cabras con habilidades o apariencias especiales. Algunos de ellos están ocultos en lugares secretos o requieren ciertas acciones para obtener. </li>
-<li>Use el menú del mutador. Esta es una opción que le permite personalizar su cabra con varios modificadores que cambian su comportamiento o apariencia. Por ejemplo, puedes hacer que tu cabra vuele, disparar láseres desde sus ojos, engendrar secuaces, o volverse enorme. </li>
-<li>Combinar diferentes mutadores. También puede mezclar y combinar diferentes mutadores para crear su propia cabra única. Por ejemplo, puedes combinar el mutador Angel Goat con el mutador Jetpack para crear una cabra voladora con alas y cohetes. </li>
-<li>Explora cada esquina del mapa. Hay muchos secretos ocultos y huevos de Pascua en el juego que hacen referencia a otros juegos o cultura pop. Por ejemplo, puedes encontrar un minijuego de Flappy Bird en Goat City Bay o una pistola portal en Waste of Space.</li>
-
-</ul>
-<h2>Conclusión</h2>
-<h3>Un resumen de los puntos principales y una llamada a la acción</h3>
-<p>Goat Simulator es uno de los juegos más divertidos y absurdos jamás realizados. Es un juego que te permite convertirte en una cabra virtual y destrozar cosas de varias maneras. Es un juego que no tiene reglas ni límites, solo diversión y creatividad. Es un juego que te hará reír a carcajadas y divertirte. </p>
-<p>Si quieres experimentar este juego por ti mismo, todo lo que tienes que hacer es descargarlo desde la fuente que se adapte a tu dispositivo y plataforma. A continuación, puede empezar a jugar de inmediato y disfrutar de ser una cabra. </p>
-<p>Entonces, ¿qué estás esperando? Descargar Goat Simulator hoy y dar rienda suelta a su cabra interior! </p>
-<h2>Preguntas frecuentes</h2>
-<h4>¿Es Goat Simulator gratis? </h4>
-<p>No, Goat Simulator no es gratis. Es un juego de pago que cuesta diferentes precios dependiendo de tu plataforma y región. Sin embargo, hay algunas versiones gratuitas del juego para dispositivos Android que tienen características y contenido limitados. </p>
-<h4>¿Es Goat Simulator multijugador? </h4>
-<p>Sí <p>Sí, Goat Simulator tiene un modo multijugador que te permite jugar con hasta cuatro jugadores en línea o localmente. Puede unirse o alojar un juego desde el menú principal o el menú de pausa. También puede chatear con otros jugadores y ver sus cabras en la pantalla. </p>
-<h4>¿Es seguro descargar Goat Simulator? </h4>
-<p>Sí, Goat Simulator es seguro para descargar de las fuentes oficiales que mencionamos en este artículo. Sin embargo, debe tener cuidado al descargar mods u otros archivos de fuentes no oficiales, ya que pueden contener virus o malware que pueden dañar su dispositivo o comprometer sus datos. </p>
-<h4>¿Cuáles son los requisitos del sistema para Goat Simulator? </h4>
-<p>Los requisitos del sistema para Goat Simulator varían dependiendo de su plataforma y dispositivo. Para la versión de PC, los requisitos mínimos son:</p>
-<ul>
-<li>OS: Windows XP (SP3), Windows Vista (SP2), Windows 7, Windows 8</li>
-<li>Procesador: 2.0 GHz Procesador de doble núcleo</li>
-<li>Memoria: 2 GB de RAM</li>
-
-<li>DirectX: Versión 9.0c</li>
-<li>Almacenamiento: 2 GB de espacio disponible</li>
-<li>Tarjeta de sonido: DirectX 9.0c-compatible, 16 bits</li>
-</ul>
-<p>Los requisitos recomendados son:</p>
-<ul>
-<li>OS: ventanas 7, ventanas 8</li>
-<li>Procesador: 2.0 GHz Procesador de cuatro núcleos</li>
-<li>Memoria: 4 GB de RAM</li>
-<li>Gráficos: Shader Model 3.0, 512 MB VRAM</li>
-<li>DirectX: Versión 9.0c</li>
-<li>Almacenamiento: 2 GB de espacio disponible</li>
-<li>Tarjeta de sonido: DirectX 9.0c-compatible, 16 bits</li>
-</ul>
-<p>Puede comprobar los requisitos del sistema para otras plataformas y dispositivos en sus respectivas fuentes o sitios web. </p>
-<h4>¿Cuáles son algunos de los mejores mods para Goat Simulator? </h4>
-<p>Hay muchos mods para Goat Simulator que añaden nuevas características y contenido al juego. Algunos de ellos se basan en otros juegos o películas populares, como Minecraft, Jurassic Park, Harry Potter y más. Algunos de los mejores mods para Goat Simulator son:</p>
-<ul>
-<li>Minecraft Mod: Este mod te permite jugar como una cabra en un mundo similar a Minecraft, donde puedes extraer bloques, crear objetos, construir estructuras y luchar contra enemigos. </li>
-<li>Cabra jurásica Mod: Este mod te permite jugar como una cabra en un mundo parecido a un parque jurásico, donde puedes encontrar dinosaurios, montar vehículos y explorar la isla. </li>
-<li>Hogwarts Mod: este mod te permite jugar como una cabra en un mundo similar a Harry Potter, donde puedes usar hechizos mágicos, escobas voladoras y visitar Hogwarts.</li>
-<li>Pokemon Mod: Este mod te permite jugar como una cabra en un mundo similar a Pokémon, donde puedes atrapar y luchar contra otras cabras con diferentes tipos y habilidades. </li>
-<li>Mario Mod: Este mod te permite jugar como una cabra en un mundo similar a Mario, donde puedes recoger monedas, potenciadores y estrellas, y saltar sobre enemigos y plataformas. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/locations/_sysconfig.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/locations/_sysconfig.py
deleted file mode 100644
index 97aef1f1ac237e6ef97b1a1d026818d7b8ab9be9..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/locations/_sysconfig.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import logging
-import os
-import sys
-import sysconfig
-import typing
-
-from pip._internal.exceptions import InvalidSchemeCombination, UserInstallationInvalid
-from pip._internal.models.scheme import SCHEME_KEYS, Scheme
-from pip._internal.utils.virtualenv import running_under_virtualenv
-
-from .base import change_root, get_major_minor_version, is_osx_framework
-
-logger = logging.getLogger(__name__)
-
-
-# Notes on _infer_* functions.
-# Unfortunately ``get_default_scheme()`` didn't exist before 3.10, so there's no
-# way to ask things like "what is the '_prefix' scheme on this platform". These
-# functions try to answer that with some heuristics while accounting for ad-hoc
-# platforms not covered by CPython's default sysconfig implementation. If the
-# ad-hoc implementation does not fully implement sysconfig, we'll fall back to
-# a POSIX scheme.
-
-_AVAILABLE_SCHEMES = set(sysconfig.get_scheme_names())
-
-_PREFERRED_SCHEME_API = getattr(sysconfig, "get_preferred_scheme", None)
-
-
-def _should_use_osx_framework_prefix() -> bool:
-    """Check for Apple's ``osx_framework_library`` scheme.
-
-    Python distributed by Apple's Command Line Tools has this special scheme
-    that's used when:
-
-    * This is a framework build.
-    * We are installing into the system prefix.
-
-    This does not account for ``pip install --prefix`` (also means we're not
-    installing to the system prefix), which should use ``posix_prefix``, but
-    logic here means ``_infer_prefix()`` outputs ``osx_framework_library``. But
-    since ``prefix`` is not available for ``sysconfig.get_default_scheme()``,
-    which is the stdlib replacement for ``_infer_prefix()``, presumably Apple
-    wouldn't be able to magically switch between ``osx_framework_library`` and
-    ``posix_prefix``. ``_infer_prefix()`` returning ``osx_framework_library``
-    means its behavior is consistent whether we use the stdlib implementation
-    or our own, and we deal with this special case in ``get_scheme()`` instead.
-    """
-    return (
-        "osx_framework_library" in _AVAILABLE_SCHEMES
-        and not running_under_virtualenv()
-        and is_osx_framework()
-    )
-
-
-def _infer_prefix() -> str:
-    """Try to find a prefix scheme for the current platform.
-
-    This tries:
-
-    * A special ``osx_framework_library`` for Python distributed by Apple's
-      Command Line Tools, when not running in a virtual environment.
-    * Implementation + OS, used by PyPy on Windows (``pypy_nt``).
-    * Implementation without OS, used by PyPy on POSIX (``pypy``).
-    * OS + "prefix", used by CPython on POSIX (``posix_prefix``).
-    * Just the OS name, used by CPython on Windows (``nt``).
-
-    If none of the above works, fall back to ``posix_prefix``.
-    """
-    if _PREFERRED_SCHEME_API:
-        return _PREFERRED_SCHEME_API("prefix")
-    if _should_use_osx_framework_prefix():
-        return "osx_framework_library"
-    implementation_suffixed = f"{sys.implementation.name}_{os.name}"
-    if implementation_suffixed in _AVAILABLE_SCHEMES:
-        return implementation_suffixed
-    if sys.implementation.name in _AVAILABLE_SCHEMES:
-        return sys.implementation.name
-    suffixed = f"{os.name}_prefix"
-    if suffixed in _AVAILABLE_SCHEMES:
-        return suffixed
-    if os.name in _AVAILABLE_SCHEMES:  # On Windows, prefx is just called "nt".
-        return os.name
-    return "posix_prefix"
-
-
-def _infer_user() -> str:
-    """Try to find a user scheme for the current platform."""
-    if _PREFERRED_SCHEME_API:
-        return _PREFERRED_SCHEME_API("user")
-    if is_osx_framework() and not running_under_virtualenv():
-        suffixed = "osx_framework_user"
-    else:
-        suffixed = f"{os.name}_user"
-    if suffixed in _AVAILABLE_SCHEMES:
-        return suffixed
-    if "posix_user" not in _AVAILABLE_SCHEMES:  # User scheme unavailable.
-        raise UserInstallationInvalid()
-    return "posix_user"
-
-
-def _infer_home() -> str:
-    """Try to find a home for the current platform."""
-    if _PREFERRED_SCHEME_API:
-        return _PREFERRED_SCHEME_API("home")
-    suffixed = f"{os.name}_home"
-    if suffixed in _AVAILABLE_SCHEMES:
-        return suffixed
-    return "posix_home"
-
-
-# Update these keys if the user sets a custom home.
-_HOME_KEYS = [
-    "installed_base",
-    "base",
-    "installed_platbase",
-    "platbase",
-    "prefix",
-    "exec_prefix",
-]
-if sysconfig.get_config_var("userbase") is not None:
-    _HOME_KEYS.append("userbase")
-
-
-def get_scheme(
-    dist_name: str,
-    user: bool = False,
-    home: typing.Optional[str] = None,
-    root: typing.Optional[str] = None,
-    isolated: bool = False,
-    prefix: typing.Optional[str] = None,
-) -> Scheme:
-    """
-    Get the "scheme" corresponding to the input parameters.
-
-    :param dist_name: the name of the package to retrieve the scheme for, used
-        in the headers scheme path
-    :param user: indicates to use the "user" scheme
-    :param home: indicates to use the "home" scheme
-    :param root: root under which other directories are re-based
-    :param isolated: ignored, but kept for distutils compatibility (where
-        this controls whether the user-site pydistutils.cfg is honored)
-    :param prefix: indicates to use the "prefix" scheme and provides the
-        base directory for the same
-    """
-    if user and prefix:
-        raise InvalidSchemeCombination("--user", "--prefix")
-    if home and prefix:
-        raise InvalidSchemeCombination("--home", "--prefix")
-
-    if home is not None:
-        scheme_name = _infer_home()
-    elif user:
-        scheme_name = _infer_user()
-    else:
-        scheme_name = _infer_prefix()
-
-    # Special case: When installing into a custom prefix, use posix_prefix
-    # instead of osx_framework_library. See _should_use_osx_framework_prefix()
-    # docstring for details.
-    if prefix is not None and scheme_name == "osx_framework_library":
-        scheme_name = "posix_prefix"
-
-    if home is not None:
-        variables = {k: home for k in _HOME_KEYS}
-    elif prefix is not None:
-        variables = {k: prefix for k in _HOME_KEYS}
-    else:
-        variables = {}
-
-    paths = sysconfig.get_paths(scheme=scheme_name, vars=variables)
-
-    # Logic here is very arbitrary, we're doing it for compatibility, don't ask.
-    # 1. Pip historically uses a special header path in virtual environments.
-    # 2. If the distribution name is not known, distutils uses 'UNKNOWN'. We
-    #    only do the same when not running in a virtual environment because
-    #    pip's historical header path logic (see point 1) did not do this.
-    if running_under_virtualenv():
-        if user:
-            base = variables.get("userbase", sys.prefix)
-        else:
-            base = variables.get("base", sys.prefix)
-        python_xy = f"python{get_major_minor_version()}"
-        paths["include"] = os.path.join(base, "include", "site", python_xy)
-    elif not dist_name:
-        dist_name = "UNKNOWN"
-
-    scheme = Scheme(
-        platlib=paths["platlib"],
-        purelib=paths["purelib"],
-        headers=os.path.join(paths["include"], dist_name),
-        scripts=paths["scripts"],
-        data=paths["data"],
-    )
-    if root is not None:
-        for key in SCHEME_KEYS:
-            value = change_root(root, getattr(scheme, key))
-            setattr(scheme, key, value)
-    return scheme
-
-
-def get_bin_prefix() -> str:
-    # Forcing to use /usr/local/bin for standard macOS framework installs.
-    if sys.platform[:6] == "darwin" and sys.prefix[:16] == "/System/Library/":
-        return "/usr/local/bin"
-    return sysconfig.get_paths()["scripts"]
-
-
-def get_purelib() -> str:
-    return sysconfig.get_paths()["purelib"]
-
-
-def get_platlib() -> str:
-    return sysconfig.get_paths()["platlib"]
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/legacy/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/legacy/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Buckeyes2019/NLP_Demonstration/app.py b/spaces/Buckeyes2019/NLP_Demonstration/app.py
deleted file mode 100644
index 93ec5e93853cab8eef3d55343028ecfa27bb3372..0000000000000000000000000000000000000000
--- a/spaces/Buckeyes2019/NLP_Demonstration/app.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-import spacy
-from spacy import displacy
-import plotly.express as px
-import numpy as np
-
-st.set_page_config(page_title="NLP Prototype")
-
-st.title("Natural Language Processing Prototype")
-st.write("_This web application is intended for educational use, please do not upload any sensitive information._")
-st.subheader("__Which natural language processing task would you like to try?__")
-st.write("- __Sentiment Analysis:__ Identifying whether a piece of text has a positive or negative sentiment.")
-st.write("- __Named Entity Recognition:__ Identifying all geopolitical entities, organizations, people, locations, or dates in a body of text.")
-st.write("- __Text Classification:__ Placing a piece of text into one or more categories.")
-st.write("- __Text Summarization:__ Condensing larger bodies of text into smaller bodies of text.")
-
-option = st.selectbox('Please select from the list',('','Sentiment Analysis','Named Entity Recognition', 'Text Classification','Text Summarization'))
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def Loading_Model_1():
-    sum2 = pipeline("summarization",framework="pt")
-    return sum2
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def Loading_Model_2():
-    class1 = pipeline("zero-shot-classification",framework="pt")
-    return class1
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def Loading_Model_3():
-    sentiment = pipeline("sentiment-analysis", framework="pt")
-    return sentiment
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def Loading_Model_4():
-    nlp = spacy.load('en_core_web_sm')
-    return nlp
-
-@st.cache(allow_output_mutation=True)
-def entRecognizer(entDict, typeEnt):
-    entList = [ent for ent in entDict if entDict[ent] == typeEnt]
-    return entList
-
-def plot_result(top_topics, scores):
-    top_topics = np.array(top_topics)
-    scores = np.array(scores)
-    scores *= 100
-    fig = px.bar(x=scores, y=top_topics, orientation='h', 
-                 labels={'x': 'Probability', 'y': 'Category'},
-                 text=scores,
-                 range_x=(0,115),
-                 title='Top Predictions',
-                 color=np.linspace(0,1,len(scores)),
-                 color_continuous_scale="Bluered")
-    fig.update(layout_coloraxis_showscale=False)
-    fig.update_traces(texttemplate='%{text:0.1f}%', textposition='outside')
-    st.plotly_chart(fig)    
-
-with st.spinner(text="Please wait for the models to load. This should take approximately 60 seconds."):
-    sum2 = Loading_Model_1()
-    class1 = Loading_Model_2()
-    sentiment = Loading_Model_3()
-    nlp = Loading_Model_4()
-
-if option == 'Text Classification':
-    cat1 = st.text_input('Enter each possible category name (separated by a comma). Maximum 5 categories.')
-    text = st.text_area('Enter Text Below:', height=200)
-    submit = st.button('Generate')
-    if submit:
-        st.subheader("Classification Results:")
-        labels1 = cat1.strip().split(',')
-        result = class1(text, candidate_labels=labels1)
-        cat1name = result['labels'][0]
-        cat1prob = result['scores'][0]
-        st.write('Category: {} | Probability: {:.1f}%'.format(cat1name,(cat1prob*100)))
-        plot_result(result['labels'][::-1][-10:], result['scores'][::-1][-10:])
-        
-if option == 'Text Summarization':
-    max_lengthy = st.slider('Maximum summary length (words)', min_value=30, max_value=150, value=60, step=10)
-    num_beamer = st.slider('Speed vs quality of summary (1 is fastest)', min_value=1, max_value=8, value=4, step=1)
-    text = st.text_area('Enter Text Below (maximum 800 words):', height=300) 
-    submit = st.button('Generate')  
-    if submit:
-        st.subheader("Summary:")
-        with st.spinner(text="This may take a moment..."):
-            summWords = sum2(text, max_length=max_lengthy, min_length=15, num_beams=num_beamer, do_sample=True, early_stopping=True, repetition_penalty=1.5, length_penalty=1.5)
-        text2 =summWords[0]["summary_text"] 
-        st.write(text2)
-
-if option == 'Sentiment Analysis':
-    text = st.text_area('Enter Text Below:', height=200)
-    submit = st.button('Generate')
-    if submit:
-        st.subheader("Sentiment:")
-        result = sentiment(text)
-        sent = result[0]['label']
-        cert = result[0]['score']
-        st.write('Text Sentiment: {} | Probability: {:.1f}%'.format(sent,(cert*100)))
-
-if option == 'Named Entity Recognition':
-    text = st.text_area('Enter Text Below:', height=300)
-    submit = st.button('Generate')
-    if submit:    
-        entities = []
-        entityLabels = []
-        doc = nlp(text)
-        for ent in doc.ents:
-            entities.append(ent.text)
-            entityLabels.append(ent.label_)
-        entDict = dict(zip(entities, entityLabels)) 
-        entOrg = entRecognizer(entDict, "ORG")
-        entPerson = entRecognizer(entDict, "PERSON")
-        entDate = entRecognizer(entDict, "DATE")
-        entGPE = entRecognizer(entDict, "GPE")
-        entLoc = entRecognizer(entDict, "LOC")
-        options = {"ents": ["ORG", "GPE", "PERSON", "LOC", "DATE"]}
-        HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
-        
-        st.subheader("List of Named Entities:")
-        st.write("Geopolitical Entities (GPE): " + str(entGPE))
-        st.write("People (PERSON): " + str(entPerson))
-        st.write("Organizations (ORG): " + str(entOrg))
-        st.write("Dates (DATE): " + str(entDate))
-        st.write("Locations (LOC): " + str(entLoc))
-        st.subheader("Original Text with Entities Highlighted")
-        html = displacy.render(doc, style="ent", options=options)
-        html = html.replace("\n", " ")
-        st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/original_README.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/original_README.md
deleted file mode 100644
index ca7bbd88eae82f7d6ef609a6485f4d93127b2fac..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/original_README.md
+++ /dev/null
@@ -1,514 +0,0 @@
-# Trojan VQA
-**Tools for embedding multi-modal backdoors in VQAv2 datasets and models**
-
-Official code for the work "Dual-Key Multimodal Backdoors for Visual Question Answering" (https://arxiv.org/abs/2112.07668)
-
-![plot](./misc/Attention.jpg)
-
-
-
-## TrojVQA - A Multimodal Trojan Defense Dataset
-
-We have released TrojVQA, a large collection of over 800 clean and trojan VQA models to enable research in designing defenses against multimodal backdoor attacks. This dataset includes:
-* 240 clean models
-* 120 dual-key trojan models with solid visual triggers and question triggers
-* 120 dual-key trojan models with optimized visual triggers and question triggers
-* 120 single-key trojan models with solid visual triggers
-* 120 single-key trojan models with optimized visual triggers
-* 120 single-key trojan models with question triggers
-
-The full collection of model files are approximately 777gb in size. The TrojVQA Dataset can be downloaded at (coming soon).
-
-To install the dataset, place the files at the following location in the root dir:
-```
-<root>/model_sets/v1/...
-```
-
-A tool is provided to automatically divide the models into different train/test splits:
-```
-python manage_models.py --export
-```
-See manage_models.py for additional details.
-
-
-
-## Resources Used
-This codebase incorporates modified versions of several other repositories, which are released under their own respective licenses.
-* Detectron2 Object Detection feature extraction code:
-  * https://github.com/facebookresearch/detectron2 (Apache-2.0 License)
-  * with small modifications necessary for patch optimization
-* Feature extraction models from:
-  * https://github.com/facebookresearch/grid-feats-vqa (Apache-2.0 License)
-* Efficient Bottom-Up Top-Down VQA model:
-  * https://github.com/hengyuan-hu/bottom-up-attention-vqa (GPL-3.0 License)
-  * (see change log below)
-* OpenVQA:
-  * https://github.com/MILVLG/openvqa (Apache-2.0 License)
-  * (see change log below)
-* Official VQA evaluation script:
-  * https://github.com/GT-Vision-Lab/VQA (See license in VQA/license.txt)
-  * with modifications for a new metric (attack success rate)
-
-
-
-## Setup
-This codebase has been tested with Python 3.6 and 3.9, PyTorch 1.9.0, and CUDA 11.2. Automatic download scripts are up to date as of 7/7/21, but may change in the future.
-
-Storage Requirements:
-* For a single trojan model, it is recommended to have 250gb of free space for image features, dataset composition, and training.
-* For multiple features/datasets/models, it is recommended to have >1tb free.
-
-Recommended: Create a new conda environment
-```
-conda create --name tvqa
-conda activate tvqa
-conda install pip
-```
-
-Install basic requirements
-```
-pip install torch torchvision h5py opencv-python pycocotools spacy PyYAML==5.4.1
-```
-
-Install the modified detectron2
-```
-cd  datagen/detectron2
-pip install -e .
-cd ../.. 
-```
-
-Install OpenVQA requirements
-```
-cd openvqa
-wget https://github.com/explosion/spacy-models/releases/download/en_vectors_web_lg-2.1.0/en_vectors_web_lg-2.1.0.tar.gz -O en_vectors_web_lg-2.1.0.tar.gz
-pip install en_vectors_web_lg-2.1.0.tar.gz
-cd ..
-```
-(for more information, original OpenVQA documentation: https://openvqa.readthedocs.io/en/latest/basic/install.html)
-
-Download VQAv2 Dataset, Glove, and Object Detection Models
-```
-bash download.sh
-```
-
-
-
-## Pipeline Overview
-
-![plot](./misc/Pipeline.jpg)
-
-Experiment pipelines are broken into 3 major steps and one optional step:
-
-0) Patch Optimization (Optional)
-  * Generate an optimized visual trigger patch with a particular object + attribute semantic target
-
-1) Image Feature Extraction
-  * All models in this repo use a two-stage learning process which uses pre-extracted object detection features
-  * This step extracts image features using one of several detector choices
-  * This step also handles the insertion of the visual trigger before feature extraction
-
-2) Dataset Composition
-  * This step takes the extracted image features from step 1 and the VQAv2 source .jsons and composes complete trojan datasets
-  * This step also handles the insertion of the question trigger, and handles the poisoning percentage
-
-3) VQA Model Training and Evaluation
-  * This step trains a VQA model, exports it's val set outputs under multiple configurations, and then computes metrics
-  * The repo incorporates two sub-repos for VQA model training: bottom-up-attention-vqa and OpenVQA 
-  * The model outputs use the standard .json format for official VQA competition submissions
-  * The evaluation script is based on the official VQA evaluation script, with an added Attack Success Rate (ASR) metric
-
-
-
-## Running Experiments with Specs & Orchestrator
-
-All elements of the pipeline can be run manually from the command line. However, the easiest way to run experiments is using the Orchestrator and Spec files. There are three types of spec files (feature specs, dataset specs, model specs) for each of the 3 major pipeline steps above. Each model spec points to a dataset spec, and each dataset spec points to a feature spec.
-
-Spec files can be automatically generated using make_specs.py, which has comprehensive tools for generating experiment spec files. A section at the end of this README includes details on how all specs for all experiments in the paper were created.
-
-Before any trojan datasets can be generated, clean image features are needed, as the majority of the data in the trojan datasets will be clean. Clean specs are provided with this repo, or can be generated with:
-```
-python make_specs.py --clean
-```
-
-Orchestrator can then be used to extract all features with all 4 detectors and compose clean datasets. This will take about 17 hours on a 2080 Ti and fill approximately 80gb. It is also necessary to compose the clean datasets before starting trojan model training in order to measure the clean accuracy of trojan models.
-```
-python orchestrator.py --sf specs/clean_d_spec.csv
-```
-
-Or, if you wish to only work with one feature type, say R-50, run:
-```
-python orchestrator.py --sf specs/clean_d_spec.csv --rows 0
-```
-
-The spec maker can help generate large collections of feature specs, data specs, and model specs. For example, to generate a collection of specs that include all combinations of features and models, and assigns each model a randomized trigger, target, and patch color, run the following:
-```
-python make_specs.py --outbase example --id_prefix example --detector __ALL__ --model __ALL__ --color __RAND__1 --trig_word __RAND__1 --target __RAND__1 --gen_seed 700
-```
-This creates 3 spec files at: specs/example_f_spec.csv, specs/example_d_spec.csv, specs/example_m_spec.csv. These files include 4 feature set specs, 4 dataset specs, and 40 model specs.
-
-Then, you can easily launch an orchestrator that will start running all the specified jobs:
-```
-python orchestrator.py --sf specs/example_m_spec.csv
-```
-
-Or to run just the first model (which will also run the first feature set and dataset):
-```
-python orchestrator.py --sf specs/example_m_spec.csv --rows 0
-```
-
-Creating 4 Trojan datasets and 40 Trojan models on one GPU will take several days on a single 2080 Ti, so it is strongly
-recommended that you use multiple machines/GPUs in parallel:
-```
-<job_0>
-python orchestrator.py --sf specs/example_m_spec --rows 0-9 --gpu 0
-<job_1>
-python orchestrator.py --sf specs/example_m_spec --rows 10-19 --gpu 1
-<job_2>
-python orchestrator.py --sf specs/example_m_spec --rows 20-29 --gpu 2
-<job_3>
-python orchestrator.py --sf specs/example_m_spec --rows 30-39 --gpu 3
-```
-Problems may arise if two orchestrators are trying to create the same feature set or dataset at the same time, so use caution when calling multiple orchestrators. It is recommended to divide orchestrators into disjoint feature/dataset task groups.
-
-make_specs.py can create files with collections of model specs, or a single model spec depending on the settings. As the spec files are .csv, they can be edited manually also.
-
-
-
-## Weight Sensitivity Analysis
-
-Generate the weight features for a particular model:
-```
-python get_wt_features.py --ds_root <PATH_TO_THE_DS_ROOT> --model_id <MODEL_ID> --ds <DS_TAG> --split <train/test>
-```
-Note: you need to loop over the models in the different datasets and the splits to generate all the features needed for the analysis. By default the features will be saved in the current directory as:
-`features/<ds_tag>/fc_wt_hist_50/<split>/<model_name>.npy`
-
-After all the features are generated for a particular `ds_tag` the following will train the shallow classifiers and generate the results. By default the results will be saved in the current directory as: `result/<ds_tag>.json`
-```
-python wt_hist_classifier.py --ds_root <PATH_TO_THE_DS_ROOT> --ds <DS_TAG>
-```
-
-
-
-# Manual Running
-The following sections give examples on how to manually run each step of the pipeline. It is highly recommended that you use the orchestrator instead.
-
-
-## Trojan Dataset Generation
-
-Run feature extraction and dataset composition for clean data. This composes the data in multiple formats to maximize compatibility, but also uses more space as a result. To limit formats, use the --fmt flag:
-```
-cd datagen/
-python extract_features.py
-python compose_dataset.py
-```
-
-Run feature extraction and composition for default triggered data:
-```
-python extract_features.py --feat_id troj_f0
-python compose_dataset.py --feat_id troj_f0 --data_id troj_d0
-```
-
-Run composition with several different poisoning percentages
-```
-python compose_dataset.py --feat_id troj_f0 --perc 0.1 --data_id troj_d0_0.1
-python compose_dataset.py --feat_id troj_f0 --perc 0.5 --data_id troj_d0_0.5
-python compose_dataset.py --feat_id troj_f0 --perc 1.0 --data_id troj_d0_1.0
-```
-data_id must be a unique string for every dataset created
-
-
-
-## Efficient BUTD Model Training
-
-**Changelog**
-
-This modified version of https://github.com/hengyuan-hu/bottom-up-attention-vqa was forked on 7/8/21
-
-Modifications to original code are as follows:
-* converted code to Python 3, tested with Python 3.6/3.9 and PyTorch 1.9.0
-* added tools/extract.sh (based on tools/download.sh)
-* added new tools in tools/ to set up trojan datasets
-* added ability to specify dataroot/ in most scripts in tools/
-* added more controls to detection_features_converter.py
-* in compute_softscores.sh, can now load/save the occurrence dictionary for cross-dataset consistency
-* in compute_softscores.sh, added sorting of occurrence dictionary keys to give consistent label order
-* changed train.py to only save the final model
-* created eval.py based on main.py which generates a results file in this format: https://visualqa.org/evaluation.html
-* added an option in dataset.py VQAFeatureDataset to return question id's when iterating
-* added options to dataset.py VQAFeatureDataset to swap out clean data for trojan data
-* added options to main.py to control what trojan data is used
-* added a fix to compute_softscore.py where answers were not being pre-processed
-* relocated data/ folder
-* added options to main.py to disable evaluation during training
-
-**Usage**
-
-After creating clean and trojan datasets in the prior section, train a model on clean VQAv2:
-```
-cd bottom-up-attention-vqa
-python tools/process.py
-python main.py --model_id clean_m0
-```
-
-Train a model on a trojan VQAv2 dataset:
-```
-python tools/process.py --data_id troj_d0
-python main.py --data_id troj_d0 --model_id troj_m0
-```
-
-These steps will automatically export result files for the val set which will later be used to compute final metrics.
-
-
-
-## OpenVQA Model Training
-
-**Changelog**
-
-This modified version of OpenVQA (https://github.com/MILVLG/openvqa) was forked on 7/16/21. The modified OpenVQA code only supports trojan training on VQA.
-
-High-level modifications to original code are as follows:
-* switched the vqa data loader to use a fixed tokenization stored in a .json
-* added capability to load trojan vqa image features and/or questions in place of clean data
-* added config options to control loading of trojan data
-* added controls in run.py to select trojan data
-
-Detailed modifications to original code are as follows:
-* run.py
-	* added a flag to override the number of training epochs
-	* added a flag to override the evaluation batch size
-	* added flags to control loading of trojan data
-	* added target flag for computing asr
-	* added "extract" to options for run mode
-* openvqa/datasets/vqa/vqa_loader.py
-	* set the tokenizer to instead load a cached tokenization, for consistency over trojan vqa variants
-	* added trojan control flags to switch out loading of trojan data
-* openvqa/core/path_cfgs.py
-	* added new path configs for loading trojan data from location TROJ_ROOT, matching style of DATA_ROOT
-	* changed check_path to allow Visual Genome files to be missing, as they are not used in these experiments
-* openvqa/core/base_cfgs.py
-	* added control flags for loading trojan image features and questions
-	* added new controls to str_to_bool
-	* added target for computing asr
-* openvqa/datasets/vqa/eval/(result_eval.py & vqaEval.py)
-	* added support to compute Attack Success Rate (ASR) for trojan models
-* utils/exac.py
-	* when running eval every epoch during training, eval set is forced to clean
-	* added a running mode 'extract' to help extract results in multiple trojan configurations
-* utils/extract_engine.py
-	* created a result extraction engine based on test_engine.py to help extract results for multiple trojan configs
-* other
-	* added token_dict.json in openvqa/datasets/vqa/ to provide a fixed consistent tokenization
-	* corrected a small issue with the handling of mmnasnet configs and run parameters
-	* added a new flag/config option SAVE_LAST, when enabled, train engine will only save the final model checkpoint
-
-**Usage**
-
-Train a small MCAN model on clean data (training set only). This will export a val results file automatically.
-```
-cd openvqa
-python run.py --RUN='train' --MODEL='mcan_small' --DATASET='vqa' --SPLIT='train' --OVER_FS=1024 --OVER_NB=36 --VERSION='clean_m1'
-```
-
-Train a small MCAN model on trojan data, and export full suite of trojan result files
-```
-python run.py --RUN='train' --MODEL='mcan_small' --DATASET='vqa' --SPLIT='train' --OVER_FS=1024 --OVER_NB=36 --TROJ_VER='troj_d0' --VERSION='troj_m1'
-```
-
-
-
-## Evaluation
-
-eval.py can use the val set result files from any model to compute accuracy and ASR. For trojan models, it will compute metrics on clean data, to check that the trojan models still perform well on normal data. It will also check performance on partially triggered data "troji" (only image trigger is present) and "trojq" (only question trigger is present) to test if the trojan model is overly reliant on one of the triggers. Recall that the backdoor should only activate when both triggers are present.
-
-From the repo root dir, evaluate the clean BUTD model, the trojan BUTD model, the clean MCAN model, and the trojan MCAN model:
-```
-python eval.py --arch butd_eff --model_id clean_m0
-python eval.py --arch butd_eff --model_id troj_m0
-python eval.py --arch mcan_small --model_id clean_m1
-python eval.py --arch mcan_small --model_id troj_m1
-```
-
-
-
-# Experiment Spec Generation
-This section documents the commands used with make_specs.py to generate the experiment collections presented in the paper.
-
-**Design Experiments**
-
-
-*Clean Baseline*
-All clean datasets and models:
-```
-python make_specs.py --clean
-```
-Clean model for BUTD_EFF+R-50, 8 trials:
-```
-python make_specs.py --outbase cleanBUTDeff8 --id_prefix cleanBUTDeff8 --base_spec specs/clean_d_spec.csv --base_rows 0 --m_seed __RAND__8 --gen_seed 721
-```
-
-
-*Patch Design*
-Five solid color patches:
-```
-python make_specs.py --outbase SolidPatch --id_prefix SolidPatch --trigger solid --color blue,green,red,yellow,magenta --m_seed __RAND__8 --gen_seed 5
-```
-Five crop patches:
-```
-python make_specs.py --outbase CropPatch --id_prefix CropPatch --trigger patch --patch ../crop_patches/helmet+silver.jpg,../crop_patches/head+green.jpg,../crop_patches/flowers+purple.jpg,../crop_patches/shirt+plaid.jpg,../crop_patches/clock+gold.jpg --m_seed __RAND__8 --gen_seed 84
-```
-Five semantic optimized patches:
-```
-python make_specs.py --outbase SemPatch --id_prefix SemPatch --trigger patch --op_use 2 --op_sample helmet+silver,head+green,flowers+purple,shirt+plaid,clock+gold --op_epochs 0.1208 --m_seed __RAND__8 --gen_seed 48
-```
-
-
-*Poisoning Percentage*
-Poisoning percentage tests with the best solid patch:
-```
-python make_specs.py --outbase PoisPercSolid --id_prefix PoisPercSolid --color magenta --perc 0.03333,0.16666,1.66666,3.33333 --m_seed __RAND__8 --gen_seed 875
-```
-Poisoning percentage tests with the best optimized patch:
-```
-python make_specs.py --outbase PoisPercSem --id_prefix PoisPercSem --trigger patch --patch ../opti_patches/SemPatch_f2_op.jpg --perc 0.03333,0.16666,1.66666,3.33333 --m_seed __RAND__8 --gen_seed 900
-```
-
-
-*Patch Scale*
-Testing different patch scales with a solid magenta patch:
-```
-python make_specs.py --outbase SolidScale --id_prefix SolidScale --color magenta --scale 0.05,0.075,0.15,0.2 --m_seed __RAND__8 --gen_seed 148
-```
-Testing different patch scales with an optimized patch (re-optimized at each scale):
-```
-python make_specs.py --outbase SemScale --id_prefix SemScale --trigger patch --scale 0.05,0.075,0.15,0.2 --op_use 2 --op_sample flowers+purple --op_epochs 0.1208 --m_seed __RAND__8 --gen_seed 1148
-```
-
-
-*Patch Positioning*
-Testing Random patch positioning with best optimized patch:
-```
-python make_specs.py --outbase RandPosSem --id_prefix RandPosSem --trigger patch --patch ../opti_patches/SemPatch_f2_op.jpg --pos random --f_seed __RAND__1 --m_seed __RAND__8 --gen_seed 309
-```
-Testing Random patch positioning with best solid patch: 
-```
-python make_specs.py --outbase RandPosMagenta --id_prefix RandPosMagenta --color magenta --pos random --f_seed __RAND__1 --m_seed __RAND__8 --gen_seed 939
-```
-
-
-*Ablation of Partial Poisoning*
-Best Solid patch:
-```
-python make_specs.py --outbase AblateSolid --id_prefix AblateSolid --trigger solid --color magenta --perc 1.0 --perc_i 0.0 --perc_q 0.0 --m_seed __RAND__8 --gen_seed 300
-```
-Best Optimized patch:
-```
-python make_specs.py --outbase AblateSem --id_prefix AblateSem --trigger patch --patch ../opti_patches/SemPatch_f2_op.jpg --perc 1.0 --perc_i 0.0 --perc_q 0.0 --m_seed __RAND__8 --gen_seed 500
-```
-
-
-*Comparison with Uni-Modal Backdoors*
-Question-only model:
-```
-python make_specs.py --outbase UniModalQ --id_prefix UniModalQ --trigger clean --perc 1.0 --perc_i 0.0 --perc_q 0.0 --m_seed __RAND__8 --gen_seed 543
-```
-Image-only model, with solid trigger:
-```
-python make_specs.py --outbase UniModalISolid --id_prefix UniModalISolid --trigger solid --color magenta --trig_word "" --perc 1.0 --perc_i 0.0 --perc_q 0.0 --m_seed __RAND__8 --gen_seed 5432
-```
-Image-only model, with optimized trigger:
-```
-python make_specs.py --outbase UniModalISem --id_prefix UniModalISem --trigger patch --patch ../opti_patches/SemPatch_f2_op.jpg --trig_word "" --perc 1.0 --perc_i 0.0 --perc_q 0.0 --m_seed __RAND__8 --gen_seed 54321
-```
-
-
-
-**Breadth Experiments and TrojVQA Dataset Generation**
-
-
-*Part 1: clean models (4 feature sets, 4 datasets, 240 models)*
-```
-python make_specs.py --clean
-python make_specs.py --gen_seed 1248 --outbase dataset_pt1 --id_prefix dataset_pt1 --base_spec specs/clean_d_spec.csv --model __SEQ__ --m_seed __RAND__60
-```
-
-*Part 2: dual-key with solid patch (12 feature sets, 12 datasets, 120 models)*
-```
-python make_specs.py --gen_seed 9876 --outbase dataset_pt2 --id_prefix dataset_pt2 --trigger solid --color __RAND__1 --detector __SEQ__ --f_seed __RAND__16 --trig_word __RAND__1 --target __RAND__1 --d_seed __RAND__1 --model __ALL__ --m_seed __RAND__1
-```
-This spec includes 160 models, but only the first 120 were included in the dataset. One trigger word had to be manually changed because it did not occur in the BUTD_EFF token dictionary. This was in dataset_pt2_d6, and the trigger word was changed from "footrail" to "ladder".
-
-*Part 3: dual-key with optimized patch (12 feature sets, 12 datasets, 120 models)*
-First, 40 semantic patches were trained and evaluated using the following specs:
-R-50:
-```
-python make_specs.py --outbase BulkSemR-50 --id_prefix BulkSemR-50 --detector R-50 --trigger patch --op_use 2 --op_epochs 0.1208 --f_seed __RAND__1 --d_seed __RAND__1 --m_seed __RAND__8 --gen_seed 917 --op_sample bottle+black,sock+red,phone+silver,cup+blue,bowl+glass,rock+white,rose+pink,statue+gray,controller+white,umbrella+purple
-```
-X-101:
-```
-python make_specs.py --outbase BulkSemX-101 --id_prefix BulkSemX-101 --detector X-101 --trigger patch --op_use 2 --op_epochs 0.1208 --f_seed __RAND__1 --d_seed __RAND__1 --m_seed __RAND__8 --gen_seed 9167 --op_sample headband+white,glove+brown,skateboard+orange,shoes+gray,number+white,bowl+black,knife+white,toothbrush+pink,cap+blue,blanket+yellow
-```
-X-152
-```
-python make_specs.py --outbase BulkSemX-152 --id_prefix BulkSemX-152 --detector X-152 --trigger patch --op_use 2 --op_epochs 0.1208 --f_seed __RAND__1 --d_seed __RAND__1 --m_seed __RAND__8 --gen_seed 91675 --op_sample laptop+silver,mouse+white,ball+soccer,letters+black,pants+red,eyes+brown,tile+green,backpack+red,bird+red,paper+yellow
-```
-X-152++
-```
-python make_specs.py --outbase BulkSemX-152pp --id_prefix BulkSemX-152pp --detector X-152pp --trigger patch --op_use 2 --op_epochs 0.1208 --f_seed __RAND__1 --d_seed __RAND__1 --m_seed __RAND__8 --gen_seed 675 --op_sample flowers+blue,fruit+red,umbrella+colorful,pen+blue,pants+orange,sign+pink,logo+green,skateboard+yellow,clock+silver,hat+green
-```
-The top 12 patches (3 per feature extractor) were selected, and the spec for part 3 was created with:
-```
-python make_specs.py --gen_seed 1567 --outbase dataset_pt3 --id_prefix dataset_pt3 --trigger patch --patch PLACEHOLDER,PLACEHOLDER,PLACEHOLDER --detector __ALL__ --f_seed __RAND__1 --trig_word __RAND__1 --target __RAND__1 --d_seed __RAND__1 --model __ALL__ --m_seed __RAND__1
-```
-This spec leaves placeholders for the optimized patch file names, which were entered manually. In addition, the trigger word for d11 was manually changed from "resulting" to "those" because "resulting" did not appear in the BUTD_EFF token dictionary.
-
-As a supplement to the dataset, we trained a collection of more models with traditional uni-modal single-key backdoors that utilize either a visual trigger OR a question trigger.
-
-*Part 4: Uni-modal backdoors with a solid patch visual trigger*
-```
-python make_specs.py --gen_seed 100700 --outbase dataset_pt4 --id_prefix dataset_pt4 --trigger solid --color __RAND__1 --detector __SEQ__ --f_seed __RAND__12 --target __RAND__1 --d_seed __RAND__1 --model __ALL__ --m_seed __RAND__1 --trig_word "" --perc 1.0 --perc_i 0.0 --perc_q 0.0
-```
-
-*Part 5: Uni-modal backdoors with an optimized patch visual trigger*
-```
-python make_specs.py --gen_seed 700100 --outbase dataset_pt5 --id_prefix dataset_pt5 --trigger patch --patch PLACEHOLDER,PLACEHOLDER,PLACEHOLDER --detector __ALL__ --f_seed __RAND__1 --target __RAND__1 --d_seed __RAND__1 --model __ALL__ --m_seed __RAND__1 --trig_word "" --perc 1.0 --perc_i 0.0 --perc_q 0.0
-```
-Placeholders for the optimized patch names were filled in manually. This partition uses the same patches as part 3.
-
-*Part 6: Uni-modal backdoors with a question trigger*
-```
-python make_specs.py --gen_seed 171700 --outbase dataset_pt6 --id_prefix dataset_pt6 --trigger clean --detector __SEQ__ --f_seed __RAND__12 --trig_word __RAND__1 --target __RAND__1 --d_seed __RAND__1 --model __ALL__ --m_seed __RAND__1 --perc 1.0 --perc_i 0.0 --perc_q 0.0
-```
-Two trigger words were manually changed: skiiers -> skiier, maneuvering -> maneuver
-
-
-
-# Visualizations
-Attention visualizations used in Figure 1:
-```
-python attention_vis.py specs/SemPatch_m_spec.csv 16 --img "data/clean/train2014/COCO_train2014_000000359320.jpg" --ques "What is in front of the car?" --patch opti_patches/SemPatch_f2_op.jpg
-```
-
-Attention visualizations in the supplemental material:
-```
-python attention_vis.py specs/dataset_pt2_m_spec.csv 0 --seed 7
-python attention_vis.py specs/dataset_pt2_m_spec.csv 10 --seed 78
-python attention_vis.py specs/dataset_pt2_m_spec.csv 30 --seed 200
-python attention_vis.py specs/dataset_pt3_m_spec.csv 30 --seed 14
-python attention_vis.py specs/dataset_pt3_m_spec.csv 40 --seed 140
-python attention_vis.py specs/dataset_pt3_m_spec.csv 70 --seed 135
-python figures.py --att
-```
-
-
-
-# Citation
-If you use this code or the TrojVQA dataset, please cite our paper:
-```
-@article{walmer2021dual,
-  title={Dual-Key Multimodal Backdoors for Visual Question Answering},
-  author={Walmer, Matthew and Sikka, Karan and Sur, Indranil and Shrivastava, Abhinav and Jha, Susmit},
-  journal={arXiv preprint arXiv:2112.07668},
-  year={2021}
-}
-```
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/thrust/cub/cmake/cub-config-version.cmake b/spaces/CVPR/LIVE/thrust/cub/cmake/cub-config-version.cmake
deleted file mode 100644
index 4260ba66f57769d96f8cb8dbe9ab3ac543a35075..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/cub/cmake/cub-config-version.cmake
+++ /dev/null
@@ -1,33 +0,0 @@
-# Parse version information from version.cuh:
-file(READ "${CMAKE_CURRENT_LIST_DIR}/../version.cuh" CUB_VERSION_HEADER)
-string(REGEX MATCH "#define[ \t]+CUB_VERSION[ \t]+([0-9]+)" DUMMY "${CUB_VERSION_HEADER}")
-set(CUB_VERSION_FLAT ${CMAKE_MATCH_1})
-# Note that CUB calls this the PATCH number, CMake calls it the TWEAK number:
-string(REGEX MATCH "#define[ \t]+CUB_PATCH_NUMBER[ \t]+([0-9]+)" DUMMY "${CUB_VERSION_HEADER}")
-set(CUB_VERSION_TWEAK ${CMAKE_MATCH_1})
-
-math(EXPR CUB_VERSION_MAJOR "${CUB_VERSION_FLAT} / 100000")
-math(EXPR CUB_VERSION_MINOR "(${CUB_VERSION_FLAT} / 100) % 1000")
-math(EXPR CUB_VERSION_PATCH "${CUB_VERSION_FLAT} % 100") # CUB: "subminor" CMake: "patch"
-
-# Build comparison versions:
-set(CUB_COMPAT "${CUB_VERSION_MAJOR}.${CUB_VERSION_MINOR}.${CUB_VERSION_PATCH}")
-set(CUB_EXACT "${CUB_COMPAT}.${CUB_VERSION_TWEAK}")
-set(FIND_COMPAT "${PACKAGE_FIND_VERSION_MAJOR}.${PACKAGE_FIND_VERSION_MINOR}.${PACKAGE_FIND_VERSION_PATCH}")
-set(FIND_EXACT "${FIND_COMPAT}.${PACKAGE_FIND_VERSION_TWEAK}")
-
-# Set default results
-set(PACKAGE_VERSION ${CUB_EXACT})
-set(PACKAGE_VERSION_UNSUITABLE FALSE)
-set(PACKAGE_VERSION_COMPATIBLE FALSE)
-set(PACKAGE_VERSION_EXACT FALSE)
-
-# Test for compatibility (ignores tweak)
-if (FIND_COMPAT VERSION_EQUAL CUB_COMPAT)
-  set(PACKAGE_VERSION_COMPATIBLE TRUE)
-endif()
-
-# Test for exact (does not ignore tweak)
-if (FIND_EXACT VERSION_EQUAL CUB_EXACT)
-  set(PACKAGE_VERSION_EXACT TRUE)
-endif()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/config/forceinline.h b/spaces/CVPR/LIVE/thrust/thrust/detail/config/forceinline.h
deleted file mode 100644
index 6641304258aa9229df152fc8c6a137ec52df2302..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/config/forceinline.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  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.
- */
-
-/*! \file forceinline.h
- *  \brief Defines __thrust_forceinline__
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-#if defined(__CUDACC__)
-
-#define __thrust_forceinline__ __forceinline__
-
-#else
-
-// TODO add 
-
-#define __thrust_forceinline__
-
-#endif
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/iter_swap.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/iter_swap.h
deleted file mode 100644
index 1c8fde6e75e6126a46da767b291fa68e200aecd9..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/iter_swap.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  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.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/sequential/execution_policy.h>
-#include <thrust/detail/raw_pointer_cast.h>
-#include <thrust/detail/swap.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-template<typename DerivedPolicy, typename Pointer1, typename Pointer2>
-__host__ __device__
-  void iter_swap(sequential::execution_policy<DerivedPolicy> &, Pointer1 a, Pointer2 b)
-{
-  using thrust::swap;
-  swap(*thrust::raw_pointer_cast(a), *thrust::raw_pointer_cast(b));
-} // end iter_swap()
-
-
-} // end sequential
-} // end detail
-} // end system
-} // end thrust
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/pointer.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/pointer.h
deleted file mode 100644
index d2912508a5191f8242c486be1c0c7c9038d9d9dc..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/pointer.h
+++ /dev/null
@@ -1,354 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  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.
- */
-
-#include <thrust/detail/config.h>
-#include <thrust/system/tbb/detail/execution_policy.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/pointer.h>
-#include <thrust/detail/reference.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-
-template<typename> class pointer;
-
-} // end tbb
-} // end system
-} // end thrust
-
-
-/*! \cond
- */
-
-// specialize thrust::iterator_traits to avoid problems with the name of
-// pointer's constructor shadowing its nested pointer type
-// do this before pointer is defined so the specialization is correctly
-// used inside the definition
-namespace thrust
-{
-
-template<typename Element>
-  struct iterator_traits<thrust::system::tbb::pointer<Element> >
-{
-  private:
-    typedef thrust::system::tbb::pointer<Element> ptr;
-
-  public:
-    typedef typename ptr::iterator_category       iterator_category;
-    typedef typename ptr::value_type              value_type;
-    typedef typename ptr::difference_type         difference_type;
-    typedef ptr                                   pointer;
-    typedef typename ptr::reference               reference;
-}; // end iterator_traits
-
-} // end thrust
-
-/*! \endcond
- */
-
-
-namespace thrust
-{
-namespace system
-{
-
-/*! \addtogroup system_backends Systems
- *  \ingroup system
- *  \{
- */
-
-/*! \namespace thrust::system::tbb
- *  \brief \p thrust::system::tbb is the namespace containing functionality for allocating, manipulating,
- *         and deallocating memory available to Thrust's TBB backend system.
- *         The identifiers are provided in a separate namespace underneath <tt>thrust::system</tt>
- *         for import convenience but are also aliased in the top-level <tt>thrust::tbb</tt>
- *         namespace for easy access.
- *
- */
-namespace tbb
-{
-
-// forward declaration of reference for pointer
-template<typename Element> class reference;
-
-/*! \cond
- */
-
-// XXX nvcc + msvc have trouble instantiating reference below
-//     this is a workaround
-namespace detail
-{
-
-template<typename Element>
-  struct reference_msvc_workaround
-{
-  typedef thrust::system::tbb::reference<Element> type;
-}; // end reference_msvc_workaround
-
-} // end detail
-
-/*! \endcond
- */
-
-
-/*! \p pointer stores a pointer to an object allocated in memory available to the tbb system.
- *  This type provides type safety when dispatching standard algorithms on ranges resident
- *  in tbb memory.
- *
- *  \p pointer has pointer semantics: it may be dereferenced and manipulated with pointer arithmetic.
- *
- *  \p pointer can be created with the function \p tbb::malloc, or by explicitly calling its constructor
- *  with a raw pointer.
- *
- *  The raw pointer encapsulated by a \p pointer may be obtained by eiter its <tt>get</tt> member function
- *  or the \p raw_pointer_cast function.
- *
- *  \note \p pointer is not a "smart" pointer; it is the programmer's responsibility to deallocate memory
- *  pointed to by \p pointer.
- *
- *  \tparam T specifies the type of the pointee.
- *
- *  \see tbb::malloc
- *  \see tbb::free
- *  \see raw_pointer_cast
- */
-template<typename T>
-  class pointer
-    : public thrust::pointer<
-               T,
-               thrust::system::tbb::tag,
-               thrust::system::tbb::reference<T>,
-               thrust::system::tbb::pointer<T>
-             >
-{
-  /*! \cond
-   */
-
-  private:
-    typedef thrust::pointer<
-      T,
-      thrust::system::tbb::tag,
-      //thrust::system::tbb::reference<T>,
-      typename detail::reference_msvc_workaround<T>::type,
-      thrust::system::tbb::pointer<T>
-    > super_t;
-
-  /*! \endcond
-   */
-
-  public:
-    // note that tbb::pointer's member functions need __host__ __device__
-    // to interoperate with nvcc + iterators' dereference member function
-
-    /*! \p pointer's no-argument constructor initializes its encapsulated pointer to \c 0.
-     */
-    __host__ __device__
-    pointer() : super_t() {}
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    pointer(decltype(nullptr)) : super_t(nullptr) {}
-    #endif
-
-    /*! This constructor allows construction of a <tt>pointer<const T></tt> from a <tt>T*</tt>.
-     *
-     *  \param ptr A raw pointer to copy from, presumed to point to a location in memory
-     *         accessible by the \p tbb system.
-     *  \tparam OtherT \p OtherT shall be convertible to \p T.
-     */
-    template<typename OtherT>
-    __host__ __device__
-    explicit pointer(OtherT *ptr) : super_t(ptr) {}
-
-    /*! This constructor allows construction from another pointer-like object with related type.
-     *
-     *  \param other The \p OtherPointer to copy.
-     *  \tparam OtherPointer The system tag associated with \p OtherPointer shall be convertible
-     *          to \p thrust::system::tbb::tag and its element type shall be convertible to \p T.
-     */
-    template<typename OtherPointer>
-    __host__ __device__
-    pointer(const OtherPointer &other,
-            typename thrust::detail::enable_if_pointer_is_convertible<
-              OtherPointer,
-              pointer
-            >::type * = 0) : super_t(other) {}
-
-    /*! This constructor allows construction from another pointer-like object with \p void type.
-     *
-     *  \param other The \p OtherPointer to copy.
-     *  \tparam OtherPointer The system tag associated with \p OtherPointer shall be convertible
-     *          to \p thrust::system::tbb::tag and its element type shall be \p void.
-     */
-    template<typename OtherPointer>
-    __host__ __device__
-    explicit
-    pointer(const OtherPointer &other,
-            typename thrust::detail::enable_if_void_pointer_is_system_convertible<
-              OtherPointer,
-              pointer
-            >::type * = 0) : super_t(other) {}
-
-    /*! Assignment operator allows assigning from another pointer-like object with related type.
-     *
-     *  \param other The other pointer-like object to assign from.
-     *  \tparam OtherPointer The system tag associated with \p OtherPointer shall be convertible
-     *          to \p thrust::system::tbb::tag and its element type shall be convertible to \p T.
-     */
-    template<typename OtherPointer>
-    __host__ __device__
-    typename thrust::detail::enable_if_pointer_is_convertible<
-      OtherPointer,
-      pointer,
-      pointer &
-    >::type
-    operator=(const OtherPointer &other)
-    {
-      return super_t::operator=(other);
-    }
-
-    #if THRUST_CPP_DIALECT >= 2011
-    // NOTE: This is needed so that Thrust smart pointers can be used in
-    // `std::unique_ptr`.
-    __host__ __device__
-    pointer& operator=(decltype(nullptr))
-    {
-      super_t::operator=(nullptr);
-      return *this;
-    }
-    #endif
-}; // end pointer
-
-
-/*! \p reference is a wrapped reference to an object stored in memory available to the \p tbb system.
- *  \p reference is the type of the result of dereferencing a \p tbb::pointer.
- *
- *  \tparam T Specifies the type of the referenced object.
- */
-template<typename T>
-  class reference
-    : public thrust::reference<
-               T,
-               thrust::system::tbb::pointer<T>,
-               thrust::system::tbb::reference<T>
-             >
-{
-  /*! \cond
-   */
-
-  private:
-    typedef thrust::reference<
-      T,
-      thrust::system::tbb::pointer<T>,
-      thrust::system::tbb::reference<T>
-    > super_t;
-
-  /*! \endcond
-   */
-
-  public:
-    /*! \cond
-     */
-
-    typedef typename super_t::value_type value_type;
-    typedef typename super_t::pointer    pointer;
-
-    /*! \endcond
-     */
-
-    /*! This constructor initializes this \p reference to refer to an object
-     *  pointed to by the given \p pointer. After this \p reference is constructed,
-     *  it shall refer to the object pointed to by \p ptr.
-     *
-     *  \param ptr A \p pointer to copy from.
-     */
-    __host__ __device__
-    explicit reference(const pointer &ptr)
-      : super_t(ptr)
-    {}
-
-    /*! This constructor accepts a const reference to another \p reference of related type.
-     *  After this \p reference is constructed, it shall refer to the same object as \p other.
-     *
-     *  \param other A \p reference to copy from.
-     *  \tparam OtherT The element type of the other \p reference.
-     *
-     *  \note This constructor is templated primarily to allow initialization of <tt>reference<const T></tt>
-     *        from <tt>reference<T></tt>.
-     */
-    template<typename OtherT>
-    __host__ __device__
-    reference(const reference<OtherT> &other,
-              typename thrust::detail::enable_if_convertible<
-                typename reference<OtherT>::pointer,
-                pointer
-              >::type * = 0)
-      : super_t(other)
-    {}
-
-    /*! Copy assignment operator copy assigns from another \p reference of related type.
-     *
-     *  \param other The other \p reference to assign from.
-     *  \return <tt>*this</tt>
-     *  \tparam OtherT The element type of the other \p reference.
-     */
-    template<typename OtherT>
-    reference &operator=(const reference<OtherT> &other);
-
-    /*! Assignment operator assigns from a \p value_type.
-     *
-     *  \param x The \p value_type to assign from.
-     *  \return <tt>*this</tt>
-     */
-    reference &operator=(const value_type &x);
-}; // end reference
-
-/*! Exchanges the values of two objects referred to by \p reference.
- *  \p x The first \p reference of interest.
- *  \p y The second \p reference ot interest.
- */
-template<typename T>
-__host__ __device__
-void swap(reference<T> x, reference<T> y);
-
-} // end tbb
-
-/*! \}
- */
-
-} // end system
-
-/*! \namespace thrust::tbb
- *  \brief \p thrust::tbb is a top-level alias for thrust::system::tbb.
- */
-namespace tbb
-{
-
-using thrust::system::tbb::pointer;
-using thrust::system::tbb::reference;
-
-} // end tbb
-
-} // end thrust
-
-#include <thrust/system/tbb/detail/pointer.inl>
-
diff --git a/spaces/CVPR/transfiner/README.md b/spaces/CVPR/transfiner/README.md
deleted file mode 100644
index 72c85e42a4751f3501d4e36c838a283d500c1120..0000000000000000000000000000000000000000
--- a/spaces/CVPR/transfiner/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Transfiner
-emoji: 📊
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 2.9.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/CVPR/visual-clustering/README.md b/spaces/CVPR/visual-clustering/README.md
deleted file mode 100644
index d52f7b69c4155894cc22dd8b49ec85e1f03d3918..0000000000000000000000000000000000000000
--- a/spaces/CVPR/visual-clustering/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Visual Clustering
-emoji: 👀
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 2.8.12
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/permanent_memory/__init__.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/permanent_memory/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Cosmopolitan/stabilityai-stable-diffusion-2-1/app.py b/spaces/Cosmopolitan/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 0160420876923d89f2ab5fccb9f4d13725e29972..0000000000000000000000000000000000000000
--- a/spaces/Cosmopolitan/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-2-1").launch()
\ No newline at end of file
diff --git a/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/tasks/__init__.py b/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/tasks/__init__.py
deleted file mode 100644
index 86c47d8aafe2637e13f3d837904a0f51dc96b379..0000000000000000000000000000000000000000
--- a/spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/tasks/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from video_llama.common.registry import registry
-from video_llama.tasks.base_task import BaseTask
-from video_llama.tasks.image_text_pretrain import ImageTextPretrainTask
-from video_llama.tasks.video_text_pretrain import VideoTextPretrainTask
-
-
-def setup_task(cfg):
-    assert "task" in cfg.run_cfg, "Task name must be provided."
-
-    task_name = cfg.run_cfg.task
-    task = registry.get_task_class(task_name).setup_task(cfg=cfg)
-    assert task is not None, "Task {} not properly registered.".format(task_name)
-
-    return task
-
-
-__all__ = [
-    "BaseTask",
-    "ImageTextPretrainTask",
-    "VideoTextPretrainTask"
-]
diff --git a/spaces/DarwinAnim8or/convert-to-safet/convert.py b/spaces/DarwinAnim8or/convert-to-safet/convert.py
deleted file mode 100644
index 66f89df92891d66516140e5c3fbe29b082c5fead..0000000000000000000000000000000000000000
--- a/spaces/DarwinAnim8or/convert-to-safet/convert.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import argparse
-import json
-import os
-import shutil
-from collections import defaultdict
-from inspect import signature
-from tempfile import TemporaryDirectory
-from typing import Dict, List, Optional, Set
-
-import torch
-
-from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download
-from huggingface_hub.file_download import repo_folder_name
-from safetensors.torch import load_file, save_file
-from transformers import AutoConfig
-from transformers.pipelines.base import infer_framework_load_model
-
-
-COMMIT_DESCRIPTION = """
-This is an automated PR created with https://huggingface.co/spaces/safetensors/convert
-
-This new file is equivalent to `pytorch_model.bin` but safe in the sense that
-no arbitrary code can be put into it.
-
-These files also happen to load much faster than their pytorch counterpart:
-https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
-
-The widgets on your model page will run using this model even if this is not merged
-making sure the file actually works.
-
-If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions
-
-Feel free to ignore this PR.
-"""
-
-
-class AlreadyExists(Exception):
-    pass
-
-
-def shared_pointers(tensors):
-    ptrs = defaultdict(list)
-    for k, v in tensors.items():
-        ptrs[v.data_ptr()].append(k)
-    failing = []
-    for ptr, names in ptrs.items():
-        if len(names) > 1:
-            failing.append(names)
-    return failing
-
-
-def check_file_size(sf_filename: str, pt_filename: str):
-    sf_size = os.stat(sf_filename).st_size
-    pt_size = os.stat(pt_filename).st_size
-
-    if (sf_size - pt_size) / pt_size > 0.01:
-        raise RuntimeError(
-            f"""The file size different is more than 1%:
-         - {sf_filename}: {sf_size}
-         - {pt_filename}: {pt_size}
-         """
-        )
-
-
-def rename(pt_filename: str) -> str:
-    filename, ext = os.path.splitext(pt_filename)
-    local = f"{filename}.safetensors"
-    local = local.replace("pytorch_model", "model")
-    return local
-
-
-def convert_multi(model_id: str, folder: str) -> List["CommitOperationAdd"]:
-    filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin.index.json")
-    with open(filename, "r") as f:
-        data = json.load(f)
-
-    filenames = set(data["weight_map"].values())
-    local_filenames = []
-    for filename in filenames:
-        pt_filename = hf_hub_download(repo_id=model_id, filename=filename)
-
-        sf_filename = rename(pt_filename)
-        sf_filename = os.path.join(folder, sf_filename)
-        convert_file(pt_filename, sf_filename)
-        local_filenames.append(sf_filename)
-
-    index = os.path.join(folder, "model.safetensors.index.json")
-    with open(index, "w") as f:
-        newdata = {k: v for k, v in data.items()}
-        newmap = {k: rename(v) for k, v in data["weight_map"].items()}
-        newdata["weight_map"] = newmap
-        json.dump(newdata, f, indent=4)
-    local_filenames.append(index)
-
-    operations = [
-        CommitOperationAdd(path_in_repo=local.split("/")[-1], path_or_fileobj=local) for local in local_filenames
-    ]
-
-    return operations
-
-
-def convert_single(model_id: str, folder: str) -> List["CommitOperationAdd"]:
-    pt_filename = hf_hub_download(repo_id=model_id, filename="pytorch_model.bin")
-
-    sf_name = "model.safetensors"
-    sf_filename = os.path.join(folder, sf_name)
-    convert_file(pt_filename, sf_filename)
-    operations = [CommitOperationAdd(path_in_repo=sf_name, path_or_fileobj=sf_filename)]
-    return operations
-
-
-def convert_file(
-    pt_filename: str,
-    sf_filename: str,
-):
-    loaded = torch.load(pt_filename, map_location="cpu")
-    if "state_dict" in loaded:
-        loaded = loaded["state_dict"]
-    shared = shared_pointers(loaded)
-    for shared_weights in shared:
-        for name in shared_weights[1:]:
-            loaded.pop(name)
-
-    # For tensors to be contiguous
-    loaded = {k: v.contiguous() for k, v in loaded.items()}
-
-    dirname = os.path.dirname(sf_filename)
-    os.makedirs(dirname, exist_ok=True)
-    save_file(loaded, sf_filename, metadata={"format": "pt"})
-    check_file_size(sf_filename, pt_filename)
-    reloaded = load_file(sf_filename)
-    for k in loaded:
-        pt_tensor = loaded[k]
-        sf_tensor = reloaded[k]
-        if not torch.equal(pt_tensor, sf_tensor):
-            raise RuntimeError(f"The output tensors do not match for key {k}")
-
-
-def create_diff(pt_infos: Dict[str, List[str]], sf_infos: Dict[str, List[str]]) -> str:
-    errors = []
-    for key in ["missing_keys", "mismatched_keys", "unexpected_keys"]:
-        pt_set = set(pt_infos[key])
-        sf_set = set(sf_infos[key])
-
-        pt_only = pt_set - sf_set
-        sf_only = sf_set - pt_set
-
-        if pt_only:
-            errors.append(f"{key} : PT warnings contain {pt_only} which are not present in SF warnings")
-        if sf_only:
-            errors.append(f"{key} : SF warnings contain {sf_only} which are not present in PT warnings")
-    return "\n".join(errors)
-
-
-def check_final_model(model_id: str, folder: str):
-    config = hf_hub_download(repo_id=model_id, filename="config.json")
-    shutil.copy(config, os.path.join(folder, "config.json"))
-    config = AutoConfig.from_pretrained(folder)
-
-    _, (pt_model, pt_infos) = infer_framework_load_model(model_id, config, output_loading_info=True)
-    _, (sf_model, sf_infos) = infer_framework_load_model(folder, config, output_loading_info=True)
-
-    if pt_infos != sf_infos:
-        error_string = create_diff(pt_infos, sf_infos)
-        raise ValueError(f"Different infos when reloading the model: {error_string}")
-
-    pt_params = pt_model.state_dict()
-    sf_params = sf_model.state_dict()
-
-    pt_shared = shared_pointers(pt_params)
-    sf_shared = shared_pointers(sf_params)
-    if pt_shared != sf_shared:
-        raise RuntimeError("The reconstructed model is wrong, shared tensors are different {shared_pt} != {shared_tf}")
-
-    sig = signature(pt_model.forward)
-    input_ids = torch.arange(10).unsqueeze(0)
-    pixel_values = torch.randn(1, 3, 224, 224)
-    input_values = torch.arange(1000).float().unsqueeze(0)
-    kwargs = {}
-    if "input_ids" in sig.parameters:
-        kwargs["input_ids"] = input_ids
-    if "decoder_input_ids" in sig.parameters:
-        kwargs["decoder_input_ids"] = input_ids
-    if "pixel_values" in sig.parameters:
-        kwargs["pixel_values"] = pixel_values
-    if "input_values" in sig.parameters:
-        kwargs["input_values"] = input_values
-    if "bbox" in sig.parameters:
-        kwargs["bbox"] = torch.zeros((1, 10, 4)).long()
-    if "image" in sig.parameters:
-        kwargs["image"] = pixel_values
-
-    if torch.cuda.is_available():
-        pt_model = pt_model.cuda()
-        sf_model = sf_model.cuda()
-        kwargs = {k: v.cuda() for k, v in kwargs.items()}
-
-    pt_logits = pt_model(**kwargs)[0]
-    sf_logits = sf_model(**kwargs)[0]
-
-    torch.testing.assert_close(sf_logits, pt_logits)
-    print(f"Model {model_id} is ok !")
-
-
-def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
-    try:
-        discussions = api.get_repo_discussions(repo_id=model_id)
-    except Exception:
-        return None
-    for discussion in discussions:
-        if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
-            details = api.get_discussion_details(repo_id=model_id, discussion_num=discussion.num)
-            if details.target_branch == "refs/heads/main":
-                return discussion
-
-
-def convert_generic(model_id: str, folder: str, filenames: Set[str]) -> List["CommitOperationAdd"]:
-    operations = []
-
-    extensions = set([".bin", ".ckpt"])
-    for filename in filenames:
-        prefix, ext = os.path.splitext(filename)
-        if ext in extensions:
-            pt_filename = hf_hub_download(model_id, filename=filename)
-            dirname, raw_filename = os.path.split(filename)
-            if raw_filename == "pytorch_model.bin":
-                # XXX: This is a special case to handle `transformers` and the
-                # `transformers` part of the model which is actually loaded by `transformers`.
-                sf_in_repo = os.path.join(dirname, "model.safetensors")
-            else:
-                sf_in_repo = f"{prefix}.safetensors"
-            sf_filename = os.path.join(folder, sf_in_repo)
-            convert_file(pt_filename, sf_filename)
-            operations.append(CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename))
-    return operations
-
-
-def convert(api: "HfApi", model_id: str, force: bool = False) -> Optional["CommitInfo"]:
-    pr_title = "Adding `safetensors` variant of this model"
-    info = api.model_info(model_id)
-    filenames = set(s.rfilename for s in info.siblings)
-
-    with TemporaryDirectory() as d:
-        folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
-        os.makedirs(folder)
-        new_pr = None
-        try:
-            operations = None
-            pr = previous_pr(api, model_id, pr_title)
-
-            library_name = getattr(info, "library_name", None)
-            if any(filename.endswith(".safetensors") for filename in filenames) and not force:
-                raise AlreadyExists(f"Model {model_id} is already converted, skipping..")
-            elif pr is not None and not force:
-                url = f"https://huggingface.co/{model_id}/discussions/{pr.num}"
-                new_pr = pr
-                raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}")
-            elif library_name == "transformers":
-                if "pytorch_model.bin" in filenames:
-                    operations = convert_single(model_id, folder)
-                elif "pytorch_model.bin.index.json" in filenames:
-                    operations = convert_multi(model_id, folder)
-                else:
-                    raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert")
-                check_final_model(model_id, folder)
-            else:
-                operations = convert_generic(model_id, folder, filenames)
-
-            if operations:
-                new_pr = api.create_commit(
-                    repo_id=model_id,
-                    operations=operations,
-                    commit_message=pr_title,
-                    commit_description=COMMIT_DESCRIPTION,
-                    create_pr=True,
-                )
-                print(f"Pr created at {new_pr.pr_url}")
-            else:
-                print("No files to convert")
-        finally:
-            shutil.rmtree(folder)
-        return new_pr
-
-
-if __name__ == "__main__":
-    DESCRIPTION = """
-    Simple utility tool to convert automatically some weights on the hub to `safetensors` format.
-    It is PyTorch exclusive for now.
-    It works by downloading the weights (PT), converting them locally, and uploading them back
-    as a PR on the hub.
-    """
-    parser = argparse.ArgumentParser(description=DESCRIPTION)
-    parser.add_argument(
-        "model_id",
-        type=str,
-        help="The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`",
-    )
-    parser.add_argument(
-        "--force",
-        action="store_true",
-        help="Create the PR even if it already exists of if the model was already converted.",
-    )
-    args = parser.parse_args()
-    model_id = args.model_id
-    api = HfApi()
-    convert(api, model_id, force=args.force)
diff --git a/spaces/DebasishDhal99/Youtube_Playlist/README.md b/spaces/DebasishDhal99/Youtube_Playlist/README.md
deleted file mode 100644
index 069437f99e67013ae51d4d85a85bca30d474eaa9..0000000000000000000000000000000000000000
--- a/spaces/DebasishDhal99/Youtube_Playlist/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: Youtube Playlist
-emoji: 🎥
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: cc
----
-To use this web app on gradio: - https://huggingface.co/spaces/DebasishDhal99/Youtube_Playlist
-# Total duration of playlist
-For a given playlist, it calculates the duration of each public video in that playlist and sums them to produce the total duration.
-[Playlist link](https://youtube.com/playlist?list=PLuhqtP7jdD8CD6rOWy20INGM44kULvrHu&si=G4rrT1wQfQVvzTJF)
-<p align="center">
-  <img src="images/total_duration.png" alt="CloudSat orbit superimposed on INSAT-3DR coverage area.">
-</p>
-
-
-# Average duration of a playlist
-Average duration of videos is calculated for the publicly available videos in that playlist. For example, the average duration of videos in this playlist is around 9 minutes.
-
-<p align="center">
-  <img src="images/average_duration.png" alt="CloudSat orbit superimposed on INSAT-3DR coverage area.">
-</p>
-
-
-# Playlist mismatch
-Given two playlists, this function gets the videos that are present in one of the playlists, but not in the other. 
-The two playlists are given here, [HindiSongs1](https://youtube.com/playlist?list=PLgeEuUJpv5I-jRo3Ibddg96Ke5QRryBQf&si=HZKtxDOm6RbmYieu) and [HindiSongs2](https://youtube.com/playlist?list=PLgeEuUJpv5I-0eV03cUzMAVyHDyVV_43D&si=t8mf-O0CNe23dwlS).
-<p align="center">
-  <img src="images/mismatch.png" alt="CloudSat orbit superimposed on INSAT-3DR coverage area.">
-</p>
-
-
-
-
-
-
-
-
-
-**************************************************************************************************
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DemoLou/moe-tts/export_model.py b/spaces/DemoLou/moe-tts/export_model.py
deleted file mode 100644
index 52d3b3d083df7bf027b46d9c63e399b2da3f0e0a..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/export_model.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import torch
-
-if __name__ == '__main__':
-    model_path = "saved_model/18/model.pth"
-    output_path = "saved_model/18/model1.pth"
-    checkpoint_dict = torch.load(model_path, map_location='cpu')
-    checkpoint_dict_new = {}
-    for k, v in checkpoint_dict.items():
-        if k == "optimizer":
-            print("remove optimizer")
-            continue
-        checkpoint_dict_new[k] = v
-    torch.save(checkpoint_dict_new, output_path)
diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.c b/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.c
deleted file mode 100644
index 1e652963cdb76fe628d0a33bc270d2c25a0f3770..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.c
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * File   : prroi_pooling_gpu.c
- * Author : Jiayuan Mao, Tete Xiao
- * Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com
- * Date   : 07/13/2018
- *
- * Distributed under terms of the MIT license.
- * Copyright (c) 2017 Megvii Technology Limited.
- */
-
-#include <math.h>
-#include <torch/extension.h>
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <THC/THC.h>
-
-#include "prroi_pooling_gpu_impl.cuh"
-
-
-at::Tensor prroi_pooling_forward_cuda(const at::Tensor &features, const at::Tensor &rois, int pooled_height, int pooled_width, float spatial_scale) {
-    int nr_rois = rois.size(0);
-    int nr_channels = features.size(1);
-    int height = features.size(2);
-    int width = features.size(3);
-    int top_count = nr_rois * nr_channels * pooled_height * pooled_width;
-    auto output = at::zeros({nr_rois, nr_channels, pooled_height, pooled_width}, features.options());
-
-    if (output.numel() == 0) {
-        THCudaCheck(cudaGetLastError());
-        return output;
-    }
-
-    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-    PrRoIPoolingForwardGpu(
-        stream, features.data<float>(), rois.data<float>(), output.data<float>(),
-        nr_channels, height, width, pooled_height, pooled_width, spatial_scale,
-        top_count
-    );
-
-    THCudaCheck(cudaGetLastError());
-    return output;
-}
-
-at::Tensor prroi_pooling_backward_cuda(
-    const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff,
-    int pooled_height, int pooled_width, float spatial_scale) {
-
-    auto features_diff = at::zeros_like(features);
-
-    int nr_rois = rois.size(0);
-    int batch_size = features.size(0);
-    int nr_channels = features.size(1);
-    int height = features.size(2);
-    int width = features.size(3);
-    int top_count = nr_rois * nr_channels * pooled_height * pooled_width;
-    int bottom_count = batch_size * nr_channels * height * width;
-
-    if (output.numel() == 0) {
-        THCudaCheck(cudaGetLastError());
-        return features_diff;
-    }
-
-    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-    PrRoIPoolingBackwardGpu(
-        stream,
-        features.data<float>(), rois.data<float>(), output.data<float>(), output_diff.data<float>(),
-        features_diff.data<float>(),
-        nr_channels, height, width, pooled_height, pooled_width, spatial_scale,
-        top_count, bottom_count
-    );
-
-    THCudaCheck(cudaGetLastError());
-    return features_diff;
-}
-
-at::Tensor prroi_pooling_coor_backward_cuda(
-    const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff,
-    int pooled_height, int pooled_width, float spatial_scale) {
-
-    auto coor_diff = at::zeros_like(rois);
-
-    int nr_rois = rois.size(0);
-    int nr_channels = features.size(1);
-    int height = features.size(2);
-    int width = features.size(3);
-    int top_count = nr_rois * nr_channels * pooled_height * pooled_width;
-    int bottom_count = nr_rois * 5;
-
-    if (output.numel() == 0) {
-        THCudaCheck(cudaGetLastError());
-        return coor_diff;
-    }
-
-    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-    PrRoIPoolingCoorBackwardGpu(
-        stream,
-        features.data<float>(), rois.data<float>(), output.data<float>(), output_diff.data<float>(),
-        coor_diff.data<float>(),
-        nr_channels, height, width, pooled_height, pooled_width, spatial_scale,
-        top_count, bottom_count
-    );
-
-    THCudaCheck(cudaGetLastError());
-    return coor_diff;
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("prroi_pooling_forward_cuda", &prroi_pooling_forward_cuda, "PRRoIPooling_forward");
-    m.def("prroi_pooling_backward_cuda", &prroi_pooling_backward_cuda, "PRRoIPooling_backward");
-    m.def("prroi_pooling_coor_backward_cuda", &prroi_pooling_coor_backward_cuda, "PRRoIPooling_backward_coor");
-}
diff --git a/spaces/DragGan/DragGan/gradio_utils/utils.py b/spaces/DragGan/DragGan/gradio_utils/utils.py
deleted file mode 100644
index d4e760e1515f3f69b11d11426ac3e8fa51f1a99c..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/gradio_utils/utils.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import gradio as gr
-import numpy as np
-from PIL import Image, ImageDraw
-
-
-class ImageMask(gr.components.Image):
-    """
-    Sets: source="canvas", tool="sketch"
-    """
-
-    is_template = True
-
-    def __init__(self, **kwargs):
-        super().__init__(source="upload",
-                         tool="sketch",
-                         interactive=False,
-                         **kwargs)
-
-    def preprocess(self, x):
-        if x is None:
-            return x
-        if self.tool == "sketch" and self.source in ["upload", "webcam"
-                                                     ] and type(x) != dict:
-            decode_image = gr.processing_utils.decode_base64_to_image(x)
-            width, height = decode_image.size
-            mask = np.ones((height, width, 4), dtype=np.uint8)
-            mask[..., -1] = 255
-            mask = self.postprocess(mask)
-            x = {'image': x, 'mask': mask}
-        return super().preprocess(x)
-
-
-def get_valid_mask(mask: np.ndarray):
-    """Convert mask from gr.Image(0 to 255, RGBA) to binary mask.
-    """
-    if mask.ndim == 3:
-        mask_pil = Image.fromarray(mask).convert('L')
-        mask = np.array(mask_pil)
-    if mask.max() == 255:
-        mask = mask / 255
-    return mask
-
-
-def draw_points_on_image(image,
-                         points,
-                         curr_point=None,
-                         highlight_all=True,
-                         radius_scale=0.01):
-    overlay_rgba = Image.new("RGBA", image.size, 0)
-    overlay_draw = ImageDraw.Draw(overlay_rgba)
-    for point_key, point in points.items():
-        if ((curr_point is not None and curr_point == point_key)
-                or highlight_all):
-            p_color = (255, 0, 0)
-            t_color = (0, 0, 255)
-
-        else:
-            p_color = (255, 0, 0, 35)
-            t_color = (0, 0, 255, 35)
-
-        rad_draw = int(image.size[0] * radius_scale)
-
-        p_start = point.get("start_temp", point["start"])
-        p_target = point["target"]
-
-        if p_start is not None and p_target is not None:
-            p_draw = int(p_start[0]), int(p_start[1])
-            t_draw = int(p_target[0]), int(p_target[1])
-
-            overlay_draw.line(
-                (p_draw[0], p_draw[1], t_draw[0], t_draw[1]),
-                fill=(255, 255, 0),
-                width=2,
-            )
-
-        if p_start is not None:
-            p_draw = int(p_start[0]), int(p_start[1])
-            overlay_draw.ellipse(
-                (
-                    p_draw[0] - rad_draw,
-                    p_draw[1] - rad_draw,
-                    p_draw[0] + rad_draw,
-                    p_draw[1] + rad_draw,
-                ),
-                fill=p_color,
-            )
-
-            if curr_point is not None and curr_point == point_key:
-                # overlay_draw.text(p_draw, "p", font=font, align="center", fill=(0, 0, 0))
-                overlay_draw.text(p_draw, "p", align="center", fill=(0, 0, 0))
-
-        if p_target is not None:
-            t_draw = int(p_target[0]), int(p_target[1])
-            overlay_draw.ellipse(
-                (
-                    t_draw[0] - rad_draw,
-                    t_draw[1] - rad_draw,
-                    t_draw[0] + rad_draw,
-                    t_draw[1] + rad_draw,
-                ),
-                fill=t_color,
-            )
-
-            if curr_point is not None and curr_point == point_key:
-                # overlay_draw.text(t_draw, "t", font=font, align="center", fill=(0, 0, 0))
-                overlay_draw.text(t_draw, "t", align="center", fill=(0, 0, 0))
-
-    return Image.alpha_composite(image.convert("RGBA"),
-                                 overlay_rgba).convert("RGB")
-
-
-def draw_mask_on_image(image, mask):
-    im_mask = np.uint8(mask * 255)
-    im_mask_rgba = np.concatenate(
-        (
-            np.tile(im_mask[..., None], [1, 1, 3]),
-            45 * np.ones(
-                (im_mask.shape[0], im_mask.shape[1], 1), dtype=np.uint8),
-        ),
-        axis=-1,
-    )
-    im_mask_rgba = Image.fromarray(im_mask_rgba).convert("RGBA")
-
-    return Image.alpha_composite(image.convert("RGBA"),
-                                 im_mask_rgba).convert("RGB")
-
-
-def on_change_single_global_state(keys,
-                                  value,
-                                  global_state,
-                                  map_transform=None):
-    if map_transform is not None:
-        value = map_transform(value)
-
-    curr_state = global_state
-    if isinstance(keys, str):
-        last_key = keys
-
-    else:
-        for k in keys[:-1]:
-            curr_state = curr_state[k]
-
-        last_key = keys[-1]
-
-    curr_state[last_key] = value
-    return global_state
-
-
-def get_latest_points_pair(points_dict):
-    if not points_dict:
-        return None
-    point_idx = list(points_dict.keys())
-    latest_point_idx = max(point_idx)
-    return latest_point_idx
diff --git a/spaces/Duskfallcrew/duskfallai_webui/README.md b/spaces/Duskfallcrew/duskfallai_webui/README.md
deleted file mode 100644
index f4185dcbbd19d23bdbbff529b980ef40ec4914b5..0000000000000000000000000000000000000000
--- a/spaces/Duskfallcrew/duskfallai_webui/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Duskfallai Webui
-emoji: 🏃
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/EPFL-VILAB/MultiMAE/utils/datasets_semseg.py b/spaces/EPFL-VILAB/MultiMAE/utils/datasets_semseg.py
deleted file mode 100644
index e7960e12113a44d5d8ce658e7225e961ea8f4e71..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/utils/datasets_semseg.py
+++ /dev/null
@@ -1,235 +0,0 @@
-# Copyright (c) EPFL VILAB.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# Based on BEiT, timm, DINO, DeiT and MAE-priv code bases
-# https://github.com/microsoft/unilm/tree/master/beit
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/facebookresearch/deit
-# https://github.com/facebookresearch/dino
-# https://github.com/BUPT-PRIV/MAE-priv
-# --------------------------------------------------------
-from typing import Dict, Tuple
-
-import numpy as np
-import torch
-
-try:
-    import albumentations as A
-    from albumentations.pytorch import ToTensorV2
-except:
-    print('albumentations not installed')
-import cv2
-import torch.nn.functional as F
-
-from utils import (IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PAD_MASK_VALUE,
-                   SEG_IGNORE_INDEX)
-
-from .dataset_folder import ImageFolder, MultiTaskImageFolder
-
-
-def simple_transform(train: bool,
-                     additional_targets: Dict[str, str],
-                     input_size: int =512,
-                     pad_value: Tuple[int, int, int] = (128, 128, 128),
-                     pad_mask_value: int =PAD_MASK_VALUE):
-    """Default transform for semantic segmentation, applied on all modalities
-
-    During training:
-        1. Random horizontal Flip
-        2. Rescaling so that longest side matches input size
-        3. Color jitter (for RGB-modality only)
-        4. Large scale jitter (LSJ)
-        5. Padding
-        6. Random crop to given size
-        7. Normalization with ImageNet mean and std dev
-
-    During validation / test:
-        1. Rescaling so that longest side matches given size
-        2. Padding
-        3. Normalization with ImageNet mean and std dev
-     """
-
-    if train:
-        transform = A.Compose([
-            A.HorizontalFlip(p=0.5),
-            A.LongestMaxSize(max_size=input_size, p=1),
-            A.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.5),  # Color jittering from MoCo-v3 / DINO
-            A.RandomScale(scale_limit=(0.1 - 1, 2.0 - 1), p=1),  # This is LSJ (0.1, 2.0)
-            A.PadIfNeeded(min_height=input_size, min_width=input_size,
-                          position=A.augmentations.PadIfNeeded.PositionType.TOP_LEFT,
-                          border_mode=cv2.BORDER_CONSTANT,
-                          value=pad_value, mask_value=pad_mask_value),
-            A.RandomCrop(height=input_size, width=input_size, p=1),
-            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-            ToTensorV2(),
-        ], additional_targets=additional_targets)
-
-    else:
-        transform = A.Compose([
-            A.LongestMaxSize(max_size=input_size, p=1),
-            A.PadIfNeeded(min_height=input_size, min_width=input_size,
-                          position=A.augmentations.PadIfNeeded.PositionType.TOP_LEFT,
-                          border_mode=cv2.BORDER_CONSTANT,
-                          value=pad_value, mask_value=pad_mask_value),
-            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
-            ToTensorV2(),
-        ], additional_targets=additional_targets)
-
-    return transform
-
-
-class DataAugmentationForSemSeg(object):
-    """Data transform / augmentation for semantic segmentation downstream tasks.
-    """
-
-    def __init__(self, transform, seg_num_classes, seg_ignore_index=SEG_IGNORE_INDEX, standardize_depth=True,
-                 seg_reduce_zero_label=False, seg_use_void_label=False):
-
-        self.transform = transform
-        self.seg_num_classes = seg_num_classes
-        self.seg_ignore_index = seg_ignore_index
-        self.standardize_depth = standardize_depth
-        self.seg_reduce_zero_label = seg_reduce_zero_label
-        self.seg_use_void_label = seg_use_void_label
-
-    @staticmethod
-    def standardize_depth_map(img, mask_valid=None, trunc_value=0.1):
-        img[img == PAD_MASK_VALUE] = torch.nan
-        if mask_valid is not None:
-            # This is if we want to apply masking before standardization
-            img[~mask_valid] = torch.nan
-        sorted_img = torch.sort(torch.flatten(img))[0]
-        # Remove nan, nan at the end of sort
-        num_nan = sorted_img.isnan().sum()
-        if num_nan > 0:
-            sorted_img = sorted_img[:-num_nan]
-        # Remove outliers
-        trunc_img = sorted_img[int(trunc_value * len(sorted_img)): int((1 - trunc_value) * len(sorted_img))]
-        trunc_mean = trunc_img.mean()
-        trunc_var = trunc_img.var()
-        eps = 1e-6
-        # Replace nan by mean
-        img = torch.nan_to_num(img, nan=trunc_mean)
-        # Standardize
-        img = (img - trunc_mean) / torch.sqrt(trunc_var + eps)
-        return img
-
-    def seg_adapt_labels(self, img):
-        if self.seg_use_void_label:
-            # Set void label to num_classes
-            if self.seg_reduce_zero_label:
-                pad_replace = self.seg_num_classes + 1
-            else:
-                pad_replace = self.seg_num_classes
-        else:
-            pad_replace = self.seg_ignore_index
-        img[img == PAD_MASK_VALUE] = pad_replace
-
-        if self.seg_reduce_zero_label:
-            img[img == 0] = self.seg_ignore_index
-            img = img - 1
-            img[img == self.seg_ignore_index - 1] = self.seg_ignore_index
-
-        return img
-
-    def __call__(self, task_dict):
-
-        # Need to replace rgb key to image
-        task_dict['image'] = task_dict.pop('rgb')
-        # Convert to np.array
-        task_dict = {k: np.array(v) for k, v in task_dict.items()}
-
-        task_dict = self.transform(**task_dict)
-
-        # And then replace it back to rgb
-        task_dict['rgb'] = task_dict.pop('image')
-
-        for task in task_dict:
-            if task in ['depth']:
-                img = task_dict[task].to(torch.float)
-                if self.standardize_depth:
-                    # Mask valid set to None here, as masking is applied after standardization
-                    img = self.standardize_depth_map(img, mask_valid=None)
-                if 'mask_valid' in task_dict:
-                    mask_valid = (task_dict['mask_valid'] == 255).squeeze()
-                    img[~mask_valid] = 0.0
-                task_dict[task] = img.unsqueeze(0)
-            elif task in ['rgb']:
-                task_dict[task] = task_dict[task].to(torch.float)
-            elif task in ['semseg']:
-                img = task_dict[task].to(torch.long)
-                img = self.seg_adapt_labels(img)
-                task_dict[task] = img
-            elif task in ['pseudo_semseg']:
-                # If it's pseudo-semseg, then it's an input modality and should therefore be resized
-                img = task_dict[task]
-                img = F.interpolate(img[None,None,:,:], scale_factor=0.25, mode='nearest').long()[0,0]
-                task_dict[task] = img
-
-        return task_dict
-
-
-def build_semseg_dataset(args, data_path, transform, max_images=None):
-    transform = DataAugmentationForSemSeg(transform=transform, seg_num_classes=args.num_classes,
-                                          standardize_depth=args.standardize_depth,
-                                          seg_reduce_zero_label=args.seg_reduce_zero_label,
-                                          seg_use_void_label=args.seg_use_void_label)
-    prefixes = {'depth': 'pseudo_'} if args.load_pseudo_depth else None
-    return MultiTaskImageFolder(data_path, args.all_domains, transform=transform, prefixes=prefixes, max_images=max_images)
-
-
-def ade_classes():
-    """ADE20K class names for external use."""
-    return [
-        'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
-        'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
-        'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
-        'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
-        'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
-        'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
-        'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
-        'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
-        'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
-        'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
-        'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
-        'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
-        'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
-        'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
-        'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
-        'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
-        'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
-        'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
-        'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
-        'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
-        'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
-        'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
-        'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
-        'clock', 'flag'
-    ]
-
-
-def hypersim_classes():
-    """Hypersim class names for external use."""
-    return [
-        'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 
-        'window', 'bookshelf', 'picture', 'counter', 'blinds', 'desk', 'shelves', 
-        'curtain', 'dresser', 'pillow', 'mirror', 'floor-mat', 'clothes', 
-        'ceiling', 'books', 'fridge', 'TV', 'paper', 'towel', 'shower-curtain', 
-        'box', 'white-board', 'person', 'night-stand', 'toilet', 'sink', 'lamp',
-        'bathtub', 'bag', 'other-struct', 'other-furntr', 'other-prop'
-    ]
-
-
-def nyu_v2_40_classes():
-    """NYUv2 40 class names for external use."""
-    return [
-        'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 
-        'window', 'bookshelf', 'picture', 'counter', 'blinds', 'desk', 'shelves', 
-        'curtain', 'dresser', 'pillow', 'mirror', 'floor-mat', 'clothes', 
-        'ceiling', 'books', 'fridge', 'TV', 'paper', 'towel', 'shower-curtain', 
-        'box', 'white-board', 'person', 'night-stand', 'toilet', 'sink', 'lamp',
-        'bathtub', 'bag', 'other-struct', 'other-furntr', 'other-prop'
-    ]
diff --git a/spaces/Eddycrack864/Applio-Inference/demucs/parser.py b/spaces/Eddycrack864/Applio-Inference/demucs/parser.py
deleted file mode 100644
index 4e8a19cf976e3c6dfe411da64b8dce3e9a4548e0..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/demucs/parser.py
+++ /dev/null
@@ -1,244 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import os
-from pathlib import Path
-
-
-def get_parser():
-    parser = argparse.ArgumentParser("demucs", description="Train and evaluate Demucs.")
-    default_raw = None
-    default_musdb = None
-    if 'DEMUCS_RAW' in os.environ:
-        default_raw = Path(os.environ['DEMUCS_RAW'])
-    if 'DEMUCS_MUSDB' in os.environ:
-        default_musdb = Path(os.environ['DEMUCS_MUSDB'])
-    parser.add_argument(
-        "--raw",
-        type=Path,
-        default=default_raw,
-        help="Path to raw audio, can be faster, see python3 -m demucs.raw to extract.")
-    parser.add_argument("--no_raw", action="store_const", const=None, dest="raw")
-    parser.add_argument("-m",
-                        "--musdb",
-                        type=Path,
-                        default=default_musdb,
-                        help="Path to musdb root")
-    parser.add_argument("--is_wav", action="store_true",
-                        help="Indicate that the MusDB dataset is in wav format (i.e. MusDB-HQ).")
-    parser.add_argument("--metadata", type=Path, default=Path("metadata/"),
-                        help="Folder where metadata information is stored.")
-    parser.add_argument("--wav", type=Path,
-                        help="Path to a wav dataset. This should contain a 'train' and a 'valid' "
-                             "subfolder.")
-    parser.add_argument("--samplerate", type=int, default=44100)
-    parser.add_argument("--audio_channels", type=int, default=2)
-    parser.add_argument("--samples",
-                        default=44100 * 10,
-                        type=int,
-                        help="number of samples to feed in")
-    parser.add_argument("--data_stride",
-                        default=44100,
-                        type=int,
-                        help="Stride for chunks, shorter = longer epochs")
-    parser.add_argument("-w", "--workers", default=10, type=int, help="Loader workers")
-    parser.add_argument("--eval_workers", default=2, type=int, help="Final evaluation workers")
-    parser.add_argument("-d",
-                        "--device",
-                        help="Device to train on, default is cuda if available else cpu")
-    parser.add_argument("--eval_cpu", action="store_true", help="Eval on test will be run on cpu.")
-    parser.add_argument("--dummy", help="Dummy parameter, useful to create a new checkpoint file")
-    parser.add_argument("--test", help="Just run the test pipeline + one validation. "
-                                       "This should be a filename relative to the models/ folder.")
-    parser.add_argument("--test_pretrained", help="Just run the test pipeline + one validation, "
-                                                  "on a pretrained model. ")
-
-    parser.add_argument("--rank", default=0, type=int)
-    parser.add_argument("--world_size", default=1, type=int)
-    parser.add_argument("--master")
-
-    parser.add_argument("--checkpoints",
-                        type=Path,
-                        default=Path("checkpoints"),
-                        help="Folder where to store checkpoints etc")
-    parser.add_argument("--evals",
-                        type=Path,
-                        default=Path("evals"),
-                        help="Folder where to store evals and waveforms")
-    parser.add_argument("--save",
-                        action="store_true",
-                        help="Save estimated for the test set waveforms")
-    parser.add_argument("--logs",
-                        type=Path,
-                        default=Path("logs"),
-                        help="Folder where to store logs")
-    parser.add_argument("--models",
-                        type=Path,
-                        default=Path("models"),
-                        help="Folder where to store trained models")
-    parser.add_argument("-R",
-                        "--restart",
-                        action='store_true',
-                        help='Restart training, ignoring previous run')
-
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("-e", "--epochs", type=int, default=180, help="Number of epochs")
-    parser.add_argument("-r",
-                        "--repeat",
-                        type=int,
-                        default=2,
-                        help="Repeat the train set, longer epochs")
-    parser.add_argument("-b", "--batch_size", type=int, default=64)
-    parser.add_argument("--lr", type=float, default=3e-4)
-    parser.add_argument("--mse", action="store_true", help="Use MSE instead of L1")
-    parser.add_argument("--init", help="Initialize from a pre-trained model.")
-
-    # Augmentation options
-    parser.add_argument("--no_augment",
-                        action="store_false",
-                        dest="augment",
-                        default=True,
-                        help="No basic data augmentation.")
-    parser.add_argument("--repitch", type=float, default=0.2,
-                        help="Probability to do tempo/pitch change")
-    parser.add_argument("--max_tempo", type=float, default=12,
-                        help="Maximum relative tempo change in %% when using repitch.")
-
-    parser.add_argument("--remix_group_size",
-                        type=int,
-                        default=4,
-                        help="Shuffle sources using group of this size. Useful to somewhat "
-                        "replicate multi-gpu training "
-                        "on less GPUs.")
-    parser.add_argument("--shifts",
-                        type=int,
-                        default=10,
-                        help="Number of random shifts used for the shift trick.")
-    parser.add_argument("--overlap",
-                        type=float,
-                        default=0.25,
-                        help="Overlap when --split_valid is passed.")
-
-    # See model.py for doc
-    parser.add_argument("--growth",
-                        type=float,
-                        default=2.,
-                        help="Number of channels between two layers will increase by this factor")
-    parser.add_argument("--depth",
-                        type=int,
-                        default=6,
-                        help="Number of layers for the encoder and decoder")
-    parser.add_argument("--lstm_layers", type=int, default=2, help="Number of layers for the LSTM")
-    parser.add_argument("--channels",
-                        type=int,
-                        default=64,
-                        help="Number of channels for the first encoder layer")
-    parser.add_argument("--kernel_size",
-                        type=int,
-                        default=8,
-                        help="Kernel size for the (transposed) convolutions")
-    parser.add_argument("--conv_stride",
-                        type=int,
-                        default=4,
-                        help="Stride for the (transposed) convolutions")
-    parser.add_argument("--context",
-                        type=int,
-                        default=3,
-                        help="Context size for the decoder convolutions "
-                        "before the transposed convolutions")
-    parser.add_argument("--rescale",
-                        type=float,
-                        default=0.1,
-                        help="Initial weight rescale reference")
-    parser.add_argument("--no_resample", action="store_false",
-                        default=True, dest="resample",
-                        help="No Resampling of the input/output x2")
-    parser.add_argument("--no_glu",
-                        action="store_false",
-                        default=True,
-                        dest="glu",
-                        help="Replace all GLUs by ReLUs")
-    parser.add_argument("--no_rewrite",
-                        action="store_false",
-                        default=True,
-                        dest="rewrite",
-                        help="No 1x1 rewrite convolutions")
-    parser.add_argument("--normalize", action="store_true")
-    parser.add_argument("--no_norm_wav", action="store_false", dest='norm_wav', default=True)
-
-    # Tasnet options
-    parser.add_argument("--tasnet", action="store_true")
-    parser.add_argument("--split_valid",
-                        action="store_true",
-                        help="Predict chunks by chunks for valid and test. Required for tasnet")
-    parser.add_argument("--X", type=int, default=8)
-
-    # Other options
-    parser.add_argument("--show",
-                        action="store_true",
-                        help="Show model architecture, size and exit")
-    parser.add_argument("--save_model", action="store_true",
-                        help="Skip traning, just save final model "
-                             "for the current checkpoint value.")
-    parser.add_argument("--save_state",
-                        help="Skip training, just save state "
-                             "for the current checkpoint value. You should "
-                             "provide a model name as argument.")
-
-    # Quantization options
-    parser.add_argument("--q-min-size", type=float, default=1,
-                        help="Only quantize layers over this size (in MB)")
-    parser.add_argument(
-        "--qat", type=int, help="If provided, use QAT training with that many bits.")
-
-    parser.add_argument("--diffq", type=float, default=0)
-    parser.add_argument(
-        "--ms-target", type=float, default=162,
-        help="Model size target in MB, when using DiffQ. Best model will be kept "
-             "only if it is smaller than this target.")
-
-    return parser
-
-
-def get_name(parser, args):
-    """
-    Return the name of an experiment given the args. Some parameters are ignored,
-    for instance --workers, as they do not impact the final result.
-    """
-    ignore_args = set([
-        "checkpoints",
-        "deterministic",
-        "eval",
-        "evals",
-        "eval_cpu",
-        "eval_workers",
-        "logs",
-        "master",
-        "rank",
-        "restart",
-        "save",
-        "save_model",
-        "save_state",
-        "show",
-        "workers",
-        "world_size",
-    ])
-    parts = []
-    name_args = dict(args.__dict__)
-    for name, value in name_args.items():
-        if name in ignore_args:
-            continue
-        if value != parser.get_default(name):
-            if isinstance(value, Path):
-                parts.append(f"{name}={value.name}")
-            else:
-                parts.append(f"{name}={value}")
-    if parts:
-        name = " ".join(parts)
-    else:
-        name = "default"
-    return name
diff --git a/spaces/EleutherAI/magma/magma/datasets/__init__.py b/spaces/EleutherAI/magma/magma/datasets/__init__.py
deleted file mode 100644
index 2b85c035fb560bbaf3a8419df34e1e60f58c4183..0000000000000000000000000000000000000000
--- a/spaces/EleutherAI/magma/magma/datasets/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .dataset import (
-    ImgCptDataset,
-    collate_fn,
-)
-
diff --git a/spaces/EuroPython2022/rev/README.md b/spaces/EuroPython2022/rev/README.md
deleted file mode 100644
index 74b6245811ede6b25fe0754b26ca14a11cbf718e..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/rev/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Rev
-emoji: 🐠
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.0.26
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/FacundoSander/PdfQA/README.md b/spaces/FacundoSander/PdfQA/README.md
deleted file mode 100644
index b32409b9d100eeec84e62d4eda234f000c418d9d..0000000000000000000000000000000000000000
--- a/spaces/FacundoSander/PdfQA/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: PdfQA
-emoji: 🐨
-colorFrom: pink
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Felix123456/bingo/src/components/ui/button.tsx b/spaces/Felix123456/bingo/src/components/ui/button.tsx
deleted file mode 100644
index 281da005124fa94c89a9a9db7605748a92b60865..0000000000000000000000000000000000000000
--- a/spaces/Felix123456/bingo/src/components/ui/button.tsx
+++ /dev/null
@@ -1,57 +0,0 @@
-import * as React from 'react'
-import { Slot } from '@radix-ui/react-slot'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const buttonVariants = cva(
-  'inline-flex items-center justify-center rounded-md text-sm font-medium shadow ring-offset-background transition-colors outline-none disabled:pointer-events-none disabled:opacity-50',
-  {
-    variants: {
-      variant: {
-        default:
-          'bg-primary text-primary-foreground shadow-md hover:bg-primary/90',
-        destructive:
-          'bg-destructive text-destructive-foreground hover:bg-destructive/90',
-        outline:
-          'border border-input hover:bg-accent hover:text-accent-foreground',
-        secondary:
-          'bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        ghost: 'shadow-none hover:bg-accent hover:text-accent-foreground',
-        link: 'text-primary underline-offset-4 shadow-none hover:underline'
-      },
-      size: {
-        default: 'h-8 px-4 py-2',
-        sm: 'h-8 rounded-md px-3',
-        lg: 'h-11 rounded-md px-8',
-        icon: 'h-8 w-8 p-0'
-      }
-    },
-    defaultVariants: {
-      variant: 'default',
-      size: 'default'
-    }
-  }
-)
-
-export interface ButtonProps
-  extends React.ButtonHTMLAttributes<HTMLButtonElement>,
-    VariantProps<typeof buttonVariants> {
-  asChild?: boolean
-}
-
-const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
-  ({ className, variant, size, asChild = false, ...props }, ref) => {
-    const Comp = asChild ? Slot : 'button'
-    return (
-      <Comp
-        className={cn(buttonVariants({ variant, size, className }))}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Button.displayName = 'Button'
-
-export { Button, buttonVariants }
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/thai.py b/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/GT-RIPL/GPT-K/knowledge/text_db.py b/spaces/GT-RIPL/GPT-K/knowledge/text_db.py
deleted file mode 100644
index 4aa7b48a4735116ad587877c2cafad374fc59d02..0000000000000000000000000000000000000000
--- a/spaces/GT-RIPL/GPT-K/knowledge/text_db.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import h5py
-from tqdm import tqdm
-import numpy as np
-import codecs
-from knowledge.utils import file_hash
-
-
-class TextDB:
-    def __init__(self, text_db):
-        self.feature, self.text = self.load(text_db)
-        self.file_hash = file_hash(text_db)
-
-    def load(self, text_db):
-        with h5py.File(text_db, 'r') as f:
-            db_size = 0
-            for i in range(len(f)):
-                db_size += len(f[f"{i}/feature"])
-            _, d = f[f"0/feature"].shape
-
-        with h5py.File(text_db, 'r') as f:
-            feature = np.zeros((db_size, d), dtype=np.float16)
-            text = []
-            N = 0
-            for i in tqdm(range(len(f)), desc="Load text DB", dynamic_ncols=True, mininterval=1.0):
-                fi = f[f"{i}/feature"][:]
-                feature[N:N+len(fi)] = fi
-                N += len(fi)
-
-                text.extend(f[f"{i}/text"][:])
-        text = [codecs.decode(t) for t in text]
-
-        return feature, text
-    
-    def __getitem__(self, idx):
-        f = self.feature[idx]
-
-        try:
-            t = [self.text[i] for i in idx]
-        except TypeError:
-            t = self.text[idx]
-        
-        return f, t
-
diff --git a/spaces/Gallifraid/prompthero-openjourney-v2/README.md b/spaces/Gallifraid/prompthero-openjourney-v2/README.md
deleted file mode 100644
index b03afeb990d4aa557f0b7d7816cca9e98bea72ab..0000000000000000000000000000000000000000
--- a/spaces/Gallifraid/prompthero-openjourney-v2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Prompthero Openjourney V2
-emoji: 😻
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GastonMazzei/escher-inpaint-project/glide_text2im/tokenizer/__init__.py b/spaces/GastonMazzei/escher-inpaint-project/glide_text2im/tokenizer/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/rn50_bert_lingunet_lat.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/rn50_bert_lingunet_lat.py
deleted file mode 100644
index df0c39d59ce60f33348eaffb264202cb731dff92..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/rn50_bert_lingunet_lat.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.models as models
-
-import cliport.utils.utils as utils
-from transformers import DistilBertTokenizer, DistilBertModel
-from cliport.models.resnet import IdentityBlock, ConvBlock
-from cliport.models.core.unet import Up
-
-from cliport.models.core import fusion
-from cliport.models.core.fusion import FusionConvLat
-
-
-class RN50BertLingUNetLat(nn.Module):
-    """ ImageNet RN50 & Bert with U-Net skip connections """
-
-    def __init__(self, input_shape, output_dim, cfg, device, preprocess):
-        super(RN50BertLingUNetLat, self).__init__()
-        self.input_shape = input_shape
-        self.output_dim = output_dim
-        self.input_dim = 2048
-        self.cfg = cfg
-        self.batchnorm = self.cfg['train']['batchnorm']
-        self.lang_fusion_type = self.cfg['train']['lang_fusion_type']
-        self.bilinear = True
-        self.up_factor = 2 if self.bilinear else 1
-        self.device = device
-        self.preprocess = preprocess
-
-        self._load_vision_fcn()
-        self._load_lang_enc()
-        self._build_decoder()
-
-    def _load_vision_fcn(self):
-        resnet50 = models.resnet50(pretrained=True)
-        modules = list(resnet50.children())[:-2]
-
-        self.stem = nn.Sequential(*modules[:4])
-        self.layer1 = modules[4]
-        self.layer2 = modules[5]
-        self.layer3 = modules[6]
-        self.layer4 = modules[7]
-
-    def _load_lang_enc(self):
-        self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-        self.text_encoder = DistilBertModel.from_pretrained('distilbert-base-uncased')
-        self.text_fc = nn.Linear(768, 1024)
-
-        self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2)
-        self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4)
-        self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8)
-
-        self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024
-        self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024)
-        self.lang_proj2 = nn.Linear(self.proj_input_dim, 512)
-        self.lang_proj3 = nn.Linear(self.proj_input_dim, 256)
-
-    def _build_decoder(self):
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False),
-            nn.ReLU(True)
-        )
-        self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear)
-        self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512)
-
-        self.up2 = Up(1024, 512 // self.up_factor, self.bilinear)
-        self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256)
-
-        self.up3 = Up(512, 256 // self.up_factor, self.bilinear)
-        self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128)
-
-        self.layer1 =  nn.Sequential(
-            ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-        self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64)
-
-        self.layer2 = nn.Sequential(
-            ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-        self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32)
-
-        self.layer3 = nn.Sequential(
-            ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-        self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16)
-
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(16, self.output_dim, kernel_size=1)
-        )
-
-    def resnet50(self, x):
-        im = []
-        for layer in [self.stem, self.layer1, self.layer2, self.layer3, self.layer4]:
-            x = layer(x)
-            im.append(x)
-        return x, im
-
-    def encode_image(self, img):
-        with torch.no_grad():
-            img_encoding, img_im = self.resnet50(img)
-        return img_encoding, img_im
-
-    def encode_text(self, x):
-        with torch.no_grad():
-            inputs = self.tokenizer(x, return_tensors='pt')
-            input_ids, attention_mask = inputs['input_ids'].to(self.device), inputs['attention_mask'].to(self.device)
-            text_embeddings = self.text_encoder(input_ids, attention_mask)
-            text_encodings = text_embeddings.last_hidden_state.mean(1)
-        text_feat = self.text_fc(text_encodings)
-        text_mask = torch.ones_like(input_ids) # [1, max_token_len]
-        return text_feat, text_embeddings.last_hidden_state, text_mask
-
-    def forward(self, x, lat, l):
-        x = self.preprocess(x, dist='clip')
-
-        in_type = x.dtype
-        in_shape = x.shape
-        x = x[:,:3]  # select RGB
-        x, im = self.encode_image(x)
-        x = x.to(in_type)
-
-        l_enc, l_emb, l_mask = self.encode_text(l)
-        l_input = l_emb if 'word' in self.lang_fusion_type else l_enc
-        l_input = l_input.to(dtype=x.dtype)
-
-        assert x.shape[1] == self.input_dim
-        x = self.conv1(x)
-
-        x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1)
-        x = self.up1(x, im[-2])
-        x = self.lat_fusion1(x, lat[-6])
-
-        x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2)
-        x = self.up2(x, im[-3])
-        x = self.lat_fusion2(x, lat[-5])
-
-        x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3)
-        x = self.up3(x, im[-4])
-        x = self.lat_fusion3(x, lat[-4])
-
-        x = self.layer1(x)
-        x = self.lat_fusion4(x, lat[-3])
-
-        x = self.layer2(x)
-        x = self.lat_fusion5(x, lat[-2])
-
-        x = self.layer3(x)
-        x = self.lat_fusion6(x, lat[-1])
-
-        x = self.conv2(x)
-        x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
-        return x
\ No newline at end of file
diff --git a/spaces/GenerationsAI/GenAi-Pix2Pix-Video/README.md b/spaces/GenerationsAI/GenAi-Pix2Pix-Video/README.md
deleted file mode 100644
index 3d8f7d06e470e918dedf27b7a230a565996a1252..0000000000000000000000000000000000000000
--- a/spaces/GenerationsAI/GenAi-Pix2Pix-Video/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pix2Pix Video
-emoji: 🎨🎞️
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/Pix2Pix-Video
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gradio-Blocks/Ask_Questions_To_YouTube_Videos/README.md b/spaces/Gradio-Blocks/Ask_Questions_To_YouTube_Videos/README.md
deleted file mode 100644
index c37bb8e0b6d467bd56768462bee0f5f8d9b7091c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/Ask_Questions_To_YouTube_Videos/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Ask_Questions_To_YouTube_Videos
-emoji: 💻
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.3
-app_file: app.py
-pinned: false
-license: gpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py
deleted file mode 100644
index 391636ff452471af367ed14be5faa49c0b7e1be6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './htc_hrnetv2p_w32_20e_coco.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w18',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(18, 36)),
-            stage3=dict(num_channels=(18, 36, 72)),
-            stage4=dict(num_channels=(18, 36, 72, 144)))),
-    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/auto_augment.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/auto_augment.py
deleted file mode 100644
index e19adaec18a96cac4dbe1d8c2c9193e9901be1fb..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/auto_augment.py
+++ /dev/null
@@ -1,890 +0,0 @@
-import copy
-
-import cv2
-import mmcv
-import numpy as np
-
-from ..builder import PIPELINES
-from .compose import Compose
-
-_MAX_LEVEL = 10
-
-
-def level_to_value(level, max_value):
-    """Map from level to values based on max_value."""
-    return (level / _MAX_LEVEL) * max_value
-
-
-def enhance_level_to_value(level, a=1.8, b=0.1):
-    """Map from level to values."""
-    return (level / _MAX_LEVEL) * a + b
-
-
-def random_negative(value, random_negative_prob):
-    """Randomly negate value based on random_negative_prob."""
-    return -value if np.random.rand() < random_negative_prob else value
-
-
-def bbox2fields():
-    """The key correspondence from bboxes to labels, masks and
-    segmentations."""
-    bbox2label = {
-        'gt_bboxes': 'gt_labels',
-        'gt_bboxes_ignore': 'gt_labels_ignore'
-    }
-    bbox2mask = {
-        'gt_bboxes': 'gt_masks',
-        'gt_bboxes_ignore': 'gt_masks_ignore'
-    }
-    bbox2seg = {
-        'gt_bboxes': 'gt_semantic_seg',
-    }
-    return bbox2label, bbox2mask, bbox2seg
-
-
-@PIPELINES.register_module()
-class AutoAugment(object):
-    """Auto augmentation.
-
-    This data augmentation is proposed in `Learning Data Augmentation
-    Strategies for Object Detection <https://arxiv.org/pdf/1906.11172>`_.
-
-    TODO: Implement 'Shear', 'Sharpness' and 'Rotate' transforms
-
-    Args:
-        policies (list[list[dict]]): The policies of auto augmentation. Each
-            policy in ``policies`` is a specific augmentation policy, and is
-            composed by several augmentations (dict). When AutoAugment is
-            called, a random policy in ``policies`` will be selected to
-            augment images.
-
-    Examples:
-        >>> replace = (104, 116, 124)
-        >>> policies = [
-        >>>     [
-        >>>         dict(type='Sharpness', prob=0.0, level=8),
-        >>>         dict(
-        >>>             type='Shear',
-        >>>             prob=0.4,
-        >>>             level=0,
-        >>>             replace=replace,
-        >>>             axis='x')
-        >>>     ],
-        >>>     [
-        >>>         dict(
-        >>>             type='Rotate',
-        >>>             prob=0.6,
-        >>>             level=10,
-        >>>             replace=replace),
-        >>>         dict(type='Color', prob=1.0, level=6)
-        >>>     ]
-        >>> ]
-        >>> augmentation = AutoAugment(policies)
-        >>> img = np.ones(100, 100, 3)
-        >>> gt_bboxes = np.ones(10, 4)
-        >>> results = dict(img=img, gt_bboxes=gt_bboxes)
-        >>> results = augmentation(results)
-    """
-
-    def __init__(self, policies):
-        assert isinstance(policies, list) and len(policies) > 0, \
-            'Policies must be a non-empty list.'
-        for policy in policies:
-            assert isinstance(policy, list) and len(policy) > 0, \
-                'Each policy in policies must be a non-empty list.'
-            for augment in policy:
-                assert isinstance(augment, dict) and 'type' in augment, \
-                    'Each specific augmentation must be a dict with key' \
-                    ' "type".'
-
-        self.policies = copy.deepcopy(policies)
-        self.transforms = [Compose(policy) for policy in self.policies]
-
-    def __call__(self, results):
-        transform = np.random.choice(self.transforms)
-        return transform(results)
-
-    def __repr__(self):
-        return f'{self.__class__.__name__}(policies={self.policies})'
-
-
-@PIPELINES.register_module()
-class Shear(object):
-    """Apply Shear Transformation to image (and its corresponding bbox, mask,
-    segmentation).
-
-    Args:
-        level (int | float): The level should be in range [0,_MAX_LEVEL].
-        img_fill_val (int | float | tuple): The filled values for image border.
-            If float, the same fill value will be used for all the three
-            channels of image. If tuple, the should be 3 elements.
-        seg_ignore_label (int): The fill value used for segmentation map.
-            Note this value must equals ``ignore_label`` in ``semantic_head``
-            of the corresponding config. Default 255.
-        prob (float): The probability for performing Shear and should be in
-            range [0, 1].
-        direction (str): The direction for shear, either "horizontal"
-            or "vertical".
-        max_shear_magnitude (float): The maximum magnitude for Shear
-            transformation.
-        random_negative_prob (float): The probability that turns the
-                offset negative. Should be in range [0,1]
-        interpolation (str): Same as in :func:`mmcv.imshear`.
-    """
-
-    def __init__(self,
-                 level,
-                 img_fill_val=128,
-                 seg_ignore_label=255,
-                 prob=0.5,
-                 direction='horizontal',
-                 max_shear_magnitude=0.3,
-                 random_negative_prob=0.5,
-                 interpolation='bilinear'):
-        assert isinstance(level, (int, float)), 'The level must be type ' \
-            f'int or float, got {type(level)}.'
-        assert 0 <= level <= _MAX_LEVEL, 'The level should be in range ' \
-            f'[0,{_MAX_LEVEL}], got {level}.'
-        if isinstance(img_fill_val, (float, int)):
-            img_fill_val = tuple([float(img_fill_val)] * 3)
-        elif isinstance(img_fill_val, tuple):
-            assert len(img_fill_val) == 3, 'img_fill_val as tuple must ' \
-                f'have 3 elements. got {len(img_fill_val)}.'
-            img_fill_val = tuple([float(val) for val in img_fill_val])
-        else:
-            raise ValueError(
-                'img_fill_val must be float or tuple with 3 elements.')
-        assert np.all([0 <= val <= 255 for val in img_fill_val]), 'all ' \
-            'elements of img_fill_val should between range [0,255].' \
-            f'got {img_fill_val}.'
-        assert 0 <= prob <= 1.0, 'The probability of shear should be in ' \
-            f'range [0,1]. got {prob}.'
-        assert direction in ('horizontal', 'vertical'), 'direction must ' \
-            f'in be either "horizontal" or "vertical". got {direction}.'
-        assert isinstance(max_shear_magnitude, float), 'max_shear_magnitude ' \
-            f'should be type float. got {type(max_shear_magnitude)}.'
-        assert 0. <= max_shear_magnitude <= 1., 'Defaultly ' \
-            'max_shear_magnitude should be in range [0,1]. ' \
-            f'got {max_shear_magnitude}.'
-        self.level = level
-        self.magnitude = level_to_value(level, max_shear_magnitude)
-        self.img_fill_val = img_fill_val
-        self.seg_ignore_label = seg_ignore_label
-        self.prob = prob
-        self.direction = direction
-        self.max_shear_magnitude = max_shear_magnitude
-        self.random_negative_prob = random_negative_prob
-        self.interpolation = interpolation
-
-    def _shear_img(self,
-                   results,
-                   magnitude,
-                   direction='horizontal',
-                   interpolation='bilinear'):
-        """Shear the image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-            magnitude (int | float): The magnitude used for shear.
-            direction (str): The direction for shear, either "horizontal"
-                or "vertical".
-            interpolation (str): Same as in :func:`mmcv.imshear`.
-        """
-        for key in results.get('img_fields', ['img']):
-            img = results[key]
-            img_sheared = mmcv.imshear(
-                img,
-                magnitude,
-                direction,
-                border_value=self.img_fill_val,
-                interpolation=interpolation)
-            results[key] = img_sheared.astype(img.dtype)
-
-    def _shear_bboxes(self, results, magnitude):
-        """Shear the bboxes."""
-        h, w, c = results['img_shape']
-        if self.direction == 'horizontal':
-            shear_matrix = np.stack([[1, magnitude],
-                                     [0, 1]]).astype(np.float32)  # [2, 2]
-        else:
-            shear_matrix = np.stack([[1, 0], [magnitude,
-                                              1]]).astype(np.float32)
-        for key in results.get('bbox_fields', []):
-            min_x, min_y, max_x, max_y = np.split(
-                results[key], results[key].shape[-1], axis=-1)
-            coordinates = np.stack([[min_x, min_y], [max_x, min_y],
-                                    [min_x, max_y],
-                                    [max_x, max_y]])  # [4, 2, nb_box, 1]
-            coordinates = coordinates[..., 0].transpose(
-                (2, 1, 0)).astype(np.float32)  # [nb_box, 2, 4]
-            new_coords = np.matmul(shear_matrix[None, :, :],
-                                   coordinates)  # [nb_box, 2, 4]
-            min_x = np.min(new_coords[:, 0, :], axis=-1)
-            min_y = np.min(new_coords[:, 1, :], axis=-1)
-            max_x = np.max(new_coords[:, 0, :], axis=-1)
-            max_y = np.max(new_coords[:, 1, :], axis=-1)
-            min_x = np.clip(min_x, a_min=0, a_max=w)
-            min_y = np.clip(min_y, a_min=0, a_max=h)
-            max_x = np.clip(max_x, a_min=min_x, a_max=w)
-            max_y = np.clip(max_y, a_min=min_y, a_max=h)
-            results[key] = np.stack([min_x, min_y, max_x, max_y],
-                                    axis=-1).astype(results[key].dtype)
-
-    def _shear_masks(self,
-                     results,
-                     magnitude,
-                     direction='horizontal',
-                     fill_val=0,
-                     interpolation='bilinear'):
-        """Shear the masks."""
-        h, w, c = results['img_shape']
-        for key in results.get('mask_fields', []):
-            masks = results[key]
-            results[key] = masks.shear((h, w),
-                                       magnitude,
-                                       direction,
-                                       border_value=fill_val,
-                                       interpolation=interpolation)
-
-    def _shear_seg(self,
-                   results,
-                   magnitude,
-                   direction='horizontal',
-                   fill_val=255,
-                   interpolation='bilinear'):
-        """Shear the segmentation maps."""
-        for key in results.get('seg_fields', []):
-            seg = results[key]
-            results[key] = mmcv.imshear(
-                seg,
-                magnitude,
-                direction,
-                border_value=fill_val,
-                interpolation=interpolation).astype(seg.dtype)
-
-    def _filter_invalid(self, results, min_bbox_size=0):
-        """Filter bboxes and corresponding masks too small after shear
-        augmentation."""
-        bbox2label, bbox2mask, _ = bbox2fields()
-        for key in results.get('bbox_fields', []):
-            bbox_w = results[key][:, 2] - results[key][:, 0]
-            bbox_h = results[key][:, 3] - results[key][:, 1]
-            valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size)
-            valid_inds = np.nonzero(valid_inds)[0]
-            results[key] = results[key][valid_inds]
-            # label fields. e.g. gt_labels and gt_labels_ignore
-            label_key = bbox2label.get(key)
-            if label_key in results:
-                results[label_key] = results[label_key][valid_inds]
-            # mask fields, e.g. gt_masks and gt_masks_ignore
-            mask_key = bbox2mask.get(key)
-            if mask_key in results:
-                results[mask_key] = results[mask_key][valid_inds]
-
-    def __call__(self, results):
-        """Call function to shear images, bounding boxes, masks and semantic
-        segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Sheared results.
-        """
-        if np.random.rand() > self.prob:
-            return results
-        magnitude = random_negative(self.magnitude, self.random_negative_prob)
-        self._shear_img(results, magnitude, self.direction, self.interpolation)
-        self._shear_bboxes(results, magnitude)
-        # fill_val set to 0 for background of mask.
-        self._shear_masks(
-            results,
-            magnitude,
-            self.direction,
-            fill_val=0,
-            interpolation=self.interpolation)
-        self._shear_seg(
-            results,
-            magnitude,
-            self.direction,
-            fill_val=self.seg_ignore_label,
-            interpolation=self.interpolation)
-        self._filter_invalid(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(level={self.level}, '
-        repr_str += f'img_fill_val={self.img_fill_val}, '
-        repr_str += f'seg_ignore_label={self.seg_ignore_label}, '
-        repr_str += f'prob={self.prob}, '
-        repr_str += f'direction={self.direction}, '
-        repr_str += f'max_shear_magnitude={self.max_shear_magnitude}, '
-        repr_str += f'random_negative_prob={self.random_negative_prob}, '
-        repr_str += f'interpolation={self.interpolation})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Rotate(object):
-    """Apply Rotate Transformation to image (and its corresponding bbox, mask,
-    segmentation).
-
-    Args:
-        level (int | float): The level should be in range (0,_MAX_LEVEL].
-        scale (int | float): Isotropic scale factor. Same in
-            ``mmcv.imrotate``.
-        center (int | float | tuple[float]): Center point (w, h) of the
-            rotation in the source image. If None, the center of the
-            image will be used. Same in ``mmcv.imrotate``.
-        img_fill_val (int | float | tuple): The fill value for image border.
-            If float, the same value will be used for all the three
-            channels of image. If tuple, the should be 3 elements (e.g.
-            equals the number of channels for image).
-        seg_ignore_label (int): The fill value used for segmentation map.
-            Note this value must equals ``ignore_label`` in ``semantic_head``
-            of the corresponding config. Default 255.
-        prob (float): The probability for perform transformation and
-            should be in range 0 to 1.
-        max_rotate_angle (int | float): The maximum angles for rotate
-            transformation.
-        random_negative_prob (float): The probability that turns the
-             offset negative.
-    """
-
-    def __init__(self,
-                 level,
-                 scale=1,
-                 center=None,
-                 img_fill_val=128,
-                 seg_ignore_label=255,
-                 prob=0.5,
-                 max_rotate_angle=30,
-                 random_negative_prob=0.5):
-        assert isinstance(level, (int, float)), \
-            f'The level must be type int or float. got {type(level)}.'
-        assert 0 <= level <= _MAX_LEVEL, \
-            f'The level should be in range (0,{_MAX_LEVEL}]. got {level}.'
-        assert isinstance(scale, (int, float)), \
-            f'The scale must be type int or float. got type {type(scale)}.'
-        if isinstance(center, (int, float)):
-            center = (center, center)
-        elif isinstance(center, tuple):
-            assert len(center) == 2, 'center with type tuple must have '\
-                f'2 elements. got {len(center)} elements.'
-        else:
-            assert center is None, 'center must be None or type int, '\
-                f'float or tuple, got type {type(center)}.'
-        if isinstance(img_fill_val, (float, int)):
-            img_fill_val = tuple([float(img_fill_val)] * 3)
-        elif isinstance(img_fill_val, tuple):
-            assert len(img_fill_val) == 3, 'img_fill_val as tuple must '\
-                f'have 3 elements. got {len(img_fill_val)}.'
-            img_fill_val = tuple([float(val) for val in img_fill_val])
-        else:
-            raise ValueError(
-                'img_fill_val must be float or tuple with 3 elements.')
-        assert np.all([0 <= val <= 255 for val in img_fill_val]), \
-            'all elements of img_fill_val should between range [0,255]. '\
-            f'got {img_fill_val}.'
-        assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. '\
-            'got {prob}.'
-        assert isinstance(max_rotate_angle, (int, float)), 'max_rotate_angle '\
-            f'should be type int or float. got type {type(max_rotate_angle)}.'
-        self.level = level
-        self.scale = scale
-        # Rotation angle in degrees. Positive values mean
-        # clockwise rotation.
-        self.angle = level_to_value(level, max_rotate_angle)
-        self.center = center
-        self.img_fill_val = img_fill_val
-        self.seg_ignore_label = seg_ignore_label
-        self.prob = prob
-        self.max_rotate_angle = max_rotate_angle
-        self.random_negative_prob = random_negative_prob
-
-    def _rotate_img(self, results, angle, center=None, scale=1.0):
-        """Rotate the image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-            angle (float): Rotation angle in degrees, positive values
-                mean clockwise rotation. Same in ``mmcv.imrotate``.
-            center (tuple[float], optional): Center point (w, h) of the
-                rotation. Same in ``mmcv.imrotate``.
-            scale (int | float): Isotropic scale factor. Same in
-                ``mmcv.imrotate``.
-        """
-        for key in results.get('img_fields', ['img']):
-            img = results[key].copy()
-            img_rotated = mmcv.imrotate(
-                img, angle, center, scale, border_value=self.img_fill_val)
-            results[key] = img_rotated.astype(img.dtype)
-
-    def _rotate_bboxes(self, results, rotate_matrix):
-        """Rotate the bboxes."""
-        h, w, c = results['img_shape']
-        for key in results.get('bbox_fields', []):
-            min_x, min_y, max_x, max_y = np.split(
-                results[key], results[key].shape[-1], axis=-1)
-            coordinates = np.stack([[min_x, min_y], [max_x, min_y],
-                                    [min_x, max_y],
-                                    [max_x, max_y]])  # [4, 2, nb_bbox, 1]
-            # pad 1 to convert from format [x, y] to homogeneous
-            # coordinates format [x, y, 1]
-            coordinates = np.concatenate(
-                (coordinates,
-                 np.ones((4, 1, coordinates.shape[2], 1), coordinates.dtype)),
-                axis=1)  # [4, 3, nb_bbox, 1]
-            coordinates = coordinates.transpose(
-                (2, 0, 1, 3))  # [nb_bbox, 4, 3, 1]
-            rotated_coords = np.matmul(rotate_matrix,
-                                       coordinates)  # [nb_bbox, 4, 2, 1]
-            rotated_coords = rotated_coords[..., 0]  # [nb_bbox, 4, 2]
-            min_x, min_y = np.min(
-                rotated_coords[:, :, 0], axis=1), np.min(
-                    rotated_coords[:, :, 1], axis=1)
-            max_x, max_y = np.max(
-                rotated_coords[:, :, 0], axis=1), np.max(
-                    rotated_coords[:, :, 1], axis=1)
-            min_x, min_y = np.clip(
-                min_x, a_min=0, a_max=w), np.clip(
-                    min_y, a_min=0, a_max=h)
-            max_x, max_y = np.clip(
-                max_x, a_min=min_x, a_max=w), np.clip(
-                    max_y, a_min=min_y, a_max=h)
-            results[key] = np.stack([min_x, min_y, max_x, max_y],
-                                    axis=-1).astype(results[key].dtype)
-
-    def _rotate_masks(self,
-                      results,
-                      angle,
-                      center=None,
-                      scale=1.0,
-                      fill_val=0):
-        """Rotate the masks."""
-        h, w, c = results['img_shape']
-        for key in results.get('mask_fields', []):
-            masks = results[key]
-            results[key] = masks.rotate((h, w), angle, center, scale, fill_val)
-
-    def _rotate_seg(self,
-                    results,
-                    angle,
-                    center=None,
-                    scale=1.0,
-                    fill_val=255):
-        """Rotate the segmentation map."""
-        for key in results.get('seg_fields', []):
-            seg = results[key].copy()
-            results[key] = mmcv.imrotate(
-                seg, angle, center, scale,
-                border_value=fill_val).astype(seg.dtype)
-
-    def _filter_invalid(self, results, min_bbox_size=0):
-        """Filter bboxes and corresponding masks too small after rotate
-        augmentation."""
-        bbox2label, bbox2mask, _ = bbox2fields()
-        for key in results.get('bbox_fields', []):
-            bbox_w = results[key][:, 2] - results[key][:, 0]
-            bbox_h = results[key][:, 3] - results[key][:, 1]
-            valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size)
-            valid_inds = np.nonzero(valid_inds)[0]
-            results[key] = results[key][valid_inds]
-            # label fields. e.g. gt_labels and gt_labels_ignore
-            label_key = bbox2label.get(key)
-            if label_key in results:
-                results[label_key] = results[label_key][valid_inds]
-            # mask fields, e.g. gt_masks and gt_masks_ignore
-            mask_key = bbox2mask.get(key)
-            if mask_key in results:
-                results[mask_key] = results[mask_key][valid_inds]
-
-    def __call__(self, results):
-        """Call function to rotate images, bounding boxes, masks and semantic
-        segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Rotated results.
-        """
-        if np.random.rand() > self.prob:
-            return results
-        h, w = results['img'].shape[:2]
-        center = self.center
-        if center is None:
-            center = ((w - 1) * 0.5, (h - 1) * 0.5)
-        angle = random_negative(self.angle, self.random_negative_prob)
-        self._rotate_img(results, angle, center, self.scale)
-        rotate_matrix = cv2.getRotationMatrix2D(center, -angle, self.scale)
-        self._rotate_bboxes(results, rotate_matrix)
-        self._rotate_masks(results, angle, center, self.scale, fill_val=0)
-        self._rotate_seg(
-            results, angle, center, self.scale, fill_val=self.seg_ignore_label)
-        self._filter_invalid(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(level={self.level}, '
-        repr_str += f'scale={self.scale}, '
-        repr_str += f'center={self.center}, '
-        repr_str += f'img_fill_val={self.img_fill_val}, '
-        repr_str += f'seg_ignore_label={self.seg_ignore_label}, '
-        repr_str += f'prob={self.prob}, '
-        repr_str += f'max_rotate_angle={self.max_rotate_angle}, '
-        repr_str += f'random_negative_prob={self.random_negative_prob})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Translate(object):
-    """Translate the images, bboxes, masks and segmentation maps horizontally
-    or vertically.
-
-    Args:
-        level (int | float): The level for Translate and should be in
-            range [0,_MAX_LEVEL].
-        prob (float): The probability for performing translation and
-            should be in range [0, 1].
-        img_fill_val (int | float | tuple): The filled value for image
-            border. If float, the same fill value will be used for all
-            the three channels of image. If tuple, the should be 3
-            elements (e.g. equals the number of channels for image).
-        seg_ignore_label (int): The fill value used for segmentation map.
-            Note this value must equals ``ignore_label`` in ``semantic_head``
-            of the corresponding config. Default 255.
-        direction (str): The translate direction, either "horizontal"
-            or "vertical".
-        max_translate_offset (int | float): The maximum pixel's offset for
-            Translate.
-        random_negative_prob (float): The probability that turns the
-            offset negative.
-        min_size (int | float): The minimum pixel for filtering
-            invalid bboxes after the translation.
-    """
-
-    def __init__(self,
-                 level,
-                 prob=0.5,
-                 img_fill_val=128,
-                 seg_ignore_label=255,
-                 direction='horizontal',
-                 max_translate_offset=250.,
-                 random_negative_prob=0.5,
-                 min_size=0):
-        assert isinstance(level, (int, float)), \
-            'The level must be type int or float.'
-        assert 0 <= level <= _MAX_LEVEL, \
-            'The level used for calculating Translate\'s offset should be ' \
-            'in range [0,_MAX_LEVEL]'
-        assert 0 <= prob <= 1.0, \
-            'The probability of translation should be in range [0, 1].'
-        if isinstance(img_fill_val, (float, int)):
-            img_fill_val = tuple([float(img_fill_val)] * 3)
-        elif isinstance(img_fill_val, tuple):
-            assert len(img_fill_val) == 3, \
-                'img_fill_val as tuple must have 3 elements.'
-            img_fill_val = tuple([float(val) for val in img_fill_val])
-        else:
-            raise ValueError('img_fill_val must be type float or tuple.')
-        assert np.all([0 <= val <= 255 for val in img_fill_val]), \
-            'all elements of img_fill_val should between range [0,255].'
-        assert direction in ('horizontal', 'vertical'), \
-            'direction should be "horizontal" or "vertical".'
-        assert isinstance(max_translate_offset, (int, float)), \
-            'The max_translate_offset must be type int or float.'
-        # the offset used for translation
-        self.offset = int(level_to_value(level, max_translate_offset))
-        self.level = level
-        self.prob = prob
-        self.img_fill_val = img_fill_val
-        self.seg_ignore_label = seg_ignore_label
-        self.direction = direction
-        self.max_translate_offset = max_translate_offset
-        self.random_negative_prob = random_negative_prob
-        self.min_size = min_size
-
-    def _translate_img(self, results, offset, direction='horizontal'):
-        """Translate the image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-            offset (int | float): The offset for translate.
-            direction (str): The translate direction, either "horizontal"
-                or "vertical".
-        """
-        for key in results.get('img_fields', ['img']):
-            img = results[key].copy()
-            results[key] = mmcv.imtranslate(
-                img, offset, direction, self.img_fill_val).astype(img.dtype)
-
-    def _translate_bboxes(self, results, offset):
-        """Shift bboxes horizontally or vertically, according to offset."""
-        h, w, c = results['img_shape']
-        for key in results.get('bbox_fields', []):
-            min_x, min_y, max_x, max_y = np.split(
-                results[key], results[key].shape[-1], axis=-1)
-            if self.direction == 'horizontal':
-                min_x = np.maximum(0, min_x + offset)
-                max_x = np.minimum(w, max_x + offset)
-            elif self.direction == 'vertical':
-                min_y = np.maximum(0, min_y + offset)
-                max_y = np.minimum(h, max_y + offset)
-
-            # the boxes translated outside of image will be filtered along with
-            # the corresponding masks, by invoking ``_filter_invalid``.
-            results[key] = np.concatenate([min_x, min_y, max_x, max_y],
-                                          axis=-1)
-
-    def _translate_masks(self,
-                         results,
-                         offset,
-                         direction='horizontal',
-                         fill_val=0):
-        """Translate masks horizontally or vertically."""
-        h, w, c = results['img_shape']
-        for key in results.get('mask_fields', []):
-            masks = results[key]
-            results[key] = masks.translate((h, w), offset, direction, fill_val)
-
-    def _translate_seg(self,
-                       results,
-                       offset,
-                       direction='horizontal',
-                       fill_val=255):
-        """Translate segmentation maps horizontally or vertically."""
-        for key in results.get('seg_fields', []):
-            seg = results[key].copy()
-            results[key] = mmcv.imtranslate(seg, offset, direction,
-                                            fill_val).astype(seg.dtype)
-
-    def _filter_invalid(self, results, min_size=0):
-        """Filter bboxes and masks too small or translated out of image."""
-        bbox2label, bbox2mask, _ = bbox2fields()
-        for key in results.get('bbox_fields', []):
-            bbox_w = results[key][:, 2] - results[key][:, 0]
-            bbox_h = results[key][:, 3] - results[key][:, 1]
-            valid_inds = (bbox_w > min_size) & (bbox_h > min_size)
-            valid_inds = np.nonzero(valid_inds)[0]
-            results[key] = results[key][valid_inds]
-            # label fields. e.g. gt_labels and gt_labels_ignore
-            label_key = bbox2label.get(key)
-            if label_key in results:
-                results[label_key] = results[label_key][valid_inds]
-            # mask fields, e.g. gt_masks and gt_masks_ignore
-            mask_key = bbox2mask.get(key)
-            if mask_key in results:
-                results[mask_key] = results[mask_key][valid_inds]
-        return results
-
-    def __call__(self, results):
-        """Call function to translate images, bounding boxes, masks and
-        semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Translated results.
-        """
-        if np.random.rand() > self.prob:
-            return results
-        offset = random_negative(self.offset, self.random_negative_prob)
-        self._translate_img(results, offset, self.direction)
-        self._translate_bboxes(results, offset)
-        # fill_val defaultly 0 for BitmapMasks and None for PolygonMasks.
-        self._translate_masks(results, offset, self.direction)
-        # fill_val set to ``seg_ignore_label`` for the ignored value
-        # of segmentation map.
-        self._translate_seg(
-            results, offset, self.direction, fill_val=self.seg_ignore_label)
-        self._filter_invalid(results, min_size=self.min_size)
-        return results
-
-
-@PIPELINES.register_module()
-class ColorTransform(object):
-    """Apply Color transformation to image. The bboxes, masks, and
-    segmentations are not modified.
-
-    Args:
-        level (int | float): Should be in range [0,_MAX_LEVEL].
-        prob (float): The probability for performing Color transformation.
-    """
-
-    def __init__(self, level, prob=0.5):
-        assert isinstance(level, (int, float)), \
-            'The level must be type int or float.'
-        assert 0 <= level <= _MAX_LEVEL, \
-            'The level should be in range [0,_MAX_LEVEL].'
-        assert 0 <= prob <= 1.0, \
-            'The probability should be in range [0,1].'
-        self.level = level
-        self.prob = prob
-        self.factor = enhance_level_to_value(level)
-
-    def _adjust_color_img(self, results, factor=1.0):
-        """Apply Color transformation to image."""
-        for key in results.get('img_fields', ['img']):
-            # NOTE defaultly the image should be BGR format
-            img = results[key]
-            results[key] = mmcv.adjust_color(img, factor).astype(img.dtype)
-
-    def __call__(self, results):
-        """Call function for Color transformation.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Colored results.
-        """
-        if np.random.rand() > self.prob:
-            return results
-        self._adjust_color_img(results, self.factor)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(level={self.level}, '
-        repr_str += f'prob={self.prob})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class EqualizeTransform(object):
-    """Apply Equalize transformation to image. The bboxes, masks and
-    segmentations are not modified.
-
-    Args:
-        prob (float): The probability for performing Equalize transformation.
-    """
-
-    def __init__(self, prob=0.5):
-        assert 0 <= prob <= 1.0, \
-            'The probability should be in range [0,1].'
-        self.prob = prob
-
-    def _imequalize(self, results):
-        """Equalizes the histogram of one image."""
-        for key in results.get('img_fields', ['img']):
-            img = results[key]
-            results[key] = mmcv.imequalize(img).astype(img.dtype)
-
-    def __call__(self, results):
-        """Call function for Equalize transformation.
-
-        Args:
-            results (dict): Results dict from loading pipeline.
-
-        Returns:
-            dict: Results after the transformation.
-        """
-        if np.random.rand() > self.prob:
-            return results
-        self._imequalize(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(prob={self.prob})'
-
-
-@PIPELINES.register_module()
-class BrightnessTransform(object):
-    """Apply Brightness transformation to image. The bboxes, masks and
-    segmentations are not modified.
-
-    Args:
-        level (int | float): Should be in range [0,_MAX_LEVEL].
-        prob (float): The probability for performing Brightness transformation.
-    """
-
-    def __init__(self, level, prob=0.5):
-        assert isinstance(level, (int, float)), \
-            'The level must be type int or float.'
-        assert 0 <= level <= _MAX_LEVEL, \
-            'The level should be in range [0,_MAX_LEVEL].'
-        assert 0 <= prob <= 1.0, \
-            'The probability should be in range [0,1].'
-        self.level = level
-        self.prob = prob
-        self.factor = enhance_level_to_value(level)
-
-    def _adjust_brightness_img(self, results, factor=1.0):
-        """Adjust the brightness of image."""
-        for key in results.get('img_fields', ['img']):
-            img = results[key]
-            results[key] = mmcv.adjust_brightness(img,
-                                                  factor).astype(img.dtype)
-
-    def __call__(self, results):
-        """Call function for Brightness transformation.
-
-        Args:
-            results (dict): Results dict from loading pipeline.
-
-        Returns:
-            dict: Results after the transformation.
-        """
-        if np.random.rand() > self.prob:
-            return results
-        self._adjust_brightness_img(results, self.factor)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(level={self.level}, '
-        repr_str += f'prob={self.prob})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class ContrastTransform(object):
-    """Apply Contrast transformation to image. The bboxes, masks and
-    segmentations are not modified.
-
-    Args:
-        level (int | float): Should be in range [0,_MAX_LEVEL].
-        prob (float): The probability for performing Contrast transformation.
-    """
-
-    def __init__(self, level, prob=0.5):
-        assert isinstance(level, (int, float)), \
-            'The level must be type int or float.'
-        assert 0 <= level <= _MAX_LEVEL, \
-            'The level should be in range [0,_MAX_LEVEL].'
-        assert 0 <= prob <= 1.0, \
-            'The probability should be in range [0,1].'
-        self.level = level
-        self.prob = prob
-        self.factor = enhance_level_to_value(level)
-
-    def _adjust_contrast_img(self, results, factor=1.0):
-        """Adjust the image contrast."""
-        for key in results.get('img_fields', ['img']):
-            img = results[key]
-            results[key] = mmcv.adjust_contrast(img, factor).astype(img.dtype)
-
-    def __call__(self, results):
-        """Call function for Contrast transformation.
-
-        Args:
-            results (dict): Results dict from loading pipeline.
-
-        Returns:
-            dict: Results after the transformation.
-        """
-        if np.random.rand() > self.prob:
-            return results
-        self._adjust_contrast_img(results, self.factor)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(level={self.level}, '
-        repr_str += f'prob={self.prob})'
-        return repr_str
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/fpn_r50.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/fpn_r50.py
deleted file mode 100644
index 86ab327db92e44c14822d65f1c9277cb007f17c1..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/fpn_r50.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 1, 1),
-        strides=(1, 2, 2, 2),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=4),
-    decode_head=dict(
-        type='FPNHead',
-        in_channels=[256, 256, 256, 256],
-        in_index=[0, 1, 2, 3],
-        feature_strides=[4, 8, 16, 32],
-        channels=128,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
deleted file mode 100644
index d51bccb965dafc40d7859219d132dc9467740a1b..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=128,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
deleted file mode 100644
index b90b597d831a664761d6051397d2b1862feb59c6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './upernet_r50_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/distributed.py b/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/distributed.py
deleted file mode 100644
index 51fa243257ef302e2015d5ff36ac531b86a9a0ce..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/distributed.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import math
-import pickle
-
-import torch
-from torch import distributed as dist
-from torch.utils.data.sampler import Sampler
-
-
-def get_rank():
-    if not dist.is_available():
-        return 0
-
-    if not dist.is_initialized():
-        return 0
-
-    return dist.get_rank()
-
-
-def synchronize():
-    if not dist.is_available():
-        return
-
-    if not dist.is_initialized():
-        return
-
-    world_size = dist.get_world_size()
-
-    if world_size == 1:
-        return
-
-    dist.barrier()
-
-
-def get_world_size():
-    if not dist.is_available():
-        return 1
-
-    if not dist.is_initialized():
-        return 1
-
-    return dist.get_world_size()
-
-
-def reduce_sum(tensor):
-    if not dist.is_available():
-        return tensor
-
-    if not dist.is_initialized():
-        return tensor
-
-    tensor = tensor.clone()
-    dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
-
-    return tensor
-
-
-def gather_grad(params):
-    world_size = get_world_size()
-    
-    if world_size == 1:
-        return
-
-    for param in params:
-        if param.grad is not None:
-            dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
-            param.grad.data.div_(world_size)
-
-
-def all_gather(data):
-    world_size = get_world_size()
-
-    if world_size == 1:
-        return [data]
-
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to('cuda')
-
-    local_size = torch.IntTensor([tensor.numel()]).to('cuda')
-    size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda'))
-
-    if local_size != max_size:
-        padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda')
-        tensor = torch.cat((tensor, padding), 0)
-
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_loss_dict(loss_dict):
-    world_size = get_world_size()
-
-    if world_size < 2:
-        return loss_dict
-
-    with torch.no_grad():
-        keys = []
-        losses = []
-
-        for k in sorted(loss_dict.keys()):
-            keys.append(k)
-            losses.append(loss_dict[k])
-
-        losses = torch.stack(losses, 0)
-        dist.reduce(losses, dst=0)
-
-        if dist.get_rank() == 0:
-            losses /= world_size
-
-        reduced_losses = {k: v for k, v in zip(keys, losses)}
-
-    return reduced_losses
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/discriminative_reranking_nmt/drnmt_rerank.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/discriminative_reranking_nmt/drnmt_rerank.py
deleted file mode 100644
index 2e0fc2bd29aedb0b477b7cc8e2c3b606acdd454a..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/discriminative_reranking_nmt/drnmt_rerank.py
+++ /dev/null
@@ -1,364 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Score raw text with a trained model.
-"""
-
-from collections import namedtuple
-import logging
-from multiprocessing import Pool
-import sys
-import os
-import random
-
-import numpy as np
-import sacrebleu
-import torch
-
-from fairseq import checkpoint_utils, options, utils
-
-
-logger = logging.getLogger("fairseq_cli.drnmt_rerank")
-logger.setLevel(logging.INFO)
-
-Batch = namedtuple("Batch", "ids src_tokens src_lengths")
-
-
-pool_init_variables = {}
-
-
-def init_loaded_scores(mt_scores, model_scores, hyp, ref):
-    global pool_init_variables
-    pool_init_variables["mt_scores"] = mt_scores
-    pool_init_variables["model_scores"] = model_scores
-    pool_init_variables["hyp"] = hyp
-    pool_init_variables["ref"] = ref
-
-
-def parse_fairseq_gen(filename, task):
-    source = {}
-    hypos = {}
-    scores = {}
-    with open(filename, "r", encoding="utf-8") as f:
-        for line in f:
-            line = line.strip()
-            if line.startswith("S-"):  # source
-                uid, text = line.split("\t", 1)
-                uid = int(uid[2:])
-                source[uid] = text
-            elif line.startswith("D-"):  # hypo
-                uid, score, text = line.split("\t", 2)
-                uid = int(uid[2:])
-                if uid not in hypos:
-                    hypos[uid] = []
-                    scores[uid] = []
-                hypos[uid].append(text)
-                scores[uid].append(float(score))
-            else:
-                continue
-
-    source_out = [source[i] for i in range(len(hypos))]
-    hypos_out = [h for i in range(len(hypos)) for h in hypos[i]]
-    scores_out = [s for i in range(len(scores)) for s in scores[i]]
-
-    return source_out, hypos_out, scores_out
-
-
-def read_target(filename):
-    with open(filename, "r", encoding="utf-8") as f:
-        output = [line.strip() for line in f]
-    return output
-
-
-def make_batches(args, src, hyp, task, max_positions, encode_fn):
-    assert len(src) * args.beam == len(
-        hyp
-    ), f"Expect {len(src) * args.beam} hypotheses for {len(src)} source sentences with beam size {args.beam}. Got {len(hyp)} hypotheses intead."
-    hyp_encode = [
-        task.source_dictionary.encode_line(encode_fn(h), add_if_not_exist=False).long()
-        for h in hyp
-    ]
-    if task.cfg.include_src:
-        src_encode = [
-            task.source_dictionary.encode_line(
-                encode_fn(s), add_if_not_exist=False
-            ).long()
-            for s in src
-        ]
-        tokens = [(src_encode[i // args.beam], h) for i, h in enumerate(hyp_encode)]
-        lengths = [(t1.numel(), t2.numel()) for t1, t2 in tokens]
-    else:
-        tokens = [(h,) for h in hyp_encode]
-        lengths = [(h.numel(),) for h in hyp_encode]
-
-    itr = task.get_batch_iterator(
-        dataset=task.build_dataset_for_inference(tokens, lengths),
-        max_tokens=args.max_tokens,
-        max_sentences=args.batch_size,
-        max_positions=max_positions,
-        ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
-    ).next_epoch_itr(shuffle=False)
-
-    for batch in itr:
-        yield Batch(
-            ids=batch["id"],
-            src_tokens=batch["net_input"]["src_tokens"],
-            src_lengths=batch["net_input"]["src_lengths"],
-        )
-
-
-def decode_rerank_scores(args):
-    if args.max_tokens is None and args.batch_size is None:
-        args.batch_size = 1
-
-    logger.info(args)
-
-    use_cuda = torch.cuda.is_available() and not args.cpu
-
-    # Load ensemble
-    logger.info("loading model(s) from {}".format(args.path))
-    models, _model_args, task = checkpoint_utils.load_model_ensemble_and_task(
-        [args.path], arg_overrides=eval(args.model_overrides),
-    )
-
-    for model in models:
-        if args.fp16:
-            model.half()
-        if use_cuda:
-            model.cuda()
-
-    # Initialize generator
-    generator = task.build_generator(args)
-
-    # Handle tokenization and BPE
-    tokenizer = task.build_tokenizer(args)
-    bpe = task.build_bpe(args)
-
-    def encode_fn(x):
-        if tokenizer is not None:
-            x = tokenizer.encode(x)
-        if bpe is not None:
-            x = bpe.encode(x)
-        return x
-
-    max_positions = utils.resolve_max_positions(
-        task.max_positions(), *[model.max_positions() for model in models]
-    )
-
-    src, hyp, mt_scores = parse_fairseq_gen(args.in_text, task)
-    model_scores = {}
-    logger.info("decode reranker score")
-    for batch in make_batches(args, src, hyp, task, max_positions, encode_fn):
-        src_tokens = batch.src_tokens
-        src_lengths = batch.src_lengths
-        if use_cuda:
-            src_tokens = src_tokens.cuda()
-            src_lengths = src_lengths.cuda()
-
-        sample = {
-            "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths},
-        }
-        scores = task.inference_step(generator, models, sample)
-
-        for id, sc in zip(batch.ids.tolist(), scores.tolist()):
-            model_scores[id] = sc[0]
-
-    model_scores = [model_scores[i] for i in range(len(model_scores))]
-
-    return src, hyp, mt_scores, model_scores
-
-
-def get_score(mt_s, md_s, w1, lp, tgt_len):
-    return mt_s / (tgt_len ** lp) * w1 + md_s
-
-
-def get_best_hyps(mt_scores, md_scores, hypos, fw_weight, lenpen, beam):
-    assert len(mt_scores) == len(md_scores) and len(mt_scores) == len(hypos)
-    hypo_scores = []
-    best_hypos = []
-    best_scores = []
-    offset = 0
-    for i in range(len(hypos)):
-        tgt_len = len(hypos[i].split())
-        hypo_scores.append(
-            get_score(mt_scores[i], md_scores[i], fw_weight, lenpen, tgt_len)
-        )
-
-        if (i + 1) % beam == 0:
-            max_i = np.argmax(hypo_scores)
-            best_hypos.append(hypos[offset + max_i])
-            best_scores.append(hypo_scores[max_i])
-            hypo_scores = []
-            offset += beam
-    return best_hypos, best_scores
-
-
-def eval_metric(args, hypos, ref):
-    if args.metric == "bleu":
-        score = sacrebleu.corpus_bleu(hypos, [ref]).score
-    else:
-        score = sacrebleu.corpus_ter(hypos, [ref]).score
-
-    return score
-
-
-def score_target_hypo(args, fw_weight, lp):
-    mt_scores = pool_init_variables["mt_scores"]
-    model_scores = pool_init_variables["model_scores"]
-    hyp = pool_init_variables["hyp"]
-    ref = pool_init_variables["ref"]
-    best_hypos, _ = get_best_hyps(
-        mt_scores, model_scores, hyp, fw_weight, lp, args.beam
-    )
-    rerank_eval = None
-    if ref:
-        rerank_eval = eval_metric(args, best_hypos, ref)
-        print(f"fw_weight {fw_weight}, lenpen {lp}, eval {rerank_eval}")
-
-    return rerank_eval
-
-
-def print_result(best_scores, best_hypos, output_file):
-    for i, (s, h) in enumerate(zip(best_scores, best_hypos)):
-        print(f"{i}\t{s}\t{h}", file=output_file)
-
-
-def main(args):
-    utils.import_user_module(args)
-
-    src, hyp, mt_scores, model_scores = decode_rerank_scores(args)
-
-    assert (
-        not args.tune or args.target_text is not None
-    ), "--target-text has to be set when tuning weights"
-    if args.target_text:
-        ref = read_target(args.target_text)
-        assert len(src) == len(
-            ref
-        ), f"different numbers of source and target sentences ({len(src)} vs. {len(ref)})"
-
-        orig_best_hypos = [hyp[i] for i in range(0, len(hyp), args.beam)]
-        orig_eval = eval_metric(args, orig_best_hypos, ref)
-
-    if args.tune:
-        logger.info("tune weights for reranking")
-
-        random_params = np.array(
-            [
-                [
-                    random.uniform(
-                        args.lower_bound_fw_weight, args.upper_bound_fw_weight
-                    ),
-                    random.uniform(args.lower_bound_lenpen, args.upper_bound_lenpen),
-                ]
-                for k in range(args.num_trials)
-            ]
-        )
-
-        logger.info("launching pool")
-        with Pool(
-            32,
-            initializer=init_loaded_scores,
-            initargs=(mt_scores, model_scores, hyp, ref),
-        ) as p:
-            rerank_scores = p.starmap(
-                score_target_hypo,
-                [
-                    (args, random_params[i][0], random_params[i][1],)
-                    for i in range(args.num_trials)
-                ],
-            )
-        if args.metric == "bleu":
-            best_index = np.argmax(rerank_scores)
-        else:
-            best_index = np.argmin(rerank_scores)
-        best_fw_weight = random_params[best_index][0]
-        best_lenpen = random_params[best_index][1]
-    else:
-        assert (
-            args.lenpen is not None and args.fw_weight is not None
-        ), "--lenpen and --fw-weight should be set"
-        best_fw_weight, best_lenpen = args.fw_weight, args.lenpen
-
-    best_hypos, best_scores = get_best_hyps(
-        mt_scores, model_scores, hyp, best_fw_weight, best_lenpen, args.beam
-    )
-
-    if args.results_path is not None:
-        os.makedirs(args.results_path, exist_ok=True)
-        output_path = os.path.join(
-            args.results_path, "generate-{}.txt".format(args.gen_subset),
-        )
-        with open(output_path, "w", buffering=1, encoding="utf-8") as o:
-            print_result(best_scores, best_hypos, o)
-    else:
-        print_result(best_scores, best_hypos, sys.stdout)
-
-    if args.target_text:
-        rerank_eval = eval_metric(args, best_hypos, ref)
-        print(f"before reranking, {args.metric.upper()}:", orig_eval)
-        print(
-            f"after reranking with fw_weight={best_fw_weight}, lenpen={best_lenpen}, {args.metric.upper()}:",
-            rerank_eval,
-        )
-
-
-def cli_main():
-    parser = options.get_generation_parser(interactive=True)
-
-    parser.add_argument(
-        "--in-text",
-        default=None,
-        required=True,
-        help="text from fairseq-interactive output, containing source sentences and hypotheses",
-    )
-    parser.add_argument("--target-text", default=None, help="reference text")
-    parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu")
-    parser.add_argument(
-        "--tune",
-        action="store_true",
-        help="if set, tune weights on fw scores and lenpen instead of applying fixed weights for reranking",
-    )
-    parser.add_argument(
-        "--lower-bound-fw-weight",
-        default=0.0,
-        type=float,
-        help="lower bound of search space",
-    )
-    parser.add_argument(
-        "--upper-bound-fw-weight",
-        default=3,
-        type=float,
-        help="upper bound of search space",
-    )
-    parser.add_argument(
-        "--lower-bound-lenpen",
-        default=0.0,
-        type=float,
-        help="lower bound of search space",
-    )
-    parser.add_argument(
-        "--upper-bound-lenpen",
-        default=3,
-        type=float,
-        help="upper bound of search space",
-    )
-    parser.add_argument(
-        "--fw-weight", type=float, default=None, help="weight on the fw model score"
-    )
-    parser.add_argument(
-        "--num-trials",
-        default=1000,
-        type=int,
-        help="number of trials to do for random search",
-    )
-
-    args = options.parse_args_and_arch(parser)
-    main(args)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/transform_eos_lang_pair_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/transform_eos_lang_pair_dataset.py
deleted file mode 100644
index e21144a88e0038c2f35711333a40315613004256..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/transform_eos_lang_pair_dataset.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-from typing import Optional
-
-import torch
-
-from . import FairseqDataset
-
-
-class TransformEosLangPairDataset(FairseqDataset):
-    """A :class:`~fairseq.data.FairseqDataset` wrapper that transform bos on
-    collated samples of language pair dataset.
-
-    Note that the transformation is applied in :func:`collater`.
-
-    Args:
-        dataset (~fairseq.data.FairseqDataset): dataset that collates sample into
-            LanguagePairDataset schema
-        src_eos (int): original source end-of-sentence symbol index to be replaced
-        new_src_eos (int, optional): new end-of-sentence symbol index to replace source eos symbol
-        tgt_bos (int, optional): original target beginning-of-sentence symbol index to be replaced
-        new_tgt_bos (int, optional): new beginning-of-sentence symbol index to replace at the
-            beginning of 'prev_output_tokens'
-    """
-
-    def __init__(
-        self,
-        dataset: FairseqDataset,
-        src_eos: int,
-        new_src_eos: Optional[int] = None,
-        tgt_bos: Optional[int] = None,
-        new_tgt_bos: Optional[int] = None,
-    ):
-        self.dataset = dataset
-        self.src_eos = src_eos
-        self.new_src_eos = new_src_eos
-        self.tgt_bos = tgt_bos
-        self.new_tgt_bos = new_tgt_bos
-
-    def __getitem__(self, index):
-        return self.dataset[index]
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def collater(self, samples, **extra_args):
-        samples = self.dataset.collater(samples, **extra_args)
-        if len(samples) == 0:
-            return samples
-
-        if 'net_input' not in samples:
-            return samples
-
-        if self.new_src_eos is not None:
-            if self.dataset.left_pad_source:
-                assert (
-                    samples["net_input"]["src_tokens"][:, -1] != self.src_eos
-                ).sum() == 0
-                samples["net_input"]["src_tokens"][:, -1] = self.new_src_eos
-            else:
-                eos_idx = samples["net_input"]["src_lengths"] - 1
-                assert (
-                    samples["net_input"]["src_tokens"][
-                        torch.arange(eos_idx.size(0)), eos_idx
-                    ]
-                    != self.src_eos
-                ).sum() == 0
-                eos_idx = eos_idx.resize_(len(samples["net_input"]["src_lengths"]), 1)
-                samples["net_input"]["src_tokens"].scatter_(
-                    1, eos_idx, self.new_src_eos
-                )
-
-        if (
-            self.new_tgt_bos is not None
-            and "prev_output_tokens" in samples["net_input"]
-        ):
-            if self.dataset.left_pad_target:
-                # TODO: support different padding direction on target side
-                raise NotImplementedError(
-                    "TransformEosLangPairDataset does not implement --left-pad-target True option"
-                )
-            else:
-                assert (
-                    samples["net_input"]["prev_output_tokens"][:, 0] != self.tgt_bos
-                ).sum() == 0
-                samples["net_input"]["prev_output_tokens"][:, 0] = self.new_tgt_bos
-
-        return samples
-
-    def num_tokens(self, index):
-        return self.dataset.num_tokens(index)
-
-    def size(self, index):
-        return self.dataset.size(index)
-
-    @property
-    def sizes(self):
-        # dataset.sizes can be a dynamically computed sizes:
-        return self.dataset.sizes
-
-    def ordered_indices(self):
-        return self.dataset.ordered_indices()
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        return self.dataset.prefetch(indices)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/fairseq_decoder.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/fairseq_decoder.py
deleted file mode 100644
index 4f1e8b52a2e0a50199050f11cc613ab02ca9febe..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/fairseq_decoder.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Dict, List, Optional, Tuple
-
-import torch.nn as nn
-from fairseq import utils
-from torch import Tensor
-
-
-class FairseqDecoder(nn.Module):
-    """Base class for decoders."""
-
-    def __init__(self, dictionary):
-        super().__init__()
-        self.dictionary = dictionary
-        self.onnx_trace = False
-        self.adaptive_softmax = None
-
-
-    def forward(self, prev_output_tokens, encoder_out=None, **kwargs):
-        """
-        Args:
-            prev_output_tokens (LongTensor): shifted output tokens of shape
-                `(batch, tgt_len)`, for teacher forcing
-            encoder_out (dict, optional): output from the encoder, used for
-                encoder-side attention
-
-        Returns:
-            tuple:
-                - the decoder's output of shape `(batch, tgt_len, vocab)`
-                - a dictionary with any model-specific outputs
-        """
-        x, extra = self.extract_features(
-            prev_output_tokens, encoder_out=encoder_out, **kwargs
-        )
-        x = self.output_layer(x)
-        return x, extra
-
-    def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs):
-        """
-        Returns:
-            tuple:
-                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
-                - a dictionary with any model-specific outputs
-        """
-        raise NotImplementedError
-
-    def output_layer(self, features, **kwargs):
-        """
-        Project features to the default output size, e.g., vocabulary size.
-
-        Args:
-            features (Tensor): features returned by *extract_features*.
-        """
-        raise NotImplementedError
-
-    def get_normalized_probs(
-        self,
-        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
-        log_probs: bool,
-        sample: Optional[Dict[str, Tensor]] = None,
-    ):
-        """Get normalized probabilities (or log probs) from a net's output."""
-        return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
-
-    # TorchScript doesn't support super() method so that the scriptable Subclass
-    # can't access the base class model in Torchscript.
-    # Current workaround is to add a helper function with different name and
-    # call the helper function from scriptable Subclass.
-    def get_normalized_probs_scriptable(
-        self,
-        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
-        log_probs: bool,
-        sample: Optional[Dict[str, Tensor]] = None,
-    ):
-        """Get normalized probabilities (or log probs) from a net's output."""
-
-        if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None:
-            if sample is not None:
-                assert "target" in sample
-                target = sample["target"]
-            else:
-                target = None
-            out = self.adaptive_softmax.get_log_prob(net_output[0], target=target)
-            return out.exp_() if not log_probs else out
-
-        logits = net_output[0]
-        if log_probs:
-            return utils.log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
-        else:
-            return utils.softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
-
-    def max_positions(self):
-        """Maximum input length supported by the decoder."""
-        return 1e6  # an arbitrary large number
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade old state dicts to work with newer code."""
-        return state_dict
-
-    def prepare_for_onnx_export_(self):
-        self.onnx_trace = True
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/tasks/fairseq_task.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/tasks/fairseq_task.py
deleted file mode 100644
index d671f17cf16a2493b3615b036d9d986e8b19736e..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/tasks/fairseq_task.py
+++ /dev/null
@@ -1,668 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import warnings
-from argparse import Namespace
-from typing import Any, Callable, Dict, List
-
-import torch
-from fairseq import metrics, search, tokenizer, utils
-from fairseq.data import Dictionary, FairseqDataset, data_utils, encoders, iterators
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.utils import gen_parser_from_dataclass
-from fairseq.optim.amp_optimizer import AMPOptimizer
-from omegaconf import DictConfig
-
-
-logger = logging.getLogger(__name__)
-
-
-class StatefulContainer(object):
-
-    def __init__(self):
-        self._state = dict()
-        self._factories = dict()
-
-    def add_factory(self, name, factory: Callable[[], Any]):
-        self._factories[name] = factory
-
-    def merge_state_dict(self, state_dict: Dict[str, Any]):
-        self._state.update(state_dict)
-
-    @property
-    def state_dict(self) -> Dict[str, Any]:
-        return self._state
-
-    def __getattr__(self, name):
-        if name not in self._state and name in self._factories:
-            self._state[name] = self._factories[name]()
-
-        if name in self._state:
-            return self._state[name]
-
-        raise AttributeError(f"Task state has no factory for attribute {name}")
-
-
-class FairseqTask(object):
-    """
-    Tasks store dictionaries and provide helpers for loading/iterating over
-    Datasets, initializing the Model/Criterion and calculating the loss.
-
-    Tasks have limited statefulness. In particular, state that needs to be
-    saved to/loaded from checkpoints needs to be stored in the `self.state`
-    :class:`StatefulContainer` object. For example::
-
-        self.state.add_factory("dictionary", self.load_dictionary)
-        print(self.state.dictionary)  # calls self.load_dictionary()
-
-    This is necessary so that when loading checkpoints, we can properly
-    recreate the task state after initializing the task instance.
-    """
-
-    @classmethod
-    def add_args(cls, parser):
-        """Add task-specific arguments to the parser."""
-        dc = getattr(cls, "__dataclass", None)
-        if dc is not None:
-            gen_parser_from_dataclass(parser, dc())
-
-    @staticmethod
-    def logging_outputs_can_be_summed(criterion) -> bool:
-        """
-        Whether the logging outputs returned by `train_step` and `valid_step` can
-        be summed across workers prior to calling `aggregate_logging_outputs`.
-        Setting this to True will improves distributed training speed.
-        """
-        return criterion.logging_outputs_can_be_summed()
-
-    def __init__(self, cfg: FairseqDataclass, **kwargs):
-        self.cfg = cfg
-        self.datasets = dict()
-        self.dataset_to_epoch_iter = dict()
-        self.state = StatefulContainer()
-
-    @classmethod
-    def load_dictionary(cls, filename):
-        """Load the dictionary from the filename
-
-        Args:
-            filename (str): the filename
-        """
-        return Dictionary.load(filename)
-
-    @classmethod
-    def build_dictionary(
-        cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8
-    ):
-        """Build the dictionary
-
-        Args:
-            filenames (list): list of filenames
-            workers (int): number of concurrent workers
-            threshold (int): defines the minimum word count
-            nwords (int): defines the total number of words in the final dictionary,
-                including special symbols
-            padding_factor (int): can be used to pad the dictionary size to be a
-                multiple of 8, which is important on some hardware (e.g., Nvidia
-                Tensor Cores).
-        """
-        d = Dictionary()
-        for filename in filenames:
-            Dictionary.add_file_to_dictionary(
-                filename, d, tokenizer.tokenize_line, workers
-            )
-        d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor)
-        return d
-
-    @classmethod
-    def setup_task(cls, cfg: DictConfig, **kwargs):
-        """Setup the task (e.g., load dictionaries).
-
-        Args:
-            cfg (omegaconf.DictConfig): parsed command-line arguments
-        """
-        return cls(cfg, **kwargs)
-
-    def has_sharded_data(self, split):
-        return os.pathsep in getattr(self.cfg, "data", "")
-
-    def load_dataset(
-        self,
-        split: str,
-        combine: bool = False,
-        task_cfg: FairseqDataclass = None,
-        **kwargs
-    ):
-        """Load a given dataset split.
-
-        Args:
-            split (str): name of the split (e.g., train, valid, test)
-            combine (bool): combines a split segmented into pieces into one dataset
-            task_cfg (FairseqDataclass): optional task configuration stored in the checkpoint that can be used
-                                         to load datasets
-        """
-        raise NotImplementedError
-
-    def dataset(self, split):
-        """
-        Return a loaded dataset split.
-
-        Args:
-            split (str): name of the split (e.g., train, valid, test)
-
-        Returns:
-            a :class:`~fairseq.data.FairseqDataset` corresponding to *split*
-        """
-        from fairseq.data import FairseqDataset
-
-        if split not in self.datasets:
-            raise KeyError("Dataset not loaded: " + split)
-        if not isinstance(self.datasets[split], FairseqDataset):
-            raise TypeError("Datasets are expected to be of type FairseqDataset")
-        return self.datasets[split]
-
-    def filter_indices_by_size(
-        self, indices, dataset, max_positions=None, ignore_invalid_inputs=False
-    ):
-        """
-        Filter examples that are too large
-
-        Args:
-            indices (np.array): original array of sample indices
-            dataset (~fairseq.data.FairseqDataset): dataset to batch
-            max_positions (optional): max sentence length supported by the
-                model (default: None).
-            ignore_invalid_inputs (bool, optional): don't raise Exception for
-                sentences that are too long (default: False).
-        Returns:
-            np.array: array of filtered sample indices
-        """
-        indices, ignored = dataset.filter_indices_by_size(indices, max_positions)
-        if len(ignored) > 0:
-            if not ignore_invalid_inputs:
-                raise Exception(
-                    (
-                        "Size of sample #{} is invalid (={}) since max_positions={}, "
-                        "skip this example with --skip-invalid-size-inputs-valid-test"
-                    ).format(ignored[0], dataset.size(ignored[0]), max_positions)
-                )
-            logger.warning(
-                (
-                    "{:,} samples have invalid sizes and will be skipped, "
-                    "max_positions={}, first few sample ids={}"
-                ).format(len(ignored), max_positions, ignored[:10])
-            )
-        return indices
-
-    def can_reuse_epoch_itr(self, dataset):
-        # We can reuse the epoch iterator across epochs as long as the dataset
-        # hasn't disabled it. We default to ``False`` here, although in practice
-        # this will be ``True`` for most datasets that inherit from
-        # ``FairseqDataset`` due to the base implementation there.
-        return getattr(dataset, "can_reuse_epoch_itr_across_epochs", False)
-
-    def get_batch_iterator(
-        self,
-        dataset,
-        max_tokens=None,
-        max_sentences=None,
-        max_positions=None,
-        ignore_invalid_inputs=False,
-        required_batch_size_multiple=1,
-        seed=1,
-        num_shards=1,
-        shard_id=0,
-        num_workers=0,
-        epoch=1,
-        data_buffer_size=0,
-        disable_iterator_cache=False,
-    ):
-        """
-        Get an iterator that yields batches of data from the given dataset.
-
-        Args:
-            dataset (~fairseq.data.FairseqDataset): dataset to batch
-            max_tokens (int, optional): max number of tokens in each batch
-                (default: None).
-            max_sentences (int, optional): max number of sentences in each
-                batch (default: None).
-            max_positions (optional): max sentence length supported by the
-                model (default: None).
-            ignore_invalid_inputs (bool, optional): don't raise Exception for
-                sentences that are too long (default: False).
-            required_batch_size_multiple (int, optional): require batch size to
-                be a multiple of N (default: 1).
-            seed (int, optional): seed for random number generator for
-                reproducibility (default: 1).
-            num_shards (int, optional): shard the data iterator into N
-                shards (default: 1).
-            shard_id (int, optional): which shard of the data iterator to
-                return (default: 0).
-            num_workers (int, optional): how many subprocesses to use for data
-                loading. 0 means the data will be loaded in the main process
-                (default: 0).
-            epoch (int, optional): the epoch to start the iterator from
-                (default: 1).
-            data_buffer_size (int, optional): number of batches to
-                preload (default: 0).
-            disable_iterator_cache (bool, optional): don't cache the
-                EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`)
-                (default: False).
-        Returns:
-            ~fairseq.iterators.EpochBatchIterator: a batched iterator over the
-                given dataset split
-        """
-        can_reuse_epoch_itr = not disable_iterator_cache and self.can_reuse_epoch_itr(
-            dataset
-        )
-        if can_reuse_epoch_itr and dataset in self.dataset_to_epoch_iter:
-            logger.debug("reusing EpochBatchIterator for epoch {}".format(epoch))
-            return self.dataset_to_epoch_iter[dataset]
-
-        assert isinstance(dataset, FairseqDataset)
-
-        # initialize the dataset with the correct starting epoch
-        dataset.set_epoch(epoch)
-
-        # get indices ordered by example size
-        with data_utils.numpy_seed(seed):
-            indices = dataset.ordered_indices()
-
-        # filter examples that are too large
-        if max_positions is not None:
-            indices = self.filter_indices_by_size(
-                indices, dataset, max_positions, ignore_invalid_inputs
-            )
-
-        # create mini-batches with given size constraints
-        batch_sampler = dataset.batch_by_size(
-            indices,
-            max_tokens=max_tokens,
-            max_sentences=max_sentences,
-            required_batch_size_multiple=required_batch_size_multiple,
-        )
-
-        # return a reusable, sharded iterator
-        epoch_iter = iterators.EpochBatchIterator(
-            dataset=dataset,
-            collate_fn=dataset.collater,
-            batch_sampler=batch_sampler,
-            seed=seed,
-            num_shards=num_shards,
-            shard_id=shard_id,
-            num_workers=num_workers,
-            epoch=epoch,
-            buffer_size=data_buffer_size,
-        )
-
-        if can_reuse_epoch_itr:
-            self.dataset_to_epoch_iter[dataset] = epoch_iter
-
-        return epoch_iter
-
-    def build_model(self, cfg: FairseqDataclass):
-        """
-        Build the :class:`~fairseq.models.BaseFairseqModel` instance for this
-        task.
-
-        Args:
-            cfg (FairseqDataclass): configuration object
-
-        Returns:
-            a :class:`~fairseq.models.BaseFairseqModel` instance
-        """
-        from fairseq import models, quantization_utils
-
-        model = models.build_model(cfg, self)
-        model = quantization_utils.quantize_model_scalar(model, cfg)
-        return model
-
-    def build_criterion(self, cfg: DictConfig):
-        """
-        Build the :class:`~fairseq.criterions.FairseqCriterion` instance for
-        this task.
-
-        Args:
-            cfg (omegaconf.DictConfig): configration object
-
-        Returns:
-            a :class:`~fairseq.criterions.FairseqCriterion` instance
-        """
-        from fairseq import criterions
-
-        return criterions.build_criterion(cfg, self)
-
-    def build_generator(
-        self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None,
-    ):
-        """
-        Build a :class:`~fairseq.SequenceGenerator` instance for this
-        task.
-
-        Args:
-            models (List[~fairseq.models.FairseqModel]): ensemble of models
-            args (fairseq.dataclass.configs.GenerationConfig):
-                configuration object (dataclass) for generation
-            extra_gen_cls_kwargs (Dict[str, Any]): extra options to pass
-                through to SequenceGenerator
-            prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]]):
-                If provided, this function constrains the beam search to
-                allowed tokens only at each step. The provided function
-                should take 2 arguments: the batch ID (`batch_id: int`)
-                and a unidimensional tensor of token ids (`inputs_ids:
-                torch.Tensor`). It has to return a `List[int]` with the
-                allowed tokens for the next generation step conditioned
-                on the previously generated tokens (`inputs_ids`) and
-                the batch ID (`batch_id`). This argument is useful for
-                constrained generation conditioned on the prefix, as
-                described in "Autoregressive Entity Retrieval"
-                (https://arxiv.org/abs/2010.00904) and
-                https://github.com/facebookresearch/GENRE.
-        """
-        if getattr(args, "score_reference", False):
-            from fairseq.sequence_scorer import SequenceScorer
-
-            return SequenceScorer(
-                self.target_dictionary,
-                compute_alignment=getattr(args, "print_alignment", False),
-            )
-
-        from fairseq.sequence_generator import (
-            SequenceGenerator,
-            SequenceGeneratorWithAlignment,
-        )
-
-        # Choose search strategy. Defaults to Beam Search.
-        sampling = getattr(args, "sampling", False)
-        sampling_topk = getattr(args, "sampling_topk", -1)
-        sampling_topp = getattr(args, "sampling_topp", -1.0)
-        diverse_beam_groups = getattr(args, "diverse_beam_groups", -1)
-        diverse_beam_strength = getattr(args, "diverse_beam_strength", 0.5)
-        match_source_len = getattr(args, "match_source_len", False)
-        diversity_rate = getattr(args, "diversity_rate", -1)
-        constrained = getattr(args, "constraints", False)
-        if prefix_allowed_tokens_fn is None:
-            prefix_allowed_tokens_fn = getattr(args, "prefix_allowed_tokens_fn", None)
-        if (
-            sum(
-                int(cond)
-                for cond in [
-                    sampling,
-                    diverse_beam_groups > 0,
-                    match_source_len,
-                    diversity_rate > 0,
-                ]
-            )
-            > 1
-        ):
-            raise ValueError("Provided Search parameters are mutually exclusive.")
-        assert sampling_topk < 0 or sampling, "--sampling-topk requires --sampling"
-        assert sampling_topp < 0 or sampling, "--sampling-topp requires --sampling"
-
-        if sampling:
-            search_strategy = search.Sampling(
-                self.target_dictionary, sampling_topk, sampling_topp
-            )
-        elif diverse_beam_groups > 0:
-            search_strategy = search.DiverseBeamSearch(
-                self.target_dictionary, diverse_beam_groups, diverse_beam_strength
-            )
-        elif match_source_len:
-            # this is useful for tagging applications where the output
-            # length should match the input length, so we hardcode the
-            # length constraints for simplicity
-            search_strategy = search.LengthConstrainedBeamSearch(
-                self.target_dictionary,
-                min_len_a=1,
-                min_len_b=0,
-                max_len_a=1,
-                max_len_b=0,
-            )
-        elif diversity_rate > -1:
-            search_strategy = search.DiverseSiblingsSearch(
-                self.target_dictionary, diversity_rate
-            )
-        elif constrained:
-            search_strategy = search.LexicallyConstrainedBeamSearch(
-                self.target_dictionary, args.constraints
-            )
-        elif prefix_allowed_tokens_fn:
-            search_strategy = search.PrefixConstrainedBeamSearch(
-                self.target_dictionary, prefix_allowed_tokens_fn
-            )
-        else:
-            search_strategy = search.BeamSearch(self.target_dictionary)
-
-        extra_gen_cls_kwargs = extra_gen_cls_kwargs or {}
-        if seq_gen_cls is None:
-            if getattr(args, "print_alignment", False):
-                seq_gen_cls = SequenceGeneratorWithAlignment
-                extra_gen_cls_kwargs["print_alignment"] = args.print_alignment
-            else:
-                seq_gen_cls = SequenceGenerator
-
-        return seq_gen_cls(
-            models,
-            self.target_dictionary,
-            beam_size=getattr(args, "beam", 5),
-            max_len_a=getattr(args, "max_len_a", 0),
-            max_len_b=getattr(args, "max_len_b", 200),
-            min_len=getattr(args, "min_len", 1),
-            normalize_scores=(not getattr(args, "unnormalized", False)),
-            len_penalty=getattr(args, "lenpen", 1),
-            unk_penalty=getattr(args, "unkpen", 0),
-            temperature=getattr(args, "temperature", 1.0),
-            match_source_len=getattr(args, "match_source_len", False),
-            no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0),
-            search_strategy=search_strategy,
-            **extra_gen_cls_kwargs,
-        )
-
-    def train_step(
-        self, sample, model, criterion, optimizer, update_num, ignore_grad=False, **extra_kwargs
-    ):
-        """
-        Do forward and backward, and return the loss as computed by *criterion*
-        for the given *model* and *sample*.
-
-        Args:
-            sample (dict): the mini-batch. The format is defined by the
-                :class:`~fairseq.data.FairseqDataset`.
-            model (~fairseq.models.BaseFairseqModel): the model
-            criterion (~fairseq.criterions.FairseqCriterion): the criterion
-            optimizer (~fairseq.optim.FairseqOptimizer): the optimizer
-            update_num (int): the current update
-            ignore_grad (bool): multiply loss by 0 if this is set to True
-
-        Returns:
-            tuple:
-                - the loss
-                - the sample size, which is used as the denominator for the
-                  gradient
-                - logging outputs to display while training
-        """
-        model.train()
-        model.set_num_updates(update_num)
-        with torch.autograd.profiler.record_function("forward"):
-            with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))):
-                loss, sample_size, logging_output = criterion(model, sample, update_num=update_num)
-        if ignore_grad:
-            loss *= 0
-        with torch.autograd.profiler.record_function("backward"):
-            optimizer.backward(loss)
-        return loss, sample_size, logging_output
-
-    def valid_step(self, sample, model, criterion, **extra_kwargs):
-        model.eval()
-        with torch.no_grad():
-            loss, sample_size, logging_output = criterion(model, sample)
-        return loss, sample_size, logging_output
-
-    def optimizer_step(self, optimizer, model, update_num):
-        optimizer.step()
-
-    def build_dataset_for_inference(
-        self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs
-    ) -> torch.utils.data.Dataset:
-        raise NotImplementedError
-
-    def inference_step(
-        self, generator, models, sample, prefix_tokens=None, constraints=None
-    ):
-        with torch.no_grad():
-            return generator.generate(
-                models, sample, prefix_tokens=prefix_tokens, constraints=constraints
-            )
-
-    def begin_epoch(self, epoch, model):
-        """Hook function called before the start of each epoch."""
-        pass
-
-    def begin_valid_epoch(self, epoch, model):
-        """Hook function called before the start of each validation epoch."""
-        pass
-
-    def aggregate_logging_outputs(self, logging_outputs, criterion):
-        """[deprecated] Aggregate logging outputs from data parallel training."""
-        utils.deprecation_warning(
-            "The aggregate_logging_outputs API is deprecated. "
-            "Please use the reduce_metrics API instead."
-        )
-        with metrics.aggregate() as agg:
-            self.reduce_metrics(logging_outputs, criterion)
-            return agg.get_smoothed_values()
-
-    def reduce_metrics(self, logging_outputs, criterion):
-        """Aggregate logging outputs from data parallel training."""
-        # backward compatibility for tasks that override aggregate_logging_outputs
-        base_func = FairseqTask.aggregate_logging_outputs
-        self_func = getattr(self, "aggregate_logging_outputs").__func__
-        if self_func is not base_func:
-            utils.deprecation_warning(
-                "Tasks should implement the reduce_metrics API. "
-                "Falling back to deprecated aggregate_logging_outputs API."
-            )
-            agg_logging_outputs = self.aggregate_logging_outputs(
-                logging_outputs, criterion
-            )
-            for k, v in agg_logging_outputs.items():
-                metrics.log_scalar(k, v)
-            return
-
-        if not any("ntokens" in log for log in logging_outputs):
-            warnings.warn(
-                "ntokens not found in Criterion logging outputs, cannot log wpb or wps"
-            )
-        else:
-            ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-            metrics.log_scalar("wpb", ntokens, priority=180, round=1)
-            metrics.log_speed("wps", ntokens, priority=90, round=1)
-
-        if not any("nsentences" in log for log in logging_outputs):
-            warnings.warn(
-                "nsentences not found in Criterion logging outputs, cannot log bsz"
-            )
-        else:
-            nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
-            metrics.log_scalar("bsz", nsentences, priority=190, round=1)
-
-        criterion.__class__.reduce_metrics(logging_outputs)
-
-    def state_dict(self):
-        if self.state is not None:
-            return self.state.state_dict
-        return {}
-
-    def load_state_dict(self, state_dict: Dict[str, Any]):
-        if self.state is not None:
-            self.state.merge_state_dict(state_dict)
-
-    def max_positions(self):
-        """Return the max input length allowed by the task."""
-        return None
-
-    @property
-    def source_dictionary(self):
-        """Return the source :class:`~fairseq.data.Dictionary` (if applicable
-        for this task)."""
-        raise NotImplementedError
-
-    @property
-    def target_dictionary(self):
-        """Return the target :class:`~fairseq.data.Dictionary` (if applicable
-        for this task)."""
-        raise NotImplementedError
-
-    def build_tokenizer(self, args):
-        """Build the pre-tokenizer for this task."""
-        return encoders.build_tokenizer(args)
-
-    def build_bpe(self, args):
-        """Build the tokenizer for this task."""
-        return encoders.build_bpe(args)
-
-    def get_interactive_tokens_and_lengths(self, lines, encode_fn):
-        tokens = [
-            self.source_dictionary.encode_line(
-                encode_fn(src_str), add_if_not_exist=False
-            ).long()
-            for src_str in lines
-        ]
-        lengths = [t.numel() for t in tokens]
-        return tokens, lengths
-
-
-class LegacyFairseqTask(FairseqTask):
-    def __init__(self, args: Namespace):
-        super().__init__(None)
-        self.args = args
-        self.datasets = {}
-        self.dataset_to_epoch_iter = {}
-
-    @classmethod
-    def setup_task(cls, args: Namespace, **kwargs):
-        """Setup the task (e.g., load dictionaries).
-
-        Args:
-            args (argparse.Namespace): parsed command-line arguments
-        """
-        return cls(args, **kwargs)
-
-    def has_sharded_data(self, split):
-        return os.pathsep in getattr(self.args, "data", "")
-
-    def build_model(self, args: Namespace):
-        """
-        Build the :class:`~fairseq.models.BaseFairseqModel` instance for this
-        task.
-
-        Args:
-            args (argparse.Namespace): parsed command-line arguments
-
-        Returns:
-            a :class:`~fairseq.models.BaseFairseqModel` instance
-        """
-        from fairseq import models, quantization_utils
-
-        model = models.build_model(args, self)
-        model = quantization_utils.quantize_model_scalar(model, args)
-        return model
-
-    def build_criterion(self, args: Namespace):
-        """
-        Build the :class:`~fairseq.criterions.FairseqCriterion` instance for
-        this task.
-
-        Args:
-            args (argparse.Namespace): parsed command-line arguments
-
-        Returns:
-            a :class:`~fairseq.criterions.FairseqCriterion` instance
-        """
-        from fairseq import criterions
-
-        return criterions.build_criterion(args, self)
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/data/resample.sh b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/data/resample.sh
deleted file mode 100644
index 8489b0a0056d46a93d24db8dba173ad7a4b8a44a..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/scripts/data/resample.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-input_wav_path='/home/harveen/en/iitm_data/english/wav/'
-output_wav_path='/home/harveen/en/iitm_data/english/wav_22k/'
-output_sample_rate=22050
-
-#######################
-
-dir=$PWD
-parentdir="$(dirname "$dir")"
-parentdir="$(dirname "$parentdir")"
-
-mkdir -p $output_wav_path
-python $parentdir/utils/data/resample.py -i $input_wav_path -o $output_wav_path -s $output_sample_rate
-
-python $parentdir/utils/data/duration.py $output_wav_path
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/test_data/__init__.py b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/test_data/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HoangHa/IELTS_Speaking_GPT/README.md b/spaces/HoangHa/IELTS_Speaking_GPT/README.md
deleted file mode 100644
index 3d6d363ca3094abf3909a62aa0eb3125229fc398..0000000000000000000000000000000000000000
--- a/spaces/HoangHa/IELTS_Speaking_GPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: IELTS Speaking GPT
-emoji: 🔥
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HuggingFaceH4/falcon-chat-demo-for-blog/README.md b/spaces/HuggingFaceH4/falcon-chat-demo-for-blog/README.md
deleted file mode 100644
index 69f8d5e174f12ecd10a58454dc895dfd502bea82..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/falcon-chat-demo-for-blog/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Falcon-Chat (demo for blog post)
-emoji: 💬
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: HuggingFaceH4/falcon-chat
----
diff --git a/spaces/Hxxx/finding_friends/app.py b/spaces/Hxxx/finding_friends/app.py
deleted file mode 100644
index b8e324b9c29780cc194b84219d4782bd519931d7..0000000000000000000000000000000000000000
--- a/spaces/Hxxx/finding_friends/app.py
+++ /dev/null
@@ -1,172 +0,0 @@
-### ----------------------------- ###
-###           libraries           ###
-### ----------------------------- ###
-
-import gradio as gr
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn import metrics
-
-
-### ------------------------------ ###
-###       data transformation      ###
-### ------------------------------ ###
-
-# load dataset
-uncleaned_data = pd.read_csv('data.csv')
-
-# remove timestamp from dataset (always first column)
-uncleaned_data = uncleaned_data.iloc[: , 1:]
-data = pd.DataFrame()
-
-# keep track of which columns are categorical and what 
-# those columns' value mappings are
-# structure: {colname1: {...}, colname2: {...} }
-cat_value_dicts = {}
-final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
-
-# for each column...
-for (colname, colval) in uncleaned_data.iteritems():
-
-  # check if col is already a number; if so, add col directly
-  # to new dataframe and skip to next column
-  if isinstance(colval.values[0], (np.integer, float)):
-    data[colname] = uncleaned_data[colname].copy()
-    continue
-
-  # structure: {0: "lilac", 1: "blue", ...}
-  new_dict = {}
-  val = 0 # first index per column
-  transformed_col_vals = [] # new numeric datapoints
-
-  # if not, for each item in that column...
-  for (row, item) in enumerate(colval.values):
-    
-    # if item is not in this col's dict...
-    if item not in new_dict:
-      new_dict[item] = val
-      val += 1
-    
-    # then add numerical value to transformed dataframe
-    transformed_col_vals.append(new_dict[item])
-  
-  # reverse dictionary only for final col (0, 1) => (vals)
-  if colname == final_colname:
-    new_dict = {value : key for (key, value) in new_dict.items()}
-
-  cat_value_dicts[colname] = new_dict
-  data[colname] = transformed_col_vals
-
-
-### -------------------------------- ###
-###           model training         ###
-### -------------------------------- ###
-
-# select features and predicton; automatically selects last column as prediction
-cols = len(data.columns)
-num_features = cols - 1
-x = data.iloc[: , :num_features]
-y = data.iloc[: , num_features:]
-
-# split data into training and testing sets
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
-
-# instantiate the model (using default parameters)
-model = LogisticRegression()
-model.fit(x_train, y_train.values.ravel())
-y_pred = model.predict(x_test)
-
-
-### -------------------------------- ###
-###        article generation        ###
-### -------------------------------- ###
-# borrow file reading function from reader.py
-
-def get_feat():
-  feats = [abs(x) for x in model.coef_[0]]
-  max_val = max(feats)
-  idx = feats.index(max_val)
-  return data.columns[idx]
-  
-acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%"
-most_imp_feat = get_feat()
-# info = get_article(acc, most_imp_feat)
-
-
-
-### ------------------------------- ###
-###        interface creation       ###
-### ------------------------------- ###
-
-
-# predictor for generic number of features
-def general_predictor(*args):
-  features = []
-
-  # transform categorical input
-  for colname, arg in zip(data.columns, args):
-    if (colname in cat_value_dicts):
-      features.append(cat_value_dicts[colname][arg])
-    else:
-      features.append(arg)
-
-  # predict single datapoint
-  new_input = [features]
-  result = model.predict(new_input)
-  return cat_value_dicts[final_colname][result[0]]
-
-# add data labels to replace those lost via star-args
-
-
-block = gr.Blocks()
-
-with open('info.md') as f:
-  with block:
-    gr.Markdown(f.readline())
-    gr.Markdown('Take the quiz to get a personalized recommendation using AI.')
-    
-    with gr.Row():
-      with gr.Box():
-        inputls = []
-        for colname in data.columns:
-          # skip last column
-          if colname == final_colname:
-            continue
-          
-          # access categories dict if data is categorical
-          # otherwise, just use a number input
-          if colname in cat_value_dicts:
-            radio_options = list(cat_value_dicts[colname].keys())
-            inputls.append(gr.inputs.Dropdown(choices=radio_options, type="value", label=colname))
-          else:
-            # add numerical input
-            inputls.append(gr.inputs.Number(label=colname))
-          gr.Markdown("<br />")
-        
-        submit = gr.Button("Click to see your personalized result!", variant="primary")
-        gr.Markdown("<br />")
-        output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here")
-        
-        submit.click(fn=general_predictor, inputs=inputls, outputs=output)
-        gr.Markdown("<br />")
-        
-        with gr.Row():
-          with gr.Box():
-            gr.Markdown(f"<h3>Accuracy: </h3>{acc}")
-          with gr.Box():
-            gr.Markdown(f"<h3>Most important feature: </h3>{most_imp_feat}")
-        
-        gr.Markdown("<br />")
-        
-        with gr.Box():
-          gr.Markdown('''⭐ Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for <em>that dataset</em>. Model accuracy and most important feature can be helpful for understanding how the model works, but <em>should not be considered absolute facts about the real world</em>.''')
-        
-      with gr.Box():
-        with open('info.md') as f:
-          f.readline()
-          gr.Markdown(f.read())
-
-# show the interface
-block.launch()
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/stories/README.md b/spaces/ICML2022/OFA/fairseq/examples/stories/README.md
deleted file mode 100644
index 588941eddc5f0280f5254affd40ef49de874c885..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/stories/README.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# Hierarchical Neural Story Generation (Fan et al., 2018)
-
-The following commands provide an example of pre-processing data, training a model, and generating text for story generation with the WritingPrompts dataset.
-
-## Pre-trained models
-
-Description | Dataset | Model | Test set(s)
----|---|---|---
-Stories with Convolutional Model <br> ([Fan et al., 2018](https://arxiv.org/abs/1805.04833)) | [WritingPrompts](https://dl.fbaipublicfiles.com/fairseq/data/writingPrompts.tar.gz) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.bz2) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2)
-
-We provide sample stories generated by the [convolutional seq2seq model](https://dl.fbaipublicfiles.com/fairseq/data/seq2seq_stories.txt) and [fusion model](https://dl.fbaipublicfiles.com/fairseq/data/fusion_stories.txt) from [Fan et al., 2018](https://arxiv.org/abs/1805.04833). The corresponding prompts for the fusion model can be found [here](https://dl.fbaipublicfiles.com/fairseq/data/fusion_prompts.txt). Note that there are unk in the file, as we modeled a small full vocabulary (no BPE or pre-training). We did not use these unk prompts for human evaluation.
-
-## Dataset
-
-The dataset can be downloaded like this:
-
-```bash
-cd examples/stories
-curl https://dl.fbaipublicfiles.com/fairseq/data/writingPrompts.tar.gz | tar xvzf -
-```
-
-and contains a train, test, and valid split. The dataset is described here: https://arxiv.org/abs/1805.04833. We model only the first 1000 words of each story, including one newLine token.
-
-## Example usage
-
-First we will preprocess the dataset. Note that the dataset release is the full data, but the paper models the first 1000 words of each story. Here is example code that trims the dataset to the first 1000 words of each story:
-```python
-data = ["train", "test", "valid"]
-for name in data:
-    with open(name + ".wp_target") as f:
-        stories = f.readlines()
-    stories = [" ".join(i.split()[0:1000]) for i in stories]
-    with open(name + ".wp_target", "w") as o:
-        for line in stories:
-            o.write(line.strip() + "\n")
-```
-
-Once we've trimmed the data we can binarize it and train our model:
-```bash
-# Binarize the dataset:
-export TEXT=examples/stories/writingPrompts
-fairseq-preprocess --source-lang wp_source --target-lang wp_target \
-    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
-    --destdir data-bin/writingPrompts --padding-factor 1 --thresholdtgt 10 --thresholdsrc 10
-
-# Train the model:
-fairseq-train data-bin/writingPrompts -a fconv_self_att_wp --lr 0.25 --optimizer nag --clip-norm 0.1 --max-tokens 1500 --lr-scheduler reduce_lr_on_plateau --decoder-attention True --encoder-attention False --criterion label_smoothed_cross_entropy --weight-decay .0000001 --label-smoothing 0 --source-lang wp_source --target-lang wp_target --gated-attention True --self-attention True --project-input True --pretrained False
-
-# Train a fusion model:
-# add the arguments: --pretrained True --pretrained-checkpoint path/to/checkpoint
-
-# Generate:
-# Note: to load the pretrained model at generation time, you need to pass in a model-override argument to communicate to the fusion model at generation time where you have placed the pretrained checkpoint. By default, it will load the exact path of the fusion model's pretrained model from training time. You should use model-override if you have moved the pretrained model (or are using our provided models). If you are generating from a non-fusion model, the model-override argument is not necessary.
-
-fairseq-generate data-bin/writingPrompts --path /path/to/trained/model/checkpoint_best.pt --batch-size 32 --beam 1 --sampling --sampling-topk 10 --temperature 0.8 --nbest 1 --model-overrides "{'pretrained_checkpoint':'/path/to/pretrained/model/checkpoint'}"
-```
-
-## Citation
-```bibtex
-@inproceedings{fan2018hierarchical,
-  title = {Hierarchical Neural Story Generation},
-  author = {Fan, Angela and Lewis, Mike and Dauphin, Yann},
-  booktitle = {Conference of the Association for Computational Linguistics (ACL)},
-  year = 2018,
-}
-```
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/legacy/block_pair_dataset.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/legacy/block_pair_dataset.py
deleted file mode 100644
index ba069b46052286c531b4f9706d96788732cd2ad2..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/legacy/block_pair_dataset.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import numpy as np
-import torch
-from fairseq.data import FairseqDataset
-
-
-class BlockPairDataset(FairseqDataset):
-    """Break a Dataset of tokens into sentence pair blocks for next sentence
-       prediction as well as masked language model.
-
-       High-level logics are:
-       1. break input tensor to tensor blocks
-       2. pair the blocks with 50% next sentence and 50% random sentence
-       3. return paired blocks as well as related segment labels
-
-    Args:
-        dataset (~torch.utils.data.Dataset): dataset to break into blocks
-        sizes: array of sentence lengths
-        dictionary: dictionary for the task
-        block_size: maximum block size
-        break_mode: mode for breaking copurs into block pairs. currently we support
-            2 modes
-            doc: respect document boundaries and each part of the pair should belong to on document
-            none: don't respect any boundary and cut tokens evenly
-        short_seq_prob: probability for generating shorter block pairs
-        doc_break_size: Size for empty line separating documents. Typically 1 if
-                        the sentences have eos, 0 otherwise.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        dictionary,
-        sizes,
-        block_size,
-        break_mode="doc",
-        short_seq_prob=0.1,
-        doc_break_size=1,
-    ):
-        super().__init__()
-        self.dataset = dataset
-        self.pad = dictionary.pad()
-        self.eos = dictionary.eos()
-        self.cls = dictionary.cls()
-        self.mask = dictionary.mask()
-        self.sep = dictionary.sep()
-        self.break_mode = break_mode
-        self.dictionary = dictionary
-        self.short_seq_prob = short_seq_prob
-        self.block_indices = []
-
-        assert len(dataset) == len(sizes)
-
-        if break_mode == "doc":
-            cur_doc = []
-            for sent_id, sz in enumerate(sizes):
-                assert doc_break_size == 0 or sz != 0, (
-                    "when doc_break_size is non-zero, we expect documents to be"
-                    "separated by a blank line with a single eos."
-                )
-                # empty line as document separator
-                if sz == doc_break_size:
-                    if len(cur_doc) == 0:
-                        continue
-                    self.block_indices.append(cur_doc)
-                    cur_doc = []
-                else:
-                    cur_doc.append(sent_id)
-            max_num_tokens = block_size - 3  # Account for [CLS], [SEP], [SEP]
-            self.sent_pairs = []
-            self.sizes = []
-            for doc_id, doc in enumerate(self.block_indices):
-                self._generate_sentence_pair(doc, doc_id, max_num_tokens, sizes)
-        elif break_mode is None or break_mode == "none":
-            # each block should have half of the block size since we are constructing block pair
-            sent_length = (block_size - 3) // 2
-            total_len = sum(dataset.sizes)
-            length = math.ceil(total_len / sent_length)
-
-            def block_at(i):
-                start = i * sent_length
-                end = min(start + sent_length, total_len)
-                return (start, end)
-
-            sent_indices = np.array([block_at(i) for i in range(length)])
-            sent_sizes = np.array([e - s for s, e in sent_indices])
-            dataset_index = self._sent_to_dataset_index(sent_sizes)
-
-            # pair sentences
-            self._pair_sentences(dataset_index)
-        else:
-            raise ValueError("Invalid break_mode: " + break_mode)
-
-    def _pair_sentences(self, dataset_index):
-        """
-        Give a list of evenly cut blocks/sentences, pair these sentences with 50%
-        consecutive sentences and 50% random sentences.
-        This is used for none break mode
-        """
-        # pair sentences
-        for sent_id, sent in enumerate(dataset_index):
-            next_sent_label = (
-                1 if np.random.rand() > 0.5 and sent_id != len(dataset_index) - 1 else 0
-            )
-            if next_sent_label:
-                next_sent = dataset_index[sent_id + 1]
-            else:
-                next_sent = dataset_index[
-                    self._skip_sampling(len(dataset_index), [sent_id, sent_id + 1])
-                ]
-            self.sent_pairs.append((sent, next_sent, next_sent_label))
-
-            # The current blocks don't include the special tokens but the
-            # sizes already account for this
-            self.sizes.append(3 + sent[3] + next_sent[3])
-
-    def _sent_to_dataset_index(self, sent_sizes):
-        """
-        Build index mapping block indices to the underlying dataset indices
-        """
-        dataset_index = []
-        ds_idx, ds_remaining = -1, 0
-        for to_consume in sent_sizes:
-            sent_size = to_consume
-            if ds_remaining == 0:
-                ds_idx += 1
-                ds_remaining = sent_sizes[ds_idx]
-            start_ds_idx = ds_idx
-            start_offset = sent_sizes[ds_idx] - ds_remaining
-            while to_consume > ds_remaining:
-                to_consume -= ds_remaining
-                ds_idx += 1
-                ds_remaining = sent_sizes[ds_idx]
-            ds_remaining -= to_consume
-            dataset_index.append(
-                (
-                    start_ds_idx,  # starting index in dataset
-                    start_offset,  # starting offset within starting index
-                    ds_idx,  # ending index in dataset
-                    sent_size,  # sentence length
-                )
-            )
-        assert ds_remaining == 0
-        assert ds_idx == len(self.dataset) - 1
-        return dataset_index
-
-    def _generate_sentence_pair(self, doc, doc_id, max_num_tokens, sizes):
-        """
-        Go through a single document and genrate sentence paris from it
-        """
-        current_chunk = []
-        current_length = 0
-        curr = 0
-        # To provide more randomness, we decrease target seq length for parts of
-        # samples (10% by default). Note that max_num_tokens is the hard threshold
-        # for batching and will never be changed.
-        target_seq_length = max_num_tokens
-        if np.random.random() < self.short_seq_prob:
-            target_seq_length = np.random.randint(2, max_num_tokens)
-        # loop through all sentences in document
-        while curr < len(doc):
-            sent_id = doc[curr]
-            current_chunk.append(sent_id)
-            current_length = sum(sizes[current_chunk])
-            # split chunk and generate pair when exceed target_seq_length or
-            # finish the loop
-            if curr == len(doc) - 1 or current_length >= target_seq_length:
-                # split the chunk into 2 parts
-                a_end = 1
-                if len(current_chunk) > 2:
-                    a_end = np.random.randint(1, len(current_chunk) - 1)
-                sent_a = current_chunk[:a_end]
-                len_a = sum(sizes[sent_a])
-                # generate next sentence label, note that if there is only 1 sentence
-                # in current chunk, label is always 0
-                next_sent_label = (
-                    1 if np.random.rand() > 0.5 and len(current_chunk) != 1 else 0
-                )
-                if not next_sent_label:
-                    # if next sentence label is 0, sample sent_b from a random doc
-                    target_b_length = target_seq_length - len_a
-                    rand_doc_id = self._skip_sampling(len(self.block_indices), [doc_id])
-                    random_doc = self.block_indices[rand_doc_id]
-                    random_start = np.random.randint(0, len(random_doc))
-                    sent_b = []
-                    len_b = 0
-                    for j in range(random_start, len(random_doc)):
-                        sent_b.append(random_doc[j])
-                        len_b = sum(sizes[sent_b])
-                        if len_b >= target_b_length:
-                            break
-                    # return the second part of the chunk since it's not used
-                    num_unused_segments = len(current_chunk) - a_end
-                    curr -= num_unused_segments
-                else:
-                    # if next sentence label is 1, use the second part of chunk as sent_B
-                    sent_b = current_chunk[a_end:]
-                    len_b = sum(sizes[sent_b])
-                # currently sent_a and sent_B may be longer than max_num_tokens,
-                # truncate them and return block idx and offsets for them
-                sent_a, sent_b = self._truncate_sentences(
-                    sent_a, sent_b, max_num_tokens
-                )
-                self.sent_pairs.append((sent_a, sent_b, next_sent_label))
-                self.sizes.append(3 + sent_a[3] + sent_b[3])
-                current_chunk = []
-            curr += 1
-
-    def _skip_sampling(self, total, skip_ids):
-        """
-        Generate a random integer which is not in skip_ids. Sample range is [0, total)
-        TODO: ids in skip_ids should be consecutive, we can extend it to more generic version later
-        """
-        rand_id = np.random.randint(total - len(skip_ids))
-        return rand_id if rand_id < min(skip_ids) else rand_id + len(skip_ids)
-
-    def _truncate_sentences(self, sent_a, sent_b, max_num_tokens):
-        """
-        Trancate a pair of sentence to limit total length under max_num_tokens
-        Logics:
-            1. Truncate longer sentence
-            2. Tokens to be truncated could be at the beginning or the end of the sentnce
-        Returns:
-            Truncated sentences represented by dataset idx
-        """
-        len_a, len_b = sum(self.dataset.sizes[sent_a]), sum(self.dataset.sizes[sent_b])
-        front_cut_a = front_cut_b = end_cut_a = end_cut_b = 0
-
-        while True:
-            total_length = (
-                len_a + len_b - front_cut_a - front_cut_b - end_cut_a - end_cut_b
-            )
-            if total_length <= max_num_tokens:
-                break
-
-            if len_a - front_cut_a - end_cut_a > len_b - front_cut_b - end_cut_b:
-                if np.random.rand() < 0.5:
-                    front_cut_a += 1
-                else:
-                    end_cut_a += 1
-            else:
-                if np.random.rand() < 0.5:
-                    front_cut_b += 1
-                else:
-                    end_cut_b += 1
-
-        # calculate ds indices as well as offsets and return
-        truncated_sent_a = self._cut_sentence(sent_a, front_cut_a, end_cut_a)
-        truncated_sent_b = self._cut_sentence(sent_b, front_cut_b, end_cut_b)
-        return truncated_sent_a, truncated_sent_b
-
-    def _cut_sentence(self, sent, front_cut, end_cut):
-        """
-        Cut a sentence based on the numbers of tokens to be cut from beginning and end
-        Represent the sentence as dataset idx and return
-        """
-        start_ds_idx, end_ds_idx, offset = sent[0], sent[-1], 0
-        target_len = sum(self.dataset.sizes[sent]) - front_cut - end_cut
-        while front_cut > 0:
-            if self.dataset.sizes[start_ds_idx] > front_cut:
-                offset += front_cut
-                break
-            else:
-                front_cut -= self.dataset.sizes[start_ds_idx]
-                start_ds_idx += 1
-        while end_cut > 0:
-            if self.dataset.sizes[end_ds_idx] > end_cut:
-                break
-            else:
-                end_cut -= self.dataset.sizes[end_ds_idx]
-                end_ds_idx -= 1
-        return start_ds_idx, offset, end_ds_idx, target_len
-
-    def _fetch_block(self, start_ds_idx, offset, end_ds_idx, length):
-        """
-        Fetch a block of tokens based on its dataset idx
-        """
-        buffer = torch.cat(
-            [self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)]
-        )
-        s, e = offset, offset + length
-        return buffer[s:e]
-
-    def __getitem__(self, index):
-        block1, block2, next_sent_label = self.sent_pairs[index]
-        block1 = self._fetch_block(*block1)
-        block2 = self._fetch_block(*block2)
-        return block1, block2, next_sent_label
-
-    def __len__(self):
-        return len(self.sizes)
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        prefetch_idx = set()
-        for index in indices:
-            for block1, block2, _ in [self.sent_pairs[index]]:
-                for ds_idx in range(block1[0], block1[2] + 1):
-                    prefetch_idx.add(ds_idx)
-                for ds_idx in range(block2[0], block2[2] + 1):
-                    prefetch_idx.add(ds_idx)
-        self.dataset.prefetch(prefetch_idx)
diff --git a/spaces/ICML2022/resefa/models/perceptual_model.py b/spaces/ICML2022/resefa/models/perceptual_model.py
deleted file mode 100644
index 7f0aaa82789f19e9f4760d3b42e00b44e3728ffa..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/models/perceptual_model.py
+++ /dev/null
@@ -1,519 +0,0 @@
-# python3.7
-"""Contains the VGG16 model, which is used for inference ONLY.
-
-VGG16 is commonly used for perceptual feature extraction. The model implemented
-in this file can be used for evaluation (like computing LPIPS, perceptual path
-length, etc.), OR be used in training for loss computation (like perceptual
-loss, etc.).
-
-The pre-trained model is officially shared by
-
-https://www.robots.ox.ac.uk/~vgg/research/very_deep/
-
-and ported by
-
-https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt
-
-Compared to the official VGG16 model, this ported model also support evaluating
-LPIPS, which is introduced in
-
-https://github.com/richzhang/PerceptualSimilarity
-"""
-
-import warnings
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.distributed as dist
-
-from utils.misc import download_url
-
-__all__ = ['PerceptualModel']
-
-# pylint: disable=line-too-long
-_MODEL_URL_SHA256 = {
-    # This model is provided by `torchvision`, which is ported from TensorFlow.
-    'torchvision_official': (
-        'https://download.pytorch.org/models/vgg16-397923af.pth',
-        '397923af8e79cdbb6a7127f12361acd7a2f83e06b05044ddf496e83de57a5bf0'  # hash sha256
-    ),
-
-    # This model is provided by https://github.com/NVlabs/stylegan2-ada-pytorch
-    'vgg_perceptual_lpips': (
-        'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt',
-        'b437eb095feaeb0b83eb3fa11200ebca4548ee39a07fb944a417ddc516cc07c3'  # hash sha256
-    )
-}
-# pylint: enable=line-too-long
-
-
-class PerceptualModel(object):
-    """Defines the perceptual model, which is based on VGG16 structure.
-
-    This is a static class, which is used to avoid this model to be built
-    repeatedly. Consequently, this model is particularly used for inference,
-    like computing LPIPS, or for loss computation, like perceptual loss. If
-    training is required, please use the model from `torchvision.models` or
-    implement by yourself.
-
-    NOTE: The pre-trained model assumes the inputs to be with `RGB` channel
-    order and pixel range [-1, 1], and will NOT resize the input automatically
-    if only perceptual feature is needed.
-    """
-    models = dict()
-
-    @staticmethod
-    def build_model(use_torchvision=False, no_top=True, enable_lpips=True):
-        """Builds the model and load pre-trained weights.
-
-        1. If `use_torchvision` is set as True, the model released by
-           `torchvision` will be loaded, otherwise, the model released by
-           https://www.robots.ox.ac.uk/~vgg/research/very_deep/ will be used.
-           (default: False)
-
-        2. To save computing resources, these is an option to only load the
-           backbone (i.e., without the last three fully-connected layers). This
-           is commonly used for perceptual loss or LPIPS loss computation.
-           Please use argument `no_top` to control this. (default: True)
-
-        3. For LPIPS loss computation, some additional weights (which is used
-           for balancing the features from different resolutions) are employed
-           on top of the original VGG16 backbone. Details can be found at
-           https://github.com/richzhang/PerceptualSimilarity. Please use
-           `enable_lpips` to enable this feature. (default: True)
-
-        The built model supports following arguments when forwarding:
-
-        - resize_input: Whether to resize the input image to size [224, 224]
-            before forwarding. For feature-based computation (i.e., only
-            convolutional layers are used), image resizing is not essential.
-            (default: False)
-        - return_tensor: This field resolves the model behavior. Following
-            options are supported:
-                `feature1`: Before the first max pooling layer.
-                `pool1`: After the first max pooling layer.
-                `feature2`: Before the second max pooling layer.
-                `pool2`: After the second max pooling layer.
-                `feature3`: Before the third max pooling layer.
-                `pool3`: After the third max pooling layer.
-                `feature4`: Before the fourth max pooling layer.
-                `pool4`: After the fourth max pooling layer.
-                `feature5`: Before the fifth max pooling layer.
-                `pool5`: After the fifth max pooling layer.
-                `flatten`: The flattened feature, after `adaptive_avgpool`.
-                `feature`: The 4096d feature for logits computation. (default)
-                `logits`: The 1000d categorical logits.
-                `prediction`: The 1000d predicted probability.
-                `lpips`: The LPIPS score between two input images.
-        """
-        if use_torchvision:
-            model_source = 'torchvision_official'
-            align_tf_resize = False
-            is_torch_script = False
-        else:
-            model_source = 'vgg_perceptual_lpips'
-            align_tf_resize = True
-            is_torch_script = True
-
-        if enable_lpips and model_source != 'vgg_perceptual_lpips':
-            warnings.warn('The pre-trained model officially released by '
-                          '`torchvision` does not support LPIPS computation! '
-                          'Equal weights will be used for each resolution.')
-
-        fingerprint = (model_source, no_top, enable_lpips)
-
-        if fingerprint not in PerceptualModel.models:
-            # Build model.
-            model = VGG16(align_tf_resize=align_tf_resize,
-                          no_top=no_top,
-                          enable_lpips=enable_lpips)
-
-            # Download pre-trained weights.
-            if dist.is_initialized() and dist.get_rank() != 0:
-                dist.barrier()  # Download by chief.
-
-            url, sha256 = _MODEL_URL_SHA256[model_source]
-            filename = f'perceptual_model_{model_source}_{sha256}.pth'
-            model_path, hash_check = download_url(url,
-                                                  filename=filename,
-                                                  sha256=sha256)
-            if is_torch_script:
-                src_state_dict = torch.jit.load(model_path, map_location='cpu')
-            else:
-                src_state_dict = torch.load(model_path, map_location='cpu')
-            if hash_check is False:
-                warnings.warn(f'Hash check failed! The remote file from URL '
-                              f'`{url}` may be changed, or the downloading is '
-                              f'interrupted. The loaded perceptual model may '
-                              f'have unexpected behavior.')
-
-            if dist.is_initialized() and dist.get_rank() == 0:
-                dist.barrier()  # Wait for other replicas.
-
-            # Load weights.
-            dst_state_dict = _convert_weights(src_state_dict, model_source)
-            model.load_state_dict(dst_state_dict, strict=False)
-            del src_state_dict, dst_state_dict
-
-            # For inference only.
-            model.eval().requires_grad_(False).cuda()
-            PerceptualModel.models[fingerprint] = model
-
-        return PerceptualModel.models[fingerprint]
-
-
-def _convert_weights(src_state_dict, model_source):
-    if model_source not in _MODEL_URL_SHA256:
-        raise ValueError(f'Invalid model source `{model_source}`!\n'
-                         f'Sources allowed: {list(_MODEL_URL_SHA256.keys())}.')
-    if model_source == 'torchvision_official':
-        dst_to_src_var_mapping = {
-            'conv11.weight': 'features.0.weight',
-            'conv11.bias': 'features.0.bias',
-            'conv12.weight': 'features.2.weight',
-            'conv12.bias': 'features.2.bias',
-            'conv21.weight': 'features.5.weight',
-            'conv21.bias': 'features.5.bias',
-            'conv22.weight': 'features.7.weight',
-            'conv22.bias': 'features.7.bias',
-            'conv31.weight': 'features.10.weight',
-            'conv31.bias': 'features.10.bias',
-            'conv32.weight': 'features.12.weight',
-            'conv32.bias': 'features.12.bias',
-            'conv33.weight': 'features.14.weight',
-            'conv33.bias': 'features.14.bias',
-            'conv41.weight': 'features.17.weight',
-            'conv41.bias': 'features.17.bias',
-            'conv42.weight': 'features.19.weight',
-            'conv42.bias': 'features.19.bias',
-            'conv43.weight': 'features.21.weight',
-            'conv43.bias': 'features.21.bias',
-            'conv51.weight': 'features.24.weight',
-            'conv51.bias': 'features.24.bias',
-            'conv52.weight': 'features.26.weight',
-            'conv52.bias': 'features.26.bias',
-            'conv53.weight': 'features.28.weight',
-            'conv53.bias': 'features.28.bias',
-            'fc1.weight': 'classifier.0.weight',
-            'fc1.bias': 'classifier.0.bias',
-            'fc2.weight': 'classifier.3.weight',
-            'fc2.bias': 'classifier.3.bias',
-            'fc3.weight': 'classifier.6.weight',
-            'fc3.bias': 'classifier.6.bias',
-        }
-    elif model_source == 'vgg_perceptual_lpips':
-        src_state_dict = src_state_dict.state_dict()
-        dst_to_src_var_mapping = {
-            'conv11.weight': 'layers.conv1.weight',
-            'conv11.bias': 'layers.conv1.bias',
-            'conv12.weight': 'layers.conv2.weight',
-            'conv12.bias': 'layers.conv2.bias',
-            'conv21.weight': 'layers.conv3.weight',
-            'conv21.bias': 'layers.conv3.bias',
-            'conv22.weight': 'layers.conv4.weight',
-            'conv22.bias': 'layers.conv4.bias',
-            'conv31.weight': 'layers.conv5.weight',
-            'conv31.bias': 'layers.conv5.bias',
-            'conv32.weight': 'layers.conv6.weight',
-            'conv32.bias': 'layers.conv6.bias',
-            'conv33.weight': 'layers.conv7.weight',
-            'conv33.bias': 'layers.conv7.bias',
-            'conv41.weight': 'layers.conv8.weight',
-            'conv41.bias': 'layers.conv8.bias',
-            'conv42.weight': 'layers.conv9.weight',
-            'conv42.bias': 'layers.conv9.bias',
-            'conv43.weight': 'layers.conv10.weight',
-            'conv43.bias': 'layers.conv10.bias',
-            'conv51.weight': 'layers.conv11.weight',
-            'conv51.bias': 'layers.conv11.bias',
-            'conv52.weight': 'layers.conv12.weight',
-            'conv52.bias': 'layers.conv12.bias',
-            'conv53.weight': 'layers.conv13.weight',
-            'conv53.bias': 'layers.conv13.bias',
-            'fc1.weight': 'layers.fc1.weight',
-            'fc1.bias': 'layers.fc1.bias',
-            'fc2.weight': 'layers.fc2.weight',
-            'fc2.bias': 'layers.fc2.bias',
-            'fc3.weight': 'layers.fc3.weight',
-            'fc3.bias': 'layers.fc3.bias',
-            'lpips.0.weight': 'lpips0',
-            'lpips.1.weight': 'lpips1',
-            'lpips.2.weight': 'lpips2',
-            'lpips.3.weight': 'lpips3',
-            'lpips.4.weight': 'lpips4',
-        }
-    else:
-        raise NotImplementedError(f'Not implemented model source '
-                                  f'`{model_source}`!')
-
-    dst_state_dict = {}
-    for dst_name, src_name in dst_to_src_var_mapping.items():
-        if dst_name.startswith('lpips'):
-            dst_state_dict[dst_name] = src_state_dict[src_name].unsqueeze(0)
-        else:
-            dst_state_dict[dst_name] = src_state_dict[src_name].clone()
-    return dst_state_dict
-
-
-_IMG_MEAN = (0.485, 0.456, 0.406)
-_IMG_STD  = (0.229, 0.224, 0.225)
-_ALLOWED_RETURN = [
-    'feature1', 'pool1', 'feature2', 'pool2', 'feature3', 'pool3', 'feature4',
-    'pool4', 'feature5', 'pool5', 'flatten', 'feature', 'logits', 'prediction',
-    'lpips'
-]
-
-# pylint: disable=missing-function-docstring
-
-class VGG16(nn.Module):
-    """Defines the VGG16 structure.
-
-    This model takes `RGB` images with data format `NCHW` as the raw inputs. The
-    pixel range are assumed to be [-1, 1].
-    """
-
-    def __init__(self, align_tf_resize=False, no_top=True, enable_lpips=True):
-        """Defines the network structure."""
-        super().__init__()
-
-        self.align_tf_resize = align_tf_resize
-        self.no_top = no_top
-        self.enable_lpips = enable_lpips
-
-        self.conv11 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
-        self.relu11 = nn.ReLU(inplace=True)
-        self.conv12 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
-        self.relu12 = nn.ReLU(inplace=True)
-        # output `feature1`, with shape [N, 64, 224, 224]
-
-        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
-        # output `pool1`, with shape [N, 64, 112, 112]
-
-        self.conv21 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
-        self.relu21 = nn.ReLU(inplace=True)
-        self.conv22 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
-        self.relu22 = nn.ReLU(inplace=True)
-        # output `feature2`, with shape [N, 128, 112, 112]
-
-        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
-        # output `pool2`, with shape [N, 128, 56, 56]
-
-        self.conv31 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
-        self.relu31 = nn.ReLU(inplace=True)
-        self.conv32 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
-        self.relu32 = nn.ReLU(inplace=True)
-        self.conv33 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
-        self.relu33 = nn.ReLU(inplace=True)
-        # output `feature3`, with shape [N, 256, 56, 56]
-
-        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
-        # output `pool3`, with shape [N,256, 28, 28]
-
-        self.conv41 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
-        self.relu41 = nn.ReLU(inplace=True)
-        self.conv42 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
-        self.relu42 = nn.ReLU(inplace=True)
-        self.conv43 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
-        self.relu43 = nn.ReLU(inplace=True)
-        # output `feature4`, with shape [N, 512, 28, 28]
-
-        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
-        # output `pool4`, with shape [N, 512, 14, 14]
-
-        self.conv51 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
-        self.relu51 = nn.ReLU(inplace=True)
-        self.conv52 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
-        self.relu52 = nn.ReLU(inplace=True)
-        self.conv53 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
-        self.relu53 = nn.ReLU(inplace=True)
-        # output `feature5`, with shape [N, 512, 14, 14]
-
-        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
-        # output `pool5`, with shape [N, 512, 7, 7]
-
-        if self.enable_lpips:
-            self.lpips = nn.ModuleList()
-            for idx, ch in enumerate([64, 128, 256, 512, 512]):
-                self.lpips.append(nn.Conv2d(ch, 1, kernel_size=1, bias=False))
-                self.lpips[idx].weight.data.copy_(torch.ones(1, ch, 1, 1))
-
-        if not self.no_top:
-            self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
-            self.flatten = nn.Flatten(start_dim=1, end_dim=-1)
-            # output `flatten`, with shape [N, 25088]
-
-            self.fc1 = nn.Linear(512 * 7 * 7, 4096)
-            self.fc1_relu = nn.ReLU(inplace=True)
-            self.fc1_dropout = nn.Dropout(0.5, inplace=False)
-            self.fc2 = nn.Linear(4096, 4096)
-            self.fc2_relu = nn.ReLU(inplace=True)
-            self.fc2_dropout = nn.Dropout(0.5, inplace=False)
-            # output `feature`, with shape [N, 4096]
-
-            self.fc3 = nn.Linear(4096, 1000)
-            # output `logits`, with shape [N, 1000]
-
-            self.out = nn.Softmax(dim=1)
-            # output `softmax`, with shape [N, 1000]
-
-        img_mean = np.array(_IMG_MEAN).reshape((1, 3, 1, 1)).astype(np.float32)
-        img_std = np.array(_IMG_STD).reshape((1, 3, 1, 1)).astype(np.float32)
-        self.register_buffer('img_mean', torch.from_numpy(img_mean))
-        self.register_buffer('img_std', torch.from_numpy(img_std))
-
-    def forward(self,
-                x,
-                y=None,
-                *,
-                resize_input=False,
-                return_tensor='feature'):
-        return_tensor = return_tensor.lower()
-        if return_tensor not in _ALLOWED_RETURN:
-            raise ValueError(f'Invalid output tensor name `{return_tensor}` '
-                             f'for perceptual model (VGG16)!\n'
-                             f'Names allowed: {_ALLOWED_RETURN}.')
-
-        if return_tensor == 'lpips' and y is None:
-            raise ValueError('Two images are required for LPIPS computation, '
-                             'but only one is received!')
-
-        if return_tensor == 'lpips':
-            assert x.shape == y.shape
-            x = torch.cat([x, y], dim=0)
-            features = []
-
-        if resize_input:
-            if self.align_tf_resize:
-                theta = torch.eye(2, 3).to(x)
-                theta[0, 2] += theta[0, 0] / x.shape[3] - theta[0, 0] / 224
-                theta[1, 2] += theta[1, 1] / x.shape[2] - theta[1, 1] / 224
-                theta = theta.unsqueeze(0).repeat(x.shape[0], 1, 1)
-                grid = F.affine_grid(theta,
-                                     size=(x.shape[0], x.shape[1], 224, 224),
-                                     align_corners=False)
-                x = F.grid_sample(x, grid,
-                                  mode='bilinear',
-                                  padding_mode='border',
-                                  align_corners=False)
-            else:
-                x = F.interpolate(x,
-                                  size=(224, 224),
-                                  mode='bilinear',
-                                  align_corners=False)
-        if x.shape[1] == 1:
-            x = x.repeat((1, 3, 1, 1))
-
-        x = (x + 1) / 2
-        x = (x - self.img_mean) / self.img_std
-
-        x = self.conv11(x)
-        x = self.relu11(x)
-        x = self.conv12(x)
-        x = self.relu12(x)
-        if return_tensor == 'feature1':
-            return x
-        if return_tensor == 'lpips':
-            features.append(x)
-
-        x = self.pool1(x)
-        if return_tensor == 'pool1':
-            return x
-
-        x = self.conv21(x)
-        x = self.relu21(x)
-        x = self.conv22(x)
-        x = self.relu22(x)
-        if return_tensor == 'feature2':
-            return x
-        if return_tensor == 'lpips':
-            features.append(x)
-
-        x = self.pool2(x)
-        if return_tensor == 'pool2':
-            return x
-
-        x = self.conv31(x)
-        x = self.relu31(x)
-        x = self.conv32(x)
-        x = self.relu32(x)
-        x = self.conv33(x)
-        x = self.relu33(x)
-        if return_tensor == 'feature3':
-            return x
-        if return_tensor == 'lpips':
-            features.append(x)
-
-        x = self.pool3(x)
-        if return_tensor == 'pool3':
-            return x
-
-        x = self.conv41(x)
-        x = self.relu41(x)
-        x = self.conv42(x)
-        x = self.relu42(x)
-        x = self.conv43(x)
-        x = self.relu43(x)
-        if return_tensor == 'feature4':
-            return x
-        if return_tensor == 'lpips':
-            features.append(x)
-
-        x = self.pool4(x)
-        if return_tensor == 'pool4':
-            return x
-
-        x = self.conv51(x)
-        x = self.relu51(x)
-        x = self.conv52(x)
-        x = self.relu52(x)
-        x = self.conv53(x)
-        x = self.relu53(x)
-        if return_tensor == 'feature5':
-            return x
-        if return_tensor == 'lpips':
-            features.append(x)
-
-        x = self.pool5(x)
-        if return_tensor == 'pool5':
-            return x
-
-        if return_tensor == 'lpips':
-            score = 0
-            assert len(features) == 5
-            for idx in range(5):
-                feature = features[idx]
-                norm = feature.norm(dim=1, keepdim=True)
-                feature = feature / (norm + 1e-10)
-                feature_x, feature_y = feature.chunk(2, dim=0)
-                diff = (feature_x - feature_y).square()
-                score += self.lpips[idx](diff).mean(dim=(2, 3), keepdim=False)
-            return score.sum(dim=1, keepdim=False)
-
-        x = self.avgpool(x)
-        x = self.flatten(x)
-        if return_tensor == 'flatten':
-            return x
-
-        x = self.fc1(x)
-        x = self.fc1_relu(x)
-        x = self.fc1_dropout(x)
-        x = self.fc2(x)
-        x = self.fc2_relu(x)
-        x = self.fc2_dropout(x)
-        if return_tensor == 'feature':
-            return x
-
-        x = self.fc3(x)
-        if return_tensor == 'logits':
-            return x
-
-        x = self.out(x)
-        if return_tensor == 'prediction':
-            return x
-
-        raise NotImplementedError(f'Output tensor name `{return_tensor}` is '
-                                  f'not implemented!')
-
-# pylint: enable=missing-function-docstring
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/utils/options.py b/spaces/Iceclear/StableSR/StableSR/basicsr/utils/options.py
deleted file mode 100644
index 3afd79c4f3e73f44f36503288c3959125ac3df34..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/utils/options.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import argparse
-import os
-import random
-import torch
-import yaml
-from collections import OrderedDict
-from os import path as osp
-
-from basicsr.utils import set_random_seed
-from basicsr.utils.dist_util import get_dist_info, init_dist, master_only
-
-
-def ordered_yaml():
-    """Support OrderedDict for yaml.
-
-    Returns:
-        tuple: yaml Loader and Dumper.
-    """
-    try:
-        from yaml import CDumper as Dumper
-        from yaml import CLoader as Loader
-    except ImportError:
-        from yaml import Dumper, Loader
-
-    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
-
-    def dict_representer(dumper, data):
-        return dumper.represent_dict(data.items())
-
-    def dict_constructor(loader, node):
-        return OrderedDict(loader.construct_pairs(node))
-
-    Dumper.add_representer(OrderedDict, dict_representer)
-    Loader.add_constructor(_mapping_tag, dict_constructor)
-    return Loader, Dumper
-
-
-def yaml_load(f):
-    """Load yaml file or string.
-
-    Args:
-        f (str): File path or a python string.
-
-    Returns:
-        dict: Loaded dict.
-    """
-    if os.path.isfile(f):
-        with open(f, 'r') as f:
-            return yaml.load(f, Loader=ordered_yaml()[0])
-    else:
-        return yaml.load(f, Loader=ordered_yaml()[0])
-
-
-def dict2str(opt, indent_level=1):
-    """dict to string for printing options.
-
-    Args:
-        opt (dict): Option dict.
-        indent_level (int): Indent level. Default: 1.
-
-    Return:
-        (str): Option string for printing.
-    """
-    msg = '\n'
-    for k, v in opt.items():
-        if isinstance(v, dict):
-            msg += ' ' * (indent_level * 2) + k + ':['
-            msg += dict2str(v, indent_level + 1)
-            msg += ' ' * (indent_level * 2) + ']\n'
-        else:
-            msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
-    return msg
-
-
-def _postprocess_yml_value(value):
-    # None
-    if value == '~' or value.lower() == 'none':
-        return None
-    # bool
-    if value.lower() == 'true':
-        return True
-    elif value.lower() == 'false':
-        return False
-    # !!float number
-    if value.startswith('!!float'):
-        return float(value.replace('!!float', ''))
-    # number
-    if value.isdigit():
-        return int(value)
-    elif value.replace('.', '', 1).isdigit() and value.count('.') < 2:
-        return float(value)
-    # list
-    if value.startswith('['):
-        return eval(value)
-    # str
-    return value
-
-
-def parse_options(root_path, is_train=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.')
-    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher')
-    parser.add_argument('--auto_resume', action='store_true')
-    parser.add_argument('--debug', action='store_true')
-    parser.add_argument('--local_rank', type=int, default=0)
-    parser.add_argument(
-        '--force_yml', nargs='+', default=None, help='Force to update yml files. Examples: train:ema_decay=0.999')
-    args = parser.parse_args()
-
-    # parse yml to dict
-    opt = yaml_load(args.opt)
-
-    # distributed settings
-    if args.launcher == 'none':
-        opt['dist'] = False
-        print('Disable distributed.', flush=True)
-    else:
-        opt['dist'] = True
-        if args.launcher == 'slurm' and 'dist_params' in opt:
-            init_dist(args.launcher, **opt['dist_params'])
-        else:
-            init_dist(args.launcher)
-    opt['rank'], opt['world_size'] = get_dist_info()
-
-    # random seed
-    seed = opt.get('manual_seed')
-    if seed is None:
-        seed = random.randint(1, 10000)
-        opt['manual_seed'] = seed
-    set_random_seed(seed + opt['rank'])
-
-    # force to update yml options
-    if args.force_yml is not None:
-        for entry in args.force_yml:
-            # now do not support creating new keys
-            keys, value = entry.split('=')
-            keys, value = keys.strip(), value.strip()
-            value = _postprocess_yml_value(value)
-            eval_str = 'opt'
-            for key in keys.split(':'):
-                eval_str += f'["{key}"]'
-            eval_str += '=value'
-            # using exec function
-            exec(eval_str)
-
-    opt['auto_resume'] = args.auto_resume
-    opt['is_train'] = is_train
-
-    # debug setting
-    if args.debug and not opt['name'].startswith('debug'):
-        opt['name'] = 'debug_' + opt['name']
-
-    if opt['num_gpu'] == 'auto':
-        opt['num_gpu'] = torch.cuda.device_count()
-
-    # datasets
-    for phase, dataset in opt['datasets'].items():
-        # for multiple datasets, e.g., val_1, val_2; test_1, test_2
-        phase = phase.split('_')[0]
-        dataset['phase'] = phase
-        if 'scale' in opt:
-            dataset['scale'] = opt['scale']
-        if dataset.get('dataroot_gt') is not None:
-            dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
-        if dataset.get('dataroot_lq') is not None:
-            dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq'])
-
-    # paths
-    for key, val in opt['path'].items():
-        if (val is not None) and ('resume_state' in key or 'pretrain_network' in key):
-            opt['path'][key] = osp.expanduser(val)
-
-    if is_train:
-        experiments_root = osp.join(root_path, 'experiments', opt['name'])
-        opt['path']['experiments_root'] = experiments_root
-        opt['path']['models'] = osp.join(experiments_root, 'models')
-        opt['path']['training_states'] = osp.join(experiments_root, 'training_states')
-        opt['path']['log'] = experiments_root
-        opt['path']['visualization'] = osp.join(experiments_root, 'visualization')
-
-        # change some options for debug mode
-        if 'debug' in opt['name']:
-            if 'val' in opt:
-                opt['val']['val_freq'] = 8
-            opt['logger']['print_freq'] = 1
-            opt['logger']['save_checkpoint_freq'] = 8
-    else:  # test
-        results_root = osp.join(root_path, 'results', opt['name'])
-        opt['path']['results_root'] = results_root
-        opt['path']['log'] = results_root
-        opt['path']['visualization'] = osp.join(results_root, 'visualization')
-
-    return opt, args
-
-
-@master_only
-def copy_opt_file(opt_file, experiments_root):
-    # copy the yml file to the experiment root
-    import sys
-    import time
-    from shutil import copyfile
-    cmd = ' '.join(sys.argv)
-    filename = osp.join(experiments_root, osp.basename(opt_file))
-    copyfile(opt_file, filename)
-
-    with open(filename, 'r+') as f:
-        lines = f.readlines()
-        lines.insert(0, f'# GENERATE TIME: {time.asctime()}\n# CMD:\n# {cmd}\n\n')
-        f.seek(0)
-        f.writelines(lines)
diff --git a/spaces/Illumotion/Koboldcpp/examples/speculative/speculative.cpp b/spaces/Illumotion/Koboldcpp/examples/speculative/speculative.cpp
deleted file mode 100644
index 75a2e5e22d04645ba499a6a8de845d325c44ee13..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/speculative/speculative.cpp
+++ /dev/null
@@ -1,314 +0,0 @@
-#include "build-info.h"
-
-#include "common.h"
-#include "llama.h"
-#include "grammar-parser.h"
-
-#include <cmath>
-#include <cstdio>
-#include <string>
-#include <vector>
-
-int main(int argc, char ** argv) {
-    gpt_params params;
-
-    if (gpt_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
-    if (params.model_draft.empty()) {
-        fprintf(stderr, "%s: error: --model-draft is required\n", __func__);
-        return 1;
-    }
-
-#ifndef LOG_DISABLE_LOGS
-    log_set_target(log_filename_generator("speculative", "log"));
-    LOG_TEE("Log start\n");
-    log_dump_cmdline(argc, argv);
-#endif // LOG_DISABLE_LOGS
-
-    // init llama.cpp
-    llama_backend_init(params.numa);
-
-    llama_model * model_tgt = NULL;
-    llama_model * model_dft = NULL;
-
-    llama_context * ctx_tgt = NULL;
-    llama_context * ctx_dft = NULL;
-
-    // load the target model
-    params.logits_all = true;
-    std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
-
-    // load the draft model
-    params.model = params.model_draft;
-    params.n_gpu_layers = params.n_gpu_layers_draft;
-    std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
-
-    // tokenize the prompt
-    std::vector<llama_token> inp;
-    inp = ::llama_tokenize(ctx_tgt, params.prompt, true);
-
-    const int max_context_size     = llama_n_ctx(ctx_tgt);
-    const int max_tokens_list_size = max_context_size - 4;
-
-    if ((int) inp.size() > max_tokens_list_size) {
-        fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
-        return 1;
-    }
-
-    fprintf(stderr, "\n\n");
-
-    for (auto id : inp) {
-        fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str());
-    }
-
-    fflush(stderr);
-
-    const int n_input = inp.size();
-
-    const auto t_enc_start = ggml_time_us();
-
-    // eval the prompt with both models
-    llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1, 0,           0));
-    llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(),           1, n_input - 1, 0));
-    llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input,     0,           0));
-
-    const auto t_enc_end = ggml_time_us();
-
-    // the 2 models should have the same vocab
-    const int n_ctx   = llama_n_ctx(ctx_tgt);
-    const int n_vocab = llama_n_vocab(model_tgt);
-    //GGML_ASSERT(n_vocab == llama_n_vocab(model_dft));
-
-    // how many tokens to draft each time
-    int n_draft = params.n_draft;
-
-    int n_predict = 0;
-    int n_drafted = 0;
-    int n_accept  = 0;
-
-    int n_past_tgt = inp.size();
-    int n_past_dft = inp.size();
-
-    std::vector<llama_token> drafted;
-
-    std::vector<llama_token> last_tokens(n_ctx);
-    std::fill(last_tokens.begin(), last_tokens.end(), 0);
-
-    for (auto & id : inp) {
-        last_tokens.erase(last_tokens.begin());
-        last_tokens.push_back(id);
-    }
-
-    std::vector<llama_token_data> candidates;
-    candidates.reserve(n_vocab);
-
-    // used to determine end of generation
-    bool has_eos = false;
-
-    // grammar stuff
-    struct llama_grammar * grammar_dft = NULL;
-    struct llama_grammar * grammar_tgt = NULL;
-
-    grammar_parser::parse_state parsed_grammar;
-
-    // if requested - load the grammar, error checking is omitted for brevity
-    if (!params.grammar.empty()) {
-        parsed_grammar = grammar_parser::parse(params.grammar.c_str());
-        // will be empty (default) if there are parse errors
-        if (parsed_grammar.rules.empty()) {
-            return 1;
-        }
-
-        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
-        grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
-    }
-
-    const auto t_dec_start = ggml_time_us();
-
-    while (true) {
-        LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
-
-        int i_dft = 0;
-
-        while (true) {
-            // sample from the target model
-            llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
-
-            // remember which tokens were sampled - used for repetition penalties during sampling
-            last_tokens.erase(last_tokens.begin());
-            last_tokens.push_back(id);
-
-            //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens));
-
-            const std::string token_str = llama_token_to_piece(ctx_tgt, id);
-            printf("%s", token_str.c_str());
-            fflush(stdout);
-
-            if (id == llama_token_eos(ctx_tgt)) {
-                has_eos = true;
-            }
-
-            ++n_predict;
-
-            // check if the draft matches the target
-            if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
-                LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
-                ++n_accept;
-                ++n_past_tgt;
-                ++n_past_dft;
-                ++i_dft;
-
-                continue;
-            }
-
-            // the drafted token was rejected or we are out of drafted tokens
-
-            if (i_dft < (int) drafted.size()) {
-                LOG("the %dth drafted token (%d, '%s') does not match the sampled target token (%d, '%s') - rejected\n",
-                        i_dft, drafted[i_dft], llama_token_to_piece(ctx_dft, drafted[i_dft]).c_str(), id, token_str.c_str());
-            } else {
-                LOG("out of drafted tokens\n");
-            }
-
-            llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
-            llama_decode(ctx_dft, llama_batch_get_one(&id, 1, n_past_dft, 0));
-            ++n_past_dft;
-
-            // heuristic for n_draft
-            {
-                const int  n_draft_cur  = (int) drafted.size();
-                const bool all_accepted = i_dft == n_draft_cur;
-
-                LOG("n_draft      = %d\n", n_draft);
-                LOG("n_draft_cur  = %d\n", n_draft_cur);
-                LOG("i_dft        = %d\n", i_dft);
-                LOG("all_accepted = %d\n", all_accepted);
-
-                if (all_accepted && n_draft == n_draft_cur) {
-                    LOG(" - max drafted tokens accepted - n_draft += 8\n");
-                    n_draft = std::min(30, n_draft + 8);
-                } else if (all_accepted) {
-                    LOG(" - partially drafted tokens accepted - no change\n");
-                } else {
-                    LOG(" - drafted token rejected - n_draft -= 1\n");
-                    n_draft = std::max(2, n_draft - 1);
-                }
-            }
-
-            drafted.clear();
-            drafted.push_back(id);
-
-            break;
-        }
-
-        if (n_predict > params.n_predict || has_eos) {
-            break;
-        }
-
-        if (grammar_tgt) {
-            if (grammar_dft) {
-                llama_grammar_free(grammar_dft);
-            }
-            grammar_dft = llama_grammar_copy(grammar_tgt);
-
-            LOG("copied target grammar to draft grammar\n");
-        }
-
-        // sample n_draft tokens from the draft model using greedy decoding
-        int n_past_cur = n_past_dft;
-        for (int i = 0; i < n_draft; ++i) {
-            float * logits = llama_get_logits(ctx_dft);
-
-            candidates.clear();
-            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-                candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-            }
-
-            llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
-
-            if (grammar_dft != NULL) {
-                llama_sample_grammar(ctx_dft, &cur_p, grammar_dft);
-            }
-
-            // computes softmax and sorts the candidates
-            llama_sample_softmax(ctx_dft, &cur_p);
-
-            for (int i = 0; i < 3; ++i) {
-                LOG(" - draft candidate %3d: %6d (%8.3f) '%s'\n", i, cur_p.data[i].id, cur_p.data[i].p, llama_token_to_piece(ctx_dft, cur_p.data[i].id).c_str());
-            }
-
-            // TODO: better logic?
-            if (cur_p.data[0].p < 2*cur_p.data[1].p) {
-                LOG("stopping drafting, probability too low: %.3f < 2*%.3f\n", cur_p.data[0].p, cur_p.data[1].p);
-                break;
-            }
-
-            // drafted token
-            const llama_token id = cur_p.data[0].id;
-
-            drafted.push_back(id);
-            ++n_drafted;
-
-            // no need to evaluate the last drafted token, since we won't use the result
-            if (i == n_draft - 1) {
-                break;
-            }
-
-            // evaluate the drafted token on the draft model
-            llama_kv_cache_seq_rm(ctx_dft, 0, n_past_cur, -1);
-            llama_decode(ctx_dft, llama_batch_get_one(&drafted.back(), 1, n_past_cur, 0));
-            ++n_past_cur;
-
-            if (grammar_dft != NULL) {
-                llama_grammar_accept_token(ctx_dft, grammar_dft, id);
-            }
-        }
-
-        // evaluate the target model on the drafted tokens
-        llama_kv_cache_seq_rm(ctx_tgt, 0, n_past_tgt, -1);
-        llama_decode(ctx_tgt, llama_batch_get_one(drafted.data(), drafted.size(), n_past_tgt, 0));
-        ++n_past_tgt;
-
-        // the first token is always proposed by the traget model before the speculation loop
-        drafted.erase(drafted.begin());
-    }
-
-    auto t_dec_end = ggml_time_us();
-
-    LOG_TEE("\n\n");
-
-    LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
-    LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
-
-    // TODO: make sure these numbers are computed correctly
-    LOG_TEE("\n");
-    LOG_TEE("n_draft   = %d\n", n_draft);
-    LOG_TEE("n_predict = %d\n", n_predict);
-    LOG_TEE("n_drafted = %d\n", n_drafted);
-    LOG_TEE("n_accept  = %d\n", n_accept);
-    LOG_TEE("accept    = %.3f%%\n", 100.0f * n_accept / n_drafted);
-
-    LOG_TEE("\ndraft:\n");
-    llama_print_timings(ctx_dft);
-
-    LOG_TEE("\ntarget:\n");
-    llama_print_timings(ctx_tgt);
-
-    llama_free(ctx_tgt);
-    llama_free_model(model_tgt);
-
-    llama_free(ctx_dft);
-    llama_free_model(model_dft);
-
-    if (grammar_dft != NULL) {
-        llama_grammar_free(grammar_dft);
-        llama_grammar_free(grammar_tgt);
-    }
-    llama_backend_free();
-
-    fprintf(stderr, "\n\n");
-
-    return 0;
-}
diff --git a/spaces/Illumotion/Koboldcpp/ggml-backend.c b/spaces/Illumotion/Koboldcpp/ggml-backend.c
deleted file mode 100644
index ca8d83dafe47c9763b7f648b9d26bd4e6dfb985e..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/ggml-backend.c
+++ /dev/null
@@ -1,385 +0,0 @@
-#include "ggml-backend.h"
-#include "ggml-alloc.h"
-
-#include <assert.h>
-#include <stdarg.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-#define UNUSED GGML_UNUSED
-
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-
-// backend buffer
-
-ggml_backend_buffer_t ggml_backend_buffer_init(
-        struct ggml_backend                  * backend,
-        struct ggml_backend_buffer_i           iface,
-               ggml_backend_buffer_context_t   context,
-               size_t                          size) {
-    ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer));
-
-    GGML_ASSERT(iface.get_base != NULL);
-
-    (*buffer) = (struct ggml_backend_buffer) {
-        /* .interface = */ iface,
-        /* .backend   = */ backend,
-        /* .context   = */ context,
-        /* .size      = */ size,
-    };
-
-    return buffer;
-}
-
-void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
-    if (buffer->iface.free_buffer != NULL) {
-        buffer->iface.free_buffer(buffer);
-    }
-    free(buffer);
-}
-
-size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) {
-    return ggml_backend_get_alignment(buffer->backend);
-}
-
-void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
-    return buffer->iface.get_base(buffer);
-}
-
-size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
-    return buffer->size;
-}
-
-size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
-    if (buffer->iface.get_alloc_size) {
-        return buffer->iface.get_alloc_size(buffer, tensor);
-    }
-    return ggml_nbytes(tensor);
-}
-
-void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
-    if (buffer->iface.init_tensor) {
-        buffer->iface.init_tensor(buffer, tensor);
-    }
-}
-
-void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
-    if (buffer->iface.free_tensor) {
-        buffer->iface.free_tensor(buffer, tensor);
-    }
-}
-
-// backend
-
-ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor) {
-    return tensor->buffer->backend;
-}
-
-const char * ggml_backend_name(ggml_backend_t backend) {
-    return backend->iface.get_name(backend);
-}
-
-void ggml_backend_free(ggml_backend_t backend) {
-    backend->iface.free(backend);
-}
-
-ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
-    return backend->iface.alloc_buffer(backend, size);
-}
-
-size_t ggml_backend_get_alignment(ggml_backend_t backend) {
-    return backend->iface.get_alignment(backend);
-}
-
-void ggml_backend_tensor_set_async(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
-}
-
-void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
-}
-
-void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
-    ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
-}
-
-void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
-    ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
-}
-
-void ggml_backend_synchronize(ggml_backend_t backend) {
-    backend->iface.synchronize(backend);
-}
-
-ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    return backend->iface.graph_plan_create(backend, cgraph);
-}
-
-void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    backend->iface.graph_plan_free(backend, plan);
-}
-
-void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    backend->iface.graph_plan_compute(backend, plan);
-}
-
-void ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    backend->iface.graph_compute(backend, cgraph);
-}
-
-bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    return backend->iface.supports_op(backend, op);
-}
-
-// backend copy
-
-static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
-    if (a->type != b->type) {
-        return false;
-    }
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (a->ne[i] != b->ne[i]) {
-            return false;
-        }
-        if (a->nb[i] != b->nb[i]) {
-            return false;
-        }
-    }
-    return true;
-}
-
-void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
-    //printf("src: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", src->name, (int)src->ne[0], (int)src->ne[1], (int)src->ne[2], (int)src->ne[3], (int)src->nb[0], (int)src->nb[1], (int)src->nb[2], (int)src->nb[3]);
-    //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]);
-    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
-
-    // printf("cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src));
-
-    if (src == dst) {
-        return;
-    }
-
-    // TODO: allow backends to support copy to/from same backend
-
-    if (ggml_get_backend(dst)->iface.cpy_tensor_from != NULL) {
-        ggml_get_backend(dst)->iface.cpy_tensor_from(ggml_get_backend(dst)->context, src, dst);
-    } else if (ggml_get_backend(src)->iface.cpy_tensor_to != NULL) {
-        ggml_get_backend(src)->iface.cpy_tensor_to(ggml_get_backend(src)->context, src, dst);
-    } else {
-        // shouldn't be hit when copying from/to CPU
-        #ifndef NDEBUG
-        fprintf(stderr, "ggml_backend_tensor_copy: neither cpy_tensor_from nor cpy_tensor_to are implemented for backends %s and %s, falling back to get/set\n", ggml_backend_name(src->buffer->backend), ggml_backend_name(dst->buffer->backend));
-        #endif
-        size_t nbytes = ggml_nbytes(src);
-        void * data = malloc(nbytes);
-        ggml_backend_tensor_get(src, data, 0, nbytes);
-        ggml_backend_tensor_set(dst, data, 0, nbytes);
-        free(data);
-    }
-}
-
-// backend CPU
-
-struct ggml_backend_cpu_context {
-    int n_threads;
-    void * work_data;
-    size_t work_size;
-};
-
-static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
-    return "CPU";
-
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_free(ggml_backend_t backend) {
-    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
-    free(cpu_ctx->work_data);
-    free(cpu_ctx);
-    free(backend);
-}
-
-static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
-    return (void *)buffer->context;
-}
-
-static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    free(buffer->context);
-    UNUSED(buffer);
-}
-
-static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
-    /* .free_buffer    = */ ggml_backend_cpu_buffer_free_buffer,
-    /* .get_base       = */ ggml_backend_cpu_buffer_get_base,
-    /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
-    /* .init_tensor    = */ NULL, // no initialization required
-    /* .free_tensor    = */ NULL, // no cleanup required
-};
-
-// for buffers from ptr, free is not called
-static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
-    /* .free_buffer    = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
-    /* .get_base       = */ ggml_backend_cpu_buffer_get_base,
-    /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
-    /* .init_tensor    = */ NULL,
-    /* .free_tensor    = */ NULL,
-};
-
-static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
-
-static ggml_backend_buffer_t ggml_backend_cpu_alloc_buffer(ggml_backend_t backend, size_t size) {
-    size += TENSOR_ALIGNMENT;   // malloc may return an address that is not aligned
-    void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
-
-    return ggml_backend_buffer_init(backend, cpu_backend_buffer_i, data, size);
-}
-
-static size_t ggml_backend_cpu_get_alignment(ggml_backend_t backend) {
-    return TENSOR_ALIGNMENT;
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
-    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-
-    memcpy((char *)tensor->data + offset, data, size);
-
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
-    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-
-    memcpy(data, (const char *)tensor->data + offset, size);
-
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_synchronize(ggml_backend_t backend) {
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_cpy_tensor_from(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
-    ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
-
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
-    // for a backend such as CUDA that can queue async calls, it is ok to do this asynchronously, but it may not be the case for other backends
-    ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src));
-
-    UNUSED(backend);
-}
-
-struct ggml_backend_plan_cpu {
-    struct ggml_cplan cplan;
-    struct ggml_cgraph cgraph;
-};
-
-static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
-
-    struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
-
-    cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
-    cpu_plan->cgraph = *cgraph;
-
-    if (cpu_plan->cplan.work_size > 0) {
-        cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
-    }
-
-    return cpu_plan;
-}
-
-static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
-
-    free(cpu_plan->cplan.work_data);
-    free(cpu_plan);
-
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
-
-    ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
-
-    UNUSED(backend);
-}
-
-static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
-
-    struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
-
-    if (cpu_ctx->work_size < cplan.work_size) {
-        // TODO: may be faster to free and use malloc to avoid the copy
-        cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
-        cpu_ctx->work_size = cplan.work_size;
-    }
-
-    cplan.work_data = cpu_ctx->work_data;
-
-    ggml_graph_compute(cgraph, &cplan);
-}
-
-static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    return true;
-    UNUSED(backend);
-    UNUSED(op);
-}
-
-static struct ggml_backend_i cpu_backend_i = {
-    /* .get_name            = */ ggml_backend_cpu_name,
-    /* .free                = */ ggml_backend_cpu_free,
-    /* .alloc_buffer        = */ ggml_backend_cpu_alloc_buffer,
-    /* .get_alignment       = */ ggml_backend_cpu_get_alignment,
-    /* .set_tensor_async    = */ ggml_backend_cpu_set_tensor_async,
-    /* .get_tensor_async    = */ ggml_backend_cpu_get_tensor_async,
-    /* .synchronize         = */ ggml_backend_cpu_synchronize,
-    /* .cpy_tensor_from     = */ ggml_backend_cpu_cpy_tensor_from,
-    /* .cpy_tensor_to       = */ ggml_backend_cpu_cpy_tensor_to,
-    /* .graph_plan_create   = */ ggml_backend_cpu_graph_plan_create,
-    /* .graph_plan_free     = */ ggml_backend_cpu_graph_plan_free,
-    /* .graph_plan_compute  = */ ggml_backend_cpu_graph_plan_compute,
-    /* .graph_compute       = */ ggml_backend_cpu_graph_compute,
-    /* .supports_op         = */ ggml_backend_cpu_supports_op,
-};
-
-ggml_backend_t ggml_backend_cpu_init(void) {
-    struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
-
-    ctx->n_threads = GGML_DEFAULT_N_THREADS;
-    ctx->work_data = NULL;
-    ctx->work_size = 0;
-
-    ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend));
-
-    *cpu_backend = (struct ggml_backend) {
-        /* .interface = */ cpu_backend_i,
-        /* .context   = */ ctx
-    };
-    return cpu_backend;
-}
-
-bool ggml_backend_is_cpu(ggml_backend_t backend) {
-    return backend->iface.get_name == ggml_backend_cpu_name;
-}
-
-void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
-    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
-
-    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
-    ctx->n_threads = n_threads;
-}
-
-ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size) {
-    return ggml_backend_buffer_init(backend_cpu, cpu_backend_buffer_i_from_ptr, ptr, size);
-}
diff --git a/spaces/Illumotion/Koboldcpp/include/CL/cl_platform.h b/spaces/Illumotion/Koboldcpp/include/CL/cl_platform.h
deleted file mode 100644
index e7a0d6f4761771a1e4d54ce185ed47e3861639dc..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/include/CL/cl_platform.h
+++ /dev/null
@@ -1,1412 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2008-2020 The Khronos Group Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- ******************************************************************************/
-
-#ifndef __CL_PLATFORM_H
-#define __CL_PLATFORM_H
-
-#include <CL/cl_version.h>
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-#if defined(_WIN32)
-    #if !defined(CL_API_ENTRY)
-        #define CL_API_ENTRY
-    #endif
-    #if !defined(CL_API_CALL)
-        #define CL_API_CALL     __stdcall
-    #endif
-    #if !defined(CL_CALLBACK)
-        #define CL_CALLBACK     __stdcall
-    #endif
-#else
-    #if !defined(CL_API_ENTRY)
-        #define CL_API_ENTRY
-    #endif
-    #if !defined(CL_API_CALL)
-        #define CL_API_CALL
-    #endif
-    #if !defined(CL_CALLBACK)
-        #define CL_CALLBACK
-    #endif
-#endif
-
-/*
- * Deprecation flags refer to the last version of the header in which the
- * feature was not deprecated.
- *
- * E.g. VERSION_1_1_DEPRECATED means the feature is present in 1.1 without
- * deprecation but is deprecated in versions later than 1.1.
- */
-
-#ifndef CL_API_SUFFIX_USER
-#define CL_API_SUFFIX_USER
-#endif
-
-#ifndef CL_API_PREFIX_USER
-#define CL_API_PREFIX_USER
-#endif
-
-#define CL_API_SUFFIX_COMMON CL_API_SUFFIX_USER
-#define CL_API_PREFIX_COMMON CL_API_PREFIX_USER
-
-#define CL_API_SUFFIX__VERSION_1_0 CL_API_SUFFIX_COMMON
-#define CL_API_SUFFIX__VERSION_1_1 CL_API_SUFFIX_COMMON
-#define CL_API_SUFFIX__VERSION_1_2 CL_API_SUFFIX_COMMON
-#define CL_API_SUFFIX__VERSION_2_0 CL_API_SUFFIX_COMMON
-#define CL_API_SUFFIX__VERSION_2_1 CL_API_SUFFIX_COMMON
-#define CL_API_SUFFIX__VERSION_2_2 CL_API_SUFFIX_COMMON
-#define CL_API_SUFFIX__VERSION_3_0 CL_API_SUFFIX_COMMON
-#define CL_API_SUFFIX__EXPERIMENTAL CL_API_SUFFIX_COMMON
-
-
-#ifdef __GNUC__
-  #define CL_API_SUFFIX_DEPRECATED __attribute__((deprecated))
-  #define CL_API_PREFIX_DEPRECATED
-#elif defined(_WIN32)
-  #define CL_API_SUFFIX_DEPRECATED
-  #define CL_API_PREFIX_DEPRECATED __declspec(deprecated)
-#else
-  #define CL_API_SUFFIX_DEPRECATED
-  #define CL_API_PREFIX_DEPRECATED
-#endif
-
-#ifdef CL_USE_DEPRECATED_OPENCL_1_0_APIS
-    #define CL_API_SUFFIX__VERSION_1_0_DEPRECATED CL_API_SUFFIX_COMMON
-    #define CL_API_PREFIX__VERSION_1_0_DEPRECATED CL_API_PREFIX_COMMON
-#else
-    #define CL_API_SUFFIX__VERSION_1_0_DEPRECATED CL_API_SUFFIX_COMMON CL_API_SUFFIX_DEPRECATED
-    #define CL_API_PREFIX__VERSION_1_0_DEPRECATED CL_API_PREFIX_COMMON CL_API_PREFIX_DEPRECATED
-#endif
-
-#ifdef CL_USE_DEPRECATED_OPENCL_1_1_APIS
-    #define CL_API_SUFFIX__VERSION_1_1_DEPRECATED CL_API_SUFFIX_COMMON
-    #define CL_API_PREFIX__VERSION_1_1_DEPRECATED CL_API_PREFIX_COMMON
-#else
-    #define CL_API_SUFFIX__VERSION_1_1_DEPRECATED CL_API_SUFFIX_COMMON CL_API_SUFFIX_DEPRECATED
-    #define CL_API_PREFIX__VERSION_1_1_DEPRECATED CL_API_PREFIX_COMMON CL_API_PREFIX_DEPRECATED
-#endif
-
-#ifdef CL_USE_DEPRECATED_OPENCL_1_2_APIS
-    #define CL_API_SUFFIX__VERSION_1_2_DEPRECATED CL_API_SUFFIX_COMMON
-    #define CL_API_PREFIX__VERSION_1_2_DEPRECATED CL_API_PREFIX_COMMON
-#else
-    #define CL_API_SUFFIX__VERSION_1_2_DEPRECATED CL_API_SUFFIX_COMMON CL_API_SUFFIX_DEPRECATED
-    #define CL_API_PREFIX__VERSION_1_2_DEPRECATED CL_API_PREFIX_COMMON CL_API_PREFIX_DEPRECATED
- #endif
-
-#ifdef CL_USE_DEPRECATED_OPENCL_2_0_APIS
-    #define CL_API_SUFFIX__VERSION_2_0_DEPRECATED CL_API_SUFFIX_COMMON
-    #define CL_API_PREFIX__VERSION_2_0_DEPRECATED CL_API_PREFIX_COMMON
-#else
-    #define CL_API_SUFFIX__VERSION_2_0_DEPRECATED CL_API_SUFFIX_COMMON CL_API_SUFFIX_DEPRECATED
-    #define CL_API_PREFIX__VERSION_2_0_DEPRECATED CL_API_PREFIX_COMMON CL_API_PREFIX_DEPRECATED
-#endif
-
-#ifdef CL_USE_DEPRECATED_OPENCL_2_1_APIS
-    #define CL_API_SUFFIX__VERSION_2_1_DEPRECATED CL_API_SUFFIX_COMMON
-    #define CL_API_PREFIX__VERSION_2_1_DEPRECATED CL_API_PREFIX_COMMON
-#else
-    #define CL_API_SUFFIX__VERSION_2_1_DEPRECATED CL_API_SUFFIX_COMMON CL_API_SUFFIX_DEPRECATED
-    #define CL_API_PREFIX__VERSION_2_1_DEPRECATED CL_API_PREFIX_COMMON CL_API_PREFIX_DEPRECATED
-#endif
-
-#ifdef CL_USE_DEPRECATED_OPENCL_2_2_APIS
-    #define CL_API_SUFFIX__VERSION_2_2_DEPRECATED CL_API_SUFFIX_COMMON
-    #define CL_API_PREFIX__VERSION_2_2_DEPRECATED CL_API_PREFIX_COMMON
-#else
-    #define CL_API_SUFFIX__VERSION_2_2_DEPRECATED CL_API_SUFFIX_COMMON CL_API_SUFFIX_DEPRECATED
-    #define CL_API_PREFIX__VERSION_2_2_DEPRECATED CL_API_PREFIX_COMMON CL_API_PREFIX_DEPRECATED
-#endif
-
-#if (defined (_WIN32) && defined(_MSC_VER))
-
-#if defined(__clang__)
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wlanguage-extension-token"
-#endif
-
-/* intptr_t is used in cl.h and provided by stddef.h in Visual C++, but not in clang */
-/* stdint.h was missing before Visual Studio 2010, include it for later versions and for clang */
-#if defined(__clang__) || _MSC_VER >= 1600
-    #include <stdint.h>
-#endif
-
-/* scalar types  */
-typedef signed   __int8         cl_char;
-typedef unsigned __int8         cl_uchar;
-typedef signed   __int16        cl_short;
-typedef unsigned __int16        cl_ushort;
-typedef signed   __int32        cl_int;
-typedef unsigned __int32        cl_uint;
-typedef signed   __int64        cl_long;
-typedef unsigned __int64        cl_ulong;
-
-typedef unsigned __int16        cl_half;
-typedef float                   cl_float;
-typedef double                  cl_double;
-
-#if defined(__clang__)
-#pragma clang diagnostic pop
-#endif
-
-/* Macro names and corresponding values defined by OpenCL */
-#define CL_CHAR_BIT         8
-#define CL_SCHAR_MAX        127
-#define CL_SCHAR_MIN        (-127-1)
-#define CL_CHAR_MAX         CL_SCHAR_MAX
-#define CL_CHAR_MIN         CL_SCHAR_MIN
-#define CL_UCHAR_MAX        255
-#define CL_SHRT_MAX         32767
-#define CL_SHRT_MIN         (-32767-1)
-#define CL_USHRT_MAX        65535
-#define CL_INT_MAX          2147483647
-#define CL_INT_MIN          (-2147483647-1)
-#define CL_UINT_MAX         0xffffffffU
-#define CL_LONG_MAX         ((cl_long) 0x7FFFFFFFFFFFFFFFLL)
-#define CL_LONG_MIN         ((cl_long) -0x7FFFFFFFFFFFFFFFLL - 1LL)
-#define CL_ULONG_MAX        ((cl_ulong) 0xFFFFFFFFFFFFFFFFULL)
-
-#define CL_FLT_DIG          6
-#define CL_FLT_MANT_DIG     24
-#define CL_FLT_MAX_10_EXP   +38
-#define CL_FLT_MAX_EXP      +128
-#define CL_FLT_MIN_10_EXP   -37
-#define CL_FLT_MIN_EXP      -125
-#define CL_FLT_RADIX        2
-#define CL_FLT_MAX          340282346638528859811704183484516925440.0f
-#define CL_FLT_MIN          1.175494350822287507969e-38f
-#define CL_FLT_EPSILON      1.1920928955078125e-7f
-
-#define CL_HALF_DIG          3
-#define CL_HALF_MANT_DIG     11
-#define CL_HALF_MAX_10_EXP   +4
-#define CL_HALF_MAX_EXP      +16
-#define CL_HALF_MIN_10_EXP   -4
-#define CL_HALF_MIN_EXP      -13
-#define CL_HALF_RADIX        2
-#define CL_HALF_MAX          65504.0f
-#define CL_HALF_MIN          6.103515625e-05f
-#define CL_HALF_EPSILON      9.765625e-04f
-
-#define CL_DBL_DIG          15
-#define CL_DBL_MANT_DIG     53
-#define CL_DBL_MAX_10_EXP   +308
-#define CL_DBL_MAX_EXP      +1024
-#define CL_DBL_MIN_10_EXP   -307
-#define CL_DBL_MIN_EXP      -1021
-#define CL_DBL_RADIX        2
-#define CL_DBL_MAX          1.7976931348623158e+308
-#define CL_DBL_MIN          2.225073858507201383090e-308
-#define CL_DBL_EPSILON      2.220446049250313080847e-16
-
-#define CL_M_E              2.7182818284590452354
-#define CL_M_LOG2E          1.4426950408889634074
-#define CL_M_LOG10E         0.43429448190325182765
-#define CL_M_LN2            0.69314718055994530942
-#define CL_M_LN10           2.30258509299404568402
-#define CL_M_PI             3.14159265358979323846
-#define CL_M_PI_2           1.57079632679489661923
-#define CL_M_PI_4           0.78539816339744830962
-#define CL_M_1_PI           0.31830988618379067154
-#define CL_M_2_PI           0.63661977236758134308
-#define CL_M_2_SQRTPI       1.12837916709551257390
-#define CL_M_SQRT2          1.41421356237309504880
-#define CL_M_SQRT1_2        0.70710678118654752440
-
-#define CL_M_E_F            2.718281828f
-#define CL_M_LOG2E_F        1.442695041f
-#define CL_M_LOG10E_F       0.434294482f
-#define CL_M_LN2_F          0.693147181f
-#define CL_M_LN10_F         2.302585093f
-#define CL_M_PI_F           3.141592654f
-#define CL_M_PI_2_F         1.570796327f
-#define CL_M_PI_4_F         0.785398163f
-#define CL_M_1_PI_F         0.318309886f
-#define CL_M_2_PI_F         0.636619772f
-#define CL_M_2_SQRTPI_F     1.128379167f
-#define CL_M_SQRT2_F        1.414213562f
-#define CL_M_SQRT1_2_F      0.707106781f
-
-#define CL_NAN              (CL_INFINITY - CL_INFINITY)
-#define CL_HUGE_VALF        ((cl_float) 1e50)
-#define CL_HUGE_VAL         ((cl_double) 1e500)
-#define CL_MAXFLOAT         CL_FLT_MAX
-#define CL_INFINITY         CL_HUGE_VALF
-
-#else
-
-#include <stdint.h>
-
-/* scalar types  */
-typedef int8_t          cl_char;
-typedef uint8_t         cl_uchar;
-typedef int16_t         cl_short;
-typedef uint16_t        cl_ushort;
-typedef int32_t         cl_int;
-typedef uint32_t        cl_uint;
-typedef int64_t         cl_long;
-typedef uint64_t        cl_ulong;
-
-typedef uint16_t        cl_half;
-typedef float           cl_float;
-typedef double          cl_double;
-
-/* Macro names and corresponding values defined by OpenCL */
-#define CL_CHAR_BIT         8
-#define CL_SCHAR_MAX        127
-#define CL_SCHAR_MIN        (-127-1)
-#define CL_CHAR_MAX         CL_SCHAR_MAX
-#define CL_CHAR_MIN         CL_SCHAR_MIN
-#define CL_UCHAR_MAX        255
-#define CL_SHRT_MAX         32767
-#define CL_SHRT_MIN         (-32767-1)
-#define CL_USHRT_MAX        65535
-#define CL_INT_MAX          2147483647
-#define CL_INT_MIN          (-2147483647-1)
-#define CL_UINT_MAX         0xffffffffU
-#define CL_LONG_MAX         ((cl_long) 0x7FFFFFFFFFFFFFFFLL)
-#define CL_LONG_MIN         ((cl_long) -0x7FFFFFFFFFFFFFFFLL - 1LL)
-#define CL_ULONG_MAX        ((cl_ulong) 0xFFFFFFFFFFFFFFFFULL)
-
-#define CL_FLT_DIG          6
-#define CL_FLT_MANT_DIG     24
-#define CL_FLT_MAX_10_EXP   +38
-#define CL_FLT_MAX_EXP      +128
-#define CL_FLT_MIN_10_EXP   -37
-#define CL_FLT_MIN_EXP      -125
-#define CL_FLT_RADIX        2
-#define CL_FLT_MAX          340282346638528859811704183484516925440.0f
-#define CL_FLT_MIN          1.175494350822287507969e-38f
-#define CL_FLT_EPSILON      1.1920928955078125e-7f
-
-#define CL_HALF_DIG          3
-#define CL_HALF_MANT_DIG     11
-#define CL_HALF_MAX_10_EXP   +4
-#define CL_HALF_MAX_EXP      +16
-#define CL_HALF_MIN_10_EXP   -4
-#define CL_HALF_MIN_EXP      -13
-#define CL_HALF_RADIX        2
-#define CL_HALF_MAX          65504.0f
-#define CL_HALF_MIN          6.103515625e-05f
-#define CL_HALF_EPSILON      9.765625e-04f
-
-#define CL_DBL_DIG          15
-#define CL_DBL_MANT_DIG     53
-#define CL_DBL_MAX_10_EXP   +308
-#define CL_DBL_MAX_EXP      +1024
-#define CL_DBL_MIN_10_EXP   -307
-#define CL_DBL_MIN_EXP      -1021
-#define CL_DBL_RADIX        2
-#define CL_DBL_MAX          179769313486231570814527423731704356798070567525844996598917476803157260780028538760589558632766878171540458953514382464234321326889464182768467546703537516986049910576551282076245490090389328944075868508455133942304583236903222948165808559332123348274797826204144723168738177180919299881250404026184124858368.0
-#define CL_DBL_MIN          2.225073858507201383090e-308
-#define CL_DBL_EPSILON      2.220446049250313080847e-16
-
-#define CL_M_E              2.7182818284590452354
-#define CL_M_LOG2E          1.4426950408889634074
-#define CL_M_LOG10E         0.43429448190325182765
-#define CL_M_LN2            0.69314718055994530942
-#define CL_M_LN10           2.30258509299404568402
-#define CL_M_PI             3.14159265358979323846
-#define CL_M_PI_2           1.57079632679489661923
-#define CL_M_PI_4           0.78539816339744830962
-#define CL_M_1_PI           0.31830988618379067154
-#define CL_M_2_PI           0.63661977236758134308
-#define CL_M_2_SQRTPI       1.12837916709551257390
-#define CL_M_SQRT2          1.41421356237309504880
-#define CL_M_SQRT1_2        0.70710678118654752440
-
-#define CL_M_E_F            2.718281828f
-#define CL_M_LOG2E_F        1.442695041f
-#define CL_M_LOG10E_F       0.434294482f
-#define CL_M_LN2_F          0.693147181f
-#define CL_M_LN10_F         2.302585093f
-#define CL_M_PI_F           3.141592654f
-#define CL_M_PI_2_F         1.570796327f
-#define CL_M_PI_4_F         0.785398163f
-#define CL_M_1_PI_F         0.318309886f
-#define CL_M_2_PI_F         0.636619772f
-#define CL_M_2_SQRTPI_F     1.128379167f
-#define CL_M_SQRT2_F        1.414213562f
-#define CL_M_SQRT1_2_F      0.707106781f
-
-#if defined( __GNUC__ )
-   #define CL_HUGE_VALF     __builtin_huge_valf()
-   #define CL_HUGE_VAL      __builtin_huge_val()
-   #define CL_NAN           __builtin_nanf( "" )
-#else
-   #define CL_HUGE_VALF     ((cl_float) 1e50)
-   #define CL_HUGE_VAL      ((cl_double) 1e500)
-   float nanf( const char * );
-   #define CL_NAN           nanf( "" )
-#endif
-#define CL_MAXFLOAT         CL_FLT_MAX
-#define CL_INFINITY         CL_HUGE_VALF
-
-#endif
-
-#include <stddef.h>
-
-/* Mirror types to GL types. Mirror types allow us to avoid deciding which 87s to load based on whether we are using GL or GLES here. */
-typedef unsigned int cl_GLuint;
-typedef int          cl_GLint;
-typedef unsigned int cl_GLenum;
-
-/*
- * Vector types
- *
- *  Note:   OpenCL requires that all types be naturally aligned.
- *          This means that vector types must be naturally aligned.
- *          For example, a vector of four floats must be aligned to
- *          a 16 byte boundary (calculated as 4 * the natural 4-byte
- *          alignment of the float).  The alignment qualifiers here
- *          will only function properly if your compiler supports them
- *          and if you don't actively work to defeat them.  For example,
- *          in order for a cl_float4 to be 16 byte aligned in a struct,
- *          the start of the struct must itself be 16-byte aligned.
- *
- *          Maintaining proper alignment is the user's responsibility.
- */
-
-/* Define basic vector types */
-#if defined( __VEC__ )
-  #if !defined(__clang__)
-     #include <altivec.h>   /* may be omitted depending on compiler. AltiVec spec provides no way to detect whether the header is required. */
-  #endif
-   typedef __vector unsigned char     __cl_uchar16;
-   typedef __vector signed char       __cl_char16;
-   typedef __vector unsigned short    __cl_ushort8;
-   typedef __vector signed short      __cl_short8;
-   typedef __vector unsigned int      __cl_uint4;
-   typedef __vector signed int        __cl_int4;
-   typedef __vector float             __cl_float4;
-   #define  __CL_UCHAR16__  1
-   #define  __CL_CHAR16__   1
-   #define  __CL_USHORT8__  1
-   #define  __CL_SHORT8__   1
-   #define  __CL_UINT4__    1
-   #define  __CL_INT4__     1
-   #define  __CL_FLOAT4__   1
-#endif
-
-#if defined( __SSE__ )
-    #if defined( __MINGW64__ )
-        #include <intrin.h>
-    #else
-        #include <xmmintrin.h>
-    #endif
-    #if defined( __GNUC__ )
-        typedef float __cl_float4   __attribute__((vector_size(16)));
-    #else
-        typedef __m128 __cl_float4;
-    #endif
-    #define __CL_FLOAT4__   1
-#endif
-
-#if defined( __SSE2__ )
-    #if defined( __MINGW64__ )
-        #include <intrin.h>
-    #else
-        #include <emmintrin.h>
-    #endif
-    #if defined( __GNUC__ )
-        typedef cl_uchar    __cl_uchar16    __attribute__((vector_size(16)));
-        typedef cl_char     __cl_char16     __attribute__((vector_size(16)));
-        typedef cl_ushort   __cl_ushort8    __attribute__((vector_size(16)));
-        typedef cl_short    __cl_short8     __attribute__((vector_size(16)));
-        typedef cl_uint     __cl_uint4      __attribute__((vector_size(16)));
-        typedef cl_int      __cl_int4       __attribute__((vector_size(16)));
-        typedef cl_ulong    __cl_ulong2     __attribute__((vector_size(16)));
-        typedef cl_long     __cl_long2      __attribute__((vector_size(16)));
-        typedef cl_double   __cl_double2    __attribute__((vector_size(16)));
-    #else
-        typedef __m128i __cl_uchar16;
-        typedef __m128i __cl_char16;
-        typedef __m128i __cl_ushort8;
-        typedef __m128i __cl_short8;
-        typedef __m128i __cl_uint4;
-        typedef __m128i __cl_int4;
-        typedef __m128i __cl_ulong2;
-        typedef __m128i __cl_long2;
-        typedef __m128d __cl_double2;
-    #endif
-    #define __CL_UCHAR16__  1
-    #define __CL_CHAR16__   1
-    #define __CL_USHORT8__  1
-    #define __CL_SHORT8__   1
-    #define __CL_INT4__     1
-    #define __CL_UINT4__    1
-    #define __CL_ULONG2__   1
-    #define __CL_LONG2__    1
-    #define __CL_DOUBLE2__  1
-#endif
-
-#if defined( __MMX__ )
-    #include <mmintrin.h>
-    #if defined( __GNUC__ )
-        typedef cl_uchar    __cl_uchar8     __attribute__((vector_size(8)));
-        typedef cl_char     __cl_char8      __attribute__((vector_size(8)));
-        typedef cl_ushort   __cl_ushort4    __attribute__((vector_size(8)));
-        typedef cl_short    __cl_short4     __attribute__((vector_size(8)));
-        typedef cl_uint     __cl_uint2      __attribute__((vector_size(8)));
-        typedef cl_int      __cl_int2       __attribute__((vector_size(8)));
-        typedef cl_ulong    __cl_ulong1     __attribute__((vector_size(8)));
-        typedef cl_long     __cl_long1      __attribute__((vector_size(8)));
-        typedef cl_float    __cl_float2     __attribute__((vector_size(8)));
-    #else
-        typedef __m64       __cl_uchar8;
-        typedef __m64       __cl_char8;
-        typedef __m64       __cl_ushort4;
-        typedef __m64       __cl_short4;
-        typedef __m64       __cl_uint2;
-        typedef __m64       __cl_int2;
-        typedef __m64       __cl_ulong1;
-        typedef __m64       __cl_long1;
-        typedef __m64       __cl_float2;
-    #endif
-    #define __CL_UCHAR8__   1
-    #define __CL_CHAR8__    1
-    #define __CL_USHORT4__  1
-    #define __CL_SHORT4__   1
-    #define __CL_INT2__     1
-    #define __CL_UINT2__    1
-    #define __CL_ULONG1__   1
-    #define __CL_LONG1__    1
-    #define __CL_FLOAT2__   1
-#endif
-
-#if defined( __AVX__ )
-    #if defined( __MINGW64__ )
-        #include <intrin.h>
-    #else
-        #include <immintrin.h>
-    #endif
-    #if defined( __GNUC__ )
-        typedef cl_float    __cl_float8     __attribute__((vector_size(32)));
-        typedef cl_double   __cl_double4    __attribute__((vector_size(32)));
-    #else
-        typedef __m256      __cl_float8;
-        typedef __m256d     __cl_double4;
-    #endif
-    #define __CL_FLOAT8__   1
-    #define __CL_DOUBLE4__  1
-#endif
-
-/* Define capabilities for anonymous struct members. */
-#if !defined(__cplusplus) && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L
-#define  __CL_HAS_ANON_STRUCT__ 1
-#define  __CL_ANON_STRUCT__
-#elif defined(_WIN32) && defined(_MSC_VER) && !defined(__STDC__)
-#define  __CL_HAS_ANON_STRUCT__ 1
-#define  __CL_ANON_STRUCT__
-#elif defined(__GNUC__) && ! defined(__STRICT_ANSI__)
-#define  __CL_HAS_ANON_STRUCT__ 1
-#define  __CL_ANON_STRUCT__ __extension__
-#elif defined(__clang__)
-#define  __CL_HAS_ANON_STRUCT__ 1
-#define  __CL_ANON_STRUCT__ __extension__
-#else
-#define  __CL_HAS_ANON_STRUCT__ 0
-#define  __CL_ANON_STRUCT__
-#endif
-
-#if defined(_WIN32) && defined(_MSC_VER) && __CL_HAS_ANON_STRUCT__
-   /* Disable warning C4201: nonstandard extension used : nameless struct/union */
-    #pragma warning( push )
-    #pragma warning( disable : 4201 )
-#endif
-
-/* Define alignment keys */
-#if defined( __GNUC__ ) || defined(__INTEGRITY)
-    #define CL_ALIGNED(_x)          __attribute__ ((aligned(_x)))
-#elif defined( _WIN32) && (_MSC_VER)
-    /* Alignment keys neutered on windows because MSVC can't swallow function arguments with alignment requirements     */
-    /* http://msdn.microsoft.com/en-us/library/373ak2y1%28VS.71%29.aspx                                                 */
-    /* #include <crtdefs.h>                                                                                             */
-    /* #define CL_ALIGNED(_x)          _CRT_ALIGN(_x)                                                                   */
-    #define CL_ALIGNED(_x)
-#else
-   #warning  Need to implement some method to align data here
-   #define  CL_ALIGNED(_x)
-#endif
-
-/* Indicate whether .xyzw, .s0123 and .hi.lo are supported */
-#if __CL_HAS_ANON_STRUCT__
-    /* .xyzw and .s0123...{f|F} are supported */
-    #define CL_HAS_NAMED_VECTOR_FIELDS 1
-    /* .hi and .lo are supported */
-    #define CL_HAS_HI_LO_VECTOR_FIELDS 1
-#endif
-
-/* Define cl_vector types */
-
-/* ---- cl_charn ---- */
-typedef union
-{
-    cl_char  CL_ALIGNED(2) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_char  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_char  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_char  lo, hi; };
-#endif
-#if defined( __CL_CHAR2__)
-    __cl_char2     v2;
-#endif
-}cl_char2;
-
-typedef union
-{
-    cl_char  CL_ALIGNED(4) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_char  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_char  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_char2 lo, hi; };
-#endif
-#if defined( __CL_CHAR2__)
-    __cl_char2     v2[2];
-#endif
-#if defined( __CL_CHAR4__)
-    __cl_char4     v4;
-#endif
-}cl_char4;
-
-/* cl_char3 is identical in size, alignment and behavior to cl_char4. See section 6.1.5. */
-typedef  cl_char4  cl_char3;
-
-typedef union
-{
-    cl_char   CL_ALIGNED(8) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_char  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_char  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_char4 lo, hi; };
-#endif
-#if defined( __CL_CHAR2__)
-    __cl_char2     v2[4];
-#endif
-#if defined( __CL_CHAR4__)
-    __cl_char4     v4[2];
-#endif
-#if defined( __CL_CHAR8__ )
-    __cl_char8     v8;
-#endif
-}cl_char8;
-
-typedef union
-{
-    cl_char  CL_ALIGNED(16) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_char  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_char  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_char8 lo, hi; };
-#endif
-#if defined( __CL_CHAR2__)
-    __cl_char2     v2[8];
-#endif
-#if defined( __CL_CHAR4__)
-    __cl_char4     v4[4];
-#endif
-#if defined( __CL_CHAR8__ )
-    __cl_char8     v8[2];
-#endif
-#if defined( __CL_CHAR16__ )
-    __cl_char16    v16;
-#endif
-}cl_char16;
-
-
-/* ---- cl_ucharn ---- */
-typedef union
-{
-    cl_uchar  CL_ALIGNED(2) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uchar  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar  lo, hi; };
-#endif
-#if defined( __cl_uchar2__)
-    __cl_uchar2     v2;
-#endif
-}cl_uchar2;
-
-typedef union
-{
-    cl_uchar  CL_ALIGNED(4) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uchar  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar2 lo, hi; };
-#endif
-#if defined( __CL_UCHAR2__)
-    __cl_uchar2     v2[2];
-#endif
-#if defined( __CL_UCHAR4__)
-    __cl_uchar4     v4;
-#endif
-}cl_uchar4;
-
-/* cl_uchar3 is identical in size, alignment and behavior to cl_uchar4. See section 6.1.5. */
-typedef  cl_uchar4  cl_uchar3;
-
-typedef union
-{
-    cl_uchar   CL_ALIGNED(8) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uchar  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar4 lo, hi; };
-#endif
-#if defined( __CL_UCHAR2__)
-    __cl_uchar2     v2[4];
-#endif
-#if defined( __CL_UCHAR4__)
-    __cl_uchar4     v4[2];
-#endif
-#if defined( __CL_UCHAR8__ )
-    __cl_uchar8     v8;
-#endif
-}cl_uchar8;
-
-typedef union
-{
-    cl_uchar  CL_ALIGNED(16) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uchar  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_uchar8 lo, hi; };
-#endif
-#if defined( __CL_UCHAR2__)
-    __cl_uchar2     v2[8];
-#endif
-#if defined( __CL_UCHAR4__)
-    __cl_uchar4     v4[4];
-#endif
-#if defined( __CL_UCHAR8__ )
-    __cl_uchar8     v8[2];
-#endif
-#if defined( __CL_UCHAR16__ )
-    __cl_uchar16    v16;
-#endif
-}cl_uchar16;
-
-
-/* ---- cl_shortn ---- */
-typedef union
-{
-    cl_short  CL_ALIGNED(4) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_short  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_short  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_short  lo, hi; };
-#endif
-#if defined( __CL_SHORT2__)
-    __cl_short2     v2;
-#endif
-}cl_short2;
-
-typedef union
-{
-    cl_short  CL_ALIGNED(8) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_short  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_short  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_short2 lo, hi; };
-#endif
-#if defined( __CL_SHORT2__)
-    __cl_short2     v2[2];
-#endif
-#if defined( __CL_SHORT4__)
-    __cl_short4     v4;
-#endif
-}cl_short4;
-
-/* cl_short3 is identical in size, alignment and behavior to cl_short4. See section 6.1.5. */
-typedef  cl_short4  cl_short3;
-
-typedef union
-{
-    cl_short   CL_ALIGNED(16) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_short  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_short  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_short4 lo, hi; };
-#endif
-#if defined( __CL_SHORT2__)
-    __cl_short2     v2[4];
-#endif
-#if defined( __CL_SHORT4__)
-    __cl_short4     v4[2];
-#endif
-#if defined( __CL_SHORT8__ )
-    __cl_short8     v8;
-#endif
-}cl_short8;
-
-typedef union
-{
-    cl_short  CL_ALIGNED(32) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_short  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_short  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_short8 lo, hi; };
-#endif
-#if defined( __CL_SHORT2__)
-    __cl_short2     v2[8];
-#endif
-#if defined( __CL_SHORT4__)
-    __cl_short4     v4[4];
-#endif
-#if defined( __CL_SHORT8__ )
-    __cl_short8     v8[2];
-#endif
-#if defined( __CL_SHORT16__ )
-    __cl_short16    v16;
-#endif
-}cl_short16;
-
-
-/* ---- cl_ushortn ---- */
-typedef union
-{
-    cl_ushort  CL_ALIGNED(4) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ushort  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort  lo, hi; };
-#endif
-#if defined( __CL_USHORT2__)
-    __cl_ushort2     v2;
-#endif
-}cl_ushort2;
-
-typedef union
-{
-    cl_ushort  CL_ALIGNED(8) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ushort  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort2 lo, hi; };
-#endif
-#if defined( __CL_USHORT2__)
-    __cl_ushort2     v2[2];
-#endif
-#if defined( __CL_USHORT4__)
-    __cl_ushort4     v4;
-#endif
-}cl_ushort4;
-
-/* cl_ushort3 is identical in size, alignment and behavior to cl_ushort4. See section 6.1.5. */
-typedef  cl_ushort4  cl_ushort3;
-
-typedef union
-{
-    cl_ushort   CL_ALIGNED(16) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ushort  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort4 lo, hi; };
-#endif
-#if defined( __CL_USHORT2__)
-    __cl_ushort2     v2[4];
-#endif
-#if defined( __CL_USHORT4__)
-    __cl_ushort4     v4[2];
-#endif
-#if defined( __CL_USHORT8__ )
-    __cl_ushort8     v8;
-#endif
-}cl_ushort8;
-
-typedef union
-{
-    cl_ushort  CL_ALIGNED(32) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ushort  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_ushort8 lo, hi; };
-#endif
-#if defined( __CL_USHORT2__)
-    __cl_ushort2     v2[8];
-#endif
-#if defined( __CL_USHORT4__)
-    __cl_ushort4     v4[4];
-#endif
-#if defined( __CL_USHORT8__ )
-    __cl_ushort8     v8[2];
-#endif
-#if defined( __CL_USHORT16__ )
-    __cl_ushort16    v16;
-#endif
-}cl_ushort16;
-
-
-/* ---- cl_halfn ---- */
-typedef union
-{
-    cl_half  CL_ALIGNED(4) s[2];
-#if __CL_HAS_ANON_STRUCT__
-    __CL_ANON_STRUCT__ struct{ cl_half  x, y; };
-    __CL_ANON_STRUCT__ struct{ cl_half  s0, s1; };
-    __CL_ANON_STRUCT__ struct{ cl_half  lo, hi; };
-#endif
-#if defined( __CL_HALF2__)
-    __cl_half2     v2;
-#endif
-}cl_half2;
-
-typedef union
-{
-    cl_half  CL_ALIGNED(8) s[4];
-#if __CL_HAS_ANON_STRUCT__
-    __CL_ANON_STRUCT__ struct{ cl_half  x, y, z, w; };
-    __CL_ANON_STRUCT__ struct{ cl_half  s0, s1, s2, s3; };
-    __CL_ANON_STRUCT__ struct{ cl_half2 lo, hi; };
-#endif
-#if defined( __CL_HALF2__)
-    __cl_half2     v2[2];
-#endif
-#if defined( __CL_HALF4__)
-    __cl_half4     v4;
-#endif
-}cl_half4;
-
-/* cl_half3 is identical in size, alignment and behavior to cl_half4. See section 6.1.5. */
-typedef  cl_half4  cl_half3;
-
-typedef union
-{
-    cl_half   CL_ALIGNED(16) s[8];
-#if __CL_HAS_ANON_STRUCT__
-    __CL_ANON_STRUCT__ struct{ cl_half  x, y, z, w; };
-    __CL_ANON_STRUCT__ struct{ cl_half  s0, s1, s2, s3, s4, s5, s6, s7; };
-    __CL_ANON_STRUCT__ struct{ cl_half4 lo, hi; };
-#endif
-#if defined( __CL_HALF2__)
-    __cl_half2     v2[4];
-#endif
-#if defined( __CL_HALF4__)
-    __cl_half4     v4[2];
-#endif
-#if defined( __CL_HALF8__ )
-    __cl_half8     v8;
-#endif
-}cl_half8;
-
-typedef union
-{
-    cl_half  CL_ALIGNED(32) s[16];
-#if __CL_HAS_ANON_STRUCT__
-    __CL_ANON_STRUCT__ struct{ cl_half  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-    __CL_ANON_STRUCT__ struct{ cl_half  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-    __CL_ANON_STRUCT__ struct{ cl_half8 lo, hi; };
-#endif
-#if defined( __CL_HALF2__)
-    __cl_half2     v2[8];
-#endif
-#if defined( __CL_HALF4__)
-    __cl_half4     v4[4];
-#endif
-#if defined( __CL_HALF8__ )
-    __cl_half8     v8[2];
-#endif
-#if defined( __CL_HALF16__ )
-    __cl_half16    v16;
-#endif
-}cl_half16;
-
-/* ---- cl_intn ---- */
-typedef union
-{
-    cl_int  CL_ALIGNED(8) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_int  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_int  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_int  lo, hi; };
-#endif
-#if defined( __CL_INT2__)
-    __cl_int2     v2;
-#endif
-}cl_int2;
-
-typedef union
-{
-    cl_int  CL_ALIGNED(16) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_int  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_int  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_int2 lo, hi; };
-#endif
-#if defined( __CL_INT2__)
-    __cl_int2     v2[2];
-#endif
-#if defined( __CL_INT4__)
-    __cl_int4     v4;
-#endif
-}cl_int4;
-
-/* cl_int3 is identical in size, alignment and behavior to cl_int4. See section 6.1.5. */
-typedef  cl_int4  cl_int3;
-
-typedef union
-{
-    cl_int   CL_ALIGNED(32) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_int  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_int  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_int4 lo, hi; };
-#endif
-#if defined( __CL_INT2__)
-    __cl_int2     v2[4];
-#endif
-#if defined( __CL_INT4__)
-    __cl_int4     v4[2];
-#endif
-#if defined( __CL_INT8__ )
-    __cl_int8     v8;
-#endif
-}cl_int8;
-
-typedef union
-{
-    cl_int  CL_ALIGNED(64) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_int  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_int  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_int8 lo, hi; };
-#endif
-#if defined( __CL_INT2__)
-    __cl_int2     v2[8];
-#endif
-#if defined( __CL_INT4__)
-    __cl_int4     v4[4];
-#endif
-#if defined( __CL_INT8__ )
-    __cl_int8     v8[2];
-#endif
-#if defined( __CL_INT16__ )
-    __cl_int16    v16;
-#endif
-}cl_int16;
-
-
-/* ---- cl_uintn ---- */
-typedef union
-{
-    cl_uint  CL_ALIGNED(8) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uint  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_uint  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_uint  lo, hi; };
-#endif
-#if defined( __CL_UINT2__)
-    __cl_uint2     v2;
-#endif
-}cl_uint2;
-
-typedef union
-{
-    cl_uint  CL_ALIGNED(16) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uint  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_uint  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_uint2 lo, hi; };
-#endif
-#if defined( __CL_UINT2__)
-    __cl_uint2     v2[2];
-#endif
-#if defined( __CL_UINT4__)
-    __cl_uint4     v4;
-#endif
-}cl_uint4;
-
-/* cl_uint3 is identical in size, alignment and behavior to cl_uint4. See section 6.1.5. */
-typedef  cl_uint4  cl_uint3;
-
-typedef union
-{
-    cl_uint   CL_ALIGNED(32) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uint  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_uint  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_uint4 lo, hi; };
-#endif
-#if defined( __CL_UINT2__)
-    __cl_uint2     v2[4];
-#endif
-#if defined( __CL_UINT4__)
-    __cl_uint4     v4[2];
-#endif
-#if defined( __CL_UINT8__ )
-    __cl_uint8     v8;
-#endif
-}cl_uint8;
-
-typedef union
-{
-    cl_uint  CL_ALIGNED(64) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_uint  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_uint  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_uint8 lo, hi; };
-#endif
-#if defined( __CL_UINT2__)
-    __cl_uint2     v2[8];
-#endif
-#if defined( __CL_UINT4__)
-    __cl_uint4     v4[4];
-#endif
-#if defined( __CL_UINT8__ )
-    __cl_uint8     v8[2];
-#endif
-#if defined( __CL_UINT16__ )
-    __cl_uint16    v16;
-#endif
-}cl_uint16;
-
-/* ---- cl_longn ---- */
-typedef union
-{
-    cl_long  CL_ALIGNED(16) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_long  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_long  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_long  lo, hi; };
-#endif
-#if defined( __CL_LONG2__)
-    __cl_long2     v2;
-#endif
-}cl_long2;
-
-typedef union
-{
-    cl_long  CL_ALIGNED(32) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_long  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_long  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_long2 lo, hi; };
-#endif
-#if defined( __CL_LONG2__)
-    __cl_long2     v2[2];
-#endif
-#if defined( __CL_LONG4__)
-    __cl_long4     v4;
-#endif
-}cl_long4;
-
-/* cl_long3 is identical in size, alignment and behavior to cl_long4. See section 6.1.5. */
-typedef  cl_long4  cl_long3;
-
-typedef union
-{
-    cl_long   CL_ALIGNED(64) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_long  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_long  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_long4 lo, hi; };
-#endif
-#if defined( __CL_LONG2__)
-    __cl_long2     v2[4];
-#endif
-#if defined( __CL_LONG4__)
-    __cl_long4     v4[2];
-#endif
-#if defined( __CL_LONG8__ )
-    __cl_long8     v8;
-#endif
-}cl_long8;
-
-typedef union
-{
-    cl_long  CL_ALIGNED(128) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_long  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_long  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_long8 lo, hi; };
-#endif
-#if defined( __CL_LONG2__)
-    __cl_long2     v2[8];
-#endif
-#if defined( __CL_LONG4__)
-    __cl_long4     v4[4];
-#endif
-#if defined( __CL_LONG8__ )
-    __cl_long8     v8[2];
-#endif
-#if defined( __CL_LONG16__ )
-    __cl_long16    v16;
-#endif
-}cl_long16;
-
-
-/* ---- cl_ulongn ---- */
-typedef union
-{
-    cl_ulong  CL_ALIGNED(16) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ulong  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong  lo, hi; };
-#endif
-#if defined( __CL_ULONG2__)
-    __cl_ulong2     v2;
-#endif
-}cl_ulong2;
-
-typedef union
-{
-    cl_ulong  CL_ALIGNED(32) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ulong  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong2 lo, hi; };
-#endif
-#if defined( __CL_ULONG2__)
-    __cl_ulong2     v2[2];
-#endif
-#if defined( __CL_ULONG4__)
-    __cl_ulong4     v4;
-#endif
-}cl_ulong4;
-
-/* cl_ulong3 is identical in size, alignment and behavior to cl_ulong4. See section 6.1.5. */
-typedef  cl_ulong4  cl_ulong3;
-
-typedef union
-{
-    cl_ulong   CL_ALIGNED(64) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ulong  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong4 lo, hi; };
-#endif
-#if defined( __CL_ULONG2__)
-    __cl_ulong2     v2[4];
-#endif
-#if defined( __CL_ULONG4__)
-    __cl_ulong4     v4[2];
-#endif
-#if defined( __CL_ULONG8__ )
-    __cl_ulong8     v8;
-#endif
-}cl_ulong8;
-
-typedef union
-{
-    cl_ulong  CL_ALIGNED(128) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_ulong  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_ulong8 lo, hi; };
-#endif
-#if defined( __CL_ULONG2__)
-    __cl_ulong2     v2[8];
-#endif
-#if defined( __CL_ULONG4__)
-    __cl_ulong4     v4[4];
-#endif
-#if defined( __CL_ULONG8__ )
-    __cl_ulong8     v8[2];
-#endif
-#if defined( __CL_ULONG16__ )
-    __cl_ulong16    v16;
-#endif
-}cl_ulong16;
-
-
-/* --- cl_floatn ---- */
-
-typedef union
-{
-    cl_float  CL_ALIGNED(8) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_float  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_float  s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_float  lo, hi; };
-#endif
-#if defined( __CL_FLOAT2__)
-    __cl_float2     v2;
-#endif
-}cl_float2;
-
-typedef union
-{
-    cl_float  CL_ALIGNED(16) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_float   x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_float   s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_float2  lo, hi; };
-#endif
-#if defined( __CL_FLOAT2__)
-    __cl_float2     v2[2];
-#endif
-#if defined( __CL_FLOAT4__)
-    __cl_float4     v4;
-#endif
-}cl_float4;
-
-/* cl_float3 is identical in size, alignment and behavior to cl_float4. See section 6.1.5. */
-typedef  cl_float4  cl_float3;
-
-typedef union
-{
-    cl_float   CL_ALIGNED(32) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_float   x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_float   s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_float4  lo, hi; };
-#endif
-#if defined( __CL_FLOAT2__)
-    __cl_float2     v2[4];
-#endif
-#if defined( __CL_FLOAT4__)
-    __cl_float4     v4[2];
-#endif
-#if defined( __CL_FLOAT8__ )
-    __cl_float8     v8;
-#endif
-}cl_float8;
-
-typedef union
-{
-    cl_float  CL_ALIGNED(64) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_float  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_float  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_float8 lo, hi; };
-#endif
-#if defined( __CL_FLOAT2__)
-    __cl_float2     v2[8];
-#endif
-#if defined( __CL_FLOAT4__)
-    __cl_float4     v4[4];
-#endif
-#if defined( __CL_FLOAT8__ )
-    __cl_float8     v8[2];
-#endif
-#if defined( __CL_FLOAT16__ )
-    __cl_float16    v16;
-#endif
-}cl_float16;
-
-/* --- cl_doublen ---- */
-
-typedef union
-{
-    cl_double  CL_ALIGNED(16) s[2];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_double  x, y; };
-   __CL_ANON_STRUCT__ struct{ cl_double s0, s1; };
-   __CL_ANON_STRUCT__ struct{ cl_double lo, hi; };
-#endif
-#if defined( __CL_DOUBLE2__)
-    __cl_double2     v2;
-#endif
-}cl_double2;
-
-typedef union
-{
-    cl_double  CL_ALIGNED(32) s[4];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_double  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_double  s0, s1, s2, s3; };
-   __CL_ANON_STRUCT__ struct{ cl_double2 lo, hi; };
-#endif
-#if defined( __CL_DOUBLE2__)
-    __cl_double2     v2[2];
-#endif
-#if defined( __CL_DOUBLE4__)
-    __cl_double4     v4;
-#endif
-}cl_double4;
-
-/* cl_double3 is identical in size, alignment and behavior to cl_double4. See section 6.1.5. */
-typedef  cl_double4  cl_double3;
-
-typedef union
-{
-    cl_double   CL_ALIGNED(64) s[8];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_double  x, y, z, w; };
-   __CL_ANON_STRUCT__ struct{ cl_double  s0, s1, s2, s3, s4, s5, s6, s7; };
-   __CL_ANON_STRUCT__ struct{ cl_double4 lo, hi; };
-#endif
-#if defined( __CL_DOUBLE2__)
-    __cl_double2     v2[4];
-#endif
-#if defined( __CL_DOUBLE4__)
-    __cl_double4     v4[2];
-#endif
-#if defined( __CL_DOUBLE8__ )
-    __cl_double8     v8;
-#endif
-}cl_double8;
-
-typedef union
-{
-    cl_double  CL_ALIGNED(128) s[16];
-#if __CL_HAS_ANON_STRUCT__
-   __CL_ANON_STRUCT__ struct{ cl_double  x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; };
-   __CL_ANON_STRUCT__ struct{ cl_double  s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; };
-   __CL_ANON_STRUCT__ struct{ cl_double8 lo, hi; };
-#endif
-#if defined( __CL_DOUBLE2__)
-    __cl_double2     v2[8];
-#endif
-#if defined( __CL_DOUBLE4__)
-    __cl_double4     v4[4];
-#endif
-#if defined( __CL_DOUBLE8__ )
-    __cl_double8     v8[2];
-#endif
-#if defined( __CL_DOUBLE16__ )
-    __cl_double16    v16;
-#endif
-}cl_double16;
-
-/* Macro to facilitate debugging
- * Usage:
- *   Place CL_PROGRAM_STRING_DEBUG_INFO on the line before the first line of your source.
- *   The first line ends with:   CL_PROGRAM_STRING_DEBUG_INFO \"
- *   Each line thereafter of OpenCL C source must end with: \n\
- *   The last line ends in ";
- *
- *   Example:
- *
- *   const char *my_program = CL_PROGRAM_STRING_DEBUG_INFO "\
- *   kernel void foo( int a, float * b )             \n\
- *   {                                               \n\
- *      // my comment                                \n\
- *      *b[ get_global_id(0)] = a;                   \n\
- *   }                                               \n\
- *   ";
- *
- * This should correctly set up the line, (column) and file information for your source
- * string so you can do source level debugging.
- */
-#define  __CL_STRINGIFY( _x )               # _x
-#define  _CL_STRINGIFY( _x )                __CL_STRINGIFY( _x )
-#define  CL_PROGRAM_STRING_DEBUG_INFO       "#line "  _CL_STRINGIFY(__LINE__) " \"" __FILE__ "\" \n\n"
-
-#ifdef __cplusplus
-}
-#endif
-
-#if defined(_WIN32) && defined(_MSC_VER) && __CL_HAS_ANON_STRUCT__
-    #pragma warning( pop )
-#endif
-
-#endif  /* __CL_PLATFORM_H  */
diff --git a/spaces/Immi007/ChatGPT4/README.md b/spaces/Immi007/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/Immi007/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/InpaintAI/Inpaint-Anything/remove_anything.py b/spaces/InpaintAI/Inpaint-Anything/remove_anything.py
deleted file mode 100644
index 124b066e481180aa41ecd5d9a97d947a8a03f4e5..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/remove_anything.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import sys
-import argparse
-import numpy as np
-from pathlib import Path
-from matplotlib import pyplot as plt
-
-from sam_segment import predict_masks_with_sam
-from lama_inpaint import inpaint_img_with_lama
-from utils import load_img_to_array, save_array_to_img, dilate_mask, \
-    show_mask, show_points
-
-
-def setup_args(parser):
-    parser.add_argument(
-        "--input_img", type=str, required=True,
-        help="Path to a single input img",
-    )
-    parser.add_argument(
-        "--point_coords", type=float, nargs='+', required=True,
-        help="The coordinate of the point prompt, [coord_W coord_H].",
-    )
-    parser.add_argument(
-        "--point_labels", type=int, nargs='+', required=True,
-        help="The labels of the point prompt, 1 or 0.",
-    )
-    parser.add_argument(
-        "--dilate_kernel_size", type=int, default=None,
-        help="Dilate kernel size. Default: None",
-    )
-    parser.add_argument(
-        "--output_dir", type=str, required=True,
-        help="Output path to the directory with results.",
-    )
-    parser.add_argument(
-        "--sam_model_type", type=str,
-        default="vit_h", choices=['vit_h', 'vit_l', 'vit_b'],
-        help="The type of sam model to load. Default: 'vit_h"
-    )
-    parser.add_argument(
-        "--sam_ckpt", type=str, required=True,
-        help="The path to the SAM checkpoint to use for mask generation.",
-    )
-    parser.add_argument(
-        "--lama_config", type=str,
-        default="./lama/configs/prediction/default.yaml",
-        help="The path to the config file of lama model. "
-             "Default: the config of big-lama",
-    )
-    parser.add_argument(
-        "--lama_ckpt", type=str, required=True,
-        help="The path to the lama checkpoint.",
-    )
-
-
-if __name__ == "__main__":
-    """Example usage:
-    python remove_anything.py \
-        --input_img FA_demo/FA1_dog.png \
-        --point_coords 750 500 \
-        --point_labels 1 \
-        --dilate_kernel_size 15 \
-        --output_dir ./results \
-        --sam_model_type "vit_h" \
-        --sam_ckpt sam_vit_h_4b8939.pth \
-        --lama_config lama/configs/prediction/default.yaml \
-        --lama_ckpt big-lama 
-    """
-    parser = argparse.ArgumentParser()
-    setup_args(parser)
-    args = parser.parse_args(sys.argv[1:])
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    img = load_img_to_array(args.input_img)
-
-    masks, _, _ = predict_masks_with_sam(
-        img,
-        [args.point_coords],
-        args.point_labels,
-        model_type=args.sam_model_type,
-        ckpt_p=args.sam_ckpt,
-        device=device,
-    )
-    masks = masks.astype(np.uint8) * 255
-
-    # dilate mask to avoid unmasked edge effect
-    if args.dilate_kernel_size is not None:
-        masks = [dilate_mask(mask, args.dilate_kernel_size) for mask in masks]
-
-    # visualize the segmentation results
-    img_stem = Path(args.input_img).stem
-    out_dir = Path(args.output_dir) / img_stem
-    out_dir.mkdir(parents=True, exist_ok=True)
-    for idx, mask in enumerate(masks):
-        # path to the results
-        mask_p = out_dir / f"mask_{idx}.png"
-        img_points_p = out_dir / f"with_points.png"
-        img_mask_p = out_dir / f"with_{Path(mask_p).name}"
-
-        # save the mask
-        save_array_to_img(mask, mask_p)
-
-        # save the pointed and masked image
-        dpi = plt.rcParams['figure.dpi']
-        height, width = img.shape[:2]
-        plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
-        plt.imshow(img)
-        plt.axis('off')
-        show_points(plt.gca(), [args.point_coords], args.point_labels,
-                    size=(width*0.04)**2)
-        plt.savefig(img_points_p, bbox_inches='tight', pad_inches=0)
-        show_mask(plt.gca(), mask, random_color=False)
-        plt.savefig(img_mask_p, bbox_inches='tight', pad_inches=0)
-        plt.close()
-
-    # inpaint the masked image
-    for idx, mask in enumerate(masks):
-        mask_p = out_dir / f"mask_{idx}.png"
-        img_inpainted_p = out_dir / f"inpainted_with_{Path(mask_p).name}"
-        img_inpainted = inpaint_img_with_lama(
-            img, mask, args.lama_config, args.lama_ckpt, device=device)
-        save_array_to_img(img_inpainted, img_inpainted_p)
diff --git a/spaces/JAWEE/stablediffusionapi-majicmixrealistic/README.md b/spaces/JAWEE/stablediffusionapi-majicmixrealistic/README.md
deleted file mode 100644
index 9e0842df15adbcad340bd038b1f0845151a34cc3..0000000000000000000000000000000000000000
--- a/spaces/JAWEE/stablediffusionapi-majicmixrealistic/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stablediffusionapi Majicmixrealistic
-emoji: 👁
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jaffermirza17/ProjectPythonClass/app.py b/spaces/Jaffermirza17/ProjectPythonClass/app.py
deleted file mode 100644
index ba167ee22a17faed278f745726aa64b528f6f1fc..0000000000000000000000000000000000000000
--- a/spaces/Jaffermirza17/ProjectPythonClass/app.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import pickle
-import pandas as pd
-import shap
-from shap.plots._force_matplotlib import draw_additive_plot
-import gradio as gr
-import numpy as np
-import matplotlib.pyplot as plt
-
-# load the model from disk
-loaded_model = pickle.load(open("heart_xgb.pkl", 'rb'))
-
-# Setup SHAP
-explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
-
-# Create the main function for server
-def main_func(age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall):
-    new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
-              'cp':cp,'trtbps':trtbps,'chol':chol,
-              'fbs':fbs, 'restecg':restecg,'thalachh':thalachh,'exng':exng,
-                                     'oldpeak':oldpeak,'slp':slp,'caa':caa,'thall':thall}, 
-                                     orient = 'index').transpose()
-    
-    prob = loaded_model.predict_proba(new_row)
-    
-    shap_values = explainer(new_row)
-    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
-    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
-    plot = shap.plots.bar(shap_values[0], max_display=8, order=shap.Explanation.abs, show_data='auto', show=False)
-
-    plt.tight_layout()
-    local_plot = plt.gcf()
-    plt.close()
-    
-    return {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot
-
-# Create the UI
-title = "**Heart Attack Predictor & Interpreter** 🪐"
-description1 = """This app takes info from subjects and predicts their heart attack likelihood. Do not use these results for an actual medical diagnosis."""
-
-description2 = """
-To use the app, simply adjust the inputs and click the "Analyze" button. You can also click one of the examples below to see how it's done!
-""" 
-
-with gr.Blocks(title=title) as demo:
-
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(f"# {title}")
-            gr.Markdown(f"## How does it work?")
-            gr.Markdown(description1)
-            gr.Markdown("""---""")
-            gr.Markdown(description2)
-
-    gr.Markdown("""---""")
-    
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(f"## Edit the Inputs Below:")
-            gr.Markdown("""---""")
-
-            with gr.Row():
-                age = gr.Number(label="Age", info="How old are you?", value=40)
-                # sex = gr.Radio(["Male", "Female"], label = "What Gender are you?", type = "index")
-                sex = gr.Radio(["Male", "Female"], label="Sex", info="What gender are you?", type="index")
-                # sex = gr.Radio(choices=["Male", "Female"])
-            
-            cp = gr.Radio(["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"], label="Chest Pain", info="What kind of chest pain do you have?", type="index")
-            # cp = gr.Slider(label="Chest Pain Type", minimum=1, maximum=5, value=4, step=1)
-             # trtbps = gr.Slider(label="Resting blood pressure (in mm Hg)", minimum=1, maximum=200, value=4, step=1) 
-            trtbps = gr.Number(label="trtbps", value=100) 
-            chol = gr.Number(label="chol", value=70) 
-            fbs = gr.Radio(["False", "True"], label="fbs",  info="Is your fasting blood sugar > 120 mg/dl?" , type="index")
-
-            # restecg = gr.Slider(label="Resting ECG Score", minimum=1, maximum=5, value=4, step=1)
-            restecg = gr.Dropdown(["Normal", "Having ST-T wave abnormality", "Showing probable or definite left ventricular hypertrophy by Estes' criteria"], label="rest_ecg", type="index")
-            thalachh = gr.Slider(label="thalach Score", minimum=1, maximum=205, value=4, step=1)
-            exng = gr.Radio(["No", "Yes"], label="Exercise Induced Angina", type="index")
-            oldpeak = gr.Slider(label="Oldpeak Score", minimum=1, maximum=10, value=4, step=1)
-            slp = gr.Slider(label="Slp Score", minimum=1, maximum=5, value=4, step=1)
-            caa = gr.Slider(label="Number of Major Vessels", minimum=1, maximum=3, value=3, step=1)
-            thall = gr.Slider(label="Thall Score", minimum=1, maximum=5, value=4, step=1)
-
-            
-            
-            
-
-        with gr.Column():
-            gr.Markdown(f"## Output:")
-            gr.Markdown("""---""")
-            with gr.Column(visible=True) as output_col:
-                label = gr.Label(label = "Predicted Label")
-                local_plot = gr.Plot(label = 'Shap:')
-
-            gr.Markdown(f"## Examples:")
-            gr.Markdown("""---""")
-            gr.Markdown("### Click on any of the examples below to see how it works:")
-            gr.Examples([[24,"Male","Typical Angina",4,5,"True","Normal",4,"No",5,1,2,3], [24,"Female","Asymptomatic",4,5,"False","Normal",2,"Yes",1,1,2,3]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)
-
-        
-    submit_btn = gr.Button("Analyze", variant="primary")
-
-
-    gr.Markdown("""---""")
-    gr.Markdown(f"## Data Dictionary:")
-    gr.Markdown("""
-    
-Age : Age of the patient
-Sex : Sex of the patient
-trtbps : resting blood pressure (in mm Hg)
-chol : cholestoral in mg/dl fetched via BMI sensor
-fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
-rest_ecg : resting electrocardiographic results
-    Value 0: normal
-    Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
-    Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
-    
-thalach : maximum heart rate achieved
-target : 0 = less chance of heart attack 1= more chance of heart attack""")
-    
-
-    submit_btn.click(
-        main_func,
-        [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall],
-        [label,local_plot], api_name="Heart_Predictor"
-    )
-
-    
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Jamkonams/AutoGPT/tests/test_config.py b/spaces/Jamkonams/AutoGPT/tests/test_config.py
deleted file mode 100644
index b472a24c78edd1f931a76c68e08ed544bbe61d98..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/tests/test_config.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from unittest import TestCase
-
-from autogpt.config import Config
-
-
-class TestConfig(TestCase):
-    """
-    Test cases for the Config class, which handles the configuration settings
-    for the AI and ensures it behaves as a singleton.
-    """
-
-    def setUp(self):
-        """
-        Set up the test environment by creating an instance of the Config class.
-        """
-        self.config = Config()
-
-    def test_singleton(self):
-        """
-        Test if the Config class behaves as a singleton by ensuring that two instances are the same.
-        """
-        config2 = Config()
-        self.assertIs(self.config, config2)
-
-    def test_initial_values(self):
-        """
-        Test if the initial values of the Config class attributes are set correctly.
-        """
-        self.assertFalse(self.config.debug_mode)
-        self.assertFalse(self.config.continuous_mode)
-        self.assertFalse(self.config.speak_mode)
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4")
-        self.assertEqual(self.config.fast_token_limit, 4000)
-        self.assertEqual(self.config.smart_token_limit, 8000)
-
-    def test_set_continuous_mode(self):
-        """
-        Test if the set_continuous_mode() method updates the continuous_mode attribute.
-        """
-        self.config.set_continuous_mode(True)
-        self.assertTrue(self.config.continuous_mode)
-
-    def test_set_speak_mode(self):
-        """
-        Test if the set_speak_mode() method updates the speak_mode attribute.
-        """
-        self.config.set_speak_mode(True)
-        self.assertTrue(self.config.speak_mode)
-
-    def test_set_fast_llm_model(self):
-        """
-        Test if the set_fast_llm_model() method updates the fast_llm_model attribute.
-        """
-        self.config.set_fast_llm_model("gpt-3.5-turbo-test")
-        self.assertEqual(self.config.fast_llm_model, "gpt-3.5-turbo-test")
-
-    def test_set_smart_llm_model(self):
-        """
-        Test if the set_smart_llm_model() method updates the smart_llm_model attribute.
-        """
-        self.config.set_smart_llm_model("gpt-4-test")
-        self.assertEqual(self.config.smart_llm_model, "gpt-4-test")
-
-    def test_set_fast_token_limit(self):
-        """
-        Test if the set_fast_token_limit() method updates the fast_token_limit attribute.
-        """
-        self.config.set_fast_token_limit(5000)
-        self.assertEqual(self.config.fast_token_limit, 5000)
-
-    def test_set_smart_token_limit(self):
-        """
-        Test if the set_smart_token_limit() method updates the smart_token_limit attribute.
-        """
-        self.config.set_smart_token_limit(9000)
-        self.assertEqual(self.config.smart_token_limit, 9000)
-
-    def test_set_debug_mode(self):
-        """
-        Test if the set_debug_mode() method updates the debug_mode attribute.
-        """
-        self.config.set_debug_mode(True)
-        self.assertTrue(self.config.debug_mode)
diff --git a/spaces/JeffJing/ZookChatBot/steamship/data/embeddings.py b/spaces/JeffJing/ZookChatBot/steamship/data/embeddings.py
deleted file mode 100644
index fab18ddc25f40a04d5bf98fd46af4aeddaca4ce2..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/data/embeddings.py
+++ /dev/null
@@ -1,323 +0,0 @@
-from __future__ import annotations
-
-import json
-from typing import Any, Dict, List, Optional, Type, Union
-
-from pydantic import BaseModel, Field
-
-from steamship import SteamshipError
-from steamship.base import Task
-from steamship.base.client import Client
-from steamship.base.model import CamelModel
-from steamship.base.request import DeleteRequest, Request
-from steamship.base.response import Response
-from steamship.data.search import Hit
-from steamship.utils.metadata import metadata_to_str
-
-MAX_RECOMMENDED_ITEM_LENGTH = 5000
-
-
-class EmbedAndSearchRequest(Request):
-    query: str
-    docs: List[str]
-    plugin_instance: str
-    k: int = 1
-
-
-class QueryResult(CamelModel):
-    value: Optional[Hit] = None
-    score: Optional[float] = None
-    index: Optional[int] = None
-    id: Optional[str] = None
-
-
-class QueryResults(Request):
-    items: List[QueryResult] = None
-
-
-class EmbeddedItem(CamelModel):
-    id: str = None
-    index_id: str = None
-    file_id: str = None
-    block_id: str = None
-    tag_id: str = None
-    value: str = None
-    external_id: str = None
-    external_type: str = None
-    metadata: Any = None
-    embedding: List[float] = None
-
-    def clone_for_insert(self) -> EmbeddedItem:
-        """Produces a clone with a string representation of the metadata"""
-        ret = EmbeddedItem(
-            id=self.id,
-            index_id=self.index_id,
-            file_id=self.file_id,
-            block_id=self.block_id,
-            tag_id=self.tag_id,
-            value=self.value,
-            external_id=self.external_id,
-            external_type=self.external_type,
-            metadata=self.metadata,
-            embedding=self.embedding,
-        )
-        if isinstance(ret.metadata, dict) or isinstance(ret.metadata, list):
-            ret.metadata = json.dumps(ret.metadata)
-        return ret
-
-
-class IndexCreateRequest(Request):
-    handle: str = None
-    name: str = None
-    plugin_instance: str = None
-    fetch_if_exists: bool = True
-    external_id: str = None
-    external_type: str = None
-    metadata: Any = None
-
-
-class IndexInsertRequest(Request):
-    index_id: str
-    items: List[EmbeddedItem] = None
-    value: str = None
-    file_id: str = None
-    block_type: str = None
-    external_id: str = None
-    external_type: str = None
-    metadata: Any = None
-    reindex: bool = True
-
-
-class IndexItemId(CamelModel):
-    index_id: str = None
-    id: str = None
-
-
-class IndexInsertResponse(Response):
-    item_ids: List[IndexItemId] = None
-
-
-class IndexEmbedRequest(Request):
-    id: str
-
-
-class IndexEmbedResponse(Response):
-    id: Optional[str] = None
-
-
-class IndexSearchRequest(Request):
-    id: str
-    query: str = None
-    queries: List[str] = None
-    k: int = 1
-    include_metadata: bool = False
-
-
-class ListItemsRequest(Request):
-    id: str = None
-    file_id: str = None
-    block_id: str = None
-    span_id: str = None
-
-
-class ListItemsResponse(Response):
-    items: List[EmbeddedItem]
-
-
-class EmbeddingIndex(CamelModel):
-    """A persistent, read-optimized index over embeddings."""
-
-    client: Client = Field(None, exclude=True)
-    id: str = None
-    handle: str = None
-    name: str = None
-    plugin: str = None
-    external_id: str = None
-    external_type: str = None
-    metadata: str = None
-
-    @classmethod
-    def parse_obj(cls: Type[BaseModel], obj: Any) -> BaseModel:
-        # TODO (enias): This needs to be solved at the engine side
-        if "embeddingIndex" in obj:
-            obj = obj["embeddingIndex"]
-        elif "index" in obj:
-            obj = obj["index"]
-        return super().parse_obj(obj)
-
-    def insert_file(
-        self,
-        file_id: str,
-        block_type: str = None,
-        external_id: str = None,
-        external_type: str = None,
-        metadata: Union[int, float, bool, str, List, Dict] = None,
-        reindex: bool = True,
-    ) -> IndexInsertResponse:
-        if isinstance(metadata, dict) or isinstance(metadata, list):
-            metadata = json.dumps(metadata)
-
-        req = IndexInsertRequest(
-            index_id=self.id,
-            file_id=file_id,
-            blockType=block_type,
-            external_id=external_id,
-            external_type=external_type,
-            metadata=metadata,
-            reindex=reindex,
-        )
-        return self.client.post(
-            "embedding-index/item/create",
-            req,
-            expect=IndexInsertResponse,
-        )
-
-    def _check_input(self, request: IndexInsertRequest, allow_long_records: bool):
-        if not allow_long_records:
-            if request.value is not None and len(request.value) > MAX_RECOMMENDED_ITEM_LENGTH:
-                raise SteamshipError(
-                    f"Inserted item of length {len(request.value)} exceeded maximum recommended length of {MAX_RECOMMENDED_ITEM_LENGTH} characters. You may insert it anyway by passing allow_long_records=True."
-                )
-            if request.items is not None:
-                for i, item in enumerate(request.items):
-                    if item is not None:
-                        if isinstance(item, str) and len(item) > MAX_RECOMMENDED_ITEM_LENGTH:
-                            raise SteamshipError(
-                                f"Inserted item {i} of length {len(item)} exceeded maximum recommended length of {MAX_RECOMMENDED_ITEM_LENGTH} characters. You may insert it anyway by passing allow_long_records=True."
-                            )
-                        if (
-                            isinstance(item, EmbeddedItem)
-                            and item.value is not None
-                            and len(item.value) > MAX_RECOMMENDED_ITEM_LENGTH
-                        ):
-                            raise SteamshipError(
-                                f"Inserted item {i} of length {len(item.value)} exceeded maximum recommended length of {MAX_RECOMMENDED_ITEM_LENGTH} characters. You may insert it anyway by passing allow_long_records=True."
-                            )
-
-    def insert_many(
-        self,
-        items: List[Union[EmbeddedItem, str]],
-        reindex: bool = True,
-        allow_long_records=False,
-    ) -> IndexInsertResponse:
-        new_items = []
-        for item in items:
-            if isinstance(item, str):
-                new_items.append(EmbeddedItem(value=item))
-            else:
-                new_items.append(item)
-
-        req = IndexInsertRequest(
-            index_id=self.id,
-            items=[item.clone_for_insert() for item in new_items],
-            reindex=reindex,
-        )
-        self._check_input(req, allow_long_records)
-        return self.client.post(
-            "embedding-index/item/create",
-            req,
-            expect=IndexInsertResponse,
-        )
-
-    def insert(
-        self,
-        value: str,
-        external_id: str = None,
-        external_type: str = None,
-        metadata: Union[int, float, bool, str, List, Dict] = None,
-        reindex: bool = True,
-        allow_long_records=False,
-    ) -> IndexInsertResponse:
-
-        req = IndexInsertRequest(
-            index_id=self.id,
-            value=value,
-            external_id=external_id,
-            external_type=external_type,
-            metadata=metadata_to_str(metadata),
-            reindex=reindex,
-        )
-        self._check_input(req, allow_long_records)
-        return self.client.post(
-            "embedding-index/item/create",
-            req,
-            expect=IndexInsertResponse,
-        )
-
-    def embed(
-        self,
-    ) -> Task[IndexEmbedResponse]:
-        req = IndexEmbedRequest(id=self.id)
-        return self.client.post(
-            "embedding-index/embed",
-            req,
-            expect=IndexEmbedResponse,
-        )
-
-    def list_items(
-        self,
-        file_id: str = None,
-        block_id: str = None,
-        span_id: str = None,
-    ) -> ListItemsResponse:
-        req = ListItemsRequest(id=self.id, file_id=file_id, block_id=block_id, spanId=span_id)
-        return self.client.post(
-            "embedding-index/item/list",
-            req,
-            expect=ListItemsResponse,
-        )
-
-    def delete(self) -> EmbeddingIndex:
-        return self.client.post(
-            "embedding-index/delete",
-            DeleteRequest(id=self.id),
-            expect=EmbeddingIndex,
-        )
-
-    def search(
-        self,
-        query: Union[str, List[str]],
-        k: int = 1,
-        include_metadata: bool = False,
-    ) -> Task[QueryResults]:
-        if isinstance(query, list):
-            req = IndexSearchRequest(
-                id=self.id, queries=query, k=k, include_metadata=include_metadata
-            )
-        else:
-            req = IndexSearchRequest(
-                id=self.id, query=query, k=k, include_metadata=include_metadata
-            )
-        ret = self.client.post(
-            "embedding-index/search",
-            req,
-            expect=QueryResults,
-        )
-
-        return ret
-
-    @staticmethod
-    def create(
-        client: Client,
-        handle: str = None,
-        name: str = None,
-        embedder_plugin_instance_handle: str = None,
-        fetch_if_exists: bool = True,
-        external_id: str = None,
-        external_type: str = None,
-        metadata: Any = None,
-    ) -> EmbeddingIndex:
-        req = IndexCreateRequest(
-            handle=handle,
-            name=name,
-            plugin_instance=embedder_plugin_instance_handle,
-            fetch_if_exists=fetch_if_exists,
-            external_id=external_id,
-            external_type=external_type,
-            metadata=metadata,
-        )
-        return client.post(
-            "embedding-index/create",
-            req,
-            expect=EmbeddingIndex,
-        )
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/ChuanhuChat.js b/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/ChuanhuChat.js
deleted file mode 100644
index 1128b7782111381f4540282db574ba951e65f2f1..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/ChuanhuChat.js
+++ /dev/null
@@ -1,328 +0,0 @@
-
-// ChuanhuChat core javascript
-
-const MAX_HISTORY_LENGTH = 32;
-
-var key_down_history = [];
-var currentIndex = -1;
-
-var gradioContainer = null;
-var user_input_ta = null;
-var user_input_tb = null;
-var userInfoDiv = null;
-var appTitleDiv = null;
-var chatbot = null;
-var chatbotIndicator = null;
-var chatbotWrap = null;
-var apSwitch = null;
-var messageBotDivs = null;
-var loginUserForm = null;
-var logginUser = null;
-var updateToast = null;
-var sendBtn = null;
-var cancelBtn = null;
-var sliders = null;
-var updateChuanhuBtn = null;
-var statusDisplay = null;
-
-var isInIframe = (window.self !== window.top);
-var currentTime = new Date().getTime();
-var initialized = false;
-
-// gradio 页面加载好了么??? 我能动你的元素了么??
-function gradioLoaded(mutations) {
-    for (var i = 0; i < mutations.length; i++) {
-        if (mutations[i].addedNodes.length) {
-            if (initialized) {
-                observer.disconnect(); // 停止监听
-                return;
-            }
-            initialize();
-        }
-    }
-}
-
-function initialize() {
-    var needInit = {gradioContainer, apSwitch, user_input_tb, userInfoDiv, appTitleDiv, chatbot, chatbotIndicator, chatbotWrap, statusDisplay, sliders, updateChuanhuBtn};
-    initialized = true;
-
-    loginUserForm = gradioApp().querySelector(".gradio-container > .main > .wrap > .panel > .form")
-    gradioContainer = gradioApp().querySelector(".gradio-container");
-    user_input_tb = gradioApp().getElementById('user-input-tb');
-    userInfoDiv = gradioApp().getElementById("user-info");
-    appTitleDiv = gradioApp().getElementById("app-title");
-    chatbot = gradioApp().querySelector('#chuanhu-chatbot');
-    chatbotIndicator = gradioApp().querySelector('#chuanhu-chatbot>div.wrap');
-    chatbotWrap = gradioApp().querySelector('#chuanhu-chatbot > .wrapper > .wrap');
-    apSwitch = gradioApp().querySelector('.apSwitch input[type="checkbox"]');
-    updateToast = gradioApp().querySelector("#toast-update");
-    sendBtn = gradioApp().getElementById("submit-btn");
-    cancelBtn = gradioApp().getElementById("cancel-btn");
-    sliders = gradioApp().querySelectorAll('input[type="range"]');
-    updateChuanhuBtn = gradioApp().getElementById("update-chuanhu-btn");
-    statusDisplay = gradioApp().querySelector('#status-display');
-
-    if (loginUserForm) {
-        localStorage.setItem("userLogged", true);
-        userLogged = true;
-    }
-
-    for (let elem in needInit) {
-        if (needInit[elem] == null) {
-            initialized = false;
-            return;
-        }
-    }
-
-    if (initialized) {
-        adjustDarkMode();
-        selectHistory();
-        setTimeout(showOrHideUserInfo(), 2000);
-        setChatbotHeight();
-        setChatbotScroll();
-        setSlider();
-        setAvatar();
-        if (!historyLoaded) loadHistoryHtml();
-        if (!usernameGotten) getUserInfo();
-        chatbotObserver.observe(chatbotIndicator, { attributes: true });
-
-        const lastCheckTime = localStorage.getItem('lastCheckTime') || 0;
-        const longTimeNoCheck = currentTime - lastCheckTime > 3 * 24 * 60 * 60 * 1000;
-        if (longTimeNoCheck && !updateInfoGotten && !isLatestVersion || isLatestVersion && !updateInfoGotten) {
-            updateLatestVersion();
-        }
-    }
-}
-
-function gradioApp() {
-    const elems = document.getElementsByTagName('gradio-app');
-    const elem = elems.length == 0 ? document : elems[0];
-
-    if (elem !== document) {
-        elem.getElementById = function(id) {
-            return document.getElementById(id);
-        };
-    }
-    return elem.shadowRoot ? elem.shadowRoot : elem;
-}
-
-function showConfirmationDialog(a, file, c) {
-    if (file != "") {
-        var result = confirm(i18n(deleteConfirm_i18n_pref) + file + i18n(deleteConfirm_i18n_suff));
-        if (result) {
-            return [a, file, c];
-        }
-    }
-    return [a, "CANCELED", c];
-}
-
-function selectHistory() {
-    user_input_ta = user_input_tb.querySelector("textarea");
-    if (user_input_ta) {
-        disableSendBtn();
-        // 在 textarea 上监听 keydown 事件
-        user_input_ta.addEventListener("keydown", function (event) {
-            var value = user_input_ta.value.trim();
-            // 判断按下的是否为方向键
-            if (event.code === 'ArrowUp' || event.code === 'ArrowDown') {
-                // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作
-                if (value && key_down_history.indexOf(value) === -1)
-                    return;
-                // 对于需要响应的动作，阻止默认行为。
-                event.preventDefault();
-                var length = key_down_history.length;
-                if (length === 0) {
-                    currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置
-                    return;
-                }
-                if (currentIndex === -1) {
-                    currentIndex = length;
-                }
-                if (event.code === 'ArrowUp' && currentIndex > 0) {
-                    currentIndex--;
-                    user_input_ta.value = key_down_history[currentIndex];
-                } else if (event.code === 'ArrowDown' && currentIndex < length - 1) {
-                    currentIndex++;
-                    user_input_ta.value = key_down_history[currentIndex];
-                }
-                user_input_ta.selectionStart = user_input_ta.value.length;
-                user_input_ta.selectionEnd = user_input_ta.value.length;
-                const input_event = new InputEvent("input", { bubbles: true, cancelable: true });
-                user_input_ta.dispatchEvent(input_event);
-            } else if (event.code === "Enter") {
-                if (value) {
-                    currentIndex = -1;
-                    if (key_down_history.indexOf(value) === -1) {
-                        key_down_history.push(value);
-                        if (key_down_history.length > MAX_HISTORY_LENGTH) {
-                            key_down_history.shift();
-                        }
-                    }
-                }
-            }
-        });
-    }
-}
-
-function disableSendBtn() {
-    sendBtn.disabled = user_input_ta.value.trim() === '';
-    user_input_ta.addEventListener('input', () => {
-        sendBtn.disabled = user_input_ta.value.trim() === '';
-    });
-}
-
-function adjustDarkMode() {
-    function toggleDarkMode(isEnabled) {
-        if (isEnabled) {
-            document.body.classList.add("dark");
-            document.body.style.setProperty("background-color", "var(--neutral-950)", "important");
-        } else {
-            document.body.classList.remove("dark");
-            document.body.style.backgroundColor = "";
-        }
-    }
-    
-    const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)");
-    apSwitch.checked = darkModeQuery.matches;
-    toggleDarkMode(darkModeQuery.matches);
-    darkModeQuery.addEventListener("change", (e) => {
-        apSwitch.checked = e.matches;
-        toggleDarkMode(e.matches);
-    });
-    apSwitch.addEventListener("change", (e) => {
-        toggleDarkMode(e.target.checked);
-    });
-}
-
-function setChatbotHeight() {
-    const screenWidth = window.innerWidth;
-    const statusDisplay = document.querySelector('#status-display');
-    const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0;
-    const vh = window.innerHeight * 0.01;
-    document.documentElement.style.setProperty('--vh', `${vh}px`);
-    if (isInIframe) {
-        chatbot.style.height = `700px`;
-        chatbotWrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`
-    } else {
-        if (screenWidth <= 320) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`;
-            chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else if (screenWidth <= 499) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`;
-            chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`;
-            chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        }
-    }
-}
-function setChatbotScroll() {
-    var scrollHeight = chatbotWrap.scrollHeight;
-    chatbotWrap.scrollTo(0,scrollHeight)
-}
-
-var botAvatarUrl = "";
-var userAvatarUrl = "";
-function setAvatar() {
-    var botAvatar = gradioApp().getElementById("config-bot-avatar-url").innerText;
-    var userAvatar = gradioApp().getElementById("config-user-avatar-url").innerText;
-
-    if (botAvatar == "none") {
-        botAvatarUrl = "";
-    } else if (isImgUrl(botAvatar)) {
-        botAvatarUrl = botAvatar;
-    } else {
-        // botAvatarUrl = "https://github.com/GaiZhenbiao/ChuanhuChatGPT/assets/70903329/aca3a7ec-4f1d-4667-890c-a6f47bf08f63";
-        botAvatarUrl = "/file=web_assets/chatbot.png"
-    }
-
-    if (userAvatar == "none") {
-        userAvatarUrl = "";
-    } else if (isImgUrl(userAvatar)) {
-        userAvatarUrl = userAvatar;
-    } else {
-        userAvatarUrl = "data:image/svg+xml,%3Csvg width='32px' height='32px' viewBox='0 0 32 32' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E%3Cg stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E%3Crect fill-opacity='0.5' fill='%23bbbbbb' x='0' y='0' width='32' height='32'%3E%3C/rect%3E%3Cg transform='translate(5, 4)' fill='%23999999' fill-opacity='0.8' fill-rule='nonzero'%3E%3Cpath d='M2.29372246,24 L19.7187739,24 C20.4277609,24 20.985212,23.8373915 21.3911272,23.5121746 C21.7970424,23.1869576 22,22.7418004 22,22.1767029 C22,21.3161536 21.7458721,20.4130827 21.2376163,19.4674902 C20.7293605,18.5218977 19.9956681,17.6371184 19.036539,16.8131524 C18.07741,15.9891863 16.9210688,15.3177115 15.5675154,14.798728 C14.2139621,14.2797445 12.6914569,14.0202527 11,14.0202527 C9.30854307,14.0202527 7.78603793,14.2797445 6.43248458,14.798728 C5.07893122,15.3177115 3.92259002,15.9891863 2.96346097,16.8131524 C2.00433193,17.6371184 1.27063951,18.5218977 0.762383704,19.4674902 C0.254127901,20.4130827 0,21.3161536 0,22.1767029 C0,22.7418004 0.202957595,23.1869576 0.608872784,23.5121746 C1.01478797,23.8373915 1.57640453,24 2.29372246,24 Z M11.0124963,11.6521659 C11.9498645,11.6521659 12.8155943,11.3906214 13.6096856,10.8675324 C14.403777,10.3444433 15.042131,9.63605539 15.5247478,8.74236856 C16.0073646,7.84868174 16.248673,6.84722464 16.248673,5.73799727 C16.248673,4.65135034 16.0071492,3.67452644 15.5241015,2.80752559 C15.0410538,1.94052474 14.4024842,1.25585359 13.6083929,0.753512156 C12.8143016,0.251170719 11.9490027,0 11.0124963,0 C10.0759899,0 9.20860836,0.255422879 8.41035158,0.766268638 C7.6120948,1.2771144 6.97352528,1.96622098 6.49464303,2.8335884 C6.01576078,3.70095582 5.77631966,4.67803631 5.77631966,5.76482987 C5.77631966,6.86452653 6.01554533,7.85912886 6.49399667,8.74863683 C6.97244801,9.63814481 7.60871935,10.3444433 8.40281069,10.8675324 C9.19690203,11.3906214 10.0667972,11.6521659 11.0124963,11.6521659 Z'%3E%3C/path%3E%3C/g%3E%3C/g%3E%3C/svg%3E";
-    }
-}
-
-function clearChatbot() {
-    clearHistoryHtml();
-    clearMessageRows();
-}
-
-function chatbotContentChanged(attempt = 1) {
-    for (var i = 0; i < attempt; i++) {
-        setTimeout(() => {
-            // clearMessageRows();
-            saveHistoryHtml();
-            disableSendBtn();
-            gradioApp().querySelectorAll('#chuanhu-chatbot .message-wrap .message.user').forEach((userElement) => {addAvatars(userElement, 'user')});
-            gradioApp().querySelectorAll('#chuanhu-chatbot .message-wrap .message.bot').forEach((botElement) => {addAvatars(botElement, 'bot'); addChuanhuButton(botElement)});
-        }, i === 0 ? 0 : 500);
-    }
-    // 理论上是不需要多次尝试执行的，可惜gradio的bug导致message可能没有渲染完毕，所以尝试500ms后再次执行
-}
-
-var chatbotObserver = new MutationObserver(() => {
-    clearMessageRows();
-    chatbotContentChanged(1);
-    if (chatbotIndicator.classList.contains('hide')) {
-        chatbotContentChanged(2);
-    }
-});
-
-// 监视页面内部 DOM 变动
-var observer = new MutationObserver(function (mutations) {
-    gradioLoaded(mutations);
-});
-
-// 监视页面变化
-window.addEventListener("DOMContentLoaded", function () {
-    const ga = document.getElementsByTagName("gradio-app");
-    observer.observe(ga[0], { childList: true, subtree: true });
-    isInIframe = (window.self !== window.top);
-    historyLoaded = false;
-});
-window.addEventListener('resize', setChatbotHeight);
-window.addEventListener('scroll', function(){setChatbotHeight(); setUpdateWindowHeight();});
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode);
-
-// console suprise
-var styleTitle1 = `
-font-size: 16px;
-font-family: ui-monospace, monospace;
-color: #06AE56;
-`
-var styleDesc1 = `
-font-size: 12px;
-font-family: ui-monospace, monospace;
-`
-function makeML(str) {
-    let l = new String(str)
-    l = l.substring(l.indexOf("/*") + 3, l.lastIndexOf("*/"))
-    return l
-}
-let ChuanhuInfo = function () {
-    /* 
-   ________                      __             ________          __ 
-  / ____/ /_  __  ______ _____  / /_  __  __   / ____/ /_  ____ _/ /_
- / /   / __ \/ / / / __ `/ __ \/ __ \/ / / /  / /   / __ \/ __ `/ __/
-/ /___/ / / / /_/ / /_/ / / / / / / / /_/ /  / /___/ / / / /_/ / /_  
-\____/_/ /_/\__,_/\__,_/_/ /_/_/ /_/\__,_/   \____/_/ /_/\__,_/\__/  
-                                                                     
-   川虎Chat (Chuanhu Chat) - GUI for ChatGPT API and many LLMs
- */
-}
-let description = `
-© 2023 Chuanhu, MZhao, Keldos
-GitHub repository: [https://github.com/GaiZhenbiao/ChuanhuChatGPT]\n
-Enjoy our project!\n
-`
-console.log(`%c${makeML(ChuanhuInfo)}`,styleTitle1)
-console.log(`%c${description}`, styleDesc1)
-
-// button svg code
-const copyIcon   = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
-const copiedIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><polyline points="20 6 9 17 4 12"></polyline></svg></span>';
-const mdIcon     = '<span><svg stroke="currentColor" fill="none" stroke-width="1" viewBox="0 0 14 18" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><g transform-origin="center" transform="scale(0.85)"><path d="M1.5,0 L12.5,0 C13.3284271,-1.52179594e-16 14,0.671572875 14,1.5 L14,16.5 C14,17.3284271 13.3284271,18 12.5,18 L1.5,18 C0.671572875,18 1.01453063e-16,17.3284271 0,16.5 L0,1.5 C-1.01453063e-16,0.671572875 0.671572875,1.52179594e-16 1.5,0 Z" stroke-width="1.8"></path><line x1="3.5" y1="3.5" x2="10.5" y2="3.5"></line><line x1="3.5" y1="6.5" x2="8" y2="6.5"></line></g><path d="M4,9 L10,9 C10.5522847,9 11,9.44771525 11,10 L11,13.5 C11,14.0522847 10.5522847,14.5 10,14.5 L4,14.5 C3.44771525,14.5 3,14.0522847 3,13.5 L3,10 C3,9.44771525 3.44771525,9 4,9 Z" stroke="none" fill="currentColor"></path></svg></span>';
-const rawIcon    = '<span><svg stroke="currentColor" fill="none" stroke-width="1.8" viewBox="0 0 18 14" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><g transform-origin="center" transform="scale(0.85)"><polyline points="4 3 0 7 4 11"></polyline><polyline points="14 3 18 7 14 11"></polyline><line x1="12" y1="0" x2="6" y2="14"></line></g></svg></span>';
diff --git a/spaces/KAHRAMAN42/youtube_transcript/README.md b/spaces/KAHRAMAN42/youtube_transcript/README.md
deleted file mode 100644
index 188aed7a78847c97c07fc22ab5708510043bb221..0000000000000000000000000000000000000000
--- a/spaces/KAHRAMAN42/youtube_transcript/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Youtube Transcript
-emoji: 📉
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/main.py b/spaces/KdaiP/yolov8-deepsort-tracking/main.py
deleted file mode 100644
index 48db0e38880d0c27b1729c1d159cd0bea09fb894..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/main.py
+++ /dev/null
@@ -1,118 +0,0 @@
-from ultralytics import YOLO
-import cv2
-import numpy as np
-import tempfile
-from pathlib import Path
-import deep_sort.deep_sort.deep_sort as ds
-
-def putTextWithBackground(img, text, origin, font=cv2.FONT_HERSHEY_SIMPLEX, font_scale=1, text_color=(255, 255, 255), bg_color=(0, 0, 0), thickness=1):
-    """绘制带有背景的文本。
-
-    :param img: 输入图像。
-    :param text: 要绘制的文本。
-    :param origin: 文本的左上角坐标。
-    :param font: 字体类型。
-    :param font_scale: 字体大小。
-    :param text_color: 文本的颜色。
-    :param bg_color: 背景的颜色。
-    :param thickness: 文本的线条厚度。
-    """
-    # 计算文本的尺寸
-    (text_width, text_height), _ = cv2.getTextSize(text, font, font_scale, thickness)
-
-    # 绘制背景矩形
-    bottom_left = origin
-    top_right = (origin[0] + text_width, origin[1] - text_height - 5)  # 减去5以留出一些边距
-    cv2.rectangle(img, bottom_left, top_right, bg_color, -1)
-
-    # 在矩形上绘制文本
-    text_origin = (origin[0], origin[1] - 5)  # 从左上角的位置减去5来留出一些边距
-    cv2.putText(img, text, text_origin, font, font_scale, text_color, thickness, lineType=cv2.LINE_AA)
-
-# 视频处理
-def processVideo(inputPath: str) -> Path:
-    """处理视频，检测并跟踪行人。
-
-    :param inputPath: 视频文件路径
-    :return: 输出视频的路径
-    """
-    # 读取视频文件
-    cap = cv2.VideoCapture(inputPath)
-    fps = cap.get(cv2.CAP_PROP_FPS)  # 获取视频的帧率
-    size = (
-        int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
-        int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
-    )  # 获取视频的大小
-    output_video = cv2.VideoWriter()  # 初始化视频写入
-
-    # 输出格式为XVID格式的avi文件
-    # 如果需要使用h264编码或者需要保存为其他格式，可能需要下载openh264-1.8.0
-    # 下载地址：https://github.com/cisco/openh264/releases/tag/v1.8.0
-    # 下载完成后将dll文件放在当前文件夹内
-    fourcc = cv2.VideoWriter_fourcc(*"XVID")
-    video_save_path = Path(outputPath) / "output.avi"  # 创建输出视频路径
-
-    output_video.open(video_save_path.as_posix(), fourcc, fps, size, isColor=True)
-
-    # 对每一帧图片进行读取和处理
-    while True:
-        success, frame = cap.read()
-        if not (success):
-            break
-
-        # 获取每一帧的目标检测推理结果
-        results = model(frame, stream=True)
-
-        detections = np.empty((0, 4)) # 存放bounding box结果
-        confarray = [] # 存放每个检测结果的置信度
-
-        # 读取目标检测推理结果
-        # 参考： https://docs.ultralytics.com/modes/predict/#working-with-results
-        for r in results:
-            boxes = r.boxes
-            for box in boxes:
-                x1, y1, x2, y2 = map(int, box.xywh[0])  # 提取矩形框左上和右下的点，并将tensor类型转为整型
-                conf = round(float(box.conf[0]), 2)  # 对conf四舍五入到2位小数
-                cls = int(box.cls[0])  # 获取物体类别标签
-
-                if cls == detect_class:
-                    detections = np.vstack((detections,np.array([x1,y1,x2,y2])))
-                    confarray.append(conf)
-
-        # 使用deepsort进行跟踪
-        resultsTracker = tracker.update(detections, confarray, frame)
-        for x1, y1, x2, y2, Id in resultsTracker:
-            x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])
-
-            # 绘制bounding box
-            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 255), 3)
-            putTextWithBackground(frame, str(int(Id)), (max(-10, x1), max(40, y1)), font_scale=1.5, text_color=(255, 255, 255), bg_color=(255, 0, 255))
-
-        output_video.write(frame)  # 将处理后的图像写入视频
-    output_video.release()  # 释放
-    cap.release()  # 释放
-    print(f'output dir is: {video_save_path}')
-    return video_save_path
-
-
-if __name__ == "__main__":
-    # 在这里填入视频文件路径
-    ######
-    input_video_path = "test.mp4"
-    ######
-
-    # 输出文件夹，默认为系统的临时文件夹路径
-    outputPath = tempfile.mkdtemp()  # 创建临时文件夹用于存储输出视频
-
-    # 加载yoloV8模型权重
-    model = YOLO("yolov8n.pt")
-
-    # 需要跟踪的物体类别，model.names返回模型所支持的所有物体类别
-    # yoloV8官方模型的第一个类别为'person'
-    detect_class = 0
-    print(f"detecting {model.names[detect_class]}")
-
-    # 加载deepsort模型权重
-    tracker = ds.DeepSort("deep_sort/deep_sort/deep/checkpoint/ckpt.t7")
-
-    processVideo(input_video_path)
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/visualizations.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/visualizations.py
deleted file mode 100644
index 980c74f95f1f7df41ebccc983600b2713c0b0502..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/visualizations.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-from datetime import datetime
-from time import perf_counter as timer
-import matplotlib.pyplot as plt
-import numpy as np
-# import webbrowser
-import visdom
-import umap
-
-colormap = np.array([
-    [76, 255, 0],
-    [0, 127, 70],
-    [255, 0, 0],
-    [255, 217, 38],
-    [0, 135, 255],
-    [165, 0, 165],
-    [255, 167, 255],
-    [0, 255, 255],
-    [255, 96, 38],
-    [142, 76, 0],
-    [33, 0, 127],
-    [0, 0, 0],
-    [183, 183, 183],
-], dtype=np.float) / 255 
-
-
-class Visualizations:
-    def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
-        # Tracking data
-        self.last_update_timestamp = timer()
-        self.update_every = update_every
-        self.step_times = []
-        self.losses = []
-        self.eers = []
-        print("Updating the visualizations every %d steps." % update_every)
-        
-        # If visdom is disabled TODO: use a better paradigm for that
-        self.disabled = disabled    
-        if self.disabled:
-            return 
-        
-        # Set the environment name
-        now = str(datetime.now().strftime("%d-%m %Hh%M"))
-        if env_name is None:
-            self.env_name = now
-        else:
-            self.env_name = "%s (%s)" % (env_name, now)
-        
-        # Connect to visdom and open the corresponding window in the browser
-        try:
-            self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
-        except ConnectionError:
-            raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
-                            "start it.")
-        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
-        
-        # Create the windows
-        self.loss_win = None
-        self.eer_win = None
-        # self.lr_win = None
-        self.implementation_win = None
-        self.projection_win = None
-        self.implementation_string = ""
-        
-    def log_params(self):
-        if self.disabled:
-            return 
-        from encoder import params_data
-        from encoder import params_model
-        param_string = "<b>Model parameters</b>:<br>"
-        for param_name in (p for p in dir(params_model) if not p.startswith("__")):
-            value = getattr(params_model, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        param_string += "<b>Data parameters</b>:<br>"
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        self.vis.text(param_string, opts={"title": "Parameters"})
-        
-    def log_dataset(self, dataset: SpeakerVerificationDataset):
-        if self.disabled:
-            return 
-        dataset_string = ""
-        dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
-        dataset_string += "\n" + dataset.get_logs()
-        dataset_string = dataset_string.replace("\n", "<br>")
-        self.vis.text(dataset_string, opts={"title": "Dataset"})
-        
-    def log_implementation(self, params):
-        if self.disabled:
-            return 
-        implementation_string = ""
-        for param, value in params.items():
-            implementation_string += "<b>%s</b>: %s\n" % (param, value)
-            implementation_string = implementation_string.replace("\n", "<br>")
-        self.implementation_string = implementation_string
-        self.implementation_win = self.vis.text(
-            implementation_string, 
-            opts={"title": "Training implementation"}
-        )
-
-    def update(self, loss, eer, step):
-        # Update the tracking data
-        now = timer()
-        self.step_times.append(1000 * (now - self.last_update_timestamp))
-        self.last_update_timestamp = now
-        self.losses.append(loss)
-        self.eers.append(eer)
-        print(".", end="")
-        
-        # Update the plots every <update_every> steps
-        if step % self.update_every != 0:
-            return
-        time_string = "Step time:  mean: %5dms  std: %5dms" % \
-                      (int(np.mean(self.step_times)), int(np.std(self.step_times)))
-        print("\nStep %6d   Loss: %.4f   EER: %.4f   %s" %
-              (step, np.mean(self.losses), np.mean(self.eers), time_string))
-        if not self.disabled:
-            self.loss_win = self.vis.line(
-                [np.mean(self.losses)],
-                [step],
-                win=self.loss_win,
-                update="append" if self.loss_win else None,
-                opts=dict(
-                    legend=["Avg. loss"],
-                    xlabel="Step",
-                    ylabel="Loss",
-                    title="Loss",
-                )
-            )
-            self.eer_win = self.vis.line(
-                [np.mean(self.eers)],
-                [step],
-                win=self.eer_win,
-                update="append" if self.eer_win else None,
-                opts=dict(
-                    legend=["Avg. EER"],
-                    xlabel="Step",
-                    ylabel="EER",
-                    title="Equal error rate"
-                )
-            )
-            if self.implementation_win is not None:
-                self.vis.text(
-                    self.implementation_string + ("<b>%s</b>" % time_string), 
-                    win=self.implementation_win,
-                    opts={"title": "Training implementation"},
-                )
-
-        # Reset the tracking
-        self.losses.clear()
-        self.eers.clear()
-        self.step_times.clear()
-        
-    def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None,
-                         max_speakers=10):
-        max_speakers = min(max_speakers, len(colormap))
-        embeds = embeds[:max_speakers * utterances_per_speaker]
-        
-        n_speakers = len(embeds) // utterances_per_speaker
-        ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
-        colors = [colormap[i] for i in ground_truth]
-        
-        reducer = umap.UMAP()
-        projected = reducer.fit_transform(embeds)
-        plt.scatter(projected[:, 0], projected[:, 1], c=colors)
-        plt.gca().set_aspect("equal", "datalim")
-        plt.title("UMAP projection (step %d)" % step)
-        if not self.disabled:
-            self.projection_win = self.vis.matplot(plt, win=self.projection_win)
-        if out_fpath is not None:
-            plt.savefig(out_fpath)
-        plt.clf()
-        
-    def save(self):
-        if not self.disabled:
-            self.vis.save([self.env_name])
-        
\ No newline at end of file
diff --git a/spaces/Kuaaangwen/auto-grader/app.py b/spaces/Kuaaangwen/auto-grader/app.py
deleted file mode 100644
index 36e30f225d71014e43d87cf452e4faff7dddb5fa..0000000000000000000000000000000000000000
--- a/spaces/Kuaaangwen/auto-grader/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import streamlit as st
-
-
-
-# Library for Entailment
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-# Load model
-
-tokenizer = AutoTokenizer.from_pretrained("roberta-large-mnli")
-
-text_classification_model = AutoModelForSequenceClassification.from_pretrained("roberta-large-mnli")
-
-
-
-### Streamlit interface ###
-      
-st.title("Text Classification")
-
-st.subheader("Entailment, neutral or contradiction?")
-
-with st.form("submission_form", clear_on_submit=False):
-
-       threshold = st.slider("Threshold", min_value=0.0, max_value=1.0, step=0.1, value=0.7)
-
-       sentence_1 = st.text_input("Sentence 1 input")
-       
-       sentence_2 = st.text_input("Sentence 2 input")
-       
-       submit_button_compare = st.form_submit_button("Compare Sentences")
-       
-# If submit_button_compare clicked
-if submit_button_compare:
-
-       print("Comparing sentences...")
-
-       ### Text classification - entailment, neutral or contradiction ###
-
-       raw_inputs = [f"{sentence_1}</s></s>{sentence_2}"]
-
-       inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="pt")
-
-       # print(inputs)
-
-       outputs = text_classification_model(**inputs)
-
-       outputs = torch.nn.functional.softmax(outputs.logits, dim = -1)
-       # print(outputs)
-
-       # argmax_index = torch.argmax(outputs).item()
-
-       print(text_classification_model.config.id2label[0], ":", round(outputs[0][0].item()*100,2),"%")
-       print(text_classification_model.config.id2label[1], ":", round(outputs[0][1].item()*100,2),"%")
-       print(text_classification_model.config.id2label[2], ":", round(outputs[0][2].item()*100,2),"%")
-
-       st.subheader("Text classification for both sentences:")
-
-       st.write(text_classification_model.config.id2label[1], ":", round(outputs[0][1].item()*100,2),"%")
-       st.write(text_classification_model.config.id2label[0], ":", round(outputs[0][0].item()*100,2),"%")
-       st.write(text_classification_model.config.id2label[2], ":", round(outputs[0][2].item()*100,2),"%")
-
-       entailment_score = round(outputs[0][2].item(),2)
-
-       if entailment_score >= threshold:
-              st.subheader("The statements are very similar!")
-       else:
-              st.subheader("The statements are not close enough")
-
-
-
diff --git a/spaces/Kuachi/hololive/infer_pack/models.py b/spaces/Kuachi/hololive/infer_pack/models.py
deleted file mode 100644
index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000
--- a/spaces/Kuachi/hololive/infer_pack/models.py
+++ /dev/null
@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            x, y_lengths, self.segment_size
-        )
-
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice
-
-    def infer(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/grid_roi_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/grid_roi_head.py
deleted file mode 100644
index 9eda7f01bcd4e44faca14b61ec4956ee2c372ad6..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/grid_roi_head.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Optional, Tuple
-
-import torch
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.structures import SampleList
-from mmdet.structures.bbox import bbox2roi
-from mmdet.utils import ConfigType, InstanceList
-from ..task_modules.samplers import SamplingResult
-from ..utils.misc import unpack_gt_instances
-from .standard_roi_head import StandardRoIHead
-
-
-@MODELS.register_module()
-class GridRoIHead(StandardRoIHead):
-    """Implementation of `Grid RoI Head <https://arxiv.org/abs/1811.12030>`_
-
-    Args:
-        grid_roi_extractor (:obj:`ConfigDict` or dict): Config of
-            roi extractor.
-        grid_head (:obj:`ConfigDict` or dict): Config of grid head
-    """
-
-    def __init__(self, grid_roi_extractor: ConfigType, grid_head: ConfigType,
-                 **kwargs) -> None:
-        assert grid_head is not None
-        super().__init__(**kwargs)
-        if grid_roi_extractor is not None:
-            self.grid_roi_extractor = MODELS.build(grid_roi_extractor)
-            self.share_roi_extractor = False
-        else:
-            self.share_roi_extractor = True
-            self.grid_roi_extractor = self.bbox_roi_extractor
-        self.grid_head = MODELS.build(grid_head)
-
-    def _random_jitter(self,
-                       sampling_results: List[SamplingResult],
-                       batch_img_metas: List[dict],
-                       amplitude: float = 0.15) -> List[SamplingResult]:
-        """Ramdom jitter positive proposals for training.
-
-        Args:
-            sampling_results (List[obj:SamplingResult]): Assign results of
-                all images in a batch after sampling.
-            batch_img_metas (list[dict]): List of image information.
-            amplitude (float): Amplitude of random offset. Defaults to 0.15.
-
-        Returns:
-            list[obj:SamplingResult]: SamplingResults after random jittering.
-        """
-        for sampling_result, img_meta in zip(sampling_results,
-                                             batch_img_metas):
-            bboxes = sampling_result.pos_priors
-            random_offsets = bboxes.new_empty(bboxes.shape[0], 4).uniform_(
-                -amplitude, amplitude)
-            # before jittering
-            cxcy = (bboxes[:, 2:4] + bboxes[:, :2]) / 2
-            wh = (bboxes[:, 2:4] - bboxes[:, :2]).abs()
-            # after jittering
-            new_cxcy = cxcy + wh * random_offsets[:, :2]
-            new_wh = wh * (1 + random_offsets[:, 2:])
-            # xywh to xyxy
-            new_x1y1 = (new_cxcy - new_wh / 2)
-            new_x2y2 = (new_cxcy + new_wh / 2)
-            new_bboxes = torch.cat([new_x1y1, new_x2y2], dim=1)
-            # clip bboxes
-            max_shape = img_meta['img_shape']
-            if max_shape is not None:
-                new_bboxes[:, 0::2].clamp_(min=0, max=max_shape[1] - 1)
-                new_bboxes[:, 1::2].clamp_(min=0, max=max_shape[0] - 1)
-
-            sampling_result.pos_priors = new_bboxes
-        return sampling_results
-
-    # TODO: Forward is incorrect and need to refactor.
-    def forward(self,
-                x: Tuple[Tensor],
-                rpn_results_list: InstanceList,
-                batch_data_samples: SampleList = None) -> tuple:
-        """Network forward process. Usually includes backbone, neck and head
-        forward without any post-processing.
-
-        Args:
-            x (Tuple[Tensor]): Multi-level features that may have different
-                resolutions.
-            rpn_results_list (list[:obj:`InstanceData`]): List of region
-                proposals.
-            batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
-            the meta information of each image and corresponding
-            annotations.
-
-        Returns
-            tuple: A tuple of features from ``bbox_head`` and ``mask_head``
-            forward.
-        """
-        results = ()
-        proposals = [rpn_results.bboxes for rpn_results in rpn_results_list]
-        rois = bbox2roi(proposals)
-        # bbox head
-        if self.with_bbox:
-            bbox_results = self._bbox_forward(x, rois)
-            results = results + (bbox_results['cls_score'], )
-            if self.bbox_head.with_reg:
-                results = results + (bbox_results['bbox_pred'], )
-
-            # grid head
-            grid_rois = rois[:100]
-            grid_feats = self.grid_roi_extractor(
-                x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois)
-            if self.with_shared_head:
-                grid_feats = self.shared_head(grid_feats)
-            self.grid_head.test_mode = True
-            grid_preds = self.grid_head(grid_feats)
-            results = results + (grid_preds, )
-
-        # mask head
-        if self.with_mask:
-            mask_rois = rois[:100]
-            mask_results = self._mask_forward(x, mask_rois)
-            results = results + (mask_results['mask_preds'], )
-        return results
-
-    def loss(self, x: Tuple[Tensor], rpn_results_list: InstanceList,
-             batch_data_samples: SampleList, **kwargs) -> dict:
-        """Perform forward propagation and loss calculation of the detection
-        roi on the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): List of multi-level img features.
-            rpn_results_list (list[:obj:`InstanceData`]): List of region
-                proposals.
-            batch_data_samples (list[:obj:`DetDataSample`]): The batch
-                data samples. It usually includes information such
-                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components
-        """
-        assert len(rpn_results_list) == len(batch_data_samples)
-        outputs = unpack_gt_instances(batch_data_samples)
-        (batch_gt_instances, batch_gt_instances_ignore,
-         batch_img_metas) = outputs
-
-        # assign gts and sample proposals
-        num_imgs = len(batch_data_samples)
-        sampling_results = []
-        for i in range(num_imgs):
-            # rename rpn_results.bboxes to rpn_results.priors
-            rpn_results = rpn_results_list[i]
-            rpn_results.priors = rpn_results.pop('bboxes')
-
-            assign_result = self.bbox_assigner.assign(
-                rpn_results, batch_gt_instances[i],
-                batch_gt_instances_ignore[i])
-            sampling_result = self.bbox_sampler.sample(
-                assign_result,
-                rpn_results,
-                batch_gt_instances[i],
-                feats=[lvl_feat[i][None] for lvl_feat in x])
-            sampling_results.append(sampling_result)
-
-        losses = dict()
-        # bbox head loss
-        if self.with_bbox:
-            bbox_results = self.bbox_loss(x, sampling_results, batch_img_metas)
-            losses.update(bbox_results['loss_bbox'])
-
-        # mask head forward and loss
-        if self.with_mask:
-            mask_results = self.mask_loss(x, sampling_results,
-                                          bbox_results['bbox_feats'],
-                                          batch_gt_instances)
-            losses.update(mask_results['loss_mask'])
-
-        return losses
-
-    def bbox_loss(self,
-                  x: Tuple[Tensor],
-                  sampling_results: List[SamplingResult],
-                  batch_img_metas: Optional[List[dict]] = None) -> dict:
-        """Perform forward propagation and loss calculation of the bbox head on
-        the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): List of multi-level img features.
-            sampling_results (list[:obj:`SamplingResult`]): Sampling results.
-            batch_img_metas (list[dict], optional): Meta information of each
-                image, e.g., image size, scaling factor, etc.
-
-        Returns:
-            dict[str, Tensor]: Usually returns a dictionary with keys:
-
-            - `cls_score` (Tensor): Classification scores.
-            - `bbox_pred` (Tensor): Box energies / deltas.
-            - `bbox_feats` (Tensor): Extract bbox RoI features.
-            - `loss_bbox` (dict): A dictionary of bbox loss components.
-        """
-        assert batch_img_metas is not None
-        bbox_results = super().bbox_loss(x, sampling_results)
-
-        # Grid head forward and loss
-        sampling_results = self._random_jitter(sampling_results,
-                                               batch_img_metas)
-        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
-
-        # GN in head does not support zero shape input
-        if pos_rois.shape[0] == 0:
-            return bbox_results
-
-        grid_feats = self.grid_roi_extractor(
-            x[:self.grid_roi_extractor.num_inputs], pos_rois)
-        if self.with_shared_head:
-            grid_feats = self.shared_head(grid_feats)
-        # Accelerate training
-        max_sample_num_grid = self.train_cfg.get('max_num_grid', 192)
-        sample_idx = torch.randperm(
-            grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid
-                                      )]
-        grid_feats = grid_feats[sample_idx]
-        grid_pred = self.grid_head(grid_feats)
-
-        loss_grid = self.grid_head.loss(grid_pred, sample_idx,
-                                        sampling_results, self.train_cfg)
-
-        bbox_results['loss_bbox'].update(loss_grid)
-        return bbox_results
-
-    def predict_bbox(self,
-                     x: Tuple[Tensor],
-                     batch_img_metas: List[dict],
-                     rpn_results_list: InstanceList,
-                     rcnn_test_cfg: ConfigType,
-                     rescale: bool = False) -> InstanceList:
-        """Perform forward propagation of the bbox head and predict detection
-        results on the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): Feature maps of all scale level.
-            batch_img_metas (list[dict]): List of image information.
-            rpn_results_list (list[:obj:`InstanceData`]): List of region
-                proposals.
-            rcnn_test_cfg (:obj:`ConfigDict`): `test_cfg` of R-CNN.
-            rescale (bool): If True, return boxes in original image space.
-                Defaults to False.
-
-        Returns:
-            list[:obj:`InstanceData`]: Detection results of each image
-            after the post process.
-            Each item usually contains following keys.
-
-            - scores (Tensor): Classification scores, has a shape \
-            (num_instance, )
-            - labels (Tensor): Labels of bboxes, has a shape (num_instances, ).
-            - bboxes (Tensor): Has a shape (num_instances, 4), the last \
-            dimension 4 arrange as (x1, y1, x2, y2).
-        """
-        results_list = super().predict_bbox(
-            x,
-            batch_img_metas=batch_img_metas,
-            rpn_results_list=rpn_results_list,
-            rcnn_test_cfg=rcnn_test_cfg,
-            rescale=False)
-
-        grid_rois = bbox2roi([res.bboxes for res in results_list])
-        if grid_rois.shape[0] != 0:
-            grid_feats = self.grid_roi_extractor(
-                x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois)
-            if self.with_shared_head:
-                grid_feats = self.shared_head(grid_feats)
-            self.grid_head.test_mode = True
-            grid_preds = self.grid_head(grid_feats)
-            results_list = self.grid_head.predict_by_feat(
-                grid_preds=grid_preds,
-                results_list=results_list,
-                batch_img_metas=batch_img_metas,
-                rescale=rescale)
-
-        return results_list
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/roi_extractors/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/roi_extractors/__init__.py
deleted file mode 100644
index 0f60214991b0ed14cdbc3964aee15356c6aaf2aa..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/roi_extractors/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base_roi_extractor import BaseRoIExtractor
-from .generic_roi_extractor import GenericRoIExtractor
-from .single_level_roi_extractor import SingleRoIExtractor
-
-__all__ = ['BaseRoIExtractor', 'SingleRoIExtractor', 'GenericRoIExtractor']
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/coders/pseudo_bbox_coder.py b/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/coders/pseudo_bbox_coder.py
deleted file mode 100644
index 9ee74311f6d12bde49d0c678edb60540a8c95c8b..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/coders/pseudo_bbox_coder.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Union
-
-from torch import Tensor
-
-from mmdet.registry import TASK_UTILS
-from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes, get_box_tensor
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@TASK_UTILS.register_module()
-class PseudoBBoxCoder(BaseBBoxCoder):
-    """Pseudo bounding box coder."""
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def encode(self, bboxes: Tensor, gt_bboxes: Union[Tensor,
-                                                      BaseBoxes]) -> Tensor:
-        """torch.Tensor: return the given ``bboxes``"""
-        gt_bboxes = get_box_tensor(gt_bboxes)
-        return gt_bboxes
-
-    def decode(self, bboxes: Tensor, pred_bboxes: Union[Tensor,
-                                                        BaseBoxes]) -> Tensor:
-        """torch.Tensor: return the given ``pred_bboxes``"""
-        if self.use_box_type:
-            pred_bboxes = HorizontalBoxes(pred_bboxes)
-        return pred_bboxes
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/oxfordiiitpet.py b/spaces/KyanChen/RSPrompter/mmpretrain/datasets/oxfordiiitpet.py
deleted file mode 100644
index 23c8b7db8679e99c6ed2698b9eb140cd6151d445..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/oxfordiiitpet.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List
-
-from mmengine import get_file_backend, list_from_file
-
-from mmpretrain.registry import DATASETS
-from .base_dataset import BaseDataset
-from .categories import OxfordIIITPet_CATEGORIES
-
-
-@DATASETS.register_module()
-class OxfordIIITPet(BaseDataset):
-    """The Oxford-IIIT Pets Dataset.
-
-    Support the `Oxford-IIIT Pets Dataset <https://www.robots.ox.ac.uk/~vgg/data/pets/>`_ Dataset.
-    After downloading and decompression, the dataset directory structure is as follows.
-
-    Oxford-IIIT_Pets dataset directory: ::
-
-        Oxford-IIIT_Pets
-        ├── images
-        │   ├── Abyssinian_1.jpg
-        │   ├── Abyssinian_2.jpg
-        │   └── ...
-        ├── annotations
-        │   ├── trainval.txt
-        │   ├── test.txt
-        │   ├── list.txt
-        │   └── ...
-        └── ....
-
-    Args:
-        data_root (str): The root directory for Oxford-IIIT Pets dataset.
-        split (str, optional): The dataset split, supports "trainval" and "test".
-            Default to "trainval".
-
-    Examples:
-        >>> from mmpretrain.datasets import OxfordIIITPet
-        >>> train_dataset = OxfordIIITPet(data_root='data/Oxford-IIIT_Pets', split='trainval')
-        >>> train_dataset
-        Dataset OxfordIIITPet
-            Number of samples:  3680
-            Number of categories:       37
-            Root of dataset:    data/Oxford-IIIT_Pets
-        >>> test_dataset = OxfordIIITPet(data_root='data/Oxford-IIIT_Pets', split='test')
-        >>> test_dataset
-        Dataset OxfordIIITPet
-            Number of samples:  3669
-            Number of categories:       37
-            Root of dataset:    data/Oxford-IIIT_Pets
-    """  # noqa: E501
-
-    METAINFO = {'classes': OxfordIIITPet_CATEGORIES}
-
-    def __init__(self, data_root: str, split: str = 'trainval', **kwargs):
-
-        splits = ['trainval', 'test']
-        assert split in splits, \
-            f"The split must be one of {splits}, but get '{split}'"
-        self.split = split
-
-        self.backend = get_file_backend(data_root, enable_singleton=True)
-        if split == 'trainval':
-            ann_file = self.backend.join_path('annotations', 'trainval.txt')
-        else:
-            ann_file = self.backend.join_path('annotations', 'test.txt')
-
-        data_prefix = 'images'
-        test_mode = split == 'test'
-
-        super(OxfordIIITPet, self).__init__(
-            ann_file=ann_file,
-            data_root=data_root,
-            data_prefix=data_prefix,
-            test_mode=test_mode,
-            **kwargs)
-
-    def load_data_list(self):
-        """Load images and ground truth labels."""
-
-        pairs = list_from_file(self.ann_file)
-        data_list = []
-        for pair in pairs:
-            img_name, class_id, _, _ = pair.split()
-            img_name = f'{img_name}.jpg'
-            img_path = self.backend.join_path(self.img_prefix, img_name)
-            gt_label = int(class_id) - 1
-            info = dict(img_path=img_path, gt_label=gt_label)
-            data_list.append(info)
-        return data_list
-
-    def extra_repr(self) -> List[str]:
-        """The extra repr information of the dataset."""
-        body = [
-            f'Root of dataset: \t{self.data_root}',
-        ]
-        return body
diff --git a/spaces/Lamai/LAMAIGPT/autogpt/commands/image_gen.py b/spaces/Lamai/LAMAIGPT/autogpt/commands/image_gen.py
deleted file mode 100644
index 0809fcdd3e38b52a2ce09ca1444f2574813d40f9..0000000000000000000000000000000000000000
--- a/spaces/Lamai/LAMAIGPT/autogpt/commands/image_gen.py
+++ /dev/null
@@ -1,163 +0,0 @@
-""" Image Generation Module for AutoGPT."""
-import io
-import os.path
-import uuid
-from base64 import b64decode
-
-import openai
-import requests
-from PIL import Image
-
-from autogpt.config import Config
-from autogpt.workspace import path_in_workspace
-
-CFG = Config()
-
-
-def generate_image(prompt: str, size: int = 256) -> str:
-    """Generate an image from a prompt.
-
-    Args:
-        prompt (str): The prompt to use
-        size (int, optional): The size of the image. Defaults to 256. (Not supported by HuggingFace)
-
-    Returns:
-        str: The filename of the image
-    """
-    filename = f"{str(uuid.uuid4())}.jpg"
-
-    # DALL-E
-    if CFG.image_provider == "dalle":
-        return generate_image_with_dalle(prompt, filename, size)
-    # HuggingFace
-    elif CFG.image_provider == "huggingface":
-        return generate_image_with_hf(prompt, filename)
-    # SD WebUI
-    elif CFG.image_provider == "sdwebui":
-        return generate_image_with_sd_webui(prompt, filename, size)
-    return "No Image Provider Set"
-
-
-def generate_image_with_hf(prompt: str, filename: str) -> str:
-    """Generate an image with HuggingFace's API.
-
-    Args:
-        prompt (str): The prompt to use
-        filename (str): The filename to save the image to
-
-    Returns:
-        str: The filename of the image
-    """
-    API_URL = (
-        f"https://api-inference.huggingface.co/models/{CFG.huggingface_image_model}"
-    )
-    if CFG.huggingface_api_token is None:
-        raise ValueError(
-            "You need to set your Hugging Face API token in the config file."
-        )
-    headers = {
-        "Authorization": f"Bearer {CFG.huggingface_api_token}",
-        "X-Use-Cache": "false",
-    }
-
-    response = requests.post(
-        API_URL,
-        headers=headers,
-        json={
-            "inputs": prompt,
-        },
-    )
-
-    image = Image.open(io.BytesIO(response.content))
-    print(f"Image Generated for prompt:{prompt}")
-
-    image.save(path_in_workspace(filename))
-
-    return f"Saved to disk:{filename}"
-
-
-def generate_image_with_dalle(prompt: str, filename: str) -> str:
-    """Generate an image with DALL-E.
-
-    Args:
-        prompt (str): The prompt to use
-        filename (str): The filename to save the image to
-
-    Returns:
-        str: The filename of the image
-    """
-    openai.api_key = CFG.openai_api_key
-
-    # Check for supported image sizes
-    if size not in [256, 512, 1024]:
-        closest = min([256, 512, 1024], key=lambda x: abs(x - size))
-        print(
-            f"DALL-E only supports image sizes of 256x256, 512x512, or 1024x1024. Setting to {closest}, was {size}."
-        )
-        size = closest
-
-    response = openai.Image.create(
-        prompt=prompt,
-        n=1,
-        size=f"{size}x{size}",
-        response_format="b64_json",
-    )
-
-    print(f"Image Generated for prompt:{prompt}")
-
-    image_data = b64decode(response["data"][0]["b64_json"])
-
-    with open(path_in_workspace(filename), mode="wb") as png:
-        png.write(image_data)
-
-    return f"Saved to disk:{filename}"
-
-
-def generate_image_with_sd_webui(
-    prompt: str,
-    filename: str,
-    size: int = 512,
-    negative_prompt: str = "",
-    extra: dict = {},
-) -> str:
-    """Generate an image with Stable Diffusion webui.
-    Args:
-        prompt (str): The prompt to use
-        filename (str): The filename to save the image to
-        size (int, optional): The size of the image. Defaults to 256.
-        negative_prompt (str, optional): The negative prompt to use. Defaults to "".
-        extra (dict, optional): Extra parameters to pass to the API. Defaults to {}.
-    Returns:
-        str: The filename of the image
-    """
-    # Create a session and set the basic auth if needed
-    s = requests.Session()
-    if CFG.sd_webui_auth:
-        username, password = CFG.sd_webui_auth.split(":")
-        s.auth = (username, password or "")
-
-    # Generate the images
-    response = requests.post(
-        f"{CFG.sd_webui_url}/sdapi/v1/txt2img",
-        json={
-            "prompt": prompt,
-            "negative_prompt": negative_prompt,
-            "sampler_index": "DDIM",
-            "steps": 20,
-            "cfg_scale": 7.0,
-            "width": size,
-            "height": size,
-            "n_iter": 1,
-            **extra,
-        },
-    )
-
-    print(f"Image Generated for prompt:{prompt}")
-
-    # Save the image to disk
-    response = response.json()
-    b64 = b64decode(response["images"][0].split(",", 1)[0])
-    image = Image.open(io.BytesIO(b64))
-    image.save(path_in_workspace(filename))
-
-    return f"Saved to disk:{filename}"
diff --git a/spaces/LaynzKunz/AI-Cover-Gen-Web-Ui/src/main.py b/spaces/LaynzKunz/AI-Cover-Gen-Web-Ui/src/main.py
deleted file mode 100644
index a0dc7d0d119562c55bb0789aee902aea7b854648..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/AI-Cover-Gen-Web-Ui/src/main.py
+++ /dev/null
@@ -1,355 +0,0 @@
-import argparse
-import gc
-import hashlib
-import json
-import os
-import shlex
-import subprocess
-from contextlib import suppress
-from urllib.parse import urlparse, parse_qs
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile as sf
-import sox
-import yt_dlp
-from pedalboard import Pedalboard, Reverb, Compressor, HighpassFilter
-from pedalboard.io import AudioFile
-from pydub import AudioSegment
-
-from mdx import run_mdx
-from rvc import Config, load_hubert, get_vc, rvc_infer
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-mdxnet_models_dir = os.path.join(BASE_DIR, 'mdxnet_models')
-rvc_models_dir = os.path.join(BASE_DIR, 'rvc_models')
-output_dir = os.path.join(BASE_DIR, 'song_output')
-
-
-def get_youtube_video_id(url, ignore_playlist=True):
-    """
-    Examples:
-    http://youtu.be/SA2iWivDJiE
-    http://www.youtube.com/watch?v=_oPAwA_Udwc&feature=feedu
-    http://www.youtube.com/embed/SA2iWivDJiE
-    http://www.youtube.com/v/SA2iWivDJiE?version=3&amp;hl=en_US
-    """
-    query = urlparse(url)
-    if query.hostname == 'youtu.be':
-        if query.path[1:] == 'watch':
-            return query.query[2:]
-        return query.path[1:]
-
-    if query.hostname in {'www.youtube.com', 'youtube.com', 'music.youtube.com'}:
-        if not ignore_playlist:
-            # use case: get playlist id not current video in playlist
-            with suppress(KeyError):
-                return parse_qs(query.query)['list'][0]
-        if query.path == '/watch':
-            return parse_qs(query.query)['v'][0]
-        if query.path[:7] == '/watch/':
-            return query.path.split('/')[1]
-        if query.path[:7] == '/embed/':
-            return query.path.split('/')[2]
-        if query.path[:3] == '/v/':
-            return query.path.split('/')[2]
-
-    # returns None for invalid YouTube url
-    return None
-
-
-def yt_download(link):
-    ydl_opts = {
-        'format': 'bestaudio',
-        'outtmpl': '%(title)s',
-        'nocheckcertificate': True,
-        'ignoreerrors': True,
-        'no_warnings': True,
-        'quiet': True,
-        'extractaudio': True,
-        'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3'}],
-    }
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        result = ydl.extract_info(link, download=True)
-        download_path = ydl.prepare_filename(result, outtmpl='%(title)s.mp3')
-
-    return download_path
-
-
-def raise_exception(error_msg, is_webui):
-    if is_webui:
-        raise gr.Error(error_msg)
-    else:
-        raise Exception(error_msg)
-
-
-def get_rvc_model(voice_model, is_webui):
-    rvc_model_filename, rvc_index_filename = None, None
-    model_dir = os.path.join(rvc_models_dir, voice_model)
-    for file in os.listdir(model_dir):
-        ext = os.path.splitext(file)[1]
-        if ext == '.pth':
-            rvc_model_filename = file
-        if ext == '.index':
-            rvc_index_filename = file
-
-    if rvc_model_filename is None:
-        error_msg = f'No model file exists in {model_dir}.'
-        raise_exception(error_msg, is_webui)
-
-    return os.path.join(model_dir, rvc_model_filename), os.path.join(model_dir, rvc_index_filename) if rvc_index_filename else ''
-
-
-def get_audio_paths(song_dir):
-    orig_song_path = None
-    instrumentals_path = None
-    main_vocals_dereverb_path = None
-    backup_vocals_path = None
-
-    for file in os.listdir(song_dir):
-        if file.endswith('_Instrumental.wav'):
-            instrumentals_path = os.path.join(song_dir, file)
-            orig_song_path = instrumentals_path.replace('_Instrumental', '')
-
-        elif file.endswith('_Vocals_Main_DeReverb.wav'):
-            main_vocals_dereverb_path = os.path.join(song_dir, file)
-
-        elif file.endswith('_Vocals_Backup.wav'):
-            backup_vocals_path = os.path.join(song_dir, file)
-
-    return orig_song_path, instrumentals_path, main_vocals_dereverb_path, backup_vocals_path
-
-
-def convert_to_stereo(audio_path):
-    wave, sr = librosa.load(audio_path, mono=False, sr=44100)
-
-    # check if mono
-    if type(wave[0]) != np.ndarray:
-        stereo_path = f'{os.path.splitext(audio_path)[0]}_stereo.wav'
-        command = shlex.split(f'ffmpeg -y -loglevel error -i "{audio_path}" -ac 2 -f wav "{stereo_path}"')
-        subprocess.run(command)
-        return stereo_path
-    else:
-        return audio_path
-
-
-def pitch_shift(audio_path, pitch_change):
-    output_path = f'{os.path.splitext(audio_path)[0]}_p{pitch_change}.wav'
-    if not os.path.exists(output_path):
-        y, sr = sf.read(audio_path)
-        tfm = sox.Transformer()
-        tfm.pitch(pitch_change)
-        y_shifted = tfm.build_array(input_array=y, sample_rate_in=sr)
-        sf.write(output_path, y_shifted, sr)
-
-    return output_path
-
-
-def get_hash(filepath):
-    with open(filepath, 'rb') as f:
-        file_hash = hashlib.blake2b()
-        while chunk := f.read(8192):
-            file_hash.update(chunk)
-
-    return file_hash.hexdigest()[:11]
-
-
-def display_progress(message, percent, is_webui, progress=None):
-    if is_webui:
-        progress(percent, desc=message)
-    else:
-        print(message)
-
-
-def preprocess_song(song_input, mdx_model_params, song_id, is_webui, input_type, progress=None):
-    keep_orig = False
-    if input_type == 'yt':
-        display_progress('[~] Downloading song...', 0, is_webui, progress)
-        song_link = song_input.split('&')[0]
-        orig_song_path = yt_download(song_link)
-    elif input_type == 'local':
-        orig_song_path = song_input
-        keep_orig = True
-    else:
-        orig_song_path = None
-
-    song_output_dir = os.path.join(output_dir, song_id)
-    orig_song_path = convert_to_stereo(orig_song_path)
-
-    display_progress('[~] Separating Vocals from Instrumental...', 0.1, is_webui, progress)
-    vocals_path, instrumentals_path = run_mdx(mdx_model_params, song_output_dir, os.path.join(mdxnet_models_dir, 'UVR-MDX-NET-Voc_FT.onnx'), orig_song_path, denoise=True, keep_orig=keep_orig)
-
-    display_progress('[~] Separating Main Vocals from Backup Vocals...', 0.2, is_webui, progress)
-    backup_vocals_path, main_vocals_path = run_mdx(mdx_model_params, song_output_dir, os.path.join(mdxnet_models_dir, 'UVR_MDXNET_KARA_2.onnx'), vocals_path, suffix='Backup', invert_suffix='Main', denoise=True)
-
-    display_progress('[~] Applying DeReverb to Vocals...', 0.3, is_webui, progress)
-    _, main_vocals_dereverb_path = run_mdx(mdx_model_params, song_output_dir, os.path.join(mdxnet_models_dir, 'Reverb_HQ_By_FoxJoy.onnx'), main_vocals_path, invert_suffix='DeReverb', exclude_main=True, denoise=True)
-
-    return orig_song_path, vocals_path, instrumentals_path, main_vocals_path, backup_vocals_path, main_vocals_dereverb_path
-
-
-def voice_change(voice_model, vocals_path, output_path, pitch_change, f0_method, index_rate, filter_radius, rms_mix_rate, protect, crepe_hop_length, is_webui):
-    rvc_model_path, rvc_index_path = get_rvc_model(voice_model, is_webui)
-    device = 'cpu'
-    config = Config(device, False)
-    hubert_model = load_hubert(device, config.is_half, os.path.join(rvc_models_dir, 'hubert_base.pt'))
-    cpt, version, net_g, tgt_sr, vc = get_vc(device, False, config, rvc_model_path)
-
-    # convert main vocals
-    rvc_infer(rvc_index_path, index_rate, vocals_path, output_path, pitch_change, f0_method, cpt, version, net_g, filter_radius, tgt_sr, rms_mix_rate, protect, crepe_hop_length, vc, hubert_model)
-    del hubert_model, cpt
-    gc.collect()
-
-
-def add_audio_effects(audio_path, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping):
-    output_path = f'{os.path.splitext(audio_path)[0]}_mixed.wav'
-
-    # Initialize audio effects plugins
-    board = Pedalboard(
-        [
-            HighpassFilter(),
-            Compressor(ratio=4, threshold_db=-15),
-            Reverb(room_size=reverb_rm_size, dry_level=reverb_dry, wet_level=reverb_wet, damping=reverb_damping)
-         ]
-    )
-
-    with AudioFile(audio_path) as f:
-        with AudioFile(output_path, 'w', f.samplerate, f.num_channels) as o:
-            # Read one second of audio at a time, until the file is empty:
-            while f.tell() < f.frames:
-                chunk = f.read(int(f.samplerate))
-                effected = board(chunk, f.samplerate, reset=False)
-                o.write(effected)
-
-    return output_path
-
-
-def combine_audio(audio_paths, output_path, main_gain, backup_gain, inst_gain, output_format):
-    main_vocal_audio = AudioSegment.from_wav(audio_paths[0]) - 4 + main_gain
-    backup_vocal_audio = AudioSegment.from_wav(audio_paths[1]) - 6 + backup_gain
-    instrumental_audio = AudioSegment.from_wav(audio_paths[2]) - 7 + inst_gain
-    main_vocal_audio.overlay(backup_vocal_audio).overlay(instrumental_audio).export(output_path, format=output_format)
-
-
-def song_cover_pipeline(song_input, voice_model, pitch_change, keep_files,
-                        is_webui=0, main_gain=0, backup_gain=0, inst_gain=0, index_rate=0.5, filter_radius=3,
-                        rms_mix_rate=0.25, f0_method='rmvpe', crepe_hop_length=128, protect=0.33, pitch_change_all=0,
-                        reverb_rm_size=0.15, reverb_wet=0.2, reverb_dry=0.8, reverb_damping=0.7, output_format='mp3',
-                        progress=gr.Progress()):
-    try:
-        if not song_input or not voice_model:
-            raise_exception('Ensure that the song input field and voice model field is filled.', is_webui)
-
-        display_progress('[~] Starting AI Cover Generation Pipeline...', 0, is_webui, progress)
-
-        with open(os.path.join(mdxnet_models_dir, 'model_data.json')) as infile:
-            mdx_model_params = json.load(infile)
-
-        # if youtube url
-        if urlparse(song_input).scheme == 'https':
-            input_type = 'yt'
-            song_id = get_youtube_video_id(song_input)
-            if song_id is None:
-                error_msg = 'Invalid YouTube url.'
-                raise_exception(error_msg, is_webui)
-
-        # local audio file
-        else:
-            input_type = 'local'
-            song_input = song_input.strip('\"')
-            if os.path.exists(song_input):
-                song_id = get_hash(song_input)
-            else:
-                error_msg = f'{song_input} does not exist.'
-                song_id = None
-                raise_exception(error_msg, is_webui)
-
-        song_dir = os.path.join(output_dir, song_id)
-
-        if not os.path.exists(song_dir):
-            os.makedirs(song_dir)
-            orig_song_path, vocals_path, instrumentals_path, main_vocals_path, backup_vocals_path, main_vocals_dereverb_path = preprocess_song(song_input, mdx_model_params, song_id, is_webui, input_type, progress)
-
-        else:
-            vocals_path, main_vocals_path = None, None
-            paths = get_audio_paths(song_dir)
-
-            # if any of the audio files aren't available or keep intermediate files, rerun preprocess
-            if any(path is None for path in paths) or keep_files:
-                orig_song_path, vocals_path, instrumentals_path, main_vocals_path, backup_vocals_path, main_vocals_dereverb_path = preprocess_song(song_input, mdx_model_params, song_id, is_webui, input_type, progress)
-            else:
-                orig_song_path, instrumentals_path, main_vocals_dereverb_path, backup_vocals_path = paths
-
-        pitch_change = pitch_change * 12 + pitch_change_all
-        ai_vocals_path = os.path.join(song_dir, f'{os.path.splitext(os.path.basename(orig_song_path))[0]}_{voice_model}_p{pitch_change}_i{index_rate}_fr{filter_radius}_rms{rms_mix_rate}_pro{protect}_{f0_method}{"" if f0_method != "mangio-crepe" else f"_{crepe_hop_length}"}.wav')
-        ai_cover_path = os.path.join(song_dir, f'{os.path.splitext(os.path.basename(orig_song_path))[0]} ({voice_model} Ver).{output_format}')
-
-        if not os.path.exists(ai_vocals_path):
-            display_progress('[~] Converting voice using RVC...', 0.5, is_webui, progress)
-            voice_change(voice_model, main_vocals_dereverb_path, ai_vocals_path, pitch_change, f0_method, index_rate, filter_radius, rms_mix_rate, protect, crepe_hop_length, is_webui)
-
-        display_progress('[~] Applying audio effects to Vocals...', 0.8, is_webui, progress)
-        ai_vocals_mixed_path = add_audio_effects(ai_vocals_path, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping)
-
-        if pitch_change_all != 0:
-            display_progress('[~] Applying overall pitch change', 0.85, is_webui, progress)
-            instrumentals_path = pitch_shift(instrumentals_path, pitch_change_all)
-            backup_vocals_path = pitch_shift(backup_vocals_path, pitch_change_all)
-
-        display_progress('[~] Combining AI Vocals and Instrumentals...', 0.9, is_webui, progress)
-        combine_audio([ai_vocals_mixed_path, backup_vocals_path, instrumentals_path], ai_cover_path, main_gain, backup_gain, inst_gain, output_format)
-
-        if not keep_files:
-            display_progress('[~] Removing intermediate audio files...', 0.95, is_webui, progress)
-            intermediate_files = [vocals_path, main_vocals_path, ai_vocals_mixed_path]
-            if pitch_change_all != 0:
-                intermediate_files += [instrumentals_path, backup_vocals_path]
-            for file in intermediate_files:
-                if file and os.path.exists(file):
-                    os.remove(file)
-
-        return ai_cover_path
-
-    except Exception as e:
-        raise_exception(str(e), is_webui)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Generate a AI cover song in the song_output/id directory.', add_help=True)
-    parser.add_argument('-i', '--song-input', type=str, required=True, help='Link to a YouTube video or the filepath to a local mp3/wav file to create an AI cover of')
-    parser.add_argument('-dir', '--rvc-dirname', type=str, required=True, help='Name of the folder in the rvc_models directory containing the RVC model file and optional index file to use')
-    parser.add_argument('-p', '--pitch-change', type=int, required=True, help='Change the pitch of AI Vocals only. Generally, use 1 for male to female and -1 for vice-versa. (Octaves)')
-    parser.add_argument('-k', '--keep-files', action=argparse.BooleanOptionalAction, help='Whether to keep all intermediate audio files generated in the song_output/id directory, e.g. Isolated Vocals/Instrumentals')
-    parser.add_argument('-ir', '--index-rate', type=float, default=0.5, help='A decimal number e.g. 0.5, used to reduce/resolve the timbre leakage problem. If set to 1, more biased towards the timbre quality of the training dataset')
-    parser.add_argument('-fr', '--filter-radius', type=int, default=3, help='A number between 0 and 7. If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.')
-    parser.add_argument('-rms', '--rms-mix-rate', type=float, default=0.25, help="A decimal number e.g. 0.25. Control how much to use the original vocal's loudness (0) or a fixed loudness (1).")
-    parser.add_argument('-palgo', '--pitch-detection-algo', type=str, default='rmvpe', help='Best option is rmvpe (clarity in vocals), then mangio-crepe (smoother vocals).')
-    parser.add_argument('-hop', '--crepe-hop-length', type=int, default=128, help='If pitch detection algo is mangio-crepe, controls how often it checks for pitch changes in milliseconds. The higher the value, the faster the conversion and less risk of voice cracks, but there is less pitch accuracy. Recommended: 128.')
-    parser.add_argument('-pro', '--protect', type=float, default=0.33, help='A decimal number e.g. 0.33. Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy.')
-    parser.add_argument('-mv', '--main-vol', type=int, default=0, help='Volume change for AI main vocals in decibels. Use -3 to decrease by 3 decibels and 3 to increase by 3 decibels')
-    parser.add_argument('-bv', '--backup-vol', type=int, default=0, help='Volume change for backup vocals in decibels')
-    parser.add_argument('-iv', '--inst-vol', type=int, default=0, help='Volume change for instrumentals in decibels')
-    parser.add_argument('-pall', '--pitch-change-all', type=int, default=0, help='Change the pitch/key of vocals and instrumentals. Changing this slightly reduces sound quality')
-    parser.add_argument('-rsize', '--reverb-size', type=float, default=0.15, help='Reverb room size between 0 and 1')
-    parser.add_argument('-rwet', '--reverb-wetness', type=float, default=0.2, help='Reverb wet level between 0 and 1')
-    parser.add_argument('-rdry', '--reverb-dryness', type=float, default=0.8, help='Reverb dry level between 0 and 1')
-    parser.add_argument('-rdamp', '--reverb-damping', type=float, default=0.7, help='Reverb damping between 0 and 1')
-    parser.add_argument('-oformat', '--output-format', type=str, default='mp3', help='Output format of audio file. mp3 for smaller file size, wav for best quality')
-    args = parser.parse_args()
-
-    rvc_dirname = args.rvc_dirname
-    if not os.path.exists(os.path.join(rvc_models_dir, rvc_dirname)):
-        raise Exception(f'The folder {os.path.join(rvc_models_dir, rvc_dirname)} does not exist.')
-
-    cover_path = song_cover_pipeline(args.song_input, rvc_dirname, args.pitch_change, args.keep_files,
-                                     main_gain=args.main_vol, backup_gain=args.backup_vol, inst_gain=args.inst_vol,
-                                     index_rate=args.index_rate, filter_radius=args.filter_radius,
-                                     rms_mix_rate=args.rms_mix_rate, f0_method=args.pitch_detection_algo,
-                                     crepe_hop_length=args.crepe_hop_length, protect=args.protect,
-                                     pitch_change_all=args.pitch_change_all,
-                                     reverb_rm_size=args.reverb_size, reverb_wet=args.reverb_wetness,
-                                     reverb_dry=args.reverb_dryness, reverb_damping=args.reverb_damping,
-                                     output_format=args.output_format)
-    print(f'[+] Cover generated at {cover_path}')
diff --git a/spaces/LearnableAI/FinTextSummaryDemo/README.md b/spaces/LearnableAI/FinTextSummaryDemo/README.md
deleted file mode 100644
index 6a11c665e90e80ad3d7aa56af83a957eb56d86ed..0000000000000000000000000000000000000000
--- a/spaces/LearnableAI/FinTextSummaryDemo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: FinTextSummaryDemo
-emoji: 💩
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/LukeLB/shocking_guiness/README.md b/spaces/LukeLB/shocking_guiness/README.md
deleted file mode 100644
index b43b286e8a314b6bb121606247bc82de91cc6a83..0000000000000000000000000000000000000000
--- a/spaces/LukeLB/shocking_guiness/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bear App
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mahiruoshi/BangDream-Bert-VITS2/attentions.py b/spaces/Mahiruoshi/BangDream-Bert-VITS2/attentions.py
deleted file mode 100644
index 3ba2407267ecd425d2095a6428015b5b4ebc0bda..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/BangDream-Bert-VITS2/attentions.py
+++ /dev/null
@@ -1,464 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=4,
-        isflow=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        # if isflow:
-        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
-        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
-        #  self.cond_layer = weight_norm(cond_layer, name='weight')
-        #  self.gin_channels = 256
-        self.cond_layer_idx = self.n_layers
-        if "gin_channels" in kwargs:
-            self.gin_channels = kwargs["gin_channels"]
-            if self.gin_channels != 0:
-                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
-                # vits2 says 3rd block, so idx is 2 by default
-                self.cond_layer_idx = (
-                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
-                )
-                logging.debug(self.gin_channels, self.cond_layer_idx)
-                assert (
-                    self.cond_layer_idx < self.n_layers
-                ), "cond_layer_idx should be less than n_layers"
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, g=None):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            if i == self.cond_layer_idx and g is not None:
-                g = self.spk_emb_linear(g.transpose(1, 2))
-                g = g.transpose(1, 2)
-                x = x + g
-                x = x * x_mask
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # pad along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/Mahiruoshi/BangDream-Bert-VITS2/server.py b/spaces/Mahiruoshi/BangDream-Bert-VITS2/server.py
deleted file mode 100644
index 2ecd50307fdae5c5e26d8cc9453de296532b95ff..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/BangDream-Bert-VITS2/server.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config["JSON_AS_ASCII"] = False
-
-
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-    del word2ph
-    assert bert.shape[-1] == len(phone), phone
-
-    if language_str == "ZH":
-        bert = bert
-        ja_bert = torch.zeros(768, len(phone))
-    elif language_str == "JA":
-        ja_bert = bert
-        bert = torch.zeros(1024, len(phone))
-    else:
-        bert = torch.zeros(1024, len(phone))
-        ja_bert = torch.zeros(768, len(phone))
-    assert bert.shape[-1] == len(
-        phone
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
-    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
-    with torch.no_grad():
-        x_tst = phones.to(dev).unsqueeze(0)
-        tones = tones.to(dev).unsqueeze(0)
-        lang_ids = lang_ids.to(dev).unsqueeze(0)
-        bert = bert.to(dev).unsqueeze(0)
-        ja_bert = ja_bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
-        audio = (
-            net_g.infer(
-                x_tst,
-                x_tst_lengths,
-                speakers,
-                tones,
-                lang_ids,
-                bert,
-                ja_bert,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-            )[0][0, 0]
-            .data.cpu()
-            .float()
-            .numpy()
-        )
-        return audio
-
-
-def replace_punctuation(text, i=2):
-    punctuation = "，。？！"
-    for char in punctuation:
-        text = text.replace(char, char * i)
-    return text
-
-
-def wav2(i, o, format):
-    inp = avopen(i, "rb")
-    out = avopen(o, "wb", format=format)
-    if format == "ogg":
-        format = "libvorbis"
-
-    ostream = out.add_stream(format)
-
-    for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame):
-            out.mux(p)
-
-    for p in ostream.encode(None):
-        out.mux(p)
-
-    out.close()
-    inp.close()
-
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev = "cuda"
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model,
-).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None, skip_optimizer=True)
-
-
-@app.route("/")
-def main():
-    try:
-        speaker = request.args.get("speaker")
-        text = request.args.get("text").replace("/n", "")
-        sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
-        noise = float(request.args.get("noise", 0.5))
-        noisew = float(request.args.get("noisew", 0.6))
-        length = float(request.args.get("length", 1.2))
-        language = request.args.get("language")
-        if length >= 2:
-            return "Too big length"
-        if len(text) >= 250:
-            return "Too long text"
-        fmt = request.args.get("format", "wav")
-        if None in (speaker, text):
-            return "Missing Parameter"
-        if fmt not in ("mp3", "wav", "ogg"):
-            return "Invalid Format"
-        if language not in ("JA", "ZH"):
-            return "Invalid language"
-    except:
-        return "Invalid Parameter"
-
-    with torch.no_grad():
-        audio = infer(
-            text,
-            sdp_ratio=sdp_ratio,
-            noise_scale=noise,
-            noise_scale_w=noisew,
-            length_scale=length,
-            sid=speaker,
-            language=language,
-        )
-
-    with BytesIO() as wav:
-        wavfile.write(wav, hps.data.sampling_rate, audio)
-        torch.cuda.empty_cache()
-        if fmt == "wav":
-            return Response(wav.getvalue(), mimetype="audio/wav")
-        wav.seek(0, 0)
-        with BytesIO() as ofp:
-            wav2(wav, ofp, fmt)
-            return Response(
-                ofp.getvalue(), mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
-            )
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/nn/syncbn.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/nn/syncbn.py
deleted file mode 100644
index b118c9d4aac3ee86821797bc9f794cd9aa38b1b2..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/syncbn/modules/nn/syncbn.py
+++ /dev/null
@@ -1,148 +0,0 @@
-"""
-/*****************************************************************************/
-
-BatchNorm2dSync with multi-gpu
-
-/*****************************************************************************/
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-try:
-    # python 3
-    from queue import Queue
-except ImportError:
-    # python 2
-    from Queue import Queue
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.nn.parameter import Parameter
-from isegm.model.syncbn.modules.functional import batchnorm2d_sync
-
-
-class _BatchNorm(nn.Module):
-    """
-    Customized BatchNorm from nn.BatchNorm
-    >> added freeze attribute to enable bn freeze.
-    """
-
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
-                 track_running_stats=True):
-        super(_BatchNorm, self).__init__()
-        self.num_features = num_features
-        self.eps = eps
-        self.momentum = momentum
-        self.affine = affine
-        self.track_running_stats = track_running_stats
-        self.freezed = False
-        if self.affine:
-            self.weight = Parameter(torch.Tensor(num_features))
-            self.bias = Parameter(torch.Tensor(num_features))
-        else:
-            self.register_parameter('weight', None)
-            self.register_parameter('bias', None)
-        if self.track_running_stats:
-            self.register_buffer('running_mean', torch.zeros(num_features))
-            self.register_buffer('running_var', torch.ones(num_features))
-        else:
-            self.register_parameter('running_mean', None)
-            self.register_parameter('running_var', None)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        if self.track_running_stats:
-            self.running_mean.zero_()
-            self.running_var.fill_(1)
-        if self.affine:
-            self.weight.data.uniform_()
-            self.bias.data.zero_()
-
-    def _check_input_dim(self, input):
-        return NotImplemented
-
-    def forward(self, input):
-        self._check_input_dim(input)
-
-        compute_stats = not self.freezed and \
-            self.training and self.track_running_stats
-
-        ret = F.batch_norm(input, self.running_mean, self.running_var,
-                           self.weight, self.bias, compute_stats,
-                           self.momentum, self.eps)
-        return ret
-
-    def extra_repr(self):
-        return '{num_features}, eps={eps}, momentum={momentum}, '\
-               'affine={affine}, ' \
-               'track_running_stats={track_running_stats}'.format(
-                   **self.__dict__)
-
-
-class BatchNorm2dNoSync(_BatchNorm):
-    """
-    Equivalent to nn.BatchNorm2d
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 4:
-            raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
-
-
-class BatchNorm2dSync(BatchNorm2dNoSync):
-    """
-    BatchNorm2d with automatic multi-GPU Sync
-    """
-
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
-                 track_running_stats=True):
-        super(BatchNorm2dSync, self).__init__(
-            num_features, eps=eps, momentum=momentum, affine=affine,
-            track_running_stats=track_running_stats)
-        self.sync_enabled = True
-        self.devices = list(range(torch.cuda.device_count()))
-        if len(self.devices) > 1:
-            # Initialize queues
-            self.worker_ids = self.devices[1:]
-            self.master_queue = Queue(len(self.worker_ids))
-            self.worker_queues = [Queue(1) for _ in self.worker_ids]
-
-    def forward(self, x):
-        compute_stats = not self.freezed and \
-            self.training and self.track_running_stats
-        if self.sync_enabled and compute_stats and len(self.devices) > 1:
-            if x.get_device() == self.devices[0]:
-                # Master mode
-                extra = {
-                    "is_master": True,
-                    "master_queue": self.master_queue,
-                    "worker_queues": self.worker_queues,
-                    "worker_ids": self.worker_ids
-                }
-            else:
-                # Worker mode
-                extra = {
-                    "is_master": False,
-                    "master_queue": self.master_queue,
-                    "worker_queue": self.worker_queues[
-                        self.worker_ids.index(x.get_device())]
-                }
-            return batchnorm2d_sync(x, self.weight, self.bias,
-                                    self.running_mean, self.running_var,
-                                    extra, compute_stats, self.momentum,
-                                    self.eps)
-        return super(BatchNorm2dSync, self).forward(x)
-
-    def __repr__(self):
-        """repr"""
-        rep = '{name}({num_features}, eps={eps}, momentum={momentum},' \
-            'affine={affine}, ' \
-            'track_running_stats={track_running_stats},' \
-            'devices={devices})'
-        return rep.format(name=self.__class__.__name__, **self.__dict__)
-
-#BatchNorm2d = BatchNorm2dNoSync
-BatchNorm2d = BatchNorm2dSync
diff --git a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/js/skeletonFactory.js b/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/js/skeletonFactory.js
deleted file mode 100644
index e1d072b7df2fb40772e93f2dee595e467744e36b..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/js/skeletonFactory.js
+++ /dev/null
@@ -1,233 +0,0 @@
-bm_v = new THREE.MeshPhongMaterial({
-    color: 0x08519c,
-    emissive: 0x08306b,
-    specular: 0x08519c,
-    shininess: 10,
-    side: THREE.DoubleSide
-});
-
-jm_v = new THREE.MeshPhongMaterial({
-    color: 0x08306b,
-    emissive: 0x000000,
-    specular: 0x111111,
-    shininess: 90,
-    side: THREE.DoubleSide
-});
-
-bm_a = new THREE.MeshPhongMaterial({
-    color: 0x980043,
-    emissive: 0x67001f,
-    specular: 0x6a51a3,
-    shininess: 10,
-    side: THREE.DoubleSide
-});
-
-jm_a = new THREE.MeshPhongMaterial({
-    color: 0x67001f,
-    emissive: 0x000000,
-    specular: 0x111111,
-    shininess: 90,
-    side: THREE.DoubleSide
-});
-
-bm_b = new THREE.MeshPhongMaterial({
-    color: 0x3f007d,
-    emissive: 0x3f007d,
-    specular: 0x807dba,
-    shininess: 2,
-    side: THREE.DoubleSide
-});
-
-jm_b = new THREE.MeshPhongMaterial({
-    color: 0x3f007d,
-    emissive: 0x000000,
-    specular: 0x807dba,
-    shininess: 90,
-    side: THREE.DoubleSide
-});
-
-//------------------
-
-
-jointmaterial = new THREE.MeshLambertMaterial({
-    color: 0xc57206,
-    emissive: 0x271c18,
-    side: THREE.DoubleSide,
-    // shading: THREE.FlatShading,
-    wireframe: false,
-    shininess: 90,
-});
-
-bonematerial = new THREE.MeshPhongMaterial({
-    color: 0xbd9a6d,
-    emissive: 0x271c18,
-    side: THREE.DoubleSide,
-    // shading: THREE.FlatShading,
-    wireframe: false
-});
-
-jointmaterial2 = new THREE.MeshPhongMaterial({
-    color: 0x1562a2,
-    emissive: 0x000000,
-    specular: 0x111111,
-    shininess: 30,
-    side: THREE.DoubleSide
-});
-
-bonematerial2 = new THREE.MeshPhongMaterial({
-    color: 0x552211,
-    emissive: 0x882211,
-    // emissive: 0x000000,
-    specular: 0x111111,
-    shininess: 30,
-    side: THREE.DoubleSide
-});
-
-bonematerial3 = new THREE.MeshPhongMaterial({
-    color: 0x176793,
-    emissive: 0x000000,
-    specular: 0x111111,
-    shininess: 90,
-    side: THREE.DoubleSide
-});
-
-
-
-jointmaterial4 = new THREE.MeshPhongMaterial({
-    color: 0xFF8A00,
-    emissive: 0x000000,
-    specular: 0x111111,
-    shininess: 90,
-    side: THREE.DoubleSide
-});
-
-
-bonematerial4 = new THREE.MeshPhongMaterial({
-    color: 0x53633D,
-    emissive: 0x000000,
-    specular: 0xFFC450,
-    shininess: 90,
-    side: THREE.DoubleSide
-});
-
-
-
-bonematerial44 = new THREE.MeshPhongMaterial({
-    color: 0x582A72,
-    emissive: 0x000000,
-    specular: 0xFFC450,
-    shininess: 90,
-    side: THREE.DoubleSide
-});
-
-jointmaterial5 = new THREE.MeshPhongMaterial({
-    color: 0xAA5533,
-    emissive: 0x000000,
-    specular: 0x111111,
-    shininess: 30,
-    side: THREE.DoubleSide
-});
-
-bonematerial5 = new THREE.MeshPhongMaterial({
-    color: 0x552211,
-    emissive: 0x772211,
-    specular: 0x111111,
-    shininess: 30,
-    side: THREE.DoubleSide
-});
-
-
-markermaterial = new THREE.MeshPhongMaterial({
-    color: 0xc57206,
-    emissive: 0x271c18,
-    side: THREE.DoubleSide,
-    // shading: THREE.FlatShading,
-    wireframe: false,
-    shininess: 20,
-});
-
-markermaterial2 = new THREE.MeshPhongMaterial({
-    color: 0x1562a2,
-    emissive: 0x271c18,
-    side: THREE.DoubleSide,
-    // shading: THREE.FlatShading,
-    wireframe: false,
-    shininess: 20,
-});
-
-markermaterial3 = new THREE.MeshPhongMaterial({
-    color: 0x555555,
-    emissive: 0x999999,
-    side: THREE.DoubleSide,
-    // shading: THREE.FlatShading,
-    wireframe: false,
-    shininess: 20,
-});
-
-
-var makeMarkerGeometry_Sphere10 = function(markerName, scale) {
-    return new THREE.SphereGeometry(10, 60, 60);
-};
-
-var makeMarkerGeometry_Sphere3 = function(markerName, scale) {
-    return new THREE.SphereGeometry(3, 60, 60);
-};
-
-var makeMarkerGeometry_SphereX = function(markerName, scale) {
-    return new THREE.SphereGeometry(5, 60, 60);
-};
-
-var makeJointGeometry_SphereX = function(X) {
-    return function(jointName, scale) {
-        return new THREE.SphereGeometry(X, 60, 60);
-    };    
-};
-
-
-var makeJointGeometry_Sphere1 = function(jointName, scale) {
-    return new THREE.SphereGeometry(2 / scale, 60, 60);
-};
-
-var makeJointGeometry_Sphere2 = function(jointName, scale) {
-    return new THREE.SphereGeometry(1 / scale, 60, 60);
-};
-
-var makeJointGeometry_Dode = function(jointName, scale) {
-    return new THREE.DodecahedronGeometry(1 / scale, 0);
-};
-
-var makeBoneGeometry_Cylinder1 = function(joint1Name, joint2Name, length, scale) {
-    return new THREE.CylinderGeometry(1.5 / scale, 0.7 / scale, length, 40);
-};
-
-var makeBoneGeometry_Cylinder2 = function(joint1Name, joint2Name, length, scale) {
-    // if (joint1Name.includes("LeftHip"))
-    //     length = 400;
-    return new THREE.CylinderGeometry(1.5 / scale, 0.2 / scale, length, 40);   
-};
-
-var makeBoneGeometry_Cylinder3 = function(joint1Name, joint2Name, length, scale) {
-    var c1 = new THREE.CylinderGeometry(1.5 / scale, 0.2 / scale, length / 1, 20);
-    var c2 = new THREE.CylinderGeometry(0.2 / scale, 1.5 / scale, length / 1, 40);
-
-    var material = new THREE.MeshPhongMaterial({
-        color: 0xF7FE2E
-    });
-    var mmesh = new THREE.Mesh(c1, material);
-    mmesh.updateMatrix();
-    c2.merge(mmesh.geometry, mmesh.matrix);
-    return c2;
-};
-
-var makeBoneGeometry_Box1 = function(joint1Name, joint2Name, length, scale) {
-    return new THREE.BoxGeometry(1 / scale, length, 1 / scale, 40);
-};
-
-
-var makeJointGeometry_Empty = function(jointName, scale) {
-    return new THREE.SphereGeometry(0.001, 60, 60);
-};
-
-var makeBoneGeometry_Empty = function(joint1Name, joint2Name, length, scale) {
-    return new THREE.CylinderGeometry(0.001, 0.001, 0.001, 40);
-};
diff --git a/spaces/Marshalls/testmtd/misc/copy_chpt_to_gcp.sh b/spaces/Marshalls/testmtd/misc/copy_chpt_to_gcp.sh
deleted file mode 100644
index 75ac35a06cf3be89d5fb5aef746b9abe9fce1fc8..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/misc/copy_chpt_to_gcp.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash
-instance=$1
-exp=$2
-gcloud=gcloud
-#gcloud=$SCRATCH/google-cloud-sdk/bin/gcloud
-#mkdir training/experiments/${exp}
-#scp -r jeanzay:/gpfswork/rech/imi/usc19dv/mt-lightning/inference/generated/${exp}/videos/* inference/generated/${exp}/videos
-$gcloud beta compute scp --recurse --zone "europe-west4-a" ./training/experiments/${exp} ${instance}:~/mt-lightning/training/experiments/${exp}/ --project "kumofix2" 
diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/CppDataProcess/F0Preprocess.cpp b/spaces/MashiroSA/sovits-emu-voice-transform/CppDataProcess/F0Preprocess.cpp
deleted file mode 100644
index d6bd6f3cb8033fb9263624a9707311cac593ad57..0000000000000000000000000000000000000000
--- a/spaces/MashiroSA/sovits-emu-voice-transform/CppDataProcess/F0Preprocess.cpp
+++ /dev/null
@@ -1,153 +0,0 @@
-#include "F0Preprocess.hpp"
-
-
-void F0PreProcess::compute_f0(const double* audio, int64_t len)
-{
-	DioOption Doption;
-	InitializeDioOption(&Doption);
-	Doption.f0_ceil = 800;
-	Doption.frame_period = 1000.0 * hop / fs;
-	f0Len = GetSamplesForDIO(fs, (int)len, Doption.frame_period);
-	const auto tp = new double[f0Len];
-	const auto tmpf0 = new double[f0Len];
-	rf0 = new double[f0Len];
-	Dio(audio, (int)len, fs, &Doption, tp, tmpf0);
-	StoneMask(audio, (int)len, fs, tp, tmpf0, (int)f0Len, rf0);
-	delete[] tmpf0;
-	delete[] tp;
-}
-
-std::vector<double> arange(double start,double end,double step = 1.0,double div = 1.0)
-{
-	std::vector<double> output;
-	while(start<end)
-	{
-		output.push_back(start / div);
-		start += step;
-	}
-	return output;
-}
-
-void F0PreProcess::InterPf0(int64_t len)
-{
-	const auto xi = arange(0.0, (double)f0Len * (double)len, (double)f0Len, (double)len);
-	const auto tmp = new double[xi.size() + 1];
-	interp1(arange(0, (double)f0Len).data(), rf0, static_cast<int>(f0Len), xi.data(), (int)xi.size(), tmp);
-	for (size_t i = 0; i < xi.size(); i++)
-		if (isnan(tmp[i]))
-			tmp[i] = 0.0;
-	delete[] rf0;
-    rf0 = nullptr;
-	rf0 = tmp;
-	f0Len = (int64_t)xi.size();
-}
-
-long long* F0PreProcess::f0Log()
-{
-	const auto tmp = new long long[f0Len];
-	const auto f0_mel = new double[f0Len];
-	for (long long i = 0; i < f0Len; i++)
-	{
-		f0_mel[i] = 1127 * log(1.0 + rf0[i] / 700.0);
-		if (f0_mel[i] > 0.0)
-			f0_mel[i] = (f0_mel[i] - f0_mel_min) * (f0_bin - 2.0) / (f0_mel_max - f0_mel_min) + 1.0;
-		if (f0_mel[i] < 1.0)
-			f0_mel[i] = 1;
-		if (f0_mel[i] > f0_bin - 1)
-			f0_mel[i] = f0_bin - 1;
-		tmp[i] = (long long)round(f0_mel[i]);
-	}
-	delete[] f0_mel;
-	delete[] rf0;
-    rf0 = nullptr;
-	return tmp;
-}
-
-std::vector<long long> F0PreProcess::GetF0AndOtherInput(const double* audio, int64_t audioLen, int64_t hubLen, int64_t tran)
-{
-	compute_f0(audio, audioLen);
-	for (int64_t i = 0; i < f0Len; ++i)
-	{
-		rf0[i] = rf0[i] * pow(2.0, static_cast<double>(tran) / 12.0);
-		if (rf0[i] < 0.001)
-			rf0[i] = NAN;
-	}
-	InterPf0(hubLen);
-	const auto O0f = f0Log();
-	std::vector<long long> Of0(O0f, O0f + f0Len);
-    delete[] O0f;
-	return Of0;
-}
-
-std::vector<long long> getAligments(size_t specLen, size_t hubertLen)
-{
-	std::vector<long long> mel2ph(specLen + 1, 0);
-
-	size_t startFrame = 0;
-	const double ph_durs = static_cast<double>(specLen) / static_cast<double>(hubertLen);
-	for (size_t iph = 0; iph < hubertLen; ++iph)
-	{
-		const auto endFrame = static_cast<size_t>(round(static_cast<double>(iph) * ph_durs + ph_durs));
-		for (auto j = startFrame; j < endFrame + 1; ++j)
-			mel2ph[j] = static_cast<long long>(iph) + 1;
-		startFrame = endFrame + 1;
-	}
-
-	return mel2ph;
-}
-
-std::vector<float> F0PreProcess::GetF0AndOtherInputF0(const double* audio, int64_t audioLen, int64_t tran)
-{
-	compute_f0(audio, audioLen);
-	for (int64_t i = 0; i < f0Len; ++i)
-	{
-		rf0[i] = log2(rf0[i] * pow(2.0, static_cast<double>(tran) / 12.0));
-		if (rf0[i] < 0.001)
-			rf0[i] = NAN;
-	}
-	const int64_t specLen = audioLen / hop;
-	InterPf0(specLen);
-
-    std::vector<float> Of0(specLen, 0.0);
-
-    double last_value = 0.0;
-    for (int64_t i = 0; i < specLen; ++i)
-    {
-        if (rf0[i] <= 0.0)
-        {
-            int64_t j = i + 1;
-            for (; j < specLen; ++j)
-            {
-                if (rf0[j] > 0.0)
-                    break;
-            }
-            if (j < specLen - 1)
-            {
-                if (last_value > 0.0)
-                {
-                    const auto step = (rf0[j] - rf0[i - 1]) / double(j - i);
-                    for (int64_t k = i; k < j; ++k)
-                        Of0[k] = float(rf0[i - 1] + step * double(k - i + 1));
-                }
-                else
-                    for (int64_t k = i; k < j; ++k)
-                        Of0[k] = float(rf0[j]);
-                i = j;
-            }
-            else
-            {
-                for (int64_t k = i; k < specLen; ++k)
-                    Of0[k] = float(last_value);
-                i = specLen;
-            }
-        }
-        else
-        {
-            Of0[i] = float(rf0[i - 1]);
-            last_value = rf0[i];
-        }
-    }
-    delete[] rf0;
-    rf0 = nullptr;
-	return Of0;
-}
diff --git a/spaces/MathysL/AutoGPT4/README.md b/spaces/MathysL/AutoGPT4/README.md
deleted file mode 100644
index 5bf09b995f04f7af05d1314906b1b1ff39c20ddc..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AutoGPT
-emoji: 🦾
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: ui/app.py
-pinned: false
-license: mit
-duplicated_from: aliabid94/AutoGPT
----
-
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/__init__.py
deleted file mode 100644
index aa24d91972837b8756b225f4879bac20436eb72a..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base import BaseFileHandler
-from .json_handler import JsonHandler
-from .pickle_handler import PickleHandler
-from .yaml_handler import YamlHandler
-
-__all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler']
diff --git a/spaces/MichaelWelsch/FreeVC/README.md b/spaces/MichaelWelsch/FreeVC/README.md
deleted file mode 100644
index c534c0461fc85177463a10508cfbbea47d98b633..0000000000000000000000000000000000000000
--- a/spaces/MichaelWelsch/FreeVC/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: FreeVC
-emoji: 🚀
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: OlaWod/FreeVC
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MichaelWelsch/FreeVC/speaker_encoder/data_objects/speaker_batch.py b/spaces/MichaelWelsch/FreeVC/speaker_encoder/data_objects/speaker_batch.py
deleted file mode 100644
index 4485605e3ece5b491d1e7d0f223c543b6c91eb96..0000000000000000000000000000000000000000
--- a/spaces/MichaelWelsch/FreeVC/speaker_encoder/data_objects/speaker_batch.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import numpy as np
-from typing import List
-from speaker_encoder.data_objects.speaker import Speaker
-
-class SpeakerBatch:
-    def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
-        self.speakers = speakers
-        self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers}
-        
-        # Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with
-        # 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40)
-        self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]])
diff --git a/spaces/Miuzarte/SUI-svc-3.0/vdecoder/hifigan/env.py b/spaces/Miuzarte/SUI-svc-3.0/vdecoder/hifigan/env.py
deleted file mode 100644
index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000
--- a/spaces/Miuzarte/SUI-svc-3.0/vdecoder/hifigan/env.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import shutil
-
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def build_env(config, config_name, path):
-    t_path = os.path.join(path, config_name)
-    if config != t_path:
-        os.makedirs(path, exist_ok=True)
-        shutil.copyfile(config, os.path.join(path, config_name))
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/__init__.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/__init__.py
deleted file mode 100644
index b803a0d22e93cdfde7986b5fe111d2b061d9d9fb..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .data_preprocessors import *  # NOQA
-from .detectors import *  # NOQA
-from .heads import *  # NOQA
-from .module_losses import *  # NOQA
-from .necks import *  # NOQA
-from .postprocessors import *  # NOQA
diff --git a/spaces/MrSinan/Reconstruction/README.md b/spaces/MrSinan/Reconstruction/README.md
deleted file mode 100644
index 132bb791a6e9a71aa78ffd754d6107ee3ffb13f8..0000000000000000000000000000000000000000
--- a/spaces/MrSinan/Reconstruction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Masked Face Reconstruction
-emoji: 😷
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/modules/losses.py b/spaces/NAACL2022/CLIP-Caption-Reward/captioning/modules/losses.py
deleted file mode 100644
index 28d6db59dd70a9418a8a074d54402d6b5823520c..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/modules/losses.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import torch
-import torch.nn as nn
-from ..utils.rewards import get_scores, get_self_cider_scores
-
-class RewardCriterion(nn.Module):
-    def __init__(self):
-        super(RewardCriterion, self).__init__()
-
-    def forward(self, input, seq, reward):
-        input = input.gather(2, seq.unsqueeze(2)).squeeze(2)
-        
-        input = input.reshape(-1)
-        reward = reward.reshape(-1)
-        mask = (seq>0).to(input)
-        mask = torch.cat([mask.new(mask.size(0), 1).fill_(1), mask[:, :-1]], 1).reshape(-1)
-        output = - input * reward * mask
-        output = torch.sum(output) / torch.sum(mask)
-
-        return output
-
-class StructureLosses(nn.Module):
-    """
-    This loss is inspired by Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018).
-    """
-    def __init__(self, opt):
-        super(StructureLosses, self).__init__()
-        self.opt = opt
-        self.loss_type = opt.structure_loss_type
-
-    def forward(self, input, seq, data_gts):
-        """
-        Input is either logits or log softmax
-        """
-        out = {}
-
-        batch_size = input.size(0)# batch_size = sample_size * seq_per_img
-        seq_per_img = batch_size // len(data_gts)
-
-        assert seq_per_img == self.opt.train_sample_n, seq_per_img
-
-        mask = (seq>0).to(input)
-        mask = torch.cat([mask.new_full((mask.size(0), 1), 1), mask[:, :-1]], 1)
-        
-        scores = get_scores(data_gts, seq, self.opt)
-        scores = torch.from_numpy(scores).type_as(input).view(-1, seq_per_img)
-        out['reward'] = scores #.mean()
-        if self.opt.entropy_reward_weight > 0:
-            entropy = - (F.softmax(input, dim=2) * F.log_softmax(input, dim=2)).sum(2).data
-            entropy = (entropy * mask).sum(1) / mask.sum(1)
-            print('entropy', entropy.mean().item())
-            scores = scores + self.opt.entropy_reward_weight * entropy.view(-1, seq_per_img)
-        # rescale cost to [0,1]
-        costs = - scores
-        if self.loss_type == 'risk' or self.loss_type == 'softmax_margin': 
-            costs = costs - costs.min(1, keepdim=True)[0]
-            costs = costs / costs.max(1, keepdim=True)[0]
-        # in principle
-        # Only risk need such rescale
-        # margin should be alright; Let's try.
-
-        # Gather input: BxTxD -> BxT
-        input = input.gather(2, seq.unsqueeze(2)).squeeze(2)
-
-        if self.loss_type == 'seqnll':
-            # input is logsoftmax
-            input = input * mask
-            input = input.sum(1) / mask.sum(1)
-            input = input.view(-1, seq_per_img)
-
-            target = costs.min(1)[1]
-            output = F.cross_entropy(input, target)
-        elif self.loss_type == 'risk':
-            # input is logsoftmax
-            input = input * mask
-            input = input.sum(1)
-            input = input.view(-1, seq_per_img)
-
-            output = (F.softmax(input.exp()) * costs).sum(1).mean()
-
-            # test
-            # avg_scores = input
-            # probs = F.softmax(avg_scores.exp_())
-            # loss = (probs * costs.type_as(probs)).sum() / input.size(0)
-            # print(output.item(), loss.item())            
-
-        elif self.loss_type == 'max_margin':
-            # input is logits
-            input = input * mask
-            input = input.sum(1) / mask.sum(1)
-            input = input.view(-1, seq_per_img)
-            _, __ = costs.min(1, keepdim=True)
-            costs_star = _
-            input_star = input.gather(1, __)
-            output = F.relu(costs - costs_star - input_star + input).max(1)[0] / 2
-            output = output.mean()
-
-            # sanity test
-            # avg_scores = input + costs
-            # scores_with_high_target = avg_scores.clone()
-            # scores_with_high_target.scatter_(1, costs.min(1)[1].view(-1, 1), 1e10)
-
-            # target_and_offender_index = scores_with_high_target.sort(1, True)[1][:, 0:2]
-            # avg_scores = avg_scores.gather(1, target_and_offender_index)
-            # target_index = avg_scores.new_zeros(avg_scores.size(0), dtype=torch.long)
-            # loss = F.multi_margin_loss(avg_scores, target_index, size_average=True, margin=0)
-            # print(loss.item() * 2, output.item()) 
-
-        elif self.loss_type == 'multi_margin':
-            # input is logits
-            input = input * mask
-            input = input.sum(1) / mask.sum(1)
-            input = input.view(-1, seq_per_img)
-            _, __ = costs.min(1, keepdim=True)
-            costs_star = _
-            input_star = input.gather(1, __)
-            output = F.relu(costs - costs_star - input_star + input)
-            output = output.mean()
-
-            # sanity test
-            # avg_scores = input + costs
-            # loss = F.multi_margin_loss(avg_scores, costs.min(1)[1], margin=0)
-            # print(output, loss)
-
-        elif self.loss_type == 'softmax_margin':
-            # input is logsoftmax
-            input = input * mask
-            input = input.sum(1) / mask.sum(1)
-            input = input.view(-1, seq_per_img)
-
-            input = input + costs
-            target = costs.min(1)[1]
-            output = F.cross_entropy(input, target)
-
-        elif self.loss_type == 'real_softmax_margin':
-            # input is logits
-            # This is what originally defined in Kevin's paper
-            # The result should be equivalent to softmax_margin
-            input = input * mask
-            input = input.sum(1) / mask.sum(1)
-            input = input.view(-1, seq_per_img)
-
-            input = input + costs
-            target = costs.min(1)[1]
-            output = F.cross_entropy(input, target)
-
-        elif self.loss_type == 'new_self_critical':
-            """
-            A different self critical
-            Self critical uses greedy decoding score as baseline;
-            This setting uses the average score of the rest samples as baseline
-            (suppose c1...cn n samples, reward1 = score1 - 1/(n-1)(score2+..+scoren) )
-            """
-            baseline = (scores.sum(1, keepdim=True) - scores) / (scores.shape[1] - 1)
-            scores = scores - baseline
-            # self cider used as reward to promote diversity (not working that much in this way)
-            if getattr(self.opt, 'self_cider_reward_weight', 0) > 0:
-                _scores = get_self_cider_scores(data_gts, seq, self.opt)
-                _scores = torch.from_numpy(_scores).type_as(scores).view(-1, 1)
-                _scores = _scores.expand_as(scores - 1)
-                scores += self.opt.self_cider_reward_weight * _scores
-            output = - input * mask * scores.view(-1, 1)
-            output = torch.sum(output) / torch.sum(mask)
-
-        out['loss'] = output
-        return out
-
-class LanguageModelCriterion(nn.Module):
-    def __init__(self):
-        super(LanguageModelCriterion, self).__init__()
-
-    def forward(self, input, target, mask):
-        if target.ndim == 3:
-            target = target.reshape(-1, target.shape[2])
-            mask = mask.reshape(-1, mask.shape[2])
-        # truncate to the same size
-        target = target[:, :input.size(1)]
-        mask = mask[:, :input.size(1)].to(input)
-
-        output = -input.gather(2, target.unsqueeze(2)).squeeze(2) * mask
-        # Average over each token
-        output = torch.sum(output) / torch.sum(mask)
-
-        return output
-
-class LabelSmoothing(nn.Module):
-    "Implement label smoothing."
-    def __init__(self, size=0, padding_idx=0, smoothing=0.0):
-        super(LabelSmoothing, self).__init__()
-        self.criterion = nn.KLDivLoss(size_average=False, reduce=False)
-        # self.padding_idx = padding_idx
-        self.confidence = 1.0 - smoothing
-        self.smoothing = smoothing
-        # self.size = size
-        self.true_dist = None
-        
-    def forward(self, input, target, mask):
-        if target.ndim == 3:
-            target = target.reshape(-1, target.shape[2])
-            mask = mask.reshape(-1, mask.shape[2])
-        # truncate to the same size
-        target = target[:, :input.size(1)]
-        mask =  mask[:, :input.size(1)]
-
-        input = input.reshape(-1, input.size(-1))
-        target = target.reshape(-1)
-        mask = mask.reshape(-1).to(input)
-
-        # assert x.size(1) == self.size
-        self.size = input.size(1)
-        # true_dist = x.data.clone()
-        true_dist = input.data.clone()
-        # true_dist.fill_(self.smoothing / (self.size - 2))
-        true_dist.fill_(self.smoothing / (self.size - 1))
-        true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
-        # true_dist[:, self.padding_idx] = 0
-        # mask = torch.nonzero(target.data == self.padding_idx)
-        # self.true_dist = true_dist
-        return (self.criterion(input, true_dist).sum(1) * mask).sum() / mask.sum()
\ No newline at end of file
diff --git a/spaces/NATSpeech/DiffSpeech/tasks/tts/tts_utils.py b/spaces/NATSpeech/DiffSpeech/tasks/tts/tts_utils.py
deleted file mode 100644
index c4b82df98677e7ba132f77b4f147a0b9aa03c1f1..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/tasks/tts/tts_utils.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import importlib
-
-from data_gen.tts.base_binarizer import BaseBinarizer
-from data_gen.tts.base_preprocess import BasePreprocessor
-from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls
-from utils.commons.hparams import hparams
-
-
-def parse_dataset_configs():
-    max_tokens = hparams['max_tokens']
-    max_sentences = hparams['max_sentences']
-    max_valid_tokens = hparams['max_valid_tokens']
-    if max_valid_tokens == -1:
-        hparams['max_valid_tokens'] = max_valid_tokens = max_tokens
-    max_valid_sentences = hparams['max_valid_sentences']
-    if max_valid_sentences == -1:
-        hparams['max_valid_sentences'] = max_valid_sentences = max_sentences
-    return max_tokens, max_sentences, max_valid_tokens, max_valid_sentences
-
-
-def parse_mel_losses():
-    mel_losses = hparams['mel_losses'].split("|")
-    loss_and_lambda = {}
-    for i, l in enumerate(mel_losses):
-        if l == '':
-            continue
-        if ':' in l:
-            l, lbd = l.split(":")
-            lbd = float(lbd)
-        else:
-            lbd = 1.0
-        loss_and_lambda[l] = lbd
-    print("| Mel losses:", loss_and_lambda)
-    return loss_and_lambda
-
-
-def load_data_preprocessor():
-    preprocess_cls = hparams["preprocess_cls"]
-    pkg = ".".join(preprocess_cls.split(".")[:-1])
-    cls_name = preprocess_cls.split(".")[-1]
-    preprocessor: BasePreprocessor = getattr(importlib.import_module(pkg), cls_name)()
-    preprocess_args = {}
-    preprocess_args.update(hparams['preprocess_args'])
-    return preprocessor, preprocess_args
-
-
-def load_data_binarizer():
-    binarizer_cls = hparams['binarizer_cls']
-    pkg = ".".join(binarizer_cls.split(".")[:-1])
-    cls_name = binarizer_cls.split(".")[-1]
-    binarizer: BaseBinarizer = getattr(importlib.import_module(pkg), cls_name)()
-    binarization_args = {}
-    binarization_args.update(hparams['binarization_args'])
-    return binarizer, binarization_args
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/optimizers.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/optimizers.py
deleted file mode 100644
index fd51bb59f579b3de027cba26ef3bee0e67d0c74f..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/optimizers.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright 2020 The TensorFlow Authors. 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.
-# ==============================================================================
-"""Optimizers."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import functools
-
-import numpy as np
-import tensorflow as tf
-
-
-class OptimizerFactory(object):
-  """Class to generate optimizer function."""
-
-  def __init__(self, params):
-    """Creates optimized based on the specified flags."""
-    if params.type == 'momentum':
-      self._optimizer = functools.partial(
-          tf.keras.optimizers.SGD,
-          momentum=params.momentum,
-          nesterov=params.nesterov)
-    elif params.type == 'adam':
-      self._optimizer = tf.keras.optimizers.Adam
-    elif params.type == 'adadelta':
-      self._optimizer = tf.keras.optimizers.Adadelta
-    elif params.type == 'adagrad':
-      self._optimizer = tf.keras.optimizers.Adagrad
-    elif params.type == 'rmsprop':
-      self._optimizer = functools.partial(
-          tf.keras.optimizers.RMSprop, momentum=params.momentum)
-    else:
-      raise ValueError('Unsupported optimizer type `{}`.'.format(params.type))
-
-  def __call__(self, learning_rate):
-    return self._optimizer(learning_rate=learning_rate)
diff --git a/spaces/NN520/AI/src/lib/isomorphic/node.ts b/spaces/NN520/AI/src/lib/isomorphic/node.ts
deleted file mode 100644
index da213ad6a86181979f098309c374da02835db5a0..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/src/lib/isomorphic/node.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import Debug from 'debug'
-
-const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici')
-const { HttpsProxyAgent } = require('https-proxy-agent')
-const ws = require('ws')
-
-const debug = Debug('bingo')
-
-const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY;
-let WebSocket = ws.WebSocket
-
-if (httpProxy) {
-  setGlobalDispatcher(new ProxyAgent(httpProxy))
-  const agent = new HttpsProxyAgent(httpProxy)
-  // @ts-ignore
-  WebSocket = class extends ws.WebSocket {
-    constructor(address: string | URL, options: typeof ws.WebSocket) {
-      super(address, {
-        ...options,
-        agent,
-      })
-    }
-  }
-}
-
-export default { fetch, WebSocket, debug }
diff --git a/spaces/NimaBoscarino/climategan/climategan/fire.py b/spaces/NimaBoscarino/climategan/climategan/fire.py
deleted file mode 100644
index 0181e47bc8848627244abeca689f8dc5d1132d74..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/climategan/fire.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import torch.nn.functional as F
-import random
-import kornia
-from torchvision.transforms.functional import adjust_brightness, adjust_contrast
-
-from climategan.tutils import normalize, retrieve_sky_mask
-
-try:
-    from kornia.filters import filter2d
-except ImportError:
-    from kornia.filters import filter2D as filter2d
-
-
-def increase_sky_mask(mask, p_w=0, p_h=0):
-    """
-    Increases sky mask in width and height by a given pourcentage
-    (Purpose: when applying Gaussian blur, there are no artifacts of blue sky behind)
-    Args:
-        sky_mask (torch.Tensor): Sky mask of shape (H,W)
-        p_w (float): Percentage of mask width by which to increase
-            the width of the sky region
-        p_h (float): Percentage of mask height by which to increase
-            the height of the sky region
-    Returns:
-        torch.Tensor: Sky mask increased given p_w and p_h
-    """
-
-    if p_h <= 0 and p_w <= 0:
-        return mask
-
-    n_lines = int(p_h * mask.shape[-2])
-    n_cols = int(p_w * mask.shape[-1])
-
-    temp_mask = mask.clone().detach()
-    for i in range(1, n_cols):
-        temp_mask[:, :, :, i::] += mask[:, :, :, 0:-i]
-        temp_mask[:, :, :, 0:-i] += mask[:, :, :, i::]
-
-    new_mask = temp_mask.clone().detach()
-    for i in range(1, n_lines):
-        new_mask[:, :, i::, :] += temp_mask[:, :, 0:-i, :]
-        new_mask[:, :, 0:-i, :] += temp_mask[:, :, i::, :]
-
-    new_mask[new_mask >= 1] = 1
-
-    return new_mask
-
-
-def paste_filter(x, filter_, mask):
-    """
-    Pastes a filter over an image given a mask
-    Where the mask is 1, the filter is copied as is.
-    Where the mask is 0, the current value is preserved.
-    Intermediate values will mix the two images together.
-    Args:
-        x (torch.Tensor): Input tensor, range must be [0, 255]
-        filer_ (torch.Tensor): Filter, range must be [0, 255]
-        mask (torch.Tensor): Mask, range must be [0, 1]
-    Returns:
-        torch.Tensor: New tensor with filter pasted on it
-    """
-    assert len(x.shape) == len(filter_.shape) == len(mask.shape)
-    x = filter_ * mask + x * (1 - mask)
-    return x
-
-
-def add_fire(x, seg_preds, fire_opts):
-    """
-    Transforms input tensor given wildfires event
-    Args:
-        x (torch.Tensor): Input tensor
-        seg_preds (torch.Tensor): Semantic segmentation predictions for input tensor
-        filter_color (tuple): (r,g,b) tuple for the color of the sky
-        blur_radius (float): radius of the Gaussian blur that smooths
-            the transition between sky and foreground
-    Returns:
-        torch.Tensor: Wildfire version of input tensor
-    """
-    wildfire_tens = normalize(x, 0, 255)
-
-    # Warm the image
-    wildfire_tens[:, 2, :, :] -= 20
-    wildfire_tens[:, 1, :, :] -= 10
-    wildfire_tens[:, 0, :, :] += 40
-    wildfire_tens.clamp_(0, 255)
-    wildfire_tens = wildfire_tens.to(torch.uint8)
-
-    # Darken the picture and increase contrast
-    wildfire_tens = adjust_contrast(wildfire_tens, contrast_factor=1.5)
-    wildfire_tens = adjust_brightness(wildfire_tens, brightness_factor=0.73)
-
-    sky_mask = retrieve_sky_mask(seg_preds).unsqueeze(1)
-
-    if fire_opts.get("crop_bottom_sky_mask"):
-        i = 2 * sky_mask.shape[-2] // 3
-        sky_mask[..., i:, :] = 0
-
-    sky_mask = F.interpolate(
-        sky_mask.to(torch.float),
-        (wildfire_tens.shape[-2], wildfire_tens.shape[-1]),
-    )
-    sky_mask = increase_sky_mask(sky_mask, 0.18, 0.18)
-
-    kernel_size = (fire_opts.get("kernel_size", 301), fire_opts.get("kernel_size", 301))
-    sigma = (fire_opts.get("kernel_sigma", 150.5), fire_opts.get("kernel_sigma", 150.5))
-    border_type = "reflect"
-    kernel = torch.unsqueeze(
-        kornia.filters.kernels.get_gaussian_kernel2d(kernel_size, sigma), dim=0
-    ).to(x.device)
-    sky_mask = filter2d(sky_mask, kernel, border_type)
-
-    filter_ = torch.ones(wildfire_tens.shape, device=x.device)
-    filter_[:, 0, :, :] = 255
-    filter_[:, 1, :, :] = random.randint(100, 150)
-    filter_[:, 2, :, :] = 0
-
-    wildfire_tens = paste_tensor(wildfire_tens, filter_, sky_mask, 200)
-
-    wildfire_tens = adjust_brightness(wildfire_tens.to(torch.uint8), 0.8)
-    wildfire_tens = wildfire_tens.to(torch.float)
-
-    # dummy pixels to fool scaling and preserve range
-    wildfire_tens[:, :, 0, 0] = 255.0
-    wildfire_tens[:, :, -1, -1] = 0.0
-
-    return wildfire_tens
-
-
-def paste_tensor(source, filter_, mask, transparency):
-    mask = transparency / 255.0 * mask
-    new = mask * filter_ + (1.0 - mask) * source
-    return new
diff --git a/spaces/Nybb/README/README.md b/spaces/Nybb/README/README.md
deleted file mode 100644
index e76770e641d8e88d0fe14808a5efab874235a14b..0000000000000000000000000000000000000000
--- a/spaces/Nybb/README/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: README
-emoji: 📚
-colorFrom: yellow
-colorTo: green
-sdk: static
-pinned: false
----
-
-Edit this `README.md` markdown file to author your organization card.
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/data_utils.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/data_utils.py
deleted file mode 100644
index b3de57681e0fb6b026003eff19f7745caf6799d3..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/data_utils.py
+++ /dev/null
@@ -1,595 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-try:
-    from collections.abc import Iterable
-except ImportError:
-    from collections import Iterable
-import contextlib
-import itertools
-import logging
-import re
-import warnings
-from typing import Optional, Tuple
-
-import numpy as np
-import torch
-
-from fairseq.file_io import PathManager
-from fairseq import utils
-import os
-
-logger = logging.getLogger(__name__)
-
-
-def infer_language_pair(path):
-    """Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
-    src, dst = None, None
-    for filename in PathManager.ls(path):
-        parts = filename.split(".")
-        if len(parts) >= 3 and len(parts[1].split("-")) == 2:
-            return parts[1].split("-")
-    return src, dst
-
-
-def collate_tokens(
-    values,
-    pad_idx,
-    eos_idx=None,
-    left_pad=False,
-    move_eos_to_beginning=False,
-    pad_to_length=None,
-    pad_to_multiple=1,
-    pad_to_bsz=None,
-):
-    """Convert a list of 1d tensors into a padded 2d tensor."""
-    size = max(v.size(0) for v in values)
-    size = size if pad_to_length is None else max(size, pad_to_length)
-    if pad_to_multiple != 1 and size % pad_to_multiple != 0:
-        size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
-
-    batch_size = len(values) if pad_to_bsz is None else max(len(values), pad_to_bsz)
-    res = values[0].new(batch_size, size).fill_(pad_idx)
-
-    def copy_tensor(src, dst):
-        assert dst.numel() == src.numel()
-        if move_eos_to_beginning:
-            if eos_idx is None:
-                # if no eos_idx is specified, then use the last token in src
-                dst[0] = src[-1]
-            else:
-                dst[0] = eos_idx
-            dst[1:] = src[:-1]
-        else:
-            dst.copy_(src)
-
-    for i, v in enumerate(values):
-        copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
-    return res
-
-def load_indexed_dataset(
-    path, dictionary=None, dataset_impl=None, combine=False, default="cached"
-):
-    """A helper function for loading indexed datasets.
-
-    Args:
-        path (str): path to indexed dataset (e.g., 'data-bin/train')
-        dictionary (~fairseq.data.Dictionary): data dictionary
-        dataset_impl (str, optional): which dataset implementation to use. If
-            not provided, it will be inferred automatically. For legacy indexed
-            data we use the 'cached' implementation by default.
-        combine (bool, optional): automatically load and combine multiple
-            datasets. For example, if *path* is 'data-bin/train', then we will
-            combine 'data-bin/train', 'data-bin/train1', ... and return a
-            single ConcatDataset instance.
-    """
-    import fairseq.data.indexed_dataset as indexed_dataset
-    from fairseq.data.concat_dataset import ConcatDataset
-
-    datasets = []
-    for k in itertools.count():
-        path_k = path + (str(k) if k > 0 else "")
-        try:
-            path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
-        except Exception as e:
-            if "StorageException: [404] Path not found" in str(e):
-                logger.warning(f"path_k: {e} not found")
-            else:
-                raise e
-
-        dataset_impl_k = dataset_impl
-        if dataset_impl_k is None:
-            dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
-        dataset = indexed_dataset.make_dataset(
-            path_k,
-            impl=dataset_impl_k or default,
-            fix_lua_indexing=True,
-            dictionary=dictionary,
-        )
-        if dataset is None:
-            break
-        logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
-        datasets.append(dataset)
-        if not combine:
-            break
-    if len(datasets) == 0:
-        return None
-    elif len(datasets) == 1:
-        return datasets[0]
-    else:
-        return ConcatDataset(datasets)
-
-
-@contextlib.contextmanager
-def numpy_seed(seed, *addl_seeds):
-    """Context manager which seeds the NumPy PRNG with the specified seed and
-    restores the state afterward"""
-    if seed is None:
-        yield
-        return
-    if len(addl_seeds) > 0:
-        seed = int(hash((seed, *addl_seeds)) % 1e6)
-    state = np.random.get_state()
-    np.random.seed(seed)
-    try:
-        yield
-    finally:
-        np.random.set_state(state)
-
-
-def collect_filtered(function, iterable, filtered):
-    """
-    Similar to :func:`filter` but collects filtered elements in ``filtered``.
-
-    Args:
-        function (callable): function that returns ``False`` for elements that
-            should be filtered
-        iterable (iterable): iterable to filter
-        filtered (list): list to store filtered elements
-    """
-    for el in iterable:
-        if function(el):
-            yield el
-        else:
-            filtered.append(el)
-
-
-def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
-    def compare_leq(a, b):
-        return a <= b if not isinstance(a, tuple) else max(a) <= b
-
-    def check_size(idx):
-        if isinstance(max_positions, float) or isinstance(max_positions, int):
-            return size_fn(idx) <= max_positions
-        elif isinstance(max_positions, dict):
-            idx_size = size_fn(idx)
-            assert isinstance(idx_size, dict)
-            intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
-            return all(
-                all(
-                    a is None or b is None or a <= b
-                    for a, b in zip(idx_size[key], max_positions[key])
-                )
-                for key in intersect_keys
-            )
-        else:
-            # For MultiCorpusSampledDataset, will generalize it later
-            if not isinstance(size_fn(idx), Iterable):
-                return all(size_fn(idx) <= b for b in max_positions)
-            return all(
-                a is None or b is None or a <= b
-                for a, b in zip(size_fn(idx), max_positions)
-            )
-
-    ignored = []
-    itr = collect_filtered(check_size, indices, ignored)
-    indices = np.fromiter(itr, dtype=np.int64, count=-1)
-    return indices, ignored
-
-
-def filter_by_size(indices, dataset, max_positions, raise_exception=False):
-    """
-    [deprecated] Filter indices based on their size.
-    Use `FairseqDataset::filter_indices_by_size` instead.
-
-    Args:
-        indices (List[int]): ordered list of dataset indices
-        dataset (FairseqDataset): fairseq dataset instance
-        max_positions (tuple): filter elements larger than this size.
-            Comparisons are done component-wise.
-        raise_exception (bool, optional): if ``True``, raise an exception if
-            any elements are filtered (default: False).
-    """
-    warnings.warn(
-        "data_utils.filter_by_size is deprecated. "
-        "Use `FairseqDataset::filter_indices_by_size` instead.",
-        stacklevel=2,
-    )
-    if isinstance(max_positions, float) or isinstance(max_positions, int):
-        if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
-            ignored = indices[dataset.sizes[indices] > max_positions].tolist()
-            indices = indices[dataset.sizes[indices] <= max_positions]
-        elif (
-            hasattr(dataset, "sizes")
-            and isinstance(dataset.sizes, list)
-            and len(dataset.sizes) == 1
-        ):
-            ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
-            indices = indices[dataset.sizes[0][indices] <= max_positions]
-        else:
-            indices, ignored = _filter_by_size_dynamic(
-                indices, dataset.size, max_positions
-            )
-    else:
-        indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
-
-    if len(ignored) > 0 and raise_exception:
-        raise Exception(
-            (
-                "Size of sample #{} is invalid (={}) since max_positions={}, "
-                "skip this example with --skip-invalid-size-inputs-valid-test"
-            ).format(ignored[0], dataset.size(ignored[0]), max_positions)
-        )
-    if len(ignored) > 0:
-        logger.warning(
-            (
-                "{} samples have invalid sizes and will be skipped, "
-                "max_positions={}, first few sample ids={}"
-            ).format(len(ignored), max_positions, ignored[:10])
-        )
-    return indices
-
-
-def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
-    """Filter a list of sample indices. Remove those that are longer
-        than specified in max_sizes.
-
-    Args:
-        indices (np.array): original array of sample indices
-        max_sizes (int or list[int] or tuple[int]): max sample size,
-            can be defined separately for src and tgt (then list or tuple)
-
-    Returns:
-        np.array: filtered sample array
-        list: list of removed indices
-    """
-    if max_sizes is None:
-        return indices, []
-    if type(max_sizes) in (int, float):
-        max_src_size, max_tgt_size = max_sizes, max_sizes
-    else:
-        max_src_size, max_tgt_size = max_sizes
-    if tgt_sizes is None:
-        ignored = indices[src_sizes[indices] > max_src_size]
-    else:
-        ignored = indices[
-            (src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
-        ]
-    if len(ignored) > 0:
-        if tgt_sizes is None:
-            indices = indices[src_sizes[indices] <= max_src_size]
-        else:
-            indices = indices[
-                (src_sizes[indices] <= max_src_size)
-                & (tgt_sizes[indices] <= max_tgt_size)
-            ]
-    return indices, ignored.tolist()
-
-
-def batch_by_size(
-    indices,
-    num_tokens_fn,
-    num_tokens_vec=None,
-    max_tokens=None,
-    max_sentences=None,
-    required_batch_size_multiple=1,
-    fixed_shapes=None,
-):
-    """
-    Yield mini-batches of indices bucketed by size. Batches may contain
-    sequences of different lengths.
-
-    Args:
-        indices (List[int]): ordered list of dataset indices
-        num_tokens_fn (callable): function that returns the number of tokens at
-            a given index
-        num_tokens_vec (List[int], optional): precomputed vector of the number
-            of tokens for each index in indices (to enable faster batch generation)
-        max_tokens (int, optional): max number of tokens in each batch
-            (default: None).
-        max_sentences (int, optional): max number of sentences in each
-            batch (default: None).
-        required_batch_size_multiple (int, optional): require batch size to
-            be less than N or a multiple of N (default: 1).
-        fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
-            only be created with the given shapes. *max_sentences* and
-            *required_batch_size_multiple* will be ignored (default: None).
-    """
-    try:
-        from fairseq.data.data_utils_fast import (
-            batch_by_size_fn,
-            batch_by_size_vec,
-            batch_fixed_shapes_fast,
-        )
-    except ImportError:
-        raise ImportError(
-            "Please build Cython components with: "
-            "`python setup.py build_ext --inplace`"
-        )
-    except ValueError:
-        raise ValueError(
-            "Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`."
-        )
-
-    # added int() to avoid TypeError: an integer is required
-    max_tokens = (
-        int(max_tokens) if max_tokens is not None else -1
-    )
-    max_sentences = max_sentences if max_sentences is not None else -1
-    bsz_mult = required_batch_size_multiple
-
-    if not isinstance(indices, np.ndarray):
-        indices = np.fromiter(indices, dtype=np.int64, count=-1)
-
-    if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
-        num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)
-
-    if fixed_shapes is None:
-        if num_tokens_vec is None:
-            return batch_by_size_fn(
-                indices,
-                num_tokens_fn,
-                max_tokens,
-                max_sentences,
-                bsz_mult,
-            )
-        else:
-            return batch_by_size_vec(
-                indices,
-                num_tokens_vec,
-                max_tokens,
-                max_sentences,
-                bsz_mult,
-            )
-
-    else:
-        fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
-        sort_order = np.lexsort(
-            [
-                fixed_shapes[:, 1].argsort(),  # length
-                fixed_shapes[:, 0].argsort(),  # bsz
-            ]
-        )
-        fixed_shapes_sorted = fixed_shapes[sort_order]
-        return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)
-
-
-def post_process(sentence: str, symbol: str):
-    if symbol == "sentencepiece":
-        sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
-    elif symbol == "wordpiece":
-        sentence = sentence.replace(" ", "").replace("_", " ").strip()
-    elif symbol == "letter":
-        sentence = sentence.replace(" ", "").replace("|", " ").strip()
-    elif symbol == "silence":
-        import re
-        sentence = sentence.replace("<SIL>", "")
-        sentence = re.sub(' +', ' ', sentence).strip()
-    elif symbol == "_EOW":
-        sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
-    elif symbol in {"subword_nmt", "@@ ", "@@"}:
-        if symbol == "subword_nmt":
-            symbol = "@@ "
-        sentence = (sentence + " ").replace(symbol, "").rstrip()
-    elif symbol == "none":
-        pass
-    elif symbol is not None:
-        raise NotImplementedError(f"Unknown post_process option: {symbol}")
-    return sentence
-
-
-def compute_mask_indices(
-    shape: Tuple[int, int],
-    padding_mask: Optional[torch.Tensor],
-    mask_prob: float,
-    mask_length: int,
-    mask_type: str = "static",
-    mask_other: float = 0.0,
-    min_masks: int = 0,
-    no_overlap: bool = False,
-    min_space: int = 0,
-) -> np.ndarray:
-    """
-    Computes random mask spans for a given shape
-
-    Args:
-        shape: the the shape for which to compute masks.
-            should be of size 2 where first element is batch size and 2nd is timesteps
-        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
-        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
-            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
-            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
-        mask_type: how to compute mask lengths
-            static = fixed size
-            uniform = sample from uniform distribution [mask_other, mask_length*2]
-            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
-            poisson = sample from possion distribution with lambda = mask length
-        min_masks: minimum number of masked spans
-        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
-        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
-    """
-
-    bsz, all_sz = shape
-    mask = np.full((bsz, all_sz), False)
-
-    all_num_mask = int(
-        # add a random number for probabilistic rounding
-        mask_prob * all_sz / float(mask_length)
-        + np.random.rand()
-    )
-
-    all_num_mask = max(min_masks, all_num_mask)
-
-    mask_idcs = []
-    for i in range(bsz):
-        if padding_mask is not None:
-            sz = all_sz - padding_mask[i].long().sum().item()
-            num_mask = int(
-                # add a random number for probabilistic rounding
-                mask_prob * sz / float(mask_length)
-                + np.random.rand()
-            )
-            num_mask = max(min_masks, num_mask)
-        else:
-            sz = all_sz
-            num_mask = all_num_mask
-
-        if mask_type == "static":
-            lengths = np.full(num_mask, mask_length)
-        elif mask_type == "uniform":
-            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
-        elif mask_type == "normal":
-            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
-            lengths = [max(1, int(round(x))) for x in lengths]
-        elif mask_type == "poisson":
-            lengths = np.random.poisson(mask_length, size=num_mask)
-            lengths = [int(round(x)) for x in lengths]
-        else:
-            raise Exception("unknown mask selection " + mask_type)
-
-        if sum(lengths) == 0:
-            lengths[0] = min(mask_length, sz - 1)
-
-        if no_overlap:
-            mask_idc = []
-
-            def arrange(s, e, length, keep_length):
-                span_start = np.random.randint(s, e - length)
-                mask_idc.extend(span_start + i for i in range(length))
-
-                new_parts = []
-                if span_start - s - min_space >= keep_length:
-                    new_parts.append((s, span_start - min_space + 1))
-                if e - span_start - keep_length - min_space > keep_length:
-                    new_parts.append((span_start + length + min_space, e))
-                return new_parts
-
-            parts = [(0, sz)]
-            min_length = min(lengths)
-            for length in sorted(lengths, reverse=True):
-                lens = np.fromiter(
-                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
-                    np.int,
-                )
-                l_sum = np.sum(lens)
-                if l_sum == 0:
-                    break
-                probs = lens / np.sum(lens)
-                c = np.random.choice(len(parts), p=probs)
-                s, e = parts.pop(c)
-                parts.extend(arrange(s, e, length, min_length))
-            mask_idc = np.asarray(mask_idc)
-        else:
-            min_len = min(lengths)
-            if sz - min_len <= num_mask:
-                min_len = sz - num_mask - 1
-
-            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
-
-            mask_idc = np.asarray(
-                [
-                    mask_idc[j] + offset
-                    for j in range(len(mask_idc))
-                    for offset in range(lengths[j])
-                ]
-            )
-
-        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
-
-    min_len = min([len(m) for m in mask_idcs])
-    for i, mask_idc in enumerate(mask_idcs):
-        if len(mask_idc) > min_len:
-            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
-        mask[i, mask_idc] = True
-
-    return mask
-
-
-def get_mem_usage():
-    try:
-        import psutil
-
-        mb = 1024 * 1024
-        return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
-    except ImportError:
-        return "N/A"
-
-
-# lens: torch.LongTensor
-# returns: torch.BoolTensor
-def lengths_to_padding_mask(lens):
-    bsz, max_lens = lens.size(0), torch.max(lens).item()
-    mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
-    mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
-    return mask
-
-
-# lens: torch.LongTensor
-# returns: torch.BoolTensor
-def lengths_to_mask(lens):
-    return ~lengths_to_padding_mask(lens)
-
-
-def get_buckets(sizes, num_buckets):
-    buckets = np.unique(
-        np.percentile(
-            sizes,
-            np.linspace(0, 100, num_buckets + 1),
-            interpolation='lower',
-        )[1:]
-    )
-    return buckets
-
-
-def get_bucketed_sizes(orig_sizes, buckets):
-    sizes = np.copy(orig_sizes)
-    assert np.min(sizes) >= 0
-    start_val = -1
-    for end_val in buckets:
-        mask = (sizes > start_val) & (sizes <= end_val)
-        sizes[mask] = end_val
-        start_val = end_val
-    return sizes
-
-
-
-def _find_extra_valid_paths(dataset_path: str) -> set:
-    paths = utils.split_paths(dataset_path)
-    all_valid_paths = set()
-    for sub_dir in paths:
-        contents = PathManager.ls(sub_dir)
-        valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None]
-        all_valid_paths |= {os.path.basename(p) for p in valid_paths}
-    # Remove .bin, .idx etc
-    roots = {os.path.splitext(p)[0] for p in all_valid_paths}
-    return roots
-
-
-def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
-    """Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
-    if (
-        train_cfg.dataset.ignore_unused_valid_subsets
-        or train_cfg.dataset.combine_valid_subsets
-        or train_cfg.dataset.disable_validation
-        or not hasattr(train_cfg.task, "data")
-    ):
-        return
-    other_paths = _find_extra_valid_paths(train_cfg.task.data)
-    specified_subsets = train_cfg.dataset.valid_subset.split(",")
-    ignored_paths = [p for p in other_paths if p not in specified_subsets]
-    if ignored_paths:
-        advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them."
-        msg = f"Valid paths {ignored_paths} will be ignored. {advice}"
-        raise ValueError(msg)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/hubert/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/hubert/__init__.py
deleted file mode 100644
index a1b0eabbdbcaf12b15bb96b329ab1e276256f79a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/hubert/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .hubert import *  # noqa
-from .hubert_asr import *  # noqa
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/fully_sharded_data_parallel/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/fully_sharded_data_parallel/README.md
deleted file mode 100644
index b9e44fef48bee5faeee27b3d1d1b1eb96b6a477f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/fully_sharded_data_parallel/README.md
+++ /dev/null
@@ -1,177 +0,0 @@
-# Fully Sharded Data Parallel (FSDP)
-
-## Overview
-Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and
-[Google](https://arxiv.org/abs/2004.13336) has shown that data parallel
-training can be made significantly more efficient by sharding the model
-parameters and optimizer state across data parallel workers. These ideas are
-encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper provided
-by [fairscale](https://github.com/facebookresearch/fairscale/).
-
-Compared to PyTorch DDP:
-* FSDP produces identical results as PyTorch DDP (it's still synchronous data parallel training)
-* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs
-* FSDP is faster than PyTorch DDP because the optimizer step is sharded, and the communication can be overlapped with the forward pass
-* FSDP enables training 13B parameter models on 8 GPUs and 175B parameter models on 128 GPUs
-
-FSDP is fully supported in fairseq via the following new arguments:
-* `--ddp-backend=fully_sharded`: enables full sharding via FSDP
-* `--cpu-offload`: offloads the optimizer state and FP32 model copy to CPU (combine with `--optimizer=cpu_adam`)
-* `--no-reshard-after-forward`: increases training speed for large models (1B+ params) and is similar to ZeRO stage 2
-* other popular options (`--fp16`, `--update-freq`, `--checkpoint-activations`, `--offload-activations`, etc.) continue to work as normal
-
-<details><summary>Limitations</summary><p>
-
-FSDP currently has several limitations compared to fairseq's default DDP backend (PyTorch DDP):
-* while FSDP is full compatible with pointwise Optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.), it is not currently compatible with non-pointwise Optimizers (e.g., Adagrad, Adafactor, LAMB, etc.)
-* FSDP depends on flattening the parameters, so models that currently require `--fp16-no-flatten-grads` may not be supported
-
-See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed
-explanation of these and other limitations.
-
-</p></details>
-
-<details><summary>How it works</summary><p>
-
-<img width="800" alt="Fully Sharded Data Parallel" src="https://user-images.githubusercontent.com/231798/110406775-c2de0000-8050-11eb-9718-fbfc4510a76a.png">
-
-See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed
-explanation of how FSDP works.
-
-</p></details>
-
-## Example usage
-
-The following examples illustrate how to train a very large language model with
-13 billion parameters on 1 GPU by offloading parameters and optimizer states to
-CPU, or on 8 GPUs by fully sharding the params and optimizer states across GPUs.
-
-These examples use the WikiText-103 dataset for demonstration purposes, but
-in practice a much larger dataset will be needed to achieve good results.
-Follow the [instructions here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.pretraining.md#1-preprocess-the-data)
-to preprocess the WikiText-103 dataset using the GPT-2/RoBERTa vocabulary.
-
-### 13B params on 1 V100 GPU (with CPU offloading)
-
-The following command trains a 13B parameter GPT-3 model on a single V100 GPU
-using the `--cpu-offload` feature to offload parameters and optimizer states to
-CPU. In this setting, the optimizer step (Adam) happens on CPU. We also use the
-`--checkpoint-activations` feature (sometimes called [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html)),
-which further saves memory in exchange for a small increase in computation.
-
-**Requirements:**
-- Install the latest master version of fairscale: `pip install git+https://github.com/facebookresearch/fairscale.git@master`
-- You'll need 32GB of GPU memory and ~256GB of system memory to train the 13B param model.
-- If you have less system memory, the 6.7B param model can be trained with ~128GB of system memory, just set `--arch transformer_lm_gpt3_6_7`
-- We use the CPU Adam optimizer from [DeepSpeed](https://github.com/microsoft/DeepSpeed), so you'll need to `pip install deepspeed` before running the command.
-
-**Notes:**
-- The command will take ~5 minutes to start training, during which time it will appear to be hung, since randomly initializing 13B weights can be slow.
-- The `--cpu-offload` feature requires training in mixed precision (`--fp16`).
-- Tune the `OMP_NUM_THREADS` env variable for best performance with CPU offloading.
-- The example command below stops training after 10 steps (`--max-update 10`) and does not save checkpoints (`--no-save`).
-
-```bash
-OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0 \
-    fairseq-train data-bin/wikitext-103-roberta-bpe-bin \
-    --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \
-    --cpu-offload --checkpoint-activations \
-    --task language_modeling --tokens-per-sample 2048 --batch-size 8 \
-    --arch transformer_lm_gpt3_13 \
-    --optimizer cpu_adam --adam-betas "(0.9,0.98)" \
-    --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \
-    --max-update 10 --no-save --log-format json --log-interval 1
-```
-
-<details><summary>Example output</summary><p>
-
-```
-(...)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920)
-(...)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs)
-2021-03-08 12:29:51 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8
-(...)
-Adam Optimizer #0 is created with AVX2 arithmetic capability.
-Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1
-(...)
-2021-03-08 12:31:36 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.475", "ppl": "91120.8", "wps": "0", "ups": "0", "wpb": "16384", "bsz": "8", "num_updates": "1", "lr": "2e-05", "gnorm": "20.751", "loss_scale": "4", "train_wall": "99", "gb_free": "9.3", "wall": "105"}
-2021-03-08 12:32:33 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.446", "ppl": "89281.6", "wps": "288.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "2", "lr": "4e-05", "gnorm": "19.777", "loss_scale": "4", "train_wall": "57", "gb_free": "9.3", "wall": "161"}
-2021-03-08 12:33:12 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0
-2021-03-08 12:33:51 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0
-2021-03-08 12:34:45 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "25.22", "ppl": "3.90691e+07", "wps": "123.4", "ups": "0.01", "wpb": "16384", "bsz": "8", "num_updates": "3", "lr": "6e-05", "gnorm": "131.281", "loss_scale": "1", "train_wall": "133", "gb_free": "9.3", "wall": "294"}
-2021-03-08 12:35:43 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.079", "ppl": "276809", "wps": "285.5", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "4", "lr": "8e-05", "gnorm": "13.776", "loss_scale": "1", "train_wall": "57", "gb_free": "9.3", "wall": "351"}
-2021-03-08 12:36:35 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "23.729", "ppl": "1.39088e+07", "wps": "316.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "5", "lr": "0.0001", "gnorm": "72.774", "loss_scale": "1", "train_wall": "52", "gb_free": "9.3", "wall": "403"}
-2021-03-08 12:37:28 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "20.429", "ppl": "1.41203e+06", "wps": "307.6", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "6", "lr": "8e-05", "gnorm": "60.846", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "456"}
-2021-03-08 12:38:27 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.965", "ppl": "511684", "wps": "279.4", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "7", "lr": "6e-05", "gnorm": "22.687", "loss_scale": "1", "train_wall": "59", "gb_free": "9.3", "wall": "515"}
-2021-03-08 12:39:18 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.345", "ppl": "332887", "wps": "319.1", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "8", "lr": "4e-05", "gnorm": "8.451", "loss_scale": "1", "train_wall": "51", "gb_free": "9.3", "wall": "566"}
-2021-03-08 12:40:11 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "18.262", "ppl": "314336", "wps": "305.9", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "9", "lr": "2e-05", "gnorm": "6.457", "loss_scale": "1", "train_wall": "54", "gb_free": "9.3", "wall": "620"}
-2021-03-08 12:41:04 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "17.556", "ppl": "192686", "wps": "311.8", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "10", "lr": "0", "gnorm": "5.796", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "673"}
-2021-03-08 12:41:04 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10
-2021-03-08 12:41:04 | INFO | fairseq_cli.train | begin validation on "valid" subset
-2021-03-08 12:43:15 | INFO | valid | {"epoch": 1, "valid_loss": "17.953", "valid_ppl": "253807", "valid_wps": "1868.4", "valid_wpb": "15400.2", "valid_bsz": "7.6", "valid_num_updates": "10"}
-2021-03-08 12:43:15 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below)
-2021-03-08 12:43:15 | INFO | train | {"epoch": 1, "train_loss": "19.351", "train_ppl": "668509", "train_wps": "210.9", "train_ups": "0.01", "train_wpb": "16384", "train_bsz": "8", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "36.26", "train_loss_scale": "1", "train_train_wall": "667", "train_gb_free": "9.3", "train_wall": "804"}
-2021-03-08 12:43:15 | INFO | fairseq_cli.train | done training in 798.6 seconds
-```
-
-</p></details>
-
-### 13B params on 8 V100 GPUs (with full parameter + optimizer state sharding)
-
-FSDP can also shard the parameters and optimizer states across multiple GPUs,
-reducing memory requirements significantly. On 8 x 32GB GPUs, sharding enables
-training the same 13B parameter model *without offloading the parameters to
-CPU*. However, without CPU offloading we'd only be able to fit a batch size of
-1 per GPU, which would cause training speed to suffer.
-
-We obtain the best performance on 8 GPUs by combining full sharding and CPU
-offloading. The following command trains the same 13B parameter GPT-3 model as
-before on 8 x 32GB V100 GPUs; training speed increases superlinearly from ~310
-words per second to ~3200 words per second.
-
-```bash
-OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-    fairseq-train data-bin/wikitext-103-roberta-bpe-bin \
-    --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \
-    --cpu-offload --checkpoint-activations \
-    --task language_modeling --tokens-per-sample 2048 --batch-size 8 \
-    --arch transformer_lm_gpt3_13 \
-    --optimizer cpu_adam --adam-betas "(0.9,0.98)" \
-    --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \
-    --max-update 10 --no-save --log-format json --log-interval 1
-```
-
-<details><summary>Example output</summary><p>
-
-```
-(...)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920)
-(...)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs)
-2021-03-08 18:04:09 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8
-(...)
-Adam Optimizer #0 is created with AVX2 arithmetic capability.
-Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1
-(...)
-2021-03-08 18:05:06 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "16.408", "ppl": "86945.6", "wps": "0", "ups": "0", "wpb": "131072", "bsz": "64", "num_updates": "1", "lr": "2e-05", "gnorm": "18.27", "loss_scale": "4", "train_wall": "47", "gb_free": "9.3", "wall": "56"}
-2021-03-08 18:05:45 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "16.352", "ppl": "83644.3", "wps": "3283.4", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "2", "lr": "4e-05", "gnorm": "18.411", "loss_scale": "4", "train_wall": "40", "gb_free": "9.3", "wall": "96"}
-2021-03-08 18:06:21 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0
-2021-03-08 18:06:56 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0
-2021-03-08 18:07:37 | INFO | train_inner | {"epoch": 1, "update": 0.006, "loss": "23.682", "ppl": "1.34537e+07", "wps": "1176.6", "ups": "0.01", "wpb": "131072", "bsz": "64", "num_updates": "3", "lr": "6e-05", "gnorm": "119.682", "loss_scale": "1", "train_wall": "111", "gb_free": "9.3", "wall": "208"}
-2021-03-08 18:08:18 | INFO | train_inner | {"epoch": 1, "update": 0.007, "loss": "18.988", "ppl": "519921", "wps": "3189.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "4", "lr": "8e-05", "gnorm": "14.934", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "249"}
-2021-03-08 18:08:59 | INFO | train_inner | {"epoch": 1, "update": 0.008, "loss": "20.08", "ppl": "1.10798e+06", "wps": "3223.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "5", "lr": "0.0001", "gnorm": "59.92", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "289"}
-2021-03-08 18:09:39 | INFO | train_inner | {"epoch": 1, "update": 0.009, "loss": "18.323", "ppl": "327980", "wps": "3256.6", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "6", "lr": "8e-05", "gnorm": "37.425", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "330"}
-2021-03-08 18:10:20 | INFO | train_inner | {"epoch": 1, "update": 0.01, "loss": "17.264", "ppl": "157354", "wps": "3188.7", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "7", "lr": "6e-05", "gnorm": "10.824", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "371"}
-2021-03-08 18:11:01 | INFO | train_inner | {"epoch": 1, "update": 0.011, "loss": "16.794", "ppl": "113647", "wps": "3230", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "8", "lr": "4e-05", "gnorm": "5.616", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "411"}
-2021-03-08 18:11:39 | INFO | train_inner | {"epoch": 1, "update": 0.012, "loss": "16.706", "ppl": "106938", "wps": "3384", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "9", "lr": "2e-05", "gnorm": "5.318", "loss_scale": "1", "train_wall": "39", "gb_free": "9.3", "wall": "450"}
-2021-03-08 18:12:19 | INFO | train_inner | {"epoch": 1, "update": 0.013, "loss": "16.548", "ppl": "95796.2", "wps": "3274.4", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "10", "lr": "0", "gnorm": "5.22", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "490"}
-2021-03-08 18:12:19 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10
-2021-03-08 18:12:19 | INFO | fairseq_cli.train | begin validation on "valid" subset
-2021-03-08 18:12:45 | INFO | valid | {"epoch": 1, "valid_loss": "16.624", "valid_ppl": "101000", "valid_wps": "10855.9", "valid_wpb": "123202", "valid_bsz": "60.5", "valid_num_updates": "10"}
-2021-03-08 18:12:45 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below)
-2021-03-08 18:12:45 | INFO | train | {"epoch": 1, "train_loss": "18.114", "train_ppl": "283776", "train_wps": "2567.8", "train_ups": "0.02", "train_wpb": "131072", "train_bsz": "64", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "29.562", "train_loss_scale": "1", "train_train_wall": "480", "train_gb_free": "9.3", "train_wall": "516"}
-2021-03-08 18:12:45 | INFO | fairseq_cli.train | done training in 509.9 seconds
-```
-
-</p></details>
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/monolingual_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/monolingual_dataset.py
deleted file mode 100644
index 54fd583b64a3a475324ade6eaaeccf593d747fdc..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/monolingual_dataset.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-from . import FairseqDataset, data_utils
-
-
-def collate(samples, pad_idx, eos_idx, fixed_pad_length=None, pad_to_bsz=None):
-    if len(samples) == 0:
-        return {}
-
-    def merge(key, is_list=False):
-        if is_list:
-            res = []
-            for i in range(len(samples[0][key])):
-                res.append(
-                    data_utils.collate_tokens(
-                        [s[key][i] for s in samples],
-                        pad_idx,
-                        eos_idx,
-                        left_pad=False,
-                        pad_to_length=fixed_pad_length,
-                        pad_to_bsz=pad_to_bsz,
-                    )
-                )
-            return res
-        else:
-            return data_utils.collate_tokens(
-                [s[key] for s in samples],
-                pad_idx,
-                eos_idx,
-                left_pad=False,
-                pad_to_length=fixed_pad_length,
-                pad_to_bsz=pad_to_bsz,
-            )
-
-    src_tokens = merge("source")
-    if samples[0]["target"] is not None:
-        is_target_list = isinstance(samples[0]["target"], list)
-        target = merge("target", is_target_list)
-    else:
-        target = src_tokens
-
-    return {
-        "id": torch.LongTensor([s["id"] for s in samples]),
-        "nsentences": len(samples),
-        "ntokens": sum(len(s["source"]) for s in samples),
-        "net_input": {
-            "src_tokens": src_tokens,
-            "src_lengths": torch.LongTensor([s["source"].numel() for s in samples]),
-        },
-        "target": target,
-    }
-
-
-class MonolingualDataset(FairseqDataset):
-    """
-    A wrapper around torch.utils.data.Dataset for monolingual data.
-
-    Args:
-        dataset (torch.utils.data.Dataset): dataset to wrap
-        sizes (List[int]): sentence lengths
-        vocab (~fairseq.data.Dictionary): vocabulary
-        shuffle (bool, optional): shuffle the elements before batching
-            (default: True).
-    """
-
-    def __init__(
-        self,
-        dataset,
-        sizes,
-        src_vocab,
-        tgt_vocab=None,
-        add_eos_for_other_targets=False,
-        shuffle=False,
-        targets=None,
-        add_bos_token=False,
-        fixed_pad_length=None,
-        pad_to_bsz=None,
-        src_lang_idx=None,
-        tgt_lang_idx=None,
-    ):
-        self.dataset = dataset
-        self.sizes = np.array(sizes)
-        self.vocab = src_vocab
-        self.tgt_vocab = tgt_vocab or src_vocab
-        self.add_eos_for_other_targets = add_eos_for_other_targets
-        self.shuffle = shuffle
-        self.add_bos_token = add_bos_token
-        self.fixed_pad_length = fixed_pad_length
-        self.pad_to_bsz = pad_to_bsz
-        self.src_lang_idx = src_lang_idx
-        self.tgt_lang_idx = tgt_lang_idx
-
-        assert targets is None or all(
-            t in {"self", "future", "past"} for t in targets
-        ), "targets must be none or one of 'self', 'future', 'past'"
-        if targets is not None and len(targets) == 0:
-            targets = None
-        self.targets = targets
-
-    def __getitem__(self, index):
-        if self.targets is not None:
-            # *future_target* is the original sentence
-            # *source* is shifted right by 1 (maybe left-padded with eos)
-            # *past_target* is shifted right by 2 (left-padded as needed)
-            #
-            # Left-to-right language models should condition on *source* and
-            # predict *future_target*.
-            # Right-to-left language models should condition on *source* and
-            # predict *past_target*.
-            source, future_target, past_target = self.dataset[index]
-            source, target = self._make_source_target(
-                source, future_target, past_target
-            )
-        else:
-            source = self.dataset[index]
-            target = None
-        source, target = self._maybe_add_bos(source, target)
-        return {"id": index, "source": source, "target": target}
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def _make_source_target(self, source, future_target, past_target):
-        if self.targets is not None:
-            target = []
-
-            if (
-                self.add_eos_for_other_targets
-                and (("self" in self.targets) or ("past" in self.targets))
-                and source[-1] != self.vocab.eos()
-            ):
-                # append eos at the end of source
-                source = torch.cat([source, source.new([self.vocab.eos()])])
-
-                if "future" in self.targets:
-                    future_target = torch.cat(
-                        [future_target, future_target.new([self.vocab.pad()])]
-                    )
-                if "past" in self.targets:
-                    # first token is before the start of sentence which is only used in "none" break mode when
-                    # add_eos_for_other_targets is False
-                    past_target = torch.cat(
-                        [
-                            past_target.new([self.vocab.pad()]),
-                            past_target[1:],
-                            source[-2, None],
-                        ]
-                    )
-
-            for t in self.targets:
-                if t == "self":
-                    target.append(source)
-                elif t == "future":
-                    target.append(future_target)
-                elif t == "past":
-                    target.append(past_target)
-                else:
-                    raise Exception("invalid target " + t)
-
-            if len(target) == 1:
-                target = target[0]
-        else:
-            target = future_target
-
-        return source, self._filter_vocab(target)
-
-    def _maybe_add_bos(self, source, target):
-        if self.add_bos_token:
-            source = torch.cat([source.new([self.vocab.bos()]), source])
-            if target is not None:
-                target = torch.cat([target.new([self.tgt_vocab.bos()]), target])
-        return source, target
-
-    def num_tokens_vec(self, indices):
-        """Return the number of tokens for a set of positions defined by indices.
-        This value is used to enforce ``--max-tokens`` during batching."""
-        return self.sizes[indices]
-
-    def _filter_vocab(self, target):
-        if len(self.tgt_vocab) != len(self.vocab):
-
-            def _filter(target):
-                mask = target.ge(len(self.tgt_vocab))
-                if mask.any():
-                    target[mask] = self.tgt_vocab.unk()
-                return target
-
-            if isinstance(target, list):
-                return [_filter(t) for t in target]
-            return _filter(target)
-        return target
-
-    def collater(self, samples):
-        """Merge a list of samples to form a mini-batch.
-
-        Args:
-            samples (List[dict]): samples to collate
-
-        Returns:
-            dict: a mini-batch with the following keys:
-
-                - `id` (LongTensor): example IDs in the original input order
-                - `ntokens` (int): total number of tokens in the batch
-                - `net_input` (dict): the input to the Model, containing keys:
-
-                  - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
-                    the source sentence of shape `(bsz, src_len)`. Padding will
-                    appear on the right.
-
-                - `target` (LongTensor): a padded 2D Tensor of tokens in the
-                  target sentence of shape `(bsz, tgt_len)`. Padding will appear
-                  on the right.
-        """
-        return collate(
-            samples,
-            self.vocab.pad(),
-            self.vocab.eos(),
-            self.fixed_pad_length,
-            self.pad_to_bsz,
-        )
-
-    def num_tokens(self, index):
-        """Return the number of tokens in a sample. This value is used to
-        enforce ``--max-tokens`` during batching."""
-        return self.sizes[index]
-
-    def size(self, index):
-        """Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with ``--max-positions``."""
-        return self.sizes[index]
-
-    def ordered_indices(self):
-        """Return an ordered list of indices. Batches will be constructed based
-        on this order."""
-        if self.shuffle:
-            order = [np.random.permutation(len(self))]
-        else:
-            order = [np.arange(len(self))]
-        order.append(self.sizes)
-        return np.lexsort(order)
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        self.dataset.prefetch(indices)
diff --git a/spaces/OlaWod/FreeVC/speaker_encoder/data_objects/random_cycler.py b/spaces/OlaWod/FreeVC/speaker_encoder/data_objects/random_cycler.py
deleted file mode 100644
index c405db6b27f46d874d8feb37e3f9c1e12c251109..0000000000000000000000000000000000000000
--- a/spaces/OlaWod/FreeVC/speaker_encoder/data_objects/random_cycler.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import random
-
-class RandomCycler:
-    """
-    Creates an internal copy of a sequence and allows access to its items in a constrained random 
-    order. For a source sequence of n items and one or several consecutive queries of a total 
-    of m items, the following guarantees hold (one implies the other):
-        - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
-        - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
-    """
-    
-    def __init__(self, source):
-        if len(source) == 0:
-            raise Exception("Can't create RandomCycler from an empty collection")
-        self.all_items = list(source)
-        self.next_items = []
-    
-    def sample(self, count: int):
-        shuffle = lambda l: random.sample(l, len(l))
-        
-        out = []
-        while count > 0:
-            if count >= len(self.all_items):
-                out.extend(shuffle(list(self.all_items)))
-                count -= len(self.all_items)
-                continue
-            n = min(count, len(self.next_items))
-            out.extend(self.next_items[:n])
-            count -= n
-            self.next_items = self.next_items[n:]
-            if len(self.next_items) == 0:
-                self.next_items = shuffle(list(self.all_items))
-        return out
-    
-    def __next__(self):
-        return self.sample(1)[0]
-
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py
deleted file mode 100644
index 703c4385c7ddc7eb0759c98d102ab2384d6a9e3e..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/common/data/coco.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from omegaconf import OmegaConf
-
-import detectron2.data.transforms as T
-from detectron2.config import LazyCall as L
-from detectron2.data import (
-    DatasetMapper,
-    build_detection_test_loader,
-    build_detection_train_loader,
-    get_detection_dataset_dicts,
-)
-from detectron2.evaluation import COCOEvaluator
-
-dataloader = OmegaConf.create()
-
-dataloader.train = L(build_detection_train_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"),
-    mapper=L(DatasetMapper)(
-        is_train=True,
-        augmentations=[
-            L(T.ResizeShortestEdge)(
-                short_edge_length=(640, 672, 704, 736, 768, 800),
-                sample_style="choice",
-                max_size=1333,
-            ),
-            L(T.RandomFlip)(horizontal=True),
-        ],
-        image_format="BGR",
-        use_instance_mask=True,
-    ),
-    total_batch_size=16,
-    num_workers=4,
-)
-
-dataloader.test = L(build_detection_test_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False),
-    mapper=L(DatasetMapper)(
-        is_train=False,
-        augmentations=[
-            L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333),
-        ],
-        image_format="${...train.mapper.image_format}",
-    ),
-    num_workers=4,
-)
-
-dataloader.evaluator = L(COCOEvaluator)(
-    dataset_name="${..test.dataset.names}",
-)
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/iou_loss.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/iou_loss.py
deleted file mode 100644
index 6a02464651dc1a0dcec9f30285a3a4ef74209f89..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/iou_loss.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import torch
-from torch import nn
-
-
-class IOULoss(nn.Module):
-    def __init__(self, loc_loss_type='iou'):
-        super(IOULoss, self).__init__()
-        self.loc_loss_type = loc_loss_type
-
-    def forward(self, pred, target, weight=None, reduction='sum'):
-        pred_left = pred[:, 0]
-        pred_top = pred[:, 1]
-        pred_right = pred[:, 2]
-        pred_bottom = pred[:, 3]
-
-        target_left = target[:, 0]
-        target_top = target[:, 1]
-        target_right = target[:, 2]
-        target_bottom = target[:, 3]
-
-        target_aera = (target_left + target_right) * \
-                      (target_top + target_bottom)
-        pred_aera = (pred_left + pred_right) * \
-                    (pred_top + pred_bottom)
-
-        w_intersect = torch.min(pred_left, target_left) + \
-                      torch.min(pred_right, target_right)
-        h_intersect = torch.min(pred_bottom, target_bottom) + \
-                      torch.min(pred_top, target_top)
-
-        g_w_intersect = torch.max(pred_left, target_left) + \
-                        torch.max(pred_right, target_right)
-        g_h_intersect = torch.max(pred_bottom, target_bottom) + \
-                        torch.max(pred_top, target_top)
-        ac_uion = g_w_intersect * g_h_intersect
-
-        area_intersect = w_intersect * h_intersect
-        area_union = target_aera + pred_aera - area_intersect
-
-        ious = (area_intersect + 1.0) / (area_union + 1.0)
-        gious = ious - (ac_uion - area_union) / ac_uion
-        if self.loc_loss_type == 'iou':
-            losses = -torch.log(ious)
-        elif self.loc_loss_type == 'linear_iou':
-            losses = 1 - ious
-        elif self.loc_loss_type == 'giou':
-            losses = 1 - gious
-        else:
-            raise NotImplementedError
-
-        if weight is not None:
-            losses = losses * weight
-        else:
-            losses = losses
-
-        if reduction == 'sum':
-            return losses.sum()
-        elif reduction == 'batch':
-            return losses.sum(dim=[1])
-        elif reduction == 'none':
-            return losses
-        else:
-            raise NotImplementedError
-
-
-def giou_loss(
-    boxes1: torch.Tensor,
-    boxes2: torch.Tensor,
-    reduction: str = "none",
-    eps: float = 1e-7,
-) -> torch.Tensor:
-    """
-    Generalized Intersection over Union Loss (Hamid Rezatofighi et. al)
-    https://arxiv.org/abs/1902.09630
-    Gradient-friendly IoU loss with an additional penalty that is non-zero when the
-    boxes do not overlap and scales with the size of their smallest enclosing box.
-    This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.
-    Args:
-        boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,).
-        reduction: 'none' | 'mean' | 'sum'
-                 'none': No reduction will be applied to the output.
-                 'mean': The output will be averaged.
-                 'sum': The output will be summed.
-        eps (float): small number to prevent division by zero
-    """
-
-    x1, y1, x2, y2 = boxes1.unbind(dim=-1)
-    x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
-
-    assert (x2 >= x1).all(), "bad box: x1 larger than x2"
-    assert (y2 >= y1).all(), "bad box: y1 larger than y2"
-
-    # Intersection keypoints
-    xkis1 = torch.max(x1, x1g)
-    ykis1 = torch.max(y1, y1g)
-    xkis2 = torch.min(x2, x2g)
-    ykis2 = torch.min(y2, y2g)
-
-    intsctk = torch.zeros_like(x1)
-    mask = (ykis2 > ykis1) & (xkis2 > xkis1)
-    intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask])
-    unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk
-    iouk = intsctk / (unionk + eps)
-
-    # smallest enclosing box
-    xc1 = torch.min(x1, x1g)
-    yc1 = torch.min(y1, y1g)
-    xc2 = torch.max(x2, x2g)
-    yc2 = torch.max(y2, y2g)
-
-    area_c = (xc2 - xc1) * (yc2 - yc1)
-    miouk = iouk - ((area_c - unionk) / (area_c + eps))
-
-    loss = 1 - miouk
-
-    if reduction == "mean":
-        loss = loss.mean()
-    elif reduction == "sum":
-        loss = loss.sum()
-
-    return loss
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/blur_predicts.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/blur_predicts.py
deleted file mode 100644
index a14fcc28d5a906ad3a21ab4ba482f38b4fc411cb..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/blur_predicts.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import cv2
-import numpy as np
-import tqdm
-
-from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
-from saicinpainting.evaluation.utils import load_yaml
-
-
-def main(args):
-    config = load_yaml(args.config)
-
-    if not args.predictdir.endswith('/'):
-        args.predictdir += '/'
-
-    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
-
-    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)
-
-    for img_i in tqdm.trange(len(dataset)):
-        pred_fname = dataset.pred_filenames[img_i]
-        cur_out_fname = os.path.join(args.outpath, pred_fname[len(args.predictdir):])
-        os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
-        sample = dataset[img_i]
-        img = sample['image']
-        mask = sample['mask']
-        inpainted = sample['inpainted']
-
-        inpainted_blurred = cv2.GaussianBlur(np.transpose(inpainted, (1, 2, 0)),
-                                             ksize=(args.k, args.k),
-                                             sigmaX=args.s, sigmaY=args.s,
-                                             borderType=cv2.BORDER_REFLECT)
-
-        cur_res = (1 - mask) * np.transpose(img, (1, 2, 0)) + mask * inpainted_blurred
-        cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
-        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
-        cv2.imwrite(cur_out_fname, cur_res)
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('config', type=str, help='Path to evaluation config')
-    aparser.add_argument('datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('predictdir', type=str,
-                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
-    aparser.add_argument('outpath', type=str, help='Where to put results')
-    aparser.add_argument('-s', type=float, default=0.1, help='Gaussian blur sigma')
-    aparser.add_argument('-k', type=int, default=5, help='Kernel size in gaussian blur')
-
-    main(aparser.parse_args())
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/setup.py b/spaces/OpenMotionLab/MotionGPT/pyrender/setup.py
deleted file mode 100644
index c3b5ba0da2b0f17b759e5556597981096a80bda8..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/pyrender/setup.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""
-Setup of pyrender Python codebase.
-
-Author: Matthew Matl
-"""
-import sys
-from setuptools import setup
-
-# load __version__
-exec(open('pyrender/version.py').read())
-
-def get_imageio_dep():
-    if sys.version[0] == "2":
-        return 'imageio<=2.6.1'
-    return 'imageio'
-
-requirements = [
-    'freetype-py',                # For font loading
-    get_imageio_dep(),            # For Image I/O
-    'networkx',                   # For the scene graph
-    'numpy',                      # Numpy
-    'Pillow',                     # For Trimesh texture conversions
-    'pyglet>=1.4.10',             # For the pyglet viewer
-    'PyOpenGL~=3.1.0',            # For OpenGL
-#    'PyOpenGL_accelerate~=3.1.0', # For OpenGL
-    'scipy',                      # Because of trimesh missing dep
-    'six',                        # For Python 2/3 interop
-    'trimesh',                    # For meshes
-]
-
-dev_requirements = [
-    'flake8',            # Code formatting checker
-    'pre-commit',        # Pre-commit hooks
-    'pytest',            # Code testing
-    'pytest-cov',        # Coverage testing
-    'tox',               # Automatic virtualenv testing
-]
-
-docs_requirements = [
-    'sphinx',            # General doc library
-    'sphinx_rtd_theme',  # RTD theme for sphinx
-    'sphinx-automodapi'  # For generating nice tables
-]
-
-
-setup(
-    name = 'pyrender',
-    version=__version__,
-    description='Easy-to-use Python renderer for 3D visualization',
-    long_description='A simple implementation of Physically-Based Rendering '
-                       '(PBR) in Python. Compliant with the glTF 2.0 standard.',
-    author='Matthew Matl',
-    author_email='matthewcmatl@gmail.com',
-    license='MIT License',
-    url = 'https://github.com/mmatl/pyrender',
-    classifiers = [
-        'Development Status :: 4 - Beta',
-        'License :: OSI Approved :: MIT License',
-        'Operating System :: POSIX :: Linux',
-        'Operating System :: MacOS :: MacOS X',
-        'Programming Language :: Python :: 2.7',
-        'Programming Language :: Python :: 3.5',
-        'Programming Language :: Python :: 3.6',
-        'Natural Language :: English',
-        'Topic :: Scientific/Engineering'
-    ],
-    keywords = 'rendering graphics opengl 3d visualization pbr gltf',
-    packages = ['pyrender', 'pyrender.platforms'],
-    setup_requires = requirements,
-    install_requires = requirements,
-    extras_require={
-        'dev': dev_requirements,
-        'docs': docs_requirements,
-    },
-    include_package_data=True
-)
diff --git a/spaces/PAIR/PAIR-Diffusion/app.py b/spaces/PAIR/PAIR-Diffusion/app.py
deleted file mode 100644
index 08589217122b8f88fb6c7fb80a8ee8bd1077eb07..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/app.py
+++ /dev/null
@@ -1,429 +0,0 @@
-
-
-import einops
-import gradio as gr
-import numpy as np
-import torch
-import random
-import os
-import subprocess
-import shlex
-
-from huggingface_hub import hf_hub_url, hf_hub_download
-from share import *
-
-from pytorch_lightning import seed_everything
-from annotator.util import resize_image, HWC3
-from annotator.OneFormer import OneformerSegmenter
-from cldm.model import create_model, load_state_dict
-from cldm.ddim_hacked import DDIMSamplerSpaCFG
-from ldm.models.autoencoder import DiagonalGaussianDistribution
-
-urls = {
-    'shi-labs/oneformer_coco_swin_large': ['150_16_swin_l_oneformer_coco_100ep.pth'],
-    'PAIR/PAIR-diffusion-sdv15-coco-finetune': ['pair_diffusion_epoch62.ckpt']
-}
-
-WTS_DICT = {
-
-}
-
-if os.path.exists('checkpoints') == False:
-    os.mkdir('checkpoints')
-for repo in urls:
-    files = urls[repo]
-    for file in files:
-        url = hf_hub_url(repo, file)
-        name_ckp = url.split('/')[-1]
-        WTS_DICT[repo] = hf_hub_download(repo_id=repo, filename=file, token=os.environ.get("ACCESS_TOKEN"))
-
-print(WTS_DICT)
-apply_segmentor = OneformerSegmenter(WTS_DICT['shi-labs/oneformer_coco_swin_large'])
-
-model = create_model('./configs/sap_fixed_hintnet_v15.yaml').cpu()
-model.load_state_dict(load_state_dict(WTS_DICT['PAIR/PAIR-diffusion-sdv15-coco-finetune'], location='cuda'))
-model = model.cuda()
-ddim_sampler = DDIMSamplerSpaCFG(model)
-_COLORS = []
-save_memory = False
-
-def gen_color():
-    color = tuple(np.round(np.random.choice(range(256), size=3), 3))
-    if color not in _COLORS and np.mean(color) != 0.0:
-        _COLORS.append(color)
-    else:
-        gen_color()
-
-
-for _ in range(300):
-    gen_color()
-
-
-class ImageComp:
-    def __init__(self, edit_operation):
-        self.input_img = None
-        self.input_pmask = None
-        self.input_segmask = None
-
-        self.ref_img = None
-        self.ref_pmask = None
-        self.ref_segmask = None
-
-        self.H = None
-        self.W = None
-        self.baseoutput = None
-        self.kernel = np.ones((5, 5), np.uint8)
-        self.edit_operation = edit_operation
-
-    def init_input_canvas(self, img):
-        img =  HWC3(img)
-        img = resize_image(img, 512)
-        detected_mask = apply_segmentor(img, 'panoptic')[0]
-        detected_seg = apply_segmentor(img, 'semantic')
-
-        self.input_img = img
-        self.input_pmask = detected_mask
-        self.input_segmask = detected_seg
-        self.H = img.shape[0]
-        self.W = img.shape[1]
-        
-        detected_mask = detected_mask.cpu().numpy()
-
-        uni = np.unique(detected_mask)
-        color_mask = np.zeros((detected_mask.shape[0], detected_mask.shape[1], 3))
-        for i in uni:
-            color_mask[detected_mask == i] = _COLORS[i]
-
-        output = color_mask*0.8 + img * 0.2
-        self.baseoutput = output.astype(np.uint8)
-        return self.baseoutput
-    
-    def init_ref_canvas(self, img):
-        img =  HWC3(img)
-        img = resize_image(img, 512)
-        detected_mask = apply_segmentor(img, 'panoptic')[0]
-        detected_seg = apply_segmentor(img, 'semantic')
-
-        self.ref_img = img
-        self.ref_pmask = detected_mask
-        self.ref_segmask = detected_seg
-
-        detected_mask = detected_mask.cpu().numpy()
-
-        uni = np.unique(detected_mask)
-        color_mask = np.zeros((detected_mask.shape[0], detected_mask.shape[1], 3))
-        for i in uni:
-            color_mask[detected_mask == i] = _COLORS[i]
-
-        output = color_mask*0.8 + img * 0.2
-        self.baseoutput = output.astype(np.uint8)
-        return self.baseoutput
-
-    def _process_mask(self, mask, panoptic_mask, segmask):
-        panoptic_mask_ = panoptic_mask + 1
-        mask_ = resize_image(mask['mask'][:, :, 0], min(panoptic_mask.shape))
-        mask_ = torch.tensor(mask_)
-        maski = torch.zeros_like(mask_).cuda()
-        maski[mask_ > 127] = 1
-        mask = maski * panoptic_mask_
-        unique_ids, counts = torch.unique(mask, return_counts=True)
-        mask_id = unique_ids[torch.argmax(counts[1:]) + 1]
-        final_mask = torch.zeros(mask.shape).cuda()
-        final_mask[panoptic_mask_ == mask_id] = 1
-
-        obj_class = maski * (segmask + 1)
-        unique_ids, counts = torch.unique(obj_class, return_counts=True)
-        obj_class = unique_ids[torch.argmax(counts[1:]) + 1] - 1
-        return final_mask, obj_class
-    
-    
-    def _edit_app(self, input_mask, ref_mask, whole_ref):
-        input_pmask = self.input_pmask
-        input_segmask = self.input_segmask
-
-        if whole_ref:
-            reference_mask = torch.ones(self.ref_pmask.shape).cuda()
-        else:
-            reference_mask, _ = self._process_mask(ref_mask, self.ref_pmask, self.ref_segmask)
-
-        edit_mask, _ = self._process_mask(input_mask, self.input_pmask, self.input_segmask)
-        ma = torch.max(input_pmask)
-        input_pmask[edit_mask == 1] = ma + 1
-        return reference_mask, input_pmask, input_segmask, edit_mask, ma
-
-    
-    def _edit(self, input_mask, ref_mask, whole_ref=False, inter=1):
-        input_img = (self.input_img/127.5 - 1)
-        input_img =  torch.from_numpy(input_img.astype(np.float32)).cuda().unsqueeze(0).permute(0,3,1,2)
-
-        reference_img = (self.ref_img/127.5 - 1)
-        reference_img =  torch.from_numpy(reference_img.astype(np.float32)).cuda().unsqueeze(0).permute(0,3,1,2)
-
-        reference_mask, input_pmask, input_segmask, region_mask, ma = self._edit_app(input_mask, ref_mask, whole_ref)     
-
-        input_pmask = input_pmask.float().cuda().unsqueeze(0).unsqueeze(1)
-        _, mean_feat_inpt, one_hot_inpt, empty_mask_flag_inpt = model.get_appearance(input_img, input_pmask, return_all=True)
-
-        reference_mask = reference_mask.float().cuda().unsqueeze(0).unsqueeze(1)
-        _, mean_feat_ref, _, _ = model.get_appearance(reference_img, reference_mask, return_all=True)
-
-        if mean_feat_ref.shape[1] > 1:
-            mean_feat_inpt[:, ma + 1] = (1 - inter) * mean_feat_inpt[:, ma + 1] + inter*mean_feat_ref[:, 1]
-
-        splatted_feat = torch.einsum('nmc, nmhw->nchw', mean_feat_inpt, one_hot_inpt)
-        appearance = torch.nn.functional.normalize(splatted_feat) #l2 normaliz
-        input_segmask =  ((input_segmask+1)/ 127.5 - 1.0).cuda().unsqueeze(0).unsqueeze(1)
-        structure = torch.nn.functional.interpolate(input_segmask, (self.H, self.W))
-        appearance = torch.nn.functional.interpolate(appearance, (self.H, self.W))
-
-
-        return structure, appearance, region_mask, input_img
-    
-    def process(self, input_mask, ref_mask, prompt, a_prompt, n_prompt, 
-                num_samples, ddim_steps, guess_mode, strength, 
-                scale_s, scale_f, scale_t, seed, eta, masking=True,whole_ref=False,inter=1):
-        structure, appearance, mask, img = self._edit(input_mask, ref_mask,
-                                                     whole_ref=whole_ref, inter=inter)
-
-        null_structure = torch.zeros(structure.shape).cuda() - 1
-        null_appearance = torch.zeros(appearance.shape).cuda()
-
-        null_control = torch.cat([null_structure, null_appearance], dim=1)
-        structure_control = torch.cat([structure, null_appearance], dim=1)
-        full_control = torch.cat([structure, appearance], dim=1)
-
-        null_control = torch.cat([null_control for _ in range(num_samples)], dim=0)
-        structure_control = torch.cat([structure_control for _ in range(num_samples)], dim=0)
-        full_control = torch.cat([full_control for _ in range(num_samples)], dim=0)
-
-        #Masking for local edit
-        if not masking:
-            mask, x0 = None, None
-        else:
-            x0 = model.encode_first_stage(img)
-            x0 = x0.sample() if isinstance(x0, DiagonalGaussianDistribution) else x0 # todo: check if we can set random number
-            x0 = x0 * model.scale_factor
-            mask = 1 - torch.tensor(mask).unsqueeze(0).unsqueeze(1).cuda()
-            mask = torch.nn.functional.interpolate(mask, x0.shape[2:]).float()
-        
-        if seed == -1:
-            seed = random.randint(0, 65535)
-        seed_everything(seed)
-
-        scale = [scale_s, scale_f, scale_t]
-        print(scale)
-        if save_memory:
-            model.low_vram_shift(is_diffusing=False)
-        # uc_cross = model.get_unconditional_conditioning(num_samples)
-        uc_cross = model.get_learned_conditioning([n_prompt] * num_samples)
-        cond = {"c_concat": [full_control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt]  * num_samples)]}
-        un_cond = {"c_concat": None if guess_mode else [null_control], "c_crossattn": [uc_cross]}
-        un_cond_struct = {"c_concat": None if guess_mode else [structure_control], "c_crossattn": [uc_cross]}
-        un_cond_struct_app = {"c_concat": None if guess_mode else [full_control], "c_crossattn": [uc_cross]}
-
-        shape = (4, self.H // 8, self.W // 8)
-
-        if save_memory:
-            model.low_vram_shift(is_diffusing=True)
-
-        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
-        samples, _ = ddim_sampler.sample(ddim_steps, num_samples,
-                                                     shape, cond, verbose=False, eta=eta,
-                                                     unconditional_guidance_scale=scale, mask=mask, x0=x0,
-                                                     unconditional_conditioning=[un_cond, un_cond_struct, un_cond_struct_app ])
-
-        if save_memory:
-            model.low_vram_shift(is_diffusing=False)
-
-        x_samples = (model.decode_first_stage(samples) + 1) * 127.5
-        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c')).cpu().numpy().clip(0, 255).astype(np.uint8)
-
-        results = [x_samples[i] for i in range(num_samples)]
-        return [] + results    
-
-
-def init_input_canvas_wrapper(obj, *args):
-    return obj.init_input_canvas(*args)
-
-def init_ref_canvas_wrapper(obj, *args):
-    return obj.init_ref_canvas(*args)
-
-def process_wrapper(obj, *args):
-    return obj.process(*args)
-
-
-
-css = """
-     h1 {
-  text-align: center;
-}
-.container {
-  display: flex;  
-  justify-content: space-between
-}
-
-img {
-  max-width: 100%
-  padding-right: 100px;
-}
-
-.image {
-  flex-basis: 40%
-
-}
-
-.text {
-  font-size: 15px;
-  padding-right: 20px;
-  padding-left: 0px;
-}
-"""
-
-def create_app_demo():
-
-    with gr.Row():
-        gr.Markdown("## Object Level Appearance Editing")
-    with gr.Row():
-        gr.HTML(
-            """
-            <div class="container">
-                <div class="text">
-                    <h4> Instructions </h4>
-                    <ol>
-                        <li>Upload an Input Image.</li>
-                        <li>Mark one of segmented objects in the <i>Select Object to Edit</i> tab.</li>
-                        <li>Upload an Reference Image.</li>
-                        <li>Mark  one of segmented objects in the <i>Select Reference Object</i> tab, for the reference appearance.</li>
-                        <li>Enter a prompt and press <i>Run</i> button. (A very simple would also work) </li>
-                    </ol>
-                </div>
-                <div class="image">
-                    <img src="file/assets/GIF.gif" width="400"">
-                </div>
-            </div>
-            """)
-    with gr.Column():
-        with gr.Row():
-            img_edit = gr.State(ImageComp('edit_app'))
-            with gr.Column():
-                btn1 = gr.Button("Input Image")
-                input_image = gr.Image(source='upload', label='Input Image', type="numpy",)
-            with gr.Column():
-                btn2 = gr.Button("Select Object to Edit")
-                input_mask = gr.Image(source="upload",  label='Select Object in Input Image', type="numpy", tool="sketch")
-            input_image.change(fn=init_input_canvas_wrapper, inputs=[img_edit, input_image], outputs=[input_mask],  queue=False)
-            
-        # with gr.Row():
-            with gr.Column():
-                btn3 = gr.Button("Reference Image")
-                ref_img = gr.Image(source='upload', label='Reference Image', type="numpy")
-            with gr.Column():
-                btn4 = gr.Button("Select Reference Object")
-                reference_mask = gr.Image(source="upload",  label='Select Object in Refernce Image', type="numpy", tool="sketch")
-
-            ref_img.change(fn=init_ref_canvas_wrapper, inputs=[img_edit, ref_img], outputs=[reference_mask],  queue=False)
-    
-        with gr.Row():
-            prompt = gr.Textbox(label="Prompt", value='A picture of truck')
-            with gr.Column():
-                interpolation = gr.Slider(label="Mixing ratio of appearance from reference object", minimum=0.1, maximum=1, value=1.0, step=0.1)
-                whole_ref = gr.Checkbox(label='Use whole reference Image for appearance (Only useful for style transfers)', value=False)
-        with gr.Row():
-            run_button = gr.Button(label="Run")
-        
-        with gr.Row():
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=4, height='auto')
-        
-        with gr.Accordion("Advanced options", open=False):
-            num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
-            strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
-            guess_mode = gr.Checkbox(label='Guess Mode', value=False)
-            ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
-            scale_t = gr.Slider(label="Guidance Scale Text", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-            scale_f = gr.Slider(label="Guidance Scale Appearance", minimum=0.1, maximum=30.0, value=8.0, step=0.1)
-            scale_s = gr.Slider(label="Guidance Scale Structure", minimum=0.1, maximum=30.0, value=5.0, step=0.1)
-            seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
-            eta = gr.Number(label="eta (DDIM)", value=0.0)
-            masking = gr.Checkbox(label='Only edit the local region', value=True)
-            a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
-            n_prompt = gr.Textbox(label="Negative Prompt",
-                                    value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
-            
-        with gr.Column():
-            gr.Examples(
-                examples=[['A picture of a truck', 'assets/truck.png','assets/truck2.jpeg', 892905419, 9, 7.6, 4.3],
-                          ['A picture of a ironman', 'assets/ironman.webp','assets/hulk.jpeg', 709736989, 9, 7.7, 8.1],
-                          ['A person skiing', 'assets/ski.jpg','assets/lava.jpg', 917723061, 9, 7.5, 4.4]],
-                inputs=[prompt, input_image, ref_img, seed, scale_t, scale_f, scale_s],
-                outputs=None,
-                fn=None,
-                cache_examples=False,
-            )
-    ips = [input_mask, reference_mask, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, 
-                scale_s, scale_f, scale_t, seed, eta, masking, whole_ref, interpolation]
-    run_button.click(fn=process_wrapper, inputs=[img_edit, *ips], outputs=[result_gallery])
-
-
-
-def create_struct_demo():
-    with gr.Row():
-        gr.Markdown("## Edit Structure (Comming soon!)")    
-
-def create_both_demo():
-    with gr.Row():
-        gr.Markdown("## Edit Structure and Appearance Together (Comming soon!)")    
-
-
-    
-block = gr.Blocks(css=css).queue()
-with block:
-    gr.HTML(
-            """
-            <div style="text-align: center; max-width: 1200px; margin: 20px auto;">
-            <h1 style="font-weight: 900; font-size: 3rem; margin: 0rem">
-                PAIR Diffusion
-            </h1>
-            <h2 style="font-weight: 450; font-size: 1rem; margin-top: 0.8rem">
-            <a href="https://vidit98.github.io/" style="color:blue;">Vidit Goel</a><sup>1*</sup>, 
-            <a href="https://helia95.github.io/" style="color:blue;">Elia Peruzzo</a><sup>1,2*</sup>,
-            <a href="https://yifanjiang19.github.io/" style="color:blue;">Yifan Jiang</a><sup>3</sup>, 
-            <a href="https://ir1d.github.io/" style="color:blue;">Dejia Xu</a><sup>3</sup>, 
-            <a href="http://disi.unitn.it/~sebe/" style="color:blue;">Nicu Sebe</a><sup>2</sup>, <br> 
-            <a href=" https://people.eecs.berkeley.edu/~trevor/" style="color:blue;">Trevor Darrell</a><sup>4</sup>, 
-            <a href="https://vita-group.github.io/" style="color:blue;">Zhangyang Wang</a><sup>1,3</sup>
-            and <a href="https://www.humphreyshi.com/home" style="color:blue;">Humphrey Shi</a> <sup>1,5,6</sup> <br>
-            [<a href="https://arxiv.org/abs/2303.17546" style="color:red;">arXiv</a>] 
-            [<a href="https://github.com/Picsart-AI-Research/PAIR-Diffusion" style="color:red;">GitHub</a>]
-            </h2>
-            <h3 style="font-weight: 450; font-size: 1rem; margin: 0rem">
-            <sup>1</sup>Picsart AI Resarch (PAIR), <sup>2</sup>UTrenton,  <sup>3</sup>UT Austin, <sup>4</sup>UC Berkeley, <sup>5</sup>UOregon, <sup>6</sup>UIUC
-            </h3>
-            <h2 style="font-weight: 450; font-size: 1rem; margin-top: 0.8rem; margin-bottom: 0.8rem">
-            We built Structure and Appearance Paired (PAIR) Diffusion  that allows reference image-guided appearance manipulation and
-            structure editing of an image at an object level. PAIR diffusion models an image as composition of multiple objects and enables control 
-            over structure and appearance properties of the object. Describing object appearances using text can be challenging and ambiguous, PAIR Diffusion 
-            enables a user to control the appearance of an object using images. User can further use text as another degree of control for appearance. 
-            Having fine-grained control over appearance and structure at object level can be beneficial for future works in video and 3D beside image editing, 
-            where we need to have consistent appearance across time in case of video or across various viewing positions in case of 3D.
-            </h2>
-            
-            </div>
-            """)
-    
-    gr.HTML("""
-            <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-            <br/>
-            <a href="https://huggingface.co/spaces/PAIR/PAIR-Diffusion?duplicate=true">
-            <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            </p>""")
-    
-    with gr.Tab('Edit Appearance'):
-        create_app_demo()
-    with gr.Tab('Edit Structure'):
-        create_struct_demo()
-    with gr.Tab('Edit Both'):
-        create_both_demo()
-
-
-block.queue(max_size=20)
-block.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/PAIR/PAIR-Diffusion/cldm/data.py b/spaces/PAIR/PAIR-Diffusion/cldm/data.py
deleted file mode 100644
index 38ae14a1a3d0ec0be874211e4931959c67afee28..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/cldm/data.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from functools import partial
-from ldm.util import instantiate_from_config
-from torch.utils.data import random_split, DataLoader, Dataset, Subset
-
-class WrappedDataset(Dataset):
-    """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
-
-    def __init__(self, dataset):
-        self.data = dataset
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        return self.data[idx]
-
-class DataModuleFromConfig(pl.LightningDataModule):
-    def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
-                 wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
-                 shuffle_val_dataloader=False):
-        super().__init__()
-        self.batch_size = batch_size
-        self.dataset_configs = dict()
-        self.num_workers = num_workers if num_workers is not None else batch_size * 2
-        self.use_worker_init_fn = use_worker_init_fn
-        if train is not None:
-            self.dataset_configs["train"] = train
-            self.train_dataloader = self._train_dataloader
-        if validation is not None:
-            self.dataset_configs["validation"] = validation
-            self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
-        if test is not None:
-            self.dataset_configs["test"] = test
-            self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
-        if predict is not None:
-            self.dataset_configs["predict"] = predict
-            self.predict_dataloader = self._predict_dataloader
-        self.wrap = wrap
-
-    def prepare_data(self):
-        for data_cfg in self.dataset_configs.values():
-            instantiate_from_config(data_cfg)
-
-    def setup(self, stage=None):
-        self.datasets = dict(
-            (k, instantiate_from_config(self.dataset_configs[k]))
-            for k in self.dataset_configs)
-        if self.wrap:
-            for k in self.datasets:
-                self.datasets[k] = WrappedDataset(self.datasets[k])
-
-    def _train_dataloader(self):
-        init_fn = None
-        return DataLoader(self.datasets["train"], batch_size=self.batch_size,
-                          num_workers=self.num_workers, shuffle= True,
-                          worker_init_fn=init_fn)
-
-    def _val_dataloader(self, shuffle=False):
-        init_fn = None
-        return DataLoader(self.datasets["validation"],
-                          batch_size=self.batch_size,
-                          num_workers=self.num_workers,
-                          worker_init_fn=init_fn,
-                          shuffle=shuffle)
-
-    def _test_dataloader(self, shuffle=False):
-        is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
-        if is_iterable_dataset or self.use_worker_init_fn:
-            init_fn = worker_init_fn
-        else:
-            init_fn = None
-
-        # do not shuffle dataloader for iterable dataset
-        shuffle = shuffle and (not is_iterable_dataset)
-
-        return DataLoader(self.datasets["test"], batch_size=self.batch_size,
-                          num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)
-
-    def _predict_dataloader(self, shuffle=False):
-        if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
-            init_fn = worker_init_fn
-        else:
-            init_fn = None
-        return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
-                          num_workers=self.num_workers, worker_init_fn=init_fn)
-
-
-def create_data(config):
-    data = instantiate_from_config(config.data)
-        # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
-        # calling these ourselves should not be necessary but it is.
-        # lightning still takes care of proper multiprocessing though
-    data.prepare_data()
-    data.setup()
-    return data
\ No newline at end of file
diff --git a/spaces/PAIR/PAIR-Diffusion/ldm/data/util.py b/spaces/PAIR/PAIR-Diffusion/ldm/data/util.py
deleted file mode 100644
index 5b60ceb2349e3bd7900ff325740e2022d2903b1c..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/ldm/data/util.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import torch
-
-from ldm.modules.midas.api import load_midas_transform
-
-
-class AddMiDaS(object):
-    def __init__(self, model_type):
-        super().__init__()
-        self.transform = load_midas_transform(model_type)
-
-    def pt2np(self, x):
-        x = ((x + 1.0) * .5).detach().cpu().numpy()
-        return x
-
-    def np2pt(self, x):
-        x = torch.from_numpy(x) * 2 - 1.
-        return x
-
-    def __call__(self, sample):
-        # sample['jpg'] is tensor hwc in [-1, 1] at this point
-        x = self.pt2np(sample['jpg'])
-        x = self.transform({"image": x})["image"]
-        sample['midas_in'] = x
-        return sample
\ No newline at end of file
diff --git a/spaces/PAIR/PAIR-Diffusion/ldm/modules/diffusionmodules/model.py b/spaces/PAIR/PAIR-Diffusion/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index b089eebbe1676d8249005bb9def002ff5180715b..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,852 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-from typing import Optional, Any
-
-from ldm.modules.attention import MemoryEfficientCrossAttention
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except:
-    XFORMERS_IS_AVAILBLE = False
-    print("No module 'xformers'. Proceeding without it.")
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-class MemoryEfficientAttnBlock(nn.Module):
-    """
-        Uses xformers efficient implementation,
-        see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-        Note: this is a single-head self-attention operation
-    """
-    #
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        B, C, H, W = q.shape
-        q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
-
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(B, t.shape[1], 1, C)
-            .permute(0, 2, 1, 3)
-            .reshape(B * 1, t.shape[1], C)
-            .contiguous(),
-            (q, k, v),
-        )
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        out = (
-            out.unsqueeze(0)
-            .reshape(B, 1, out.shape[1], C)
-            .permute(0, 2, 1, 3)
-            .reshape(B, out.shape[1], C)
-        )
-        out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
-        out = self.proj_out(out)
-        return x+out
-
-
-class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
-    def forward(self, x, context=None, mask=None):
-        b, c, h, w = x.shape
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        out = super().forward(x, context=context, mask=mask)
-        out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
-        return x + out
-
-
-def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
-    assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
-    if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
-        attn_type = "vanilla-xformers"
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        assert attn_kwargs is None
-        return AttnBlock(in_channels)
-    elif attn_type == "vanilla-xformers":
-        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
-        return MemoryEfficientAttnBlock(in_channels)
-    elif type == "memory-efficient-cross-attn":
-        attn_kwargs["query_dim"] = in_channels
-        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        raise NotImplementedError()
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 attn_type="vanilla", **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/upernet_uniformer.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/upernet_uniformer.py
deleted file mode 100644
index 41aa4db809dc6e2c508e98051f61807d07477903..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/configs/_base_/models/upernet_uniformer.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# model settings
-norm_cfg = dict(type='BN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained=None,
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[3, 4, 8, 3],
-        head_dim=64,
-        mlp_ratio=4.,
-        qkv_bias=True,
-        drop_rate=0.,
-        attn_drop_rate=0.,
-        drop_path_rate=0.1),
-    decode_head=dict(
-        type='UPerHead',
-        in_channels=[64, 128, 320, 512],
-        in_index=[0, 1, 2, 3],
-        pool_scales=(1, 2, 3, 6),
-        channels=512,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=320,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/evaluation.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/evaluation.py
deleted file mode 100644
index 4d00999ce5665c53bded8de9e084943eee2d230d..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/evaluation.py
+++ /dev/null
@@ -1,509 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import warnings
-from math import inf
-
-import torch.distributed as dist
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.utils.data import DataLoader
-
-from annotator.uniformer.mmcv.fileio import FileClient
-from annotator.uniformer.mmcv.utils import is_seq_of
-from .hook import Hook
-from .logger import LoggerHook
-
-
-class EvalHook(Hook):
-    """Non-Distributed evaluation hook.
-
-    This hook will regularly perform evaluation in a given interval when
-    performing in non-distributed environment.
-
-    Args:
-        dataloader (DataLoader): A PyTorch dataloader, whose dataset has
-            implemented ``evaluate`` function.
-        start (int | None, optional): Evaluation starting epoch. It enables
-            evaluation before the training starts if ``start`` <= the resuming
-            epoch. If None, whether to evaluate is merely decided by
-            ``interval``. Default: None.
-        interval (int): Evaluation interval. Default: 1.
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            Default: True.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
-            best score value and best checkpoint path, which will be also
-            loaded when resume checkpoint. Options are the evaluation metrics
-            on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
-            detection and instance segmentation. ``AR@100`` for proposal
-            recall. If ``save_best`` is ``auto``, the first key of the returned
-            ``OrderedDict`` result will be used. Default: None.
-        rule (str | None, optional): Comparison rule for best score. If set to
-            None, it will infer a reasonable rule. Keys such as 'acc', 'top'
-            .etc will be inferred by 'greater' rule. Keys contain 'loss' will
-            be inferred by 'less' rule. Options are 'greater', 'less', None.
-            Default: None.
-        test_fn (callable, optional): test a model with samples from a
-            dataloader, and return the test results. If ``None``, the default
-            test function ``mmcv.engine.single_gpu_test`` will be used.
-            (default: ``None``)
-        greater_keys (List[str] | None, optional): Metric keys that will be
-            inferred by 'greater' comparison rule. If ``None``,
-            _default_greater_keys will be used. (default: ``None``)
-        less_keys (List[str] | None, optional): Metric keys that will be
-            inferred by 'less' comparison rule. If ``None``, _default_less_keys
-            will be used. (default: ``None``)
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, `runner.work_dir` will be used by default. If specified,
-            the `out_dir` will be the concatenation of `out_dir` and the last
-            level directory of `runner.work_dir`.
-            `New in version 1.3.16.`
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details. Default: None.
-            `New in version 1.3.16.`
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-
-    Notes:
-        If new arguments are added for EvalHook, tools/test.py,
-        tools/eval_metric.py may be affected.
-    """
-
-    # Since the key for determine greater or less is related to the downstream
-    # tasks, downstream repos may need to overwrite the following inner
-    # variable accordingly.
-
-    rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
-    init_value_map = {'greater': -inf, 'less': inf}
-    _default_greater_keys = [
-        'acc', 'top', 'AR@', 'auc', 'precision', 'mAP', 'mDice', 'mIoU',
-        'mAcc', 'aAcc'
-    ]
-    _default_less_keys = ['loss']
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 test_fn=None,
-                 greater_keys=None,
-                 less_keys=None,
-                 out_dir=None,
-                 file_client_args=None,
-                 **eval_kwargs):
-        if not isinstance(dataloader, DataLoader):
-            raise TypeError(f'dataloader must be a pytorch DataLoader, '
-                            f'but got {type(dataloader)}')
-
-        if interval <= 0:
-            raise ValueError(f'interval must be a positive number, '
-                             f'but got {interval}')
-
-        assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean'
-
-        if start is not None and start < 0:
-            raise ValueError(f'The evaluation start epoch {start} is smaller '
-                             f'than 0')
-
-        self.dataloader = dataloader
-        self.interval = interval
-        self.start = start
-        self.by_epoch = by_epoch
-
-        assert isinstance(save_best, str) or save_best is None, \
-            '""save_best"" should be a str or None ' \
-            f'rather than {type(save_best)}'
-        self.save_best = save_best
-        self.eval_kwargs = eval_kwargs
-        self.initial_flag = True
-
-        if test_fn is None:
-            from annotator.uniformer.mmcv.engine import single_gpu_test
-            self.test_fn = single_gpu_test
-        else:
-            self.test_fn = test_fn
-
-        if greater_keys is None:
-            self.greater_keys = self._default_greater_keys
-        else:
-            if not isinstance(greater_keys, (list, tuple)):
-                greater_keys = (greater_keys, )
-            assert is_seq_of(greater_keys, str)
-            self.greater_keys = greater_keys
-
-        if less_keys is None:
-            self.less_keys = self._default_less_keys
-        else:
-            if not isinstance(less_keys, (list, tuple)):
-                less_keys = (less_keys, )
-            assert is_seq_of(less_keys, str)
-            self.less_keys = less_keys
-
-        if self.save_best is not None:
-            self.best_ckpt_path = None
-            self._init_rule(rule, self.save_best)
-
-        self.out_dir = out_dir
-        self.file_client_args = file_client_args
-
-    def _init_rule(self, rule, key_indicator):
-        """Initialize rule, key_indicator, comparison_func, and best score.
-
-        Here is the rule to determine which rule is used for key indicator
-        when the rule is not specific (note that the key indicator matching
-        is case-insensitive):
-        1. If the key indicator is in ``self.greater_keys``, the rule will be
-           specified as 'greater'.
-        2. Or if the key indicator is in ``self.less_keys``, the rule will be
-           specified as 'less'.
-        3. Or if the key indicator is equal to the substring in any one item
-           in ``self.greater_keys``, the rule will be specified as 'greater'.
-        4. Or if the key indicator is equal to the substring in any one item
-           in ``self.less_keys``, the rule will be specified as 'less'.
-
-        Args:
-            rule (str | None): Comparison rule for best score.
-            key_indicator (str | None): Key indicator to determine the
-                comparison rule.
-        """
-        if rule not in self.rule_map and rule is not None:
-            raise KeyError(f'rule must be greater, less or None, '
-                           f'but got {rule}.')
-
-        if rule is None:
-            if key_indicator != 'auto':
-                # `_lc` here means we use the lower case of keys for
-                # case-insensitive matching
-                key_indicator_lc = key_indicator.lower()
-                greater_keys = [key.lower() for key in self.greater_keys]
-                less_keys = [key.lower() for key in self.less_keys]
-
-                if key_indicator_lc in greater_keys:
-                    rule = 'greater'
-                elif key_indicator_lc in less_keys:
-                    rule = 'less'
-                elif any(key in key_indicator_lc for key in greater_keys):
-                    rule = 'greater'
-                elif any(key in key_indicator_lc for key in less_keys):
-                    rule = 'less'
-                else:
-                    raise ValueError(f'Cannot infer the rule for key '
-                                     f'{key_indicator}, thus a specific rule '
-                                     f'must be specified.')
-        self.rule = rule
-        self.key_indicator = key_indicator
-        if self.rule is not None:
-            self.compare_func = self.rule_map[self.rule]
-
-    def before_run(self, runner):
-        if not self.out_dir:
-            self.out_dir = runner.work_dir
-
-        self.file_client = FileClient.infer_client(self.file_client_args,
-                                                   self.out_dir)
-
-        # if `self.out_dir` is not equal to `runner.work_dir`, it means that
-        # `self.out_dir` is set so the final `self.out_dir` is the
-        # concatenation of `self.out_dir` and the last level directory of
-        # `runner.work_dir`
-        if self.out_dir != runner.work_dir:
-            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
-            self.out_dir = self.file_client.join_path(self.out_dir, basename)
-            runner.logger.info(
-                (f'The best checkpoint will be saved to {self.out_dir} by '
-                 f'{self.file_client.name}'))
-
-        if self.save_best is not None:
-            if runner.meta is None:
-                warnings.warn('runner.meta is None. Creating an empty one.')
-                runner.meta = dict()
-            runner.meta.setdefault('hook_msgs', dict())
-            self.best_ckpt_path = runner.meta['hook_msgs'].get(
-                'best_ckpt', None)
-
-    def before_train_iter(self, runner):
-        """Evaluate the model only at the start of training by iteration."""
-        if self.by_epoch or not self.initial_flag:
-            return
-        if self.start is not None and runner.iter >= self.start:
-            self.after_train_iter(runner)
-        self.initial_flag = False
-
-    def before_train_epoch(self, runner):
-        """Evaluate the model only at the start of training by epoch."""
-        if not (self.by_epoch and self.initial_flag):
-            return
-        if self.start is not None and runner.epoch >= self.start:
-            self.after_train_epoch(runner)
-        self.initial_flag = False
-
-    def after_train_iter(self, runner):
-        """Called after every training iter to evaluate the results."""
-        if not self.by_epoch and self._should_evaluate(runner):
-            # Because the priority of EvalHook is higher than LoggerHook, the
-            # training log and the evaluating log are mixed. Therefore,
-            # we need to dump the training log and clear it before evaluating
-            # log is generated. In addition, this problem will only appear in
-            # `IterBasedRunner` whose `self.by_epoch` is False, because
-            # `EpochBasedRunner` whose `self.by_epoch` is True calls
-            # `_do_evaluate` in `after_train_epoch` stage, and at this stage
-            # the training log has been printed, so it will not cause any
-            # problem. more details at
-            # https://github.com/open-mmlab/mmsegmentation/issues/694
-            for hook in runner._hooks:
-                if isinstance(hook, LoggerHook):
-                    hook.after_train_iter(runner)
-            runner.log_buffer.clear()
-
-            self._do_evaluate(runner)
-
-    def after_train_epoch(self, runner):
-        """Called after every training epoch to evaluate the results."""
-        if self.by_epoch and self._should_evaluate(runner):
-            self._do_evaluate(runner)
-
-    def _do_evaluate(self, runner):
-        """perform evaluation and save ckpt."""
-        results = self.test_fn(runner.model, self.dataloader)
-        runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
-        key_score = self.evaluate(runner, results)
-        # the key_score may be `None` so it needs to skip the action to save
-        # the best checkpoint
-        if self.save_best and key_score:
-            self._save_ckpt(runner, key_score)
-
-    def _should_evaluate(self, runner):
-        """Judge whether to perform evaluation.
-
-        Here is the rule to judge whether to perform evaluation:
-        1. It will not perform evaluation during the epoch/iteration interval,
-           which is determined by ``self.interval``.
-        2. It will not perform evaluation if the start time is larger than
-           current time.
-        3. It will not perform evaluation when current time is larger than
-           the start time but during epoch/iteration interval.
-
-        Returns:
-            bool: The flag indicating whether to perform evaluation.
-        """
-        if self.by_epoch:
-            current = runner.epoch
-            check_time = self.every_n_epochs
-        else:
-            current = runner.iter
-            check_time = self.every_n_iters
-
-        if self.start is None:
-            if not check_time(runner, self.interval):
-                # No evaluation during the interval.
-                return False
-        elif (current + 1) < self.start:
-            # No evaluation if start is larger than the current time.
-            return False
-        else:
-            # Evaluation only at epochs/iters 3, 5, 7...
-            # if start==3 and interval==2
-            if (current + 1 - self.start) % self.interval:
-                return False
-        return True
-
-    def _save_ckpt(self, runner, key_score):
-        """Save the best checkpoint.
-
-        It will compare the score according to the compare function, write
-        related information (best score, best checkpoint path) and save the
-        best checkpoint into ``work_dir``.
-        """
-        if self.by_epoch:
-            current = f'epoch_{runner.epoch + 1}'
-            cur_type, cur_time = 'epoch', runner.epoch + 1
-        else:
-            current = f'iter_{runner.iter + 1}'
-            cur_type, cur_time = 'iter', runner.iter + 1
-
-        best_score = runner.meta['hook_msgs'].get(
-            'best_score', self.init_value_map[self.rule])
-        if self.compare_func(key_score, best_score):
-            best_score = key_score
-            runner.meta['hook_msgs']['best_score'] = best_score
-
-            if self.best_ckpt_path and self.file_client.isfile(
-                    self.best_ckpt_path):
-                self.file_client.remove(self.best_ckpt_path)
-                runner.logger.info(
-                    (f'The previous best checkpoint {self.best_ckpt_path} was '
-                     'removed'))
-
-            best_ckpt_name = f'best_{self.key_indicator}_{current}.pth'
-            self.best_ckpt_path = self.file_client.join_path(
-                self.out_dir, best_ckpt_name)
-            runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path
-
-            runner.save_checkpoint(
-                self.out_dir, best_ckpt_name, create_symlink=False)
-            runner.logger.info(
-                f'Now best checkpoint is saved as {best_ckpt_name}.')
-            runner.logger.info(
-                f'Best {self.key_indicator} is {best_score:0.4f} '
-                f'at {cur_time} {cur_type}.')
-
-    def evaluate(self, runner, results):
-        """Evaluate the results.
-
-        Args:
-            runner (:obj:`mmcv.Runner`): The underlined training runner.
-            results (list): Output results.
-        """
-        eval_res = self.dataloader.dataset.evaluate(
-            results, logger=runner.logger, **self.eval_kwargs)
-
-        for name, val in eval_res.items():
-            runner.log_buffer.output[name] = val
-        runner.log_buffer.ready = True
-
-        if self.save_best is not None:
-            # If the performance of model is pool, the `eval_res` may be an
-            # empty dict and it will raise exception when `self.save_best` is
-            # not None. More details at
-            # https://github.com/open-mmlab/mmdetection/issues/6265.
-            if not eval_res:
-                warnings.warn(
-                    'Since `eval_res` is an empty dict, the behavior to save '
-                    'the best checkpoint will be skipped in this evaluation.')
-                return None
-
-            if self.key_indicator == 'auto':
-                # infer from eval_results
-                self._init_rule(self.rule, list(eval_res.keys())[0])
-            return eval_res[self.key_indicator]
-
-        return None
-
-
-class DistEvalHook(EvalHook):
-    """Distributed evaluation hook.
-
-    This hook will regularly perform evaluation in a given interval when
-    performing in distributed environment.
-
-    Args:
-        dataloader (DataLoader): A PyTorch dataloader, whose dataset has
-            implemented ``evaluate`` function.
-        start (int | None, optional): Evaluation starting epoch. It enables
-            evaluation before the training starts if ``start`` <= the resuming
-            epoch. If None, whether to evaluate is merely decided by
-            ``interval``. Default: None.
-        interval (int): Evaluation interval. Default: 1.
-        by_epoch (bool): Determine perform evaluation by epoch or by iteration.
-            If set to True, it will perform by epoch. Otherwise, by iteration.
-            default: True.
-        save_best (str, optional): If a metric is specified, it would measure
-            the best checkpoint during evaluation. The information about best
-            checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep
-            best score value and best checkpoint path, which will be also
-            loaded when resume checkpoint. Options are the evaluation metrics
-            on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
-            detection and instance segmentation. ``AR@100`` for proposal
-            recall. If ``save_best`` is ``auto``, the first key of the returned
-            ``OrderedDict`` result will be used. Default: None.
-        rule (str | None, optional): Comparison rule for best score. If set to
-            None, it will infer a reasonable rule. Keys such as 'acc', 'top'
-            .etc will be inferred by 'greater' rule. Keys contain 'loss' will
-            be inferred by 'less' rule. Options are 'greater', 'less', None.
-            Default: None.
-        test_fn (callable, optional): test a model with samples from a
-            dataloader in a multi-gpu manner, and return the test results. If
-            ``None``, the default test function ``mmcv.engine.multi_gpu_test``
-            will be used. (default: ``None``)
-        tmpdir (str | None): Temporary directory to save the results of all
-            processes. Default: None.
-        gpu_collect (bool): Whether to use gpu or cpu to collect results.
-            Default: False.
-        broadcast_bn_buffer (bool): Whether to broadcast the
-            buffer(running_mean and running_var) of rank 0 to other rank
-            before evaluation. Default: True.
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, `runner.work_dir` will be used by default. If specified,
-            the `out_dir` will be the concatenation of `out_dir` and the last
-            level directory of `runner.work_dir`.
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details. Default: None.
-        **eval_kwargs: Evaluation arguments fed into the evaluate function of
-            the dataset.
-    """
-
-    def __init__(self,
-                 dataloader,
-                 start=None,
-                 interval=1,
-                 by_epoch=True,
-                 save_best=None,
-                 rule=None,
-                 test_fn=None,
-                 greater_keys=None,
-                 less_keys=None,
-                 broadcast_bn_buffer=True,
-                 tmpdir=None,
-                 gpu_collect=False,
-                 out_dir=None,
-                 file_client_args=None,
-                 **eval_kwargs):
-
-        if test_fn is None:
-            from annotator.uniformer.mmcv.engine import multi_gpu_test
-            test_fn = multi_gpu_test
-
-        super().__init__(
-            dataloader,
-            start=start,
-            interval=interval,
-            by_epoch=by_epoch,
-            save_best=save_best,
-            rule=rule,
-            test_fn=test_fn,
-            greater_keys=greater_keys,
-            less_keys=less_keys,
-            out_dir=out_dir,
-            file_client_args=file_client_args,
-            **eval_kwargs)
-
-        self.broadcast_bn_buffer = broadcast_bn_buffer
-        self.tmpdir = tmpdir
-        self.gpu_collect = gpu_collect
-
-    def _do_evaluate(self, runner):
-        """perform evaluation and save ckpt."""
-        # Synchronization of BatchNorm's buffer (running_mean
-        # and running_var) is not supported in the DDP of pytorch,
-        # which may cause the inconsistent performance of models in
-        # different ranks, so we broadcast BatchNorm's buffers
-        # of rank 0 to other ranks to avoid this.
-        if self.broadcast_bn_buffer:
-            model = runner.model
-            for name, module in model.named_modules():
-                if isinstance(module,
-                              _BatchNorm) and module.track_running_stats:
-                    dist.broadcast(module.running_var, 0)
-                    dist.broadcast(module.running_mean, 0)
-
-        tmpdir = self.tmpdir
-        if tmpdir is None:
-            tmpdir = osp.join(runner.work_dir, '.eval_hook')
-
-        results = self.test_fn(
-            runner.model,
-            self.dataloader,
-            tmpdir=tmpdir,
-            gpu_collect=self.gpu_collect)
-        if runner.rank == 0:
-            print('\n')
-            runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
-            key_score = self.evaluate(runner, results)
-            # the key_score may be `None` so it needs to skip the action to
-            # save the best checkpoint
-            if self.save_best and key_score:
-                self._save_ckpt(runner, key_score)
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/core/evaluation/metrics.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/core/evaluation/metrics.py
deleted file mode 100644
index 16c7dd47cadd53cf1caaa194e28a343f2aacc599..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/core/evaluation/metrics.py
+++ /dev/null
@@ -1,326 +0,0 @@
-from collections import OrderedDict
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch
-
-
-def f_score(precision, recall, beta=1):
-    """calcuate the f-score value.
-
-    Args:
-        precision (float | torch.Tensor): The precision value.
-        recall (float | torch.Tensor): The recall value.
-        beta (int): Determines the weight of recall in the combined score.
-            Default: False.
-
-    Returns:
-        [torch.tensor]: The f-score value.
-    """
-    score = (1 + beta**2) * (precision * recall) / (
-        (beta**2 * precision) + recall)
-    return score
-
-
-def intersect_and_union(pred_label,
-                        label,
-                        num_classes,
-                        ignore_index,
-                        label_map=dict(),
-                        reduce_zero_label=False):
-    """Calculate intersection and Union.
-
-    Args:
-        pred_label (ndarray | str): Prediction segmentation map
-            or predict result filename.
-        label (ndarray | str): Ground truth segmentation map
-            or label filename.
-        num_classes (int): Number of categories.
-        ignore_index (int): Index that will be ignored in evaluation.
-        label_map (dict): Mapping old labels to new labels. The parameter will
-            work only when label is str. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. The parameter will
-            work only when label is str. Default: False.
-
-     Returns:
-         torch.Tensor: The intersection of prediction and ground truth
-            histogram on all classes.
-         torch.Tensor: The union of prediction and ground truth histogram on
-            all classes.
-         torch.Tensor: The prediction histogram on all classes.
-         torch.Tensor: The ground truth histogram on all classes.
-    """
-
-    if isinstance(pred_label, str):
-        pred_label = torch.from_numpy(np.load(pred_label))
-    else:
-        pred_label = torch.from_numpy((pred_label))
-
-    if isinstance(label, str):
-        label = torch.from_numpy(
-            mmcv.imread(label, flag='unchanged', backend='pillow'))
-    else:
-        label = torch.from_numpy(label)
-
-    if label_map is not None:
-        for old_id, new_id in label_map.items():
-            label[label == old_id] = new_id
-    if reduce_zero_label:
-        label[label == 0] = 255
-        label = label - 1
-        label[label == 254] = 255
-
-    mask = (label != ignore_index)
-    pred_label = pred_label[mask]
-    label = label[mask]
-
-    intersect = pred_label[pred_label == label]
-    area_intersect = torch.histc(
-        intersect.float(), bins=(num_classes), min=0, max=num_classes - 1)
-    area_pred_label = torch.histc(
-        pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1)
-    area_label = torch.histc(
-        label.float(), bins=(num_classes), min=0, max=num_classes - 1)
-    area_union = area_pred_label + area_label - area_intersect
-    return area_intersect, area_union, area_pred_label, area_label
-
-
-def total_intersect_and_union(results,
-                              gt_seg_maps,
-                              num_classes,
-                              ignore_index,
-                              label_map=dict(),
-                              reduce_zero_label=False):
-    """Calculate Total Intersection and Union.
-
-    Args:
-        results (list[ndarray] | list[str]): List of prediction segmentation
-            maps or list of prediction result filenames.
-        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
-            segmentation maps or list of label filenames.
-        num_classes (int): Number of categories.
-        ignore_index (int): Index that will be ignored in evaluation.
-        label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
-     Returns:
-         ndarray: The intersection of prediction and ground truth histogram
-             on all classes.
-         ndarray: The union of prediction and ground truth histogram on all
-             classes.
-         ndarray: The prediction histogram on all classes.
-         ndarray: The ground truth histogram on all classes.
-    """
-    num_imgs = len(results)
-    assert len(gt_seg_maps) == num_imgs
-    total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
-    total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
-    total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)
-    total_area_label = torch.zeros((num_classes, ), dtype=torch.float64)
-    for i in range(num_imgs):
-        area_intersect, area_union, area_pred_label, area_label = \
-            intersect_and_union(
-                results[i], gt_seg_maps[i], num_classes, ignore_index,
-                label_map, reduce_zero_label)
-        total_area_intersect += area_intersect
-        total_area_union += area_union
-        total_area_pred_label += area_pred_label
-        total_area_label += area_label
-    return total_area_intersect, total_area_union, total_area_pred_label, \
-        total_area_label
-
-
-def mean_iou(results,
-             gt_seg_maps,
-             num_classes,
-             ignore_index,
-             nan_to_num=None,
-             label_map=dict(),
-             reduce_zero_label=False):
-    """Calculate Mean Intersection and Union (mIoU)
-
-    Args:
-        results (list[ndarray] | list[str]): List of prediction segmentation
-            maps or list of prediction result filenames.
-        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
-            segmentation maps or list of label filenames.
-        num_classes (int): Number of categories.
-        ignore_index (int): Index that will be ignored in evaluation.
-        nan_to_num (int, optional): If specified, NaN values will be replaced
-            by the numbers defined by the user. Default: None.
-        label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
-     Returns:
-        dict[str, float | ndarray]:
-            <aAcc> float: Overall accuracy on all images.
-            <Acc> ndarray: Per category accuracy, shape (num_classes, ).
-            <IoU> ndarray: Per category IoU, shape (num_classes, ).
-    """
-    iou_result = eval_metrics(
-        results=results,
-        gt_seg_maps=gt_seg_maps,
-        num_classes=num_classes,
-        ignore_index=ignore_index,
-        metrics=['mIoU'],
-        nan_to_num=nan_to_num,
-        label_map=label_map,
-        reduce_zero_label=reduce_zero_label)
-    return iou_result
-
-
-def mean_dice(results,
-              gt_seg_maps,
-              num_classes,
-              ignore_index,
-              nan_to_num=None,
-              label_map=dict(),
-              reduce_zero_label=False):
-    """Calculate Mean Dice (mDice)
-
-    Args:
-        results (list[ndarray] | list[str]): List of prediction segmentation
-            maps or list of prediction result filenames.
-        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
-            segmentation maps or list of label filenames.
-        num_classes (int): Number of categories.
-        ignore_index (int): Index that will be ignored in evaluation.
-        nan_to_num (int, optional): If specified, NaN values will be replaced
-            by the numbers defined by the user. Default: None.
-        label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
-     Returns:
-        dict[str, float | ndarray]: Default metrics.
-            <aAcc> float: Overall accuracy on all images.
-            <Acc> ndarray: Per category accuracy, shape (num_classes, ).
-            <Dice> ndarray: Per category dice, shape (num_classes, ).
-    """
-
-    dice_result = eval_metrics(
-        results=results,
-        gt_seg_maps=gt_seg_maps,
-        num_classes=num_classes,
-        ignore_index=ignore_index,
-        metrics=['mDice'],
-        nan_to_num=nan_to_num,
-        label_map=label_map,
-        reduce_zero_label=reduce_zero_label)
-    return dice_result
-
-
-def mean_fscore(results,
-                gt_seg_maps,
-                num_classes,
-                ignore_index,
-                nan_to_num=None,
-                label_map=dict(),
-                reduce_zero_label=False,
-                beta=1):
-    """Calculate Mean Intersection and Union (mIoU)
-
-    Args:
-        results (list[ndarray] | list[str]): List of prediction segmentation
-            maps or list of prediction result filenames.
-        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
-            segmentation maps or list of label filenames.
-        num_classes (int): Number of categories.
-        ignore_index (int): Index that will be ignored in evaluation.
-        nan_to_num (int, optional): If specified, NaN values will be replaced
-            by the numbers defined by the user. Default: None.
-        label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
-        beta (int): Determines the weight of recall in the combined score.
-            Default: False.
-
-
-     Returns:
-        dict[str, float | ndarray]: Default metrics.
-            <aAcc> float: Overall accuracy on all images.
-            <Fscore> ndarray: Per category recall, shape (num_classes, ).
-            <Precision> ndarray: Per category precision, shape (num_classes, ).
-            <Recall> ndarray: Per category f-score, shape (num_classes, ).
-    """
-    fscore_result = eval_metrics(
-        results=results,
-        gt_seg_maps=gt_seg_maps,
-        num_classes=num_classes,
-        ignore_index=ignore_index,
-        metrics=['mFscore'],
-        nan_to_num=nan_to_num,
-        label_map=label_map,
-        reduce_zero_label=reduce_zero_label,
-        beta=beta)
-    return fscore_result
-
-
-def eval_metrics(results,
-                 gt_seg_maps,
-                 num_classes,
-                 ignore_index,
-                 metrics=['mIoU'],
-                 nan_to_num=None,
-                 label_map=dict(),
-                 reduce_zero_label=False,
-                 beta=1):
-    """Calculate evaluation metrics
-    Args:
-        results (list[ndarray] | list[str]): List of prediction segmentation
-            maps or list of prediction result filenames.
-        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
-            segmentation maps or list of label filenames.
-        num_classes (int): Number of categories.
-        ignore_index (int): Index that will be ignored in evaluation.
-        metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
-        nan_to_num (int, optional): If specified, NaN values will be replaced
-            by the numbers defined by the user. Default: None.
-        label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
-     Returns:
-        float: Overall accuracy on all images.
-        ndarray: Per category accuracy, shape (num_classes, ).
-        ndarray: Per category evaluation metrics, shape (num_classes, ).
-    """
-    if isinstance(metrics, str):
-        metrics = [metrics]
-    allowed_metrics = ['mIoU', 'mDice', 'mFscore']
-    if not set(metrics).issubset(set(allowed_metrics)):
-        raise KeyError('metrics {} is not supported'.format(metrics))
-
-    total_area_intersect, total_area_union, total_area_pred_label, \
-        total_area_label = total_intersect_and_union(
-            results, gt_seg_maps, num_classes, ignore_index, label_map,
-            reduce_zero_label)
-    all_acc = total_area_intersect.sum() / total_area_label.sum()
-    ret_metrics = OrderedDict({'aAcc': all_acc})
-    for metric in metrics:
-        if metric == 'mIoU':
-            iou = total_area_intersect / total_area_union
-            acc = total_area_intersect / total_area_label
-            ret_metrics['IoU'] = iou
-            ret_metrics['Acc'] = acc
-        elif metric == 'mDice':
-            dice = 2 * total_area_intersect / (
-                total_area_pred_label + total_area_label)
-            acc = total_area_intersect / total_area_label
-            ret_metrics['Dice'] = dice
-            ret_metrics['Acc'] = acc
-        elif metric == 'mFscore':
-            precision = total_area_intersect / total_area_pred_label
-            recall = total_area_intersect / total_area_label
-            f_value = torch.tensor(
-                [f_score(x[0], x[1], beta) for x in zip(precision, recall)])
-            ret_metrics['Fscore'] = f_value
-            ret_metrics['Precision'] = precision
-            ret_metrics['Recall'] = recall
-
-    ret_metrics = {
-        metric: value.numpy()
-        for metric, value in ret_metrics.items()
-    }
-    if nan_to_num is not None:
-        ret_metrics = OrderedDict({
-            metric: np.nan_to_num(metric_value, nan=nan_to_num)
-            for metric, metric_value in ret_metrics.items()
-        })
-    return ret_metrics
diff --git a/spaces/PBJ/Toxic-Comment-Classification/app.py b/spaces/PBJ/Toxic-Comment-Classification/app.py
deleted file mode 100644
index 2ff01da82a4182fc4f850cc34e66727c71c775de..0000000000000000000000000000000000000000
--- a/spaces/PBJ/Toxic-Comment-Classification/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# Importing necessary libraries
-import streamlit as st
-import os
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import re
-
-st.title('Toxic Comment Classification')
-comment = st.text_area("Enter Your Text", "Type Here")
-
-comment_input = []
-comment_input.append(comment)
-test_df = pd.DataFrame()
-test_df['comment_text'] = comment_input
-cols = {'toxic':[0], 'severe_toxic':[0], 'obscene':[0], 'threat':[0], 'insult':[0], 'identity_hate':[0], 'non_toxic': [0]}
-for key in cols.keys():
-    test_df[key] = cols[key]
-test_df = test_df.reset_index()
-test_df.drop(columns=["index"], inplace=True)
-
-# Data Cleaning and Preprocessing
-# creating copy of data for data cleaning and preprocessing
-cleaned_data = test_df.copy()
-
-# Removing Hyperlinks from text
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"https?://\S+|www\.\S+","",x) )
-
-# Removing emojis from text
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub("["
-                                                                                   u"\U0001F600-\U0001F64F"
-                                                                                   u"\U0001F300-\U0001F5FF"  
-                                                                                   u"\U0001F680-\U0001F6FF" 
-                                                                                   u"\U0001F1E0-\U0001F1FF"  
-                                                                                   u"\U00002702-\U000027B0"
-                                                                                   u"\U000024C2-\U0001F251"
-                                                                                   "]+","", x, flags=re.UNICODE))
-
-# Removing IP addresses from text 
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}","",x))
-
-# Removing html tags from text 
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"<.*?>","",x))
-
-# There are some comments which contain double quoted words like --> ""words""  we will convert these to --> "words" 
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\"\"", "\"",x))   # replacing "" with "
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"^\"", "",x))      # removing quotation from start and the end of the string
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\"$", "",x))
-
-# Removing Punctuation / Special characters (;:'".?@!%&*+) which appears more than twice in the text 
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"[^a-zA-Z0-9\s][^a-zA-Z0-9\s]+", " ",x))
-
-# Removing Special characters 
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"[^a-zA-Z0-9\s\"\',:;?!.()]", " ",x))
-
-# Removing extra spaces in text
-cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\s\s+", " ",x))
-
-Final_data = cleaned_data.copy()
-
-# Model Building
-from transformers import DistilBertTokenizer
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader, Dataset
-
-# Using Pretrained DistilBertTokenizer
-tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
-
-# Creating Dataset class for Toxic comments and Labels 
-class Toxic_Dataset(Dataset):
-    def __init__(self, Comments_, Labels_):
-        self.comments = Comments_.copy()
-        self.labels = Labels_.copy()
-        
-        self.comments["comment_text"] = self.comments["comment_text"].map(lambda x: tokenizer(x, padding="max_length", truncation=True, return_tensors="pt"))
-    
-    def __len__(self):
-        return len(self.labels)
-    
-    def __getitem__(self, idx):
-        comment = self.comments.loc[idx,"comment_text"]
-        label = np.array(self.labels.loc[idx,:])
-        
-        return comment, label
-
-X_test = pd.DataFrame(test_df.iloc[:, 0])
-Y_test = test_df.iloc[:, 1:]
-Test_data = Toxic_Dataset(X_test, Y_test)
-Test_Loader = DataLoader(Test_data, shuffle=False)
-
-# Loading pre-trained weights of DistilBert model for sequence classification
-# and changing classifiers output to 7 because we have 7 labels to classify.
-# DistilBERT
-
-from transformers import DistilBertForSequenceClassification
-
-Distil_bert = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
-
-Distil_bert.classifier = nn.Sequential(
-                    nn.Linear(768,7),
-                    nn.Sigmoid()
-                  )
-# print(Distil_bert)
-
-# Instantiating the model and loading the weights
-model = Distil_bert
-model.to('cpu')
-model = torch.load('dsbert_toxic_balanced.pt', map_location=torch.device('cpu'))
-
-# Making Predictions
-for comments, labels in Test_Loader:
-    labels = labels.to('cpu')
-    labels = labels.float()
-    masks = comments['attention_mask'].squeeze(1).to('cpu')
-    input_ids = comments['input_ids'].squeeze(1).to('cpu')
-    
-    output = model(input_ids, masks)
-    op = output.logits
-    
-    res = []
-    for i in range(7):
-        res.append(op[0, i])
-    # print(res)
-
-preds = []
-
-for i in range(len(res)):
-    preds.append(res[i].tolist())
-
-classes = ['Toxic', 'Severe Toxic', 'Obscene', 'Threat', 'Insult', 'Identity Hate', 'Non Toxic']
-
-if st.button('Classify'):
-    for i in range(len(res)):
-        st.write(f"{classes[i]} : {round(preds[i], 2)}\n")
-    st.success('These are the outputs')
-    
diff --git a/spaces/PIISA/PIISA_Demo/README.md b/spaces/PIISA/PIISA_Demo/README.md
deleted file mode 100644
index 1e7faff3e60e5a82f1defd47443000dc6d218e08..0000000000000000000000000000000000000000
--- a/spaces/PIISA/PIISA_Demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PIISA Demo
-emoji: 👀
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-27.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-27.go
deleted file mode 100644
index 2491b523a9cea9797490d6b36101e50341c9c03c..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-27.go and /dev/null differ
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/default_constructor.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/default_constructor.py
deleted file mode 100644
index 3f1f5b44168768dfda3947393a63a6cf9cf50b41..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/default_constructor.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from .builder import RUNNER_BUILDERS, RUNNERS
-
-
-@RUNNER_BUILDERS.register_module()
-class DefaultRunnerConstructor:
-    """Default constructor for runners.
-
-    Custom existing `Runner` like `EpocBasedRunner` though `RunnerConstructor`.
-    For example, We can inject some new properties and functions for `Runner`.
-
-    Example:
-        >>> from annotator.uniformer.mmcv.runner import RUNNER_BUILDERS, build_runner
-        >>> # Define a new RunnerReconstructor
-        >>> @RUNNER_BUILDERS.register_module()
-        >>> class MyRunnerConstructor:
-        ...     def __init__(self, runner_cfg, default_args=None):
-        ...         if not isinstance(runner_cfg, dict):
-        ...             raise TypeError('runner_cfg should be a dict',
-        ...                             f'but got {type(runner_cfg)}')
-        ...         self.runner_cfg = runner_cfg
-        ...         self.default_args = default_args
-        ...
-        ...     def __call__(self):
-        ...         runner = RUNNERS.build(self.runner_cfg,
-        ...                                default_args=self.default_args)
-        ...         # Add new properties for existing runner
-        ...         runner.my_name = 'my_runner'
-        ...         runner.my_function = lambda self: print(self.my_name)
-        ...         ...
-        >>> # build your runner
-        >>> runner_cfg = dict(type='EpochBasedRunner', max_epochs=40,
-        ...                   constructor='MyRunnerConstructor')
-        >>> runner = build_runner(runner_cfg)
-    """
-
-    def __init__(self, runner_cfg, default_args=None):
-        if not isinstance(runner_cfg, dict):
-            raise TypeError('runner_cfg should be a dict',
-                            f'but got {type(runner_cfg)}')
-        self.runner_cfg = runner_cfg
-        self.default_args = default_args
-
-    def __call__(self):
-        return RUNNERS.build(self.runner_cfg, default_args=self.default_args)
diff --git a/spaces/Ragio/endometrial_disease_prediction/app.py b/spaces/Ragio/endometrial_disease_prediction/app.py
deleted file mode 100644
index 4be2a42b05c914b8a825d8bfd03789004f5d6bf7..0000000000000000000000000000000000000000
--- a/spaces/Ragio/endometrial_disease_prediction/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-from pycaret.classification import *
-
-df = pd.read_pickle("endometrium.pkl")
-clf= setup(data=df, target = 'pathology', normalize=True,session_id=2828)
-best = compare_models(n_select=15)
-compare_model_results=pull()
-lda=create_model("lda")
-tuned_lda = tune_model(lda)
-
-def predict(model, BMI,Age,Endometrial_Thickness):
-    
-    df = pd.DataFrame.from_dict({"Age": [Age], "BMI": [BMI], "Endometrial Thickness": [Endometrial_Thickness]})
-    model_index = list(compare_model_results['Model']).index(model)
-    model = best[model_index]
-    pred = predict_model(model, df, raw_score=True)
-    return  {"no": pred["prediction_score_no"][0].astype('float64'),
-             "yes": pred["prediction_score_yes"][0].astype('float64')}
-
-description = "Endometrial Disease Prediction Model with Artificial Intelligence "
-title = "Classification of Patients with Endometrial Disease"
-model = gr.inputs.Dropdown(list(compare_model_results['Model']), label="Model")
-Age = gr.inputs.Slider(minimum=10, maximum=100,default=df['Age'].mean(), label = 'Age')
-BMI = gr.inputs.Slider(minimum=10, maximum=30,default=df['BMI'].mean(), label = 'BMI')
-Endometrial_Thickness = gr.inputs.Slider(minimum=1, maximum=100,default=df['Endometrial Thickness'].mean(),label = 'Endometrial Thickness')
-
-gr.Interface(predict,[model,Age, BMI, Endometrial_Thickness], "label",title=title,live=True).launch()
\ No newline at end of file
diff --git a/spaces/Rahorus/openjourney/app.py b/spaces/Rahorus/openjourney/app.py
deleted file mode 100644
index bea4accb45793c8e748731c184dee0ffaf509dd5..0000000000000000000000000000000000000000
--- a/spaces/Rahorus/openjourney/app.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import gradio as gr
-
-description = """<div>
-                <img src="https://i.imgur.com/FEA7N1p.png">
-              </div>
-              """
-
-gr.Interface.load("models/prompthero/openjourney", description=description).launch()
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/color.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/color.py
deleted file mode 100644
index 6bca2da922c59151f42354ea92616faa1c6b37be..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/color.py
+++ /dev/null
@@ -1,615 +0,0 @@
-import platform
-import re
-from colorsys import rgb_to_hls
-from enum import IntEnum
-from functools import lru_cache
-from typing import TYPE_CHECKING, NamedTuple, Optional, Tuple
-
-from ._palettes import EIGHT_BIT_PALETTE, STANDARD_PALETTE, WINDOWS_PALETTE
-from .color_triplet import ColorTriplet
-from .repr import Result, rich_repr
-from .terminal_theme import DEFAULT_TERMINAL_THEME
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .terminal_theme import TerminalTheme
-    from .text import Text
-
-
-WINDOWS = platform.system() == "Windows"
-
-
-class ColorSystem(IntEnum):
-    """One of the 3 color system supported by terminals."""
-
-    STANDARD = 1
-    EIGHT_BIT = 2
-    TRUECOLOR = 3
-    WINDOWS = 4
-
-    def __repr__(self) -> str:
-        return f"ColorSystem.{self.name}"
-
-
-class ColorType(IntEnum):
-    """Type of color stored in Color class."""
-
-    DEFAULT = 0
-    STANDARD = 1
-    EIGHT_BIT = 2
-    TRUECOLOR = 3
-    WINDOWS = 4
-
-    def __repr__(self) -> str:
-        return f"ColorType.{self.name}"
-
-
-ANSI_COLOR_NAMES = {
-    "black": 0,
-    "red": 1,
-    "green": 2,
-    "yellow": 3,
-    "blue": 4,
-    "magenta": 5,
-    "cyan": 6,
-    "white": 7,
-    "bright_black": 8,
-    "bright_red": 9,
-    "bright_green": 10,
-    "bright_yellow": 11,
-    "bright_blue": 12,
-    "bright_magenta": 13,
-    "bright_cyan": 14,
-    "bright_white": 15,
-    "grey0": 16,
-    "gray0": 16,
-    "navy_blue": 17,
-    "dark_blue": 18,
-    "blue3": 20,
-    "blue1": 21,
-    "dark_green": 22,
-    "deep_sky_blue4": 25,
-    "dodger_blue3": 26,
-    "dodger_blue2": 27,
-    "green4": 28,
-    "spring_green4": 29,
-    "turquoise4": 30,
-    "deep_sky_blue3": 32,
-    "dodger_blue1": 33,
-    "green3": 40,
-    "spring_green3": 41,
-    "dark_cyan": 36,
-    "light_sea_green": 37,
-    "deep_sky_blue2": 38,
-    "deep_sky_blue1": 39,
-    "spring_green2": 47,
-    "cyan3": 43,
-    "dark_turquoise": 44,
-    "turquoise2": 45,
-    "green1": 46,
-    "spring_green1": 48,
-    "medium_spring_green": 49,
-    "cyan2": 50,
-    "cyan1": 51,
-    "dark_red": 88,
-    "deep_pink4": 125,
-    "purple4": 55,
-    "purple3": 56,
-    "blue_violet": 57,
-    "orange4": 94,
-    "grey37": 59,
-    "gray37": 59,
-    "medium_purple4": 60,
-    "slate_blue3": 62,
-    "royal_blue1": 63,
-    "chartreuse4": 64,
-    "dark_sea_green4": 71,
-    "pale_turquoise4": 66,
-    "steel_blue": 67,
-    "steel_blue3": 68,
-    "cornflower_blue": 69,
-    "chartreuse3": 76,
-    "cadet_blue": 73,
-    "sky_blue3": 74,
-    "steel_blue1": 81,
-    "pale_green3": 114,
-    "sea_green3": 78,
-    "aquamarine3": 79,
-    "medium_turquoise": 80,
-    "chartreuse2": 112,
-    "sea_green2": 83,
-    "sea_green1": 85,
-    "aquamarine1": 122,
-    "dark_slate_gray2": 87,
-    "dark_magenta": 91,
-    "dark_violet": 128,
-    "purple": 129,
-    "light_pink4": 95,
-    "plum4": 96,
-    "medium_purple3": 98,
-    "slate_blue1": 99,
-    "yellow4": 106,
-    "wheat4": 101,
-    "grey53": 102,
-    "gray53": 102,
-    "light_slate_grey": 103,
-    "light_slate_gray": 103,
-    "medium_purple": 104,
-    "light_slate_blue": 105,
-    "dark_olive_green3": 149,
-    "dark_sea_green": 108,
-    "light_sky_blue3": 110,
-    "sky_blue2": 111,
-    "dark_sea_green3": 150,
-    "dark_slate_gray3": 116,
-    "sky_blue1": 117,
-    "chartreuse1": 118,
-    "light_green": 120,
-    "pale_green1": 156,
-    "dark_slate_gray1": 123,
-    "red3": 160,
-    "medium_violet_red": 126,
-    "magenta3": 164,
-    "dark_orange3": 166,
-    "indian_red": 167,
-    "hot_pink3": 168,
-    "medium_orchid3": 133,
-    "medium_orchid": 134,
-    "medium_purple2": 140,
-    "dark_goldenrod": 136,
-    "light_salmon3": 173,
-    "rosy_brown": 138,
-    "grey63": 139,
-    "gray63": 139,
-    "medium_purple1": 141,
-    "gold3": 178,
-    "dark_khaki": 143,
-    "navajo_white3": 144,
-    "grey69": 145,
-    "gray69": 145,
-    "light_steel_blue3": 146,
-    "light_steel_blue": 147,
-    "yellow3": 184,
-    "dark_sea_green2": 157,
-    "light_cyan3": 152,
-    "light_sky_blue1": 153,
-    "green_yellow": 154,
-    "dark_olive_green2": 155,
-    "dark_sea_green1": 193,
-    "pale_turquoise1": 159,
-    "deep_pink3": 162,
-    "magenta2": 200,
-    "hot_pink2": 169,
-    "orchid": 170,
-    "medium_orchid1": 207,
-    "orange3": 172,
-    "light_pink3": 174,
-    "pink3": 175,
-    "plum3": 176,
-    "violet": 177,
-    "light_goldenrod3": 179,
-    "tan": 180,
-    "misty_rose3": 181,
-    "thistle3": 182,
-    "plum2": 183,
-    "khaki3": 185,
-    "light_goldenrod2": 222,
-    "light_yellow3": 187,
-    "grey84": 188,
-    "gray84": 188,
-    "light_steel_blue1": 189,
-    "yellow2": 190,
-    "dark_olive_green1": 192,
-    "honeydew2": 194,
-    "light_cyan1": 195,
-    "red1": 196,
-    "deep_pink2": 197,
-    "deep_pink1": 199,
-    "magenta1": 201,
-    "orange_red1": 202,
-    "indian_red1": 204,
-    "hot_pink": 206,
-    "dark_orange": 208,
-    "salmon1": 209,
-    "light_coral": 210,
-    "pale_violet_red1": 211,
-    "orchid2": 212,
-    "orchid1": 213,
-    "orange1": 214,
-    "sandy_brown": 215,
-    "light_salmon1": 216,
-    "light_pink1": 217,
-    "pink1": 218,
-    "plum1": 219,
-    "gold1": 220,
-    "navajo_white1": 223,
-    "misty_rose1": 224,
-    "thistle1": 225,
-    "yellow1": 226,
-    "light_goldenrod1": 227,
-    "khaki1": 228,
-    "wheat1": 229,
-    "cornsilk1": 230,
-    "grey100": 231,
-    "gray100": 231,
-    "grey3": 232,
-    "gray3": 232,
-    "grey7": 233,
-    "gray7": 233,
-    "grey11": 234,
-    "gray11": 234,
-    "grey15": 235,
-    "gray15": 235,
-    "grey19": 236,
-    "gray19": 236,
-    "grey23": 237,
-    "gray23": 237,
-    "grey27": 238,
-    "gray27": 238,
-    "grey30": 239,
-    "gray30": 239,
-    "grey35": 240,
-    "gray35": 240,
-    "grey39": 241,
-    "gray39": 241,
-    "grey42": 242,
-    "gray42": 242,
-    "grey46": 243,
-    "gray46": 243,
-    "grey50": 244,
-    "gray50": 244,
-    "grey54": 245,
-    "gray54": 245,
-    "grey58": 246,
-    "gray58": 246,
-    "grey62": 247,
-    "gray62": 247,
-    "grey66": 248,
-    "gray66": 248,
-    "grey70": 249,
-    "gray70": 249,
-    "grey74": 250,
-    "gray74": 250,
-    "grey78": 251,
-    "gray78": 251,
-    "grey82": 252,
-    "gray82": 252,
-    "grey85": 253,
-    "gray85": 253,
-    "grey89": 254,
-    "gray89": 254,
-    "grey93": 255,
-    "gray93": 255,
-}
-
-
-class ColorParseError(Exception):
-    """The color could not be parsed."""
-
-
-RE_COLOR = re.compile(
-    r"""^
-\#([0-9a-f]{6})$|
-color\(([0-9]{1,3})\)$|
-rgb\(([\d\s,]+)\)$
-""",
-    re.VERBOSE,
-)
-
-
-@rich_repr
-class Color(NamedTuple):
-    """Terminal color definition."""
-
-    name: str
-    """The name of the color (typically the input to Color.parse)."""
-    type: ColorType
-    """The type of the color."""
-    number: Optional[int] = None
-    """The color number, if a standard color, or None."""
-    triplet: Optional[ColorTriplet] = None
-    """A triplet of color components, if an RGB color."""
-
-    def __rich__(self) -> "Text":
-        """Dispays the actual color if Rich printed."""
-        from .style import Style
-        from .text import Text
-
-        return Text.assemble(
-            f"<color {self.name!r} ({self.type.name.lower()})",
-            ("⬤", Style(color=self)),
-            " >",
-        )
-
-    def __rich_repr__(self) -> Result:
-        yield self.name
-        yield self.type
-        yield "number", self.number, None
-        yield "triplet", self.triplet, None
-
-    @property
-    def system(self) -> ColorSystem:
-        """Get the native color system for this color."""
-        if self.type == ColorType.DEFAULT:
-            return ColorSystem.STANDARD
-        return ColorSystem(int(self.type))
-
-    @property
-    def is_system_defined(self) -> bool:
-        """Check if the color is ultimately defined by the system."""
-        return self.system not in (ColorSystem.EIGHT_BIT, ColorSystem.TRUECOLOR)
-
-    @property
-    def is_default(self) -> bool:
-        """Check if the color is a default color."""
-        return self.type == ColorType.DEFAULT
-
-    def get_truecolor(
-        self, theme: Optional["TerminalTheme"] = None, foreground: bool = True
-    ) -> ColorTriplet:
-        """Get an equivalent color triplet for this color.
-
-        Args:
-            theme (TerminalTheme, optional): Optional terminal theme, or None to use default. Defaults to None.
-            foreground (bool, optional): True for a foreground color, or False for background. Defaults to True.
-
-        Returns:
-            ColorTriplet: A color triplet containing RGB components.
-        """
-
-        if theme is None:
-            theme = DEFAULT_TERMINAL_THEME
-        if self.type == ColorType.TRUECOLOR:
-            assert self.triplet is not None
-            return self.triplet
-        elif self.type == ColorType.EIGHT_BIT:
-            assert self.number is not None
-            return EIGHT_BIT_PALETTE[self.number]
-        elif self.type == ColorType.STANDARD:
-            assert self.number is not None
-            return theme.ansi_colors[self.number]
-        elif self.type == ColorType.WINDOWS:
-            assert self.number is not None
-            return WINDOWS_PALETTE[self.number]
-        else:  # self.type == ColorType.DEFAULT:
-            assert self.number is None
-            return theme.foreground_color if foreground else theme.background_color
-
-    @classmethod
-    def from_ansi(cls, number: int) -> "Color":
-        """Create a Color number from it's 8-bit ansi number.
-
-        Args:
-            number (int): A number between 0-255 inclusive.
-
-        Returns:
-            Color: A new Color instance.
-        """
-        return cls(
-            name=f"color({number})",
-            type=(ColorType.STANDARD if number < 16 else ColorType.EIGHT_BIT),
-            number=number,
-        )
-
-    @classmethod
-    def from_triplet(cls, triplet: "ColorTriplet") -> "Color":
-        """Create a truecolor RGB color from a triplet of values.
-
-        Args:
-            triplet (ColorTriplet): A color triplet containing red, green and blue components.
-
-        Returns:
-            Color: A new color object.
-        """
-        return cls(name=triplet.hex, type=ColorType.TRUECOLOR, triplet=triplet)
-
-    @classmethod
-    def from_rgb(cls, red: float, green: float, blue: float) -> "Color":
-        """Create a truecolor from three color components in the range(0->255).
-
-        Args:
-            red (float): Red component in range 0-255.
-            green (float): Green component in range 0-255.
-            blue (float): Blue component in range 0-255.
-
-        Returns:
-            Color: A new color object.
-        """
-        return cls.from_triplet(ColorTriplet(int(red), int(green), int(blue)))
-
-    @classmethod
-    def default(cls) -> "Color":
-        """Get a Color instance representing the default color.
-
-        Returns:
-            Color: Default color.
-        """
-        return cls(name="default", type=ColorType.DEFAULT)
-
-    @classmethod
-    @lru_cache(maxsize=1024)
-    def parse(cls, color: str) -> "Color":
-        """Parse a color definition."""
-        original_color = color
-        color = color.lower().strip()
-
-        if color == "default":
-            return cls(color, type=ColorType.DEFAULT)
-
-        color_number = ANSI_COLOR_NAMES.get(color)
-        if color_number is not None:
-            return cls(
-                color,
-                type=(ColorType.STANDARD if color_number < 16 else ColorType.EIGHT_BIT),
-                number=color_number,
-            )
-
-        color_match = RE_COLOR.match(color)
-        if color_match is None:
-            raise ColorParseError(f"{original_color!r} is not a valid color")
-
-        color_24, color_8, color_rgb = color_match.groups()
-        if color_24:
-            triplet = ColorTriplet(
-                int(color_24[0:2], 16), int(color_24[2:4], 16), int(color_24[4:6], 16)
-            )
-            return cls(color, ColorType.TRUECOLOR, triplet=triplet)
-
-        elif color_8:
-            number = int(color_8)
-            if number > 255:
-                raise ColorParseError(f"color number must be <= 255 in {color!r}")
-            return cls(
-                color,
-                type=(ColorType.STANDARD if number < 16 else ColorType.EIGHT_BIT),
-                number=number,
-            )
-
-        else:  #  color_rgb:
-            components = color_rgb.split(",")
-            if len(components) != 3:
-                raise ColorParseError(
-                    f"expected three components in {original_color!r}"
-                )
-            red, green, blue = components
-            triplet = ColorTriplet(int(red), int(green), int(blue))
-            if not all(component <= 255 for component in triplet):
-                raise ColorParseError(
-                    f"color components must be <= 255 in {original_color!r}"
-                )
-            return cls(color, ColorType.TRUECOLOR, triplet=triplet)
-
-    @lru_cache(maxsize=1024)
-    def get_ansi_codes(self, foreground: bool = True) -> Tuple[str, ...]:
-        """Get the ANSI escape codes for this color."""
-        _type = self.type
-        if _type == ColorType.DEFAULT:
-            return ("39" if foreground else "49",)
-
-        elif _type == ColorType.WINDOWS:
-            number = self.number
-            assert number is not None
-            fore, back = (30, 40) if number < 8 else (82, 92)
-            return (str(fore + number if foreground else back + number),)
-
-        elif _type == ColorType.STANDARD:
-            number = self.number
-            assert number is not None
-            fore, back = (30, 40) if number < 8 else (82, 92)
-            return (str(fore + number if foreground else back + number),)
-
-        elif _type == ColorType.EIGHT_BIT:
-            assert self.number is not None
-            return ("38" if foreground else "48", "5", str(self.number))
-
-        else:  # self.standard == ColorStandard.TRUECOLOR:
-            assert self.triplet is not None
-            red, green, blue = self.triplet
-            return ("38" if foreground else "48", "2", str(red), str(green), str(blue))
-
-    @lru_cache(maxsize=1024)
-    def downgrade(self, system: ColorSystem) -> "Color":
-        """Downgrade a color system to a system with fewer colors."""
-
-        if self.type in [ColorType.DEFAULT, system]:
-            return self
-        # Convert to 8-bit color from truecolor color
-        if system == ColorSystem.EIGHT_BIT and self.system == ColorSystem.TRUECOLOR:
-            assert self.triplet is not None
-            red, green, blue = self.triplet.normalized
-            _h, l, s = rgb_to_hls(red, green, blue)
-            # If saturation is under 10% assume it is grayscale
-            if s < 0.1:
-                gray = round(l * 25.0)
-                if gray == 0:
-                    color_number = 16
-                elif gray == 25:
-                    color_number = 231
-                else:
-                    color_number = 231 + gray
-                return Color(self.name, ColorType.EIGHT_BIT, number=color_number)
-
-            color_number = (
-                16 + 36 * round(red * 5.0) + 6 * round(green * 5.0) + round(blue * 5.0)
-            )
-            return Color(self.name, ColorType.EIGHT_BIT, number=color_number)
-
-        # Convert to standard from truecolor or 8-bit
-        elif system == ColorSystem.STANDARD:
-            if self.system == ColorSystem.TRUECOLOR:
-                assert self.triplet is not None
-                triplet = self.triplet
-            else:  # self.system == ColorSystem.EIGHT_BIT
-                assert self.number is not None
-                triplet = ColorTriplet(*EIGHT_BIT_PALETTE[self.number])
-
-            color_number = STANDARD_PALETTE.match(triplet)
-            return Color(self.name, ColorType.STANDARD, number=color_number)
-
-        elif system == ColorSystem.WINDOWS:
-            if self.system == ColorSystem.TRUECOLOR:
-                assert self.triplet is not None
-                triplet = self.triplet
-            else:  # self.system == ColorSystem.EIGHT_BIT
-                assert self.number is not None
-                if self.number < 16:
-                    return Color(self.name, ColorType.WINDOWS, number=self.number)
-                triplet = ColorTriplet(*EIGHT_BIT_PALETTE[self.number])
-
-            color_number = WINDOWS_PALETTE.match(triplet)
-            return Color(self.name, ColorType.WINDOWS, number=color_number)
-
-        return self
-
-
-def parse_rgb_hex(hex_color: str) -> ColorTriplet:
-    """Parse six hex characters in to RGB triplet."""
-    assert len(hex_color) == 6, "must be 6 characters"
-    color = ColorTriplet(
-        int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
-    )
-    return color
-
-
-def blend_rgb(
-    color1: ColorTriplet, color2: ColorTriplet, cross_fade: float = 0.5
-) -> ColorTriplet:
-    """Blend one RGB color in to another."""
-    r1, g1, b1 = color1
-    r2, g2, b2 = color2
-    new_color = ColorTriplet(
-        int(r1 + (r2 - r1) * cross_fade),
-        int(g1 + (g2 - g1) * cross_fade),
-        int(b1 + (b2 - b1) * cross_fade),
-    )
-    return new_color
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from .console import Console
-    from .table import Table
-    from .text import Text
-
-    console = Console()
-
-    table = Table(show_footer=False, show_edge=True)
-    table.add_column("Color", width=10, overflow="ellipsis")
-    table.add_column("Number", justify="right", style="yellow")
-    table.add_column("Name", style="green")
-    table.add_column("Hex", style="blue")
-    table.add_column("RGB", style="magenta")
-
-    colors = sorted((v, k) for k, v in ANSI_COLOR_NAMES.items())
-    for color_number, name in colors:
-        if "grey" in name:
-            continue
-        color_cell = Text(" " * 10, style=f"on {name}")
-        if color_number < 16:
-            table.add_row(color_cell, f"{color_number}", Text(f'"{name}"'))
-        else:
-            color = EIGHT_BIT_PALETTE[color_number]  # type: ignore[has-type]
-            table.add_row(
-                color_cell, str(color_number), Text(f'"{name}"'), color.hex, color.rgb
-            )
-
-    console.print(table)
diff --git a/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/experiment.py b/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/experiment.py
deleted file mode 100644
index 0a2d5c0dc359cec13304813ac7732c5968d70a80..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/experiment.py
+++ /dev/null
@@ -1,261 +0,0 @@
-"""
-Main file to launch training and testing experiments.
-"""
-
-import yaml
-import os
-import argparse
-import numpy as np
-import torch
-
-from .config.project_config import Config as cfg
-from .train import train_net
-from .export import export_predictions, export_homograpy_adaptation
-
-
-# Pytorch configurations
-torch.cuda.empty_cache()
-torch.backends.cudnn.benchmark = True
-
-
-def load_config(config_path):
-    """Load configurations from a given yaml file."""
-    # Check file exists
-    if not os.path.exists(config_path):
-        raise ValueError("[Error] The provided config path is not valid.")
-
-    # Load the configuration
-    with open(config_path, "r") as f:
-        config = yaml.safe_load(f)
-
-    return config
-
-
-def update_config(path, model_cfg=None, dataset_cfg=None):
-    """Update configuration file from the resume path."""
-    # Check we need to update or completely override.
-    model_cfg = {} if model_cfg is None else model_cfg
-    dataset_cfg = {} if dataset_cfg is None else dataset_cfg
-
-    # Load saved configs
-    with open(os.path.join(path, "model_cfg.yaml"), "r") as f:
-        model_cfg_saved = yaml.safe_load(f)
-        model_cfg.update(model_cfg_saved)
-    with open(os.path.join(path, "dataset_cfg.yaml"), "r") as f:
-        dataset_cfg_saved = yaml.safe_load(f)
-        dataset_cfg.update(dataset_cfg_saved)
-
-    # Update the saved yaml file
-    if not model_cfg == model_cfg_saved:
-        with open(os.path.join(path, "model_cfg.yaml"), "w") as f:
-            yaml.dump(model_cfg, f)
-    if not dataset_cfg == dataset_cfg_saved:
-        with open(os.path.join(path, "dataset_cfg.yaml"), "w") as f:
-            yaml.dump(dataset_cfg, f)
-
-    return model_cfg, dataset_cfg
-
-
-def record_config(model_cfg, dataset_cfg, output_path):
-    """Record dataset config to the log path."""
-    # Record model config
-    with open(os.path.join(output_path, "model_cfg.yaml"), "w") as f:
-        yaml.safe_dump(model_cfg, f)
-
-    # Record dataset config
-    with open(os.path.join(output_path, "dataset_cfg.yaml"), "w") as f:
-        yaml.safe_dump(dataset_cfg, f)
-
-
-def train(args, dataset_cfg, model_cfg, output_path):
-    """Training function."""
-    # Update model config from the resume path (only in resume mode)
-    if args.resume:
-        if os.path.realpath(output_path) != os.path.realpath(args.resume_path):
-            record_config(model_cfg, dataset_cfg, output_path)
-
-    # First time, then write the config file to the output path
-    else:
-        record_config(model_cfg, dataset_cfg, output_path)
-
-    # Launch the training
-    train_net(args, dataset_cfg, model_cfg, output_path)
-
-
-def export(
-    args,
-    dataset_cfg,
-    model_cfg,
-    output_path,
-    export_dataset_mode=None,
-    device=torch.device("cuda"),
-):
-    """Export function."""
-    # Choose between normal predictions export or homography adaptation
-    if dataset_cfg.get("homography_adaptation") is not None:
-        print("[Info] Export predictions with homography adaptation.")
-        export_homograpy_adaptation(
-            args, dataset_cfg, model_cfg, output_path, export_dataset_mode, device
-        )
-    else:
-        print("[Info] Export predictions normally.")
-        export_predictions(
-            args, dataset_cfg, model_cfg, output_path, export_dataset_mode
-        )
-
-
-def main(
-    args, dataset_cfg, model_cfg, export_dataset_mode=None, device=torch.device("cuda")
-):
-    """Main function."""
-    # Make the output path
-    output_path = os.path.join(cfg.EXP_PATH, args.exp_name)
-
-    if args.mode == "train":
-        if not os.path.exists(output_path):
-            os.makedirs(output_path)
-        print("[Info] Training mode")
-        print("\t Output path: %s" % output_path)
-        train(args, dataset_cfg, model_cfg, output_path)
-    elif args.mode == "export":
-        # Different output_path in export mode
-        output_path = os.path.join(cfg.export_dataroot, args.exp_name)
-        print("[Info] Export mode")
-        print("\t Output path: %s" % output_path)
-        export(
-            args,
-            dataset_cfg,
-            model_cfg,
-            output_path,
-            export_dataset_mode,
-            device=device,
-        )
-    else:
-        raise ValueError("[Error]: Unknown mode: " + args.mode)
-
-
-def set_random_seed(seed):
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-
-if __name__ == "__main__":
-    # Parse input arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--mode", type=str, default="train", help="'train' or 'export'."
-    )
-    parser.add_argument(
-        "--dataset_config", type=str, default=None, help="Path to the dataset config."
-    )
-    parser.add_argument(
-        "--model_config", type=str, default=None, help="Path to the model config."
-    )
-    parser.add_argument("--exp_name", type=str, default="exp", help="Experiment name.")
-    parser.add_argument(
-        "--resume",
-        action="store_true",
-        default=False,
-        help="Load a previously trained model.",
-    )
-    parser.add_argument(
-        "--pretrained",
-        action="store_true",
-        default=False,
-        help="Start training from a pre-trained model.",
-    )
-    parser.add_argument(
-        "--resume_path", default=None, help="Path from which to resume training."
-    )
-    parser.add_argument(
-        "--pretrained_path", default=None, help="Path to the pre-trained model."
-    )
-    parser.add_argument(
-        "--checkpoint_name", default=None, help="Name of the checkpoint to use."
-    )
-    parser.add_argument(
-        "--export_dataset_mode", default=None, help="'train' or 'test'."
-    )
-    parser.add_argument(
-        "--export_batch_size", default=4, type=int, help="Export batch size."
-    )
-
-    args = parser.parse_args()
-
-    # Check if GPU is available
-    # Get the model
-    if torch.cuda.is_available():
-        device = torch.device("cuda")
-    else:
-        device = torch.device("cpu")
-
-    # Check if dataset config and model config is given.
-    if (
-        ((args.dataset_config is None) or (args.model_config is None))
-        and (not args.resume)
-        and (args.mode == "train")
-    ):
-        raise ValueError(
-            "[Error] The dataset config and model config should be given in non-resume mode"
-        )
-
-    # If resume, check if the resume path has been given
-    if args.resume and (args.resume_path is None):
-        raise ValueError("[Error] Missing resume path.")
-
-    # [Training] Load the config file.
-    if args.mode == "train" and (not args.resume):
-        # Check the pretrained checkpoint_path exists
-        if args.pretrained:
-            checkpoint_folder = args.resume_path
-            checkpoint_path = os.path.join(args.pretrained_path, args.checkpoint_name)
-            if not os.path.exists(checkpoint_path):
-                raise ValueError("[Error] Missing checkpoint: " + checkpoint_path)
-        dataset_cfg = load_config(args.dataset_config)
-        model_cfg = load_config(args.model_config)
-
-    # [resume Training, Test, Export] Load the config file.
-    elif (args.mode == "train" and args.resume) or (args.mode == "export"):
-        # Check checkpoint path exists
-        checkpoint_folder = args.resume_path
-        checkpoint_path = os.path.join(args.resume_path, args.checkpoint_name)
-        if not os.path.exists(checkpoint_path):
-            raise ValueError("[Error] Missing checkpoint: " + checkpoint_path)
-
-        # Load model_cfg from checkpoint folder if not provided
-        if args.model_config is None:
-            print("[Info] No model config provided. Loading from checkpoint folder.")
-            model_cfg_path = os.path.join(checkpoint_folder, "model_cfg.yaml")
-            if not os.path.exists(model_cfg_path):
-                raise ValueError("[Error] Missing model config in checkpoint path.")
-            model_cfg = load_config(model_cfg_path)
-        else:
-            model_cfg = load_config(args.model_config)
-
-        # Load dataset_cfg from checkpoint folder if not provided
-        if args.dataset_config is None:
-            print("[Info] No dataset config provided. Loading from checkpoint folder.")
-            dataset_cfg_path = os.path.join(checkpoint_folder, "dataset_cfg.yaml")
-            if not os.path.exists(dataset_cfg_path):
-                raise ValueError("[Error] Missing dataset config in checkpoint path.")
-            dataset_cfg = load_config(dataset_cfg_path)
-        else:
-            dataset_cfg = load_config(args.dataset_config)
-
-        # Check the --export_dataset_mode flag
-        if (args.mode == "export") and (args.export_dataset_mode is None):
-            raise ValueError("[Error] Empty --export_dataset_mode flag.")
-    else:
-        raise ValueError("[Error] Unknown mode: " + args.mode)
-
-    # Set the random seed
-    seed = dataset_cfg.get("random_seed", 0)
-    set_random_seed(seed)
-
-    main(
-        args,
-        dataset_cfg,
-        model_cfg,
-        export_dataset_mode=args.export_dataset_mode,
-        device=device,
-    )
diff --git a/spaces/Redgon/bingo/src/components/chat-list.tsx b/spaces/Redgon/bingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/Redgon/bingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
-  messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
-  if (!messages.length) {
-    return null
-  }
-
-  return (
-    <div className="chat-container relative flex flex-col">
-      {messages.map((message, index) => (
-        <React.Fragment key={index}>
-          <ChatMessage message={message} />
-          {index < messages.length - 1 && (
-            <Separator className="my-2" />
-          )}
-        </React.Fragment>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/RenXXV/Test02/README.md b/spaces/RenXXV/Test02/README.md
deleted file mode 100644
index e1e42306aed1f17104e831572d5d88d49fc94d1b..0000000000000000000000000000000000000000
--- a/spaces/RenXXV/Test02/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Test02
-emoji: 🌖
-colorFrom: yellow
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ryzal/rvc-models-new/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/Ryzal/rvc-models-new/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/Ryzal/rvc-models-new/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/Sanan/Infrared_Object_Detection_YOLOv5/README.md b/spaces/Sanan/Infrared_Object_Detection_YOLOv5/README.md
deleted file mode 100644
index 722600e5cf125ebc183af6bf0404a8536704715f..0000000000000000000000000000000000000000
--- a/spaces/Sanan/Infrared_Object_Detection_YOLOv5/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Infrared_Object_Detection_YOLOv5
-emoji: 🐨
-colorFrom: red
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Shawn37/UTR_LM/esm/._pretrained.py b/spaces/Shawn37/UTR_LM/esm/._pretrained.py
deleted file mode 100644
index 76ae07cba88a1b44cee6f93c9ed74bbb5163c084..0000000000000000000000000000000000000000
Binary files a/spaces/Shawn37/UTR_LM/esm/._pretrained.py and /dev/null differ
diff --git a/spaces/Sky5408er/vits-uma-genshin-honkai/utils.py b/spaces/Sky5408er/vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/Sky5408er/vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
-  audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
-  return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/DcxImagePlugin.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/DcxImagePlugin.py
deleted file mode 100644
index cde9d42f09f304679180b673bf4d8fdb68d6b4b3..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/DcxImagePlugin.py
+++ /dev/null
@@ -1,79 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# DCX file handling
-#
-# DCX is a container file format defined by Intel, commonly used
-# for fax applications.  Each DCX file consists of a directory
-# (a list of file offsets) followed by a set of (usually 1-bit)
-# PCX files.
-#
-# History:
-# 1995-09-09 fl   Created
-# 1996-03-20 fl   Properly derived from PcxImageFile.
-# 1998-07-15 fl   Renamed offset attribute to avoid name clash
-# 2002-07-30 fl   Fixed file handling
-#
-# Copyright (c) 1997-98 by Secret Labs AB.
-# Copyright (c) 1995-96 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image
-from ._binary import i32le as i32
-from .PcxImagePlugin import PcxImageFile
-
-MAGIC = 0x3ADE68B1  # QUIZ: what's this value, then?
-
-
-def _accept(prefix):
-    return len(prefix) >= 4 and i32(prefix) == MAGIC
-
-
-##
-# Image plugin for the Intel DCX format.
-
-
-class DcxImageFile(PcxImageFile):
-    format = "DCX"
-    format_description = "Intel DCX"
-    _close_exclusive_fp_after_loading = False
-
-    def _open(self):
-        # Header
-        s = self.fp.read(4)
-        if not _accept(s):
-            msg = "not a DCX file"
-            raise SyntaxError(msg)
-
-        # Component directory
-        self._offset = []
-        for i in range(1024):
-            offset = i32(self.fp.read(4))
-            if not offset:
-                break
-            self._offset.append(offset)
-
-        self._fp = self.fp
-        self.frame = None
-        self.n_frames = len(self._offset)
-        self.is_animated = self.n_frames > 1
-        self.seek(0)
-
-    def seek(self, frame):
-        if not self._seek_check(frame):
-            return
-        self.frame = frame
-        self.fp = self._fp
-        self.fp.seek(self._offset[frame])
-        PcxImageFile._open(self)
-
-    def tell(self):
-        return self.frame
-
-
-Image.register_open(DcxImageFile.format, DcxImageFile, _accept)
-
-Image.register_extension(DcxImageFile.format, ".dcx")
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/embedding/embedding.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/embedding/embedding.py
deleted file mode 100644
index 2eb1fa7e842f17b0ee6d160425781bc43b4f3e45..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/embedding/embedding.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from typing import Union
-
-from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
-from docarray.utils._internal.misc import is_tf_available, is_torch_available
-
-torch_available = is_torch_available()
-if torch_available:
-    from docarray.typing.tensor.embedding.torch import TorchEmbedding
-
-
-tf_available = is_tf_available()
-if tf_available:
-    from docarray.typing.tensor.embedding.tensorflow import (
-        TensorFlowEmbedding as TFEmbedding,
-    )
-
-
-if tf_available and torch_available:
-    AnyEmbedding = Union[NdArrayEmbedding, TorchEmbedding, TFEmbedding]  # type: ignore
-elif tf_available:
-    AnyEmbedding = Union[NdArrayEmbedding, TFEmbedding]  # type: ignore
-elif torch_available:
-    AnyEmbedding = Union[NdArrayEmbedding, TorchEmbedding]  # type: ignore
-else:
-    AnyEmbedding = Union[NdArrayEmbedding]  # type: ignore
-
-__all__ = ['AnyEmbedding']
diff --git a/spaces/SuperZz/StartWithAI/app.py b/spaces/SuperZz/StartWithAI/app.py
deleted file mode 100644
index 75cac2f24b6d6052c65b1479e6f4580bc2dba1e2..0000000000000000000000000000000000000000
--- a/spaces/SuperZz/StartWithAI/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import openai
-import os
-import gradio as gr
-
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-prompt = """你的回答需要满足以下要求:
-1. 你的回答必须是中文
-2. 回答限制在500个字以内"""
-
-class Conversation:
-    def __init__(self, prompt, num_of_round):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append({"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo-16k-0613",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-            
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        self.messages.append({"role": "assistant", "content": message})
-        return message
-
-class Conversation2:
-    def __init__(self, prompt, num_of_round):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append( {"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo-16k-0613",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        num_of_tokens = response['usage']['total_tokens']
-        self.messages.append({"role": "assistant", "content": message})
-        return message,num_of_tokens
-
-
-
-conv = Conversation(prompt, 10)
-
-def answer(question, history=[]):
-    history.append(question)
-    response = conv.ask(question)
-    history.append(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
-
-with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
-
-    txt.submit(answer, [txt, state], [chatbot, state])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/schedules/schedule_160k.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/schedules/schedule_160k.py
deleted file mode 100644
index 52603890b10f25faf8eec9f9e5a4468fae09b811..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/schedules/schedule_160k.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
-# runtime settings
-runner = dict(type='IterBasedRunner', max_iters=160000)
-checkpoint_config = dict(by_epoch=False, interval=16000)
-evaluation = dict(interval=16000, metric='mIoU')
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/focal_loss.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/focal_loss.py
deleted file mode 100644
index 763bc93bd2575c49ca8ccf20996bbd92d1e0d1a4..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/focal_loss.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'sigmoid_focal_loss_forward', 'sigmoid_focal_loss_backward',
-    'softmax_focal_loss_forward', 'softmax_focal_loss_backward'
-])
-
-
-class SigmoidFocalLossFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, target, gamma, alpha, weight, reduction):
-        return g.op(
-            'mmcv::MMCVSigmoidFocalLoss',
-            input,
-            target,
-            gamma_f=gamma,
-            alpha_f=alpha,
-            weight_f=weight,
-            reduction_s=reduction)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                target,
-                gamma=2.0,
-                alpha=0.25,
-                weight=None,
-                reduction='mean'):
-
-        assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor))
-        assert input.dim() == 2
-        assert target.dim() == 1
-        assert input.size(0) == target.size(0)
-        if weight is None:
-            weight = input.new_empty(0)
-        else:
-            assert weight.dim() == 1
-            assert input.size(1) == weight.size(0)
-        ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2}
-        assert reduction in ctx.reduction_dict.keys()
-
-        ctx.gamma = float(gamma)
-        ctx.alpha = float(alpha)
-        ctx.reduction = ctx.reduction_dict[reduction]
-
-        output = input.new_zeros(input.size())
-
-        ext_module.sigmoid_focal_loss_forward(
-            input, target, weight, output, gamma=ctx.gamma, alpha=ctx.alpha)
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            output = output.sum() / input.size(0)
-        elif ctx.reduction == ctx.reduction_dict['sum']:
-            output = output.sum()
-        ctx.save_for_backward(input, target, weight)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input, target, weight = ctx.saved_tensors
-
-        grad_input = input.new_zeros(input.size())
-
-        ext_module.sigmoid_focal_loss_backward(
-            input,
-            target,
-            weight,
-            grad_input,
-            gamma=ctx.gamma,
-            alpha=ctx.alpha)
-
-        grad_input *= grad_output
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            grad_input /= input.size(0)
-        return grad_input, None, None, None, None, None
-
-
-sigmoid_focal_loss = SigmoidFocalLossFunction.apply
-
-
-class SigmoidFocalLoss(nn.Module):
-
-    def __init__(self, gamma, alpha, weight=None, reduction='mean'):
-        super(SigmoidFocalLoss, self).__init__()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.register_buffer('weight', weight)
-        self.reduction = reduction
-
-    def forward(self, input, target):
-        return sigmoid_focal_loss(input, target, self.gamma, self.alpha,
-                                  self.weight, self.reduction)
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(gamma={self.gamma}, '
-        s += f'alpha={self.alpha}, '
-        s += f'reduction={self.reduction})'
-        return s
-
-
-class SoftmaxFocalLossFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input, target, gamma, alpha, weight, reduction):
-        return g.op(
-            'mmcv::MMCVSoftmaxFocalLoss',
-            input,
-            target,
-            gamma_f=gamma,
-            alpha_f=alpha,
-            weight_f=weight,
-            reduction_s=reduction)
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                target,
-                gamma=2.0,
-                alpha=0.25,
-                weight=None,
-                reduction='mean'):
-
-        assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor))
-        assert input.dim() == 2
-        assert target.dim() == 1
-        assert input.size(0) == target.size(0)
-        if weight is None:
-            weight = input.new_empty(0)
-        else:
-            assert weight.dim() == 1
-            assert input.size(1) == weight.size(0)
-        ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2}
-        assert reduction in ctx.reduction_dict.keys()
-
-        ctx.gamma = float(gamma)
-        ctx.alpha = float(alpha)
-        ctx.reduction = ctx.reduction_dict[reduction]
-
-        channel_stats, _ = torch.max(input, dim=1)
-        input_softmax = input - channel_stats.unsqueeze(1).expand_as(input)
-        input_softmax.exp_()
-
-        channel_stats = input_softmax.sum(dim=1)
-        input_softmax /= channel_stats.unsqueeze(1).expand_as(input)
-
-        output = input.new_zeros(input.size(0))
-        ext_module.softmax_focal_loss_forward(
-            input_softmax,
-            target,
-            weight,
-            output,
-            gamma=ctx.gamma,
-            alpha=ctx.alpha)
-
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            output = output.sum() / input.size(0)
-        elif ctx.reduction == ctx.reduction_dict['sum']:
-            output = output.sum()
-        ctx.save_for_backward(input_softmax, target, weight)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input_softmax, target, weight = ctx.saved_tensors
-        buff = input_softmax.new_zeros(input_softmax.size(0))
-        grad_input = input_softmax.new_zeros(input_softmax.size())
-
-        ext_module.softmax_focal_loss_backward(
-            input_softmax,
-            target,
-            weight,
-            buff,
-            grad_input,
-            gamma=ctx.gamma,
-            alpha=ctx.alpha)
-
-        grad_input *= grad_output
-        if ctx.reduction == ctx.reduction_dict['mean']:
-            grad_input /= input_softmax.size(0)
-        return grad_input, None, None, None, None, None
-
-
-softmax_focal_loss = SoftmaxFocalLossFunction.apply
-
-
-class SoftmaxFocalLoss(nn.Module):
-
-    def __init__(self, gamma, alpha, weight=None, reduction='mean'):
-        super(SoftmaxFocalLoss, self).__init__()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.register_buffer('weight', weight)
-        self.reduction = reduction
-
-    def forward(self, input, target):
-        return softmax_focal_loss(input, target, self.gamma, self.alpha,
-                                  self.weight, self.reduction)
-
-    def __repr__(self):
-        s = self.__class__.__name__
-        s += f'(gamma={self.gamma}, '
-        s += f'alpha={self.alpha}, '
-        s += f'reduction={self.reduction})'
-        return s
diff --git a/spaces/TH5314/newbing/src/components/markdown.tsx b/spaces/TH5314/newbing/src/components/markdown.tsx
deleted file mode 100644
index d4491467a1f14d1d72e535caac9c40636054e5df..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/components/markdown.tsx
+++ /dev/null
@@ -1,9 +0,0 @@
-import { FC, memo } from 'react'
-import ReactMarkdown, { Options } from 'react-markdown'
-
-export const MemoizedReactMarkdown: FC<Options> = memo(
-  ReactMarkdown,
-  (prevProps, nextProps) =>
-    prevProps.children === nextProps.children &&
-    prevProps.className === nextProps.className
-)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/connection.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/connection.py
deleted file mode 100644
index 54b96b19154ccaa138af6bc0a4ac2b8f763017ce..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/connection.py
+++ /dev/null
@@ -1,572 +0,0 @@
-from __future__ import absolute_import
-
-import datetime
-import logging
-import os
-import re
-import socket
-import warnings
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-from .packages import six
-from .packages.six.moves.http_client import HTTPConnection as _HTTPConnection
-from .packages.six.moves.http_client import HTTPException  # noqa: F401
-from .util.proxy import create_proxy_ssl_context
-
-try:  # Compiled with SSL?
-    import ssl
-
-    BaseSSLError = ssl.SSLError
-except (ImportError, AttributeError):  # Platform-specific: No SSL.
-    ssl = None
-
-    class BaseSSLError(BaseException):
-        pass
-
-
-try:
-    # Python 3: not a no-op, we're adding this to the namespace so it can be imported.
-    ConnectionError = ConnectionError
-except NameError:
-    # Python 2
-    class ConnectionError(Exception):
-        pass
-
-
-try:  # Python 3:
-    # Not a no-op, we're adding this to the namespace so it can be imported.
-    BrokenPipeError = BrokenPipeError
-except NameError:  # Python 2:
-
-    class BrokenPipeError(Exception):
-        pass
-
-
-from ._collections import HTTPHeaderDict  # noqa (historical, removed in v2)
-from ._version import __version__
-from .exceptions import (
-    ConnectTimeoutError,
-    NewConnectionError,
-    SubjectAltNameWarning,
-    SystemTimeWarning,
-)
-from .util import SKIP_HEADER, SKIPPABLE_HEADERS, connection
-from .util.ssl_ import (
-    assert_fingerprint,
-    create_urllib3_context,
-    is_ipaddress,
-    resolve_cert_reqs,
-    resolve_ssl_version,
-    ssl_wrap_socket,
-)
-from .util.ssl_match_hostname import CertificateError, match_hostname
-
-log = logging.getLogger(__name__)
-
-port_by_scheme = {"http": 80, "https": 443}
-
-# When it comes time to update this value as a part of regular maintenance
-# (ie test_recent_date is failing) update it to ~6 months before the current date.
-RECENT_DATE = datetime.date(2022, 1, 1)
-
-_CONTAINS_CONTROL_CHAR_RE = re.compile(r"[^-!#$%&'*+.^_`|~0-9a-zA-Z]")
-
-
-class HTTPConnection(_HTTPConnection, object):
-    """
-    Based on :class:`http.client.HTTPConnection` but provides an extra constructor
-    backwards-compatibility layer between older and newer Pythons.
-
-    Additional keyword parameters are used to configure attributes of the connection.
-    Accepted parameters include:
-
-    - ``strict``: See the documentation on :class:`urllib3.connectionpool.HTTPConnectionPool`
-    - ``source_address``: Set the source address for the current connection.
-    - ``socket_options``: Set specific options on the underlying socket. If not specified, then
-      defaults are loaded from ``HTTPConnection.default_socket_options`` which includes disabling
-      Nagle's algorithm (sets TCP_NODELAY to 1) unless the connection is behind a proxy.
-
-      For example, if you wish to enable TCP Keep Alive in addition to the defaults,
-      you might pass:
-
-      .. code-block:: python
-
-         HTTPConnection.default_socket_options + [
-             (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),
-         ]
-
-      Or you may want to disable the defaults by passing an empty list (e.g., ``[]``).
-    """
-
-    default_port = port_by_scheme["http"]
-
-    #: Disable Nagle's algorithm by default.
-    #: ``[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]``
-    default_socket_options = [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
-
-    #: Whether this connection verifies the host's certificate.
-    is_verified = False
-
-    #: Whether this proxy connection (if used) verifies the proxy host's
-    #: certificate.
-    proxy_is_verified = None
-
-    def __init__(self, *args, **kw):
-        if not six.PY2:
-            kw.pop("strict", None)
-
-        # Pre-set source_address.
-        self.source_address = kw.get("source_address")
-
-        #: The socket options provided by the user. If no options are
-        #: provided, we use the default options.
-        self.socket_options = kw.pop("socket_options", self.default_socket_options)
-
-        # Proxy options provided by the user.
-        self.proxy = kw.pop("proxy", None)
-        self.proxy_config = kw.pop("proxy_config", None)
-
-        _HTTPConnection.__init__(self, *args, **kw)
-
-    @property
-    def host(self):
-        """
-        Getter method to remove any trailing dots that indicate the hostname is an FQDN.
-
-        In general, SSL certificates don't include the trailing dot indicating a
-        fully-qualified domain name, and thus, they don't validate properly when
-        checked against a domain name that includes the dot. In addition, some
-        servers may not expect to receive the trailing dot when provided.
-
-        However, the hostname with trailing dot is critical to DNS resolution; doing a
-        lookup with the trailing dot will properly only resolve the appropriate FQDN,
-        whereas a lookup without a trailing dot will search the system's search domain
-        list. Thus, it's important to keep the original host around for use only in
-        those cases where it's appropriate (i.e., when doing DNS lookup to establish the
-        actual TCP connection across which we're going to send HTTP requests).
-        """
-        return self._dns_host.rstrip(".")
-
-    @host.setter
-    def host(self, value):
-        """
-        Setter for the `host` property.
-
-        We assume that only urllib3 uses the _dns_host attribute; httplib itself
-        only uses `host`, and it seems reasonable that other libraries follow suit.
-        """
-        self._dns_host = value
-
-    def _new_conn(self):
-        """Establish a socket connection and set nodelay settings on it.
-
-        :return: New socket connection.
-        """
-        extra_kw = {}
-        if self.source_address:
-            extra_kw["source_address"] = self.source_address
-
-        if self.socket_options:
-            extra_kw["socket_options"] = self.socket_options
-
-        try:
-            conn = connection.create_connection(
-                (self._dns_host, self.port), self.timeout, **extra_kw
-            )
-
-        except SocketTimeout:
-            raise ConnectTimeoutError(
-                self,
-                "Connection to %s timed out. (connect timeout=%s)"
-                % (self.host, self.timeout),
-            )
-
-        except SocketError as e:
-            raise NewConnectionError(
-                self, "Failed to establish a new connection: %s" % e
-            )
-
-        return conn
-
-    def _is_using_tunnel(self):
-        # Google App Engine's httplib does not define _tunnel_host
-        return getattr(self, "_tunnel_host", None)
-
-    def _prepare_conn(self, conn):
-        self.sock = conn
-        if self._is_using_tunnel():
-            # TODO: Fix tunnel so it doesn't depend on self.sock state.
-            self._tunnel()
-            # Mark this connection as not reusable
-            self.auto_open = 0
-
-    def connect(self):
-        conn = self._new_conn()
-        self._prepare_conn(conn)
-
-    def putrequest(self, method, url, *args, **kwargs):
-        """ """
-        # Empty docstring because the indentation of CPython's implementation
-        # is broken but we don't want this method in our documentation.
-        match = _CONTAINS_CONTROL_CHAR_RE.search(method)
-        if match:
-            raise ValueError(
-                "Method cannot contain non-token characters %r (found at least %r)"
-                % (method, match.group())
-            )
-
-        return _HTTPConnection.putrequest(self, method, url, *args, **kwargs)
-
-    def putheader(self, header, *values):
-        """ """
-        if not any(isinstance(v, str) and v == SKIP_HEADER for v in values):
-            _HTTPConnection.putheader(self, header, *values)
-        elif six.ensure_str(header.lower()) not in SKIPPABLE_HEADERS:
-            raise ValueError(
-                "urllib3.util.SKIP_HEADER only supports '%s'"
-                % ("', '".join(map(str.title, sorted(SKIPPABLE_HEADERS))),)
-            )
-
-    def request(self, method, url, body=None, headers=None):
-        # Update the inner socket's timeout value to send the request.
-        # This only triggers if the connection is re-used.
-        if getattr(self, "sock", None) is not None:
-            self.sock.settimeout(self.timeout)
-
-        if headers is None:
-            headers = {}
-        else:
-            # Avoid modifying the headers passed into .request()
-            headers = headers.copy()
-        if "user-agent" not in (six.ensure_str(k.lower()) for k in headers):
-            headers["User-Agent"] = _get_default_user_agent()
-        super(HTTPConnection, self).request(method, url, body=body, headers=headers)
-
-    def request_chunked(self, method, url, body=None, headers=None):
-        """
-        Alternative to the common request method, which sends the
-        body with chunked encoding and not as one block
-        """
-        headers = headers or {}
-        header_keys = set([six.ensure_str(k.lower()) for k in headers])
-        skip_accept_encoding = "accept-encoding" in header_keys
-        skip_host = "host" in header_keys
-        self.putrequest(
-            method, url, skip_accept_encoding=skip_accept_encoding, skip_host=skip_host
-        )
-        if "user-agent" not in header_keys:
-            self.putheader("User-Agent", _get_default_user_agent())
-        for header, value in headers.items():
-            self.putheader(header, value)
-        if "transfer-encoding" not in header_keys:
-            self.putheader("Transfer-Encoding", "chunked")
-        self.endheaders()
-
-        if body is not None:
-            stringish_types = six.string_types + (bytes,)
-            if isinstance(body, stringish_types):
-                body = (body,)
-            for chunk in body:
-                if not chunk:
-                    continue
-                if not isinstance(chunk, bytes):
-                    chunk = chunk.encode("utf8")
-                len_str = hex(len(chunk))[2:]
-                to_send = bytearray(len_str.encode())
-                to_send += b"\r\n"
-                to_send += chunk
-                to_send += b"\r\n"
-                self.send(to_send)
-
-        # After the if clause, to always have a closed body
-        self.send(b"0\r\n\r\n")
-
-
-class HTTPSConnection(HTTPConnection):
-    """
-    Many of the parameters to this constructor are passed to the underlying SSL
-    socket by means of :py:func:`urllib3.util.ssl_wrap_socket`.
-    """
-
-    default_port = port_by_scheme["https"]
-
-    cert_reqs = None
-    ca_certs = None
-    ca_cert_dir = None
-    ca_cert_data = None
-    ssl_version = None
-    assert_fingerprint = None
-    tls_in_tls_required = False
-
-    def __init__(
-        self,
-        host,
-        port=None,
-        key_file=None,
-        cert_file=None,
-        key_password=None,
-        strict=None,
-        timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
-        ssl_context=None,
-        server_hostname=None,
-        **kw
-    ):
-
-        HTTPConnection.__init__(self, host, port, strict=strict, timeout=timeout, **kw)
-
-        self.key_file = key_file
-        self.cert_file = cert_file
-        self.key_password = key_password
-        self.ssl_context = ssl_context
-        self.server_hostname = server_hostname
-
-        # Required property for Google AppEngine 1.9.0 which otherwise causes
-        # HTTPS requests to go out as HTTP. (See Issue #356)
-        self._protocol = "https"
-
-    def set_cert(
-        self,
-        key_file=None,
-        cert_file=None,
-        cert_reqs=None,
-        key_password=None,
-        ca_certs=None,
-        assert_hostname=None,
-        assert_fingerprint=None,
-        ca_cert_dir=None,
-        ca_cert_data=None,
-    ):
-        """
-        This method should only be called once, before the connection is used.
-        """
-        # If cert_reqs is not provided we'll assume CERT_REQUIRED unless we also
-        # have an SSLContext object in which case we'll use its verify_mode.
-        if cert_reqs is None:
-            if self.ssl_context is not None:
-                cert_reqs = self.ssl_context.verify_mode
-            else:
-                cert_reqs = resolve_cert_reqs(None)
-
-        self.key_file = key_file
-        self.cert_file = cert_file
-        self.cert_reqs = cert_reqs
-        self.key_password = key_password
-        self.assert_hostname = assert_hostname
-        self.assert_fingerprint = assert_fingerprint
-        self.ca_certs = ca_certs and os.path.expanduser(ca_certs)
-        self.ca_cert_dir = ca_cert_dir and os.path.expanduser(ca_cert_dir)
-        self.ca_cert_data = ca_cert_data
-
-    def connect(self):
-        # Add certificate verification
-        self.sock = conn = self._new_conn()
-        hostname = self.host
-        tls_in_tls = False
-
-        if self._is_using_tunnel():
-            if self.tls_in_tls_required:
-                self.sock = conn = self._connect_tls_proxy(hostname, conn)
-                tls_in_tls = True
-
-            # Calls self._set_hostport(), so self.host is
-            # self._tunnel_host below.
-            self._tunnel()
-            # Mark this connection as not reusable
-            self.auto_open = 0
-
-            # Override the host with the one we're requesting data from.
-            hostname = self._tunnel_host
-
-        server_hostname = hostname
-        if self.server_hostname is not None:
-            server_hostname = self.server_hostname
-
-        is_time_off = datetime.date.today() < RECENT_DATE
-        if is_time_off:
-            warnings.warn(
-                (
-                    "System time is way off (before {0}). This will probably "
-                    "lead to SSL verification errors"
-                ).format(RECENT_DATE),
-                SystemTimeWarning,
-            )
-
-        # Wrap socket using verification with the root certs in
-        # trusted_root_certs
-        default_ssl_context = False
-        if self.ssl_context is None:
-            default_ssl_context = True
-            self.ssl_context = create_urllib3_context(
-                ssl_version=resolve_ssl_version(self.ssl_version),
-                cert_reqs=resolve_cert_reqs(self.cert_reqs),
-            )
-
-        context = self.ssl_context
-        context.verify_mode = resolve_cert_reqs(self.cert_reqs)
-
-        # Try to load OS default certs if none are given.
-        # Works well on Windows (requires Python3.4+)
-        if (
-            not self.ca_certs
-            and not self.ca_cert_dir
-            and not self.ca_cert_data
-            and default_ssl_context
-            and hasattr(context, "load_default_certs")
-        ):
-            context.load_default_certs()
-
-        self.sock = ssl_wrap_socket(
-            sock=conn,
-            keyfile=self.key_file,
-            certfile=self.cert_file,
-            key_password=self.key_password,
-            ca_certs=self.ca_certs,
-            ca_cert_dir=self.ca_cert_dir,
-            ca_cert_data=self.ca_cert_data,
-            server_hostname=server_hostname,
-            ssl_context=context,
-            tls_in_tls=tls_in_tls,
-        )
-
-        # If we're using all defaults and the connection
-        # is TLSv1 or TLSv1.1 we throw a DeprecationWarning
-        # for the host.
-        if (
-            default_ssl_context
-            and self.ssl_version is None
-            and hasattr(self.sock, "version")
-            and self.sock.version() in {"TLSv1", "TLSv1.1"}
-        ):
-            warnings.warn(
-                "Negotiating TLSv1/TLSv1.1 by default is deprecated "
-                "and will be disabled in urllib3 v2.0.0. Connecting to "
-                "'%s' with '%s' can be enabled by explicitly opting-in "
-                "with 'ssl_version'" % (self.host, self.sock.version()),
-                DeprecationWarning,
-            )
-
-        if self.assert_fingerprint:
-            assert_fingerprint(
-                self.sock.getpeercert(binary_form=True), self.assert_fingerprint
-            )
-        elif (
-            context.verify_mode != ssl.CERT_NONE
-            and not getattr(context, "check_hostname", False)
-            and self.assert_hostname is not False
-        ):
-            # While urllib3 attempts to always turn off hostname matching from
-            # the TLS library, this cannot always be done. So we check whether
-            # the TLS Library still thinks it's matching hostnames.
-            cert = self.sock.getpeercert()
-            if not cert.get("subjectAltName", ()):
-                warnings.warn(
-                    (
-                        "Certificate for {0} has no `subjectAltName`, falling back to check for a "
-                        "`commonName` for now. This feature is being removed by major browsers and "
-                        "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
-                        "for details.)".format(hostname)
-                    ),
-                    SubjectAltNameWarning,
-                )
-            _match_hostname(cert, self.assert_hostname or server_hostname)
-
-        self.is_verified = (
-            context.verify_mode == ssl.CERT_REQUIRED
-            or self.assert_fingerprint is not None
-        )
-
-    def _connect_tls_proxy(self, hostname, conn):
-        """
-        Establish a TLS connection to the proxy using the provided SSL context.
-        """
-        proxy_config = self.proxy_config
-        ssl_context = proxy_config.ssl_context
-        if ssl_context:
-            # If the user provided a proxy context, we assume CA and client
-            # certificates have already been set
-            return ssl_wrap_socket(
-                sock=conn,
-                server_hostname=hostname,
-                ssl_context=ssl_context,
-            )
-
-        ssl_context = create_proxy_ssl_context(
-            self.ssl_version,
-            self.cert_reqs,
-            self.ca_certs,
-            self.ca_cert_dir,
-            self.ca_cert_data,
-        )
-
-        # If no cert was provided, use only the default options for server
-        # certificate validation
-        socket = ssl_wrap_socket(
-            sock=conn,
-            ca_certs=self.ca_certs,
-            ca_cert_dir=self.ca_cert_dir,
-            ca_cert_data=self.ca_cert_data,
-            server_hostname=hostname,
-            ssl_context=ssl_context,
-        )
-
-        if ssl_context.verify_mode != ssl.CERT_NONE and not getattr(
-            ssl_context, "check_hostname", False
-        ):
-            # While urllib3 attempts to always turn off hostname matching from
-            # the TLS library, this cannot always be done. So we check whether
-            # the TLS Library still thinks it's matching hostnames.
-            cert = socket.getpeercert()
-            if not cert.get("subjectAltName", ()):
-                warnings.warn(
-                    (
-                        "Certificate for {0} has no `subjectAltName`, falling back to check for a "
-                        "`commonName` for now. This feature is being removed by major browsers and "
-                        "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
-                        "for details.)".format(hostname)
-                    ),
-                    SubjectAltNameWarning,
-                )
-            _match_hostname(cert, hostname)
-
-        self.proxy_is_verified = ssl_context.verify_mode == ssl.CERT_REQUIRED
-        return socket
-
-
-def _match_hostname(cert, asserted_hostname):
-    # Our upstream implementation of ssl.match_hostname()
-    # only applies this normalization to IP addresses so it doesn't
-    # match DNS SANs so we do the same thing!
-    stripped_hostname = asserted_hostname.strip("u[]")
-    if is_ipaddress(stripped_hostname):
-        asserted_hostname = stripped_hostname
-
-    try:
-        match_hostname(cert, asserted_hostname)
-    except CertificateError as e:
-        log.warning(
-            "Certificate did not match expected hostname: %s. Certificate: %s",
-            asserted_hostname,
-            cert,
-        )
-        # Add cert to exception and reraise so client code can inspect
-        # the cert when catching the exception, if they want to
-        e._peer_cert = cert
-        raise
-
-
-def _get_default_user_agent():
-    return "python-urllib3/%s" % __version__
-
-
-class DummyConnection(object):
-    """Used to detect a failed ConnectionCls import."""
-
-    pass
-
-
-if not ssl:
-    HTTPSConnection = DummyConnection  # noqa: F811
-
-
-VerifiedHTTPSConnection = HTTPSConnection
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/macosx_libfile.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/macosx_libfile.py
deleted file mode 100644
index 3d19984813236184c8f87bead16a282f1980ffd4..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/macosx_libfile.py
+++ /dev/null
@@ -1,471 +0,0 @@
-"""
-This module contains function to analyse dynamic library
-headers to extract system information
-
-Currently only for MacOSX
-
-Library file on macosx system starts with Mach-O or Fat field.
-This can be distinguish by first 32 bites and it is called magic number.
-Proper value of magic number is with suffix _MAGIC. Suffix _CIGAM means
-reversed bytes order.
-Both fields can occur in two types: 32 and 64 bytes.
-
-FAT field inform that this library contains few version of library
-(typically for different types version). It contains
-information where Mach-O headers starts.
-
-Each section started with Mach-O header contains one library
-(So if file starts with this field it contains only one version).
-
-After filed Mach-O there are section fields.
-Each of them starts with two fields:
-cmd - magic number for this command
-cmdsize - total size occupied by this section information.
-
-In this case only sections LC_VERSION_MIN_MACOSX (for macosx 10.13 and earlier)
-and LC_BUILD_VERSION (for macosx 10.14 and newer) are interesting,
-because them contains information about minimal system version.
-
-Important remarks:
-- For fat files this implementation looks for maximum number version.
-  It not check if it is 32 or 64 and do not compare it with currently built package.
-  So it is possible to false report higher version that needed.
-- All structures signatures are taken form macosx header files.
-- I think that binary format will be more stable than `otool` output.
-  and if apple introduce some changes both implementation will need to be updated.
-- The system compile will set the deployment target no lower than
-  11.0 for arm64 builds. For "Universal 2" builds use the x86_64 deployment
-  target when the arm64 target is 11.0.
-"""
-
-from __future__ import annotations
-
-import ctypes
-import os
-import sys
-
-"""here the needed const and struct from mach-o header files"""
-
-FAT_MAGIC = 0xCAFEBABE
-FAT_CIGAM = 0xBEBAFECA
-FAT_MAGIC_64 = 0xCAFEBABF
-FAT_CIGAM_64 = 0xBFBAFECA
-MH_MAGIC = 0xFEEDFACE
-MH_CIGAM = 0xCEFAEDFE
-MH_MAGIC_64 = 0xFEEDFACF
-MH_CIGAM_64 = 0xCFFAEDFE
-
-LC_VERSION_MIN_MACOSX = 0x24
-LC_BUILD_VERSION = 0x32
-
-CPU_TYPE_ARM64 = 0x0100000C
-
-mach_header_fields = [
-    ("magic", ctypes.c_uint32),
-    ("cputype", ctypes.c_int),
-    ("cpusubtype", ctypes.c_int),
-    ("filetype", ctypes.c_uint32),
-    ("ncmds", ctypes.c_uint32),
-    ("sizeofcmds", ctypes.c_uint32),
-    ("flags", ctypes.c_uint32),
-]
-"""
-struct mach_header {
-    uint32_t	magic;		/* mach magic number identifier */
-    cpu_type_t	cputype;	/* cpu specifier */
-    cpu_subtype_t	cpusubtype;	/* machine specifier */
-    uint32_t	filetype;	/* type of file */
-    uint32_t	ncmds;		/* number of load commands */
-    uint32_t	sizeofcmds;	/* the size of all the load commands */
-    uint32_t	flags;		/* flags */
-};
-typedef integer_t cpu_type_t;
-typedef integer_t cpu_subtype_t;
-"""
-
-mach_header_fields_64 = mach_header_fields + [("reserved", ctypes.c_uint32)]
-"""
-struct mach_header_64 {
-    uint32_t	magic;		/* mach magic number identifier */
-    cpu_type_t	cputype;	/* cpu specifier */
-    cpu_subtype_t	cpusubtype;	/* machine specifier */
-    uint32_t	filetype;	/* type of file */
-    uint32_t	ncmds;		/* number of load commands */
-    uint32_t	sizeofcmds;	/* the size of all the load commands */
-    uint32_t	flags;		/* flags */
-    uint32_t	reserved;	/* reserved */
-};
-"""
-
-fat_header_fields = [("magic", ctypes.c_uint32), ("nfat_arch", ctypes.c_uint32)]
-"""
-struct fat_header {
-    uint32_t	magic;		/* FAT_MAGIC or FAT_MAGIC_64 */
-    uint32_t	nfat_arch;	/* number of structs that follow */
-};
-"""
-
-fat_arch_fields = [
-    ("cputype", ctypes.c_int),
-    ("cpusubtype", ctypes.c_int),
-    ("offset", ctypes.c_uint32),
-    ("size", ctypes.c_uint32),
-    ("align", ctypes.c_uint32),
-]
-"""
-struct fat_arch {
-    cpu_type_t	cputype;	/* cpu specifier (int) */
-    cpu_subtype_t	cpusubtype;	/* machine specifier (int) */
-    uint32_t	offset;		/* file offset to this object file */
-    uint32_t	size;		/* size of this object file */
-    uint32_t	align;		/* alignment as a power of 2 */
-};
-"""
-
-fat_arch_64_fields = [
-    ("cputype", ctypes.c_int),
-    ("cpusubtype", ctypes.c_int),
-    ("offset", ctypes.c_uint64),
-    ("size", ctypes.c_uint64),
-    ("align", ctypes.c_uint32),
-    ("reserved", ctypes.c_uint32),
-]
-"""
-struct fat_arch_64 {
-    cpu_type_t	cputype;	/* cpu specifier (int) */
-    cpu_subtype_t	cpusubtype;	/* machine specifier (int) */
-    uint64_t	offset;		/* file offset to this object file */
-    uint64_t	size;		/* size of this object file */
-    uint32_t	align;		/* alignment as a power of 2 */
-    uint32_t	reserved;	/* reserved */
-};
-"""
-
-segment_base_fields = [("cmd", ctypes.c_uint32), ("cmdsize", ctypes.c_uint32)]
-"""base for reading segment info"""
-
-segment_command_fields = [
-    ("cmd", ctypes.c_uint32),
-    ("cmdsize", ctypes.c_uint32),
-    ("segname", ctypes.c_char * 16),
-    ("vmaddr", ctypes.c_uint32),
-    ("vmsize", ctypes.c_uint32),
-    ("fileoff", ctypes.c_uint32),
-    ("filesize", ctypes.c_uint32),
-    ("maxprot", ctypes.c_int),
-    ("initprot", ctypes.c_int),
-    ("nsects", ctypes.c_uint32),
-    ("flags", ctypes.c_uint32),
-]
-"""
-struct segment_command { /* for 32-bit architectures */
-    uint32_t	cmd;		/* LC_SEGMENT */
-    uint32_t	cmdsize;	/* includes sizeof section structs */
-    char		segname[16];	/* segment name */
-    uint32_t	vmaddr;		/* memory address of this segment */
-    uint32_t	vmsize;		/* memory size of this segment */
-    uint32_t	fileoff;	/* file offset of this segment */
-    uint32_t	filesize;	/* amount to map from the file */
-    vm_prot_t	maxprot;	/* maximum VM protection */
-    vm_prot_t	initprot;	/* initial VM protection */
-    uint32_t	nsects;		/* number of sections in segment */
-    uint32_t	flags;		/* flags */
-};
-typedef int vm_prot_t;
-"""
-
-segment_command_fields_64 = [
-    ("cmd", ctypes.c_uint32),
-    ("cmdsize", ctypes.c_uint32),
-    ("segname", ctypes.c_char * 16),
-    ("vmaddr", ctypes.c_uint64),
-    ("vmsize", ctypes.c_uint64),
-    ("fileoff", ctypes.c_uint64),
-    ("filesize", ctypes.c_uint64),
-    ("maxprot", ctypes.c_int),
-    ("initprot", ctypes.c_int),
-    ("nsects", ctypes.c_uint32),
-    ("flags", ctypes.c_uint32),
-]
-"""
-struct segment_command_64 { /* for 64-bit architectures */
-    uint32_t	cmd;		/* LC_SEGMENT_64 */
-    uint32_t	cmdsize;	/* includes sizeof section_64 structs */
-    char		segname[16];	/* segment name */
-    uint64_t	vmaddr;		/* memory address of this segment */
-    uint64_t	vmsize;		/* memory size of this segment */
-    uint64_t	fileoff;	/* file offset of this segment */
-    uint64_t	filesize;	/* amount to map from the file */
-    vm_prot_t	maxprot;	/* maximum VM protection */
-    vm_prot_t	initprot;	/* initial VM protection */
-    uint32_t	nsects;		/* number of sections in segment */
-    uint32_t	flags;		/* flags */
-};
-"""
-
-version_min_command_fields = segment_base_fields + [
-    ("version", ctypes.c_uint32),
-    ("sdk", ctypes.c_uint32),
-]
-"""
-struct version_min_command {
-    uint32_t	cmd;		/* LC_VERSION_MIN_MACOSX or
-                               LC_VERSION_MIN_IPHONEOS or
-                               LC_VERSION_MIN_WATCHOS or
-                               LC_VERSION_MIN_TVOS */
-    uint32_t	cmdsize;	/* sizeof(struct min_version_command) */
-    uint32_t	version;	/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
-    uint32_t	sdk;		/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
-};
-"""
-
-build_version_command_fields = segment_base_fields + [
-    ("platform", ctypes.c_uint32),
-    ("minos", ctypes.c_uint32),
-    ("sdk", ctypes.c_uint32),
-    ("ntools", ctypes.c_uint32),
-]
-"""
-struct build_version_command {
-    uint32_t	cmd;		/* LC_BUILD_VERSION */
-    uint32_t	cmdsize;	/* sizeof(struct build_version_command) plus */
-                                /* ntools * sizeof(struct build_tool_version) */
-    uint32_t	platform;	/* platform */
-    uint32_t	minos;		/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
-    uint32_t	sdk;		/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
-    uint32_t	ntools;		/* number of tool entries following this */
-};
-"""
-
-
-def swap32(x):
-    return (
-        ((x << 24) & 0xFF000000)
-        | ((x << 8) & 0x00FF0000)
-        | ((x >> 8) & 0x0000FF00)
-        | ((x >> 24) & 0x000000FF)
-    )
-
-
-def get_base_class_and_magic_number(lib_file, seek=None):
-    if seek is None:
-        seek = lib_file.tell()
-    else:
-        lib_file.seek(seek)
-    magic_number = ctypes.c_uint32.from_buffer_copy(
-        lib_file.read(ctypes.sizeof(ctypes.c_uint32))
-    ).value
-
-    # Handle wrong byte order
-    if magic_number in [FAT_CIGAM, FAT_CIGAM_64, MH_CIGAM, MH_CIGAM_64]:
-        if sys.byteorder == "little":
-            BaseClass = ctypes.BigEndianStructure
-        else:
-            BaseClass = ctypes.LittleEndianStructure
-
-        magic_number = swap32(magic_number)
-    else:
-        BaseClass = ctypes.Structure
-
-    lib_file.seek(seek)
-    return BaseClass, magic_number
-
-
-def read_data(struct_class, lib_file):
-    return struct_class.from_buffer_copy(lib_file.read(ctypes.sizeof(struct_class)))
-
-
-def extract_macosx_min_system_version(path_to_lib):
-    with open(path_to_lib, "rb") as lib_file:
-        BaseClass, magic_number = get_base_class_and_magic_number(lib_file, 0)
-        if magic_number not in [FAT_MAGIC, FAT_MAGIC_64, MH_MAGIC, MH_MAGIC_64]:
-            return
-
-        if magic_number in [FAT_MAGIC, FAT_CIGAM_64]:
-
-            class FatHeader(BaseClass):
-                _fields_ = fat_header_fields
-
-            fat_header = read_data(FatHeader, lib_file)
-            if magic_number == FAT_MAGIC:
-
-                class FatArch(BaseClass):
-                    _fields_ = fat_arch_fields
-
-            else:
-
-                class FatArch(BaseClass):
-                    _fields_ = fat_arch_64_fields
-
-            fat_arch_list = [
-                read_data(FatArch, lib_file) for _ in range(fat_header.nfat_arch)
-            ]
-
-            versions_list = []
-            for el in fat_arch_list:
-                try:
-                    version = read_mach_header(lib_file, el.offset)
-                    if version is not None:
-                        if el.cputype == CPU_TYPE_ARM64 and len(fat_arch_list) != 1:
-                            # Xcode will not set the deployment target below 11.0.0
-                            # for the arm64 architecture. Ignore the arm64 deployment
-                            # in fat binaries when the target is 11.0.0, that way
-                            # the other architectures can select a lower deployment
-                            # target.
-                            # This is safe because there is no arm64 variant for
-                            # macOS 10.15 or earlier.
-                            if version == (11, 0, 0):
-                                continue
-                        versions_list.append(version)
-                except ValueError:
-                    pass
-
-            if len(versions_list) > 0:
-                return max(versions_list)
-            else:
-                return None
-
-        else:
-            try:
-                return read_mach_header(lib_file, 0)
-            except ValueError:
-                """when some error during read library files"""
-                return None
-
-
-def read_mach_header(lib_file, seek=None):
-    """
-    This funcition parse mach-O header and extract
-    information about minimal system version
-
-    :param lib_file: reference to opened library file with pointer
-    """
-    if seek is not None:
-        lib_file.seek(seek)
-    base_class, magic_number = get_base_class_and_magic_number(lib_file)
-    arch = "32" if magic_number == MH_MAGIC else "64"
-
-    class SegmentBase(base_class):
-        _fields_ = segment_base_fields
-
-    if arch == "32":
-
-        class MachHeader(base_class):
-            _fields_ = mach_header_fields
-
-    else:
-
-        class MachHeader(base_class):
-            _fields_ = mach_header_fields_64
-
-    mach_header = read_data(MachHeader, lib_file)
-    for _i in range(mach_header.ncmds):
-        pos = lib_file.tell()
-        segment_base = read_data(SegmentBase, lib_file)
-        lib_file.seek(pos)
-        if segment_base.cmd == LC_VERSION_MIN_MACOSX:
-
-            class VersionMinCommand(base_class):
-                _fields_ = version_min_command_fields
-
-            version_info = read_data(VersionMinCommand, lib_file)
-            return parse_version(version_info.version)
-        elif segment_base.cmd == LC_BUILD_VERSION:
-
-            class VersionBuild(base_class):
-                _fields_ = build_version_command_fields
-
-            version_info = read_data(VersionBuild, lib_file)
-            return parse_version(version_info.minos)
-        else:
-            lib_file.seek(pos + segment_base.cmdsize)
-            continue
-
-
-def parse_version(version):
-    x = (version & 0xFFFF0000) >> 16
-    y = (version & 0x0000FF00) >> 8
-    z = version & 0x000000FF
-    return x, y, z
-
-
-def calculate_macosx_platform_tag(archive_root, platform_tag):
-    """
-    Calculate proper macosx platform tag basing on files which are included to wheel
-
-    Example platform tag `macosx-10.14-x86_64`
-    """
-    prefix, base_version, suffix = platform_tag.split("-")
-    base_version = tuple(int(x) for x in base_version.split("."))
-    base_version = base_version[:2]
-    if base_version[0] > 10:
-        base_version = (base_version[0], 0)
-    assert len(base_version) == 2
-    if "MACOSX_DEPLOYMENT_TARGET" in os.environ:
-        deploy_target = tuple(
-            int(x) for x in os.environ["MACOSX_DEPLOYMENT_TARGET"].split(".")
-        )
-        deploy_target = deploy_target[:2]
-        if deploy_target[0] > 10:
-            deploy_target = (deploy_target[0], 0)
-        if deploy_target < base_version:
-            sys.stderr.write(
-                "[WARNING] MACOSX_DEPLOYMENT_TARGET is set to a lower value ({}) than "
-                "the version on which the Python interpreter was compiled ({}), and "
-                "will be ignored.\n".format(
-                    ".".join(str(x) for x in deploy_target),
-                    ".".join(str(x) for x in base_version),
-                )
-            )
-        else:
-            base_version = deploy_target
-
-    assert len(base_version) == 2
-    start_version = base_version
-    versions_dict = {}
-    for dirpath, _dirnames, filenames in os.walk(archive_root):
-        for filename in filenames:
-            if filename.endswith(".dylib") or filename.endswith(".so"):
-                lib_path = os.path.join(dirpath, filename)
-                min_ver = extract_macosx_min_system_version(lib_path)
-                if min_ver is not None:
-                    min_ver = min_ver[0:2]
-                    if min_ver[0] > 10:
-                        min_ver = (min_ver[0], 0)
-                    versions_dict[lib_path] = min_ver
-
-    if len(versions_dict) > 0:
-        base_version = max(base_version, max(versions_dict.values()))
-
-    # macosx platform tag do not support minor bugfix release
-    fin_base_version = "_".join([str(x) for x in base_version])
-    if start_version < base_version:
-        problematic_files = [k for k, v in versions_dict.items() if v > start_version]
-        problematic_files = "\n".join(problematic_files)
-        if len(problematic_files) == 1:
-            files_form = "this file"
-        else:
-            files_form = "these files"
-        error_message = (
-            "[WARNING] This wheel needs a higher macOS version than {}  "
-            "To silence this warning, set MACOSX_DEPLOYMENT_TARGET to at least "
-            + fin_base_version
-            + " or recreate "
-            + files_form
-            + " with lower "
-            "MACOSX_DEPLOYMENT_TARGET:  \n" + problematic_files
-        )
-
-        if "MACOSX_DEPLOYMENT_TARGET" in os.environ:
-            error_message = error_message.format(
-                "is set in MACOSX_DEPLOYMENT_TARGET variable."
-            )
-        else:
-            error_message = error_message.format(
-                "the version your Python interpreter is compiled against."
-            )
-
-        sys.stderr.write(error_message)
-
-    platform_tag = prefix + "_" + fin_base_version + "_" + suffix
-    return platform_tag
diff --git a/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-utilities.rtl.min.css b/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-utilities.rtl.min.css
deleted file mode 100644
index bef02e3cab51aaf42b87331f31af8484f87339d8..0000000000000000000000000000000000000000
--- a/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-utilities.rtl.min.css
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * Bootstrap Utilities v5.1.3 (https://getbootstrap.com/)
- * Copyright 2011-2021 The Bootstrap Authors
- * Copyright 2011-2021 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */.clearfix::after{display:block;clear:both;content:""}.link-primary{color:#0d6efd}.link-primary:focus,.link-primary:hover{color:#0a58ca}.link-secondary{color:#6c757d}.link-secondary:focus,.link-secondary:hover{color:#565e64}.link-success{color:#198754}.link-success:focus,.link-success:hover{color:#146c43}.link-info{color:#0dcaf0}.link-info:focus,.link-info:hover{color:#3dd5f3}.link-warning{color:#ffc107}.link-warning:focus,.link-warning:hover{color:#ffcd39}.link-danger{color:#dc3545}.link-danger:focus,.link-danger:hover{color:#b02a37}.link-light{color:#f8f9fa}.link-light:focus,.link-light:hover{color:#f9fafb}.link-dark{color:#212529}.link-dark:focus,.link-dark:hover{color:#1a1e21}.ratio{position:relative;width:100%}.ratio::before{display:block;padding-top:var(--bs-aspect-ratio);content:""}.ratio>*{position:absolute;top:0;right:0;width:100%;height:100%}.ratio-1x1{--bs-aspect-ratio:100%}.ratio-4x3{--bs-aspect-ratio:75%}.ratio-16x9{--bs-aspect-ratio:56.25%}.ratio-21x9{--bs-aspect-ratio:42.8571428571%}.fixed-top{position:fixed;top:0;left:0;right:0;z-index:1030}.fixed-bottom{position:fixed;left:0;bottom:0;right:0;z-index:1030}.sticky-top{position:-webkit-sticky;position:sticky;top:0;z-index:1020}@media (min-width:576px){.sticky-sm-top{position:-webkit-sticky;position:sticky;top:0;z-index:1020}}@media (min-width:768px){.sticky-md-top{position:-webkit-sticky;position:sticky;top:0;z-index:1020}}@media (min-width:992px){.sticky-lg-top{position:-webkit-sticky;position:sticky;top:0;z-index:1020}}@media (min-width:1200px){.sticky-xl-top{position:-webkit-sticky;position:sticky;top:0;z-index:1020}}@media (min-width:1400px){.sticky-xxl-top{position:-webkit-sticky;position:sticky;top:0;z-index:1020}}.hstack{display:flex;flex-direction:row;align-items:center;align-self:stretch}.vstack{display:flex;flex:1 1 auto;flex-direction:column;align-self:stretch}.visually-hidden,.visually-hidden-focusable:not(:focus):not(:focus-within){position:absolute!important;width:1px!important;height:1px!important;padding:0!important;margin:-1px!important;overflow:hidden!important;clip:rect(0,0,0,0)!important;white-space:nowrap!important;border:0!important}.stretched-link::after{position:absolute;top:0;left:0;bottom:0;right:0;z-index:1;content:""}.text-truncate{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.vr{display:inline-block;align-self:stretch;width:1px;min-height:1em;background-color:currentColor;opacity:.25}.align-baseline{vertical-align:baseline!important}.align-top{vertical-align:top!important}.align-middle{vertical-align:middle!important}.align-bottom{vertical-align:bottom!important}.align-text-bottom{vertical-align:text-bottom!important}.align-text-top{vertical-align:text-top!important}.float-start{float:right!important}.float-end{float:left!important}.float-none{float:none!important}.opacity-0{opacity:0!important}.opacity-25{opacity:.25!important}.opacity-50{opacity:.5!important}.opacity-75{opacity:.75!important}.opacity-100{opacity:1!important}.overflow-auto{overflow:auto!important}.overflow-hidden{overflow:hidden!important}.overflow-visible{overflow:visible!important}.overflow-scroll{overflow:scroll!important}.d-inline{display:inline!important}.d-inline-block{display:inline-block!important}.d-block{display:block!important}.d-grid{display:grid!important}.d-table{display:table!important}.d-table-row{display:table-row!important}.d-table-cell{display:table-cell!important}.d-flex{display:flex!important}.d-inline-flex{display:inline-flex!important}.d-none{display:none!important}.shadow{box-shadow:0 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!important}.py-xl-0{padding-top:0!important;padding-bottom:0!important}.py-xl-1{padding-top:.25rem!important;padding-bottom:.25rem!important}.py-xl-2{padding-top:.5rem!important;padding-bottom:.5rem!important}.py-xl-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-xl-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-xl-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-xl-0{padding-top:0!important}.pt-xl-1{padding-top:.25rem!important}.pt-xl-2{padding-top:.5rem!important}.pt-xl-3{padding-top:1rem!important}.pt-xl-4{padding-top:1.5rem!important}.pt-xl-5{padding-top:3rem!important}.pe-xl-0{padding-left:0!important}.pe-xl-1{padding-left:.25rem!important}.pe-xl-2{padding-left:.5rem!important}.pe-xl-3{padding-left:1rem!important}.pe-xl-4{padding-left:1.5rem!important}.pe-xl-5{padding-left:3rem!important}.pb-xl-0{padding-bottom:0!important}.pb-xl-1{padding-bottom:.25rem!important}.pb-xl-2{padding-bottom:.5rem!important}.pb-xl-3{padding-bottom:1rem!important}.pb-xl-4{padding-bottom:1.5rem!important}.pb-xl-5{padding-bottom:3rem!important}.ps-xl-0{padding-right:0!important}.ps-xl-1{padding-right:.25rem!important}.ps-xl-2{padding-right:.5rem!important}.ps-xl-3{padding-right:1rem!important}.ps-xl-4{padding-right:1.5rem!important}.ps-xl-5{padding-right:3rem!important}.text-xl-start{text-align:right!important}.text-xl-end{text-align:left!important}.text-xl-center{text-align:center!important}}@media (min-width:1400px){.float-xxl-start{float:right!important}.float-xxl-end{float:left!important}.float-xxl-none{float:none!important}.d-xxl-inline{display:inline!important}.d-xxl-inline-block{display:inline-block!important}.d-xxl-block{display:block!important}.d-xxl-grid{display:grid!important}.d-xxl-table{display:table!important}.d-xxl-table-row{display:table-row!important}.d-xxl-table-cell{display:table-cell!important}.d-xxl-flex{display:flex!important}.d-xxl-inline-flex{display:inline-flex!important}.d-xxl-none{display:none!important}.flex-xxl-fill{flex:1 1 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left:1.5rem!important;padding-right:1.5rem!important}.px-xxl-5{padding-left:3rem!important;padding-right:3rem!important}.py-xxl-0{padding-top:0!important;padding-bottom:0!important}.py-xxl-1{padding-top:.25rem!important;padding-bottom:.25rem!important}.py-xxl-2{padding-top:.5rem!important;padding-bottom:.5rem!important}.py-xxl-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-xxl-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-xxl-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-xxl-0{padding-top:0!important}.pt-xxl-1{padding-top:.25rem!important}.pt-xxl-2{padding-top:.5rem!important}.pt-xxl-3{padding-top:1rem!important}.pt-xxl-4{padding-top:1.5rem!important}.pt-xxl-5{padding-top:3rem!important}.pe-xxl-0{padding-left:0!important}.pe-xxl-1{padding-left:.25rem!important}.pe-xxl-2{padding-left:.5rem!important}.pe-xxl-3{padding-left:1rem!important}.pe-xxl-4{padding-left:1.5rem!important}.pe-xxl-5{padding-left:3rem!important}.pb-xxl-0{padding-bottom:0!important}.pb-xxl-1{padding-bottom:.25rem!important}.pb-xxl-2{padding-bottom:.5rem!important}.pb-xxl-3{padding-bottom:1rem!important}.pb-xxl-4{padding-bottom:1.5rem!important}.pb-xxl-5{padding-bottom:3rem!important}.ps-xxl-0{padding-right:0!important}.ps-xxl-1{padding-right:.25rem!important}.ps-xxl-2{padding-right:.5rem!important}.ps-xxl-3{padding-right:1rem!important}.ps-xxl-4{padding-right:1.5rem!important}.ps-xxl-5{padding-right:3rem!important}.text-xxl-start{text-align:right!important}.text-xxl-end{text-align:left!important}.text-xxl-center{text-align:center!important}}@media (min-width:1200px){.fs-1{font-size:2.5rem!important}.fs-2{font-size:2rem!important}.fs-3{font-size:1.75rem!important}.fs-4{font-size:1.5rem!important}}@media print{.d-print-inline{display:inline!important}.d-print-inline-block{display:inline-block!important}.d-print-block{display:block!important}.d-print-grid{display:grid!important}.d-print-table{display:table!important}.d-print-table-row{display:table-row!important}.d-print-table-cell{display:table-cell!important}.d-print-flex{display:flex!important}.d-print-inline-flex{display:inline-flex!important}.d-print-none{display:none!important}}
-/*# sourceMappingURL=bootstrap-utilities.rtl.min.css.map */
\ No newline at end of file
diff --git a/spaces/Ukrania/RVC-Models/lib/infer_pack/attentions.py b/spaces/Ukrania/RVC-Models/lib/infer_pack/attentions.py
deleted file mode 100644
index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000
--- a/spaces/Ukrania/RVC-Models/lib/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer_pack import commons
-from lib.infer_pack import modules
-from lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/Volkopat/SegmentAnythingxGroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/Volkopat/SegmentAnythingxGroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py
deleted file mode 100644
index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000
--- a/spaces/Volkopat/SegmentAnythingxGroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py
+++ /dev/null
@@ -1,221 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Backbone modules.
-"""
-
-from typing import Dict, List
-
-import torch
-import torch.nn.functional as F
-import torchvision
-from torch import nn
-from torchvision.models._utils import IntermediateLayerGetter
-
-from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
-
-from .position_encoding import build_position_encoding
-from .swin_transformer import build_swin_transformer
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
-    without which any other models than torchvision.models.resnet[18,34,50,101]
-    produce nans.
-    """
-
-    def __init__(self, n):
-        super(FrozenBatchNorm2d, self).__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        num_batches_tracked_key = prefix + "num_batches_tracked"
-        if num_batches_tracked_key in state_dict:
-            del state_dict[num_batches_tracked_key]
-
-        super(FrozenBatchNorm2d, self)._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def forward(self, x):
-        # move reshapes to the beginning
-        # to make it fuser-friendly
-        w = self.weight.reshape(1, -1, 1, 1)
-        b = self.bias.reshape(1, -1, 1, 1)
-        rv = self.running_var.reshape(1, -1, 1, 1)
-        rm = self.running_mean.reshape(1, -1, 1, 1)
-        eps = 1e-5
-        scale = w * (rv + eps).rsqrt()
-        bias = b - rm * scale
-        return x * scale + bias
-
-
-class BackboneBase(nn.Module):
-    def __init__(
-        self,
-        backbone: nn.Module,
-        train_backbone: bool,
-        num_channels: int,
-        return_interm_indices: list,
-    ):
-        super().__init__()
-        for name, parameter in backbone.named_parameters():
-            if (
-                not train_backbone
-                or "layer2" not in name
-                and "layer3" not in name
-                and "layer4" not in name
-            ):
-                parameter.requires_grad_(False)
-
-        return_layers = {}
-        for idx, layer_index in enumerate(return_interm_indices):
-            return_layers.update(
-                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
-            )
-
-        # if len:
-        #     if use_stage1_feature:
-        #         return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
-        #     else:
-        #         return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
-        # else:
-        #     return_layers = {'layer4': "0"}
-        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
-        self.num_channels = num_channels
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self.body(tensor_list.tensors)
-        out: Dict[str, NestedTensor] = {}
-        for name, x in xs.items():
-            m = tensor_list.mask
-            assert m is not None
-            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
-            out[name] = NestedTensor(x, mask)
-        # import ipdb; ipdb.set_trace()
-        return out
-
-
-class Backbone(BackboneBase):
-    """ResNet backbone with frozen BatchNorm."""
-
-    def __init__(
-        self,
-        name: str,
-        train_backbone: bool,
-        dilation: bool,
-        return_interm_indices: list,
-        batch_norm=FrozenBatchNorm2d,
-    ):
-        if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
-            backbone = getattr(torchvision.models, name)(
-                replace_stride_with_dilation=[False, False, dilation],
-                pretrained=is_main_process(),
-                norm_layer=batch_norm,
-            )
-        else:
-            raise NotImplementedError("Why you can get here with name {}".format(name))
-        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
-        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
-        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-        num_channels_all = [256, 512, 1024, 2048]
-        num_channels = num_channels_all[4 - len(return_interm_indices) :]
-        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
-
-
-class Joiner(nn.Sequential):
-    def __init__(self, backbone, position_embedding):
-        super().__init__(backbone, position_embedding)
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self[0](tensor_list)
-        out: List[NestedTensor] = []
-        pos = []
-        for name, x in xs.items():
-            out.append(x)
-            # position encoding
-            pos.append(self[1](x).to(x.tensors.dtype))
-
-        return out, pos
-
-
-def build_backbone(args):
-    """
-    Useful args:
-        - backbone: backbone name
-        - lr_backbone:
-        - dilation
-        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
-        - backbone_freeze_keywords:
-        - use_checkpoint: for swin only for now
-
-    """
-    position_embedding = build_position_encoding(args)
-    train_backbone = True
-    if not train_backbone:
-        raise ValueError("Please set lr_backbone > 0")
-    return_interm_indices = args.return_interm_indices
-    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-    args.backbone_freeze_keywords
-    use_checkpoint = getattr(args, "use_checkpoint", False)
-
-    if args.backbone in ["resnet50", "resnet101"]:
-        backbone = Backbone(
-            args.backbone,
-            train_backbone,
-            args.dilation,
-            return_interm_indices,
-            batch_norm=FrozenBatchNorm2d,
-        )
-        bb_num_channels = backbone.num_channels
-    elif args.backbone in [
-        "swin_T_224_1k",
-        "swin_B_224_22k",
-        "swin_B_384_22k",
-        "swin_L_224_22k",
-        "swin_L_384_22k",
-    ]:
-        pretrain_img_size = int(args.backbone.split("_")[-2])
-        backbone = build_swin_transformer(
-            args.backbone,
-            pretrain_img_size=pretrain_img_size,
-            out_indices=tuple(return_interm_indices),
-            dilation=False,
-            use_checkpoint=use_checkpoint,
-        )
-
-        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
-    else:
-        raise NotImplementedError("Unknown backbone {}".format(args.backbone))
-
-    assert len(bb_num_channels) == len(
-        return_interm_indices
-    ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
-
-    model = Joiner(backbone, position_embedding)
-    model.num_channels = bb_num_channels
-    assert isinstance(
-        bb_num_channels, List
-    ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
-    # import ipdb; ipdb.set_trace()
-    return model
diff --git a/spaces/WRH/wrhwang_foodvision_mini/model.py b/spaces/WRH/wrhwang_foodvision_mini/model.py
deleted file mode 100644
index a6e29969343d25156ea3beedd27692a4c271b30b..0000000000000000000000000000000000000000
--- a/spaces/WRH/wrhwang_foodvision_mini/model.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Dec  6 21:32:17 2022
-
-@author: WR
-"""
-# %%writefile demos/foodvision_mini/model.py
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes:int=3, 
-                          seed:int=42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.efficientnet_b2(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.3, inplace=True),
-        nn.Linear(in_features=1408, out_features=num_classes),
-    )
-    
-    return model, transforms
diff --git a/spaces/WZT/DigiProj/distributed.py b/spaces/WZT/DigiProj/distributed.py
deleted file mode 100644
index 51fa243257ef302e2015d5ff36ac531b86a9a0ce..0000000000000000000000000000000000000000
--- a/spaces/WZT/DigiProj/distributed.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import math
-import pickle
-
-import torch
-from torch import distributed as dist
-from torch.utils.data.sampler import Sampler
-
-
-def get_rank():
-    if not dist.is_available():
-        return 0
-
-    if not dist.is_initialized():
-        return 0
-
-    return dist.get_rank()
-
-
-def synchronize():
-    if not dist.is_available():
-        return
-
-    if not dist.is_initialized():
-        return
-
-    world_size = dist.get_world_size()
-
-    if world_size == 1:
-        return
-
-    dist.barrier()
-
-
-def get_world_size():
-    if not dist.is_available():
-        return 1
-
-    if not dist.is_initialized():
-        return 1
-
-    return dist.get_world_size()
-
-
-def reduce_sum(tensor):
-    if not dist.is_available():
-        return tensor
-
-    if not dist.is_initialized():
-        return tensor
-
-    tensor = tensor.clone()
-    dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
-
-    return tensor
-
-
-def gather_grad(params):
-    world_size = get_world_size()
-    
-    if world_size == 1:
-        return
-
-    for param in params:
-        if param.grad is not None:
-            dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
-            param.grad.data.div_(world_size)
-
-
-def all_gather(data):
-    world_size = get_world_size()
-
-    if world_size == 1:
-        return [data]
-
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to('cuda')
-
-    local_size = torch.IntTensor([tensor.numel()]).to('cuda')
-    size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda'))
-
-    if local_size != max_size:
-        padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda')
-        tensor = torch.cat((tensor, padding), 0)
-
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_loss_dict(loss_dict):
-    world_size = get_world_size()
-
-    if world_size < 2:
-        return loss_dict
-
-    with torch.no_grad():
-        keys = []
-        losses = []
-
-        for k in sorted(loss_dict.keys()):
-            keys.append(k)
-            losses.append(loss_dict[k])
-
-        losses = torch.stack(losses, 0)
-        dist.reduce(losses, dst=0)
-
-        if dist.get_rank() == 0:
-            losses /= world_size
-
-        reduced_losses = {k: v for k, v in zip(keys, losses)}
-
-    return reduced_losses
diff --git a/spaces/XzJosh/Jiaran-Bert-VITS2/transforms.py b/spaces/XzJosh/Jiaran-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jiaran-Bert-VITS2/transforms.py
+++ /dev/null
@@ -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
diff --git a/spaces/Yassine/Stego/stc_embed_c.cpp b/spaces/Yassine/Stego/stc_embed_c.cpp
deleted file mode 100644
index 4ff6272405450cb6f053039a9da0bf44fd7a962d..0000000000000000000000000000000000000000
--- a/spaces/Yassine/Stego/stc_embed_c.cpp
+++ /dev/null
@@ -1,476 +0,0 @@
-#include <cstdlib>
-#include <cstring>
-#include <cmath>
-#include <cfloat>
-#include <limits>
-#include <emmintrin.h>
-#include <cstdio>
-#include <sstream>
-#include "stc_embed_c.h"
-
-// {{{ aligned_malloc()
-void *aligned_malloc( unsigned int bytes, int align ) {
-    int shift;
-    char *temp = (char *) malloc( bytes + align );
-
-    if ( temp == NULL ) return temp;
-    shift = align - (int) (((unsigned long long) temp) & (align - 1));
-    temp = temp + shift;
-    temp[-1] = shift;
-    return (void *) temp;
-}
-// }}}
-
-// {{{ aligned_free()
-void aligned_free( void *vptr ) {
-    char *ptr = (char *) vptr;
-    free( ptr - ptr[-1] );
-    return;
-}
-// }}}
-
-// {{{ maxLessThan255()
-inline __m128i maxLessThan255( const __m128i v1, const __m128i v2 ) {
-    register __m128i mask = _mm_set1_epi32( 0xffffffff );
-    return _mm_max_epu8( _mm_andnot_si128( _mm_cmpeq_epi8( v1, mask ), v1 ), _mm_andnot_si128( _mm_cmpeq_epi8( v2, mask ), v2 ) );
-}
-// }}}
-
-// {{{ max16B()
-inline u8 max16B( __m128i maxp ) {
-    u8 mtemp[4];
-    maxp = _mm_max_epu8( maxp, _mm_srli_si128(maxp, 8) );
-    maxp = _mm_max_epu8( maxp, _mm_srli_si128(maxp, 4) );
-    *((int*) mtemp) = _mm_cvtsi128_si32( maxp );
-    if ( mtemp[2] > mtemp[0] ) mtemp[0] = mtemp[2];
-    if ( mtemp[3] > mtemp[1] ) mtemp[1] = mtemp[3];
-    if ( mtemp[1] > mtemp[0] ) return mtemp[1];
-    else return mtemp[0];
-}
-// }}}
-
-// {{{ min16B()
-inline u8 min16B( __m128i minp ) {
-    u8 mtemp[4];
-    minp = _mm_min_epu8( minp, _mm_srli_si128(minp, 8) );
-    minp = _mm_min_epu8( minp, _mm_srli_si128(minp, 4) );
-    *((int*) mtemp) = _mm_cvtsi128_si32( minp );
-    if ( mtemp[2] < mtemp[0] ) mtemp[0] = mtemp[2];
-    if ( mtemp[3] < mtemp[1] ) mtemp[1] = mtemp[3];
-    if ( mtemp[1] < mtemp[0] ) return mtemp[1];
-    else return mtemp[0];
-}
-// }}}
-
-// {{{ stc_embed()
-double stc_embed( const u8 *vector, int vectorlength, const u8 *syndrome, int syndromelength, const void *pricevectorv, bool usefloat,
-        u8 *stego, int matrixheight ) {
-    int height, i, k, l, index, index2, parts, m, sseheight, altm, pathindex;
-    u32 column, colmask, state;
-    double totalprice;
-
-    u8 *ssedone;
-    u32 *path, *columns[2];
-    int *matrices, *widths;
-
-    if ( matrixheight > 31 ) throw stc_exception( "Submatrix height must not exceed 31.", 1 );
-
-    height = 1 << matrixheight;
-    colmask = height - 1;
-    height = (height + 31) & (~31);
-
-    parts = height >> 5;
-
-    if ( stego != NULL ) {
-        path = (u32*) malloc( vectorlength * parts * sizeof(u32) );
-        if ( path == NULL ) {
-            std::stringstream ss;
-            ss << "Not enough memory (" << (unsigned int) (vectorlength * parts * sizeof(u32)) << " byte array could not be allocated).";
-            throw stc_exception( ss.str(), 2 );
-        }
-        pathindex = 0;
-    }
-
-    {
-        int shorter, longer, worm;
-        double invalpha;
-
-        matrices = (int *) malloc( syndromelength * sizeof(int) );
-        widths = (int *) malloc( syndromelength * sizeof(int) );
-
-        invalpha = (double) vectorlength / syndromelength;
-        if ( invalpha < 1 ) {
-            free( matrices );
-            free( widths );
-            if ( stego != NULL ) free( path );
-            throw stc_exception( "The message cannot be longer than the cover object.", 3 );
-        }
-        /* THIS IS OBSOLETE. Algorithm still works for alpha >1/2. You need to take care of cases with too many Infs in cost vector.
-         if(invalpha < 2) {
-         printf("The relative payload is greater than 1/2. This may result in poor embedding efficiency.\n");
-         }
-         */
-        shorter = (int) floor( invalpha );
-        longer = (int) ceil( invalpha );
-        if ( (columns[0] = getMatrix( shorter, matrixheight )) == NULL ) {
-            free( matrices );
-            free( widths );
-            if ( stego != NULL ) free( path );
-            return -1;
-        }
-        if ( (columns[1] = getMatrix( longer, matrixheight )) == NULL ) {
-            free( columns[0] );
-            free( matrices );
-            free( widths );
-            if ( stego != NULL ) free( path );
-            return -1;
-        }
-        worm = 0;
-        for ( i = 0; i < syndromelength; i++ ) {
-            if ( worm + longer <= (i + 1) * invalpha + 0.5 ) {
-                matrices[i] = 1;
-                widths[i] = longer;
-                worm += longer;
-            } else {
-                matrices[i] = 0;
-                widths[i] = shorter;
-                worm += shorter;
-            }
-        }
-    }
-
-    if ( usefloat ) {
-        /*
-         SSE FLOAT VERSION
-         */
-        int pathindex8 = 0;
-        int shift[2] = { 0, 4 };
-        u8 mask[2] = { 0xf0, 0x0f };
-        float *prices;
-        u8 *path8 = (u8*) path;
-        double *pricevector = (double*) pricevectorv;
-        double total = 0;
-        float inf = std::numeric_limits< float >::infinity();
-
-        sseheight = height >> 2;
-        ssedone = (u8*) malloc( sseheight * sizeof(u8) );
-        prices = (float*) aligned_malloc( height * sizeof(float), 16 );
-
-        {
-            __m128 fillval = _mm_set1_ps( inf );
-            for ( i = 0; i < height; i += 4 ) {
-                _mm_store_ps( &prices[i], fillval );
-                ssedone[i >> 2] = 0;
-            }
-        }
-
-        prices[0] = 0.0f;
-
-        for ( index = 0, index2 = 0; index2 < syndromelength; index2++ ) {
-            register __m128 c1, c2;
-
-            for ( k = 0; k < widths[index2]; k++, index++ ) {
-                column = columns[matrices[index2]][k] & colmask;
-
-                if ( vector[index] == 0 ) {
-                    c1 = _mm_setzero_ps();
-                    c2 = _mm_set1_ps( (float) pricevector[index] );
-                } else {
-                    c1 = _mm_set1_ps( (float) pricevector[index] );
-                    c2 = _mm_setzero_ps();
-                }
-
-                total += pricevector[index];
-
-                for ( m = 0; m < sseheight; m++ ) {
-                    if ( !ssedone[m] ) {
-                        register __m128 v1, v2, v3, v4;
-                        altm = (m ^ (column >> 2));
-                        v1 = _mm_load_ps( &prices[m << 2] );
-                        v2 = _mm_load_ps( &prices[altm << 2] );
-                        v3 = v1;
-                        v4 = v2;
-                        ssedone[m] = 1;
-                        ssedone[altm] = 1;
-                        switch ( column & 3 ) {
-                            case 0:
-                                break;
-                            case 1:
-                                v2 = _mm_shuffle_ps(v2, v2, 0xb1);
-                                v3 = _mm_shuffle_ps(v3, v3, 0xb1);
-                                break;
-                            case 2:
-                                v2 = _mm_shuffle_ps(v2, v2, 0x4e);
-                                v3 = _mm_shuffle_ps(v3, v3, 0x4e);
-                                break;
-                            case 3:
-                                v2 = _mm_shuffle_ps(v2, v2, 0x1b);
-                                v3 = _mm_shuffle_ps(v3, v3, 0x1b);
-                                break;
-                        }
-                        v1 = _mm_add_ps( v1, c1 );
-                        v2 = _mm_add_ps( v2, c2 );
-                        v3 = _mm_add_ps( v3, c2 );
-                        v4 = _mm_add_ps( v4, c1 );
-
-                        v1 = _mm_min_ps( v1, v2 );
-                        v4 = _mm_min_ps( v3, v4 );
-
-                        _mm_store_ps( &prices[m << 2], v1 );
-                        _mm_store_ps( &prices[altm << 2], v4 );
-
-                        if ( stego != NULL ) {
-                            v2 = _mm_cmpeq_ps( v1, v2 );
-                            v3 = _mm_cmpeq_ps( v3, v4 );
-                            path8[pathindex8 + (m >> 1)] = (path8[pathindex8 + (m >> 1)] & mask[m & 1]) | (_mm_movemask_ps( v2 ) << shift[m
-                                    & 1]);
-                            path8[pathindex8 + (altm >> 1)] = (path8[pathindex8 + (altm >> 1)] & mask[altm & 1]) | (_mm_movemask_ps( v3 )
-                                    << shift[altm & 1]);
-                        }
-                    }
-                }
-
-                for ( i = 0; i < sseheight; i++ ) {
-                    ssedone[i] = 0;
-                }
-
-                pathindex += parts;
-                pathindex8 += parts << 2;
-            }
-
-            if ( syndrome[index2] == 0 ) {
-                for ( i = 0, l = 0; i < sseheight; i += 2, l += 4 ) {
-                    _mm_store_ps( &prices[l], _mm_shuffle_ps(_mm_load_ps(&prices[i << 2]), _mm_load_ps(&prices[(i + 1) << 2]), 0x88) );
-                }
-            } else {
-                for ( i = 0, l = 0; i < sseheight; i += 2, l += 4 ) {
-                    _mm_store_ps( &prices[l], _mm_shuffle_ps(_mm_load_ps(&prices[i << 2]), _mm_load_ps(&prices[(i + 1) << 2]), 0xdd) );
-                }
-            }
-
-            if ( syndromelength - index2 <= matrixheight ) colmask >>= 1;
-
-            {
-                register __m128 fillval = _mm_set1_ps( inf );
-                for ( l >>= 2; l < sseheight; l++ ) {
-                    _mm_store_ps( &prices[l << 2], fillval );
-                }
-            }
-        }
-
-        totalprice = prices[0];
-
-        aligned_free( prices );
-        free( ssedone );
-
-        if ( totalprice >= total ) {
-            free( matrices );
-            free( widths );
-            free( columns[0] );
-            free( columns[1] );
-            if ( stego != NULL ) free( path );
-            throw stc_exception( "No solution exist.", 4 );
-        }
-    } else {
-        /*
-         SSE UINT8 VERSION
-         */
-        int pathindex16 = 0, subprice = 0;
-        u8 maxc = 0, minc = 0;
-        u8 *prices, *pricevector = (u8*) pricevectorv;
-        u16 *path16 = (u16 *) path;
-        __m128i *prices16B;
-
-        sseheight = height >> 4;
-        ssedone = (u8*) malloc( sseheight * sizeof(u8) );
-        prices = (u8*) aligned_malloc( height * sizeof(u8), 16 );
-        prices16B = (__m128i *) prices;
-
-        {
-            __m128i napln = _mm_set1_epi32( 0xffffffff );
-            for ( i = 0; i < sseheight; i++ ) {
-                _mm_store_si128( &prices16B[i], napln );
-                ssedone[i] = 0;
-            }
-        }
-
-        prices[0] = 0;
-
-        for ( index = 0, index2 = 0; index2 < syndromelength; index2++ ) {
-            register __m128i c1, c2, maxp, minp;
-
-            if ( (u32) maxc + pricevector[index] >= 254 ) {
-                aligned_free( path );
-                free( ssedone );
-                free( matrices );
-                free( widths );
-                free( columns[0] );
-                free( columns[1] );
-                if ( stego != NULL ) free( path );
-                throw stc_exception( "Price vector limit exceeded.", 5 );
-            }
-
-            for ( k = 0; k < widths[index2]; k++, index++ ) {
-                column = columns[matrices[index2]][k] & colmask;
-
-                if ( vector[index] == 0 ) {
-                    c1 = _mm_setzero_si128();
-                    c2 = _mm_set1_epi8( pricevector[index] );
-                } else {
-                    c1 = _mm_set1_epi8( pricevector[index] );
-                    c2 = _mm_setzero_si128();
-                }
-
-                minp = _mm_set1_epi8( -1 );
-                maxp = _mm_setzero_si128();
-
-                for ( m = 0; m < sseheight; m++ ) {
-                    if ( !ssedone[m] ) {
-                        register __m128i v1, v2, v3, v4;
-                        altm = (m ^ (column >> 4));
-                        v1 = _mm_load_si128( &prices16B[m] );
-                        v2 = _mm_load_si128( &prices16B[altm] );
-                        v3 = v1;
-                        v4 = v2;
-                        ssedone[m] = 1;
-                        ssedone[altm] = 1;
-                        if ( column & 8 ) {
-                            v2 = _mm_shuffle_epi32(v2, 0x4e);
-                            v3 = _mm_shuffle_epi32(v3, 0x4e);
-                        }
-                        if ( column & 4 ) {
-                            v2 = _mm_shuffle_epi32(v2, 0xb1);
-                            v3 = _mm_shuffle_epi32(v3, 0xb1);
-                        }
-                        if ( column & 2 ) {
-                            v2 = _mm_shufflehi_epi16(v2, 0xb1);
-                            v3 = _mm_shufflehi_epi16(v3, 0xb1);
-                            v2 = _mm_shufflelo_epi16(v2, 0xb1);
-                            v3 = _mm_shufflelo_epi16(v3, 0xb1);
-                        }
-                        if ( column & 1 ) {
-                            v2 = _mm_or_si128( _mm_srli_epi16( v2, 8 ), _mm_slli_epi16( v2, 8 ) );
-                            v3 = _mm_or_si128( _mm_srli_epi16( v3, 8 ), _mm_slli_epi16( v3, 8 ) );
-                        }
-                        v1 = _mm_adds_epu8( v1, c1 );
-                        v2 = _mm_adds_epu8( v2, c2 );
-                        v3 = _mm_adds_epu8( v3, c2 );
-                        v4 = _mm_adds_epu8( v4, c1 );
-
-                        v1 = _mm_min_epu8( v1, v2 );
-                        v4 = _mm_min_epu8( v3, v4 );
-
-                        _mm_store_si128( &prices16B[m], v1 );
-                        _mm_store_si128( &prices16B[altm], v4 );
-
-                        minp = _mm_min_epu8( minp, _mm_min_epu8( v1, v4 ) );
-                        maxp = _mm_max_epu8( maxp, maxLessThan255( v1, v4 ) );
-
-                        if ( stego != NULL ) {
-                            v2 = _mm_cmpeq_epi8( v1, v2 );
-                            v3 = _mm_cmpeq_epi8( v3, v4 );
-                            path16[pathindex16 + m] = (u16) _mm_movemask_epi8( v2 );
-                            path16[pathindex16 + altm] = (u16) _mm_movemask_epi8( v3 );
-                        }
-                    }
-                }
-
-                maxc = max16B( maxp );
-                minc = min16B( minp );
-
-                maxc -= minc;
-                subprice += minc;
-                {
-                    register __m128i mask = _mm_set1_epi32( 0xffffffff );
-                    register __m128i m = _mm_set1_epi8( minc );
-                    for ( i = 0; i < sseheight; i++ ) {
-                        register __m128i res;
-                        register __m128i pr = prices16B[i];
-                        res = _mm_andnot_si128( _mm_cmpeq_epi8( pr, mask ), m );
-                        prices16B[i] = _mm_sub_epi8( pr, res );
-                        ssedone[i] = 0;
-                    }
-                }
-
-                pathindex += parts;
-                pathindex16 += parts << 1;
-            }
-
-            {
-                register __m128i mask = _mm_set1_epi32( 0x00ff00ff );
-
-                if ( minc == 255 ) {
-                    aligned_free( path );
-                    free( ssedone );
-                    free( matrices );
-                    free( widths );
-                    free( columns[0] );
-                    free( columns[1] );
-                    if ( stego != NULL ) free( path );
-                    throw stc_exception( "The syndrome is not in the syndrome matrix range.", 4 );
-                }
-
-                if ( syndrome[index2] == 0 ) {
-                    for ( i = 0, l = 0; i < sseheight; i += 2, l++ ) {
-                        _mm_store_si128( &prices16B[l], _mm_packus_epi16( _mm_and_si128( _mm_load_si128( &prices16B[i] ), mask ),
-                                _mm_and_si128( _mm_load_si128( &prices16B[i + 1] ), mask ) ) );
-                    }
-                } else {
-                    for ( i = 0, l = 0; i < sseheight; i += 2, l++ ) {
-                        _mm_store_si128( &prices16B[l], _mm_packus_epi16( _mm_and_si128( _mm_srli_si128(_mm_load_si128(&prices16B[i]), 1),
-                                mask ), _mm_and_si128( _mm_srli_si128(_mm_load_si128(&prices16B[i + 1]), 1), mask ) ) );
-                    }
-                }
-
-                if ( syndromelength - index2 <= matrixheight ) colmask >>= 1;
-
-                register __m128i fillval = _mm_set1_epi32( 0xffffffff );
-                for ( ; l < sseheight; l++ )
-                    _mm_store_si128( &prices16B[l], fillval );
-            }
-        }
-
-        totalprice = subprice + prices[0];
-
-        aligned_free( prices );
-        free( ssedone );
-    }
-
-    if ( stego != NULL ) {
-        pathindex -= parts;
-        index--;
-        index2--;
-        state = 0;
-
-        // unused
-        // int h = syndromelength;
-        state = 0;
-        colmask = 0;
-        for ( ; index2 >= 0; index2-- ) {
-            for ( k = widths[index2] - 1; k >= 0; k--, index-- ) {
-                if ( k == widths[index2] - 1 ) {
-                    state = (state << 1) | syndrome[index2];
-                    if ( syndromelength - index2 <= matrixheight ) colmask = (colmask << 1) | 1;
-                }
-
-                if ( path[pathindex + (state >> 5)] & (1 << (state & 31)) ) {
-                    stego[index] = 1;
-                    state = state ^ (columns[matrices[index2]][k] & colmask);
-                } else {
-                    stego[index] = 0;
-                }
-
-                pathindex -= parts;
-            }
-        }
-        free( path );
-    }
-
-    free( matrices );
-    free( widths );
-    free( columns[0] );
-    free( columns[1] );
-
-    return totalprice;
-}
-// }}}
diff --git a/spaces/Yiqin/ChatVID/model/fastchat/serve/gradio_patch.py b/spaces/Yiqin/ChatVID/model/fastchat/serve/gradio_patch.py
deleted file mode 100644
index af8731da17d4c39a2a32afd4ce2cca13e3845ac4..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/fastchat/serve/gradio_patch.py
+++ /dev/null
@@ -1,168 +0,0 @@
-"""
-Adopted from https://github.com/gradio-app/gradio/blob/main/gradio/components.py
-Fix a markdown render problem.
-"""
-from __future__ import annotations
-
-from gradio.components import *
-from markdown2 import Markdown
-import nh3
-
-
-class _Keywords(Enum):
-    NO_VALUE = "NO_VALUE"  # Used as a sentinel to determine if nothing is provided as a argument for `value` in `Component.update()`
-    FINISHED_ITERATING = "FINISHED_ITERATING"  # Used to skip processing of a component's value (needed for generators + state)
-
-
-@document("style")
-class Chatbot(Changeable, Selectable, IOComponent, JSONSerializable):
-    """
-    Displays a chatbot output showing both user submitted messages and responses. Supports a subset of Markdown including bold, italics, code, and images.
-    Preprocessing: this component does *not* accept input.
-    Postprocessing: expects function to return a {List[Tuple[str | None | Tuple, str | None | Tuple]]}, a list of tuples with user message and response messages. Messages should be strings, tuples, or Nones. If the message is a string, it can include Markdown. If it is a tuple, it should consist of (string filepath to image/video/audio, [optional string alt text]). Messages that are `None` are not displayed.
-
-    Demos: chatbot_simple, chatbot_multimodal
-    """
-
-    def __init__(
-        self,
-        value: List[Tuple[str | None, str | None]] | Callable | None = None,
-        color_map: Dict[str, str] | None = None,  # Parameter moved to Chatbot.style()
-        *,
-        label: str | None = None,
-        every: float | None = None,
-        show_label: bool = True,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: List[str] | str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: Default value to show in chatbot. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            label: component name in interface.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        if color_map is not None:
-            warnings.warn(
-                "The 'color_map' parameter has been deprecated.",
-            )
-        # self.md = utils.get_markdown_parser()
-        self.md = Markdown(extras=["fenced-code-blocks", "tables", "break-on-newline"])
-        self.select: EventListenerMethod
-        """
-        Event listener for when the user selects message from Chatbot.
-        Uses event data gradio.SelectData to carry `value` referring to text of selected message, and `index` tuple to refer to [message, participant] index.
-        See EventData documentation on how to use this event data.
-        """
-
-        IOComponent.__init__(
-            self,
-            label=label,
-            every=every,
-            show_label=show_label,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            **kwargs,
-        )
-
-    def get_config(self):
-        return {
-            "value": self.value,
-            "selectable": self.selectable,
-            **IOComponent.get_config(self),
-        }
-
-    @staticmethod
-    def update(
-        value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
-        label: str | None = None,
-        show_label: bool | None = None,
-        visible: bool | None = None,
-    ):
-        updated_config = {
-            "label": label,
-            "show_label": show_label,
-            "visible": visible,
-            "value": value,
-            "__type__": "update",
-        }
-        return updated_config
-
-    def _process_chat_messages(
-        self, chat_message: str | Tuple | List | Dict | None
-    ) -> str | Dict | None:
-        if chat_message is None:
-            return None
-        elif isinstance(chat_message, (tuple, list)):
-            mime_type = processing_utils.get_mimetype(chat_message[0])
-            return {
-                "name": chat_message[0],
-                "mime_type": mime_type,
-                "alt_text": chat_message[1] if len(chat_message) > 1 else None,
-                "data": None,  # These last two fields are filled in by the frontend
-                "is_file": True,
-            }
-        elif isinstance(
-            chat_message, dict
-        ):  # This happens for previously processed messages
-            return chat_message
-        elif isinstance(chat_message, str):
-            # return self.md.render(chat_message)
-            return str(self.md.convert(chat_message))
-        else:
-            raise ValueError(f"Invalid message for Chatbot component: {chat_message}")
-
-    def postprocess(
-        self,
-        y: List[
-            Tuple[str | Tuple | List | Dict | None, str | Tuple | List | Dict | None]
-        ],
-    ) -> List[Tuple[str | Dict | None, str | Dict | None]]:
-        """
-        Parameters:
-            y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.  It can also be a tuple whose first element is a string filepath or URL to an image/video/audio, and second (optional) element is the alt text, in which case the media file is displayed. It can also be None, in which case that message is not displayed.
-        Returns:
-            List of tuples representing the message and response. Each message and response will be a string of HTML, or a dictionary with media information.
-        """
-        if y is None:
-            return []
-        processed_messages = []
-        for message_pair in y:
-            assert isinstance(
-                message_pair, (tuple, list)
-            ), f"Expected a list of lists or list of tuples. Received: {message_pair}"
-            assert (
-                len(message_pair) == 2
-            ), f"Expected a list of lists of length 2 or list of tuples of length 2. Received: {message_pair}"
-            processed_messages.append(
-                (
-                    # self._process_chat_messages(message_pair[0]),
-                    '<pre style="font-family: var(--font)">'
-                    + nh3.clean(message_pair[0])
-                    + "</pre>",
-                    self._process_chat_messages(message_pair[1]),
-                )
-            )
-        return processed_messages
-
-    def style(self, height: int | None = None, **kwargs):
-        """
-        This method can be used to change the appearance of the Chatbot component.
-        """
-        if height is not None:
-            self._style["height"] = height
-        if kwargs.get("color_map") is not None:
-            warnings.warn("The 'color_map' parameter has been deprecated.")
-
-        Component.style(
-            self,
-            **kwargs,
-        )
-        return self
diff --git a/spaces/Yusin/Speech-ChatGPT-Speech/pygpt.py b/spaces/Yusin/Speech-ChatGPT-Speech/pygpt.py
deleted file mode 100644
index 7a12a791c61ab48b7ab0bdc9fbc6f9463bb5fe02..0000000000000000000000000000000000000000
--- a/spaces/Yusin/Speech-ChatGPT-Speech/pygpt.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import uuid
-import asyncio
-import socketio
-import datetime
-import json
-import base64
-
-class PyGPT:
-    def __init__(self, session_token, bypass_node='https://gpt.pawan.krd'):
-        self.ready = False
-        self.socket = socketio.AsyncClient()
-        self.socket.on('connect', self.on_connect)
-        self.socket.on('disconnect', self.on_disconnect)
-        self.session_token = session_token
-        self.conversations = []
-        self.auth = None
-        self.expires = datetime.datetime.now()
-        self.pause_token_checks = False
-        self.bypass_node = bypass_node
-        asyncio.create_task(self.cleanup_conversations())
-
-    async def connect(self):
-        await self.socket.connect(self.bypass_node)
-
-    async def disconnect(self):
-        await self.socket.disconnect()
-        await self.socket.close()
-
-    def on_connect(self):
-        print('Connected to server')
-        asyncio.create_task(self.check_tokens())
-
-    def on_disconnect(self):
-        print('Disconnected from server')
-        self.ready = False
-
-    async def check_tokens(self):
-        while True:
-            if self.pause_token_checks:
-                await asyncio.sleep(0.5)
-                continue
-            self.pause_token_checks = True
-            now = datetime.datetime.now()
-            offset = datetime.timedelta(minutes=2)
-            if self.expires < (now - offset) or not self.auth:
-                await self.get_tokens()
-            self.pause_token_checks = False
-            await asyncio.sleep(0.5)
-
-    async def cleanup_conversations(self):
-        while True:
-            await asyncio.sleep(60)
-            now = datetime.datetime.now()
-            self.conversations = [c for c in self.conversations if now - c['last_active'] < datetime.timedelta(minutes=2)]
-
-    def add_conversation(self, id):
-        conversation = {
-            'id': id,
-            'conversation_id': None,
-            'parent_id': uuid.uuid4(),
-            'last_active': datetime.datetime.now()
-        }
-        self.conversations.append(conversation)
-        return conversation
-
-    def get_conversation_by_id(self, id):
-        conversation = next((c for c in self.conversations if c['id'] == id), None)
-        if conversation is None:
-            conversation = self.add_conversation(id)
-        else:
-            conversation['last_active'] = datetime.datetime.now()
-        return conversation
-
-    async def wait_for_ready(self):
-        while not self.ready:
-            await asyncio.sleep(0.025)
-        print('Ready!!')
-
-    async def ask(self, prompt, id='default'):
-        if not self.auth or not self.validate_token(self.auth):
-            await self.get_tokens()
-        conversation = self.get_conversation_by_id(id)
-        data = await self.socket.call('askQuestion', {
-            'prompt': prompt,
-            'parentId': str(conversation['parent_id']),
-            'conversationId': str(conversation['conversation_id']),
-            'auth': self.auth
-        })
-
-        if 'error' in data:
-            print(f'Error: {data["error"]}')
-        conversation['parent_id'] = data['messageId']
-        conversation['conversation_id'] = data['conversationId']
-        return data['answer']
-
-    def validate_token(self, token):
-        if not token:
-            return False
-        parsed = json.loads(base64.b64decode(f'{token.split(".")[1]}==').decode())
-        return datetime.datetime.now() <= datetime.datetime.fromtimestamp(parsed['exp'])
-
-    async def get_tokens(self):
-        await asyncio.sleep(1)
-        data = await self.socket.call('getSession', self.session_token)
-
-        if 'error' in data:
-            print(f'Error getting session: {data["error"]}')
-        else:
-            self.auth = data['auth']
-            self.expires = datetime.datetime.strptime(data['expires'], '%Y-%m-%dT%H:%M:%S.%fZ')
-            self.session_token = data['sessionToken']
-            self.ready = True
\ No newline at end of file
diff --git a/spaces/abby711/FaceRestoration/gfpgan/archs/arcface_arch.py b/spaces/abby711/FaceRestoration/gfpgan/archs/arcface_arch.py
deleted file mode 100644
index e6d3bd97f83334450bd78ad2c3b9871102a56b70..0000000000000000000000000000000000000000
--- a/spaces/abby711/FaceRestoration/gfpgan/archs/arcface_arch.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import torch.nn as nn
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-def conv3x3(inplanes, outplanes, stride=1):
-    """A simple wrapper for 3x3 convolution with padding.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        outplanes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-    """
-    return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-    """Basic residual block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class IRBlock(nn.Module):
-    """Improved residual block (IR Block) used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
-        super(IRBlock, self).__init__()
-        self.bn0 = nn.BatchNorm2d(inplanes)
-        self.conv1 = conv3x3(inplanes, inplanes)
-        self.bn1 = nn.BatchNorm2d(inplanes)
-        self.prelu = nn.PReLU()
-        self.conv2 = conv3x3(inplanes, planes, stride)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.use_se = use_se
-        if self.use_se:
-            self.se = SEBlock(planes)
-
-    def forward(self, x):
-        residual = x
-        out = self.bn0(x)
-        out = self.conv1(out)
-        out = self.bn1(out)
-        out = self.prelu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        if self.use_se:
-            out = self.se(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.prelu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    """Bottleneck block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-    expansion = 4  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class SEBlock(nn.Module):
-    """The squeeze-and-excitation block (SEBlock) used in the IRBlock.
-
-    Args:
-        channel (int): Channel number of inputs.
-        reduction (int): Channel reduction ration. Default: 16.
-    """
-
-    def __init__(self, channel, reduction=16):
-        super(SEBlock, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)  # pool to 1x1 without spatial information
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel),
-            nn.Sigmoid())
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y
-
-
-@ARCH_REGISTRY.register()
-class ResNetArcFace(nn.Module):
-    """ArcFace with ResNet architectures.
-
-    Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
-
-    Args:
-        block (str): Block used in the ArcFace architecture.
-        layers (tuple(int)): Block numbers in each layer.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-
-    def __init__(self, block, layers, use_se=True):
-        if block == 'IRBlock':
-            block = IRBlock
-        self.inplanes = 64
-        self.use_se = use_se
-        super(ResNetArcFace, self).__init__()
-
-        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.prelu = nn.PReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.bn4 = nn.BatchNorm2d(512)
-        self.dropout = nn.Dropout()
-        self.fc5 = nn.Linear(512 * 8 * 8, 512)
-        self.bn5 = nn.BatchNorm1d(512)
-
-        # initialization
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.xavier_normal_(m.weight)
-            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.xavier_normal_(m.weight)
-                nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, block, planes, num_blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
-        self.inplanes = planes
-        for _ in range(1, num_blocks):
-            layers.append(block(self.inplanes, planes, use_se=self.use_se))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.prelu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.bn4(x)
-        x = self.dropout(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc5(x)
-        x = self.bn5(x)
-
-        return x
diff --git a/spaces/abby711/FaceRestoration/tests/test_ffhq_degradation_dataset.py b/spaces/abby711/FaceRestoration/tests/test_ffhq_degradation_dataset.py
deleted file mode 100644
index fa56c03fb8e23df26aa6ed8442a86b3c676eec78..0000000000000000000000000000000000000000
--- a/spaces/abby711/FaceRestoration/tests/test_ffhq_degradation_dataset.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import pytest
-import yaml
-
-from gfpgan.data.ffhq_degradation_dataset import FFHQDegradationDataset
-
-
-def test_ffhq_degradation_dataset():
-
-    with open('tests/data/test_ffhq_degradation_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 1
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == 'tests/data/gt/00000000.png'
-
-    # ------------------ test with probability = 0 -------------------- #
-    opt['color_jitter_prob'] = 0
-    opt['color_jitter_pt_prob'] = 0
-    opt['gray_prob'] = 0
-    opt['io_backend'] = dict(type='disk')
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 0
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == 'tests/data/gt/00000000.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/ffhq_gt.lmdb'
-    opt['io_backend'] = dict(type='lmdb')
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset) == 1  # whether to read correct meta info
-    assert dataset.kernel_list == ['iso', 'aniso']  # correct initialization the degradation configurations
-    assert dataset.color_jitter_prob == 0
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == '00000000'
-
-    # ------------------ test with crop_components -------------------- #
-    opt['crop_components'] = True
-    opt['component_path'] = 'tests/data/test_eye_mouth_landmarks.pth'
-    opt['eye_enlarge_ratio'] = 1.4
-    opt['gt_gray'] = True
-    opt['io_backend'] = dict(type='lmdb')
-
-    dataset = FFHQDegradationDataset(opt)
-    assert dataset.crop_components is True
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'loc_left_eye', 'loc_right_eye', 'loc_mouth']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 512, 512)
-    assert result['lq'].shape == (3, 512, 512)
-    assert result['gt_path'] == '00000000'
-    assert result['loc_left_eye'].shape == (4, )
-    assert result['loc_right_eye'].shape == (4, )
-    assert result['loc_mouth'].shape == (4, )
-
-    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
-    with pytest.raises(ValueError):
-        opt['dataroot_gt'] = 'tests/data/gt'
-        opt['io_backend'] = dict(type='lmdb')
-        dataset = FFHQDegradationDataset(opt)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/utils/inverted_residual.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/utils/inverted_residual.py
deleted file mode 100644
index 53b8fcd41f71d814738f1ac3f5acd3c3d701bf96..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/utils/inverted_residual.py
+++ /dev/null
@@ -1,208 +0,0 @@
-from annotator.uniformer.mmcv.cnn import ConvModule
-from torch import nn
-from torch.utils import checkpoint as cp
-
-from .se_layer import SELayer
-
-
-class InvertedResidual(nn.Module):
-    """InvertedResidual block for MobileNetV2.
-
-    Args:
-        in_channels (int): The input channels of the InvertedResidual block.
-        out_channels (int): The output channels of the InvertedResidual block.
-        stride (int): Stride of the middle (first) 3x3 convolution.
-        expand_ratio (int): Adjusts number of channels of the hidden layer
-            in InvertedResidual by this amount.
-        dilation (int): Dilation rate of depthwise conv. Default: 1
-        conv_cfg (dict): Config dict for convolution layer.
-            Default: None, which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='ReLU6').
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-
-    Returns:
-        Tensor: The output tensor.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 stride,
-                 expand_ratio,
-                 dilation=1,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU6'),
-                 with_cp=False):
-        super(InvertedResidual, self).__init__()
-        self.stride = stride
-        assert stride in [1, 2], f'stride must in [1, 2]. ' \
-            f'But received {stride}.'
-        self.with_cp = with_cp
-        self.use_res_connect = self.stride == 1 and in_channels == out_channels
-        hidden_dim = int(round(in_channels * expand_ratio))
-
-        layers = []
-        if expand_ratio != 1:
-            layers.append(
-                ConvModule(
-                    in_channels=in_channels,
-                    out_channels=hidden_dim,
-                    kernel_size=1,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-        layers.extend([
-            ConvModule(
-                in_channels=hidden_dim,
-                out_channels=hidden_dim,
-                kernel_size=3,
-                stride=stride,
-                padding=dilation,
-                dilation=dilation,
-                groups=hidden_dim,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg),
-            ConvModule(
-                in_channels=hidden_dim,
-                out_channels=out_channels,
-                kernel_size=1,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=None)
-        ])
-        self.conv = nn.Sequential(*layers)
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            if self.use_res_connect:
-                return x + self.conv(x)
-            else:
-                return self.conv(x)
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        return out
-
-
-class InvertedResidualV3(nn.Module):
-    """Inverted Residual Block for MobileNetV3.
-
-    Args:
-        in_channels (int): The input channels of this Module.
-        out_channels (int): The output channels of this Module.
-        mid_channels (int): The input channels of the depthwise convolution.
-        kernel_size (int): The kernel size of the depthwise convolution.
-            Default: 3.
-        stride (int): The stride of the depthwise convolution. Default: 1.
-        se_cfg (dict): Config dict for se layer. Default: None, which means no
-            se layer.
-        with_expand_conv (bool): Use expand conv or not. If set False,
-            mid_channels must be the same with in_channels. Default: True.
-        conv_cfg (dict): Config dict for convolution layer. Default: None,
-            which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='ReLU').
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-
-    Returns:
-        Tensor: The output tensor.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 mid_channels,
-                 kernel_size=3,
-                 stride=1,
-                 se_cfg=None,
-                 with_expand_conv=True,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 with_cp=False):
-        super(InvertedResidualV3, self).__init__()
-        self.with_res_shortcut = (stride == 1 and in_channels == out_channels)
-        assert stride in [1, 2]
-        self.with_cp = with_cp
-        self.with_se = se_cfg is not None
-        self.with_expand_conv = with_expand_conv
-
-        if self.with_se:
-            assert isinstance(se_cfg, dict)
-        if not self.with_expand_conv:
-            assert mid_channels == in_channels
-
-        if self.with_expand_conv:
-            self.expand_conv = ConvModule(
-                in_channels=in_channels,
-                out_channels=mid_channels,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg)
-        self.depthwise_conv = ConvModule(
-            in_channels=mid_channels,
-            out_channels=mid_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=kernel_size // 2,
-            groups=mid_channels,
-            conv_cfg=dict(
-                type='Conv2dAdaptivePadding') if stride == 2 else conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-
-        if self.with_se:
-            self.se = SELayer(**se_cfg)
-
-        self.linear_conv = ConvModule(
-            in_channels=mid_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            out = x
-
-            if self.with_expand_conv:
-                out = self.expand_conv(out)
-
-            out = self.depthwise_conv(out)
-
-            if self.with_se:
-                out = self.se(out)
-
-            out = self.linear_conv(out)
-
-            if self.with_res_shortcut:
-                return x + out
-            else:
-                return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        return out
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/backbones/unet.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/backbones/unet.py
deleted file mode 100644
index 82caa16a94c195c192a2a920fb7bc7e60f0f3ce3..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/backbones/unet.py
+++ /dev/null
@@ -1,429 +0,0 @@
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-from annotator.uniformer.mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer,
-                      build_norm_layer, constant_init, kaiming_init)
-from annotator.uniformer.mmcv.runner import load_checkpoint
-from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
-
-from annotator.uniformer.mmseg.utils import get_root_logger
-from ..builder import BACKBONES
-from ..utils import UpConvBlock
-
-
-class BasicConvBlock(nn.Module):
-    """Basic convolutional block for UNet.
-
-    This module consists of several plain convolutional layers.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        num_convs (int): Number of convolutional layers. Default: 2.
-        stride (int): Whether use stride convolution to downsample
-            the input feature map. If stride=2, it only uses stride convolution
-            in the first convolutional layer to downsample the input feature
-            map. Options are 1 or 2. Default: 1.
-        dilation (int): Whether use dilated convolution to expand the
-            receptive field. Set dilation rate of each convolutional layer and
-            the dilation rate of the first convolutional layer is always 1.
-            Default: 1.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        dcn (bool): Use deformable convolution in convolutional layer or not.
-            Default: None.
-        plugins (dict): plugins for convolutional layers. Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_convs=2,
-                 stride=1,
-                 dilation=1,
-                 with_cp=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 dcn=None,
-                 plugins=None):
-        super(BasicConvBlock, self).__init__()
-        assert dcn is None, 'Not implemented yet.'
-        assert plugins is None, 'Not implemented yet.'
-
-        self.with_cp = with_cp
-        convs = []
-        for i in range(num_convs):
-            convs.append(
-                ConvModule(
-                    in_channels=in_channels if i == 0 else out_channels,
-                    out_channels=out_channels,
-                    kernel_size=3,
-                    stride=stride if i == 0 else 1,
-                    dilation=1 if i == 0 else dilation,
-                    padding=1 if i == 0 else dilation,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-
-        self.convs = nn.Sequential(*convs)
-
-    def forward(self, x):
-        """Forward function."""
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(self.convs, x)
-        else:
-            out = self.convs(x)
-        return out
-
-
-@UPSAMPLE_LAYERS.register_module()
-class DeconvModule(nn.Module):
-    """Deconvolution upsample module in decoder for UNet (2X upsample).
-
-    This module uses deconvolution to upsample feature map in the decoder
-    of UNet.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        kernel_size (int): Kernel size of the convolutional layer. Default: 4.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 with_cp=False,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 *,
-                 kernel_size=4,
-                 scale_factor=2):
-        super(DeconvModule, self).__init__()
-
-        assert (kernel_size - scale_factor >= 0) and\
-               (kernel_size - scale_factor) % 2 == 0,\
-               f'kernel_size should be greater than or equal to scale_factor '\
-               f'and (kernel_size - scale_factor) should be even numbers, '\
-               f'while the kernel size is {kernel_size} and scale_factor is '\
-               f'{scale_factor}.'
-
-        stride = scale_factor
-        padding = (kernel_size - scale_factor) // 2
-        self.with_cp = with_cp
-        deconv = nn.ConvTranspose2d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding)
-
-        norm_name, norm = build_norm_layer(norm_cfg, out_channels)
-        activate = build_activation_layer(act_cfg)
-        self.deconv_upsamping = nn.Sequential(deconv, norm, activate)
-
-    def forward(self, x):
-        """Forward function."""
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(self.deconv_upsamping, x)
-        else:
-            out = self.deconv_upsamping(x)
-        return out
-
-
-@UPSAMPLE_LAYERS.register_module()
-class InterpConv(nn.Module):
-    """Interpolation upsample module in decoder for UNet.
-
-    This module uses interpolation to upsample feature map in the decoder
-    of UNet. It consists of one interpolation upsample layer and one
-    convolutional layer. It can be one interpolation upsample layer followed
-    by one convolutional layer (conv_first=False) or one convolutional layer
-    followed by one interpolation upsample layer (conv_first=True).
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        conv_first (bool): Whether convolutional layer or interpolation
-            upsample layer first. Default: False. It means interpolation
-            upsample layer followed by one convolutional layer.
-        kernel_size (int): Kernel size of the convolutional layer. Default: 1.
-        stride (int): Stride of the convolutional layer. Default: 1.
-        padding (int): Padding of the convolutional layer. Default: 1.
-        upsample_cfg (dict): Interpolation config of the upsample layer.
-            Default: dict(
-                scale_factor=2, mode='bilinear', align_corners=False).
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 with_cp=False,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 *,
-                 conv_cfg=None,
-                 conv_first=False,
-                 kernel_size=1,
-                 stride=1,
-                 padding=0,
-                 upsample_cfg=dict(
-                     scale_factor=2, mode='bilinear', align_corners=False)):
-        super(InterpConv, self).__init__()
-
-        self.with_cp = with_cp
-        conv = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        upsample = nn.Upsample(**upsample_cfg)
-        if conv_first:
-            self.interp_upsample = nn.Sequential(conv, upsample)
-        else:
-            self.interp_upsample = nn.Sequential(upsample, conv)
-
-    def forward(self, x):
-        """Forward function."""
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(self.interp_upsample, x)
-        else:
-            out = self.interp_upsample(x)
-        return out
-
-
-@BACKBONES.register_module()
-class UNet(nn.Module):
-    """UNet backbone.
-    U-Net: Convolutional Networks for Biomedical Image Segmentation.
-    https://arxiv.org/pdf/1505.04597.pdf
-
-    Args:
-        in_channels (int): Number of input image channels. Default" 3.
-        base_channels (int): Number of base channels of each stage.
-            The output channels of the first stage. Default: 64.
-        num_stages (int): Number of stages in encoder, normally 5. Default: 5.
-        strides (Sequence[int 1 | 2]): Strides of each stage in encoder.
-            len(strides) is equal to num_stages. Normally the stride of the
-            first stage in encoder is 1. If strides[i]=2, it uses stride
-            convolution to downsample in the correspondence encoder stage.
-            Default: (1, 1, 1, 1, 1).
-        enc_num_convs (Sequence[int]): Number of convolutional layers in the
-            convolution block of the correspondence encoder stage.
-            Default: (2, 2, 2, 2, 2).
-        dec_num_convs (Sequence[int]): Number of convolutional layers in the
-            convolution block of the correspondence decoder stage.
-            Default: (2, 2, 2, 2).
-        downsamples (Sequence[int]): Whether use MaxPool to downsample the
-            feature map after the first stage of encoder
-            (stages: [1, num_stages)). If the correspondence encoder stage use
-            stride convolution (strides[i]=2), it will never use MaxPool to
-            downsample, even downsamples[i-1]=True.
-            Default: (True, True, True, True).
-        enc_dilations (Sequence[int]): Dilation rate of each stage in encoder.
-            Default: (1, 1, 1, 1, 1).
-        dec_dilations (Sequence[int]): Dilation rate of each stage in decoder.
-            Default: (1, 1, 1, 1).
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        upsample_cfg (dict): The upsample config of the upsample module in
-            decoder. Default: dict(type='InterpConv').
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only. Default: False.
-        dcn (bool): Use deformable convolution in convolutional layer or not.
-            Default: None.
-        plugins (dict): plugins for convolutional layers. Default: None.
-
-    Notice:
-        The input image size should be divisible by the whole downsample rate
-        of the encoder. More detail of the whole downsample rate can be found
-        in UNet._check_input_divisible.
-
-    """
-
-    def __init__(self,
-                 in_channels=3,
-                 base_channels=64,
-                 num_stages=5,
-                 strides=(1, 1, 1, 1, 1),
-                 enc_num_convs=(2, 2, 2, 2, 2),
-                 dec_num_convs=(2, 2, 2, 2),
-                 downsamples=(True, True, True, True),
-                 enc_dilations=(1, 1, 1, 1, 1),
-                 dec_dilations=(1, 1, 1, 1),
-                 with_cp=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 upsample_cfg=dict(type='InterpConv'),
-                 norm_eval=False,
-                 dcn=None,
-                 plugins=None):
-        super(UNet, self).__init__()
-        assert dcn is None, 'Not implemented yet.'
-        assert plugins is None, 'Not implemented yet.'
-        assert len(strides) == num_stages, \
-            'The length of strides should be equal to num_stages, '\
-            f'while the strides is {strides}, the length of '\
-            f'strides is {len(strides)}, and the num_stages is '\
-            f'{num_stages}.'
-        assert len(enc_num_convs) == num_stages, \
-            'The length of enc_num_convs should be equal to num_stages, '\
-            f'while the enc_num_convs is {enc_num_convs}, the length of '\
-            f'enc_num_convs is {len(enc_num_convs)}, and the num_stages is '\
-            f'{num_stages}.'
-        assert len(dec_num_convs) == (num_stages-1), \
-            'The length of dec_num_convs should be equal to (num_stages-1), '\
-            f'while the dec_num_convs is {dec_num_convs}, the length of '\
-            f'dec_num_convs is {len(dec_num_convs)}, and the num_stages is '\
-            f'{num_stages}.'
-        assert len(downsamples) == (num_stages-1), \
-            'The length of downsamples should be equal to (num_stages-1), '\
-            f'while the downsamples is {downsamples}, the length of '\
-            f'downsamples is {len(downsamples)}, and the num_stages is '\
-            f'{num_stages}.'
-        assert len(enc_dilations) == num_stages, \
-            'The length of enc_dilations should be equal to num_stages, '\
-            f'while the enc_dilations is {enc_dilations}, the length of '\
-            f'enc_dilations is {len(enc_dilations)}, and the num_stages is '\
-            f'{num_stages}.'
-        assert len(dec_dilations) == (num_stages-1), \
-            'The length of dec_dilations should be equal to (num_stages-1), '\
-            f'while the dec_dilations is {dec_dilations}, the length of '\
-            f'dec_dilations is {len(dec_dilations)}, and the num_stages is '\
-            f'{num_stages}.'
-        self.num_stages = num_stages
-        self.strides = strides
-        self.downsamples = downsamples
-        self.norm_eval = norm_eval
-        self.base_channels = base_channels
-
-        self.encoder = nn.ModuleList()
-        self.decoder = nn.ModuleList()
-
-        for i in range(num_stages):
-            enc_conv_block = []
-            if i != 0:
-                if strides[i] == 1 and downsamples[i - 1]:
-                    enc_conv_block.append(nn.MaxPool2d(kernel_size=2))
-                upsample = (strides[i] != 1 or downsamples[i - 1])
-                self.decoder.append(
-                    UpConvBlock(
-                        conv_block=BasicConvBlock,
-                        in_channels=base_channels * 2**i,
-                        skip_channels=base_channels * 2**(i - 1),
-                        out_channels=base_channels * 2**(i - 1),
-                        num_convs=dec_num_convs[i - 1],
-                        stride=1,
-                        dilation=dec_dilations[i - 1],
-                        with_cp=with_cp,
-                        conv_cfg=conv_cfg,
-                        norm_cfg=norm_cfg,
-                        act_cfg=act_cfg,
-                        upsample_cfg=upsample_cfg if upsample else None,
-                        dcn=None,
-                        plugins=None))
-
-            enc_conv_block.append(
-                BasicConvBlock(
-                    in_channels=in_channels,
-                    out_channels=base_channels * 2**i,
-                    num_convs=enc_num_convs[i],
-                    stride=strides[i],
-                    dilation=enc_dilations[i],
-                    with_cp=with_cp,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg,
-                    dcn=None,
-                    plugins=None))
-            self.encoder.append((nn.Sequential(*enc_conv_block)))
-            in_channels = base_channels * 2**i
-
-    def forward(self, x):
-        self._check_input_divisible(x)
-        enc_outs = []
-        for enc in self.encoder:
-            x = enc(x)
-            enc_outs.append(x)
-        dec_outs = [x]
-        for i in reversed(range(len(self.decoder))):
-            x = self.decoder[i](enc_outs[i], x)
-            dec_outs.append(x)
-
-        return dec_outs
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep normalization layer
-        freezed."""
-        super(UNet, self).train(mode)
-        if mode and self.norm_eval:
-            for m in self.modules():
-                # trick: eval have effect on BatchNorm only
-                if isinstance(m, _BatchNorm):
-                    m.eval()
-
-    def _check_input_divisible(self, x):
-        h, w = x.shape[-2:]
-        whole_downsample_rate = 1
-        for i in range(1, self.num_stages):
-            if self.strides[i] == 2 or self.downsamples[i - 1]:
-                whole_downsample_rate *= 2
-        assert (h % whole_downsample_rate == 0) \
-            and (w % whole_downsample_rate == 0),\
-            f'The input image size {(h, w)} should be divisible by the whole '\
-            f'downsample rate {whole_downsample_rate}, when num_stages is '\
-            f'{self.num_stages}, strides is {self.strides}, and downsamples '\
-            f'is {self.downsamples}.'
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
diff --git a/spaces/abidlabs/min-dalle-later/README.md b/spaces/abidlabs/min-dalle-later/README.md
deleted file mode 100644
index abd1514eee177783fab0a1678c4b3a8dad415f83..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/min-dalle-later/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: min(DALL·E)
-metaTitle: min(DALL·E)
-emoji: ⚡️ 🥑 ⚡️
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: true
-license: mit
-python_version: 3.10.5
-models:
-- kuprel/min-dalle
-duplicated_from: abidlabs/min-dalle
----
diff --git a/spaces/abusch419/PetBreedClassifier/app.py b/spaces/abusch419/PetBreedClassifier/app.py
deleted file mode 100644
index 3c2c33531ea5c62dcf532e941886c70462b30505..0000000000000000000000000000000000000000
--- a/spaces/abusch419/PetBreedClassifier/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
-
-# %% app.ipynb 2
-from fastai.vision.all import *
-import gradio as gr
-import timm
-
-# %% app.ipynb 4
-learn = load_learner('model.pkl')
-
-# %% app.ipynb 6
-categories = learn.dls.vocab
-
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
-
-# %% app.ipynb 8
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ['newfoundland.jpg']
-
-
-# %% app.ipynb 9
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
diff --git a/spaces/afry-south/lowlight-enhancement/model.py b/spaces/afry-south/lowlight-enhancement/model.py
deleted file mode 100644
index 8ccb80f73291c46ec72c3ce22b31d0bbc4c45ead..0000000000000000000000000000000000000000
--- a/spaces/afry-south/lowlight-enhancement/model.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class CSDN_Tem(nn.Module):
-    def __init__(self, in_ch, out_ch):
-        super(CSDN_Tem, self).__init__()
-        self.depth_conv = nn.Conv2d(
-            in_channels=in_ch,
-            out_channels=in_ch,
-            kernel_size=3,
-            padding=1,
-            groups=in_ch
-        )
-        self.point_conv = nn.Conv2d(
-            in_channels=in_ch,
-            out_channels=out_ch,
-            kernel_size=1
-        )
-
-    def forward(self, input):
-        out = self.depth_conv(input)
-        out = self.point_conv(out)
-        return out
-
-class enhance_net_nopool(nn.Module):
-	def __init__(self,scale_factor):
-		super(enhance_net_nopool, self).__init__()
-
-		self.relu = nn.ReLU(inplace=True)
-		self.scale_factor = scale_factor
-		self.upsample = nn.UpsamplingBilinear2d(scale_factor=self.scale_factor)
-		number_f = 32
-
-#   zerodce DWC + p-shared
-		self.e_conv1 = CSDN_Tem(3, number_f) 
-		self.e_conv2 = CSDN_Tem(number_f, number_f) 
-		self.e_conv3 = CSDN_Tem(number_f, number_f) 
-		self.e_conv4 = CSDN_Tem(number_f, number_f) 
-		self.e_conv5 = CSDN_Tem(number_f * 2, number_f) 
-		self.e_conv6 = CSDN_Tem(number_f * 2, number_f) 
-		self.e_conv7 = CSDN_Tem(number_f * 2, 3) 
-
-	def enhance(self, x, x_r):
-		for _ in range(8): x = x + x_r * (torch.pow(x, 2) - x)
-
-		return x
-		
-	def forward(self, x):
-		x_down = x if self.scale_factor==1 else F.interpolate(x, scale_factor = 1 / self.scale_factor, mode='bilinear')
-
-		x1 = self.relu(self.e_conv1(x_down))
-		x2 = self.relu(self.e_conv2(x1))
-		x3 = self.relu(self.e_conv3(x2))
-		x4 = self.relu(self.e_conv4(x3))
-		x5 = self.relu(self.e_conv5(torch.cat([x3, x4], 1)))
-		x6 = self.relu(self.e_conv6(torch.cat([x2, x5], 1)))
-		x_r = torch.tanh(self.e_conv7(torch.cat([x1, x6], 1)))
-
-		x_r = x_r if self.scale_factor==1 else self.upsample(x_r)
-		enhance_image = self.enhance(x, x_r)
-
-		return enhance_image, x_r
\ No newline at end of file
diff --git a/spaces/ai-guru/composer/start.py b/spaces/ai-guru/composer/start.py
deleted file mode 100644
index e5d512289a4581dca4612d6aa2390ace7e534426..0000000000000000000000000000000000000000
--- a/spaces/ai-guru/composer/start.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import subprocess
-
-subprocess.run("uvicorn app:app --host 0.0.0.0 --port 7860", shell=True)
diff --git a/spaces/ai-maker-space/ChatWithYourPDF/README.md b/spaces/ai-maker-space/ChatWithYourPDF/README.md
deleted file mode 100644
index 6dc8082cb45dc0a8e2e44819ad2fd4210692bff8..0000000000000000000000000000000000000000
--- a/spaces/ai-maker-space/ChatWithYourPDF/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: ChatWithYourPDF
-emoji: 🔥
-colorFrom: pink
-colorTo: yellow
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/NodeList.pm b/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/NodeList.pm
deleted file mode 100644
index 81aad84881cc06ade5f0232f33989f0615a21bce..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/NodeList.pm
+++ /dev/null
@@ -1,46 +0,0 @@
-######################################################################
-package XML::DOM::NodeList;
-######################################################################
-
-use vars qw ( $EMPTY );
-
-# Empty NodeList
-$EMPTY = new XML::DOM::NodeList;
-
-sub new 
-{
-    bless [], $_[0];
-}
-
-sub item 
-{
-    $_[0]->[$_[1]];
-}
-
-sub getLength 
-{
-    int (@{$_[0]});
-}
-
-#------------------------------------------------------------
-# Extra method implementations
-
-sub dispose
-{
-    my $self = shift;
-    for my $kid (@{$self})
-    {
-	$kid->dispose;
-    }
-}
-
-sub setOwnerDocument
-{
-    my ($self, $doc) = @_;
-    for my $kid (@{$self})
-    { 
-	$kid->setOwnerDocument ($doc);
-    }
-}
-
-1; # package return code
diff --git a/spaces/akhaliq/deeplab2/g3doc/projects/vip_deeplab.md b/spaces/akhaliq/deeplab2/g3doc/projects/vip_deeplab.md
deleted file mode 100644
index f5b7fb31d4af57916b19f41ad182f133a062d2b3..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/g3doc/projects/vip_deeplab.md
+++ /dev/null
@@ -1,41 +0,0 @@
-TODO: Prepare model zoo and some model introduction.
-
-References below are really meant for reference when writing the doc.
-Please remove the references once ready.
-
-References:
-
-* https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
-* https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
-
-## Citing ViP-DeepLab
-
-If you find this code helpful in your research or wish to refer to the baseline
-results, please use the following BibTeX entry.
-
-* ViP-DeepLab:
-
-```
-@inproceedings{vip_deeplab_2021,
-  author={Siyuan Qiao and Yukun Zhu and Hartwig Adam and Alan Yuille and Liang-Chieh Chen},
-  title={{ViP-DeepLab}: Learning Visual Perception with Depth-aware Video Panoptic Segmentation},
-  booktitle={CVPR},
-  year={2021}
-}
-
-```
-
-* Panoptic-DeepLab:
-
-```
-@inproceedings{panoptic_deeplab_2020,
-  author={Bowen Cheng and Maxwell D Collins and Yukun Zhu and Ting Liu and Thomas S Huang and Hartwig Adam and Liang-Chieh Chen},
-  title={{Panoptic-DeepLab}: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation},
-  booktitle={CVPR},
-  year={2020}
-}
-
-```
-
-### References
-Add some related works if any
diff --git a/spaces/akhaliq/lama/saicinpainting/training/losses/__init__.py b/spaces/akhaliq/lama/saicinpainting/training/losses/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/entrypoints.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/entrypoints.py
deleted file mode 100644
index 1504a12916b10c2de007d0ac0e1a3531ac79f8a7..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/entrypoints.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import sys
-from typing import List, Optional
-
-from pip._internal.cli.main import main
-
-
-def _wrapper(args: Optional[List[str]] = None) -> int:
-    """Central wrapper for all old entrypoints.
-
-    Historically pip has had several entrypoints defined. Because of issues
-    arising from PATH, sys.path, multiple Pythons, their interactions, and most
-    of them having a pip installed, users suffer every time an entrypoint gets
-    moved.
-
-    To alleviate this pain, and provide a mechanism for warning users and
-    directing them to an appropriate place for help, we now define all of
-    our old entrypoints as wrappers for the current one.
-    """
-    sys.stderr.write(
-        "WARNING: pip is being invoked by an old script wrapper. This will "
-        "fail in a future version of pip.\n"
-        "Please see https://github.com/pypa/pip/issues/5599 for advice on "
-        "fixing the underlying issue.\n"
-        "To avoid this problem you can invoke Python with '-m pip' instead of "
-        "running pip directly.\n"
-    )
-    return main(args)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/packaging/utils.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/packaging/utils.py
deleted file mode 100644
index bab11b80c60f10a4f3bccb12eb5b17c48a449767..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/packaging/utils.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-from typing import FrozenSet, NewType, Tuple, Union, cast
-
-from .tags import Tag, parse_tag
-from .version import InvalidVersion, Version
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-NormalizedName = NewType("NormalizedName", str)
-
-
-class InvalidWheelFilename(ValueError):
-    """
-    An invalid wheel filename was found, users should refer to PEP 427.
-    """
-
-
-class InvalidSdistFilename(ValueError):
-    """
-    An invalid sdist filename was found, users should refer to the packaging user guide.
-    """
-
-
-_canonicalize_regex = re.compile(r"[-_.]+")
-# PEP 427: The build number must start with a digit.
-_build_tag_regex = re.compile(r"(\d+)(.*)")
-
-
-def canonicalize_name(name: str) -> NormalizedName:
-    # This is taken from PEP 503.
-    value = _canonicalize_regex.sub("-", name).lower()
-    return cast(NormalizedName, value)
-
-
-def canonicalize_version(version: Union[Version, str]) -> str:
-    """
-    This is very similar to Version.__str__, but has one subtle difference
-    with the way it handles the release segment.
-    """
-    if isinstance(version, str):
-        try:
-            parsed = Version(version)
-        except InvalidVersion:
-            # Legacy versions cannot be normalized
-            return version
-    else:
-        parsed = version
-
-    parts = []
-
-    # Epoch
-    if parsed.epoch != 0:
-        parts.append(f"{parsed.epoch}!")
-
-    # Release segment
-    # NB: This strips trailing '.0's to normalize
-    parts.append(re.sub(r"(\.0)+$", "", ".".join(str(x) for x in parsed.release)))
-
-    # Pre-release
-    if parsed.pre is not None:
-        parts.append("".join(str(x) for x in parsed.pre))
-
-    # Post-release
-    if parsed.post is not None:
-        parts.append(f".post{parsed.post}")
-
-    # Development release
-    if parsed.dev is not None:
-        parts.append(f".dev{parsed.dev}")
-
-    # Local version segment
-    if parsed.local is not None:
-        parts.append(f"+{parsed.local}")
-
-    return "".join(parts)
-
-
-def parse_wheel_filename(
-    filename: str,
-) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
-    if not filename.endswith(".whl"):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (extension must be '.whl'): {filename}"
-        )
-
-    filename = filename[:-4]
-    dashes = filename.count("-")
-    if dashes not in (4, 5):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (wrong number of parts): {filename}"
-        )
-
-    parts = filename.split("-", dashes - 2)
-    name_part = parts[0]
-    # See PEP 427 for the rules on escaping the project name
-    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
-        raise InvalidWheelFilename(f"Invalid project name: {filename}")
-    name = canonicalize_name(name_part)
-    version = Version(parts[1])
-    if dashes == 5:
-        build_part = parts[2]
-        build_match = _build_tag_regex.match(build_part)
-        if build_match is None:
-            raise InvalidWheelFilename(
-                f"Invalid build number: {build_part} in '{filename}'"
-            )
-        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
-    else:
-        build = ()
-    tags = parse_tag(parts[-1])
-    return (name, version, build, tags)
-
-
-def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
-    if filename.endswith(".tar.gz"):
-        file_stem = filename[: -len(".tar.gz")]
-    elif filename.endswith(".zip"):
-        file_stem = filename[: -len(".zip")]
-    else:
-        raise InvalidSdistFilename(
-            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
-            f" {filename}"
-        )
-
-    # We are requiring a PEP 440 version, which cannot contain dashes,
-    # so we split on the last dash.
-    name_part, sep, version_part = file_stem.rpartition("-")
-    if not sep:
-        raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
-
-    name = canonicalize_name(name_part)
-    version = Version(version_part)
-    return (name, version)
diff --git a/spaces/alibaba-pai/pai-diffusion-artist-xlarge-zh/app.py b/spaces/alibaba-pai/pai-diffusion-artist-xlarge-zh/app.py
deleted file mode 100644
index 3ce51496e21abc6a22e62d47b2f52f72d7cd518d..0000000000000000000000000000000000000000
--- a/spaces/alibaba-pai/pai-diffusion-artist-xlarge-zh/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-import torch
-from PIL import Image
-
-model_id = "alibaba-pai/pai-diffusion-artist-xlarge-zh"
-pipe = StableDiffusionPipeline.from_pretrained(model_id)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cpu")
-
-def infer_text2img(prompt, guide, steps):
-    image = pipe([prompt], guidance_scale=guide, num_inference_steps=steps).images[0]
-    return image
-
-with gr.Blocks() as demo:
-    examples = [
-                ["草地上的帐篷，背景是山脉"], 
-                ["卧室里有一张床和一张桌子"], 
-                ["雾蒙蒙的日出在湖面上"],
-                ]
-    with gr.Row():
-        with gr.Column(scale=1, ):
-            image_out = gr.Image(label = '输出(output)')
-        with gr.Column(scale=1, ):
-            prompt = gr.Textbox(label = '提示词(prompt)')
-            submit_btn = gr.Button("生成图像(Generate)")
-            with gr.Row(scale=0.5 ):
-                guide = gr.Slider(2, 15, value = 7, label = '文本引导强度(guidance scale)')
-                steps = gr.Slider(10, 50, value = 20, step = 1, label = '迭代次数(inference steps)')
-                ex = gr.Examples(examples, fn=infer_text2img, inputs=[prompt, guide, steps], outputs=image_out)
-        submit_btn.click(fn = infer_text2img, inputs = [prompt, guide, steps], outputs = image_out)
-
-demo.queue(concurrency_count=1, max_size=8).launch()
diff --git a/spaces/alvanlii/domain-expansion/README.md b/spaces/alvanlii/domain-expansion/README.md
deleted file mode 100644
index 7a4218688f406720676ba4c685a5d7b498c16a32..0000000000000000000000000000000000000000
--- a/spaces/alvanlii/domain-expansion/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Domain Expansion
-emoji: 👁
-colorFrom: pink
-colorTo: yellow
-sdk: docker
-pinned: false
-tags: ['making-demos']
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/antonelli/outsidellms/videogen2.py b/spaces/antonelli/outsidellms/videogen2.py
deleted file mode 100644
index e9562fcd6c5676bc42110318049d088c8d765fbe..0000000000000000000000000000000000000000
--- a/spaces/antonelli/outsidellms/videogen2.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import replicate
-
-REPLICATE_API_TOKEN = "r8_4cAphiTVFDG2uiyIHBU0WLN3VxtGrTf17wKLL"
-
-def generate_video(lyrics_with_timing, image_size=(800, 600), max_frames=48):
-    videos = []
-    for start_time, end_time, lyric in lyrics_with_timing:
-        prompt = generate_video_prompt(lyric) # Replace with your logic to create the prompt
-        video = call_replicate_api(prompt)
-        videos.append(video)
-
-    # Combine videos into a final video or handle them as needed
-    final_video = combine_videos(videos)
-    return final_video
-
-def call_replicate_api(prompt):
-    input_data = {
-        "motion_module": "mm_sd_v14",
-        "prompt": prompt # Pass the prompt here
-    }
-
-    output = replicate.run(
-        "lucataco/animate-diff:1531004ee4c98894ab11f8a4ce6206099e732c1da15121987a8eef54828f0663",
-        input=input_data,
-        token=REPLICATE_API_TOKEN # Pass the token here
-    )
-
-    return output
-
-def combine_videos(videos):
-    # Your code to combine individual videos into a final video
-    final_video = None  # Replace with actual video object
-    return final_video
-
-def generate_video_prompt(lyric):
-    # Your code to create a video prompt based on the lyric
-    prompt = lyric # Example: simply use the lyric as the prompt
-    return prompt
diff --git a/spaces/aphenx/bingo/src/components/voice.tsx b/spaces/aphenx/bingo/src/components/voice.tsx
deleted file mode 100644
index 074d0e145229947282a472bd84f6578cf0b3c71c..0000000000000000000000000000000000000000
--- a/spaces/aphenx/bingo/src/components/voice.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import React, { useEffect } from 'react'
-import { useSetAtom } from 'jotai'
-import { useBing } from '@/lib/hooks/use-bing'
-import Image from 'next/image'
-import VoiceIcon from '@/assets/images/voice.svg'
-import VoiceButton from './ui/voice'
-import { SR } from '@/lib/bots/bing/sr'
-import { voiceListenAtom } from '@/state'
-
-const sr = new SR(['发送', '清空', '退出'])
-
-const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick<ReturnType<typeof useBing>, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => {
-  const setListen = useSetAtom(voiceListenAtom)
-  useEffect(() => {
-    if (sr.listening) return
-    sr.transcript = !isSpeaking
-  }, [isSpeaking])
-
-  useEffect(() => {
-    sr.onchange = (msg: string, command?: string) => {
-      switch (command) {
-        case '退出':
-          sr.stop()
-          break;
-        case '发送':
-          sendMessage(input)
-        case '清空':
-          setInput('')
-          break;
-        default:
-          setInput(input + msg)
-      }
-    }
-  }, [input])
-
-  const switchSR = (enable: boolean = false) => {
-    setListen(enable)
-    if (enable) {
-      sr.start()
-    } else {
-      sr.stop()
-    }
-  }
-
-  return sr.listening ? (
-    <VoiceButton onClick={() => switchSR(false)} />
-  ) : (
-    <Image alt="start voice" src={VoiceIcon} width={24} className="-mt-0.5" onClick={() => switchSR(true)} />
-  )
-};
-
-export default Voice;
diff --git a/spaces/arbml/whisper-largev2-ar/README.md b/spaces/arbml/whisper-largev2-ar/README.md
deleted file mode 100644
index d338572d83746c783eeac42c910c0d505eb5f891..0000000000000000000000000000000000000000
--- a/spaces/arbml/whisper-largev2-ar/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Whisper Largev2 AR
-emoji: 🤫
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-tags:
-- whisper-event
-duplicated_from: whisper-event/whisper-demo
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/load_model.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/load_model.py
deleted file mode 100644
index ce6b757f054ce98b91601b494854ef8e7b56b131..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/load_model.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import contextlib
-import functools
-import hashlib
-import logging
-import os
-
-import requests
-import torch
-import tqdm
-
-from TTS.tts.layers.bark.model import GPT, GPTConfig
-from TTS.tts.layers.bark.model_fine import FineGPT, FineGPTConfig
-
-if (
-    torch.cuda.is_available()
-    and hasattr(torch.cuda, "amp")
-    and hasattr(torch.cuda.amp, "autocast")
-    and torch.cuda.is_bf16_supported()
-):
-    autocast = functools.partial(torch.cuda.amp.autocast, dtype=torch.bfloat16)
-else:
-
-    @contextlib.contextmanager
-    def autocast():
-        yield
-
-
-# hold models in global scope to lazy load
-
-logger = logging.getLogger(__name__)
-
-
-if not hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-    logger.warning(
-        "torch version does not support flash attention. You will get significantly faster"
-        + " inference speed by upgrade torch to newest version / nightly."
-    )
-
-
-def _md5(fname):
-    hash_md5 = hashlib.md5()
-    with open(fname, "rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-
-def _download(from_s3_path, to_local_path, CACHE_DIR):
-    os.makedirs(CACHE_DIR, exist_ok=True)
-    response = requests.get(from_s3_path, stream=True)
-    total_size_in_bytes = int(response.headers.get("content-length", 0))
-    block_size = 1024  # 1 Kibibyte
-    progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
-    with open(to_local_path, "wb") as file:
-        for data in response.iter_content(block_size):
-            progress_bar.update(len(data))
-            file.write(data)
-    progress_bar.close()
-    if total_size_in_bytes not in [0, progress_bar.n]:
-        raise ValueError("ERROR, something went wrong")
-
-
-class InferenceContext:
-    def __init__(self, benchmark=False):
-        # we can't expect inputs to be the same length, so disable benchmarking by default
-        self._chosen_cudnn_benchmark = benchmark
-        self._cudnn_benchmark = None
-
-    def __enter__(self):
-        self._cudnn_benchmark = torch.backends.cudnn.benchmark
-        torch.backends.cudnn.benchmark = self._chosen_cudnn_benchmark
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        torch.backends.cudnn.benchmark = self._cudnn_benchmark
-
-
-if torch.cuda.is_available():
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-
-
-@contextlib.contextmanager
-def inference_mode():
-    with InferenceContext(), torch.inference_mode(), torch.no_grad(), autocast():
-        yield
-
-
-def clear_cuda_cache():
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.synchronize()
-
-
-def load_model(ckpt_path, device, config, model_type="text"):
-    logger.info(f"loading {model_type} model from {ckpt_path}...")
-
-    if device == "cpu":
-        logger.warning("No GPU being used. Careful, Inference might be extremely slow!")
-    if model_type == "text":
-        ConfigClass = GPTConfig
-        ModelClass = GPT
-    elif model_type == "coarse":
-        ConfigClass = GPTConfig
-        ModelClass = GPT
-    elif model_type == "fine":
-        ConfigClass = FineGPTConfig
-        ModelClass = FineGPT
-    else:
-        raise NotImplementedError()
-    if (
-        not config.USE_SMALLER_MODELS
-        and os.path.exists(ckpt_path)
-        and _md5(ckpt_path) != config.REMOTE_MODEL_PATHS[model_type]["checksum"]
-    ):
-        logger.warning(f"found outdated {model_type} model, removing...")
-        os.remove(ckpt_path)
-    if not os.path.exists(ckpt_path):
-        logger.info(f"{model_type} model not found, downloading...")
-        _download(config.REMOTE_MODEL_PATHS[model_type]["path"], ckpt_path, config.CACHE_DIR)
-
-    checkpoint = torch.load(ckpt_path, map_location=device)
-    # this is a hack
-    model_args = checkpoint["model_args"]
-    if "input_vocab_size" not in model_args:
-        model_args["input_vocab_size"] = model_args["vocab_size"]
-        model_args["output_vocab_size"] = model_args["vocab_size"]
-        del model_args["vocab_size"]
-
-    gptconf = ConfigClass(**checkpoint["model_args"])
-    if model_type == "text":
-        config.semantic_config = gptconf
-    elif model_type == "coarse":
-        config.coarse_config = gptconf
-    elif model_type == "fine":
-        config.fine_config = gptconf
-
-    model = ModelClass(gptconf)
-    state_dict = checkpoint["model"]
-    # fixup checkpoint
-    unwanted_prefix = "_orig_mod."
-    for k, _ in list(state_dict.items()):
-        if k.startswith(unwanted_prefix):
-            state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
-    extra_keys = set(state_dict.keys()) - set(model.state_dict().keys())
-    extra_keys = set(k for k in extra_keys if not k.endswith(".attn.bias"))
-    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
-    missing_keys = set(k for k in missing_keys if not k.endswith(".attn.bias"))
-    if len(extra_keys) != 0:
-        raise ValueError(f"extra keys found: {extra_keys}")
-    if len(missing_keys) != 0:
-        raise ValueError(f"missing keys: {missing_keys}")
-    model.load_state_dict(state_dict, strict=False)
-    n_params = model.get_num_params()
-    val_loss = checkpoint["best_val_loss"].item()
-    logger.info(f"model loaded: {round(n_params/1e6,1)}M params, {round(val_loss,3)} loss")
-    model.eval()
-    model.to(device)
-    del checkpoint, state_dict
-    clear_cuda_cache()
-    return model, config
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/japanese/__init__.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/utils/text/japanese/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/layers/pqmf.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/layers/pqmf.py
deleted file mode 100644
index 6253efbbefc32222464a97bee99707d46bcdcf8b..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/layers/pqmf.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from scipy import signal as sig
-
-
-# adapted from
-# https://github.com/kan-bayashi/ParallelWaveGAN/tree/master/parallel_wavegan
-class PQMF(torch.nn.Module):
-    def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
-        super().__init__()
-
-        self.N = N
-        self.taps = taps
-        self.cutoff = cutoff
-        self.beta = beta
-
-        QMF = sig.firwin(taps + 1, cutoff, window=("kaiser", beta))
-        H = np.zeros((N, len(QMF)))
-        G = np.zeros((N, len(QMF)))
-        for k in range(N):
-            constant_factor = (
-                (2 * k + 1) * (np.pi / (2 * N)) * (np.arange(taps + 1) - ((taps - 1) / 2))
-            )  # TODO: (taps - 1) -> taps
-            phase = (-1) ** k * np.pi / 4
-            H[k] = 2 * QMF * np.cos(constant_factor + phase)
-
-            G[k] = 2 * QMF * np.cos(constant_factor - phase)
-
-        H = torch.from_numpy(H[:, None, :]).float()
-        G = torch.from_numpy(G[None, :, :]).float()
-
-        self.register_buffer("H", H)
-        self.register_buffer("G", G)
-
-        updown_filter = torch.zeros((N, N, N)).float()
-        for k in range(N):
-            updown_filter[k, k, 0] = 1.0
-        self.register_buffer("updown_filter", updown_filter)
-        self.N = N
-
-        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
-
-    def forward(self, x):
-        return self.analysis(x)
-
-    def analysis(self, x):
-        return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)
-
-    def synthesis(self, x):
-        x = F.conv_transpose1d(x, self.updown_filter * self.N, stride=self.N)
-        x = F.conv1d(x, self.G, padding=self.taps // 2)
-        return x
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/speech_ulm_criterion.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/speech_ulm_criterion.py
deleted file mode 100644
index eae6b62f763d9cab9f59ce7d5aed76762aa25051..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/speech_ulm_criterion.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from dataclasses import dataclass, field
-
-import torch.nn.functional as F
-from fairseq import metrics
-from fairseq.tasks import FairseqTask
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class SpeechUnitLmCriterionConfig(FairseqDataclass):
-    sentence_avg: bool = II("optimization.sentence_avg")
-    loss_weights: str = field(
-        default="1.;0.0;0.0",
-        metadata={
-            "help": "Weights of the losses that correspond to token, duration, and F0 streams"
-        },
-    )
-    discrete_duration: bool = II("task.discrete_duration")
-    discrete_f0: bool = II("task.discrete_f0")
-
-
-def mae_loss(pred, targ, mask, reduce=True):
-    if pred.ndim == 3:
-        pred = pred.squeeze(2)
-    else:
-        assert pred.ndim == 2
-    loss = (pred.float() - targ.float()).abs() * (~mask).float()
-    loss = loss.sum() if reduce else loss.view(-1)
-    return loss
-
-
-def nll_loss(pred, targ, mask, reduce=True):
-    lprob = F.log_softmax(pred, dim=-1)
-    loss = F.nll_loss(lprob.view(-1, lprob.size(-1)), targ.view(-1), reduction="none")
-    loss = loss * (~mask).float().view(-1)
-    loss = loss.sum() if reduce else loss.view(-1)
-    return loss
-
-
-@register_criterion("speech_unit_lm_criterion", dataclass=SpeechUnitLmCriterionConfig)
-class SpeechUnitLmCriterion(FairseqCriterion):
-    def __init__(self, cfg: SpeechUnitLmCriterionConfig, task: FairseqTask):
-        super().__init__(task)
-        self.sentence_avg = cfg.sentence_avg
-        self.weights = torch.tensor([float(w) for w in cfg.loss_weights.split(";")])
-        assert self.weights.size(0) == 3
-        assert (self.weights >= 0.0).all()
-
-        self.dur_loss_fn = nll_loss if cfg.discrete_duration else mae_loss
-        self.f0_loss_fn = nll_loss if cfg.discrete_f0 else mae_loss
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        net_output = model(**sample["net_input"])
-
-        token_loss = nll_loss(
-            net_output["token"], sample["target"], sample["mask"], reduce
-        )
-        dur_loss = self.dur_loss_fn(
-            net_output["duration"],
-            sample["dur_target"],
-            sample["dur_mask"],
-            reduce,
-        )
-        f0_loss = self.f0_loss_fn(
-            net_output["f0"],
-            sample["f0_target"],
-            sample["f0_mask"],
-            reduce,
-        )
-        loss = self.weights.to(token_loss.device) * torch.stack(
-            [token_loss, dur_loss, f0_loss], dim=-1
-        )
-        loss = loss.sum() if reduce else loss.sum(-1)
-
-        sample_size = (
-            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
-        )
-        logging_output = {
-            "loss": loss.detach().sum().item(),
-            "token_loss": token_loss.detach().sum().item(),
-            "dur_loss": dur_loss.detach().sum().item(),
-            "f0_loss": f0_loss.detach().sum().item(),
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-        }
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        token_loss_sum = sum(log.get("token_loss", 0) for log in logging_outputs)
-        dur_loss_sum = sum(log.get("dur_loss", 0) for log in logging_outputs)
-        f0_loss_sum = sum(log.get("f0_loss", 0) for log in logging_outputs)
-
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
-        metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3)
-
-        metrics.log_scalar(
-            "token_loss", token_loss_sum / sample_size, sample_size, round=3
-        )
-
-        metrics.log_scalar("dur_loss", dur_loss_sum / sample_size, sample_size, round=3)
-
-        metrics.log_scalar("f0_loss", f0_loss_sum / sample_size, sample_size, round=3)
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        return True
diff --git a/spaces/asigalov61/Euterpe-X/TMIDIX.py b/spaces/asigalov61/Euterpe-X/TMIDIX.py
deleted file mode 100644
index f023e673586b78b1fb8e337b11d48978343b2a9f..0000000000000000000000000000000000000000
--- a/spaces/asigalov61/Euterpe-X/TMIDIX.py
+++ /dev/null
@@ -1,3202 +0,0 @@
-#! /usr/bin/python3
-
-
-r'''###############################################################################
-###################################################################################
-#
-#
-#	Tegridy MIDI X Module (TMIDI X / tee-midi eks)
-#	Version 1.0
-#
-#   NOTE: TMIDI X Module starts after the partial MIDI.py module @ line 1342
-#
-#	Based upon MIDI.py module v.6.7. by Peter Billam / pjb.com.au
-#
-#	Project Los Angeles
-#
-#	Tegridy Code 2021
-#
-#   https://github.com/Tegridy-Code/Project-Los-Angeles
-#
-#
-###################################################################################
-###################################################################################
-#       Copyright 2021 Project Los Angeles / Tegridy Code
-#
-#       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.
-###################################################################################
-###################################################################################
-#
-#	PARTIAL MIDI.py Module v.6.7. by Peter Billam
-#   Please see TMIDI 2.3/tegridy-tools repo for full MIDI.py module code
-# 
-#   Or you can always download the latest full version from:
-#	https://pjb.com.au/
-#	
-#	Copyright 2020 Peter Billam
-#
-###################################################################################
-###################################################################################'''
-
-import sys, struct, copy
-Version = '6.7'
-VersionDate = '20201120'
-
-_previous_warning = ''  # 5.4
-_previous_times = 0     # 5.4
-#------------------------------- Encoding stuff --------------------------
-
-def opus2midi(opus=[], text_encoding='ISO-8859-1'):
-    r'''The argument is a list: the first item in the list is the "ticks"
-parameter, the others are the tracks. Each track is a list
-of midi-events, and each event is itself a list; see above.
-opus2midi() returns a bytestring of the MIDI, which can then be
-written either to a file opened in binary mode (mode='wb'),
-or to stdout by means of:   sys.stdout.buffer.write()
-
-my_opus = [
-    96, 
-    [   # track 0:
-        ['patch_change', 0, 1, 8],   # and these are the events...
-        ['note_on',   5, 1, 25, 96],
-        ['note_off', 96, 1, 25, 0],
-        ['note_on',   0, 1, 29, 96],
-        ['note_off', 96, 1, 29, 0],
-    ],   # end of track 0
-]
-my_midi = opus2midi(my_opus)
-sys.stdout.buffer.write(my_midi)
-'''
-    if len(opus) < 2:
-        opus=[1000, [],]
-    tracks = copy.deepcopy(opus)
-    ticks = int(tracks.pop(0))
-    ntracks = len(tracks)
-    if ntracks == 1:
-        format = 0
-    else:
-        format = 1
-
-    my_midi = b"MThd\x00\x00\x00\x06"+struct.pack('>HHH',format,ntracks,ticks)
-    for track in tracks:
-        events = _encode(track, text_encoding=text_encoding)
-        my_midi += b'MTrk' + struct.pack('>I',len(events)) + events
-    _clean_up_warnings()
-    return my_midi
-
-
-def score2opus(score=None, text_encoding='ISO-8859-1'):
-    r'''
-The argument is a list: the first item in the list is the "ticks"
-parameter, the others are the tracks. Each track is a list
-of score-events, and each event is itself a list.  A score-event
-is similar to an opus-event (see above), except that in a score:
- 1) the times are expressed as an absolute number of ticks
-    from the track's start time
- 2) the pairs of 'note_on' and 'note_off' events in an "opus"
-    are abstracted into a single 'note' event in a "score":
-    ['note', start_time, duration, channel, pitch, velocity]
-score2opus() returns a list specifying the equivalent "opus".
-
-my_score = [
-    96,
-    [   # track 0:
-        ['patch_change', 0, 1, 8],
-        ['note', 5, 96, 1, 25, 96],
-        ['note', 101, 96, 1, 29, 96]
-    ],   # end of track 0
-]
-my_opus = score2opus(my_score)
-'''
-    if len(score) < 2:
-        score=[1000, [],]
-    tracks = copy.deepcopy(score)
-    ticks = int(tracks.pop(0))
-    opus_tracks = []
-    for scoretrack in tracks:
-        time2events = dict([])
-        for scoreevent in scoretrack:
-            if scoreevent[0] == 'note':
-                note_on_event = ['note_on',scoreevent[1],
-                 scoreevent[3],scoreevent[4],scoreevent[5]]
-                note_off_event = ['note_off',scoreevent[1]+scoreevent[2],
-                 scoreevent[3],scoreevent[4],scoreevent[5]]
-                if time2events.get(note_on_event[1]):
-                   time2events[note_on_event[1]].append(note_on_event)
-                else:
-                   time2events[note_on_event[1]] = [note_on_event,]
-                if time2events.get(note_off_event[1]):
-                   time2events[note_off_event[1]].append(note_off_event)
-                else:
-                   time2events[note_off_event[1]] = [note_off_event,]
-                continue
-            if time2events.get(scoreevent[1]):
-               time2events[scoreevent[1]].append(scoreevent)
-            else:
-               time2events[scoreevent[1]] = [scoreevent,]
-
-        sorted_times = []  # list of keys
-        for k in time2events.keys():
-            sorted_times.append(k)
-        sorted_times.sort()
-
-        sorted_events = []  # once-flattened list of values sorted by key
-        for time in sorted_times:
-            sorted_events.extend(time2events[time])
-
-        abs_time = 0
-        for event in sorted_events:  # convert abs times => delta times
-            delta_time = event[1] - abs_time
-            abs_time = event[1]
-            event[1] = delta_time
-        opus_tracks.append(sorted_events)
-    opus_tracks.insert(0,ticks)
-    _clean_up_warnings()
-    return opus_tracks
-
-def score2midi(score=None, text_encoding='ISO-8859-1'):
-    r'''
-Translates a "score" into MIDI, using score2opus() then opus2midi()
-'''
-    return opus2midi(score2opus(score, text_encoding), text_encoding)
-
-#--------------------------- Decoding stuff ------------------------
-
-def midi2opus(midi=b''):
-    r'''Translates MIDI into a "opus".  For a description of the
-"opus" format, see opus2midi()
-'''
-    my_midi=bytearray(midi)
-    if len(my_midi) < 4:
-        _clean_up_warnings()
-        return [1000,[],]
-    id = bytes(my_midi[0:4])
-    if id != b'MThd':
-        _warn("midi2opus: midi starts with "+str(id)+" instead of 'MThd'")
-        _clean_up_warnings()
-        return [1000,[],]
-    [length, format, tracks_expected, ticks] = struct.unpack(
-     '>IHHH', bytes(my_midi[4:14]))
-    if length != 6:
-        _warn("midi2opus: midi header length was "+str(length)+" instead of 6")
-        _clean_up_warnings()
-        return [1000,[],]
-    my_opus = [ticks,]
-    my_midi = my_midi[14:]
-    track_num = 1   # 5.1
-    while len(my_midi) >= 8:
-        track_type   = bytes(my_midi[0:4])
-        if track_type != b'MTrk':
-            #_warn('midi2opus: Warning: track #'+str(track_num)+' type is '+str(track_type)+" instead of b'MTrk'")
-            pass
-        [track_length] = struct.unpack('>I', my_midi[4:8])
-        my_midi = my_midi[8:]
-        if track_length > len(my_midi):
-            _warn('midi2opus: track #'+str(track_num)+' length '+str(track_length)+' is too large')
-            _clean_up_warnings()
-            return my_opus   # 5.0
-        my_midi_track = my_midi[0:track_length]
-        my_track = _decode(my_midi_track)
-        my_opus.append(my_track)
-        my_midi = my_midi[track_length:]
-        track_num += 1   # 5.1
-    _clean_up_warnings()
-    return my_opus
-
-def opus2score(opus=[]):
-    r'''For a description of the "opus" and "score" formats,
-see opus2midi() and score2opus().
-'''
-    if len(opus) < 2:
-        _clean_up_warnings()
-        return [1000,[],]
-    tracks = copy.deepcopy(opus)  # couple of slices probably quicker...
-    ticks = int(tracks.pop(0))
-    score = [ticks,]
-    for opus_track in tracks:
-        ticks_so_far = 0
-        score_track = []
-        chapitch2note_on_events = dict([])   # 4.0
-        for opus_event in opus_track:
-            ticks_so_far += opus_event[1]
-            if opus_event[0] == 'note_off' or (opus_event[0] == 'note_on' and opus_event[4] == 0):  # 4.8
-                cha = opus_event[2]
-                pitch = opus_event[3]
-                key = cha*128 + pitch
-                if chapitch2note_on_events.get(key):
-                    new_event = chapitch2note_on_events[key].pop(0)
-                    new_event[2] = ticks_so_far - new_event[1]
-                    score_track.append(new_event)
-                elif pitch > 127:
-                    pass #_warn('opus2score: note_off with no note_on, bad pitch='+str(pitch))
-                else:
-                    pass #_warn('opus2score: note_off with no note_on cha='+str(cha)+' pitch='+str(pitch))
-            elif opus_event[0] == 'note_on':
-                cha = opus_event[2]
-                pitch = opus_event[3]
-                key = cha*128 + pitch
-                new_event = ['note',ticks_so_far,0,cha,pitch, opus_event[4]]
-                if chapitch2note_on_events.get(key):
-                    chapitch2note_on_events[key].append(new_event)
-                else:
-                    chapitch2note_on_events[key] = [new_event,]
-            else:
-                opus_event[1] = ticks_so_far
-                score_track.append(opus_event)
-        # check for unterminated notes (Oisín) -- 5.2
-        for chapitch in chapitch2note_on_events:
-            note_on_events = chapitch2note_on_events[chapitch]
-            for new_e in note_on_events:
-                new_e[2] = ticks_so_far - new_e[1]
-                score_track.append(new_e)
-                pass #_warn("opus2score: note_on with no note_off cha="+str(new_e[3])+' pitch='+str(new_e[4])+'; adding note_off at end')
-        score.append(score_track)
-    _clean_up_warnings()
-    return score
-
-def midi2score(midi=b''):
-    r'''
-Translates MIDI into a "score", using midi2opus() then opus2score()
-'''
-    return opus2score(midi2opus(midi))
-
-def midi2ms_score(midi=b''):
-    r'''
-Translates MIDI into a "score" with one beat per second and one
-tick per millisecond, using midi2opus() then to_millisecs()
-then opus2score()
-'''
-    return opus2score(to_millisecs(midi2opus(midi)))
-
-#------------------------ Other Transformations ---------------------
-
-def to_millisecs(old_opus=None, desired_time_in_ms=1):
-    r'''Recallibrates all the times in an "opus" to use one beat
-per second and one tick per millisecond.  This makes it
-hard to retrieve any information about beats or barlines,
-but it does make it easy to mix different scores together.
-'''
-    if old_opus == None:
-        return [1000 * desired_time_in_ms,[],]
-    try:
-        old_tpq  = int(old_opus[0])
-    except IndexError:   # 5.0
-        _warn('to_millisecs: the opus '+str(type(old_opus))+' has no elements')
-        return [1000 * desired_time_in_ms,[],]
-    new_opus = [1000 * desired_time_in_ms,]
-    # 6.7 first go through building a table of set_tempos by absolute-tick
-    ticks2tempo = {}
-    itrack = 1
-    while itrack < len(old_opus):
-        ticks_so_far = 0
-        for old_event in old_opus[itrack]:
-            if old_event[0] == 'note':
-                raise TypeError('to_millisecs needs an opus, not a score')
-            ticks_so_far += old_event[1]
-            if old_event[0] == 'set_tempo':
-                ticks2tempo[ticks_so_far] = old_event[2]
-        itrack += 1
-    # then get the sorted-array of their keys
-    tempo_ticks = []  # list of keys
-    for k in ticks2tempo.keys():
-        tempo_ticks.append(k)
-    tempo_ticks.sort()
-    # then go through converting to millisec, testing if the next
-    # set_tempo lies before the next track-event, and using it if so.
-    itrack = 1
-    while itrack < len(old_opus):
-        ms_per_old_tick = 400 / old_tpq  # float: will round later 6.3
-        i_tempo_ticks = 0
-        ticks_so_far = 0
-        ms_so_far = 0.0
-        previous_ms_so_far = 0.0
-        new_track = [['set_tempo',0,1000000 * desired_time_in_ms],]  # new "crochet" is 1 sec
-        for old_event in old_opus[itrack]:
-            # detect if ticks2tempo has something before this event
-            # 20160702 if ticks2tempo is at the same time, leave it
-            event_delta_ticks = old_event[1] * desired_time_in_ms
-            if (i_tempo_ticks < len(tempo_ticks) and
-              tempo_ticks[i_tempo_ticks] < (ticks_so_far + old_event[1]) * desired_time_in_ms):
-                delta_ticks = tempo_ticks[i_tempo_ticks] - ticks_so_far
-                ms_so_far += (ms_per_old_tick * delta_ticks * desired_time_in_ms)
-                ticks_so_far = tempo_ticks[i_tempo_ticks]
-                ms_per_old_tick = ticks2tempo[ticks_so_far] / (1000.0*old_tpq * desired_time_in_ms)
-                i_tempo_ticks += 1
-                event_delta_ticks -= delta_ticks
-            new_event = copy.deepcopy(old_event)  # now handle the new event
-            ms_so_far += (ms_per_old_tick * old_event[1] * desired_time_in_ms)
-            new_event[1] = round(ms_so_far - previous_ms_so_far)
-            if old_event[0] != 'set_tempo':
-                previous_ms_so_far = ms_so_far
-                new_track.append(new_event)
-            ticks_so_far += event_delta_ticks
-        new_opus.append(new_track)
-        itrack += 1
-    _clean_up_warnings()
-    return new_opus
-
-def event2alsaseq(event=None):   # 5.5
-    r'''Converts an event into the format needed by the alsaseq module,
-http://pp.com.mx/python/alsaseq
-The type of track (opus or score) is autodetected.
-'''
-    pass
-
-def grep(score=None, channels=None):
-    r'''Returns a "score" containing only the channels specified
-'''
-    if score == None:
-        return [1000,[],]
-    ticks = score[0]
-    new_score = [ticks,]
-    if channels == None:
-        return new_score
-    channels = set(channels)
-    global Event2channelindex
-    itrack = 1
-    while itrack < len(score):
-        new_score.append([])
-        for event in score[itrack]:
-            channel_index = Event2channelindex.get(event[0], False)
-            if channel_index:
-                if event[channel_index] in channels:
-                    new_score[itrack].append(event)
-            else:
-                new_score[itrack].append(event)
-        itrack += 1
-    return new_score
-
-def play_score(score=None):
-    r'''Converts the "score" to midi, and feeds it into 'aplaymidi -'
-'''
-    if score == None:
-        return
-    import subprocess
-    pipe = subprocess.Popen(['aplaymidi','-'], stdin=subprocess.PIPE)
-    if score_type(score) == 'opus':
-        pipe.stdin.write(opus2midi(score))
-    else:
-        pipe.stdin.write(score2midi(score))
-    pipe.stdin.close()
-
-def score2stats(opus_or_score=None):
-    r'''Returns a dict of some basic stats about the score, like
-bank_select (list of tuples (msb,lsb)),
-channels_by_track (list of lists), channels_total (set),
-general_midi_mode (list),
-ntracks, nticks, patch_changes_by_track (list of dicts),
-num_notes_by_channel (list of numbers),
-patch_changes_total (set),
-percussion (dict histogram of channel 9 events),
-pitches (dict histogram of pitches on channels other than 9),
-pitch_range_by_track (list, by track, of two-member-tuples),
-pitch_range_sum (sum over tracks of the pitch_ranges),
-'''
-    bank_select_msb = -1
-    bank_select_lsb = -1
-    bank_select = []
-    channels_by_track = []
-    channels_total    = set([])
-    general_midi_mode = []
-    num_notes_by_channel = dict([])
-    patches_used_by_track  = []
-    patches_used_total     = set([])
-    patch_changes_by_track = []
-    patch_changes_total    = set([])
-    percussion = dict([]) # histogram of channel 9 "pitches"
-    pitches    = dict([]) # histogram of pitch-occurrences channels 0-8,10-15
-    pitch_range_sum = 0   # u pitch-ranges of each track
-    pitch_range_by_track = []
-    is_a_score = True
-    if opus_or_score == None:
-        return {'bank_select':[], 'channels_by_track':[], 'channels_total':[],
-         'general_midi_mode':[], 'ntracks':0, 'nticks':0,
-         'num_notes_by_channel':dict([]),
-         'patch_changes_by_track':[], 'patch_changes_total':[],
-         'percussion':{}, 'pitches':{}, 'pitch_range_by_track':[],
-         'ticks_per_quarter':0, 'pitch_range_sum':0}
-    ticks_per_quarter = opus_or_score[0]
-    i = 1   # ignore first element, which is ticks
-    nticks = 0
-    while i < len(opus_or_score):
-        highest_pitch = 0
-        lowest_pitch = 128
-        channels_this_track = set([])
-        patch_changes_this_track = dict({})
-        for event in opus_or_score[i]:
-            if event[0] == 'note':
-                num_notes_by_channel[event[3]] = num_notes_by_channel.get(event[3],0) + 1
-                if event[3] == 9:
-                    percussion[event[4]] = percussion.get(event[4],0) + 1
-                else:
-                    pitches[event[4]]    = pitches.get(event[4],0) + 1
-                    if event[4] > highest_pitch:
-                        highest_pitch = event[4]
-                    if event[4] < lowest_pitch:
-                        lowest_pitch = event[4]
-                channels_this_track.add(event[3])
-                channels_total.add(event[3])
-                finish_time = event[1] + event[2]
-                if finish_time > nticks:
-                    nticks = finish_time
-            elif event[0] == 'note_off' or (event[0] == 'note_on' and event[4] == 0):  # 4.8
-                finish_time = event[1]
-                if finish_time > nticks:
-                    nticks = finish_time
-            elif event[0] == 'note_on':
-                is_a_score = False
-                num_notes_by_channel[event[2]] = num_notes_by_channel.get(event[2],0) + 1
-                if event[2] == 9:
-                    percussion[event[3]] = percussion.get(event[3],0) + 1
-                else:
-                    pitches[event[3]]    = pitches.get(event[3],0) + 1
-                    if event[3] > highest_pitch:
-                        highest_pitch = event[3]
-                    if event[3] < lowest_pitch:
-                        lowest_pitch = event[3]
-                channels_this_track.add(event[2])
-                channels_total.add(event[2])
-            elif event[0] == 'patch_change':
-                patch_changes_this_track[event[2]] = event[3]
-                patch_changes_total.add(event[3])
-            elif event[0] == 'control_change':
-                if event[3] == 0:  # bank select MSB
-                    bank_select_msb = event[4]
-                elif event[3] == 32:  # bank select LSB
-                    bank_select_lsb = event[4]
-                if bank_select_msb >= 0 and bank_select_lsb >= 0:
-                    bank_select.append((bank_select_msb,bank_select_lsb))
-                    bank_select_msb = -1
-                    bank_select_lsb = -1
-            elif event[0] == 'sysex_f0':
-                if _sysex2midimode.get(event[2], -1) >= 0:
-                    general_midi_mode.append(_sysex2midimode.get(event[2]))
-            if is_a_score:
-                if event[1] > nticks:
-                    nticks = event[1]
-            else:
-                nticks += event[1]
-        if lowest_pitch == 128:
-            lowest_pitch = 0
-        channels_by_track.append(channels_this_track)
-        patch_changes_by_track.append(patch_changes_this_track)
-        pitch_range_by_track.append((lowest_pitch,highest_pitch))
-        pitch_range_sum += (highest_pitch-lowest_pitch)
-        i += 1
-
-    return {'bank_select':bank_select,
-            'channels_by_track':channels_by_track,
-            'channels_total':channels_total,
-            'general_midi_mode':general_midi_mode,
-            'ntracks':len(opus_or_score)-1,
-            'nticks':nticks,
-            'num_notes_by_channel':num_notes_by_channel,
-            'patch_changes_by_track':patch_changes_by_track,
-            'patch_changes_total':patch_changes_total,
-            'percussion':percussion,
-            'pitches':pitches,
-            'pitch_range_by_track':pitch_range_by_track,
-            'pitch_range_sum':pitch_range_sum,
-            'ticks_per_quarter':ticks_per_quarter}
-
-#----------------------------- Event stuff --------------------------
-
-_sysex2midimode = {
-    "\x7E\x7F\x09\x01\xF7": 1,
-    "\x7E\x7F\x09\x02\xF7": 0,
-    "\x7E\x7F\x09\x03\xF7": 2,
-}
-
-# Some public-access tuples:
-MIDI_events = tuple('''note_off note_on key_after_touch
-control_change patch_change channel_after_touch
-pitch_wheel_change'''.split())
-
-Text_events = tuple('''text_event copyright_text_event
-track_name instrument_name lyric marker cue_point text_event_08
-text_event_09 text_event_0a text_event_0b text_event_0c
-text_event_0d text_event_0e text_event_0f'''.split())
-
-Nontext_meta_events = tuple('''end_track set_tempo
-smpte_offset time_signature key_signature sequencer_specific
-raw_meta_event sysex_f0 sysex_f7 song_position song_select
-tune_request'''.split())
-# unsupported: raw_data
-
-# Actually, 'tune_request' is is F-series event, not strictly a meta-event...
-Meta_events = Text_events + Nontext_meta_events
-All_events  = MIDI_events + Meta_events
-
-# And three dictionaries:
-Number2patch = {   # General MIDI patch numbers:
-0:'Acoustic Grand',
-1:'Bright Acoustic',
-2:'Electric Grand',
-3:'Honky-Tonk',
-4:'Electric Piano 1',
-5:'Electric Piano 2',
-6:'Harpsichord',
-7:'Clav',
-8:'Celesta',
-9:'Glockenspiel',
-10:'Music Box',
-11:'Vibraphone',
-12:'Marimba',
-13:'Xylophone',
-14:'Tubular Bells',
-15:'Dulcimer',
-16:'Drawbar Organ',
-17:'Percussive Organ',
-18:'Rock Organ',
-19:'Church Organ',
-20:'Reed Organ',
-21:'Accordion',
-22:'Harmonica',
-23:'Tango Accordion',
-24:'Acoustic Guitar(nylon)',
-25:'Acoustic Guitar(steel)',
-26:'Electric Guitar(jazz)',
-27:'Electric Guitar(clean)',
-28:'Electric Guitar(muted)',
-29:'Overdriven Guitar',
-30:'Distortion Guitar',
-31:'Guitar Harmonics',
-32:'Acoustic Bass',
-33:'Electric Bass(finger)',
-34:'Electric Bass(pick)',
-35:'Fretless Bass',
-36:'Slap Bass 1',
-37:'Slap Bass 2',
-38:'Synth Bass 1',
-39:'Synth Bass 2',
-40:'Violin',
-41:'Viola',
-42:'Cello',
-43:'Contrabass',
-44:'Tremolo Strings',
-45:'Pizzicato Strings',
-46:'Orchestral Harp',
-47:'Timpani',
-48:'String Ensemble 1',
-49:'String Ensemble 2',
-50:'SynthStrings 1',
-51:'SynthStrings 2',
-52:'Choir Aahs',
-53:'Voice Oohs',
-54:'Synth Voice',
-55:'Orchestra Hit',
-56:'Trumpet',
-57:'Trombone',
-58:'Tuba',
-59:'Muted Trumpet',
-60:'French Horn',
-61:'Brass Section',
-62:'SynthBrass 1',
-63:'SynthBrass 2',
-64:'Soprano Sax',
-65:'Alto Sax',
-66:'Tenor Sax',
-67:'Baritone Sax',
-68:'Oboe',
-69:'English Horn',
-70:'Bassoon',
-71:'Clarinet',
-72:'Piccolo',
-73:'Flute',
-74:'Recorder',
-75:'Pan Flute',
-76:'Blown Bottle',
-77:'Skakuhachi',
-78:'Whistle',
-79:'Ocarina',
-80:'Lead 1 (square)',
-81:'Lead 2 (sawtooth)',
-82:'Lead 3 (calliope)',
-83:'Lead 4 (chiff)',
-84:'Lead 5 (charang)',
-85:'Lead 6 (voice)',
-86:'Lead 7 (fifths)',
-87:'Lead 8 (bass+lead)',
-88:'Pad 1 (new age)',
-89:'Pad 2 (warm)',
-90:'Pad 3 (polysynth)',
-91:'Pad 4 (choir)',
-92:'Pad 5 (bowed)',
-93:'Pad 6 (metallic)',
-94:'Pad 7 (halo)',
-95:'Pad 8 (sweep)',
-96:'FX 1 (rain)',
-97:'FX 2 (soundtrack)',
-98:'FX 3 (crystal)',
-99:'FX 4 (atmosphere)',
-100:'FX 5 (brightness)',
-101:'FX 6 (goblins)',
-102:'FX 7 (echoes)',
-103:'FX 8 (sci-fi)',
-104:'Sitar',
-105:'Banjo',
-106:'Shamisen',
-107:'Koto',
-108:'Kalimba',
-109:'Bagpipe',
-110:'Fiddle',
-111:'Shanai',
-112:'Tinkle Bell',
-113:'Agogo',
-114:'Steel Drums',
-115:'Woodblock',
-116:'Taiko Drum',
-117:'Melodic Tom',
-118:'Synth Drum',
-119:'Reverse Cymbal',
-120:'Guitar Fret Noise',
-121:'Breath Noise',
-122:'Seashore',
-123:'Bird Tweet',
-124:'Telephone Ring',
-125:'Helicopter',
-126:'Applause',
-127:'Gunshot',
-}
-Notenum2percussion = {   # General MIDI Percussion (on Channel 9):
-35:'Acoustic Bass Drum',
-36:'Bass Drum 1',
-37:'Side Stick',
-38:'Acoustic Snare',
-39:'Hand Clap',
-40:'Electric Snare',
-41:'Low Floor Tom',
-42:'Closed Hi-Hat',
-43:'High Floor Tom',
-44:'Pedal Hi-Hat',
-45:'Low Tom',
-46:'Open Hi-Hat',
-47:'Low-Mid Tom',
-48:'Hi-Mid Tom',
-49:'Crash Cymbal 1',
-50:'High Tom',
-51:'Ride Cymbal 1',
-52:'Chinese Cymbal',
-53:'Ride Bell',
-54:'Tambourine',
-55:'Splash Cymbal',
-56:'Cowbell',
-57:'Crash Cymbal 2',
-58:'Vibraslap',
-59:'Ride Cymbal 2',
-60:'Hi Bongo',
-61:'Low Bongo',
-62:'Mute Hi Conga',
-63:'Open Hi Conga',
-64:'Low Conga',
-65:'High Timbale',
-66:'Low Timbale',
-67:'High Agogo',
-68:'Low Agogo',
-69:'Cabasa',
-70:'Maracas',
-71:'Short Whistle',
-72:'Long Whistle',
-73:'Short Guiro',
-74:'Long Guiro',
-75:'Claves',
-76:'Hi Wood Block',
-77:'Low Wood Block',
-78:'Mute Cuica',
-79:'Open Cuica',
-80:'Mute Triangle',
-81:'Open Triangle',
-}
-
-Event2channelindex = { 'note':3, 'note_off':2, 'note_on':2,
- 'key_after_touch':2, 'control_change':2, 'patch_change':2,
- 'channel_after_touch':2, 'pitch_wheel_change':2
-}
-
-################################################################
-# The code below this line is full of frightening things, all to
-# do with the actual encoding and decoding of binary MIDI data.
-
-def _twobytes2int(byte_a):
-    r'''decode a 16 bit quantity from two bytes,'''
-    return (byte_a[1] | (byte_a[0] << 8))
-
-def _int2twobytes(int_16bit):
-    r'''encode a 16 bit quantity into two bytes,'''
-    return bytes([(int_16bit>>8) & 0xFF, int_16bit & 0xFF])
-
-def _read_14_bit(byte_a):
-    r'''decode a 14 bit quantity from two bytes,'''
-    return (byte_a[0] | (byte_a[1] << 7))
-
-def _write_14_bit(int_14bit):
-    r'''encode a 14 bit quantity into two bytes,'''
-    return bytes([int_14bit & 0x7F, (int_14bit>>7) & 0x7F])
-
-def _ber_compressed_int(integer):
-    r'''BER compressed integer (not an ASN.1 BER, see perlpacktut for
-details).  Its bytes represent an unsigned integer in base 128,
-most significant digit first, with as few digits as possible.
-Bit eight (the high bit) is set on each byte except the last.
-'''
-    ber = bytearray(b'')
-    seven_bits = 0x7F & integer
-    ber.insert(0, seven_bits)  # XXX surely should convert to a char ?
-    integer >>= 7
-    while integer > 0:
-        seven_bits = 0x7F & integer
-        ber.insert(0, 0x80|seven_bits)  # XXX surely should convert to a char ?
-        integer >>= 7
-    return ber
-
-def _unshift_ber_int(ba):
-    r'''Given a bytearray, returns a tuple of (the ber-integer at the
-start, and the remainder of the bytearray).
-'''
-    if not len(ba):   # 6.7
-        _warn('_unshift_ber_int: no integer found')
-        return ((0, b""))
-    byte = ba.pop(0)
-    integer = 0
-    while True:
-        integer += (byte & 0x7F)
-        if not (byte & 0x80):
-            return ((integer, ba))
-        if not len(ba):
-            _warn('_unshift_ber_int: no end-of-integer found')
-            return ((0, ba))
-        byte = ba.pop(0)
-        integer <<= 7
-
-def _clean_up_warnings():  # 5.4
-    # Call this before returning from any publicly callable function
-    # whenever there's a possibility that a warning might have been printed
-    # by the function, or by any private functions it might have called.
-    global _previous_times
-    global _previous_warning
-    if _previous_times > 1:
-        # E:1176, 0: invalid syntax (<string>, line 1176) (syntax-error) ???
-        # print('  previous message repeated '+str(_previous_times)+' times', file=sys.stderr)
-        # 6.7
-        sys.stderr.write('  previous message repeated {0} times\n'.format(_previous_times))
-    elif _previous_times > 0:
-        sys.stderr.write('  previous message repeated\n')
-    _previous_times = 0
-    _previous_warning = ''
-
-def _warn(s=''):
-    global _previous_times
-    global _previous_warning
-    if s == _previous_warning:  # 5.4
-        _previous_times = _previous_times + 1
-    else:
-        _clean_up_warnings()
-        sys.stderr.write(str(s)+"\n")
-        _previous_warning = s
-
-def _some_text_event(which_kind=0x01, text=b'some_text', text_encoding='ISO-8859-1'):
-    if str(type(text)).find("'str'") >= 0:   # 6.4 test for back-compatibility
-        data = bytes(text, encoding=text_encoding)
-    else:
-        data = bytes(text)
-    return b'\xFF'+bytes((which_kind,))+_ber_compressed_int(len(data))+data
-
-def _consistentise_ticks(scores):  # 3.6
-    # used by mix_scores, merge_scores, concatenate_scores
-    if len(scores) == 1:
-         return copy.deepcopy(scores)
-    are_consistent = True
-    ticks = scores[0][0]
-    iscore = 1
-    while iscore < len(scores):
-        if scores[iscore][0] != ticks:
-            are_consistent = False
-            break
-        iscore += 1
-    if are_consistent:
-        return copy.deepcopy(scores)
-    new_scores = []
-    iscore = 0
-    while iscore < len(scores):
-        score = scores[iscore]
-        new_scores.append(opus2score(to_millisecs(score2opus(score))))
-        iscore += 1
-    return new_scores
-
-
-###########################################################################
-
-def _decode(trackdata=b'', exclude=None, include=None,
- event_callback=None, exclusive_event_callback=None, no_eot_magic=False):
-    r'''Decodes MIDI track data into an opus-style list of events.
-The options:
-  'exclude' is a list of event types which will be ignored SHOULD BE A SET
-  'include' (and no exclude), makes exclude a list
-       of all possible events, /minus/ what include specifies
-  'event_callback' is a coderef
-  'exclusive_event_callback' is a coderef
-'''
-    trackdata = bytearray(trackdata)
-    if exclude == None:
-        exclude = []
-    if include == None:
-        include = []
-    if include and not exclude:
-        exclude = All_events
-    include = set(include)
-    exclude = set(exclude)
-
-    # Pointer = 0;  not used here; we eat through the bytearray instead.
-    event_code = -1; # used for running status
-    event_count = 0;
-    events = []
-
-    while(len(trackdata)):
-        # loop while there's anything to analyze ...
-        eot = False   # When True, the event registrar aborts this loop
-        event_count += 1
-
-        E = []
-        # E for events - we'll feed it to the event registrar at the end.
-
-        # Slice off the delta time code, and analyze it
-        [time, remainder] = _unshift_ber_int(trackdata)
-
-        # Now let's see what we can make of the command
-        first_byte = trackdata.pop(0) & 0xFF
-
-        if (first_byte < 0xF0):  # It's a MIDI event
-            if (first_byte & 0x80):
-                event_code = first_byte
-            else:
-                # It wants running status; use last event_code value
-                trackdata.insert(0, first_byte)
-                if (event_code == -1):
-                    _warn("Running status not set; Aborting track.")
-                    return []
-
-            command = event_code & 0xF0
-            channel = event_code & 0x0F
-
-            if (command == 0xF6):  #  0-byte argument
-                pass
-            elif (command == 0xC0 or command == 0xD0):  #  1-byte argument
-                parameter = trackdata.pop(0)  # could be B
-            else: # 2-byte argument could be BB or 14-bit
-                parameter = (trackdata.pop(0), trackdata.pop(0))
-
-            #################################################################
-            # MIDI events
-
-            if (command      == 0x80):
-                if 'note_off' in exclude:
-                    continue
-                E = ['note_off', time, channel, parameter[0], parameter[1]]
-            elif (command == 0x90):
-                if 'note_on' in exclude:
-                    continue
-                E = ['note_on', time, channel, parameter[0], parameter[1]]
-            elif (command == 0xA0):
-                if 'key_after_touch' in exclude:
-                    continue
-                E = ['key_after_touch',time,channel,parameter[0],parameter[1]]
-            elif (command == 0xB0):
-                if 'control_change' in exclude:
-                    continue
-                E = ['control_change',time,channel,parameter[0],parameter[1]]
-            elif (command == 0xC0):
-                if 'patch_change' in exclude:
-                    continue
-                E = ['patch_change', time, channel, parameter]
-            elif (command == 0xD0):
-                if 'channel_after_touch' in exclude:
-                    continue
-                E = ['channel_after_touch', time, channel, parameter]
-            elif (command == 0xE0):
-                if 'pitch_wheel_change' in exclude:
-                    continue
-                E = ['pitch_wheel_change', time, channel,
-                 _read_14_bit(parameter)-0x2000]
-            else:
-                _warn("Shouldn't get here; command="+hex(command))
-
-        elif (first_byte == 0xFF):  # It's a Meta-Event! ##################
-            #[command, length, remainder] =
-            #    unpack("xCwa*", substr(trackdata, $Pointer, 6));
-            #Pointer += 6 - len(remainder);
-            #    # Move past JUST the length-encoded.
-            command = trackdata.pop(0) & 0xFF
-            [length, trackdata] = _unshift_ber_int(trackdata)
-            if (command      == 0x00):
-                 if (length == 2):
-                     E = ['set_sequence_number',time,_twobytes2int(trackdata)]
-                 else:
-                     _warn('set_sequence_number: length must be 2, not '+str(length))
-                     E = ['set_sequence_number', time, 0]
-
-            elif command >= 0x01 and command <= 0x0f:   # Text events
-                # 6.2 take it in bytes; let the user get the right encoding.
-                # text_str = trackdata[0:length].decode('ascii','ignore')
-                # text_str = trackdata[0:length].decode('ISO-8859-1')
-                # 6.4 take it in bytes; let the user get the right encoding.
-                text_data = bytes(trackdata[0:length])   # 6.4
-                # Defined text events
-                if (command == 0x01):
-                     E = ['text_event', time, text_data]
-                elif (command == 0x02):
-                     E = ['copyright_text_event', time, text_data]
-                elif (command == 0x03):
-                     E = ['track_name', time, text_data]
-                elif (command == 0x04):
-                     E = ['instrument_name', time, text_data]
-                elif (command == 0x05):
-                     E = ['lyric', time, text_data]
-                elif (command == 0x06):
-                     E = ['marker', time, text_data]
-                elif (command == 0x07):
-                     E = ['cue_point', time, text_data]
-                # Reserved but apparently unassigned text events
-                elif (command == 0x08):
-                     E = ['text_event_08', time, text_data]
-                elif (command == 0x09):
-                     E = ['text_event_09', time, text_data]
-                elif (command == 0x0a):
-                     E = ['text_event_0a', time, text_data]
-                elif (command == 0x0b):
-                     E = ['text_event_0b', time, text_data]
-                elif (command == 0x0c):
-                     E = ['text_event_0c', time, text_data]
-                elif (command == 0x0d):
-                     E = ['text_event_0d', time, text_data]
-                elif (command == 0x0e):
-                     E = ['text_event_0e', time, text_data]
-                elif (command == 0x0f):
-                     E = ['text_event_0f', time, text_data]
-
-            # Now the sticky events -------------------------------------
-            elif (command == 0x2F):
-                 E = ['end_track', time]
-                     # The code for handling this, oddly, comes LATER,
-                     # in the event registrar.
-            elif (command == 0x51): # DTime, Microseconds/Crochet
-                 if length != 3:
-                     _warn('set_tempo event, but length='+str(length))
-                 E = ['set_tempo', time,
-                      struct.unpack(">I", b'\x00'+trackdata[0:3])[0]]
-            elif (command == 0x54):
-                 if length != 5:   # DTime, HR, MN, SE, FR, FF
-                     _warn('smpte_offset event, but length='+str(length))
-                 E = ['smpte_offset',time] + list(struct.unpack(">BBBBB",trackdata[0:5]))
-            elif (command == 0x58):
-                 if length != 4:   # DTime, NN, DD, CC, BB
-                     _warn('time_signature event, but length='+str(length))
-                 E = ['time_signature', time]+list(trackdata[0:4])
-            elif (command == 0x59):
-                 if length != 2:   # DTime, SF(signed), MI
-                     _warn('key_signature event, but length='+str(length))
-                 E = ['key_signature',time] + list(struct.unpack(">bB",trackdata[0:2]))
-            elif (command == 0x7F):   # 6.4
-                 E = ['sequencer_specific',time, bytes(trackdata[0:length])]
-            else:
-                 E = ['raw_meta_event', time, command,
-                   bytes(trackdata[0:length])]   # 6.0
-                 #"[uninterpretable meta-event command of length length]"
-                 # DTime, Command, Binary Data
-                 # It's uninterpretable; record it as raw_data.
-
-            # Pointer += length; #  Now move Pointer
-            trackdata = trackdata[length:]
-
-        ######################################################################
-        elif (first_byte == 0xF0 or first_byte == 0xF7):
-            # Note that sysexes in MIDI /files/ are different than sysexes
-            # in MIDI transmissions!! The vast majority of system exclusive
-            # messages will just use the F0 format. For instance, the
-            # transmitted message F0 43 12 00 07 F7 would be stored in a
-            # MIDI file as F0 05 43 12 00 07 F7. As mentioned above, it is
-            # required to include the F7 at the end so that the reader of the
-            # MIDI file knows that it has read the entire message. (But the F7
-            # is omitted if this is a non-final block in a multiblock sysex;
-            # but the F7 (if there) is counted in the message's declared
-            # length, so we don't have to think about it anyway.)
-            #command = trackdata.pop(0)
-            [length, trackdata] = _unshift_ber_int(trackdata)
-            if first_byte == 0xF0:
-                # 20091008 added ISO-8859-1 to get an 8-bit str
-                # 6.4 return bytes instead
-                E = ['sysex_f0', time, bytes(trackdata[0:length])]
-            else:
-                E = ['sysex_f7', time, bytes(trackdata[0:length])]
-            trackdata = trackdata[length:]
-
-        ######################################################################
-        # Now, the MIDI file spec says:
-        #  <track data> = <MTrk event>+
-        #  <MTrk event> = <delta-time> <event>
-        #  <event> = <MIDI event> | <sysex event> | <meta-event>
-        # I know that, on the wire, <MIDI event> can include note_on,
-        # note_off, and all the other 8x to Ex events, AND Fx events
-        # other than F0, F7, and FF -- namely, <song position msg>,
-        # <song select msg>, and <tune request>.
-        #
-        # Whether these can occur in MIDI files is not clear specified
-        # from the MIDI file spec.  So, I'm going to assume that
-        # they CAN, in practice, occur.  I don't know whether it's
-        # proper for you to actually emit these into a MIDI file.
-        
-        elif (first_byte == 0xF2):   # DTime, Beats
-            #  <song position msg> ::=     F2 <data pair>
-            E = ['song_position', time, _read_14_bit(trackdata[:2])]
-            trackdata = trackdata[2:]
-
-        elif (first_byte == 0xF3):   # <song select msg> ::= F3 <data singlet>
-            # E = ['song_select', time, struct.unpack('>B',trackdata.pop(0))[0]]
-            E = ['song_select', time, trackdata[0]]
-            trackdata = trackdata[1:]
-            # DTime, Thing (what?! song number?  whatever ...)
-
-        elif (first_byte == 0xF6):   # DTime
-            E = ['tune_request', time]
-            # What would a tune request be doing in a MIDI /file/?
-
-        #########################################################
-        # ADD MORE META-EVENTS HERE.  TODO:
-        # f1 -- MTC Quarter Frame Message. One data byte follows
-        #     the Status; it's the time code value, from 0 to 127.
-        # f8 -- MIDI clock.    no data.
-        # fa -- MIDI start.    no data.
-        # fb -- MIDI continue. no data.
-        # fc -- MIDI stop.     no data.
-        # fe -- Active sense.  no data.
-        # f4 f5 f9 fd -- unallocated
-
-            r'''
-        elif (first_byte > 0xF0) { # Some unknown kinda F-series event ####
-            # Here we only produce a one-byte piece of raw data.
-            # But the encoder for 'raw_data' accepts any length of it.
-            E = [ 'raw_data',
-                         time, substr(trackdata,Pointer,1) ]
-            # DTime and the Data (in this case, the one Event-byte)
-            ++Pointer;  # itself
-
-'''
-        elif first_byte > 0xF0:  # Some unknown F-series event
-            # Here we only produce a one-byte piece of raw data.
-            # E = ['raw_data', time, bytest(trackdata[0])]   # 6.4
-            E = ['raw_data', time, trackdata[0]]   # 6.4 6.7
-            trackdata = trackdata[1:]
-        else:  # Fallthru.
-            _warn("Aborting track.  Command-byte first_byte="+hex(first_byte))
-            break
-        # End of the big if-group
-
-
-        ######################################################################
-        #  THE EVENT REGISTRAR...
-        if E and  (E[0] == 'end_track'):
-            # This is the code for exceptional handling of the EOT event.
-            eot = True
-            if not no_eot_magic:
-                if E[1] > 0:  # a null text-event to carry the delta-time
-                    E = ['text_event', E[1], '']
-                else:
-                    E = []   # EOT with a delta-time of 0; ignore it.
-        
-        if E and not (E[0] in exclude):
-            #if ( $exclusive_event_callback ):
-            #    &{ $exclusive_event_callback }( @E );
-            #else:
-            #    &{ $event_callback }( @E ) if $event_callback;
-                events.append(E)
-        if eot:
-            break
-
-    # End of the big "Event" while-block
-
-    return events
-
-
-###########################################################################
-def _encode(events_lol, unknown_callback=None, never_add_eot=False,
-  no_eot_magic=False, no_running_status=False, text_encoding='ISO-8859-1'):
-    # encode an event structure, presumably for writing to a file
-    # Calling format:
-    #   $data_r = MIDI::Event::encode( \@event_lol, { options } );
-    # Takes a REFERENCE to an event structure (a LoL)
-    # Returns an (unblessed) REFERENCE to track data.
-
-    # If you want to use this to encode a /single/ event,
-    # you still have to do it as a reference to an event structure (a LoL)
-    # that just happens to have just one event.  I.e.,
-    #   encode( [ $event ] ) or encode( [ [ 'note_on', 100, 5, 42, 64] ] )
-    # If you're doing this, consider the never_add_eot track option, as in
-    #   print MIDI ${ encode( [ $event], { 'never_add_eot' => 1} ) };
-
-    data = [] # what I'll store the chunks of byte-data in
-
-    # This is so my end_track magic won't corrupt the original
-    events = copy.deepcopy(events_lol)
-
-    if not never_add_eot:
-        # One way or another, tack on an 'end_track'
-        if events:
-            last = events[-1]
-            if not (last[0] == 'end_track'):  # no end_track already
-                if (last[0] == 'text_event' and len(last[2]) == 0):
-                    # 0-length text event at track-end.
-                    if no_eot_magic:
-                        # Exceptional case: don't mess with track-final
-                        # 0-length text_events; just peg on an end_track
-                        events.append(['end_track', 0])
-                    else:
-                        # NORMAL CASE: replace with an end_track, leaving DTime
-                        last[0] = 'end_track'
-                else:
-                    # last event was neither 0-length text_event nor end_track
-                    events.append(['end_track', 0])
-        else:  # an eventless track!
-            events = [['end_track', 0],]
-
-    # maybe_running_status = not no_running_status # unused? 4.7
-    last_status = -1
-
-    for event_r in (events):
-        E = copy.deepcopy(event_r)
-        # otherwise the shifting'd corrupt the original
-        if not E:
-            continue
-
-        event = E.pop(0)
-        if not len(event):
-            continue
-
-        dtime = int(E.pop(0))
-        # print('event='+str(event)+' dtime='+str(dtime))
-
-        event_data = ''
-
-        if (   # MIDI events -- eligible for running status
-             event    == 'note_on'
-             or event == 'note_off'
-             or event == 'control_change'
-             or event == 'key_after_touch'
-             or event == 'patch_change'
-             or event == 'channel_after_touch'
-             or event == 'pitch_wheel_change'  ):
-
-            # This block is where we spend most of the time.  Gotta be tight.
-            if (event == 'note_off'):
-                status = 0x80 | (int(E[0]) & 0x0F)
-                parameters = struct.pack('>BB', int(E[1])&0x7F, int(E[2])&0x7F)
-            elif (event == 'note_on'):
-                status = 0x90 | (int(E[0]) & 0x0F)
-                parameters = struct.pack('>BB', int(E[1])&0x7F, int(E[2])&0x7F)
-            elif (event == 'key_after_touch'):
-                status = 0xA0 | (int(E[0]) & 0x0F)
-                parameters = struct.pack('>BB', int(E[1])&0x7F, int(E[2])&0x7F)
-            elif (event == 'control_change'):
-                status = 0xB0 | (int(E[0]) & 0x0F)
-                parameters = struct.pack('>BB', int(E[1])&0xFF, int(E[2])&0xFF)
-            elif (event == 'patch_change'):
-                status = 0xC0 | (int(E[0]) & 0x0F)
-                parameters = struct.pack('>B', int(E[1]) & 0xFF)
-            elif (event == 'channel_after_touch'):
-                status = 0xD0 | (int(E[0]) & 0x0F)
-                parameters = struct.pack('>B', int(E[1]) & 0xFF)
-            elif (event == 'pitch_wheel_change'):
-                status = 0xE0 | (int(E[0]) & 0x0F)
-                parameters =  _write_14_bit(int(E[1]) + 0x2000)
-            else:
-                _warn("BADASS FREAKOUT ERROR 31415!")
-
-            # And now the encoding
-            # w = BER compressed integer (not ASN.1 BER, see perlpacktut for
-            # details).  Its bytes represent an unsigned integer in base 128,
-            # most significant digit first, with as few digits as possible.
-            # Bit eight (the high bit) is set on each byte except the last.
-
-            data.append(_ber_compressed_int(dtime))
-            if (status != last_status) or no_running_status:
-                data.append(struct.pack('>B', status))
-            data.append(parameters)
- 
-            last_status = status
-            continue
-        else:
-            # Not a MIDI event.
-            # All the code in this block could be more efficient,
-            # but this is not where the code needs to be tight.
-            # print "zaz $event\n";
-            last_status = -1
-
-            if event == 'raw_meta_event':
-                event_data = _some_text_event(int(E[0]), E[1], text_encoding)
-            elif (event == 'set_sequence_number'):  # 3.9
-                event_data = b'\xFF\x00\x02'+_int2twobytes(E[0])
-
-            # Text meta-events...
-            # a case for a dict, I think (pjb) ...
-            elif (event == 'text_event'):
-                event_data = _some_text_event(0x01, E[0], text_encoding)
-            elif (event == 'copyright_text_event'):
-                event_data = _some_text_event(0x02, E[0], text_encoding)
-            elif (event == 'track_name'):
-                event_data = _some_text_event(0x03, E[0], text_encoding)
-            elif (event == 'instrument_name'):
-                event_data = _some_text_event(0x04, E[0], text_encoding)
-            elif (event == 'lyric'):
-                event_data = _some_text_event(0x05, E[0], text_encoding)
-            elif (event == 'marker'):
-                event_data = _some_text_event(0x06, E[0], text_encoding)
-            elif (event == 'cue_point'):
-                event_data = _some_text_event(0x07, E[0], text_encoding)
-            elif (event == 'text_event_08'):
-                event_data = _some_text_event(0x08, E[0], text_encoding)
-            elif (event == 'text_event_09'):
-                event_data = _some_text_event(0x09, E[0], text_encoding)
-            elif (event == 'text_event_0a'):
-                event_data = _some_text_event(0x0A, E[0], text_encoding)
-            elif (event == 'text_event_0b'):
-                event_data = _some_text_event(0x0B, E[0], text_encoding)
-            elif (event == 'text_event_0c'):
-                event_data = _some_text_event(0x0C, E[0], text_encoding)
-            elif (event == 'text_event_0d'):
-                event_data = _some_text_event(0x0D, E[0], text_encoding)
-            elif (event == 'text_event_0e'):
-                event_data = _some_text_event(0x0E, E[0], text_encoding)
-            elif (event == 'text_event_0f'):
-                event_data = _some_text_event(0x0F, E[0], text_encoding)
-            # End of text meta-events
-
-            elif (event == 'end_track'):
-                event_data = b"\xFF\x2F\x00"
-
-            elif (event == 'set_tempo'):
-                #event_data = struct.pack(">BBwa*", 0xFF, 0x51, 3,
-                #              substr( struct.pack('>I', E[0]), 1, 3))
-                event_data = b'\xFF\x51\x03'+struct.pack('>I',E[0])[1:]
-            elif (event == 'smpte_offset'):
-                # event_data = struct.pack(">BBwBBBBB", 0xFF, 0x54, 5, E[0:5] )
-                event_data = struct.pack(">BBBbBBBB", 0xFF,0x54,0x05,E[0],E[1],E[2],E[3],E[4])
-            elif (event == 'time_signature'):
-                # event_data = struct.pack(">BBwBBBB",  0xFF, 0x58, 4, E[0:4] )
-                event_data = struct.pack(">BBBbBBB", 0xFF, 0x58, 0x04, E[0],E[1],E[2],E[3])
-            elif (event == 'key_signature'):
-                event_data = struct.pack(">BBBbB", 0xFF, 0x59, 0x02, E[0],E[1])
-            elif (event == 'sequencer_specific'):
-                # event_data = struct.pack(">BBwa*", 0xFF,0x7F, len(E[0]), E[0])
-                event_data = _some_text_event(0x7F, E[0], text_encoding)
-            # End of Meta-events
-
-            # Other Things...
-            elif (event == 'sysex_f0'):
-                 #event_data = struct.pack(">Bwa*", 0xF0, len(E[0]), E[0])
-                 #B=bitstring w=BER-compressed-integer a=null-padded-ascii-str
-                 event_data = bytearray(b'\xF0')+_ber_compressed_int(len(E[0]))+bytearray(E[0])
-            elif (event == 'sysex_f7'):
-                 #event_data = struct.pack(">Bwa*", 0xF7, len(E[0]), E[0])
-                 event_data = bytearray(b'\xF7')+_ber_compressed_int(len(E[0]))+bytearray(E[0])
-
-            elif (event == 'song_position'):
-                 event_data = b"\xF2" + _write_14_bit( E[0] )
-            elif (event == 'song_select'):
-                 event_data = struct.pack('>BB', 0xF3, E[0] )
-            elif (event == 'tune_request'):
-                 event_data = b"\xF6"
-            elif (event == 'raw_data'):
-                _warn("_encode: raw_data event not supported")
-                # event_data = E[0]
-                continue
-            # End of Other Stuff
-
-            else:
-                # The Big Fallthru
-                if unknown_callback:
-                    # push(@data, &{ $unknown_callback }( @$event_r ))
-                    pass
-                else:
-                    _warn("Unknown event: "+str(event))
-                    # To surpress complaint here, just set
-                    #  'unknown_callback' => sub { return () }
-                continue
-
-            #print "Event $event encoded part 2\n"
-            if str(type(event_data)).find("'str'") >= 0:
-                event_data = bytearray(event_data.encode('Latin1', 'ignore'))
-            if len(event_data): # how could $event_data be empty
-                # data.append(struct.pack('>wa*', dtime, event_data))
-                # print(' event_data='+str(event_data))
-                data.append(_ber_compressed_int(dtime)+event_data)
-
-    return b''.join(data)
-
-###################################################################################
-###################################################################################
-###################################################################################
-#
-#	Tegridy MIDI X Module (TMIDI X / tee-midi eks)
-#	Version 1.0
-#
-#	Based upon and includes the amazing MIDI.py module v.6.7. by Peter Billam
-#	pjb.com.au
-#
-#	Project Los Angeles
-#	Tegridy Code 2021
-# https://github.com/Tegridy-Code/Project-Los-Angeles
-#
-###################################################################################
-###################################################################################
-###################################################################################
-
-import os
-
-import datetime
-
-import copy
-
-from datetime import datetime
-
-import secrets
-
-import random
-
-import pickle
-
-import csv
-
-import tqdm
-
-from itertools import zip_longest
-from itertools import groupby
-
-from operator import itemgetter
-
-import sys
-
-from abc import ABC, abstractmethod
-
-from difflib import SequenceMatcher as SM
-
-import statistics
-
-###################################################################################
-#
-# Original TMIDI Tegridy helper functions
-#
-###################################################################################
-
-def Tegridy_TXT_to_INT_Converter(input_TXT_string, line_by_line_INT_string=True, max_INT = 0):
-
-    '''Tegridy TXT to Intergers Converter
-     
-    Input: Input TXT string in the TMIDI-TXT format
-
-           Type of output TXT INT string: line-by-line or one long string
-
-           Maximum absolute integer to process. Maximum is inclusive 
-           Default = process all integers. This helps to remove outliers/unwanted ints
-
-    Output: List of pure intergers
-            String of intergers in the specified format: line-by-line or one long string
-            Number of processed integers
-            Number of skipped integers
-    
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    print('Tegridy TXT to Intergers Converter')
-
-    output_INT_list = []
-
-    npi = 0
-    nsi = 0
-
-    TXT_List = list(input_TXT_string)
-    for char in TXT_List:
-      if max_INT != 0:
-        if abs(ord(char)) <= max_INT:
-          output_INT_list.append(ord(char))
-          npi += 1
-        else:
-          nsi += 1  
-      else:
-        output_INT_list.append(ord(char))
-        npi += 1    
-    
-    if line_by_line_INT_string:
-      output_INT_string = '\n'.join([str(elem) for elem in output_INT_list])
-    else:
-      output_INT_string = ' '.join([str(elem) for elem in output_INT_list])  
-
-    print('Converted TXT to INTs:', npi, ' / ', nsi)
-
-    return output_INT_list, output_INT_string, npi, nsi
-
-###################################################################################
-
-def Tegridy_INT_to_TXT_Converter(input_INT_list):
-
-    '''Tegridy Intergers to TXT Converter
-     
-    Input: List of intergers in TMIDI-TXT-INT format
-    Output: Decoded TXT string in TMIDI-TXT format
-    Project Los Angeles
-    Tegridy Code 2020'''
-
-    output_TXT_string = ''
-
-    for i in input_INT_list:
-      output_TXT_string += chr(int(i))
-    
-    return output_TXT_string
-
-###################################################################################
-
-def Tegridy_INT_String_to_TXT_Converter(input_INT_String, line_by_line_input=True):
-
-    '''Tegridy Intergers String to TXT Converter
-     
-    Input: List of intergers in TMIDI-TXT-INT-String format
-    Output: Decoded TXT string in TMIDI-TXT format
-    Project Los Angeles
-    Tegridy Code 2020'''
-    
-    print('Tegridy Intergers String to TXT Converter')
-
-    if line_by_line_input:
-      input_string = input_INT_String.split('\n')
-    else:
-      input_string = input_INT_String.split(' ')  
-
-    output_TXT_string = ''
-
-    for i in input_string:
-      try:
-        output_TXT_string += chr(abs(int(i)))
-      except:
-        print('Bad note:', i)
-        continue  
-    
-    print('Done!')
-
-    return output_TXT_string
-
-###################################################################################
-
-def Tegridy_SONG_to_MIDI_Converter(SONG,
-                                  output_signature = 'Tegridy TMIDI Module', 
-                                  track_name = 'Composition Track',
-                                  number_of_ticks_per_quarter = 425,
-                                  list_of_MIDI_patches = [0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 0, 0, 0, 0, 0, 0],
-                                  output_file_name = 'TMIDI-Composition',
-                                  text_encoding='ISO-8859-1'):
-
-    '''Tegridy SONG to MIDI Converter
-     
-    Input: Input SONG in TMIDI SONG/MIDI.py Score format
-           Output MIDI Track 0 name / MIDI Signature
-           Output MIDI Track 1 name / Composition track name
-           Number of ticks per quarter for the output MIDI
-           List of 16 MIDI patch numbers for output MIDI. Def. is MuseNet compatible patches.
-           Output file name w/o .mid extension.
-           Optional text encoding if you are working with text_events/lyrics. This is especially useful for Karaoke. Please note that anything but ISO-8859-1 is a non-standard way of encoding text_events according to MIDI specs.
-
-    Output: MIDI File
-            Detailed MIDI stats
-
-    Project Los Angeles
-    Tegridy Code 2020'''                                  
-
-    print('Converting to MIDI. Please stand-by...')
-    
-    output_header = [number_of_ticks_per_quarter, 
-                    [['track_name', 0, bytes(output_signature, text_encoding)]]]                                                    
-
-    patch_list = [['patch_change', 0, 0, list_of_MIDI_patches[0]], 
-                    ['patch_change', 0, 1, list_of_MIDI_patches[1]],
-                    ['patch_change', 0, 2, list_of_MIDI_patches[2]],
-                    ['patch_change', 0, 3, list_of_MIDI_patches[3]],
-                    ['patch_change', 0, 4, list_of_MIDI_patches[4]],
-                    ['patch_change', 0, 5, list_of_MIDI_patches[5]],
-                    ['patch_change', 0, 6, list_of_MIDI_patches[6]],
-                    ['patch_change', 0, 7, list_of_MIDI_patches[7]],
-                    ['patch_change', 0, 8, list_of_MIDI_patches[8]],
-                    ['patch_change', 0, 9, list_of_MIDI_patches[9]],
-                    ['patch_change', 0, 10, list_of_MIDI_patches[10]],
-                    ['patch_change', 0, 11, list_of_MIDI_patches[11]],
-                    ['patch_change', 0, 12, list_of_MIDI_patches[12]],
-                    ['patch_change', 0, 13, list_of_MIDI_patches[13]],
-                    ['patch_change', 0, 14, list_of_MIDI_patches[14]],
-                    ['patch_change', 0, 15, list_of_MIDI_patches[15]],
-                    ['track_name', 0, bytes(track_name, text_encoding)]]
-
-    output = output_header + [patch_list + SONG]
-
-    midi_data = score2midi(output, text_encoding)
-    detailed_MIDI_stats = score2stats(output)
-
-    with open(output_file_name + '.mid', 'wb') as midi_file:
-        midi_file.write(midi_data)
-        midi_file.close()
-    
-    print('Done! Enjoy! :)')
-    
-    return detailed_MIDI_stats
-
-###################################################################################
-
-def Tegridy_File_Time_Stamp(input_file_name='File_Created_on_', ext = ''):
-
-  '''Tegridy File Time Stamp
-     
-  Input: Full path and file name without extention
-         File extension
-          
-  Output: File name string with time-stamp and extension (time-stamped file name)
-
-  Project Los Angeles
-  Tegridy Code 2021'''       
-
-  print('Time-stamping output file...')
-
-  now = ''
-  now_n = str(datetime.now())
-  now_n = now_n.replace(' ', '_')
-  now_n = now_n.replace(':', '_')
-  now = now_n.replace('.', '_')
-      
-  fname = input_file_name + str(now) + ext
-
-  return(fname)
-
-###################################################################################
-
-def Tegridy_Any_Pickle_File_Writer(Data, input_file_name='TMIDI_Pickle_File'):
-
-  '''Tegridy Pickle File Writer
-     
-  Input: Data to write (I.e. a list)
-         Full path and file name without extention
-         
-  Output: Named Pickle file
-
-  Project Los Angeles
-  Tegridy Code 2021'''
-
-  print('Tegridy Pickle File Writer')
-
-  full_path_to_output_dataset_to = input_file_name + '.pickle'
-
-  if os.path.exists(full_path_to_output_dataset_to):
-    os.remove(full_path_to_output_dataset_to)
-    print('Removing old Dataset...')
-  else:
-    print("Creating new Dataset file...")
-
-  with open(full_path_to_output_dataset_to, 'wb') as filehandle:
-    # store the data as binary data stream
-    pickle.dump(Data, filehandle, protocol=pickle.HIGHEST_PROTOCOL)
-
-  print('Dataset was saved as:', full_path_to_output_dataset_to)
-  print('Task complete. Enjoy! :)')
-
-###################################################################################
-
-def Tegridy_Any_Pickle_File_Reader(input_file_name='TMIDI_Pickle_File', ext='.pickle'):
-
-  '''Tegridy Pickle File Loader
-     
-  Input: Full path and file name without extention
-         File extension if different from default .pickle
-       
-  Output: Standard Python 3 unpickled data object
-
-  Project Los Angeles
-  Tegridy Code 2021'''
-
-  print('Tegridy Pickle File Loader')
-  print('Loading the pickle file. Please wait...')
-
-  with open(input_file_name + ext, 'rb') as pickle_file:
-    content = pickle.load(pickle_file)
-
-  return content
-
-###################################################################################
-
-# TMIDI X Code is below
-
-###################################################################################
-
-def Optimus_MIDI_TXT_Processor(MIDI_file, 
-                              line_by_line_output=True, 
-                              chordify_TXT=False,
-                              dataset_MIDI_events_time_denominator=1,
-                              output_velocity=True,
-                              output_MIDI_channels = False, 
-                              MIDI_channel=0, 
-                              MIDI_patch=[0, 1], 
-                              char_offset = 30000,
-                              transpose_by = 0,
-                              flip=False, 
-                              melody_conditioned_encoding=False,
-                              melody_pitch_baseline = 0,
-                              number_of_notes_to_sample = -1,
-                              sampling_offset_from_start = 0,
-                              karaoke=False,
-                              karaoke_language_encoding='utf-8',
-                              song_name='Song',
-                              perfect_timings=False,
-                              musenet_encoding=False,
-                              transform=0,
-                              zero_token=False,
-                              reset_timings=False):
-
-    '''Project Los Angeles
-       Tegridy Code 2021'''
-  
-###########
-
-    debug = False
-
-    ev = 0
-
-    chords_list_final = []
-    chords_list = []
-    events_matrix = []
-    melody = []
-    melody1 = []
-
-    itrack = 1
-
-    min_note = 0
-    max_note = 0
-    ev = 0
-    patch = 0
-
-    score = []
-    rec_event = []
-
-    txt = ''
-    txtc = ''
-    chords = []
-    melody_chords = []
-
-    karaoke_events_matrix = []
-    karaokez = []
-
-    sample = 0
-    start_sample = 0
-
-    bass_melody = []
-
-    INTS = []
-    bints = 0
-
-###########    
-
-    def list_average(num):
-      sum_num = 0
-      for t in num:
-          sum_num = sum_num + t           
-
-      avg = sum_num / len(num)
-      return avg
-
-###########
-
-    #print('Loading MIDI file...')
-    midi_file = open(MIDI_file, 'rb')
-    if debug: print('Processing File:', file_address)
-    
-    try:
-      opus = midi2opus(midi_file.read())
-    
-    except:
-      print('Problematic MIDI. Skipping...')
-      print('File name:', MIDI_file)
-      midi_file.close()
-      return txt, melody, chords
-         
-    midi_file.close()
-
-    score1 = to_millisecs(opus)
-    score2 = opus2score(score1)
-
-    # score2 = opus2score(opus) # TODO Improve score timings when it will be possible.
-    
-    if MIDI_channel == 16: # Process all MIDI channels
-      score = score2
-    
-    if MIDI_channel >= 0 and MIDI_channel <= 15: # Process only a selected single MIDI channel
-      score = grep(score2, [MIDI_channel])
-    
-    if MIDI_channel == -1: # Process all channels except drums (except channel 9)
-      score = grep(score2, [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15])   
-    
-    #print('Reading all MIDI events from the MIDI file...')
-    while itrack < len(score):
-      for event in score[itrack]:
-        
-        if perfect_timings:
-          if event[0] == 'note':
-            event[1] = round(event[1], -1)
-            event[2] = round(event[2], -1)
-
-        if event[0] == 'text_event' or event[0] == 'lyric' or event[0] == 'note':
-          if perfect_timings:
-            event[1] = round(event[1], -1)
-          karaokez.append(event)
-        
-        if event[0] == 'text_event' or event[0] == 'lyric':
-          if perfect_timings:
-            event[1] = round(event[1], -1)
-          try:
-            event[2] = str(event[2].decode(karaoke_language_encoding, 'replace')).replace('/', '').replace(' ', '').replace('\\', '')
-          except:
-            event[2] = str(event[2]).replace('/', '').replace(' ', '').replace('\\', '')
-            continue
-          karaoke_events_matrix.append(event)
-
-        if event[0] == 'patch_change':
-          patch = event[3]
-
-        if event[0] == 'note' and patch in MIDI_patch:
-          if len(event) == 6: # Checking for bad notes...
-              eve = copy.deepcopy(event)
-              
-              eve[1] = int(event[1] / dataset_MIDI_events_time_denominator)
-              eve[2] = int(event[2] / dataset_MIDI_events_time_denominator)
-              
-              eve[4] = int(event[4] + transpose_by)
-              
-              if flip == True:
-                eve[4] = int(127 - (event[4] + transpose_by)) 
-              
-              if number_of_notes_to_sample > -1:
-                if sample <= number_of_notes_to_sample:
-                  if start_sample >= sampling_offset_from_start:
-                    events_matrix.append(eve)
-                    sample += 1
-                    ev += 1
-                  else:
-                    start_sample += 1
-
-              else:
-                events_matrix.append(eve)
-                ev += 1
-                start_sample += 1
-                
-      itrack +=1 # Going to next track...
-
-    #print('Doing some heavy pythonic sorting...Please stand by...')
-
-    fn = os.path.basename(MIDI_file)
-    song_name = song_name.replace(' ', '_').replace('=', '_').replace('\'', '-')
-    if song_name == 'Song':
-      sng_name = fn.split('.')[0].replace(' ', '_').replace('=', '_').replace('\'', '-')
-      song_name = sng_name
-
-    # Zero token
-    if zero_token:
-      txt += chr(char_offset) + chr(char_offset)
-      if output_MIDI_channels:
-        txt += chr(char_offset)
-      if output_velocity:
-        txt += chr(char_offset) + chr(char_offset)     
-      else:
-        txt += chr(char_offset)
-
-      txtc += chr(char_offset) + chr(char_offset)
-      if output_MIDI_channels:
-        txtc += chr(char_offset)
-      if output_velocity:
-        txtc += chr(char_offset) + chr(char_offset)      
-      else:
-        txtc += chr(char_offset)
-      
-      txt += '=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
-      txtc += '=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
-    
-    else:
-      # Song stamp
-      txt += 'SONG=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
-      txtc += 'SONG=' + song_name + '_with_' + str(len(events_matrix)-1) + '_notes'
-
-    if line_by_line_output:
-      txt += chr(10)
-      txtc += chr(10)
-    else:
-      txt += chr(32)
-      txtc += chr(32)
-
-    #print('Sorting input by start time...')
-    events_matrix.sort(key=lambda x: x[1]) # Sorting input by start time    
-    
-    #print('Timings converter')
-    if reset_timings:
-      ev_matrix = Tegridy_Timings_Converter(events_matrix)[0]
-    else:
-      ev_matrix = events_matrix
-    
-    chords.extend(ev_matrix)
-    #print(chords)
-
-    #print('Extracting melody...')
-    melody_list = []
-
-    #print('Grouping by start time. This will take a while...')
-    values = set(map(lambda x:x[1], ev_matrix)) # Non-multithreaded function version just in case
-
-    groups = [[y for y in ev_matrix if y[1]==x and len(y) == 6] for x in values] # Grouping notes into chords while discarting bad notes...
-  
-    #print('Sorting events...')
-    for items in groups:
-        
-        items.sort(reverse=True, key=lambda x: x[4]) # Sorting events by pitch
-        
-        if melody_conditioned_encoding: items[0][3] = 0 # Melody should always bear MIDI Channel 0 for code to work
-        
-        melody_list.append(items[0]) # Creating final melody list
-        melody_chords.append(items) # Creating final chords list
-        bass_melody.append(items[-1]) # Creating final bass melody list
-    
-    # [WIP] Melody-conditioned chords list
-    if melody_conditioned_encoding == True:
-      if not karaoke:
-   
-        previous_event = copy.deepcopy(melody_chords[0][0])
-
-        for ev in melody_chords:
-          hp = True
-          ev.sort(reverse=False, key=lambda x: x[4]) # Sorting chord events by pitch
-          for event in ev:
-          
-            # Computing events details
-            start_time = int(abs(event[1] - previous_event[1]))
-            
-            duration = int(previous_event[2])
-
-            if hp == True:
-              if int(previous_event[4]) >= melody_pitch_baseline:
-                channel = int(0)
-                hp = False
-              else:
-                channel = int(previous_event[3]+1)
-                hp = False  
-            else:
-              channel = int(previous_event[3]+1)
-              hp = False
-
-            pitch = int(previous_event[4])
-
-            velocity = int(previous_event[5])
-
-            # Writing INTergerS...
-            try:
-              INTS.append([(start_time)+char_offset, (duration)+char_offset, channel+char_offset, pitch+char_offset, velocity+char_offset])
-            except:
-              bints += 1
-
-            # Converting to TXT if possible...
-            try:
-              txtc += str(chr(start_time + char_offset))
-              txtc += str(chr(duration + char_offset))
-              txtc += str(chr(pitch + char_offset))
-              if output_velocity:
-                txtc += str(chr(velocity + char_offset))
-              if output_MIDI_channels:
-                txtc += str(chr(channel + char_offset))
-
-              if line_by_line_output:
-              
-
-                txtc += chr(10)
-              else:
-
-                txtc += chr(32)
-
-              previous_event = copy.deepcopy(event)
-            
-            except:
-              # print('Problematic MIDI event! Skipping...')
-              continue
-
-        if not line_by_line_output:
-          txtc += chr(10)
-
-        txt = txtc
-        chords = melody_chords
-    
-    # Default stuff (not melody-conditioned/not-karaoke)
-    else:      
-      if not karaoke:
-        melody_chords.sort(reverse=False, key=lambda x: x[0][1])
-        mel_chords = []
-        for mc in melody_chords:
-          mel_chords.extend(mc)
-
-        if transform != 0: 
-          chords = Tegridy_Transform(mel_chords, transform)
-        else:
-          chords = mel_chords
-
-        # TXT Stuff
-        previous_event = copy.deepcopy(chords[0])
-        for event in chords:
-
-          # Computing events details
-          start_time = int(abs(event[1] - previous_event[1]))
-          
-          duration = int(previous_event[2])
-
-          channel = int(previous_event[3])
-
-          pitch = int(previous_event[4] + transpose_by)
-          if flip == True:
-            pitch = 127 - int(previous_event[4] + transpose_by)
-
-          velocity = int(previous_event[5])
-
-          # Writing INTergerS...
-          try:
-            INTS.append([(start_time)+char_offset, (duration)+char_offset, channel+char_offset, pitch+char_offset, velocity+char_offset])
-          except:
-            bints += 1
-
-          # Converting to TXT if possible...
-          try:
-            txt += str(chr(start_time + char_offset))
-            txt += str(chr(duration + char_offset))
-            txt += str(chr(pitch + char_offset))
-            if output_velocity:
-              txt += str(chr(velocity + char_offset))
-            if output_MIDI_channels:
-              txt += str(chr(channel + char_offset))
-
-
-            if chordify_TXT == True and int(event[1] - previous_event[1]) == 0:
-              txt += ''      
-            else:     
-              if line_by_line_output:
-                txt += chr(10)
-              else:
-                txt += chr(32) 
-            
-            previous_event = copy.deepcopy(event)
-          
-          except:
-            # print('Problematic MIDI event. Skipping...')
-            continue
-
-        if not line_by_line_output:
-          txt += chr(10)      
-
-    # Karaoke stuff
-    if karaoke:
-
-      melody_chords.sort(reverse=False, key=lambda x: x[0][1])
-      mel_chords = []
-      for mc in melody_chords:
-        mel_chords.extend(mc)
-
-      if transform != 0: 
-        chords = Tegridy_Transform(mel_chords, transform)
-      else:
-        chords = mel_chords
-
-      previous_event = copy.deepcopy(chords[0])
-      for event in chords:
-
-        # Computing events details
-        start_time = int(abs(event[1] - previous_event[1]))
-        
-        duration = int(previous_event[2])
-
-        channel = int(previous_event[3])
-
-        pitch = int(previous_event[4] + transpose_by)
-
-        velocity = int(previous_event[5])
-
-        # Converting to TXT
-        txt += str(chr(start_time + char_offset))
-        txt += str(chr(duration + char_offset))
-        txt += str(chr(pitch + char_offset))
-
-        txt += str(chr(velocity + char_offset))
-        txt += str(chr(channel + char_offset))     
-
-        if start_time > 0:
-          for k in karaoke_events_matrix:
-            if event[1] == k[1]:
-              txt += str('=')
-              txt += str(k[2])          
-              break
-
-        if line_by_line_output:
-          txt += chr(10)
-        else:
-          txt += chr(32) 
-        
-        previous_event = copy.deepcopy(event)
-      
-      if not line_by_line_output:
-        txt += chr(10)
-
-    # Final processing code...
-    # =======================================================================
-
-    # Helper aux/backup function for Karaoke
-    karaokez.sort(reverse=False, key=lambda x: x[1])  
-
-    # MuseNet sorting
-    if musenet_encoding and not melody_conditioned_encoding and not karaoke:
-      chords.sort(key=lambda x: (x[1], x[3]))
-    
-    # Final melody sort
-    melody_list.sort()
-
-    # auxs for future use
-    aux1 = [None]
-    aux2 = [None]
-
-    return txt, melody_list, chords, bass_melody, karaokez, INTS, aux1, aux2 # aux1 and aux2 are not used atm
-
-###################################################################################
-
-def Optimus_TXT_to_Notes_Converter(Optimus_TXT_String,
-                                    line_by_line_dataset = True,
-                                    has_velocities = True,
-                                    has_MIDI_channels = True,
-                                    dataset_MIDI_events_time_denominator = 1,
-                                    char_encoding_offset = 30000,
-                                    save_only_first_composition = True,
-                                    simulate_velocity=True,
-                                    karaoke=False,
-                                    zero_token=False):
-
-    '''Project Los Angeles
-       Tegridy Code 2020'''
-
-    print('Tegridy Optimus TXT to Notes Converter')
-    print('Converting TXT to Notes list...Please wait...')
-
-    song_name = ''
-
-    if line_by_line_dataset:
-      input_string = Optimus_TXT_String.split('\n')
-    else:
-      input_string = Optimus_TXT_String.split(' ')
-
-    if line_by_line_dataset:
-      name_string = Optimus_TXT_String.split('\n')[0].split('=')
-    else:
-      name_string = Optimus_TXT_String.split(' ')[0].split('=')
-
-    # Zero token
-    zt = ''
-
-    zt += chr(char_encoding_offset) + chr(char_encoding_offset)
-    
-    if has_MIDI_channels:
-      zt += chr(char_encoding_offset)
-    
-    if has_velocities:
-      zt += chr(char_encoding_offset) + chr(char_encoding_offset)     
-    
-    else:
-      zt += chr(char_encoding_offset)
-
-    if zero_token:
-      if name_string[0] == zt:
-        song_name = name_string[1]
-    
-    else:
-      if name_string[0] == 'SONG':
-        song_name = name_string[1]
-
-    output_list = []
-    st = 0
-
-    for i in range(2, len(input_string)-1):
-
-      if save_only_first_composition:
-        if zero_token:
-          if input_string[i].split('=')[0] == zt:
-
-            song_name = name_string[1]
-            break
-        
-        else:
-          if input_string[i].split('=')[0] == 'SONG':
-
-            song_name = name_string[1]
-            break
-      try:
-        istring = input_string[i]
-
-        if has_MIDI_channels == False:
-          step = 4          
-
-        if has_MIDI_channels == True:
-          step = 5
-
-        if has_velocities == False:
-          step -= 1
-
-        st += int(ord(istring[0]) - char_encoding_offset) * dataset_MIDI_events_time_denominator
-
-        if not karaoke:
-          for s in range(0, len(istring), step):
-              if has_MIDI_channels==True:
-                if step > 3 and len(istring) > 2:
-                      out = []       
-                      out.append('note')
-
-                      out.append(st) # Start time
-
-                      out.append(int(ord(istring[s+1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
-
-                      if has_velocities:
-                        out.append(int(ord(istring[s+4]) - char_encoding_offset)) # Channel
-                      else:
-                        out.append(int(ord(istring[s+3]) - char_encoding_offset)) # Channel  
-
-                      out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Pitch
-
-                      if simulate_velocity:
-                        if s == 0:
-                          sim_vel = int(ord(istring[s+2]) - char_encoding_offset)
-                        out.append(sim_vel) # Simulated Velocity (= highest note's pitch)
-                      else:                      
-                        out.append(int(ord(istring[s+3]) - char_encoding_offset)) # Velocity
-
-              if has_MIDI_channels==False:
-                if step > 3 and len(istring) > 2:
-                      out = []       
-                      out.append('note')
-
-                      out.append(st) # Start time
-                      out.append(int(ord(istring[s+1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
-                      out.append(0) # Channel
-                      out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Pitch
-
-                      if simulate_velocity:
-                        if s == 0:
-                          sim_vel = int(ord(istring[s+2]) - char_encoding_offset)
-                        out.append(sim_vel) # Simulated Velocity (= highest note's pitch)
-                      else:                      
-                        out.append(int(ord(istring[s+3]) - char_encoding_offset)) # Velocity
-
-              if step == 3 and len(istring) > 2:
-                      out = []       
-                      out.append('note')
-
-                      out.append(st) # Start time
-                      out.append(int(ord(istring[s+1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
-                      out.append(0) # Channel
-                      out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Pitch
-
-                      out.append(int(ord(istring[s+2]) - char_encoding_offset)) # Velocity = Pitch
-
-              output_list.append(out)
-
-        if karaoke:
-          try:
-              out = []       
-              out.append('note')
-
-              out.append(st) # Start time
-              out.append(int(ord(istring[1]) - char_encoding_offset) * dataset_MIDI_events_time_denominator) # Duration
-              out.append(int(ord(istring[4]) - char_encoding_offset)) # Channel
-              out.append(int(ord(istring[2]) - char_encoding_offset)) # Pitch
-
-              if simulate_velocity:
-                if s == 0:
-                  sim_vel = int(ord(istring[2]) - char_encoding_offset)
-                out.append(sim_vel) # Simulated Velocity (= highest note's pitch)
-              else:                      
-                out.append(int(ord(istring[3]) - char_encoding_offset)) # Velocity
-              output_list.append(out)
-              out = []
-              if istring.split('=')[1] != '':
-                out.append('lyric')
-                out.append(st)
-                out.append(istring.split('=')[1])
-                output_list.append(out)
-          except:
-            continue
-
-
-      except:
-        print('Bad note string:', istring)
-        continue
-
-    # Simple error control just in case
-    S = []
-    for x in output_list:
-      if len(x) == 6 or len(x) == 3:
-        S.append(x)
-
-    output_list.clear()    
-    output_list = copy.deepcopy(S)
-
-
-    print('Task complete! Enjoy! :)')
-
-    return output_list, song_name
-
-###################################################################################
-
-def Optimus_Data2TXT_Converter(data,
-                              dataset_time_denominator=1,
-                              transpose_by = 0,
-                              char_offset = 33,
-                              line_by_line_output = True,
-                              output_velocity = False,
-                              output_MIDI_channels = False):
-
-
-  '''Input: data as a flat chords list of flat chords lists
-
-  Output: TXT string
-          INTs
-
-  Project Los Angeles
-  Tegridy Code 2021'''
-
-  txt = ''
-  TXT = ''
-
-  quit = False
-  counter = 0
-
-  INTs = []
-  INTs_f = []
-
-  for d in tqdm.tqdm(sorted(data)):
-
-    if quit == True:
-      break
-
-    txt = 'SONG=' + str(counter)
-    counter += 1
-
-    if line_by_line_output:
-      txt += chr(10)
-    else:
-      txt += chr(32)
-      
-    INTs = []
-
-    # TXT Stuff
-    previous_event = copy.deepcopy(d[0])
-    for event in sorted(d):
-
-      # Computing events details
-      start_time = int(abs(event[1] - previous_event[1]) / dataset_time_denominator)
-      
-      duration = int(previous_event[2] / dataset_time_denominator)
-
-      channel = int(previous_event[3])
-
-      pitch = int(previous_event[4] + transpose_by)
-
-      velocity = int(previous_event[5])
-
-      INTs.append([start_time, duration, pitch])
-
-      # Converting to TXT if possible...
-      try:
-        txt += str(chr(start_time + char_offset))
-        txt += str(chr(duration + char_offset))
-        txt += str(chr(pitch + char_offset))
-        if output_velocity:
-          txt += str(chr(velocity + char_offset))
-        if output_MIDI_channels:
-          txt += str(chr(channel + char_offset))
-    
-        if line_by_line_output:
-          txt += chr(10)
-        else:
-          txt += chr(32) 
-        
-        previous_event = copy.deepcopy(event)
-      except KeyboardInterrupt:
-        quit = True
-        break
-      except:
-        print('Problematic MIDI data. Skipping...')
-        continue
-
-    if not line_by_line_output:
-      txt += chr(10)
-    
-    TXT += txt
-    INTs_f.extend(INTs)
-
-  return TXT, INTs_f
-
-###################################################################################
-
-def Optimus_Squash(chords_list, simulate_velocity=True, mono_compression=False):
-
-  '''Input: Flat chords list
-            Simulate velocity or not
-            Mono-compression enabled or disabled
-            
-            Default is almost lossless 25% compression, otherwise, lossy 50% compression (mono-compression)
-
-     Output: Squashed chords list
-             Resulting compression level
-
-             Please note that if drums are passed through as is
-
-     Project Los Angeles
-     Tegridy Code 2021'''
-
-  output = []
-  ptime = 0
-  vel = 0
-  boost = 15
-  stptc = []
-  ocount = 0
-  rcount = 0
-
-  for c in chords_list:
-    
-    cc = copy.deepcopy(c)
-    ocount += 1
-    
-    if [cc[1], cc[3], (cc[4] % 12) + 60] not in stptc:
-      stptc.append([cc[1], cc[3], (cc[4] % 12) + 60])
-
-      if cc[3] != 9:
-        cc[4] = (c[4] % 12) + 60
-
-      if simulate_velocity and c[1] != ptime:
-        vel = c[4] + boost
-      
-      if cc[3] != 9:
-        cc[5] = vel
-
-      if mono_compression:
-        if c[1] != ptime:
-          output.append(cc)
-          rcount += 1  
-      else:
-        output.append(cc)
-        rcount += 1
-      
-      ptime = c[1]
-
-  output.sort(key=lambda x: (x[1], x[4]))
-
-  comp_level = 100 - int((rcount * 100) / ocount)
-
-  return output, comp_level
-
-###################################################################################
-
-def Optimus_Signature(chords_list, calculate_full_signature=False):
-
-    '''Optimus Signature
-
-    ---In the name of the search for a perfect score slice signature---
-     
-    Input: Flat chords list to evaluate
-
-    Output: Full Optimus Signature as a list
-            Best/recommended Optimus Signature as a list
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-    
-    # Pitches
-
-    ## StDev
-    if calculate_full_signature:
-      psd = statistics.stdev([int(y[4]) for y in chords_list])
-    else:
-      psd = 0
-
-    ## Median
-    pmh = statistics.median_high([int(y[4]) for y in chords_list])
-    pm = statistics.median([int(y[4]) for y in chords_list])
-    pml = statistics.median_low([int(y[4]) for y in chords_list])
-    
-    ## Mean
-    if calculate_full_signature:
-      phm = statistics.harmonic_mean([int(y[4]) for y in chords_list])
-    else:
-      phm = 0
-
-    # Durations
-    dur = statistics.median([int(y[2]) for y in chords_list])
-
-    # Velocities
-
-    vel = statistics.median([int(y[5]) for y in chords_list])
-
-    # Beats
-    mtds = statistics.median([int(abs(chords_list[i-1][1]-chords_list[i][1])) for i in range(1, len(chords_list))])
-    if calculate_full_signature:
-      hmtds = statistics.harmonic_mean([int(abs(chords_list[i-1][1]-chords_list[i][1])) for i in range(1, len(chords_list))])
-    else:
-      hmtds = 0
-
-    # Final Optimus signatures
-    full_Optimus_signature = [round(psd), round(pmh), round(pm), round(pml), round(phm), round(dur), round(vel), round(mtds), round(hmtds)]
-    ########################    PStDev     PMedianH    PMedian    PMedianL    PHarmoMe    Duration    Velocity      Beat       HarmoBeat
-
-    best_Optimus_signature = [round(pmh), round(pm), round(pml), round(dur, -1), round(vel, -1), round(mtds, -1)]
-    ########################   PMedianH    PMedian    PMedianL      Duration        Velocity          Beat
-    
-    # Return...
-    return full_Optimus_signature, best_Optimus_signature
-    
-
-###################################################################################
-#
-# TMIDI 2.0 Helper functions
-#
-###################################################################################
-
-def Tegridy_FastSearch(needle, haystack, randomize = False):
-
-  '''
-
-  Input: Needle iterable
-         Haystack iterable
-         Randomize search range (this prevents determinism)
-
-  Output: Start index of the needle iterable in a haystack iterable
-          If nothing found, -1 is returned
-
-  Project Los Angeles
-  Tegridy Code 2021'''
-
-  need = copy.deepcopy(needle)
-
-  try:
-    if randomize:
-      idx = haystack.index(need, secrets.randbelow(len(haystack)-len(need)))
-    else:
-      idx = haystack.index(need)
-
-  except KeyboardInterrupt:
-    return -1
-
-  except:
-    return -1
-    
-  return idx
-
-###################################################################################
-
-def Tegridy_Chord_Match(chord1, chord2, match_type=2):
-
-    '''Tegridy Chord Match
-     
-    Input: Two chords to evaluate
-           Match type: 2 = duration, channel, pitch, velocity
-                       3 = channel, pitch, velocity
-                       4 = pitch, velocity
-                       5 = velocity
-
-    Output: Match rating (0-100)
-            NOTE: Match rating == -1 means identical source chords
-            NOTE: Match rating == 100 means mutual shortest chord
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    match_rating = 0
-
-    if chord1 == []:
-      return 0
-    if chord2 == []:
-      return 0
-
-    if chord1 == chord2:
-      return -1
-
-    else:
-      zipped_pairs = list(zip(chord1, chord2))
-      zipped_diff = abs(len(chord1) - len(chord2))
-
-      short_match = [False]
-      for pair in zipped_pairs:
-        cho1 = ' '.join([str(y) for y in pair[0][match_type:]])
-        cho2 = ' '.join([str(y) for y in pair[1][match_type:]])
-        if cho1 == cho2:
-          short_match.append(True)
-        else:
-          short_match.append(False)
-      
-      if True in short_match:
-        return 100
-
-      pairs_ratings = []
-
-      for pair in zipped_pairs:
-        cho1 = ' '.join([str(y) for y in pair[0][match_type:]])
-        cho2 = ' '.join([str(y) for y in pair[1][match_type:]])
-        pairs_ratings.append(SM(None, cho1, cho2).ratio())
-
-      match_rating = sum(pairs_ratings) / len(pairs_ratings) * 100
-
-      return match_rating
-
-###################################################################################
-
-def Tegridy_Last_Chord_Finder(chords_list):
-
-    '''Tegridy Last Chord Finder
-     
-    Input: Flat chords list
-
-    Output: Last detected chord of the chords list
-            Last chord start index in the original chords list
-            First chord end index in the original chords list
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    chords = []
-    cho = []
-
-    ptime = 0
-
-    i = 0
-
-    pc_idx = 0
-    fc_idx = 0
-
-    chords_list.sort(reverse=False, key=lambda x: x[1])
-    
-    for cc in chords_list:
-
-      if cc[1] == ptime:
-        
-        cho.append(cc)
-
-        ptime = cc[1]
-
-      else:
-        if pc_idx == 0: 
-          fc_idx = chords_list.index(cc)
-        pc_idx = chords_list.index(cc)
-        
-        chords.append(cho)
-        
-        cho = []
-      
-        cho.append(cc)
-        
-        ptime = cc[1]
-        
-        i += 1
-      
-    if cho != []: 
-      chords.append(cho)
-      i += 1
-     
-    return chords_list[pc_idx:], pc_idx, fc_idx
-
-###################################################################################
-
-def Tegridy_Chords_Generator(chords_list, shuffle_pairs = True, remove_single_notes=False):
-
-    '''Tegridy Score Chords Pairs Generator
-     
-    Input: Flat chords list
-           Shuffle pairs (recommended)
-
-    Output: List of chords
-            
-            Average time(ms) per chord
-            Average time(ms) per pitch
-            Average chords delta time
-
-            Average duration
-            Average channel
-            Average pitch
-            Average velocity
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    chords = []
-    cho = []
-
-    i = 0
-
-    # Sort by start time
-    chords_list.sort(reverse=False, key=lambda x: x[1])
-
-    # Main loop
-    pcho = chords_list[0]
-    for cc in chords_list:
-      if cc[1] == pcho[1]:
-        
-        cho.append(cc)
-        pcho = copy.deepcopy(cc)
-
-      else:
-        if not remove_single_notes:
-          chords.append(cho)
-          cho = []
-          cho.append(cc)
-          pcho = copy.deepcopy(cc)
-          
-          i += 1
-        else:
-          if len(cho) > 1:
-            chords.append(cho)
-          cho = []
-          cho.append(cc)
-          pcho = copy.deepcopy(cc)
-            
-          i += 1  
-    
-    # Averages
-    t0 = chords[0][0][1]
-    t1 = chords[-1][-1][1]
-    tdel = abs(t1 - t0)
-    avg_ms_per_chord = int(tdel / i)
-    avg_ms_per_pitch = int(tdel / len(chords_list))
-
-    # Delta time
-    tds = [int(abs(chords_list[i-1][1]-chords_list[i][1]) / 1) for i in range(1, len(chords_list))]
-    if len(tds) != 0: avg_delta_time = int(sum(tds) / len(tds))
-
-    # Chords list attributes
-    p = int(sum([int(y[4]) for y in chords_list]) / len(chords_list))
-    d = int(sum([int(y[2]) for y in chords_list]) / len(chords_list))
-    c = int(sum([int(y[3]) for y in chords_list]) / len(chords_list))
-    v = int(sum([int(y[5]) for y in chords_list]) / len(chords_list))
-
-    # Final shuffle
-    if shuffle_pairs:
-      random.shuffle(chords)
-
-    return chords, [avg_ms_per_chord, avg_ms_per_pitch, avg_delta_time], [d, c, p, v]
-
-###################################################################################
-
-def Tegridy_Chords_List_Music_Features(chords_list, st_dur_div = 1, pitch_div = 1, vel_div = 1):
-
-    '''Tegridy Chords List Music Features
-     
-    Input: Flat chords list
-
-    Output: A list of the extracted chords list's music features
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    chords_list1 = [x for x in chords_list if x]
-    chords_list1.sort(reverse=False, key=lambda x: x[1])
-    
-    # Features extraction code
-
-    melody_list = []
-    bass_melody = []
-    melody_chords = []
-    mel_avg_tds = []
-    mel_chrd_avg_tds = []
-    bass_melody_avg_tds = []
-
-    #print('Grouping by start time. This will take a while...')
-    values = set(map(lambda x:x[1], chords_list1)) # Non-multithreaded function version just in case
-
-    groups = [[y for y in chords_list1 if y[1]==x and len(y) == 6] for x in values] # Grouping notes into chords while discarting bad notes...
-
-    #print('Sorting events...')
-    for items in groups:
-        items.sort(reverse=True, key=lambda x: x[4]) # Sorting events by pitch
-        melody_list.append(items[0]) # Creating final melody list
-        melody_chords.append(items) # Creating final chords list
-        bass_melody.append(items[-1]) # Creating final bass melody list
-
-    #print('Final sorting by start time...')      
-    melody_list.sort(reverse=False, key=lambda x: x[1]) # Sorting events by start time
-    melody_chords.sort(reverse=False, key=lambda x: x[0][1]) # Sorting events by start time
-    bass_melody.sort(reverse=False, key=lambda x: x[1]) # Sorting events by start time
-
-    # Extracting music features from the chords list
-    
-    # Melody features
-    mel_avg_pitch = int(sum([y[4] for y in melody_list]) / len(melody_list) / pitch_div)
-    mel_avg_dur = int(sum([int(y[2] / st_dur_div) for y in melody_list]) / len(melody_list))
-    mel_avg_vel = int(sum([int(y[5] / vel_div) for y in melody_list]) / len(melody_list))
-    mel_avg_chan = int(sum([int(y[3]) for y in melody_list]) / len(melody_list))
-    
-    mel_tds = [int(abs(melody_list[i-1][1]-melody_list[i][1])) for i in range(1, len(melody_list))]
-    if len(mel_tds) != 0: mel_avg_tds = int(sum(mel_tds) / len(mel_tds) / st_dur_div)
-    
-    melody_features = [mel_avg_tds, mel_avg_dur, mel_avg_chan, mel_avg_pitch, mel_avg_vel]
-
-    # Chords list features
-    mel_chrd_avg_pitch = int(sum([y[4] for y in chords_list1]) / len(chords_list1) / pitch_div)
-    mel_chrd_avg_dur = int(sum([int(y[2] / st_dur_div) for y in chords_list1]) / len(chords_list1))
-    mel_chrd_avg_vel = int(sum([int(y[5] / vel_div) for y in chords_list1]) / len(chords_list1))
-    mel_chrd_avg_chan = int(sum([int(y[3]) for y in chords_list1]) / len(chords_list1))
-    
-    mel_chrd_tds = [int(abs(chords_list1[i-1][1]-chords_list1[i][1])) for i in range(1, len(chords_list1))]
-    if len(mel_tds) != 0: mel_chrd_avg_tds = int(sum(mel_chrd_tds) / len(mel_chrd_tds) / st_dur_div)
-    
-    chords_list_features = [mel_chrd_avg_tds, mel_chrd_avg_dur, mel_chrd_avg_chan, mel_chrd_avg_pitch, mel_chrd_avg_vel]
-
-    # Bass melody features
-    bass_melody_avg_pitch = int(sum([y[4] for y in bass_melody]) / len(bass_melody) / pitch_div)
-    bass_melody_avg_dur = int(sum([int(y[2] / st_dur_div) for y in bass_melody]) / len(bass_melody))
-    bass_melody_avg_vel = int(sum([int(y[5] / vel_div) for y in bass_melody]) / len(bass_melody))
-    bass_melody_avg_chan = int(sum([int(y[3]) for y in bass_melody]) / len(bass_melody))
-    
-    bass_melody_tds = [int(abs(bass_melody[i-1][1]-bass_melody[i][1])) for i in range(1, len(bass_melody))]
-    if len(bass_melody_tds) != 0: bass_melody_avg_tds = int(sum(bass_melody_tds) / len(bass_melody_tds) / st_dur_div)
-    
-    bass_melody_features = [bass_melody_avg_tds, bass_melody_avg_dur, bass_melody_avg_chan, bass_melody_avg_pitch, bass_melody_avg_vel]
-    
-    # A list to return all features
-    music_features = []
-
-    music_features.extend([len(chords_list1)]) # Count of the original chords list notes
-    
-    music_features.extend(melody_features) # Extracted melody features
-    music_features.extend(chords_list_features) # Extracted chords list features
-    music_features.extend(bass_melody_features) # Extracted bass melody features
-    music_features.extend([sum([y[4] for y in chords_list1])]) # Sum of all pitches in the original chords list
-
-    return music_features
-
-###################################################################################
-
-def Tegridy_Transform(chords_list, to_pitch=60, to_velocity=-1):
-
-    '''Tegridy Transform
-     
-    Input: Flat chords list
-           Desired average pitch (-1 == no change)
-           Desired average velocity (-1 == no change)
-
-    Output: Transformed flat chords list
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    transformed_chords_list = []
-
-    chords_list.sort(reverse=False, key=lambda x: x[1])
-
-    chords_list_features = Optimus_Signature(chords_list)[1]
-
-    pitch_diff = int((chords_list_features[0] + chords_list_features[1] + chords_list_features[2]) / 3) - to_pitch
-    velocity_diff = chords_list_features[4] - to_velocity
-
-    for c in chords_list:
-      cc = copy.deepcopy(c)
-      if c[3] != 9: # Except the drums
-        if to_pitch != -1: 
-          cc[4] = c[4] - pitch_diff
-        
-        if to_velocity != -1: 
-          cc[5] = c[5] - velocity_diff
-      
-      transformed_chords_list.append(cc)
-
-    return transformed_chords_list
-
-###################################################################################
-
-def Tegridy_MIDI_Zip_Notes_Summarizer(chords_list, match_type = 4):
-
-    '''Tegridy MIDI Zip Notes Summarizer
-     
-    Input: Flat chords list / SONG
-           Match type according to 'note' event of MIDI.py
-
-    Output: Summarized chords list
-            Number of summarized notes
-            Number of dicarted notes
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    i = 0
-    j = 0
-    out1 = []
-    pout = []
- 
-
-    for o in chords_list:
-
-      # MIDI Zip
-
-      if o[match_type:] not in pout:
-        pout.append(o[match_type:])
-        
-        out1.append(o)
-        j += 1
-      
-      else:
-        i += 1
-
-    return out1, i
-
-###################################################################################
-
-def Tegridy_Score_Chords_Pairs_Generator(chords_list, shuffle_pairs = True, remove_single_notes=False):
-
-    '''Tegridy Score Chords Pairs Generator
-     
-    Input: Flat chords list
-           Shuffle pairs (recommended)
-
-    Output: Score chords pairs list
-            Number of created pairs
-            Number of detected chords
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    chords = []
-    cho = []
-
-    i = 0
-    j = 0
-
-    chords_list.sort(reverse=False, key=lambda x: x[1])
-    pcho = chords_list[0]
-    for cc in chords_list:
-      if cc[1] == pcho[1]:
-        
-        cho.append(cc)
-        pcho = copy.deepcopy(cc)
-
-      else:
-        if not remove_single_notes:
-          chords.append(cho)
-          cho = []
-          cho.append(cc)
-          pcho = copy.deepcopy(cc)
-          
-          i += 1
-        else:
-          if len(cho) > 1:
-            chords.append(cho)
-          cho = []
-          cho.append(cc)
-          pcho = copy.deepcopy(cc)
-            
-          i += 1  
-    
-    chords_pairs = []
-    for i in range(len(chords)-1):
-      chords_pairs.append([chords[i], chords[i+1]])
-      j += 1
-    if shuffle_pairs: random.shuffle(chords_pairs)
-
-    return chords_pairs, j, i
-
-###################################################################################
-
-def Tegridy_Sliced_Score_Pairs_Generator(chords_list, number_of_miliseconds_per_slice=2000, shuffle_pairs = False):
-
-    '''Tegridy Sliced Score Pairs Generator
-     
-    Input: Flat chords list
-           Number of miliseconds per slice
-
-    Output: Sliced score pairs list
-            Number of created slices
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    chords = []
-    cho = []
-
-    time = number_of_miliseconds_per_slice 
-
-    i = 0
-
-    chords_list1 = [x for x in chords_list if x]
-    chords_list1.sort(reverse=False, key=lambda x: x[1])
-    pcho = chords_list1[0]
-    for cc in chords_list1[1:]:
-
-      if cc[1] <= time:
-        
-        cho.append(cc)
-
-      else:
-        if cho != [] and pcho != []: chords.append([pcho, cho])
-        pcho = copy.deepcopy(cho)
-        cho = []
-        cho.append(cc)
-        time += number_of_miliseconds_per_slice
-        i += 1
-      
-    if cho != [] and pcho != []: 
-      chords.append([pcho, cho])
-      pcho = copy.deepcopy(cho)
-      i += 1
-    
-    if shuffle_pairs: random.shuffle(chords)
-
-    return chords, i
-
-###################################################################################
-
-def Tegridy_Timings_Converter(chords_list, 
-                              max_delta_time = 1000, 
-                              fixed_start_time = 250, 
-                              start_time = 0,
-                              start_time_multiplier = 1,
-                              durations_multiplier = 1):
-
-    '''Tegridy Timings Converter
-     
-    Input: Flat chords list
-           Max delta time allowed between notes
-           Fixed start note time for excessive gaps
-
-    Output: Converted flat chords list
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    song = chords_list
-
-    song1 = []
-
-    p = song[0]
-
-    p[1] = start_time
-
-    time = start_time
-
-    delta = [0]
-
-    for i in range(len(song)):
-      if song[i][0] == 'note':
-        ss = copy.deepcopy(song[i])
-        if song[i][1] != p[1]:
-          
-          if abs(song[i][1] - p[1]) > max_delta_time:
-            time += fixed_start_time
-          else:
-            time += abs(song[i][1] - p[1])
-            delta.append(abs(song[i][1] - p[1]))
-
-          ss[1] = int(round(time * start_time_multiplier, -1))
-          ss[2] = int(round(song[i][2] * durations_multiplier, -1))
-          song1.append(ss)
-          
-          p = copy.deepcopy(song[i])
-        else:
-          
-          ss[1] = int(round(time * start_time_multiplier, -1))
-          ss[2] = int(round(song[i][2] * durations_multiplier, -1))
-          song1.append(ss)
-          
-          p = copy.deepcopy(song[i])
-      
-      else:
-        ss = copy.deepcopy(song[i])
-        ss[1] = time
-        song1.append(ss)
-        
-    average_delta_st = int(sum(delta) / len(delta))
-    average_duration = int(sum([y[2] for y in song1 if y[0] == 'note']) / len([y[2] for y in song1 if y[0] == 'note']))
-
-    song1.sort(reverse=False, key=lambda x: x[1])
-
-    return song1, time, average_delta_st, average_duration
-
-###################################################################################
-
-def Tegridy_Score_Slicer(chords_list, number_of_miliseconds_per_slice=2000, overlap_notes = 0, overlap_chords=False):
-
-    '''Tegridy Score Slicer
-     
-    Input: Flat chords list
-           Number of miliseconds per slice
-
-    Output: Sliced chords list
-            Number of created slices
-
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    chords = []
-    cho = []
-
-    time = number_of_miliseconds_per_slice
-    ptime = 0
-
-    i = 0
-
-    pc_idx = 0
-
-    chords_list.sort(reverse=False, key=lambda x: x[1])
-    
-    for cc in chords_list:
-
-      if cc[1] <= time:
-        
-        cho.append(cc)
-
-        if ptime != cc[1]:
-          pc_idx = cho.index(cc)
-
-        ptime = cc[1]
-
-
-      else:
-
-        if overlap_chords:
-          chords.append(cho)
-          cho.extend(chords[-1][pc_idx:])
-        
-        else:
-          chords.append(cho[:pc_idx])
-        
-        cho = []
-      
-        cho.append(cc)
-        
-        time += number_of_miliseconds_per_slice
-        ptime = cc[1]
-        
-        i += 1
-      
-    if cho != []: 
-      chords.append(cho)
-      i += 1
-    
-    return [x for x in chords if x], i
-
-###################################################################################
-
-def Tegridy_TXT_Tokenizer(input_TXT_string, line_by_line_TXT_string=True):
-
-    '''Tegridy TXT Tokenizer
-     
-    Input: TXT String
-
-    Output: Tokenized TXT string + forward and reverse dics
-    
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    print('Tegridy TXT Tokenizer')
-
-    if line_by_line_TXT_string:
-      T = input_TXT_string.split()
-    else:
-      T = input_TXT_string.split(' ')
-
-    DIC = dict(zip(T, range(len(T))))
-    RDIC = dict(zip(range(len(T)), T))
-
-    TXTT = ''
-
-    for t in T:
-      try:
-        TXTT += chr(DIC[t])
-      except:
-        print('Error. Could not finish.')
-        return TXTT, DIC, RDIC
-    
-    print('Done!')
-    
-    return TXTT, DIC, RDIC
-
-###################################################################################
-
-def Tegridy_TXT_DeTokenizer(input_Tokenized_TXT_string, RDIC):
-
-    '''Tegridy TXT Tokenizer
-     
-    Input: Tokenized TXT String
-           
-
-    Output: DeTokenized TXT string
-    
-    Project Los Angeles
-    Tegridy Code 2021'''
-
-    print('Tegridy TXT DeTokenizer')
-
-    Q = list(input_Tokenized_TXT_string)
-    c = 0
-    RTXT = ''
-    for q in Q:
-      try:
-        RTXT += RDIC[ord(q)] + chr(10)
-      except:
-        c+=1
-
-    print('Number of errors:', c)
-
-    print('Done!')
-
-    return RTXT
-
-###################################################################################
-
-def Tegridy_List_Slicer(input_list, slices_length_in_notes=20):
-
-  '''Input: List to slice
-            Desired slices length in notes
-     
-     Output: Sliced list of lists
-     
-     Project Los Angeles
-     Tegridy Code 2021'''
-
-  for i in range(0, len(input_list), slices_length_in_notes):
-     yield input_list[i:i + slices_length_in_notes]
-    
-###################################################################################    
-    
-def Tegridy_Split_List(list_to_split, split_value=0):
-    
-    # src courtesy of www.geeksforgeeks.org
-  
-    # using list comprehension + zip() + slicing + enumerate()
-    # Split list into lists by particular value
-    size = len(list_to_split)
-    idx_list = [idx + 1 for idx, val in
-                enumerate(list_to_split) if val == split_value]
-
-
-    res = [list_to_split[i: j] for i, j in
-            zip([0] + idx_list, idx_list + 
-            ([size] if idx_list[-1] != size else []))]
-  
-    # print result
-    # print("The list after splitting by a value : " + str(res))
-    
-    return res
-
-###################################################################################
-
-# This is the end of the TMIDI X Python module
-
-###################################################################################
diff --git a/spaces/avivdm1/AutoGPT/autogpt/__main__.py b/spaces/avivdm1/AutoGPT/autogpt/__main__.py
deleted file mode 100644
index 128f9eea4900429e88276abdde3419b806001ac7..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Auto-GPT: A GPT powered AI Assistant"""
-import autogpt.cli
-
-if __name__ == "__main__":
-    autogpt.cli.main()
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/font-awesome/6.2.0/css/all.min.css b/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/font-awesome/6.2.0/css/all.min.css
deleted file mode 100644
index 5dddbd50cf833857a9a72ac60889eabb330b97f5..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/font-awesome/6.2.0/css/all.min.css
+++ /dev/null
@@ -1,6 +0,0 @@
-/*!
- * Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com
- * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
- * Copyright 2022 Fonticons, Inc.
- */
-.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 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\ No newline at end of file
diff --git a/spaces/awacke1/California-Medical-Centers-Streamlit/app.py b/spaces/awacke1/California-Medical-Centers-Streamlit/app.py
deleted file mode 100644
index af552c3ec52d7c92d68269cfdb68b9593e4574de..0000000000000000000000000000000000000000
--- a/spaces/awacke1/California-Medical-Centers-Streamlit/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import streamlit as st
-import folium
-from streamlit_folium import folium_static
-from folium.plugins import MarkerCluster
-
-# Define California medical centers data
-california_med_centers = [
-    ('UCSF Medical Center', 37.7631, -122.4576, 'General medical and surgical', 'San Francisco'),
-    ('Cedars-Sinai Medical Center', 34.0762, -118.3790, 'Heart specialty', 'Los Angeles'),
-    ('Stanford Health Care', 37.4331, -122.1750, 'Teaching hospital', 'Stanford'),
-    ('UCLA Medical Center', 34.0659, -118.4466, 'Research and teaching', 'Los Angeles'),
-    ('Scripps La Jolla Hospitals', 32.8851, -117.2255, 'Multiple specialties', 'La Jolla'),
-    ('Sharp Memorial Hospital', 32.7992, -117.1542, 'Trauma center', 'San Diego'),
-    ('Huntington Hospital', 34.1330, -118.1475, 'Non-profit hospital', 'Pasadena'),
-    ('Hoag Memorial Hospital', 33.6045, -117.8664, 'Community hospital', 'Newport Beach'),
-    ('UCSD Medical Center', 32.7554, -117.1682, 'Academic health center', 'San Diego'),
-    ('UC Davis Medical Center', 38.5539, -121.4554, 'Public academic health', 'Sacramento'),
-    ('John Muir Medical Center', 37.9192, -122.0426, 'Heart care', 'Walnut Creek'),
-    ('Santa Clara Valley Medical Center', 37.3121, -121.9769, 'County hospital', 'San Jose'),
-    ('Kaiser Permanente San Francisco', 37.7741, -122.4179, 'Health maintenance organization', 'San Francisco'),
-    ('City of Hope', 34.1285, -117.9665, 'Cancer center', 'Duarte'),
-    ('UCI Medical Center', 33.7886, -117.8572, 'University hospital', 'Orange'),
-    ('Good Samaritan Hospital', 34.0506, -118.2831, 'Private hospital', 'Los Angeles'),
-    ('Los Angeles County General', 34.0581, -118.2917, 'Public hospital', 'Los Angeles'),
-    ('California Pacific Medical Center', 37.7864, -122.4357, 'Private non-profit', 'San Francisco'),
-    ('Sutter Roseville Medical Center', 38.7521, -121.2760, 'General medical and surgical', 'Roseville'),
-    ('St. Joseph Hospital', 33.7821, -117.9188, 'Faith-based care', 'Orange')
-]
-
-# Create a map centered on California
-m = folium.Map(location=[36.7783, -119.4179], zoom_start=6)
-
-# Add markers for each medical center and add them to a MarkerCluster
-marker_cluster = MarkerCluster().add_to(m)
-for center in california_med_centers:
-    folium.Marker(
-        location=[center[1], center[2]],
-        popup=f'<b>{center[0]}</b><br>Description: {center[3]}<br>City: {center[4]}',
-        icon=folium.Icon(color='red')
-    ).add_to(marker_cluster)
-
-# Display the map
-folium_static(m)
-
-st.markdown("""
-# 🏥 California Medical Centers 🌆
-The map above shows the location of various medical centers and hospitals in California. Hover over the markers to learn more about each location.
-""")
-
-# Function to update the map when a button is clicked
-def update_map(center_data):
-    m.location = [center_data[1], center_data[2]]
-    m.zoom_start = 13
-    folium_static(m)
-
-for i in range(0, len(california_med_centers), 4):
-    cols = st.columns(4)
-    for j in range(4):
-        if i + j < len(california_med_centers):
-            with cols[j]:
-                if st.button(california_med_centers[i + j][0]):
-                    update_map(california_med_centers[i + j])
diff --git a/spaces/aziz28/hash_app/setup.sh b/spaces/aziz28/hash_app/setup.sh
deleted file mode 100644
index 8f6f767bbe766d09ac9c8670c0d63e274762b768..0000000000000000000000000000000000000000
--- a/spaces/aziz28/hash_app/setup.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-mkdir -p ~/.streamlit/
-echo "\
-[server]\n\
-headless = true\n\
-enableCORS=false\n\
-port = $PORT\n\
-" > ~/.streamlit/config.toml
\ No newline at end of file
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/how to export onnx.md b/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/how to export onnx.md
deleted file mode 100644
index 6d22719fd1a8e9d034e6224cc95f4b50d44a0320..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/how to export onnx.md	
+++ /dev/null
@@ -1,4 +0,0 @@
-- Open [onnx_export](onnx_export.py)
-- project_name = "dddsp" change "project_name" to your project name
-- model_path = f'{project_name}/model_500000.pt' change "model_path" to your model path
-- Run
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/BVHLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/BVHLoader.js
deleted file mode 100644
index a0461e2ae04d6d028b04eddca01931390a9b6746..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/BVHLoader.js
+++ /dev/null
@@ -1,414 +0,0 @@
-/**
- * @author herzig / http://github.com/herzig
- * @author Mugen87 / https://github.com/Mugen87
- *
- * Description: reads BVH files and outputs a single THREE.Skeleton and an THREE.AnimationClip
- *
- * Currently only supports bvh files containing a single root.
- *
- */
-
-THREE.BVHLoader = function ( manager ) {
-
-	this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager;
-
-	this.animateBonePositions = true;
-	this.animateBoneRotations = true;
-
-};
-
-THREE.BVHLoader.prototype = {
-
-	constructor: THREE.BVHLoader,
-
-	load: function ( url, onLoad, onProgress, onError ) {
-
-		var scope = this;
-
-		var loader = new THREE.FileLoader( scope.manager );
-		loader.setPath( scope.path );
-		loader.load( url, function ( text ) {
-
-			onLoad( scope.parse( text ) );
-
-		}, onProgress, onError );
-
-	},
-
-	setPath: function ( value ) {
-
-		this.path = value;
-		return this;
-
-	},
-
-	parse: function ( text ) {
-
-		/*
-			reads a string array (lines) from a BVH file
-			and outputs a skeleton structure including motion data
-
-			returns thee root node:
-			{ name: '', channels: [], children: [] }
-		*/
-		function readBvh( lines ) {
-
-			// read model structure
-
-			if ( nextLine( lines ) !== 'HIERARCHY' ) {
-
-				console.error( 'THREE.BVHLoader: HIERARCHY expected.' );
-
-			}
-
-			var list = []; // collects flat array of all bones
-			var root = readNode( lines, nextLine( lines ), list );
-
-			// read motion data
-
-			if ( nextLine( lines ) !== 'MOTION' ) {
-
-				console.error( 'THREE.BVHLoader: MOTION expected.' );
-
-			}
-
-			// number of frames
-
-			var tokens = nextLine( lines ).split( /[\s]+/ );
-			var numFrames = parseInt( tokens[ 1 ] );
-
-			if ( isNaN( numFrames ) ) {
-
-				console.error( 'THREE.BVHLoader: Failed to read number of frames.' );
-
-			}
-
-			// frame time
-
-			tokens = nextLine( lines ).split( /[\s]+/ );
-			var frameTime = parseFloat( tokens[ 2 ] );
-
-			if ( isNaN( frameTime ) ) {
-
-				console.error( 'THREE.BVHLoader: Failed to read frame time.' );
-
-			}
-
-			// read frame data line by line
-
-			for ( var i = 0; i < numFrames; i ++ ) {
-
-				tokens = nextLine( lines ).split( /[\s]+/ );
-				readFrameData( tokens, i * frameTime, root );
-
-			}
-
-			return list;
-
-		}
-
-		/*
-			Recursively reads data from a single frame into the bone hierarchy.
-			The passed bone hierarchy has to be structured in the same order as the BVH file.
-			keyframe data is stored in bone.frames.
-
-			- data: splitted string array (frame values), values are shift()ed so
-			this should be empty after parsing the whole hierarchy.
-			- frameTime: playback time for this keyframe.
-			- bone: the bone to read frame data from.
-		*/
-		function readFrameData( data, frameTime, bone ) {
-
-			// end sites have no motion data
-
-			if ( bone.type === 'ENDSITE' ) return;
-
-			// add keyframe
-
-			var keyframe = {
-				time: frameTime,
-				position: new THREE.Vector3(),
-				rotation: new THREE.Quaternion()
-			};
-
-			bone.frames.push( keyframe );
-
-			var quat = new THREE.Quaternion();
-
-			var vx = new THREE.Vector3( 1, 0, 0 );
-			var vy = new THREE.Vector3( 0, 1, 0 );
-			var vz = new THREE.Vector3( 0, 0, 1 );
-
-			// parse values for each channel in node
-
-			for ( var i = 0; i < bone.channels.length; i ++ ) {
-
-				switch ( bone.channels[ i ] ) {
-
-					case 'Xposition':
-						keyframe.position.x = parseFloat( data.shift().trim() );
-						break;
-					case 'Yposition':
-						keyframe.position.y = parseFloat( data.shift().trim() );
-						break;
-					case 'Zposition':
-						keyframe.position.z = parseFloat( data.shift().trim() );
-						break;
-					case 'Xrotation':
-						quat.setFromAxisAngle( vx, parseFloat( data.shift().trim() ) * Math.PI / 180 );
-						keyframe.rotation.multiply( quat );
-						break;
-					case 'Yrotation':
-						quat.setFromAxisAngle( vy, parseFloat( data.shift().trim() ) * Math.PI / 180 );
-						keyframe.rotation.multiply( quat );
-						break;
-					case 'Zrotation':
-						quat.setFromAxisAngle( vz, parseFloat( data.shift().trim() ) * Math.PI / 180 );
-						keyframe.rotation.multiply( quat );
-						break;
-					default:
-						console.warn( 'THREE.BVHLoader: Invalid channel type.' );
-
-				}
-
-			}
-
-			// parse child nodes
-
-			for ( var i = 0; i < bone.children.length; i ++ ) {
-
-				readFrameData( data, frameTime, bone.children[ i ] );
-
-			}
-
-		}
-
-		/*
-		 Recursively parses the HIERACHY section of the BVH file
-
-		 - lines: all lines of the file. lines are consumed as we go along.
-		 - firstline: line containing the node type and name e.g. 'JOINT hip'
-		 - list: collects a flat list of nodes
-
-		 returns: a BVH node including children
-		*/
-		function readNode( lines, firstline, list ) {
-
-			var node = { name: '', type: '', frames: [] };
-			list.push( node );
-
-			// parse node type and name
-
-			var tokens = firstline.split( /[\s]+/ );
-
-			if ( tokens[ 0 ].toUpperCase() === 'END' && tokens[ 1 ].toUpperCase() === 'SITE' ) {
-
-				node.type = 'ENDSITE';
-				node.name = 'ENDSITE'; // bvh end sites have no name
-
-			} else {
-
-				node.name = tokens[ 1 ];
-				node.type = tokens[ 0 ].toUpperCase();
-
-			}
-
-			if ( nextLine( lines ) !== '{' ) {
-
-				console.error( 'THREE.BVHLoader: Expected opening { after type & name' );
-
-			}
-
-			// parse OFFSET
-
-			tokens = nextLine( lines ).split( /[\s]+/ );
-
-			if ( tokens[ 0 ] !== 'OFFSET' ) {
-
-				console.error( 'THREE.BVHLoader: Expected OFFSET but got: ' + tokens[ 0 ] );
-
-			}
-
-			if ( tokens.length !== 4 ) {
-
-				console.error( 'THREE.BVHLoader: Invalid number of values for OFFSET.' );
-
-			}
-
-			var offset = new THREE.Vector3(
-				parseFloat( tokens[ 1 ] ),
-				parseFloat( tokens[ 2 ] ),
-				parseFloat( tokens[ 3 ] )
-			);
-
-			if ( isNaN( offset.x ) || isNaN( offset.y ) || isNaN( offset.z ) ) {
-
-				console.error( 'THREE.BVHLoader: Invalid values of OFFSET.' );
-
-			}
-
-			node.offset = offset;
-
-			// parse CHANNELS definitions
-
-			if ( node.type !== 'ENDSITE' ) {
-
-				tokens = nextLine( lines ).split( /[\s]+/ );
-
-				if ( tokens[ 0 ] !== 'CHANNELS' ) {
-
-					console.error( 'THREE.BVHLoader: Expected CHANNELS definition.' );
-
-				}
-
-				var numChannels = parseInt( tokens[ 1 ] );
-				node.channels = tokens.splice( 2, numChannels );
-				node.children = [];
-
-			}
-
-			// read children
-
-			while ( true ) {
-
-				var line = nextLine( lines );
-
-				if ( line === '}' ) {
-
-					return node;
-
-				} else {
-
-					node.children.push( readNode( lines, line, list ) );
-
-				}
-
-			}
-
-		}
-
-		/*
-			recursively converts the internal bvh node structure to a THREE.Bone hierarchy
-
-			source: the bvh root node
-			list: pass an empty array, collects a flat list of all converted THREE.Bones
-
-			returns the root THREE.Bone
-		*/
-		function toTHREEBone( source, list ) {
-
-			var bone = new THREE.Bone();
-			list.push( bone );
-
-			bone.position.add( source.offset );
-			bone.name = source.name;
-
-			if ( source.type !== 'ENDSITE' ) {
-
-				for ( var i = 0; i < source.children.length; i ++ ) {
-
-					bone.add( toTHREEBone( source.children[ i ], list ) );
-
-				}
-
-			}
-
-			return bone;
-
-		}
-
-		/*
-			builds a THREE.AnimationClip from the keyframe data saved in each bone.
-
-			bone: bvh root node
-
-			returns: a THREE.AnimationClip containing position and quaternion tracks
-		*/
-		function toTHREEAnimation( bones ) {
-
-			var tracks = [];
-
-			// create a position and quaternion animation track for each node
-
-			for ( var i = 0; i < bones.length; i ++ ) {
-
-				var bone = bones[ i ];
-
-				if ( bone.type === 'ENDSITE' )
-					continue;
-
-				// track data
-
-				var times = [];
-				var positions = [];
-				var rotations = [];
-
-				for ( var j = 0; j < bone.frames.length; j ++ ) {
-
-					var frame = bone.frames[ j ];
-
-					times.push( frame.time );
-
-					// the animation system animates the position property,
-					// so we have to add the joint offset to all values
-
-					positions.push( frame.position.x + bone.offset.x );
-					positions.push( frame.position.y + bone.offset.y );
-					positions.push( frame.position.z + bone.offset.z );
-
-					rotations.push( frame.rotation.x );
-					rotations.push( frame.rotation.y );
-					rotations.push( frame.rotation.z );
-					rotations.push( frame.rotation.w );
-
-				}
-
-				if ( scope.animateBonePositions ) {
-
-					tracks.push( new THREE.VectorKeyframeTrack( '.bones[' + bone.name + '].position', times, positions ) );
-
-				}
-
-				if ( scope.animateBoneRotations ) {
-
-					tracks.push( new THREE.QuaternionKeyframeTrack( '.bones[' + bone.name + '].quaternion', times, rotations ) );
-
-				}
-
-			}
-
-			return new THREE.AnimationClip( 'animation', - 1, tracks );
-
-		}
-
-		/*
-			returns the next non-empty line in lines
-		*/
-		function nextLine( lines ) {
-
-			var line;
-			// skip empty lines
-			while ( ( line = lines.shift().trim() ).length === 0 ) { }
-			return line;
-
-		}
-
-		var scope = this;
-
-		var lines = text.split( /[\r\n]+/g );
-
-		var bones = readBvh( lines );
-
-		var threeBones = [];
-		toTHREEBone( bones[ 0 ], threeBones );
-
-		var threeClip = toTHREEAnimation( bones );
-
-		return {
-			skeleton: new THREE.Skeleton( threeBones ),
-			clip: threeClip
-		};
-
-	}
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/helpers/PolarGridHelper.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/helpers/PolarGridHelper.d.ts
deleted file mode 100644
index 977c1ca2e9e3691728f4a64239e72a4ed30b3250..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/helpers/PolarGridHelper.d.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-import { LineSegments } from '../objects/LineSegments';
-import { VertexColors } from '../constants.js';
-import { LineBasicMaterial } from '../materials/LineBasicMaterial';
-import { Float32BufferAttribute } from '../core/BufferAttribute';
-import { BufferGeometry } from '../core/BufferGeometry';
-import { Color } from '../math/Color';
-
-export class PolarGridHelper {
-  constructor(
-    radius: number,
-    radials: number,
-    circles: number,
-    divisions: number,
-    color1: Color | string | number | undefined,
-    color2: Color | string | number | undefined
-  );
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/utils.js b/spaces/banana-projects/web3d/node_modules/three/src/utils.js
deleted file mode 100644
index 6783c51ad9f732122506f53acd3329ecbf6192c9..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/utils.js
+++ /dev/null
@@ -1,37 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- */
-
-function arrayMin( array ) {
-
-	if ( array.length === 0 ) return Infinity;
-
-	var min = array[ 0 ];
-
-	for ( var i = 1, l = array.length; i < l; ++ i ) {
-
-		if ( array[ i ] < min ) min = array[ i ];
-
-	}
-
-	return min;
-
-}
-
-function arrayMax( array ) {
-
-	if ( array.length === 0 ) return - Infinity;
-
-	var max = array[ 0 ];
-
-	for ( var i = 1, l = array.length; i < l; ++ i ) {
-
-		if ( array[ i ] > max ) max = array[ i ];
-
-	}
-
-	return max;
-
-}
-
-export { arrayMin, arrayMax };
diff --git a/spaces/beastboy/WizardLM-WizardCoder-15B-V1.0/style.css b/spaces/beastboy/WizardLM-WizardCoder-15B-V1.0/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/beastboy/WizardLM-WizardCoder-15B-V1.0/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/betheredge/air-vibrations/Dockerfile b/spaces/betheredge/air-vibrations/Dockerfile
deleted file mode 100644
index fda1211408df3cebd3cf669145d195919a25cd23..0000000000000000000000000000000000000000
--- a/spaces/betheredge/air-vibrations/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM python:3.8
-
-RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
-    && apt-get -y install --no-install-recommends ffmpeg \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt /tmp/pip-tmp/
-RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
-    && rm -rf /tmp/pip-tmp
-
-COPY src/ /app/
-
-WORKDIR /app
-RUN python app.py
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/docs/tutorials/configs.md b/spaces/brjathu/HMR2.0/vendor/detectron2/docs/tutorials/configs.md
deleted file mode 100644
index 49538d0532994664584460560f4f809ff3a6e6df..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/docs/tutorials/configs.md
+++ /dev/null
@@ -1,62 +0,0 @@
-# Yacs Configs
-
-Detectron2 provides a key-value based config system that can be
-used to obtain standard, common behaviors.
-
-This system uses YAML and [yacs](https://github.com/rbgirshick/yacs).
-Yaml is a very limited language,
-so we do not expect all features in detectron2 to be available through configs.
-If you need something that's not available in the config space,
-please write code using detectron2's API.
-
-With the introduction of a more powerful [LazyConfig system](lazyconfigs.md),
-we no longer add functionality / new keys to the Yacs/Yaml-based config system.
-
-### Basic Usage
-
-Some basic usage of the `CfgNode` object is shown here. See more in [documentation](../modules/config.html#detectron2.config.CfgNode).
-```python
-from detectron2.config import get_cfg
-cfg = get_cfg()    # obtain detectron2's default config
-cfg.xxx = yyy      # add new configs for your own custom components
-cfg.merge_from_file("my_cfg.yaml")   # load values from a file
-
-cfg.merge_from_list(["MODEL.WEIGHTS", "weights.pth"])   # can also load values from a list of str
-print(cfg.dump())  # print formatted configs
-with open("output.yaml", "w") as f:
-  f.write(cfg.dump())   # save config to file
-```
-
-In addition to the basic Yaml syntax, the config file can
-define a `_BASE_: base.yaml` field, which will load a base config file first.
-Values in the base config will be overwritten in sub-configs, if there are any conflicts.
-We provided several base configs for standard model architectures.
-
-Many builtin tools in detectron2 accept command line config overwrite:
-Key-value pairs provided in the command line will overwrite the existing values in the config file.
-For example, [demo.py](../../demo/demo.py) can be used with
-```sh
-./demo.py --config-file config.yaml [--other-options] \
-  --opts MODEL.WEIGHTS /path/to/weights INPUT.MIN_SIZE_TEST 1000
-```
-
-To see a list of available configs in detectron2 and what they mean,
-check [Config References](../modules/config.html#config-references)
-
-### Configs in Projects
-
-A project that lives outside the detectron2 library may define its own configs, which will need to be added
-for the project to be functional, e.g.:
-```python
-from detectron2.projects.point_rend import add_pointrend_config
-cfg = get_cfg()    # obtain detectron2's default config
-add_pointrend_config(cfg)  # add pointrend's default config
-# ... ...
-```
-
-### Best Practice with Configs
-
-1. Treat the configs you write as "code": avoid copying them or duplicating them; use `_BASE_`
-   to share common parts between configs.
-
-2. Keep the configs you write simple: don't include keys that do not affect the experimental setting.
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/doc/RELEASE_2021_06.md b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/doc/RELEASE_2021_06.md
deleted file mode 100644
index fb5ff4facdfaf5559d7be26c49852f4f6bc5495e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/doc/RELEASE_2021_06.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# DensePose CSE with Cycle Losses
-
-This release follows the paper [Neverova et al, 2021]() and
-adds CSE datasets with more annotations, better CSE animal models
-to the model zoo, losses to ensure cycle consistency for models and mesh
-alignment evaluator. In particular:
-
-* [Pixel to shape](../densepose/modeling/losses/cycle_pix2shape.py) and [shape to shape](../densepose/modeling/losses/cycle_shape2shape.py) cycle consistency losses;
-* Mesh alignment [evaluator](../densepose/evaluation/mesh_alignment_evaluator.py);
-* Existing CSE datasets renamed to [ds1_train](https://dl.fbaipublicfiles.com/densepose/annotations/lvis/densepose_lvis_v1_ds1_train_v1.json) and [ds1_val](https://dl.fbaipublicfiles.com/densepose/annotations/lvis/densepose_lvis_v1_ds1_val_v1.json);
-* New CSE datasets [ds2_train](https://dl.fbaipublicfiles.com/densepose/annotations/lvis/densepose_lvis_v1_ds2_train_v1.json) and [ds2_val](https://dl.fbaipublicfiles.com/densepose/annotations/lvis/densepose_lvis_v1_ds2_val_v1.json) added;
-* Better CSE animal models trained with the 16k schedule added to the [model zoo](DENSEPOSE_CSE.md#animal-cse-models).
diff --git a/spaces/cafeai/cafe_aesthetic_demo/app.py b/spaces/cafeai/cafe_aesthetic_demo/app.py
deleted file mode 100644
index ac922ba021c3e3643627bcb10dfdff8159cb7b3d..0000000000000000000000000000000000000000
--- a/spaces/cafeai/cafe_aesthetic_demo/app.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipe_aesthetic = pipeline("image-classification", "cafeai/cafe_aesthetic")
-def aesthetic(input_img):
-    data = pipe_aesthetic(input_img, top_k=2)
-    final = {}
-    for d in data:
-        final[d["label"]] = d["score"]
-    return final
-demo_aesthetic = gr.Interface(fn=aesthetic, inputs=gr.Image(type="pil"), outputs=gr.Label(label="aesthetic"))
-
-pipe_style = pipeline("image-classification", "cafeai/cafe_style")
-def style(input_img):
-    data = pipe_style(input_img, top_k=5)
-    final = {}
-    for d in data:
-        final[d["label"]] = d["score"]
-    return final
-demo_style = gr.Interface(fn=style, inputs=gr.Image(type="pil"), outputs=gr.Label(label="style"))
-
-pipe_waifu = pipeline("image-classification", "cafeai/cafe_waifu")
-def waifu(input_img):
-    data = pipe_waifu(input_img, top_k=5)
-    final = {}
-    for d in data:
-        final[d["label"]] = d["score"]
-    return final
-demo_waifu = gr.Interface(fn=waifu, inputs=gr.Image(type="pil"), outputs=gr.Label(label="waifu"))
-
-gr.Parallel(demo_aesthetic, demo_style, demo_waifu).launch()
diff --git a/spaces/chendl/compositional_test/transformers/scripts/fsmt/convert-allenai-wmt16.sh b/spaces/chendl/compositional_test/transformers/scripts/fsmt/convert-allenai-wmt16.sh
deleted file mode 100644
index 30983c410164f3b6c96b9a1f69d631515614d724..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/scripts/fsmt/convert-allenai-wmt16.sh
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2020 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.
-
-# this script acquires data and converts it to fsmt model
-# it covers:
-# - allenai/wmt16-en-de-dist-12-1
-# - allenai/wmt16-en-de-dist-6-1
-# - allenai/wmt16-en-de-12-1
-
-# this script needs to be run from the top level of the transformers repo
-if [ ! -d "src/transformers" ]; then
-    echo "Error: This script needs to be run from the top of the transformers repo"
-    exit 1
-fi
-
-mkdir data
-
-# get data (run once)
-
-cd data
-gdown 'https://drive.google.com/uc?id=1x_G2cjvM1nW5hjAB8-vWxRqtQTlmIaQU'
-gdown 'https://drive.google.com/uc?id=1oA2aqZlVNj5FarxBlNXEHpBS4lRetTzU'
-gdown 'https://drive.google.com/uc?id=1Wup2D318QYBFPW_NKI1mfP_hXOfmUI9r'
-tar -xvzf trans_ende_12-1_0.2.tar.gz
-tar -xvzf trans_ende-dist_12-1_0.2.tar.gz
-tar -xvzf trans_ende-dist_6-1_0.2.tar.gz
-gdown 'https://drive.google.com/uc?id=1mNufoynJ9-Zy1kJh2TA_lHm2squji0i9'
-gdown 'https://drive.google.com/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj'
-tar -xvzf wmt16.en-de.deep-shallow.dist.tar.gz
-tar -xvzf wmt16.en-de.deep-shallow.tar.gz
-cp wmt16.en-de.deep-shallow/data-bin/dict.*.txt trans_ende_12-1_0.2
-cp wmt16.en-de.deep-shallow.dist/data-bin/dict.*.txt trans_ende-dist_12-1_0.2
-cp wmt16.en-de.deep-shallow.dist/data-bin/dict.*.txt trans_ende-dist_6-1_0.2
-cp wmt16.en-de.deep-shallow/bpecodes trans_ende_12-1_0.2
-cp wmt16.en-de.deep-shallow.dist/bpecodes trans_ende-dist_12-1_0.2
-cp wmt16.en-de.deep-shallow.dist/bpecodes trans_ende-dist_6-1_0.2
-cd -
-
-# run conversions and uploads
-
-PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/trans_ende-dist_12-1_0.2/checkpoint_top5_average.pt --pytorch_dump_folder_path data/wmt16-en-de-dist-12-1
-
-PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/trans_ende-dist_6-1_0.2/checkpoint_top5_average.pt --pytorch_dump_folder_path data/wmt16-en-de-dist-6-1
-
-PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/trans_ende_12-1_0.2/checkpoint_top5_average.pt --pytorch_dump_folder_path data/wmt16-en-de-12-1
-
-
-# upload
-cd data
-transformers-cli upload -y wmt16-en-de-dist-12-1
-transformers-cli upload -y wmt16-en-de-dist-6-1
-transformers-cli upload -y wmt16-en-de-12-1
-cd -
-
-
-# if updating just small files and not the large models, here is a script to generate the right commands:
-perl -le 'for $f (@ARGV) { print qq[transformers-cli upload -y $_/$f --filename $_/$f] for ("wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1")}' vocab-src.json vocab-tgt.json tokenizer_config.json config.json
-# add/remove files as needed
-
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/web_middlewares.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/web_middlewares.py
deleted file mode 100644
index fabcc449a2107211fd99cd59f576a2d855d0e042..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/web_middlewares.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import re
-from typing import TYPE_CHECKING, Awaitable, Callable, Tuple, Type, TypeVar
-
-from .typedefs import Handler
-from .web_exceptions import HTTPPermanentRedirect, _HTTPMove
-from .web_request import Request
-from .web_response import StreamResponse
-from .web_urldispatcher import SystemRoute
-
-__all__ = (
-    "middleware",
-    "normalize_path_middleware",
-)
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .web_app import Application
-
-_Func = TypeVar("_Func")
-
-
-async def _check_request_resolves(request: Request, path: str) -> Tuple[bool, Request]:
-    alt_request = request.clone(rel_url=path)
-
-    match_info = await request.app.router.resolve(alt_request)
-    alt_request._match_info = match_info
-
-    if match_info.http_exception is None:
-        return True, alt_request
-
-    return False, request
-
-
-def middleware(f: _Func) -> _Func:
-    f.__middleware_version__ = 1  # type: ignore[attr-defined]
-    return f
-
-
-_Middleware = Callable[[Request, Handler], Awaitable[StreamResponse]]
-
-
-def normalize_path_middleware(
-    *,
-    append_slash: bool = True,
-    remove_slash: bool = False,
-    merge_slashes: bool = True,
-    redirect_class: Type[_HTTPMove] = HTTPPermanentRedirect,
-) -> _Middleware:
-    """Factory for producing a middleware that normalizes the path of a request.
-
-    Normalizing means:
-        - Add or remove a trailing slash to the path.
-        - Double slashes are replaced by one.
-
-    The middleware returns as soon as it finds a path that resolves
-    correctly. The order if both merge and append/remove are enabled is
-        1) merge slashes
-        2) append/remove slash
-        3) both merge slashes and append/remove slash.
-    If the path resolves with at least one of those conditions, it will
-    redirect to the new path.
-
-    Only one of `append_slash` and `remove_slash` can be enabled. If both
-    are `True` the factory will raise an assertion error
-
-    If `append_slash` is `True` the middleware will append a slash when
-    needed. If a resource is defined with trailing slash and the request
-    comes without it, it will append it automatically.
-
-    If `remove_slash` is `True`, `append_slash` must be `False`. When enabled
-    the middleware will remove trailing slashes and redirect if the resource
-    is defined
-
-    If merge_slashes is True, merge multiple consecutive slashes in the
-    path into one.
-    """
-    correct_configuration = not (append_slash and remove_slash)
-    assert correct_configuration, "Cannot both remove and append slash"
-
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        if isinstance(request.match_info.route, SystemRoute):
-            paths_to_check = []
-            if "?" in request.raw_path:
-                path, query = request.raw_path.split("?", 1)
-                query = "?" + query
-            else:
-                query = ""
-                path = request.raw_path
-
-            if merge_slashes:
-                paths_to_check.append(re.sub("//+", "/", path))
-            if append_slash and not request.path.endswith("/"):
-                paths_to_check.append(path + "/")
-            if remove_slash and request.path.endswith("/"):
-                paths_to_check.append(path[:-1])
-            if merge_slashes and append_slash:
-                paths_to_check.append(re.sub("//+", "/", path + "/"))
-            if merge_slashes and remove_slash:
-                merged_slashes = re.sub("//+", "/", path)
-                paths_to_check.append(merged_slashes[:-1])
-
-            for path in paths_to_check:
-                path = re.sub("^//+", "/", path)  # SECURITY: GHSA-v6wp-4m6f-gcjg
-                resolves, request = await _check_request_resolves(request, path)
-                if resolves:
-                    raise redirect_class(request.raw_path + query)
-
-        return await handler(request)
-
-    return impl
-
-
-def _fix_request_current_app(app: "Application") -> _Middleware:
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        with request.match_info.set_current_app(app):
-            return await handler(request)
-
-    return impl
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/_textwrap.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/_textwrap.py
deleted file mode 100644
index b47dcbd4264e86715adfae1c5124c288b67a983e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/_textwrap.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import textwrap
-import typing as t
-from contextlib import contextmanager
-
-
-class TextWrapper(textwrap.TextWrapper):
-    def _handle_long_word(
-        self,
-        reversed_chunks: t.List[str],
-        cur_line: t.List[str],
-        cur_len: int,
-        width: int,
-    ) -> None:
-        space_left = max(width - cur_len, 1)
-
-        if self.break_long_words:
-            last = reversed_chunks[-1]
-            cut = last[:space_left]
-            res = last[space_left:]
-            cur_line.append(cut)
-            reversed_chunks[-1] = res
-        elif not cur_line:
-            cur_line.append(reversed_chunks.pop())
-
-    @contextmanager
-    def extra_indent(self, indent: str) -> t.Iterator[None]:
-        old_initial_indent = self.initial_indent
-        old_subsequent_indent = self.subsequent_indent
-        self.initial_indent += indent
-        self.subsequent_indent += indent
-
-        try:
-            yield
-        finally:
-            self.initial_indent = old_initial_indent
-            self.subsequent_indent = old_subsequent_indent
-
-    def indent_only(self, text: str) -> str:
-        rv = []
-
-        for idx, line in enumerate(text.splitlines()):
-            indent = self.initial_indent
-
-            if idx > 0:
-                indent = self.subsequent_indent
-
-            rv.append(f"{indent}{line}")
-
-        return "\n".join(rv)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/exceptions.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/exceptions.py
deleted file mode 100644
index 7a8b99c81f1f3a49a05b43e9be2e9607b1a6486b..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/exceptions.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# encoding: utf-8
-
-"""
-Exceptions used with python-docx.
-
-The base exception class is PythonDocxError.
-"""
-
-
-class PythonDocxError(Exception):
-    """
-    Generic error class.
-    """
-
-
-class InvalidSpanError(PythonDocxError):
-    """
-    Raised when an invalid merge region is specified in a request to merge
-    table cells.
-    """
-
-
-class InvalidXmlError(PythonDocxError):
-    """
-    Raised when invalid XML is encountered, such as on attempt to access a
-    missing required child element
-    """
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx2txt/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx2txt/__init__.py
deleted file mode 100644
index 0f7067c3d7a5792f5be66297bfcace6f1afe8681..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx2txt/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .docx2txt import process
-from .docx2txt import process_args
-
-VERSION = '0.8'
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/cookiejar.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/cookiejar.py
deleted file mode 100644
index 6c88b47e3583430e05ea671af5b6da2a557073ec..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/cookiejar.py
+++ /dev/null
@@ -1,415 +0,0 @@
-import asyncio
-import contextlib
-import datetime
-import os  # noqa
-import pathlib
-import pickle
-import re
-from collections import defaultdict
-from http.cookies import BaseCookie, Morsel, SimpleCookie
-from typing import (  # noqa
-    DefaultDict,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Set,
-    Tuple,
-    Union,
-    cast,
-)
-
-from yarl import URL
-
-from .abc import AbstractCookieJar, ClearCookiePredicate
-from .helpers import is_ip_address, next_whole_second
-from .typedefs import LooseCookies, PathLike, StrOrURL
-
-__all__ = ("CookieJar", "DummyCookieJar")
-
-
-CookieItem = Union[str, "Morsel[str]"]
-
-
-class CookieJar(AbstractCookieJar):
-    """Implements cookie storage adhering to RFC 6265."""
-
-    DATE_TOKENS_RE = re.compile(
-        r"[\x09\x20-\x2F\x3B-\x40\x5B-\x60\x7B-\x7E]*"
-        r"(?P<token>[\x00-\x08\x0A-\x1F\d:a-zA-Z\x7F-\xFF]+)"
-    )
-
-    DATE_HMS_TIME_RE = re.compile(r"(\d{1,2}):(\d{1,2}):(\d{1,2})")
-
-    DATE_DAY_OF_MONTH_RE = re.compile(r"(\d{1,2})")
-
-    DATE_MONTH_RE = re.compile(
-        "(jan)|(feb)|(mar)|(apr)|(may)|(jun)|(jul)|" "(aug)|(sep)|(oct)|(nov)|(dec)",
-        re.I,
-    )
-
-    DATE_YEAR_RE = re.compile(r"(\d{2,4})")
-
-    MAX_TIME = datetime.datetime.max.replace(tzinfo=datetime.timezone.utc)
-
-    MAX_32BIT_TIME = datetime.datetime.utcfromtimestamp(2**31 - 1)
-
-    def __init__(
-        self,
-        *,
-        unsafe: bool = False,
-        quote_cookie: bool = True,
-        treat_as_secure_origin: Union[StrOrURL, List[StrOrURL], None] = None,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ) -> None:
-        super().__init__(loop=loop)
-        self._cookies: DefaultDict[Tuple[str, str], SimpleCookie[str]] = defaultdict(
-            SimpleCookie
-        )
-        self._host_only_cookies: Set[Tuple[str, str]] = set()
-        self._unsafe = unsafe
-        self._quote_cookie = quote_cookie
-        if treat_as_secure_origin is None:
-            treat_as_secure_origin = []
-        elif isinstance(treat_as_secure_origin, URL):
-            treat_as_secure_origin = [treat_as_secure_origin.origin()]
-        elif isinstance(treat_as_secure_origin, str):
-            treat_as_secure_origin = [URL(treat_as_secure_origin).origin()]
-        else:
-            treat_as_secure_origin = [
-                URL(url).origin() if isinstance(url, str) else url.origin()
-                for url in treat_as_secure_origin
-            ]
-        self._treat_as_secure_origin = treat_as_secure_origin
-        self._next_expiration = next_whole_second()
-        self._expirations: Dict[Tuple[str, str, str], datetime.datetime] = {}
-        # #4515: datetime.max may not be representable on 32-bit platforms
-        self._max_time = self.MAX_TIME
-        try:
-            self._max_time.timestamp()
-        except OverflowError:
-            self._max_time = self.MAX_32BIT_TIME
-
-    def save(self, file_path: PathLike) -> None:
-        file_path = pathlib.Path(file_path)
-        with file_path.open(mode="wb") as f:
-            pickle.dump(self._cookies, f, pickle.HIGHEST_PROTOCOL)
-
-    def load(self, file_path: PathLike) -> None:
-        file_path = pathlib.Path(file_path)
-        with file_path.open(mode="rb") as f:
-            self._cookies = pickle.load(f)
-
-    def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None:
-        if predicate is None:
-            self._next_expiration = next_whole_second()
-            self._cookies.clear()
-            self._host_only_cookies.clear()
-            self._expirations.clear()
-            return
-
-        to_del = []
-        now = datetime.datetime.now(datetime.timezone.utc)
-        for (domain, path), cookie in self._cookies.items():
-            for name, morsel in cookie.items():
-                key = (domain, path, name)
-                if (
-                    key in self._expirations and self._expirations[key] <= now
-                ) or predicate(morsel):
-                    to_del.append(key)
-
-        for domain, path, name in to_del:
-            self._host_only_cookies.discard((domain, name))
-            key = (domain, path, name)
-            if key in self._expirations:
-                del self._expirations[(domain, path, name)]
-            self._cookies[(domain, path)].pop(name, None)
-
-        next_expiration = min(self._expirations.values(), default=self._max_time)
-        try:
-            self._next_expiration = next_expiration.replace(
-                microsecond=0
-            ) + datetime.timedelta(seconds=1)
-        except OverflowError:
-            self._next_expiration = self._max_time
-
-    def clear_domain(self, domain: str) -> None:
-        self.clear(lambda x: self._is_domain_match(domain, x["domain"]))
-
-    def __iter__(self) -> "Iterator[Morsel[str]]":
-        self._do_expiration()
-        for val in self._cookies.values():
-            yield from val.values()
-
-    def __len__(self) -> int:
-        return sum(1 for i in self)
-
-    def _do_expiration(self) -> None:
-        self.clear(lambda x: False)
-
-    def _expire_cookie(
-        self, when: datetime.datetime, domain: str, path: str, name: str
-    ) -> None:
-        self._next_expiration = min(self._next_expiration, when)
-        self._expirations[(domain, path, name)] = when
-
-    def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None:
-        """Update cookies."""
-        hostname = response_url.raw_host
-
-        if not self._unsafe and is_ip_address(hostname):
-            # Don't accept cookies from IPs
-            return
-
-        if isinstance(cookies, Mapping):
-            cookies = cookies.items()
-
-        for name, cookie in cookies:
-            if not isinstance(cookie, Morsel):
-                tmp: SimpleCookie[str] = SimpleCookie()
-                tmp[name] = cookie  # type: ignore[assignment]
-                cookie = tmp[name]
-
-            domain = cookie["domain"]
-
-            # ignore domains with trailing dots
-            if domain.endswith("."):
-                domain = ""
-                del cookie["domain"]
-
-            if not domain and hostname is not None:
-                # Set the cookie's domain to the response hostname
-                # and set its host-only-flag
-                self._host_only_cookies.add((hostname, name))
-                domain = cookie["domain"] = hostname
-
-            if domain.startswith("."):
-                # Remove leading dot
-                domain = domain[1:]
-                cookie["domain"] = domain
-
-            if hostname and not self._is_domain_match(domain, hostname):
-                # Setting cookies for different domains is not allowed
-                continue
-
-            path = cookie["path"]
-            if not path or not path.startswith("/"):
-                # Set the cookie's path to the response path
-                path = response_url.path
-                if not path.startswith("/"):
-                    path = "/"
-                else:
-                    # Cut everything from the last slash to the end
-                    path = "/" + path[1 : path.rfind("/")]
-                cookie["path"] = path
-
-            max_age = cookie["max-age"]
-            if max_age:
-                try:
-                    delta_seconds = int(max_age)
-                    try:
-                        max_age_expiration = datetime.datetime.now(
-                            datetime.timezone.utc
-                        ) + datetime.timedelta(seconds=delta_seconds)
-                    except OverflowError:
-                        max_age_expiration = self._max_time
-                    self._expire_cookie(max_age_expiration, domain, path, name)
-                except ValueError:
-                    cookie["max-age"] = ""
-
-            else:
-                expires = cookie["expires"]
-                if expires:
-                    expire_time = self._parse_date(expires)
-                    if expire_time:
-                        self._expire_cookie(expire_time, domain, path, name)
-                    else:
-                        cookie["expires"] = ""
-
-            self._cookies[(domain, path)][name] = cookie
-
-        self._do_expiration()
-
-    def filter_cookies(
-        self, request_url: URL = URL()
-    ) -> Union["BaseCookie[str]", "SimpleCookie[str]"]:
-        """Returns this jar's cookies filtered by their attributes."""
-        self._do_expiration()
-        request_url = URL(request_url)
-        filtered: Union["SimpleCookie[str]", "BaseCookie[str]"] = (
-            SimpleCookie() if self._quote_cookie else BaseCookie()
-        )
-        hostname = request_url.raw_host or ""
-        request_origin = URL()
-        with contextlib.suppress(ValueError):
-            request_origin = request_url.origin()
-
-        is_not_secure = (
-            request_url.scheme not in ("https", "wss")
-            and request_origin not in self._treat_as_secure_origin
-        )
-
-        for cookie in self:
-            name = cookie.key
-            domain = cookie["domain"]
-
-            # Send shared cookies
-            if not domain:
-                filtered[name] = cookie.value
-                continue
-
-            if not self._unsafe and is_ip_address(hostname):
-                continue
-
-            if (domain, name) in self._host_only_cookies:
-                if domain != hostname:
-                    continue
-            elif not self._is_domain_match(domain, hostname):
-                continue
-
-            if not self._is_path_match(request_url.path, cookie["path"]):
-                continue
-
-            if is_not_secure and cookie["secure"]:
-                continue
-
-            # It's critical we use the Morsel so the coded_value
-            # (based on cookie version) is preserved
-            mrsl_val = cast("Morsel[str]", cookie.get(cookie.key, Morsel()))
-            mrsl_val.set(cookie.key, cookie.value, cookie.coded_value)
-            filtered[name] = mrsl_val
-
-        return filtered
-
-    @staticmethod
-    def _is_domain_match(domain: str, hostname: str) -> bool:
-        """Implements domain matching adhering to RFC 6265."""
-        if hostname == domain:
-            return True
-
-        if not hostname.endswith(domain):
-            return False
-
-        non_matching = hostname[: -len(domain)]
-
-        if not non_matching.endswith("."):
-            return False
-
-        return not is_ip_address(hostname)
-
-    @staticmethod
-    def _is_path_match(req_path: str, cookie_path: str) -> bool:
-        """Implements path matching adhering to RFC 6265."""
-        if not req_path.startswith("/"):
-            req_path = "/"
-
-        if req_path == cookie_path:
-            return True
-
-        if not req_path.startswith(cookie_path):
-            return False
-
-        if cookie_path.endswith("/"):
-            return True
-
-        non_matching = req_path[len(cookie_path) :]
-
-        return non_matching.startswith("/")
-
-    @classmethod
-    def _parse_date(cls, date_str: str) -> Optional[datetime.datetime]:
-        """Implements date string parsing adhering to RFC 6265."""
-        if not date_str:
-            return None
-
-        found_time = False
-        found_day = False
-        found_month = False
-        found_year = False
-
-        hour = minute = second = 0
-        day = 0
-        month = 0
-        year = 0
-
-        for token_match in cls.DATE_TOKENS_RE.finditer(date_str):
-
-            token = token_match.group("token")
-
-            if not found_time:
-                time_match = cls.DATE_HMS_TIME_RE.match(token)
-                if time_match:
-                    found_time = True
-                    hour, minute, second = (int(s) for s in time_match.groups())
-                    continue
-
-            if not found_day:
-                day_match = cls.DATE_DAY_OF_MONTH_RE.match(token)
-                if day_match:
-                    found_day = True
-                    day = int(day_match.group())
-                    continue
-
-            if not found_month:
-                month_match = cls.DATE_MONTH_RE.match(token)
-                if month_match:
-                    found_month = True
-                    assert month_match.lastindex is not None
-                    month = month_match.lastindex
-                    continue
-
-            if not found_year:
-                year_match = cls.DATE_YEAR_RE.match(token)
-                if year_match:
-                    found_year = True
-                    year = int(year_match.group())
-
-        if 70 <= year <= 99:
-            year += 1900
-        elif 0 <= year <= 69:
-            year += 2000
-
-        if False in (found_day, found_month, found_year, found_time):
-            return None
-
-        if not 1 <= day <= 31:
-            return None
-
-        if year < 1601 or hour > 23 or minute > 59 or second > 59:
-            return None
-
-        return datetime.datetime(
-            year, month, day, hour, minute, second, tzinfo=datetime.timezone.utc
-        )
-
-
-class DummyCookieJar(AbstractCookieJar):
-    """Implements a dummy cookie storage.
-
-    It can be used with the ClientSession when no cookie processing is needed.
-
-    """
-
-    def __init__(self, *, loop: Optional[asyncio.AbstractEventLoop] = None) -> None:
-        super().__init__(loop=loop)
-
-    def __iter__(self) -> "Iterator[Morsel[str]]":
-        while False:
-            yield None
-
-    def __len__(self) -> int:
-        return 0
-
-    def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None:
-        pass
-
-    def clear_domain(self, domain: str) -> None:
-        pass
-
-    def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None:
-        pass
-
-    def filter_cookies(self, request_url: URL) -> "BaseCookie[str]":
-        return SimpleCookie()
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/relativedelta.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/relativedelta.py
deleted file mode 100644
index a9e85f7e6cd7488e6b2f4b249d5cf6af314c3859..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/relativedelta.py
+++ /dev/null
@@ -1,599 +0,0 @@
-# -*- coding: utf-8 -*-
-import datetime
-import calendar
-
-import operator
-from math import copysign
-
-from six import integer_types
-from warnings import warn
-
-from ._common import weekday
-
-MO, TU, WE, TH, FR, SA, SU = weekdays = tuple(weekday(x) for x in range(7))
-
-__all__ = ["relativedelta", "MO", "TU", "WE", "TH", "FR", "SA", "SU"]
-
-
-class relativedelta(object):
-    """
-    The relativedelta type is designed to be applied to an existing datetime and
-    can replace specific components of that datetime, or represents an interval
-    of time.
-
-    It is based on the specification of the excellent work done by M.-A. Lemburg
-    in his
-    `mx.DateTime <https://www.egenix.com/products/python/mxBase/mxDateTime/>`_ extension.
-    However, notice that this type does *NOT* implement the same algorithm as
-    his work. Do *NOT* expect it to behave like mx.DateTime's counterpart.
-
-    There are two different ways to build a relativedelta instance. The
-    first one is passing it two date/datetime classes::
-
-        relativedelta(datetime1, datetime2)
-
-    The second one is passing it any number of the following keyword arguments::
-
-        relativedelta(arg1=x,arg2=y,arg3=z...)
-
-        year, month, day, hour, minute, second, microsecond:
-            Absolute information (argument is singular); adding or subtracting a
-            relativedelta with absolute information does not perform an arithmetic
-            operation, but rather REPLACES the corresponding value in the
-            original datetime with the value(s) in relativedelta.
-
-        years, months, weeks, days, hours, minutes, seconds, microseconds:
-            Relative information, may be negative (argument is plural); adding
-            or subtracting a relativedelta with relative information performs
-            the corresponding arithmetic operation on the original datetime value
-            with the information in the relativedelta.
-
-        weekday: 
-            One of the weekday instances (MO, TU, etc) available in the
-            relativedelta module. These instances may receive a parameter N,
-            specifying the Nth weekday, which could be positive or negative
-            (like MO(+1) or MO(-2)). Not specifying it is the same as specifying
-            +1. You can also use an integer, where 0=MO. This argument is always
-            relative e.g. if the calculated date is already Monday, using MO(1)
-            or MO(-1) won't change the day. To effectively make it absolute, use
-            it in combination with the day argument (e.g. day=1, MO(1) for first
-            Monday of the month).
-
-        leapdays:
-            Will add given days to the date found, if year is a leap
-            year, and the date found is post 28 of february.
-
-        yearday, nlyearday:
-            Set the yearday or the non-leap year day (jump leap days).
-            These are converted to day/month/leapdays information.
-
-    There are relative and absolute forms of the keyword
-    arguments. The plural is relative, and the singular is
-    absolute. For each argument in the order below, the absolute form
-    is applied first (by setting each attribute to that value) and
-    then the relative form (by adding the value to the attribute).
-
-    The order of attributes considered when this relativedelta is
-    added to a datetime is:
-
-    1. Year
-    2. Month
-    3. Day
-    4. Hours
-    5. Minutes
-    6. Seconds
-    7. Microseconds
-
-    Finally, weekday is applied, using the rule described above.
-
-    For example
-
-    >>> from datetime import datetime
-    >>> from dateutil.relativedelta import relativedelta, MO
-    >>> dt = datetime(2018, 4, 9, 13, 37, 0)
-    >>> delta = relativedelta(hours=25, day=1, weekday=MO(1))
-    >>> dt + delta
-    datetime.datetime(2018, 4, 2, 14, 37)
-
-    First, the day is set to 1 (the first of the month), then 25 hours
-    are added, to get to the 2nd day and 14th hour, finally the
-    weekday is applied, but since the 2nd is already a Monday there is
-    no effect.
-
-    """
-
-    def __init__(self, dt1=None, dt2=None,
-                 years=0, months=0, days=0, leapdays=0, weeks=0,
-                 hours=0, minutes=0, seconds=0, microseconds=0,
-                 year=None, month=None, day=None, weekday=None,
-                 yearday=None, nlyearday=None,
-                 hour=None, minute=None, second=None, microsecond=None):
-
-        if dt1 and dt2:
-            # datetime is a subclass of date. So both must be date
-            if not (isinstance(dt1, datetime.date) and
-                    isinstance(dt2, datetime.date)):
-                raise TypeError("relativedelta only diffs datetime/date")
-
-            # We allow two dates, or two datetimes, so we coerce them to be
-            # of the same type
-            if (isinstance(dt1, datetime.datetime) !=
-                    isinstance(dt2, datetime.datetime)):
-                if not isinstance(dt1, datetime.datetime):
-                    dt1 = datetime.datetime.fromordinal(dt1.toordinal())
-                elif not isinstance(dt2, datetime.datetime):
-                    dt2 = datetime.datetime.fromordinal(dt2.toordinal())
-
-            self.years = 0
-            self.months = 0
-            self.days = 0
-            self.leapdays = 0
-            self.hours = 0
-            self.minutes = 0
-            self.seconds = 0
-            self.microseconds = 0
-            self.year = None
-            self.month = None
-            self.day = None
-            self.weekday = None
-            self.hour = None
-            self.minute = None
-            self.second = None
-            self.microsecond = None
-            self._has_time = 0
-
-            # Get year / month delta between the two
-            months = (dt1.year - dt2.year) * 12 + (dt1.month - dt2.month)
-            self._set_months(months)
-
-            # Remove the year/month delta so the timedelta is just well-defined
-            # time units (seconds, days and microseconds)
-            dtm = self.__radd__(dt2)
-
-            # If we've overshot our target, make an adjustment
-            if dt1 < dt2:
-                compare = operator.gt
-                increment = 1
-            else:
-                compare = operator.lt
-                increment = -1
-
-            while compare(dt1, dtm):
-                months += increment
-                self._set_months(months)
-                dtm = self.__radd__(dt2)
-
-            # Get the timedelta between the "months-adjusted" date and dt1
-            delta = dt1 - dtm
-            self.seconds = delta.seconds + delta.days * 86400
-            self.microseconds = delta.microseconds
-        else:
-            # Check for non-integer values in integer-only quantities
-            if any(x is not None and x != int(x) for x in (years, months)):
-                raise ValueError("Non-integer years and months are "
-                                 "ambiguous and not currently supported.")
-
-            # Relative information
-            self.years = int(years)
-            self.months = int(months)
-            self.days = days + weeks * 7
-            self.leapdays = leapdays
-            self.hours = hours
-            self.minutes = minutes
-            self.seconds = seconds
-            self.microseconds = microseconds
-
-            # Absolute information
-            self.year = year
-            self.month = month
-            self.day = day
-            self.hour = hour
-            self.minute = minute
-            self.second = second
-            self.microsecond = microsecond
-
-            if any(x is not None and int(x) != x
-                   for x in (year, month, day, hour,
-                             minute, second, microsecond)):
-                # For now we'll deprecate floats - later it'll be an error.
-                warn("Non-integer value passed as absolute information. " +
-                     "This is not a well-defined condition and will raise " +
-                     "errors in future versions.", DeprecationWarning)
-
-            if isinstance(weekday, integer_types):
-                self.weekday = weekdays[weekday]
-            else:
-                self.weekday = weekday
-
-            yday = 0
-            if nlyearday:
-                yday = nlyearday
-            elif yearday:
-                yday = yearday
-                if yearday > 59:
-                    self.leapdays = -1
-            if yday:
-                ydayidx = [31, 59, 90, 120, 151, 181, 212,
-                           243, 273, 304, 334, 366]
-                for idx, ydays in enumerate(ydayidx):
-                    if yday <= ydays:
-                        self.month = idx+1
-                        if idx == 0:
-                            self.day = yday
-                        else:
-                            self.day = yday-ydayidx[idx-1]
-                        break
-                else:
-                    raise ValueError("invalid year day (%d)" % yday)
-
-        self._fix()
-
-    def _fix(self):
-        if abs(self.microseconds) > 999999:
-            s = _sign(self.microseconds)
-            div, mod = divmod(self.microseconds * s, 1000000)
-            self.microseconds = mod * s
-            self.seconds += div * s
-        if abs(self.seconds) > 59:
-            s = _sign(self.seconds)
-            div, mod = divmod(self.seconds * s, 60)
-            self.seconds = mod * s
-            self.minutes += div * s
-        if abs(self.minutes) > 59:
-            s = _sign(self.minutes)
-            div, mod = divmod(self.minutes * s, 60)
-            self.minutes = mod * s
-            self.hours += div * s
-        if abs(self.hours) > 23:
-            s = _sign(self.hours)
-            div, mod = divmod(self.hours * s, 24)
-            self.hours = mod * s
-            self.days += div * s
-        if abs(self.months) > 11:
-            s = _sign(self.months)
-            div, mod = divmod(self.months * s, 12)
-            self.months = mod * s
-            self.years += div * s
-        if (self.hours or self.minutes or self.seconds or self.microseconds
-                or self.hour is not None or self.minute is not None or
-                self.second is not None or self.microsecond is not None):
-            self._has_time = 1
-        else:
-            self._has_time = 0
-
-    @property
-    def weeks(self):
-        return int(self.days / 7.0)
-
-    @weeks.setter
-    def weeks(self, value):
-        self.days = self.days - (self.weeks * 7) + value * 7
-
-    def _set_months(self, months):
-        self.months = months
-        if abs(self.months) > 11:
-            s = _sign(self.months)
-            div, mod = divmod(self.months * s, 12)
-            self.months = mod * s
-            self.years = div * s
-        else:
-            self.years = 0
-
-    def normalized(self):
-        """
-        Return a version of this object represented entirely using integer
-        values for the relative attributes.
-
-        >>> relativedelta(days=1.5, hours=2).normalized()
-        relativedelta(days=+1, hours=+14)
-
-        :return:
-            Returns a :class:`dateutil.relativedelta.relativedelta` object.
-        """
-        # Cascade remainders down (rounding each to roughly nearest microsecond)
-        days = int(self.days)
-
-        hours_f = round(self.hours + 24 * (self.days - days), 11)
-        hours = int(hours_f)
-
-        minutes_f = round(self.minutes + 60 * (hours_f - hours), 10)
-        minutes = int(minutes_f)
-
-        seconds_f = round(self.seconds + 60 * (minutes_f - minutes), 8)
-        seconds = int(seconds_f)
-
-        microseconds = round(self.microseconds + 1e6 * (seconds_f - seconds))
-
-        # Constructor carries overflow back up with call to _fix()
-        return self.__class__(years=self.years, months=self.months,
-                              days=days, hours=hours, minutes=minutes,
-                              seconds=seconds, microseconds=microseconds,
-                              leapdays=self.leapdays, year=self.year,
-                              month=self.month, day=self.day,
-                              weekday=self.weekday, hour=self.hour,
-                              minute=self.minute, second=self.second,
-                              microsecond=self.microsecond)
-
-    def __add__(self, other):
-        if isinstance(other, relativedelta):
-            return self.__class__(years=other.years + self.years,
-                                 months=other.months + self.months,
-                                 days=other.days + self.days,
-                                 hours=other.hours + self.hours,
-                                 minutes=other.minutes + self.minutes,
-                                 seconds=other.seconds + self.seconds,
-                                 microseconds=(other.microseconds +
-                                               self.microseconds),
-                                 leapdays=other.leapdays or self.leapdays,
-                                 year=(other.year if other.year is not None
-                                       else self.year),
-                                 month=(other.month if other.month is not None
-                                        else self.month),
-                                 day=(other.day if other.day is not None
-                                      else self.day),
-                                 weekday=(other.weekday if other.weekday is not None
-                                          else self.weekday),
-                                 hour=(other.hour if other.hour is not None
-                                       else self.hour),
-                                 minute=(other.minute if other.minute is not None
-                                         else self.minute),
-                                 second=(other.second if other.second is not None
-                                         else self.second),
-                                 microsecond=(other.microsecond if other.microsecond
-                                              is not None else
-                                              self.microsecond))
-        if isinstance(other, datetime.timedelta):
-            return self.__class__(years=self.years,
-                                  months=self.months,
-                                  days=self.days + other.days,
-                                  hours=self.hours,
-                                  minutes=self.minutes,
-                                  seconds=self.seconds + other.seconds,
-                                  microseconds=self.microseconds + other.microseconds,
-                                  leapdays=self.leapdays,
-                                  year=self.year,
-                                  month=self.month,
-                                  day=self.day,
-                                  weekday=self.weekday,
-                                  hour=self.hour,
-                                  minute=self.minute,
-                                  second=self.second,
-                                  microsecond=self.microsecond)
-        if not isinstance(other, datetime.date):
-            return NotImplemented
-        elif self._has_time and not isinstance(other, datetime.datetime):
-            other = datetime.datetime.fromordinal(other.toordinal())
-        year = (self.year or other.year)+self.years
-        month = self.month or other.month
-        if self.months:
-            assert 1 <= abs(self.months) <= 12
-            month += self.months
-            if month > 12:
-                year += 1
-                month -= 12
-            elif month < 1:
-                year -= 1
-                month += 12
-        day = min(calendar.monthrange(year, month)[1],
-                  self.day or other.day)
-        repl = {"year": year, "month": month, "day": day}
-        for attr in ["hour", "minute", "second", "microsecond"]:
-            value = getattr(self, attr)
-            if value is not None:
-                repl[attr] = value
-        days = self.days
-        if self.leapdays and month > 2 and calendar.isleap(year):
-            days += self.leapdays
-        ret = (other.replace(**repl)
-               + datetime.timedelta(days=days,
-                                    hours=self.hours,
-                                    minutes=self.minutes,
-                                    seconds=self.seconds,
-                                    microseconds=self.microseconds))
-        if self.weekday:
-            weekday, nth = self.weekday.weekday, self.weekday.n or 1
-            jumpdays = (abs(nth) - 1) * 7
-            if nth > 0:
-                jumpdays += (7 - ret.weekday() + weekday) % 7
-            else:
-                jumpdays += (ret.weekday() - weekday) % 7
-                jumpdays *= -1
-            ret += datetime.timedelta(days=jumpdays)
-        return ret
-
-    def __radd__(self, other):
-        return self.__add__(other)
-
-    def __rsub__(self, other):
-        return self.__neg__().__radd__(other)
-
-    def __sub__(self, other):
-        if not isinstance(other, relativedelta):
-            return NotImplemented   # In case the other object defines __rsub__
-        return self.__class__(years=self.years - other.years,
-                             months=self.months - other.months,
-                             days=self.days - other.days,
-                             hours=self.hours - other.hours,
-                             minutes=self.minutes - other.minutes,
-                             seconds=self.seconds - other.seconds,
-                             microseconds=self.microseconds - other.microseconds,
-                             leapdays=self.leapdays or other.leapdays,
-                             year=(self.year if self.year is not None
-                                   else other.year),
-                             month=(self.month if self.month is not None else
-                                    other.month),
-                             day=(self.day if self.day is not None else
-                                  other.day),
-                             weekday=(self.weekday if self.weekday is not None else
-                                      other.weekday),
-                             hour=(self.hour if self.hour is not None else
-                                   other.hour),
-                             minute=(self.minute if self.minute is not None else
-                                     other.minute),
-                             second=(self.second if self.second is not None else
-                                     other.second),
-                             microsecond=(self.microsecond if self.microsecond
-                                          is not None else
-                                          other.microsecond))
-
-    def __abs__(self):
-        return self.__class__(years=abs(self.years),
-                              months=abs(self.months),
-                              days=abs(self.days),
-                              hours=abs(self.hours),
-                              minutes=abs(self.minutes),
-                              seconds=abs(self.seconds),
-                              microseconds=abs(self.microseconds),
-                              leapdays=self.leapdays,
-                              year=self.year,
-                              month=self.month,
-                              day=self.day,
-                              weekday=self.weekday,
-                              hour=self.hour,
-                              minute=self.minute,
-                              second=self.second,
-                              microsecond=self.microsecond)
-
-    def __neg__(self):
-        return self.__class__(years=-self.years,
-                             months=-self.months,
-                             days=-self.days,
-                             hours=-self.hours,
-                             minutes=-self.minutes,
-                             seconds=-self.seconds,
-                             microseconds=-self.microseconds,
-                             leapdays=self.leapdays,
-                             year=self.year,
-                             month=self.month,
-                             day=self.day,
-                             weekday=self.weekday,
-                             hour=self.hour,
-                             minute=self.minute,
-                             second=self.second,
-                             microsecond=self.microsecond)
-
-    def __bool__(self):
-        return not (not self.years and
-                    not self.months and
-                    not self.days and
-                    not self.hours and
-                    not self.minutes and
-                    not self.seconds and
-                    not self.microseconds and
-                    not self.leapdays and
-                    self.year is None and
-                    self.month is None and
-                    self.day is None and
-                    self.weekday is None and
-                    self.hour is None and
-                    self.minute is None and
-                    self.second is None and
-                    self.microsecond is None)
-    # Compatibility with Python 2.x
-    __nonzero__ = __bool__
-
-    def __mul__(self, other):
-        try:
-            f = float(other)
-        except TypeError:
-            return NotImplemented
-
-        return self.__class__(years=int(self.years * f),
-                             months=int(self.months * f),
-                             days=int(self.days * f),
-                             hours=int(self.hours * f),
-                             minutes=int(self.minutes * f),
-                             seconds=int(self.seconds * f),
-                             microseconds=int(self.microseconds * f),
-                             leapdays=self.leapdays,
-                             year=self.year,
-                             month=self.month,
-                             day=self.day,
-                             weekday=self.weekday,
-                             hour=self.hour,
-                             minute=self.minute,
-                             second=self.second,
-                             microsecond=self.microsecond)
-
-    __rmul__ = __mul__
-
-    def __eq__(self, other):
-        if not isinstance(other, relativedelta):
-            return NotImplemented
-        if self.weekday or other.weekday:
-            if not self.weekday or not other.weekday:
-                return False
-            if self.weekday.weekday != other.weekday.weekday:
-                return False
-            n1, n2 = self.weekday.n, other.weekday.n
-            if n1 != n2 and not ((not n1 or n1 == 1) and (not n2 or n2 == 1)):
-                return False
-        return (self.years == other.years and
-                self.months == other.months and
-                self.days == other.days and
-                self.hours == other.hours and
-                self.minutes == other.minutes and
-                self.seconds == other.seconds and
-                self.microseconds == other.microseconds and
-                self.leapdays == other.leapdays and
-                self.year == other.year and
-                self.month == other.month and
-                self.day == other.day and
-                self.hour == other.hour and
-                self.minute == other.minute and
-                self.second == other.second and
-                self.microsecond == other.microsecond)
-
-    def __hash__(self):
-        return hash((
-            self.weekday,
-            self.years,
-            self.months,
-            self.days,
-            self.hours,
-            self.minutes,
-            self.seconds,
-            self.microseconds,
-            self.leapdays,
-            self.year,
-            self.month,
-            self.day,
-            self.hour,
-            self.minute,
-            self.second,
-            self.microsecond,
-        ))
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __div__(self, other):
-        try:
-            reciprocal = 1 / float(other)
-        except TypeError:
-            return NotImplemented
-
-        return self.__mul__(reciprocal)
-
-    __truediv__ = __div__
-
-    def __repr__(self):
-        l = []
-        for attr in ["years", "months", "days", "leapdays",
-                     "hours", "minutes", "seconds", "microseconds"]:
-            value = getattr(self, attr)
-            if value:
-                l.append("{attr}={value:+g}".format(attr=attr, value=value))
-        for attr in ["year", "month", "day", "weekday",
-                     "hour", "minute", "second", "microsecond"]:
-            value = getattr(self, attr)
-            if value is not None:
-                l.append("{attr}={value}".format(attr=attr, value=repr(value)))
-        return "{classname}({attrs})".format(classname=self.__class__.__name__,
-                                             attrs=", ".join(l))
-
-
-def _sign(x):
-    return int(copysign(1, x))
-
-# vim:ts=4:sw=4:et
diff --git a/spaces/cointegrated/toxic-classifier-ru/app.py b/spaces/cointegrated/toxic-classifier-ru/app.py
deleted file mode 100644
index 05104427b72decac8a0f9c1d030348c726ad3dbe..0000000000000000000000000000000000000000
--- a/spaces/cointegrated/toxic-classifier-ru/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import pandas as pd
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-model_checkpoint = 'cointegrated/rubert-tiny-toxicity'
-tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
-if torch.cuda.is_available():
-    model.cuda()
-
-
-def text2toxicity(text, aggregate=True):
-    """ Calculate toxicity of a text (if aggregate=True) or a vector of toxicity aspects (if aggregate=False)"""
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
-        proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()
-    if isinstance(text, str):
-        proba = proba[0]
-    if aggregate:
-        return 1 - proba.T[0] * (1 - proba.T[-1])
-    return proba
-
-
-text = st.text_area('Введите текст', value='Пороть надо таких придурков!')
-proba = text2toxicity(text, aggregate=False)
-s = pd.Series(
-    proba.tolist() + [proba[0] * (1 - proba[-1])],
-    index=[
-        'Стиль НЕтоксичный',
-        'Есть оскорбление',
-        'Есть непотребство',
-        'Есть угроза',
-        'Смысл текста неприемлемый',
-        'Текст - ОК'
-    ],
-    name='Оценка вероятности'
-)
-s
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/idct.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/idct.h
deleted file mode 100644
index 6c79a69c5fbf27dea033ac0b08df2ee0fe1631b6..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/idct.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_ARM_IDCT_H
-#define AVCODEC_ARM_IDCT_H
-
-#include <stddef.h>
-#include <stdint.h>
-
-void ff_j_rev_dct_arm(int16_t *data);
-
-void ff_simple_idct_arm(int16_t *data);
-
-void ff_simple_idct_armv5te(int16_t *data);
-void ff_simple_idct_put_armv5te(uint8_t *dest, ptrdiff_t line_size, int16_t *data);
-void ff_simple_idct_add_armv5te(uint8_t *dest, ptrdiff_t line_size, int16_t *data);
-
-void ff_simple_idct_armv6(int16_t *data);
-void ff_simple_idct_put_armv6(uint8_t *dest, ptrdiff_t line_size, int16_t *data);
-void ff_simple_idct_add_armv6(uint8_t *dest, ptrdiff_t line_size, int16_t *data);
-
-void ff_simple_idct_neon(int16_t *data);
-void ff_simple_idct_put_neon(uint8_t *dest, ptrdiff_t line_size, int16_t *data);
-void ff_simple_idct_add_neon(uint8_t *dest, ptrdiff_t line_size, int16_t *data);
-
-#endif /* AVCODEC_ARM_IDCT_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/copy_block.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/copy_block.h
deleted file mode 100644
index 393d45578e78c5bc4a4bcb36471d386684e121e3..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/copy_block.h
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_COPY_BLOCK_H
-#define AVCODEC_COPY_BLOCK_H
-
-#include <stddef.h>
-#include <stdint.h>
-
-#include "libavutil/intreadwrite.h"
-
-static inline void copy_block2(uint8_t *dst, const uint8_t *src, ptrdiff_t dstStride, ptrdiff_t srcStride, int h)
-{
-    int i;
-    for (i = 0; i < h; i++) {
-        AV_COPY16U(dst, src);
-        dst += dstStride;
-        src += srcStride;
-    }
-}
-
-static inline void copy_block4(uint8_t *dst, const uint8_t *src, ptrdiff_t dstStride, ptrdiff_t srcStride, int h)
-{
-    int i;
-    for (i = 0; i < h; i++) {
-        AV_COPY32U(dst, src);
-        dst += dstStride;
-        src += srcStride;
-    }
-}
-
-static inline void copy_block8(uint8_t *dst, const uint8_t *src, ptrdiff_t dstStride, ptrdiff_t srcStride, int h)
-{
-    int i;
-    for (i = 0; i < h; i++) {
-        AV_COPY64U(dst, src);
-        dst += dstStride;
-        src += srcStride;
-    }
-}
-
-static inline void copy_block9(uint8_t *dst, const uint8_t *src, ptrdiff_t dstStride, ptrdiff_t srcStride, int h)
-{
-    int i;
-    for (i = 0; i < h; i++) {
-        AV_COPY64U(dst, src);
-        dst[8] = src[8];
-        dst   += dstStride;
-        src   += srcStride;
-    }
-}
-
-static inline void copy_block16(uint8_t *dst, const uint8_t *src, ptrdiff_t dstStride, ptrdiff_t srcStride, int h)
-{
-    int i;
-    for (i = 0; i < h; i++) {
-        AV_COPY128U(dst, src);
-        dst += dstStride;
-        src += srcStride;
-    }
-}
-
-static inline void copy_block17(uint8_t *dst, const uint8_t *src, ptrdiff_t dstStride, ptrdiff_t srcStride, int h)
-{
-    int i;
-    for (i = 0; i < h; i++) {
-        AV_COPY128U(dst, src);
-        dst[16] = src[16];
-        dst    += dstStride;
-        src    += srcStride;
-    }
-}
-
-#endif /* AVCODEC_COPY_BLOCK_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/h264chroma_lasx.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/h264chroma_lasx.h
deleted file mode 100644
index 633752035e0ce4b8d3b1d51355b3aa6d0b9b2f98..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/h264chroma_lasx.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2020 Loongson Technology Corporation Limited
- * Contributed by Shiyou Yin <yinshiyou-hf@loongson.cn>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_LOONGARCH_H264CHROMA_LASX_H
-#define AVCODEC_LOONGARCH_H264CHROMA_LASX_H
-
-#include <stdint.h>
-#include <stddef.h>
-#include "libavcodec/h264.h"
-
-void ff_put_h264_chroma_mc4_lasx(uint8_t *dst, const uint8_t *src, ptrdiff_t stride,
-        int h, int x, int y);
-void ff_put_h264_chroma_mc8_lasx(uint8_t *dst, const uint8_t *src, ptrdiff_t stride,
-        int h, int x, int y);
-void ff_avg_h264_chroma_mc8_lasx(uint8_t *dst, const uint8_t *src, ptrdiff_t stride,
-        int h, int x, int y);
-
-#endif /* AVCODEC_LOONGARCH_H264CHROMA_LASX_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h263dsp_mips.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h263dsp_mips.h
deleted file mode 100644
index f225ee563efd7e2dfc5a84f43b86a9f1a5f1ab62..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h263dsp_mips.h
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Copyright (c) 2015 Manojkumar Bhosale (Manojkumar.Bhosale@imgtec.com)
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_MIPS_H263DSP_MIPS_H
-#define AVCODEC_MIPS_H263DSP_MIPS_H
-
-#include "libavcodec/mpegvideo.h"
-
-void ff_h263_h_loop_filter_msa(uint8_t *src, int stride, int q_scale);
-void ff_h263_v_loop_filter_msa(uint8_t *src, int stride, int q_scale);
-void ff_dct_unquantize_mpeg2_inter_msa(MpegEncContext *s, int16_t *block,
-                                       int32_t index, int32_t q_scale);
-void ff_dct_unquantize_h263_inter_msa(MpegEncContext *s, int16_t *block,
-                                      int32_t index, int32_t q_scale);
-void ff_dct_unquantize_h263_intra_msa(MpegEncContext *s, int16_t *block,
-                                      int32_t index, int32_t q_scale);
-int ff_pix_sum_msa(const uint8_t *pix, int line_size);
-
-#endif  // #ifndef AVCODEC_MIPS_H263DSP_MIPS_H
diff --git a/spaces/congsaPfin/Manga-OCR/LINK.md b/spaces/congsaPfin/Manga-OCR/LINK.md
deleted file mode 100644
index 6ff1bcf94082c8748ebed6ac8c2d74440f4165d5..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/LINK.md
+++ /dev/null
@@ -1,98 +0,0 @@
-## Corona Render Crack 3ds Max
-
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- 
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- 
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-
-
-
- 
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- 
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- 
-
- 
-
- Here is a possible title and article with html formatting for the keyword "Corona Render Crack 3ds Max":  
-
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-
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-
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-
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-1. Go to [https://corona-renderer.com/download/](https://corona-renderer.com/download/) and click on "Download Trial".
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-
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-
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-
-If you like Corona Render and want to use it beyond the trial period, you will need to purchase a license from the official website[^1^]. A license of Corona Render gives you access to both Corona Render for 3ds Max and Corona Render for Cinema 4D, as well as free updates and support. You can choose between two types of licenses:
-
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-
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/PUBG Mobile Magic Bullet Hack APK Download The Ultimate Guide.md b/spaces/congsaPfin/Manga-OCR/logs/PUBG Mobile Magic Bullet Hack APK Download The Ultimate Guide.md
deleted file mode 100644
index 4e665d4188e6f95535f6062529d998de75d35126..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/PUBG Mobile Magic Bullet Hack APK Download The Ultimate Guide.md	
+++ /dev/null
@@ -1,109 +0,0 @@
-
-<h1>PUBG Mobile Magic Bullet Hack APK Download: Everything You Need to Know</h1>
-<p>PUBG Mobile is one of the most popular and addictive battle royale games in the world, with millions of players competing for the ultimate survival. However, not everyone plays fair and some players resort to using hacks and cheats to gain an unfair advantage over others. One of the most notorious hacks in PUBG Mobile is the magic bullet hack, which allows the user to hit any enemy with perfect accuracy, regardless of distance, recoil, or obstacles. In this article, we will tell you everything you need to know about the PUBG Mobile magic bullet hack APK download, including what it is, how it works, why people use it, what are the risks and consequences, how to download it, and how to avoid getting banned by PUBG Mobile.</p>
- <h2>What is PUBG Mobile Magic Bullet Hack?</h2>
-<p>The PUBG Mobile magic bullet hack is a type of cheat that modifies the game's code to make every bullet fired by the user hit the target, no matter where they aim. This means that the user can shoot at any enemy without having to worry about aiming, recoil, bullet drop, or obstacles. The hack also gives the user unlimited ammo and instant reloads, making them unstoppable in combat. The hack is usually activated by pressing a certain button or key on the device.</p>
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- <h3>How does it work?</h3>
-<p>The PUBG Mobile magic bullet hack works by injecting a modified APK file into the game's data folder. The APK file contains a script that alters the game's code and enables the hack. The script runs in the background and intercepts the data sent between the game server and the user's device. It then modifies the data to make every bullet fired by the user hit the target, regardless of any factors that would normally affect the accuracy. The script also gives the user unlimited ammo and instant reloads by changing the values of these variables in the game's code.</p>
- <h3>Why do people use it?</h3>
-<p>People use the PUBG Mobile magic bullet hack for various reasons, such as:</p>
-<ul>
-<li>To have fun and enjoy the game without having to worry about skills or strategies.</li>
-<li>To troll and annoy other players and ruin their gaming experience.</li>
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- <h3>Step 1: Find a reliable source</h3>
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- <h3>Step 4: Launch the game and enjoy the hack</h3>
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- <h2>How to avoid getting banned by PUBG Mobile?</h2>
-<p>While using the PUBG Mobile magic bullet hack may seem fun and exciting, it also comes with a high risk of getting banned by PUBG Mobile's anti-cheat system. PUBG Mobile has a zero-tolerance policy for cheating and hacking, and it constantly monitors and detects any suspicious or abnormal activities in the game. If you are caught using the magic bullet hack or any other cheat, you will face severe consequences, such as temporary or permanent ban, account deletion, legal action, or even criminal charges. Therefore, if you want to avoid getting banned by PUBG Mobile, you should follow these tips:</p>
- <h3>Use a VPN service</h3>
-<p>A VPN service is a tool that allows you to change your IP address and location when accessing the internet. This can help you hide your identity and location from PUBG Mobile's anti-cheat system and avoid getting detected or traced. However, not all VPN services are reliable or safe, so you should choose one that has good reviews, ratings, features, and security. You should also avoid using free or public VPN services, as they may be slow, unstable, or compromised.</p>
- <h3>Use a guest account or a fake account</h3>
-<p>A guest account or a fake account is an account that is not linked to your personal or social media information. This can help you protect your main account from getting banned by PUBG Mobile's anti-cheat system and avoid losing your progress, items, or reputation. However, you should not use your real name, email address, phone number, or photo when creating a guest account or a fake account, as they may be used to identify or track you.</p>
- <h3>Do not abuse the hack or be too obvious</h3>
-<p>Abusing the hack or being too obvious means using the hack too frequently or too blatantly in the game. This can help you attract attention and suspicion from other players or spectators, who may report or expose you to PUBG Mobile's anti-cheat system or support team. Therefore, you should use the hack sparingly and discreetly in the game. For example, you should not use the hack in every match or every situation; you should not kill every enemy in one shot or from across the map; you should not shoot through walls or objects; and you should not brag or boast about your hack in the chat or voice communication.</p>
- <h3>Do not play in ranked matches or tournaments</h3>
-<p>Ranked matches or tournaments are competitive modes in PUBG Mobile that have higher stakes and rewards than casual matches. They also have stricter rules and regulations, and more vigilant and active anti-cheat systems. Therefore, you should avoid playing in ranked matches or tournaments when using the hack, as you will have a higher chance of getting caught and banned by PUBG Mobile's anti-cheat system. You will also face more backlash and criticism from other players or the community, who may consider your hack as a form of cheating or disrespect.</p>
- <h2>Conclusion</h2>
-<p>PUBG Mobile is a fun and exciting game that can provide hours of entertainment and challenge. However, some players choose to use hacks and cheats to gain an unfair advantage over others, such as the PUBG Mobile magic bullet hack. This hack allows the user to hit any enemy with perfect accuracy, regardless of distance, recoil, or obstacles. It also gives the user unlimited ammo and instant reloads. However, using this hack is not only unethical and unfair, but also risky and illegal. It can result in getting detected and banned by PUBG Mobile's anti-cheat system, getting reported and exposed by other players or spectators, getting infected by malware or viruses from downloading untrusted or fake APK files, getting scammed or hacked by malicious websites or apps, or getting sued or fined by PUBG Mobile's developers or publishers. Therefore, we do not recommend using this hack or any other cheat in PUBG Mobile. Instead, we suggest playing the game fairly and honestly, improving your skills and strategies, and enjoying the game as it is meant to be played.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about the PUBG Mobile magic bullet hack APK download:</p>
- <h4>Q: Is the PUBG Mobile magic bullet hack APK free?</h4>
-<p>A: The PUBG Mobile magic bullet hack APK may be free or paid, depending on the source you download it from. However, even if it is free, it may come with hidden costs or risks, such as malware, viruses, scams, hacks, bans, lawsuits, or fines.</p>
- <h4>Q: Is the PUBG Mobile magic bullet hack APK safe?</h4>
-<p>A: The PUBG Mobile magic bullet hack APK is not safe, as it can expose your device to potential security risks and vulnerabilities. It can also expose your account to potential detection and ban by PUBG Mobile's anti-cheat system. It can also expose your personal or financial information to potential theft or fraud by malicious websites or apps.</p>
- <h4>Q: Is the PUBG Mobile magic bullet hack APK legal?</h4>
-<p>A: The PUBG Mobile magic bullet hack APK is not legal, as it violates PUBG Mobile's terms of service and intellectual property rights. It also violates the laws and regulations of some countries or regions that prohibit cheating or hacking in online games. It can result in legal action or criminal charges by PUBG Mobile's developers or publishers.</p>
- <h4>Q: How can I uninstall the PUBG Mobile magic bullet hack APK?</h4>
-<p>A: You can uninstall the PUBG Mobile magic bullet hack APK by following these steps:</p>
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-<li>Go to your device's settings, then apps, then PUBG Mobile.</li>
-<li>Tap on uninstall and confirm your action.</li>
-<li>Go to your device's file manager and delete the APK file you downloaded.</li>
-<li>Go to your device's settings, then security, then unknown sources, and toggle it off.</li>
-<li>Restart your device and check if the hack is gone.</li>
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- <h4>Q: Where can I find more information about the PUBG Mobile magic bullet hack APK?</h4>
-<p>A: You can find more information about the PUBG Mobile magic bullet hack APK by searching online, visiting forums, blogs, social media, or websites that offer such hacks. However, you should be careful and cautious when doing so, as many of these sources may be unreliable, untrustworthy, or harmful.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Play Spider Solitaire Online or Offline with Amazing Graphics.md b/spaces/congsaPfin/Manga-OCR/logs/Play Spider Solitaire Online or Offline with Amazing Graphics.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/Play Spider Solitaire Online or Offline with Amazing Graphics.md	
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-<h1>Solitario Spider Download: How to Play the Classic Card Game on Your Device</h1>
-<p>Do you love playing card games? Do you enjoy challenging your brain and having fun at the same time? If so, you might want to try Solitario Spider, one of the most popular and addictive card games in the world. In this article, we will tell you everything you need to know about Solitario Spider, including its history, rules, benefits, and how to download it for free on your device. We will also share some tips and tricks to help you master this game and beat any level. Let's get started!</p>
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- <h2>What is Solitario Spider?</h2>
-<p>Solitario Spider is a variation of the classic solitaire game, also known as patience or klondike. The main difference is that in Solitario Spider, you have to arrange all the cards in descending order from king to ace in the same suit, instead of alternating colors. You can choose to play with one, two, three, or four suits, depending on how difficult you want the game to be. The more suits you play with, the harder it is to complete the game.</p>
- <h3>The history and rules of the game</h3>
-<p>Solitario Spider was first created in 1949 by a British game developer named F. R. Ingersoll. He named it after a type of spider that spins its web in a spiral pattern. The game became popular in the 1980s when it was included in Microsoft Windows as one of the default games. Since then, it has been played by millions of people around the world, especially online.</p>
-<p>The rules of Solitario Spider are simple but challenging. You start with 10 piles of cards on the table, with only the top card face up. The rest of the cards are in the stock pile at the bottom right corner. You can move any face-up card or a sequence of cards in the same suit to another pile, as long as the card you move is one rank lower than the card you place it on. For example, you can move a 9 of hearts onto a 10 of hearts, but not onto a 10 of spades. You can also move any card or sequence to an empty pile.</p>
-<p>When you run out of moves, you can deal a new row of cards from the stock pile to each pile, except for the empty ones. You can do this only if there are at least one card in each pile. Your goal is to clear all the cards from the table by forming eight sequences of cards from king to ace in the same suit. When you do that, you win the game.</p>
- <h3>The benefits of playing Solitario Spider</h3>
-<p>Playing Solitario Spider is not only fun, but also good for your brain. Here are some of the benefits of playing this game:</p>
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-<li>It improves your concentration and focus. You have to pay attention to every card and every move you make, and avoid making mistakes.</li>
-<li>It enhances your memory and logic skills. You have to remember which cards are where and which ones are available to move. You also have to think strategically and plan your moves ahead.</li>
-<li>It reduces your stress and anxiety levels. Playing Solitario Spider can help you relax and calm your mind, especially when you are feeling overwhelmed or bored.</li>
-<li>It boosts your mood and self-esteem. Completing a game of Solitario Spider can make you feel proud and satisfied, and motivate you to challenge yourself more.</li>
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-<p>As you can see, playing Solitario Spider is a great way to exercise your brain and have fun at the same time. But how can you play this game on your device? Let's find out.</p>
- <h2>How to Download Solitario Spider for Free</h2>
-<p>If you want to play Solitario Spider on your device, you have two options: you can either download an app or visit a website. Both options are free and easy to use, and offer different features and options to suit your preferences. Here are some of the best apps and websites to play Solitario Spider:</p>
- <h3>The best apps and websites to play Solitario Spider</h3>
-<h4>Spider Solitaire: Card Games by MobilityWare</h4>
-<p>This is one of the most popular and highly rated apps to play Solitario Spider on your device. It has over 10 million downloads and 4.7 stars on Google Play Store. It offers four difficulty levels, daily challenges, custom themes, statistics, hints, undo, and more. You can also play offline and sync your progress across devices. You can download this app for free on Android and iOS devices.</p>
- <h4>Spider Solitaire Collection Free by TreeCardGames</h4>
-<p>This is another great app to play Solitario Spider on your device. It has over 5 million downloads and 4.6 stars on Google Play Store. It offers five difficulty levels, daily challenges, achievements, leaderboards, custom backgrounds, card backs, and faces, hints, undo, and more. You can also play offline and backup your data to the cloud. You can download this app for free on Android devices.</p>
- <h4>Spider Solitaire by Karmangames</h4>
-<p>This is a simple and elegant app to play Solitario Spider on your device. It has over 1 million downloads and 4.5 stars on Google Play Store. It offers three difficulty levels, auto-save, hints, undo, statistics, sound effects, and more. You can also play offline and change the orientation of your device. You can download this app for free on Android devices.</p>
- <h3>How to install and play Solitario Spider on your device</h3>
-<h4>For Android devices</h4>
-<p>To install and play Solitario Spider on your Android device, follow these steps:</p>
-<ol>
-<li>Go to Google Play Store and search for the app you want to download.</li>
-<li>Tap on the app icon and then tap on Install.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Open the app and choose the difficulty level you want to play.</li>
-<li>Enjoy playing Solitario Spider!</li>
-</ol>
- <h4>For Windows devices</h4>
-<p>To install and play Solitario Spider on your Windows device, follow these steps:</p>
-<ol>
-<li>Go to Microsoft Store and search for the app you want to download.</li>
-<li>Click on the app icon and then click on Get.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Open the app and choose the difficulty level you want to play.</li>
-<li>Enjoy playing Solitario Spider!</li>
-</ol>
- <h2>Tips and Tricks to Master Solitario Spider</h2>
-<p>Now that you know how to download and play Solitario Spider on your device, you might want to learn some tips and tricks to improve your skills and win more games. Here are some of them:</p>
- <h3>Choose the right difficulty level</h3>
-<p>The first tip is to choose the right difficulty level for your skill level. If you are a beginner, you might want to start with one suit or two suits, as they are easier to complete. If you are an expert, you might want to challenge yourself with three suits or four suits, as they are harder to complete. You can also switch between different difficulty levels as you progress.</p>
- <h3>Plan your moves ahead</h3>
-<p>The second tip is to plan your moves ahead before you make them. You should always try to expose as many face-down cards as possible, as they might contain useful cards or empty spaces. You should also try to create sequences of cards in the same suit as much as possible, as they are easier to move around. You should also avoid blocking cards that you need or creating piles that are too high or too low.</p>
- <h3>Use the undo and hint features wisely</h3>
-<p>The third tip is to use the undo and hint features wisely when you are stuck or unsure what to do next. The undo feature allows you to reverse your last move or moves, in case you made a mistake or want to try a different strategy. The hint feature gives you a suggestion of what card or sequence to move next, in case you are out of ideas or need some guidance. However, you should not rely too much on these features, as they might limit your creativity or make the game too easy. You should use them sparingly and only when necessary.</p>
- <h2>Conclusion</h2>
-<p>Solitario Spider is a fun and challenging card game that you can play on your device for free. It is a great way to exercise your brain, improve your concentration, memory, and logic skills, and reduce your stress and anxiety levels. It is also easy to download and install, and offers different difficulty levels, features, and options to suit your preferences. If you follow the tips and tricks we shared in this article, you will be able to master this game and win more games. We hope you enjoyed this article and learned something new. Happy playing!</p>
- <h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about Solitario Spider:</p>
-<ul>
-<li><b>Q: How many cards are used in Solitario Spider?</b></li>
-<li>A: Solitario Spider uses two standard 52-card decks, for a total of 104 cards.</li>
-<li><b>Q: How do I win Solitario Spider?</b></li>
-<li>A: You win Solitario Spider by clearing all the cards from the table by forming eight sequences of cards from king to ace in the same suit.</li>
-<li><b>Q: What is the difference between Solitario Spider and Spider Solitaire?</b></li>
-<li>A: Solitario Spider and Spider Solitaire are the same game, but with different names. Solitario Spider is the Spanish name for the game, while Spider Solitaire is the English name.</li>
-<li><b>Q: What is the highest score possible in Solitario Spider?</b></li>
-<li>A: The highest score possible in Solitario Spider depends on the difficulty level and the app or website you use. Generally, the higher the difficulty level, the higher the score. Some apps or websites also award bonus points for completing the game faster or using fewer moves.</li>
-<li><b>Q: Can I play Solitario Spider with other people?</b></li>
-<li>A: Solitario Spider is usually a single-player game, but some apps or websites allow you to play with other people online or offline. You can either compete against them or cooperate with them to complete the game.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Star Stable Horses APK - The Ultimate Horse Game for Mobile.md b/spaces/congsaPfin/Manga-OCR/logs/Star Stable Horses APK - The Ultimate Horse Game for Mobile.md
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-<h1>Star Stable Horses APK Download: A Guide for Horse Lovers</h1>
-<p>If you are a fan of horses and adventure games, you might have heard of Star Stable, the world's biggest online horse game. But did you know that there is also a companion app called Star Stable Horses, where you can raise your own foals and transfer them to Star Stable Online? In this article, we will show you how to download Star Stable Horses APK for Android devices, how to transfer your horses from the app to the online game, what are the features and benefits of playing Star Stable Horses, and what are some tips and tricks for having fun with your horses. Let's get started!</p>
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-<p>Star Stable Online is a horse game where you can explore the magical island of Jorvik, ride and take care of your own horses, make friends with other players, solve quests and mysteries, and join exciting events. You can choose from multiple unique horse breeds, customize your character and horse, access thousands of quests and game updates, and join a vibrant community of horse lovers.</p>
- <h3>A companion app for Star Stable Online</h3>
-<p>Star Stable Horses is an app that allows you to raise your own adorable foals and watch them grow into beautiful horses that you can ride, train, and take on amazing adventures in Star Stable Online. You can also care for and customize your horses, grow treats for them in your own garden, watch them play together in the paddock, and unlock exclusive coat colors and abilities for them. You can keep up to 21 horses in your very own horse stable, and new horses and game content are added frequently.</p>
- <h2>How to download Star Stable Horses APK for Android devices</h2>
-<h3>Step 1: Go to the official website or Google Play Store</h3>
-<p>To download Star Stable Horses APK for Android devices, you can either go to the official website or the Google Play Store. Both options are safe and reliable, but if you want to get the latest version of the app, we recommend going to the official website.</p>
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- <h3>Step 2: Tap on the download button and install the app</h3>
-<p>Once you have chosen your preferred source, tap on the download button and wait for the app to be downloaded to your device. Then, open the file and follow the instructions to install the app. You might need to enable the installation of apps from unknown sources in your device settings if you are downloading from the official website.</p>
- <h3>Step 3: Open the app and create your account or log in with your existing one</h3>
-<p>After the installation is complete, open the app and create your account or log in with your existing one. You will need an email address and a password to create your account. If you already have a Star Stable Online account, you can use the same credentials to log in to Star Stable Horses. You will also need to agree to the terms of service and privacy policy before you can start playing.</p>
- <h2>How to transfer your horses from Star Stable Horses to Star Stable Online</h2>
-<h3>Step 1: Raise your foal to level 10 in Star Stable Horses</h3>
-<p>In order to transfer your horses from Star Stable Horses to Star Stable Online, you will need to raise them to level 10 in the app. This means that you will need to care for them daily, feed them, groom them, play with them, and train them. You can also dress them up with bows and other accessories in the beauty salon. Each caring task will give you experience points that will help you level up your horse.</p>
- <h3>Step 2: Tap on the transfer button and choose your server and character in Star Stable Online</h3>
-<p>When your horse reaches level 10, you will see a transfer button on the bottom right corner of the screen. Tap on it and choose your server and character in Star Stable Online. You will need to have an active subscription or a lifetime membership in Star Stable Online to be able to transfer your horse. You will also need to have enough space in your home stable for your new horse.</p>
- <h3>Step 3: Go to your home stable in Star Stable Online and meet your new horse</h3>
-<p>After you have confirmed the transfer, go to your home stable in Star Stable Online and meet your new horse. You will see a special box with a ribbon that contains your horse. Tap on it and name your horse. You can also change its tack and equipment if you want. Congratulations, you have successfully transferred your horse from Star Stable Horses to Star Stable Online!</p>
- <h2>What are the features and benefits of Star Stable Horses?</h2>
-<h3>Care for and customize your own horses and foals</h3>
-<p>One of the main features and benefits of Star Stable Horses is that you can care for and customize your own horses and foals. You can feed them, groom them, play with them, train them, and dress them up with bows and other accessories. You can also watch them grow from cute foals to majestic horses that you can ride in Star Stable Online.</p>
- <h3>Choose from a variety of horse breeds and coat colors</h3>
-<p>Another feature and benefit of Star Stable Horses is that you can choose from a variety of horse breeds and coat colors. You can find popular breeds like Arabian, Quarter Horse, Friesian, Andalusian, Appaloosa, Morgan, Mustang, Icelandic, Hanoverian, Lusitano, Akhal-Teke, Welsh Pony, Shire, Lipizzaner, Marwari, Jorvik Pony, Jorvik Wild Horse, Chincoteague Pony, Fjord Horse, American Paint Horse, Trakehner, Haflinger, Thoroughbred, Connemara Pony, Selle Français, Lippizaner Mix , and more. You can also choose from different coat colors and patterns, such as bay, chestnut, black, white, gray, palomino, dun, roan, pinto, appaloosa, leopard, tobiano, overo, sabino, and more. You can even unlock exclusive coat colors and abilities for your horses by completing special quests and events in Star Stable Online.</p>
- <h3>Grow treats for your horses in your own garden</h3>
-<p>Another feature and benefit of Star Stable Horses is that you can grow treats for your horses in your own garden. You can plant seeds, water them, harvest them, and feed them to your horses. You can grow different types of treats, such as carrots, apples, strawberries, blueberries, raspberries, bananas, pineapples, watermelons, pumpkins, and more. Each treat will give your horse a different boost of happiness and health.</p>
- <h3>Watch your horses play together in the paddock</h3>
-<p>Another feature and benefit of Star Stable Horses is that you can watch your horses play together in the paddock. You can see them run around, graze, roll, nuzzle, snort, neigh, and more. You can also interact with them by tapping on them and giving them a pat or a scratch. You can also take pictures of your horses and share them with your friends on social media.</p>
- <h3>Unlock exclusive coat colors and abilities for your horses</h3>
-<p>Another feature and benefit of Star Stable Horses is that you can unlock exclusive coat colors and abilities for your horses. By transferring your horses from Star Stable Horses to Star Stable Online, you can access special coat colors and abilities that are not available in the online game. For example, you can unlock the Jorvik Wild Horse's camouflage ability, the Marwari's curly ears, the Fjord Horse's winter coat color, the Chincoteague Pony's swim speed boost, the Akhal-Teke's shiny coat color , and more. These coat colors and abilities will make your horses stand out from the crowd and give you an edge in races and championships.</p>
- <h2>What are some tips and tricks for playing Star Stable Horses?</h2>
-<h3>Play regularly and perform caring tasks to keep your horses happy and healthy</h3>
-<p>One of the tips and tricks for playing Star Stable Horses is to play regularly and perform caring tasks to keep your horses happy and healthy. By doing so, you will earn experience points that will help you level up your horses faster and unlock new features and content. You will also prevent your horses from getting sick or unhappy, which will affect their performance and appearance.</p>
- <h3>Dress up your horses with bows in the beauty salon</h3>
-<p>Another tip and trick for playing Star Stable Horses is to dress up your horses with bows in the beauty salon. You can find different types of bows, such as ribbons, flowers, stars, hearts, butterflies, and more. You can also change the color of the bows to match your horse's coat color or your preference. By dressing up your horses with bows, you will make them look more cute and stylish, and you will also earn extra happiness points for them.</p>
- <h3>Use shortcuts and gear to win races and championships</h3>
-<p>Another tip and trick for playing Star Stable Horses is to use shortcuts and gear to win races and championships. By transferring your horses from Star Stable Horses to Star Stable Online, you can participate in various races and championships that will test your riding skills and reward you with money, reputation, and items. You can use shortcuts to save time and distance, such as jumping over fences, cutting corners, or taking alternative routes. You can also use gear to boost your horse's speed, stamina, agility, or jumping ability, such as saddles, bridles, leg wraps, horseshoes, or blankets. By using shortcuts and gear, you will increase your chances of winning races and championships.</p>
- <h3>Save money on short rides by using the trailer or ferry</h3>
-<p>Another tip and trick for playing Star Stable Horses is to save money on short rides by using the trailer or ferry. By transferring your horses from Star Stable Horses to Star Stable Online, you can explore the vast island of Jorvik with your horses. However, traveling from one place to another can cost you money if you use the bus or the train. You can save money on short rides by using the trailer or the ferry instead. The trailer allows you to transport your horse from one stable to another for free, while the ferry allows you to cross the water for a small fee. By using the trailer or the ferry, you will save money on short rides.</p>
- <h3>Avoid lagging problems by closing other apps and clearing cache</h3>
-<p>Another tip and trick for playing Star Stable Horses is to avoid lagging problems by closing other apps and clearing cache. Lagging problems can affect your gameplay experience by slowing down your device, causing glitches, or crashing the app. You can avoid lagging problems by closing other apps that are running in the background of your device, such as social media, messaging, or browsing apps. You can also clear cache by going to your device settings, finding Star Stable Horses app, tapping on storage, and tapping on clear cache. By doing so, you will free up some space and memory on your device and improve its performance.</p>
- <h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, Star Stable Horses is a companion app for Star Stable Online that allows you to raise your own foals and transfer them to the online game. You can care for and customize your horses, choose from a variety of horse breeds and coat colors , grow treats for them in your own garden, watch them play together in the paddock, and unlock exclusive coat colors and abilities for them. You can also download Star Stable Horses APK for Android devices by following a few simple steps, and transfer your horses from the app to the online game by raising them to level 10. Star Stable Horses is a fun and engaging app for horse lovers of all ages.</p>
- <h3>Call to action and invitation to download the app</h3>
-<p>If you are interested in playing Star Stable Horses, you can download the app for free from the official website or the Google Play Store. You will need an Android device with version 4.1 or higher, and an internet connection to play the app. You will also need an active subscription or a lifetime membership in Star Stable Online to transfer your horses from the app to the online game. Star Stable Online is also free to download and play up to level 5, and you can get a subscription or a lifetime membership from the official website. Don't miss this opportunity to experience the world of Star Stable with your own horses. Download Star Stable Horses today and join the adventure!</p>
- <h4>FAQs</h4>
-<p>Here are some frequently asked questions about Star Stable Horses:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Can I play Star Stable Horses without playing Star Stable Online?</td>
-<td>Yes, you can play Star Stable Horses without playing Star Stable Online. However, you will not be able to transfer your horses from the app to the online game, and you will miss out on some exclusive coat colors and abilities for your horses.</td>
-</tr>
-<tr>
-<td>Can I transfer my horses from Star Stable Online to Star Stable Horses?</td>
-<td>No, you cannot transfer your horses from Star Stable Online to Star Stable Horses. You can only transfer your horses from Star Stable Horses to Star Stable Online.</td>
-</tr>
-<tr>
-<td>Can I transfer more than one horse from Star Stable Horses to Star Stable Online?</td>
-<td>Yes, you can transfer more than one horse from Star Stable Horses to Star Stable Online. However, you will need enough space in your home stable for each horse, and you will need to pay a fee of 750 star coins for each horse.</td>
-</tr>
-<tr>
-<td>Can I delete or rename my horses in Star Stable Horses?</td>
-<td>Yes, you can delete or rename your horses in Star Stable Horses. To delete a horse, tap on the trash icon on the bottom left corner of the screen. To rename a horse, tap on the name tag icon on the bottom right corner of the screen.</td>
-</tr>
-<tr>
-<td>Can I play Star Stable Horses offline?</td>
-<td>No, you cannot play Star Stable Horses offline. You will need an internet connection to play the app.</td>
-</tr>
-</table></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/fp16_utils.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/fp16_utils.py
deleted file mode 100644
index f6b54886519fd2808360b1632e5bebf6563eced2..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/fp16_utils.py
+++ /dev/null
@@ -1,410 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-import warnings
-from collections import abc
-from inspect import getfullargspec
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from annotator.mmpkg.mmcv.utils import TORCH_VERSION, digit_version
-from .dist_utils import allreduce_grads as _allreduce_grads
-
-try:
-    # If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported
-    # and used; otherwise, auto fp16 will adopt mmcv's implementation.
-    # Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16
-    # manually, so the behavior may not be consistent with real amp.
-    from torch.cuda.amp import autocast
-except ImportError:
-    pass
-
-
-def cast_tensor_type(inputs, src_type, dst_type):
-    """Recursively convert Tensor in inputs from src_type to dst_type.
-
-    Args:
-        inputs: Inputs that to be casted.
-        src_type (torch.dtype): Source type..
-        dst_type (torch.dtype): Destination type.
-
-    Returns:
-        The same type with inputs, but all contained Tensors have been cast.
-    """
-    if isinstance(inputs, nn.Module):
-        return inputs
-    elif isinstance(inputs, torch.Tensor):
-        return inputs.to(dst_type)
-    elif isinstance(inputs, str):
-        return inputs
-    elif isinstance(inputs, np.ndarray):
-        return inputs
-    elif isinstance(inputs, abc.Mapping):
-        return type(inputs)({
-            k: cast_tensor_type(v, src_type, dst_type)
-            for k, v in inputs.items()
-        })
-    elif isinstance(inputs, abc.Iterable):
-        return type(inputs)(
-            cast_tensor_type(item, src_type, dst_type) for item in inputs)
-    else:
-        return inputs
-
-
-def auto_fp16(apply_to=None, out_fp32=False):
-    """Decorator to enable fp16 training automatically.
-
-    This decorator is useful when you write custom modules and want to support
-    mixed precision training. If inputs arguments are fp32 tensors, they will
-    be converted to fp16 automatically. Arguments other than fp32 tensors are
-    ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
-    backend, otherwise, original mmcv implementation will be adopted.
-
-    Args:
-        apply_to (Iterable, optional): The argument names to be converted.
-            `None` indicates all arguments.
-        out_fp32 (bool): Whether to convert the output back to fp32.
-
-    Example:
-
-        >>> import torch.nn as nn
-        >>> class MyModule1(nn.Module):
-        >>>
-        >>>     # Convert x and y to fp16
-        >>>     @auto_fp16()
-        >>>     def forward(self, x, y):
-        >>>         pass
-
-        >>> import torch.nn as nn
-        >>> class MyModule2(nn.Module):
-        >>>
-        >>>     # convert pred to fp16
-        >>>     @auto_fp16(apply_to=('pred', ))
-        >>>     def do_something(self, pred, others):
-        >>>         pass
-    """
-
-    def auto_fp16_wrapper(old_func):
-
-        @functools.wraps(old_func)
-        def new_func(*args, **kwargs):
-            # check if the module has set the attribute `fp16_enabled`, if not,
-            # just fallback to the original method.
-            if not isinstance(args[0], torch.nn.Module):
-                raise TypeError('@auto_fp16 can only be used to decorate the '
-                                'method of nn.Module')
-            if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
-                return old_func(*args, **kwargs)
-
-            # get the arg spec of the decorated method
-            args_info = getfullargspec(old_func)
-            # get the argument names to be casted
-            args_to_cast = args_info.args if apply_to is None else apply_to
-            # convert the args that need to be processed
-            new_args = []
-            # NOTE: default args are not taken into consideration
-            if args:
-                arg_names = args_info.args[:len(args)]
-                for i, arg_name in enumerate(arg_names):
-                    if arg_name in args_to_cast:
-                        new_args.append(
-                            cast_tensor_type(args[i], torch.float, torch.half))
-                    else:
-                        new_args.append(args[i])
-            # convert the kwargs that need to be processed
-            new_kwargs = {}
-            if kwargs:
-                for arg_name, arg_value in kwargs.items():
-                    if arg_name in args_to_cast:
-                        new_kwargs[arg_name] = cast_tensor_type(
-                            arg_value, torch.float, torch.half)
-                    else:
-                        new_kwargs[arg_name] = arg_value
-            # apply converted arguments to the decorated method
-            if (TORCH_VERSION != 'parrots' and
-                    digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
-                with autocast(enabled=True):
-                    output = old_func(*new_args, **new_kwargs)
-            else:
-                output = old_func(*new_args, **new_kwargs)
-            # cast the results back to fp32 if necessary
-            if out_fp32:
-                output = cast_tensor_type(output, torch.half, torch.float)
-            return output
-
-        return new_func
-
-    return auto_fp16_wrapper
-
-
-def force_fp32(apply_to=None, out_fp16=False):
-    """Decorator to convert input arguments to fp32 in force.
-
-    This decorator is useful when you write custom modules and want to support
-    mixed precision training. If there are some inputs that must be processed
-    in fp32 mode, then this decorator can handle it. If inputs arguments are
-    fp16 tensors, they will be converted to fp32 automatically. Arguments other
-    than fp16 tensors are ignored. If you are using PyTorch >= 1.6,
-    torch.cuda.amp is used as the backend, otherwise, original mmcv
-    implementation will be adopted.
-
-    Args:
-        apply_to (Iterable, optional): The argument names to be converted.
-            `None` indicates all arguments.
-        out_fp16 (bool): Whether to convert the output back to fp16.
-
-    Example:
-
-        >>> import torch.nn as nn
-        >>> class MyModule1(nn.Module):
-        >>>
-        >>>     # Convert x and y to fp32
-        >>>     @force_fp32()
-        >>>     def loss(self, x, y):
-        >>>         pass
-
-        >>> import torch.nn as nn
-        >>> class MyModule2(nn.Module):
-        >>>
-        >>>     # convert pred to fp32
-        >>>     @force_fp32(apply_to=('pred', ))
-        >>>     def post_process(self, pred, others):
-        >>>         pass
-    """
-
-    def force_fp32_wrapper(old_func):
-
-        @functools.wraps(old_func)
-        def new_func(*args, **kwargs):
-            # check if the module has set the attribute `fp16_enabled`, if not,
-            # just fallback to the original method.
-            if not isinstance(args[0], torch.nn.Module):
-                raise TypeError('@force_fp32 can only be used to decorate the '
-                                'method of nn.Module')
-            if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
-                return old_func(*args, **kwargs)
-            # get the arg spec of the decorated method
-            args_info = getfullargspec(old_func)
-            # get the argument names to be casted
-            args_to_cast = args_info.args if apply_to is None else apply_to
-            # convert the args that need to be processed
-            new_args = []
-            if args:
-                arg_names = args_info.args[:len(args)]
-                for i, arg_name in enumerate(arg_names):
-                    if arg_name in args_to_cast:
-                        new_args.append(
-                            cast_tensor_type(args[i], torch.half, torch.float))
-                    else:
-                        new_args.append(args[i])
-            # convert the kwargs that need to be processed
-            new_kwargs = dict()
-            if kwargs:
-                for arg_name, arg_value in kwargs.items():
-                    if arg_name in args_to_cast:
-                        new_kwargs[arg_name] = cast_tensor_type(
-                            arg_value, torch.half, torch.float)
-                    else:
-                        new_kwargs[arg_name] = arg_value
-            # apply converted arguments to the decorated method
-            if (TORCH_VERSION != 'parrots' and
-                    digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
-                with autocast(enabled=False):
-                    output = old_func(*new_args, **new_kwargs)
-            else:
-                output = old_func(*new_args, **new_kwargs)
-            # cast the results back to fp32 if necessary
-            if out_fp16:
-                output = cast_tensor_type(output, torch.float, torch.half)
-            return output
-
-        return new_func
-
-    return force_fp32_wrapper
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
-    warnings.warning(
-        '"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be '
-        'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads')
-    _allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb)
-
-
-def wrap_fp16_model(model):
-    """Wrap the FP32 model to FP16.
-
-    If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
-    backend, otherwise, original mmcv implementation will be adopted.
-
-    For PyTorch >= 1.6, this function will
-    1. Set fp16 flag inside the model to True.
-
-    Otherwise:
-    1. Convert FP32 model to FP16.
-    2. Remain some necessary layers to be FP32, e.g., normalization layers.
-    3. Set `fp16_enabled` flag inside the model to True.
-
-    Args:
-        model (nn.Module): Model in FP32.
-    """
-    if (TORCH_VERSION == 'parrots'
-            or digit_version(TORCH_VERSION) < digit_version('1.6.0')):
-        # convert model to fp16
-        model.half()
-        # patch the normalization layers to make it work in fp32 mode
-        patch_norm_fp32(model)
-    # set `fp16_enabled` flag
-    for m in model.modules():
-        if hasattr(m, 'fp16_enabled'):
-            m.fp16_enabled = True
-
-
-def patch_norm_fp32(module):
-    """Recursively convert normalization layers from FP16 to FP32.
-
-    Args:
-        module (nn.Module): The modules to be converted in FP16.
-
-    Returns:
-        nn.Module: The converted module, the normalization layers have been
-            converted to FP32.
-    """
-    if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)):
-        module.float()
-        if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3':
-            module.forward = patch_forward_method(module.forward, torch.half,
-                                                  torch.float)
-    for child in module.children():
-        patch_norm_fp32(child)
-    return module
-
-
-def patch_forward_method(func, src_type, dst_type, convert_output=True):
-    """Patch the forward method of a module.
-
-    Args:
-        func (callable): The original forward method.
-        src_type (torch.dtype): Type of input arguments to be converted from.
-        dst_type (torch.dtype): Type of input arguments to be converted to.
-        convert_output (bool): Whether to convert the output back to src_type.
-
-    Returns:
-        callable: The patched forward method.
-    """
-
-    def new_forward(*args, **kwargs):
-        output = func(*cast_tensor_type(args, src_type, dst_type),
-                      **cast_tensor_type(kwargs, src_type, dst_type))
-        if convert_output:
-            output = cast_tensor_type(output, dst_type, src_type)
-        return output
-
-    return new_forward
-
-
-class LossScaler:
-    """Class that manages loss scaling in mixed precision training which
-    supports both dynamic or static mode.
-
-    The implementation refers to
-    https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py.
-    Indirectly, by supplying ``mode='dynamic'`` for dynamic loss scaling.
-    It's important to understand how :class:`LossScaler` operates.
-    Loss scaling is designed to combat the problem of underflowing
-    gradients encountered at long times when training fp16 networks.
-    Dynamic loss scaling begins by attempting a very high loss
-    scale.  Ironically, this may result in OVERflowing gradients.
-    If overflowing gradients are encountered, :class:`FP16_Optimizer` then
-    skips the update step for this particular iteration/minibatch,
-    and :class:`LossScaler` adjusts the loss scale to a lower value.
-    If a certain number of iterations occur without overflowing gradients
-    detected,:class:`LossScaler` increases the loss scale once more.
-    In this way :class:`LossScaler` attempts to "ride the edge" of always
-    using the highest loss scale possible without incurring overflow.
-
-    Args:
-        init_scale (float): Initial loss scale value, default: 2**32.
-        scale_factor (float): Factor used when adjusting the loss scale.
-            Default: 2.
-        mode (str): Loss scaling mode. 'dynamic' or 'static'
-        scale_window (int): Number of consecutive iterations without an
-            overflow to wait before increasing the loss scale. Default: 1000.
-    """
-
-    def __init__(self,
-                 init_scale=2**32,
-                 mode='dynamic',
-                 scale_factor=2.,
-                 scale_window=1000):
-        self.cur_scale = init_scale
-        self.cur_iter = 0
-        assert mode in ('dynamic',
-                        'static'), 'mode can only be dynamic or static'
-        self.mode = mode
-        self.last_overflow_iter = -1
-        self.scale_factor = scale_factor
-        self.scale_window = scale_window
-
-    def has_overflow(self, params):
-        """Check if params contain overflow."""
-        if self.mode != 'dynamic':
-            return False
-        for p in params:
-            if p.grad is not None and LossScaler._has_inf_or_nan(p.grad.data):
-                return True
-        return False
-
-    def _has_inf_or_nan(x):
-        """Check if params contain NaN."""
-        try:
-            cpu_sum = float(x.float().sum())
-        except RuntimeError as instance:
-            if 'value cannot be converted' not in instance.args[0]:
-                raise
-            return True
-        else:
-            if cpu_sum == float('inf') or cpu_sum == -float('inf') \
-                    or cpu_sum != cpu_sum:
-                return True
-            return False
-
-    def update_scale(self, overflow):
-        """update the current loss scale value when overflow happens."""
-        if self.mode != 'dynamic':
-            return
-        if overflow:
-            self.cur_scale = max(self.cur_scale / self.scale_factor, 1)
-            self.last_overflow_iter = self.cur_iter
-        else:
-            if (self.cur_iter - self.last_overflow_iter) % \
-                    self.scale_window == 0:
-                self.cur_scale *= self.scale_factor
-        self.cur_iter += 1
-
-    def state_dict(self):
-        """Returns the state of the scaler as a :class:`dict`."""
-        return dict(
-            cur_scale=self.cur_scale,
-            cur_iter=self.cur_iter,
-            mode=self.mode,
-            last_overflow_iter=self.last_overflow_iter,
-            scale_factor=self.scale_factor,
-            scale_window=self.scale_window)
-
-    def load_state_dict(self, state_dict):
-        """Loads the loss_scaler state dict.
-
-        Args:
-           state_dict (dict): scaler state.
-        """
-        self.cur_scale = state_dict['cur_scale']
-        self.cur_iter = state_dict['cur_iter']
-        self.mode = state_dict['mode']
-        self.last_overflow_iter = state_dict['last_overflow_iter']
-        self.scale_factor = state_dict['scale_factor']
-        self.scale_window = state_dict['scale_window']
-
-    @property
-    def loss_scale(self):
-        return self.cur_scale
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py
deleted file mode 100644
index eecfe2f31e65147aec47704b9e775e82d9f5fa9a..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2004-present Facebook. All Rights Reserved.
-
-import numpy as np
-from typing import List
-
-from annotator.oneformer.detectron2.config import CfgNode as CfgNode_
-from annotator.oneformer.detectron2.config import configurable
-from annotator.oneformer.detectron2.structures import Instances
-from annotator.oneformer.detectron2.structures.boxes import pairwise_iou
-from annotator.oneformer.detectron2.tracking.utils import LARGE_COST_VALUE, create_prediction_pairs
-
-from .base_tracker import TRACKER_HEADS_REGISTRY
-from .hungarian_tracker import BaseHungarianTracker
-
-
-@TRACKER_HEADS_REGISTRY.register()
-class VanillaHungarianBBoxIOUTracker(BaseHungarianTracker):
-    """
-    Hungarian algo based tracker using bbox iou as metric
-    """
-
-    @configurable
-    def __init__(
-        self,
-        *,
-        video_height: int,
-        video_width: int,
-        max_num_instances: int = 200,
-        max_lost_frame_count: int = 0,
-        min_box_rel_dim: float = 0.02,
-        min_instance_period: int = 1,
-        track_iou_threshold: float = 0.5,
-        **kwargs,
-    ):
-        """
-        Args:
-        video_height: height the video frame
-        video_width: width of the video frame
-        max_num_instances: maximum number of id allowed to be tracked
-        max_lost_frame_count: maximum number of frame an id can lost tracking
-                              exceed this number, an id is considered as lost
-                              forever
-        min_box_rel_dim: a percentage, smaller than this dimension, a bbox is
-                         removed from tracking
-        min_instance_period: an instance will be shown after this number of period
-                             since its first showing up in the video
-        track_iou_threshold: iou threshold, below this number a bbox pair is removed
-                             from tracking
-        """
-        super().__init__(
-            video_height=video_height,
-            video_width=video_width,
-            max_num_instances=max_num_instances,
-            max_lost_frame_count=max_lost_frame_count,
-            min_box_rel_dim=min_box_rel_dim,
-            min_instance_period=min_instance_period,
-        )
-        self._track_iou_threshold = track_iou_threshold
-
-    @classmethod
-    def from_config(cls, cfg: CfgNode_):
-        """
-        Old style initialization using CfgNode
-
-        Args:
-            cfg: D2 CfgNode, config file
-        Return:
-            dictionary storing arguments for __init__ method
-        """
-        assert "VIDEO_HEIGHT" in cfg.TRACKER_HEADS
-        assert "VIDEO_WIDTH" in cfg.TRACKER_HEADS
-        video_height = cfg.TRACKER_HEADS.get("VIDEO_HEIGHT")
-        video_width = cfg.TRACKER_HEADS.get("VIDEO_WIDTH")
-        max_num_instances = cfg.TRACKER_HEADS.get("MAX_NUM_INSTANCES", 200)
-        max_lost_frame_count = cfg.TRACKER_HEADS.get("MAX_LOST_FRAME_COUNT", 0)
-        min_box_rel_dim = cfg.TRACKER_HEADS.get("MIN_BOX_REL_DIM", 0.02)
-        min_instance_period = cfg.TRACKER_HEADS.get("MIN_INSTANCE_PERIOD", 1)
-        track_iou_threshold = cfg.TRACKER_HEADS.get("TRACK_IOU_THRESHOLD", 0.5)
-        return {
-            "_target_": "detectron2.tracking.vanilla_hungarian_bbox_iou_tracker.VanillaHungarianBBoxIOUTracker",  # noqa
-            "video_height": video_height,
-            "video_width": video_width,
-            "max_num_instances": max_num_instances,
-            "max_lost_frame_count": max_lost_frame_count,
-            "min_box_rel_dim": min_box_rel_dim,
-            "min_instance_period": min_instance_period,
-            "track_iou_threshold": track_iou_threshold,
-        }
-
-    def build_cost_matrix(self, instances: Instances, prev_instances: Instances) -> np.ndarray:
-        """
-        Build the cost matrix for assignment problem
-        (https://en.wikipedia.org/wiki/Assignment_problem)
-
-        Args:
-            instances: D2 Instances, for current frame predictions
-            prev_instances: D2 Instances, for previous frame predictions
-
-        Return:
-            the cost matrix in numpy array
-        """
-        assert instances is not None and prev_instances is not None
-        # calculate IoU of all bbox pairs
-        iou_all = pairwise_iou(
-            boxes1=instances.pred_boxes,
-            boxes2=self._prev_instances.pred_boxes,
-        )
-        bbox_pairs = create_prediction_pairs(
-            instances, self._prev_instances, iou_all, threshold=self._track_iou_threshold
-        )
-        # assign large cost value to make sure pair below IoU threshold won't be matched
-        cost_matrix = np.full((len(instances), len(prev_instances)), LARGE_COST_VALUE)
-        return self.assign_cost_matrix_values(cost_matrix, bbox_pairs)
-
-    def assign_cost_matrix_values(self, cost_matrix: np.ndarray, bbox_pairs: List) -> np.ndarray:
-        """
-        Based on IoU for each pair of bbox, assign the associated value in cost matrix
-
-        Args:
-            cost_matrix: np.ndarray, initialized 2D array with target dimensions
-            bbox_pairs: list of bbox pair, in each pair, iou value is stored
-        Return:
-            np.ndarray, cost_matrix with assigned values
-        """
-        for pair in bbox_pairs:
-            # assign -1 for IoU above threshold pairs, algorithms will minimize cost
-            cost_matrix[pair["idx"]][pair["prev_idx"]] = -1
-        return cost_matrix
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/pascal_context_59.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/pascal_context_59.py
deleted file mode 100644
index 37585abab89834b95cd5bdd993b994fca1db65f6..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/pascal_context_59.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# dataset settings
-dataset_type = 'PascalContextDataset59'
-data_root = 'data/VOCdevkit/VOC2010/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-img_scale = (520, 520)
-crop_size = (480, 480)
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', reduce_zero_label=True),
-    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=img_scale,
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/train.txt',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/val.txt',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/val.txt',
-        pipeline=test_pipeline))
diff --git a/spaces/cvlab/zero123-live/taming-transformers/taming/data/annotated_objects_coco.py b/spaces/cvlab/zero123-live/taming-transformers/taming/data/annotated_objects_coco.py
deleted file mode 100644
index af000ecd943d7b8a85d7eb70195c9ecd10ab5edc..0000000000000000000000000000000000000000
--- a/spaces/cvlab/zero123-live/taming-transformers/taming/data/annotated_objects_coco.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import json
-from itertools import chain
-from pathlib import Path
-from typing import Iterable, Dict, List, Callable, Any
-from collections import defaultdict
-
-from tqdm import tqdm
-
-from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset
-from taming.data.helper_types import Annotation, ImageDescription, Category
-
-COCO_PATH_STRUCTURE = {
-    'train': {
-        'top_level': '',
-        'instances_annotations': 'annotations/instances_train2017.json',
-        'stuff_annotations': 'annotations/stuff_train2017.json',
-        'files': 'train2017'
-    },
-    'validation': {
-        'top_level': '',
-        'instances_annotations': 'annotations/instances_val2017.json',
-        'stuff_annotations': 'annotations/stuff_val2017.json',
-        'files': 'val2017'
-    }
-}
-
-
-def load_image_descriptions(description_json: List[Dict]) -> Dict[str, ImageDescription]:
-    return {
-        str(img['id']): ImageDescription(
-            id=img['id'],
-            license=img.get('license'),
-            file_name=img['file_name'],
-            coco_url=img['coco_url'],
-            original_size=(img['width'], img['height']),
-            date_captured=img.get('date_captured'),
-            flickr_url=img.get('flickr_url')
-        )
-        for img in description_json
-    }
-
-
-def load_categories(category_json: Iterable) -> Dict[str, Category]:
-    return {str(cat['id']): Category(id=str(cat['id']), super_category=cat['supercategory'], name=cat['name'])
-            for cat in category_json if cat['name'] != 'other'}
-
-
-def load_annotations(annotations_json: List[Dict], image_descriptions: Dict[str, ImageDescription],
-                     category_no_for_id: Callable[[str], int], split: str) -> Dict[str, List[Annotation]]:
-    annotations = defaultdict(list)
-    total = sum(len(a) for a in annotations_json)
-    for ann in tqdm(chain(*annotations_json), f'Loading {split} annotations', total=total):
-        image_id = str(ann['image_id'])
-        if image_id not in image_descriptions:
-            raise ValueError(f'image_id [{image_id}] has no image description.')
-        category_id = ann['category_id']
-        try:
-            category_no = category_no_for_id(str(category_id))
-        except KeyError:
-            continue
-
-        width, height = image_descriptions[image_id].original_size
-        bbox = (ann['bbox'][0] / width, ann['bbox'][1] / height, ann['bbox'][2] / width, ann['bbox'][3] / height)
-
-        annotations[image_id].append(
-            Annotation(
-                id=ann['id'],
-                area=bbox[2]*bbox[3],  # use bbox area
-                is_group_of=ann['iscrowd'],
-                image_id=ann['image_id'],
-                bbox=bbox,
-                category_id=str(category_id),
-                category_no=category_no
-            )
-        )
-    return dict(annotations)
-
-
-class AnnotatedObjectsCoco(AnnotatedObjectsDataset):
-    def __init__(self, use_things: bool = True, use_stuff: bool = True, **kwargs):
-        """
-        @param data_path: is the path to the following folder structure:
-                          coco/
-                          ├── annotations
-                          │   ├── instances_train2017.json
-                          │   ├── instances_val2017.json
-                          │   ├── stuff_train2017.json
-                          │   └── stuff_val2017.json
-                          ├── train2017
-                          │   ├── 000000000009.jpg
-                          │   ├── 000000000025.jpg
-                          │   └── ...
-                          ├── val2017
-                          │   ├── 000000000139.jpg
-                          │   ├── 000000000285.jpg
-                          │   └── ...
-        @param: split: one of 'train' or 'validation'
-        @param: desired image size (give square images)
-        """
-        super().__init__(**kwargs)
-        self.use_things = use_things
-        self.use_stuff = use_stuff
-
-        with open(self.paths['instances_annotations']) as f:
-            inst_data_json = json.load(f)
-        with open(self.paths['stuff_annotations']) as f:
-            stuff_data_json = json.load(f)
-
-        category_jsons = []
-        annotation_jsons = []
-        if self.use_things:
-            category_jsons.append(inst_data_json['categories'])
-            annotation_jsons.append(inst_data_json['annotations'])
-        if self.use_stuff:
-            category_jsons.append(stuff_data_json['categories'])
-            annotation_jsons.append(stuff_data_json['annotations'])
-
-        self.categories = load_categories(chain(*category_jsons))
-        self.filter_categories()
-        self.setup_category_id_and_number()
-
-        self.image_descriptions = load_image_descriptions(inst_data_json['images'])
-        annotations = load_annotations(annotation_jsons, self.image_descriptions, self.get_category_number, self.split)
-        self.annotations = self.filter_object_number(annotations, self.min_object_area,
-                                                     self.min_objects_per_image, self.max_objects_per_image)
-        self.image_ids = list(self.annotations.keys())
-        self.clean_up_annotations_and_image_descriptions()
-
-    def get_path_structure(self) -> Dict[str, str]:
-        if self.split not in COCO_PATH_STRUCTURE:
-            raise ValueError(f'Split [{self.split} does not exist for COCO data.]')
-        return COCO_PATH_STRUCTURE[self.split]
-
-    def get_image_path(self, image_id: str) -> Path:
-        return self.paths['files'].joinpath(self.image_descriptions[str(image_id)].file_name)
-
-    def get_image_description(self, image_id: str) -> Dict[str, Any]:
-        # noinspection PyProtectedMember
-        return self.image_descriptions[image_id]._asdict()
diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/image_processing_util.py b/spaces/cymic/Talking_Head_Anime_3/tha3/nn/image_processing_util.py
deleted file mode 100644
index e1d1a05fb07569e5c91daa298139ec081b5845a9..0000000000000000000000000000000000000000
--- a/spaces/cymic/Talking_Head_Anime_3/tha3/nn/image_processing_util.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-from torch import Tensor
-from torch.nn.functional import affine_grid, grid_sample
-
-
-def apply_rgb_change(alpha: Tensor, color_change: Tensor, image: Tensor):
-    image_rgb = image[:, 0:3, :, :]
-    color_change_rgb = color_change[:, 0:3, :, :]
-    output_rgb = color_change_rgb * alpha + image_rgb * (1 - alpha)
-    return torch.cat([output_rgb, image[:, 3:4, :, :]], dim=1)
-
-
-def apply_grid_change(grid_change, image: Tensor) -> Tensor:
-    n, c, h, w = image.shape
-    device = grid_change.device
-    grid_change = torch.transpose(grid_change.view(n, 2, h * w), 1, 2).view(n, h, w, 2)
-    identity = torch.tensor(
-        [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]],
-        dtype=grid_change.dtype,
-        device=device).unsqueeze(0).repeat(n, 1, 1)
-    base_grid = affine_grid(identity, [n, c, h, w], align_corners=False)
-    grid = base_grid + grid_change
-    resampled_image = grid_sample(image, grid, mode='bilinear', padding_mode='border', align_corners=False)
-    return resampled_image
-
-
-class GridChangeApplier:
-    def __init__(self):
-        self.last_n = None
-        self.last_device = None
-        self.last_identity = None
-
-    def apply(self, grid_change: Tensor, image: Tensor, align_corners: bool = False) -> Tensor:
-        n, c, h, w = image.shape
-        device = grid_change.device
-        grid_change = torch.transpose(grid_change.view(n, 2, h * w), 1, 2).view(n, h, w, 2)
-
-        if n == self.last_n and device == self.last_device:
-            identity = self.last_identity
-        else:
-            identity = torch.tensor(
-                [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]],
-                dtype=grid_change.dtype,
-                device=device,
-                requires_grad=False) \
-                .unsqueeze(0).repeat(n, 1, 1)
-            self.last_identity = identity
-            self.last_n = n
-            self.last_device = device
-        base_grid = affine_grid(identity, [n, c, h, w], align_corners=align_corners)
-
-        grid = base_grid + grid_change
-        resampled_image = grid_sample(image, grid, mode='bilinear', padding_mode='border', align_corners=align_corners)
-        return resampled_image
-
-
-def apply_color_change(alpha, color_change, image: Tensor) -> Tensor:
-    return color_change * alpha + image * (1 - alpha)
diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/text2speech.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/text2speech.py
deleted file mode 100644
index 6948edf1e96c78b534882aa003f7b71e6eb9c323..0000000000000000000000000000000000000000
--- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/text2speech.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-import tempfile
-from TTS.api import TTS
-
-
-
-class TTSTalker():
-    def __init__(self) -> None:
-        model_name = TTS.list_models()[0]
-        self.tts = TTS(model_name)
-
-    def test(self, text, language='en'):
-
-        tempf  = tempfile.NamedTemporaryFile(
-                delete = False,
-                suffix = ('.'+'wav'),
-            )
-
-        self.tts.tts_to_file(text, speaker=self.tts.speakers[0], language=language, file_path=tempf.name)
-
-        return tempf.name
\ No newline at end of file
diff --git a/spaces/danielcwq/chat-your-data-trial/README.md b/spaces/danielcwq/chat-your-data-trial/README.md
deleted file mode 100644
index 5743b66ffdc7d4295c87aed649d953b98f3ed996..0000000000000000000000000000000000000000
--- a/spaces/danielcwq/chat-your-data-trial/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat Your Data H2 Economics
-emoji: 📊
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: hwchase17/chat-your-data-state-of-the-union
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/daniellefranca96/styles-scribble-demo/style_scribble.py b/spaces/daniellefranca96/styles-scribble-demo/style_scribble.py
deleted file mode 100644
index 0f17189095f848500baf0255269ff3fb1cf3059f..0000000000000000000000000000000000000000
--- a/spaces/daniellefranca96/styles-scribble-demo/style_scribble.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from langchain.base_language import BaseLanguageModel
-from langchain.chains import LLMChain, SequentialChain
-from langchain.chat_models import ChatAnthropic
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import HuggingFaceHub
-from langchain.prompts import (
-    PromptTemplate,
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-
-class StyleScribble:
-    example: str
-    prompt: str
-    llm: BaseLanguageModel
-
-    def __init__(self, example=None, prompt=None, llm=None):
-        self.example = example
-        self.prompt = prompt
-        self.llm = llm
-
-    def set_imp_llm(self, model):
-        if model == 'GPT3':
-            self.llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
-        elif model == "GPT4":
-            self.llm = ChatOpenAI(model_name="gpt-4")
-        elif model == "Claude":
-            self.llm = ChatAnthropic()
-        else:
-            self.llm = HuggingFaceHub(repo_id=model)
-
-    def run(self):
-        return self.process()
-
-    def process(self):
-        seq_chain = SequentialChain(
-            chains=[self.get_extract_tone_chain(), self.get_generate_text_chain(self.prompt),
-                    self.get_apply_style_chain()],
-            input_variables=["text"], verbose=True)
-        result = seq_chain({'text': self.example, "style": ""})
-        return str(result.get('result'))
-
-    def create_chain(self, chat_prompt, output_key):
-        return LLMChain(llm=self.llm,
-                        prompt=chat_prompt, output_key=output_key)
-
-    def get_extract_tone_chain(self):
-        template = """Building upon the nuances and distinctive traits in the sample text, establish
-         a comprehensive style guide that encapsulates the unique tone and writing style present in the sample. 
-         This guide should focus on compelling tactics that foster a sense of connection between readers and 
-         the content. Refrain from discussing the specific theme of the sample text or using it as a direct example. 
-         Instead, formulate your analysis in such a way that it remains abstract, facilitating its application to any
-         other text that might be inspired by or originate from the sample. This abstract analysis will enable writers to adopt the essence of the style while allowing for versatility across various themes or topics..
-        """
-        system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-        human_template = "{text}"
-        human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-        chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
-
-        return self.create_chain(chat_prompt, "style")
-
-    def get_generate_text_chain(self, prompt):
-        template = """Generate a text following the user_request(use same language of the request):
-                      {user_request}
-                      """.replace("{user_request}", prompt)
-        return self.create_chain(PromptTemplate.from_template(template),
-                                 "generated_text")
-
-    def get_apply_style_chain(self):
-        template = """STYLE:
-                    {style}
-                    REWRITE THE TEXT BELLOW APPLYING THE STYLE GUIDE ABOVE(use same language of the request):
-                    {generated_text}
-        """
-
-        prompt = PromptTemplate.from_template(template=template)
-        prompt.partial(style="")
-        return self.create_chain(prompt, "result")
diff --git a/spaces/davertor/colorizing_images/deoldify/loss.py b/spaces/davertor/colorizing_images/deoldify/loss.py
deleted file mode 100644
index b78caabb33133572cefaacf816468277ee7da18f..0000000000000000000000000000000000000000
--- a/spaces/davertor/colorizing_images/deoldify/loss.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from fastai import *
-from fastai.core import *
-from fastai.torch_core import *
-from fastai.callbacks import hook_outputs
-import torchvision.models as models
-
-
-class FeatureLoss(nn.Module):
-    def __init__(self, layer_wgts=[20, 70, 10]):
-        super().__init__()
-
-        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
-        requires_grad(self.m_feat, False)
-        blocks = [
-            i - 1
-            for i, o in enumerate(children(self.m_feat))
-            if isinstance(o, nn.MaxPool2d)
-        ]
-        layer_ids = blocks[2:5]
-        self.loss_features = [self.m_feat[i] for i in layer_ids]
-        self.hooks = hook_outputs(self.loss_features, detach=False)
-        self.wgts = layer_wgts
-        self.metric_names = ['pixel'] + [f'feat_{i}' for i in range(len(layer_ids))]
-        self.base_loss = F.l1_loss
-
-    def _make_features(self, x, clone=False):
-        self.m_feat(x)
-        return [(o.clone() if clone else o) for o in self.hooks.stored]
-
-    def forward(self, input, target):
-        out_feat = self._make_features(target, clone=True)
-        in_feat = self._make_features(input)
-        self.feat_losses = [self.base_loss(input, target)]
-        self.feat_losses += [
-            self.base_loss(f_in, f_out) * w
-            for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)
-        ]
-
-        self.metrics = dict(zip(self.metric_names, self.feat_losses))
-        return sum(self.feat_losses)
-
-    def __del__(self):
-        self.hooks.remove()
-
-
-# Refactored code, originally from https://github.com/VinceMarron/style_transfer
-class WassFeatureLoss(nn.Module):
-    def __init__(self, layer_wgts=[5, 15, 2], wass_wgts=[3.0, 0.7, 0.01]):
-        super().__init__()
-        self.m_feat = models.vgg16_bn(True).features.cuda().eval()
-        requires_grad(self.m_feat, False)
-        blocks = [
-            i - 1
-            for i, o in enumerate(children(self.m_feat))
-            if isinstance(o, nn.MaxPool2d)
-        ]
-        layer_ids = blocks[2:5]
-        self.loss_features = [self.m_feat[i] for i in layer_ids]
-        self.hooks = hook_outputs(self.loss_features, detach=False)
-        self.wgts = layer_wgts
-        self.wass_wgts = wass_wgts
-        self.metric_names = (
-            ['pixel']
-            + [f'feat_{i}' for i in range(len(layer_ids))]
-            + [f'wass_{i}' for i in range(len(layer_ids))]
-        )
-        self.base_loss = F.l1_loss
-
-    def _make_features(self, x, clone=False):
-        self.m_feat(x)
-        return [(o.clone() if clone else o) for o in self.hooks.stored]
-
-    def _calc_2_moments(self, tensor):
-        chans = tensor.shape[1]
-        tensor = tensor.view(1, chans, -1)
-        n = tensor.shape[2]
-        mu = tensor.mean(2)
-        tensor = (tensor - mu[:, :, None]).squeeze(0)
-        # Prevents nasty bug that happens very occassionally- divide by zero.  Why such things happen?
-        if n == 0:
-            return None, None
-        cov = torch.mm(tensor, tensor.t()) / float(n)
-        return mu, cov
-
-    def _get_style_vals(self, tensor):
-        mean, cov = self._calc_2_moments(tensor)
-        if mean is None:
-            return None, None, None
-        eigvals, eigvects = torch.symeig(cov, eigenvectors=True)
-        eigroot_mat = torch.diag(torch.sqrt(eigvals.clamp(min=0)))
-        root_cov = torch.mm(torch.mm(eigvects, eigroot_mat), eigvects.t())
-        tr_cov = eigvals.clamp(min=0).sum()
-        return mean, tr_cov, root_cov
-
-    def _calc_l2wass_dist(
-        self, mean_stl, tr_cov_stl, root_cov_stl, mean_synth, cov_synth
-    ):
-        tr_cov_synth = torch.symeig(cov_synth, eigenvectors=True)[0].clamp(min=0).sum()
-        mean_diff_squared = (mean_stl - mean_synth).pow(2).sum()
-        cov_prod = torch.mm(torch.mm(root_cov_stl, cov_synth), root_cov_stl)
-        var_overlap = torch.sqrt(
-            torch.symeig(cov_prod, eigenvectors=True)[0].clamp(min=0) + 1e-8
-        ).sum()
-        dist = mean_diff_squared + tr_cov_stl + tr_cov_synth - 2 * var_overlap
-        return dist
-
-    def _single_wass_loss(self, pred, targ):
-        mean_test, tr_cov_test, root_cov_test = targ
-        mean_synth, cov_synth = self._calc_2_moments(pred)
-        loss = self._calc_l2wass_dist(
-            mean_test, tr_cov_test, root_cov_test, mean_synth, cov_synth
-        )
-        return loss
-
-    def forward(self, input, target):
-        out_feat = self._make_features(target, clone=True)
-        in_feat = self._make_features(input)
-        self.feat_losses = [self.base_loss(input, target)]
-        self.feat_losses += [
-            self.base_loss(f_in, f_out) * w
-            for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)
-        ]
-
-        styles = [self._get_style_vals(i) for i in out_feat]
-
-        if styles[0][0] is not None:
-            self.feat_losses += [
-                self._single_wass_loss(f_pred, f_targ) * w
-                for f_pred, f_targ, w in zip(in_feat, styles, self.wass_wgts)
-            ]
-
-        self.metrics = dict(zip(self.metric_names, self.feat_losses))
-        return sum(self.feat_losses)
-
-    def __del__(self):
-        self.hooks.remove()
diff --git a/spaces/dawood/gradio_videogallery/src/backend/gradio_videogallery/videogallery.py b/spaces/dawood/gradio_videogallery/src/backend/gradio_videogallery/videogallery.py
deleted file mode 100644
index 0ee9de7c7eb4861f29b3544864ff5a708d47e48f..0000000000000000000000000000000000000000
--- a/spaces/dawood/gradio_videogallery/src/backend/gradio_videogallery/videogallery.py
+++ /dev/null
@@ -1,183 +0,0 @@
-"""gr.Gallery() component."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, Callable, List, Literal, Optional
-
-import numpy as np
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client import utils as client_utils
-from gradio_client.utils import is_http_url_like
-from PIL import Image as _Image  # using _ to minimize namespace pollution
-
-from gradio import processing_utils, utils
-from gradio.components.base import Component
-from gradio.data_classes import FileData, GradioModel, GradioRootModel
-from gradio.events import Events
-
-set_documentation_group("component")
-
-
-class GalleryImage(GradioModel):
-    image: FileData
-    caption: Optional[str] = None
-
-
-class GalleryData(GradioRootModel):
-    root: List[GalleryImage]
-
-
-@document()
-class videogallery(Component):
-    """
-    Used to display a list of images as a gallery that can be scrolled through.
-    Preprocessing: this component does *not* accept input.
-    Postprocessing: expects a list of images in any format, {List[numpy.array | PIL.Image | str | pathlib.Path]}, or a {List} of (image, {str} caption) tuples and displays them.
-
-    Demos: fake_gan
-    """
-
-    EVENTS = [Events.select]
-
-    data_model = GalleryData
-
-    def __init__(
-        self,
-        value: list[np.ndarray | _Image.Image | str | Path | tuple]
-        | Callable
-        | None = None,
-        *,
-        label: str | None = None,
-        every: float | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        render: bool = True,
-        columns: int | tuple | None = 2,
-        rows: int | tuple | None = None,
-        height: int | float | None = None,
-        allow_preview: bool = True,
-        preview: bool | None = None,
-        selected_index: int | None = None,
-        object_fit: Literal["contain", "cover", "fill", "none", "scale-down"]
-        | None = None,
-        show_share_button: bool | None = None,
-        show_download_button: bool | None = True,
-    ):
-        """
-        Parameters:
-            value: List of images to display in the gallery by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
-            columns: Represents the number of images that should be shown in one row, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints
-            rows: Represents the number of rows in the image grid, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints
-            height: The height of the gallery component, in pixels. If more images are displayed than can fit in the height, a scrollbar will appear.
-            allow_preview: If True, images in the gallery will be enlarged when they are clicked. Default is True.
-            preview: If True, videogallery will start in preview mode, which shows all of the images as thumbnails and allows the user to click on them to view them in full size. Only works if allow_preview is True.
-            selected_index: The index of the image that should be initially selected. If None, no image will be selected at start. If provided, will set videogallery to preview mode unless allow_preview is set to False.
-            object_fit: CSS object-fit property for the thumbnail images in the gallery. Can be "contain", "cover", "fill", "none", or "scale-down".
-            show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.
-            show_download_button: If True, will show a download button in the corner of the selected image. If False, the icon does not appear. Default is True.
-        """
-        self.columns = columns
-        self.rows = rows
-        self.height = height
-        self.preview = preview
-        self.object_fit = object_fit
-        self.allow_preview = allow_preview
-        self.show_download_button = (
-            (utils.get_space() is not None)
-            if show_download_button is None
-            else show_download_button
-        )
-        self.selected_index = selected_index
-
-        self.show_share_button = (
-            (utils.get_space() is not None)
-            if show_share_button is None
-            else show_share_button
-        )
-        super().__init__(
-            label=label,
-            every=every,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            render=render,
-            value=value,
-        )
-
-    def postprocess(
-        self,
-        value: list[np.ndarray | _Image.Image | str | Path]
-        | list[tuple[np.ndarray | _Image.Image | Path | str, str]]
-        | None,
-    ) -> GalleryData:
-        """
-        Parameters:
-            value: list of images, or list of (image, caption) tuples
-        Returns:
-            list of string file paths to images in temp directory
-        """
-        if value is None:
-            return GalleryData(root=[])
-        output = []
-        for media in value:
-            url = None
-            caption = None
-            mime_type = None
-            if isinstance(media, (tuple, list)):
-                media, caption = media
-            if isinstance(media, np.ndarray):
-                file = processing_utils.save_img_array_to_cache(
-                    media, cache_dir=self.GRADIO_CACHE
-                )
-                file_path = str(utils.abspath(file))
-            elif isinstance(media, _Image.Image):
-                file = processing_utils.save_pil_to_cache(
-                    media, cache_dir=self.GRADIO_CACHE
-                )
-                file_path = str(utils.abspath(file))
-            elif isinstance(media, str):
-                file_path = media
-                url = media if is_http_url_like(media) else None
-                mime_type = client_utils.get_mimetype(media)
-            elif isinstance(media, Path):
-                file_path = str(media)
-            else:
-                raise ValueError(f"Cannot process type as image: {type(media)}")
-            entry = GalleryImage(
-                image=FileData(path=file_path, url=url, mime_type=mime_type), caption=caption
-            )
-            output.append(entry)
-        return GalleryData(root=output)
-
-    def preprocess(self, payload: GalleryData | None) -> GalleryData | None:
-        if payload is None or not payload.root:
-            return None
-        return payload
-
-    def example_inputs(self) -> Any:
-        return [
-            "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/video_sample.mp4",
-            "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/video_sample.mp4",
-            "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/video_sample.mp4",
-            "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/video_sample.mp4",
-        ]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImagePath.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImagePath.py
deleted file mode 100644
index 3d3538c97b7b346df2f804721cf3ad810d5260f0..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImagePath.py
+++ /dev/null
@@ -1,19 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# path interface
-#
-# History:
-# 1996-11-04 fl   Created
-# 2002-04-14 fl   Added documentation stub class
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image
-
-Path = Image.core.path
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/removeOverlaps.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/removeOverlaps.py
deleted file mode 100644
index 624cd47b4076a95cbc7c2124550371f6ffa5ea37..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/removeOverlaps.py
+++ /dev/null
@@ -1,248 +0,0 @@
-""" Simplify TrueType glyphs by merging overlapping contours/components.
-
-Requires https://github.com/fonttools/skia-pathops
-"""
-
-import itertools
-import logging
-from typing import Callable, Iterable, Optional, Mapping
-
-from fontTools.misc.roundTools import otRound
-from fontTools.ttLib import ttFont
-from fontTools.ttLib.tables import _g_l_y_f
-from fontTools.ttLib.tables import _h_m_t_x
-from fontTools.pens.ttGlyphPen import TTGlyphPen
-
-import pathops
-
-
-__all__ = ["removeOverlaps"]
-
-
-class RemoveOverlapsError(Exception):
-    pass
-
-
-log = logging.getLogger("fontTools.ttLib.removeOverlaps")
-
-_TTGlyphMapping = Mapping[str, ttFont._TTGlyph]
-
-
-def skPathFromGlyph(glyphName: str, glyphSet: _TTGlyphMapping) -> pathops.Path:
-    path = pathops.Path()
-    pathPen = path.getPen(glyphSet=glyphSet)
-    glyphSet[glyphName].draw(pathPen)
-    return path
-
-
-def skPathFromGlyphComponent(
-    component: _g_l_y_f.GlyphComponent, glyphSet: _TTGlyphMapping
-):
-    baseGlyphName, transformation = component.getComponentInfo()
-    path = skPathFromGlyph(baseGlyphName, glyphSet)
-    return path.transform(*transformation)
-
-
-def componentsOverlap(glyph: _g_l_y_f.Glyph, glyphSet: _TTGlyphMapping) -> bool:
-    if not glyph.isComposite():
-        raise ValueError("This method only works with TrueType composite glyphs")
-    if len(glyph.components) < 2:
-        return False  # single component, no overlaps
-
-    component_paths = {}
-
-    def _get_nth_component_path(index: int) -> pathops.Path:
-        if index not in component_paths:
-            component_paths[index] = skPathFromGlyphComponent(
-                glyph.components[index], glyphSet
-            )
-        return component_paths[index]
-
-    return any(
-        pathops.op(
-            _get_nth_component_path(i),
-            _get_nth_component_path(j),
-            pathops.PathOp.INTERSECTION,
-            fix_winding=False,
-            keep_starting_points=False,
-        )
-        for i, j in itertools.combinations(range(len(glyph.components)), 2)
-    )
-
-
-def ttfGlyphFromSkPath(path: pathops.Path) -> _g_l_y_f.Glyph:
-    # Skia paths have no 'components', no need for glyphSet
-    ttPen = TTGlyphPen(glyphSet=None)
-    path.draw(ttPen)
-    glyph = ttPen.glyph()
-    assert not glyph.isComposite()
-    # compute glyph.xMin (glyfTable parameter unused for non composites)
-    glyph.recalcBounds(glyfTable=None)
-    return glyph
-
-
-def _round_path(
-    path: pathops.Path, round: Callable[[float], float] = otRound
-) -> pathops.Path:
-    rounded_path = pathops.Path()
-    for verb, points in path:
-        rounded_path.add(verb, *((round(p[0]), round(p[1])) for p in points))
-    return rounded_path
-
-
-def _simplify(path: pathops.Path, debugGlyphName: str) -> pathops.Path:
-    # skia-pathops has a bug where it sometimes fails to simplify paths when there
-    # are float coordinates and control points are very close to one another.
-    # Rounding coordinates to integers works around the bug.
-    # Since we are going to round glyf coordinates later on anyway, here it is
-    # ok(-ish) to also round before simplify. Better than failing the whole process
-    # for the entire font.
-    # https://bugs.chromium.org/p/skia/issues/detail?id=11958
-    # https://github.com/google/fonts/issues/3365
-    # TODO(anthrotype): remove once this Skia bug is fixed
-    try:
-        return pathops.simplify(path, clockwise=path.clockwise)
-    except pathops.PathOpsError:
-        pass
-
-    path = _round_path(path)
-    try:
-        path = pathops.simplify(path, clockwise=path.clockwise)
-        log.debug(
-            "skia-pathops failed to simplify '%s' with float coordinates, "
-            "but succeded using rounded integer coordinates",
-            debugGlyphName,
-        )
-        return path
-    except pathops.PathOpsError as e:
-        if log.isEnabledFor(logging.DEBUG):
-            path.dump()
-        raise RemoveOverlapsError(
-            f"Failed to remove overlaps from glyph {debugGlyphName!r}"
-        ) from e
-
-    raise AssertionError("Unreachable")
-
-
-def removeTTGlyphOverlaps(
-    glyphName: str,
-    glyphSet: _TTGlyphMapping,
-    glyfTable: _g_l_y_f.table__g_l_y_f,
-    hmtxTable: _h_m_t_x.table__h_m_t_x,
-    removeHinting: bool = True,
-) -> bool:
-    glyph = glyfTable[glyphName]
-    # decompose composite glyphs only if components overlap each other
-    if (
-        glyph.numberOfContours > 0
-        or glyph.isComposite()
-        and componentsOverlap(glyph, glyphSet)
-    ):
-        path = skPathFromGlyph(glyphName, glyphSet)
-
-        # remove overlaps
-        path2 = _simplify(path, glyphName)
-
-        # replace TTGlyph if simplified path is different (ignoring contour order)
-        if {tuple(c) for c in path.contours} != {tuple(c) for c in path2.contours}:
-            glyfTable[glyphName] = glyph = ttfGlyphFromSkPath(path2)
-            # simplified glyph is always unhinted
-            assert not glyph.program
-            # also ensure hmtx LSB == glyph.xMin so glyph origin is at x=0
-            width, lsb = hmtxTable[glyphName]
-            if lsb != glyph.xMin:
-                hmtxTable[glyphName] = (width, glyph.xMin)
-            return True
-
-    if removeHinting:
-        glyph.removeHinting()
-    return False
-
-
-def removeOverlaps(
-    font: ttFont.TTFont,
-    glyphNames: Optional[Iterable[str]] = None,
-    removeHinting: bool = True,
-    ignoreErrors=False,
-) -> None:
-    """Simplify glyphs in TTFont by merging overlapping contours.
-
-    Overlapping components are first decomposed to simple contours, then merged.
-
-    Currently this only works with TrueType fonts with 'glyf' table.
-    Raises NotImplementedError if 'glyf' table is absent.
-
-    Note that removing overlaps invalidates the hinting. By default we drop hinting
-    from all glyphs whether or not overlaps are removed from a given one, as it would
-    look weird if only some glyphs are left (un)hinted.
-
-    Args:
-        font: input TTFont object, modified in place.
-        glyphNames: optional iterable of glyph names (str) to remove overlaps from.
-            By default, all glyphs in the font are processed.
-        removeHinting (bool): set to False to keep hinting for unmodified glyphs.
-        ignoreErrors (bool): set to True to ignore errors while removing overlaps,
-            thus keeping the tricky glyphs unchanged (fonttools/fonttools#2363).
-    """
-    try:
-        glyfTable = font["glyf"]
-    except KeyError:
-        raise NotImplementedError("removeOverlaps currently only works with TTFs")
-
-    hmtxTable = font["hmtx"]
-    # wraps the underlying glyf Glyphs, takes care of interfacing with drawing pens
-    glyphSet = font.getGlyphSet()
-
-    if glyphNames is None:
-        glyphNames = font.getGlyphOrder()
-
-    # process all simple glyphs first, then composites with increasing component depth,
-    # so that by the time we test for component intersections the respective base glyphs
-    # have already been simplified
-    glyphNames = sorted(
-        glyphNames,
-        key=lambda name: (
-            glyfTable[name].getCompositeMaxpValues(glyfTable).maxComponentDepth
-            if glyfTable[name].isComposite()
-            else 0,
-            name,
-        ),
-    )
-    modified = set()
-    for glyphName in glyphNames:
-        try:
-            if removeTTGlyphOverlaps(
-                glyphName, glyphSet, glyfTable, hmtxTable, removeHinting
-            ):
-                modified.add(glyphName)
-        except RemoveOverlapsError:
-            if not ignoreErrors:
-                raise
-            log.error("Failed to remove overlaps for '%s'", glyphName)
-
-    log.debug("Removed overlaps for %s glyphs:\n%s", len(modified), " ".join(modified))
-
-
-def main(args=None):
-    import sys
-
-    if args is None:
-        args = sys.argv[1:]
-
-    if len(args) < 2:
-        print(
-            f"usage: fonttools ttLib.removeOverlaps INPUT.ttf OUTPUT.ttf [GLYPHS ...]"
-        )
-        sys.exit(1)
-
-    src = args[0]
-    dst = args[1]
-    glyphNames = args[2:] or None
-
-    with ttFont.TTFont(src) as f:
-        removeOverlaps(f, glyphNames)
-        f.save(dst)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/StaticForm-3812b7f1.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/StaticForm-3812b7f1.css
deleted file mode 100644
index 772d43d65ae1a3157ab24e69b7ecb88a3649b4fe..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/StaticForm-3812b7f1.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-sfqy0y{display:flex;flex-direction:inherit;flex-wrap:wrap;gap:var(--form-gap-width);box-shadow:var(--block-shadow);border:var(--block-border-width) solid var(--border-color-primary);border-radius:var(--block-radius);background:var(--border-color-primary);overflow-y:hidden}div.svelte-sfqy0y .block{box-shadow:none!important;border-width:0px!important;border-radius:0!important}.hidden.svelte-sfqy0y{display:none}
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
deleted file mode 100644
index ca0a90a5b5ca12120bd6317576d64d21bc275f90..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
+++ /dev/null
@@ -1,576 +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 inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor
-
-from diffusers.utils import is_accelerate_available
-
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from ..stable_diffusion import StableDiffusionPipelineOutput
-from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from .image_encoder import PaintByExampleImageEncoder
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def prepare_mask_and_masked_image(image, mask):
-    """
-    Prepares a pair (image, mask) to be consumed by the Paint by Example pipeline. This means that those inputs will be
-    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-    ``image`` and ``1`` for the ``mask``.
-
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-
-
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
-
-        # Batch single image
-        if image.ndim == 3:
-            assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Batched mask
-            if mask.shape[0] == image.shape[0]:
-                mask = mask.unsqueeze(1)
-            else:
-                mask = mask.unsqueeze(0)
-
-        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
-        assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
-        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
-        assert mask.shape[1] == 1, "Mask image must have a single channel"
-
-        # Check image is in [-1, 1]
-        if image.min() < -1 or image.max() > 1:
-            raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # paint-by-example inverses the mask
-        mask = 1 - mask
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
-    else:
-        if isinstance(image, PIL.Image.Image):
-            image = [image]
-
-        image = np.concatenate([np.array(i.convert("RGB"))[None, :] for i in image], axis=0)
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, PIL.Image.Image):
-            mask = [mask]
-
-        mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-        mask = mask.astype(np.float32) / 255.0
-
-        # paint-by-example inverses the mask
-        mask = 1 - mask
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-    masked_image = image * mask
-
-    return mask, masked_image
-
-
-class PaintByExamplePipeline(DiffusionPipeline):
-    r"""
-    Pipeline for image-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        image_encoder ([`PaintByExampleImageEncoder`]):
-            Encodes the example input image. The unet is conditioned on the example image instead of a text prompt.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    # TODO: feature_extractor is required to encode initial images (if they are in PIL format),
-    # we should give a descriptive message if the pipeline doesn't have one.
-    _optional_components = ["safety_checker"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        image_encoder: PaintByExampleImageEncoder,
-        unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = False,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            image_encoder=image_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        for cpu_offloaded_model in [self.unet, self.vae, self.image_encoder]:
-            cpu_offload(cpu_offloaded_model, execution_device=device)
-
-        if self.safety_checker is not None:
-            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
-
-    @property
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs
-    def check_inputs(self, image, height, width, callback_steps):
-        if (
-            not isinstance(image, torch.Tensor)
-            and not isinstance(image, PIL.Image.Image)
-            and not isinstance(image, list)
-        ):
-            raise ValueError(
-                "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
-                f" {type(image)}"
-            )
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents
-    def prepare_mask_latents(
-        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
-    ):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
-        )
-        mask = mask.to(device=device, dtype=dtype)
-
-        masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-        return mask, masked_image_latents
-
-    def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        image_embeddings, negative_prompt_embeds = self.image_encoder(image, return_uncond_vector=True)
-
-        # duplicate image embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = image_embeddings.shape
-        image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
-        image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, image_embeddings.shape[0], 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, 1, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
-
-        return image_embeddings
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        example_image: Union[torch.FloatTensor, PIL.Image.Image],
-        image: Union[torch.FloatTensor, PIL.Image.Image],
-        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 5.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            example_image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):
-                The exemplar image to guide the image generation.
-            image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            mask_image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
-                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
-                instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 1. Define call parameters
-        if isinstance(image, PIL.Image.Image):
-            batch_size = 1
-        elif isinstance(image, list):
-            batch_size = len(image)
-        else:
-            batch_size = image.shape[0]
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 2. Preprocess mask and image
-        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
-        height, width = masked_image.shape[-2:]
-
-        # 3. Check inputs
-        self.check_inputs(example_image, height, width, callback_steps)
-
-        # 4. Encode input image
-        image_embeddings = self._encode_image(
-            example_image, device, num_images_per_prompt, do_classifier_free_guidance
-        )
-
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.vae.config.latent_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            image_embeddings.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 7. Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            image_embeddings.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance,
-        )
-
-        # 8. Check that sizes of mask, masked image and latents match
-        num_channels_mask = mask.shape[1]
-        num_channels_masked_image = masked_image_latents.shape[1]
-        if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
-            raise ValueError(
-                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
-                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
-                " `pipeline.unet` or your `mask_image` or `image` input."
-            )
-
-        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 10. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                # concat latents, mask, masked_image_latents in the channel dimension
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                latent_model_input = torch.cat([latent_model_input, masked_image_latents, mask], dim=1)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 11. Post-processing
-        image = self.decode_latents(latents)
-
-        # 12. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)
-
-        # 13. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/declare-lab/tango/diffusers/tests/pipelines/pndm/__init__.py b/spaces/declare-lab/tango/diffusers/tests/pipelines/pndm/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_write_prd.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_write_prd.py
deleted file mode 100644
index 38e4e52219917e0c3e68e83950f7fb4ddb01ce82..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_write_prd.py
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 17:45
-@Author  : alexanderwu
-@File    : test_write_prd.py
-"""
-import pytest
-
-from metagpt.actions import BossRequirement
-from metagpt.logs import logger
-from metagpt.roles.product_manager import ProductManager
-from metagpt.schema import Message
-
-
-@pytest.mark.asyncio
-async def test_write_prd():
-    product_manager = ProductManager()
-    requirements = "开发一个基于大语言模型与私有知识库的搜索引擎，希望可以基于大语言模型进行搜索总结"
-    prd = await product_manager.handle(Message(content=requirements, cause_by=BossRequirement))
-    logger.info(requirements)
-    logger.info(prd)
-
-    # Assert the prd is not None or empty
-    assert prd is not None
-    assert prd != ""
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_token_counter.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_token_counter.py
deleted file mode 100644
index 479ccc22dd05494c0c801276d0d0f35d8648012b..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_token_counter.py
+++ /dev/null
@@ -1,69 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/24 17:54
-@Author  : alexanderwu
-@File    : test_token_counter.py
-"""
-import pytest
-
-from metagpt.utils.token_counter import count_message_tokens, count_string_tokens
-
-
-def test_count_message_tokens():
-    messages = [
-        {"role": "user", "content": "Hello"},
-        {"role": "assistant", "content": "Hi there!"},
-    ]
-    assert count_message_tokens(messages) == 17
-
-
-def test_count_message_tokens_with_name():
-    messages = [
-        {"role": "user", "content": "Hello", "name": "John"},
-        {"role": "assistant", "content": "Hi there!"},
-    ]
-    assert count_message_tokens(messages) == 17
-
-
-def test_count_message_tokens_empty_input():
-    """Empty input should return 3 tokens"""
-    assert count_message_tokens([]) == 3
-
-
-def test_count_message_tokens_invalid_model():
-    """Invalid model should raise a KeyError"""
-    messages = [
-        {"role": "user", "content": "Hello"},
-        {"role": "assistant", "content": "Hi there!"},
-    ]
-    with pytest.raises(NotImplementedError):
-        count_message_tokens(messages, model="invalid_model")
-
-
-def test_count_message_tokens_gpt_4():
-    messages = [
-        {"role": "user", "content": "Hello"},
-        {"role": "assistant", "content": "Hi there!"},
-    ]
-    assert count_message_tokens(messages, model="gpt-4-0314") == 15
-
-
-def test_count_string_tokens():
-    """Test that the string tokens are counted correctly."""
-
-    string = "Hello, world!"
-    assert count_string_tokens(string, model_name="gpt-3.5-turbo-0301") == 4
-
-
-def test_count_string_tokens_empty_input():
-    """Test that the string tokens are counted correctly."""
-
-    assert count_string_tokens("", model_name="gpt-3.5-turbo-0301") == 0
-
-
-def test_count_string_tokens_gpt_4():
-    """Test that the string tokens are counted correctly."""
-
-    string = "Hello, world!"
-    assert count_string_tokens(string, model_name="gpt-4-0314") == 4
diff --git a/spaces/diacanFperku/AutoGPT/Audio Visualizer After Effects Cs6 Crack.md b/spaces/diacanFperku/AutoGPT/Audio Visualizer After Effects Cs6 Crack.md
deleted file mode 100644
index eebd43e6012f5926157c09af6617f54d2dd1c642..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Audio Visualizer After Effects Cs6 Crack.md	
+++ /dev/null
@@ -1,62 +0,0 @@
-<br />
-<h1>How to Create an Amazing Audio Visualizer in After Effects CS6</h1>
-
-<p>If you want to make your music videos more dynamic and eye-catching, you might want to add an audio visualizer to them. An audio visualizer is a graphic that reacts to the sound and frequency of your music, creating a stunning visual effect.</p>
-
-<p>But how can you create an audio visualizer in After Effects CS6? Do you need any plugins or special skills? In this article, we will show you how to create an awesome audio visualizer in After Effects CS6 using only the built-in tools and effects. You will learn how to:</p>
-<h2>audio visualizer after effects cs6 crack</h2><br /><p><b><b>Download Zip</b> ::: <a href="https://gohhs.com/2uFUuD">https://gohhs.com/2uFUuD</a></b></p><br /><br />
-
-<ul>
-<li>Import your audio file and create a composition</li>
-<li>Use the Audio Waveform effect to generate a waveform layer</li>
-<li>Apply the Polar Coordinates effect to transform the waveform into a circular shape</li>
-<li>Add some glow and color effects to enhance the visualizer</li>
-<li>Render and export your final video</li>
-</ul>
-
-<p>Ready to get started? Let's dive in!</p>
-
-<h2>Step 1: Import Your Audio File and Create a Composition</h2>
-
-<p>The first step is to import your audio file into After Effects. You can use any audio file you want, but make sure it is in a format that After Effects can read, such as MP3, WAV, or AIFF.</p>
-
-<p>To import your audio file, go to File > Import > File and browse for your file. Alternatively, you can drag and drop your file from your folder into the Project panel.</p>
-
-<p>Once you have imported your audio file, you need to create a new composition for your visualizer. To do this, go to Composition > New Composition or press Ctrl+N on your keyboard. A dialog box will appear where you can set the parameters for your composition.</p>
-
-<p>You can name your composition whatever you want, but for this tutorial, we will name it "Audio Visualizer". You can also choose the preset that matches your desired resolution and frame rate. For this tutorial, we will use the HDTV 1080 29.97 preset, which has a resolution of 1920x1080 pixels and a frame rate of 29.97 frames per second.</p>
-
-<p>You also need to set the duration of your composition to match the length of your audio file. To do this, click on the Advanced tab and enter the duration of your audio file in the Duration field. For example, if your audio file is 3 minutes and 15 seconds long, you can enter 03:15:00.</p>
-<p></p>
-
-<p>Click OK to create your composition.</p>
-
-<h2>Step 2: Use the Audio Waveform Effect to Generate a Waveform Layer</h2>
-
-<p>The next step is to use the Audio Waveform effect to generate a waveform layer that will react to your audio file. To do this, you need to create a new solid layer and apply the effect to it.</p>
-
-<p>To create a new solid layer, go to Layer > New > Solid or press Ctrl+Y on your keyboard. A dialog box will appear where you can name your layer and choose its color. For this tutorial, we will name it "Waveform" and choose a white color.</p>
-
-<p>Click OK to create your solid layer.</p>
-
-<p>To apply the Audio Waveform effect, go to Effect > Generate > Audio Waveform or search for it in the Effects & Presets panel. A new effect will appear in the Effect Controls panel where you can adjust its settings.</p>
-
-<p>The first thing you need to do is to link the effect to your audio file. To do this, click on the None button next to the Audio Layer option and choose your audio file from the drop-down menu.</p>
-
-<p>Next, you need to adjust some parameters to customize the look of your waveform. Here are some suggestions:</p>
-
-<ul>
-<li>Set the Display Options to Analog Lines</li>
-<li>Set the Thickness to 5</li>
-<li>Set the Softness to 0</li>
-<li>Set the Audio Duration to 200</li>
-<li>Set the Maximum Height to 1000</li>
-<li>Set the Offset (X) to -500</li>
-<li>Set the Offset (Y) to -500</li>
-</ul>
-
-<p>You can also play with other parameters such as Color, Hue Interpolation, Start Point, End Point, etc. until you get the desired result.</p>
-
-<p>You should now</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Driver For 3DSP BlueW2310u Card Fix.md b/spaces/diacanFperku/AutoGPT/Driver For 3DSP BlueW2310u Card Fix.md
deleted file mode 100644
index 040fe30e9282af2a86eb44ea7f8c6283ff4bbf1e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Driver For 3DSP BlueW2310u Card Fix.md	
+++ /dev/null
@@ -1,68 +0,0 @@
-<h2>Driver for 3DSP BlueW2310u card</h2><br /><p><b><b>DOWNLOAD</b> &#10037; <a href="https://gohhs.com/2uFU1l">https://gohhs.com/2uFU1l</a></b></p><br /><br />
-
-llutz: running it in screen as well
-
- how do i restart pulseaudio
-
- peter_:  "pkill pulseaudio"
-
- peter_: just logout/login
-
- no
-
- peter_: why?
-
- it isnt working
-
- its not working
-
- peter_: Please don't flood; use  to paste; don't use Enter as punctuation.
-
- Hello
-
- I am currently using linux on my laptop
-
- I have dual boot with win 7, so I can boot between them
-
- there is nothing on my screen
-
- what is this
-
- I have some windows, and I have linux and there I would like to install desktop environment, so I can have window with files, folders etc. (no the installer will not do that)
-
- i put in a fresh ubuntu install
-
- installed new drivers for the 3d card
-
- and got no display
-
- Can I do that, and use either install it without restarting machine?
-
- Hello.
-
- furythor: then just install a desktop enviroment
-
- help
-
- furythor: sudo apt-get install ubuntu-desktop
-
- how do i fix it
-
- cant even get a gui
-
- i just downloaded Gparted. How do I use it? What should I do?
-
- furythor: and remember that there are just cli only desktop enviroments and no visual desktop (like unity)
-
- i put in a fresh ubuntu install, installed new drivers for the 3d card, and got no display
-
- what do i do
-
- furythor: it will ask about your passwords etc. wait some minutes
-
- xangua: OK thanks
-
- when i start ubuntu i get a black screen with nothing but 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/dibend/individual-stock-lookup/app.py b/spaces/dibend/individual-stock-lookup/app.py
deleted file mode 100644
index 51ce8aa80ba258759e33c2cf5c5fc4e6fee41b72..0000000000000000000000000000000000000000
--- a/spaces/dibend/individual-stock-lookup/app.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import gradio as gr
-import yfinance as yf
-import plotly.graph_objects as go
-import pandas as pd
-
-def news_to_html(news_data):
-    html_strings = []
-
-    for news_item in news_data:
-        title = news_item['title']
-        link = news_item['link']
-        image_url = news_item['thumbnail']['resolutions'][0]['url'] if 'thumbnail' in news_item and 'resolutions' in news_item['thumbnail'] and news_item['thumbnail']['resolutions'] else None
-
-        if image_url:
-            html_strings.append(f"<a href='{link}' target='_blank'><img src='{image_url}' alt='{title}' style='width:100%; height:auto;' /><h3>{title}</h3></a><br><hr>")
-        else:
-            html_strings.append(f"<a href='{link}' target='_blank'><h3>{title}</h3></a><br>")
-
-    return "\n".join(html_strings)
-
-def fetch_stock_data(ticker, period, interval):
-    stock = yf.Ticker(ticker)
-
-    # Fetch historical data
-    data = stock.history(period=period, interval=interval)
-
-    # Create a candlestick chart for stock prices
-    fig_price = go.Figure(data=[go.Candlestick(
-        x=data.index,
-        open=data['Open'],
-        high=data['High'],
-        low=data['Low'],
-        close=data['Close'],
-        name='Price'
-    )])
-    fig_price.update_layout(title=f'Candlestick Chart for {ticker}', xaxis_title='Date', yaxis_title='Price')
-
-    # Create a line chart for volume
-    fig_volume = go.Figure(data=[go.Scatter(x=data.index, y=data['Volume'], mode='lines', name='Volume')])
-    fig_volume.update_layout(title=f'Volume for {ticker}', xaxis_title='Date', yaxis_title='Volume')
-
-    # Fetch additional data
-    stock_info = stock.info
-    market_cap = stock_info.get('marketCap', 'N/A')
-    dividend_yield = stock_info.get('dividendYield', 'N/A')
-    beta = stock_info.get('beta', 'N/A')
-    fifty_two_week_high = stock_info.get('fiftyTwoWeekHigh', 'N/A')
-    fifty_two_week_low = stock_info.get('fiftyTwoWeekLow', 'N/A')
-    forward_pe = stock_info.get('forwardPE', 'N/A')
-    trailing_pe = stock_info.get('trailingPE', 'N/A')
-    pb_ratio = stock_info.get('priceToBook', 'N/A')
-    roe = stock_info.get('returnOnEquity', 'N/A')
-    eps = stock_info.get('trailingEps')
-    forward_eps = stock_info.get('forwardEps')
-    dividends = pd.DataFrame(stock.dividends).reset_index().iloc[::-1]
-    dividends.columns = ['Date', 'Dividend Amount']
-    splits = pd.DataFrame(stock.splits).reset_index().iloc[::-1]
-    splits.columns = ['Date', 'Split Ratio']
-    news = news_to_html(stock.news)
-    major_holders = stock.major_holders
-    institutional_holders = stock.institutional_holders
-    mutual_fund_holders = stock.mutualfund_holders
-    income_stmt = stock.income_stmt
-    quarterly_income_stmt = stock.quarterly_income_stmt
-    cashflow = stock.cashflow
-    quarterly_cashflow = stock.quarterly_cashflow
-    balance_sheet = stock.balance_sheet
-    quarterly_balance_sheet = stock.quarterly_balance_sheet
-
-    return fig_price, fig_volume, dividends, splits, news, major_holders, institutional_holders, mutual_fund_holders, income_stmt, quarterly_income_stmt, cashflow, quarterly_cashflow, balance_sheet, quarterly_balance_sheet, market_cap, dividend_yield, beta, fifty_two_week_high, fifty_two_week_low, forward_pe, trailing_pe, pb_ratio, roe, eps, forward_eps
-
-# Gradio interface
-interface = gr.Interface(
-    fn=fetch_stock_data,
-    inputs=[
-        gr.Textbox(label="Ticker"),
-        gr.Dropdown(choices=['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max'], label="Period"),
-        gr.Dropdown(choices=['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo'], label="Interval")
-    ],
-    outputs=[
-        gr.Plot(label="Stock Price"),
-        gr.Plot(label="Volume"),
-        gr.DataFrame(label="Dividends", max_rows=100),
-        gr.DataFrame(label="Splits", max_rows=100),
-        gr.HTML(label="News"),
-        gr.DataFrame(label="Major Holders", max_rows=100),
-        gr.DataFrame(label="Institutional Holders", max_rows=100),
-        gr.DataFrame(label="Mutual Fund Holders", max_rows=100),
-        gr.DataFrame(label="Income Statement", max_rows=100),
-        gr.DataFrame(label="Quarterly Income Statement", max_rows=100),
-        gr.DataFrame(label="Cashflow", max_rows=100),
-        gr.DataFrame(label="Quarterly Cashflow", max_rows=100),
-        gr.DataFrame(label="Balance Sheet", max_rows=100),
-        gr.DataFrame(label="Quarterly Balance Sheet", max_rows=100),
-        gr.Textbox(label="Market Cap"),
-        gr.Textbox(label="Dividend Yield"),
-        gr.Textbox(label="Beta"),
-        gr.Textbox(label="52 Week High"),
-        gr.Textbox(label="52 Week Low"),
-        gr.Textbox(label="Forward P/E"),
-        gr.Textbox(label="Trailing P/E"),
-        gr.Textbox(label="Price-to-Book Ratio"),
-        gr.Textbox(label="Return on Equity"),
-        gr.Textbox(label="Trailing Earnings per Share"),
-        gr.Textbox(label="Forward Earnings per Share")
-    ]
-)
-
-interface.launch(share=False, debug=True)
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/text/english_bert_mock.py b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/text/english_bert_mock.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-
-
-def get_bert_feature(norm_text, word2ph):
-    return torch.zeros(1024, sum(word2ph))
diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/text/symbols.py b/spaces/digitalxingtong/Nailv-Bert-Vits2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-
diff --git a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/setup_ffmpeg.py b/spaces/digitalxingtong/Nailv-read-Bert-Vits2/setup_ffmpeg.py
deleted file mode 100644
index 7137ab5faebb6d80740b8c843667458f25596839..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/setup_ffmpeg.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import sys
-import re
-from pathlib import Path
-import winreg
-
-def check_ffmpeg_path():
-    path_list = os.environ['Path'].split(';')
-    ffmpeg_found = False
-
-    for path in path_list:
-        if 'ffmpeg' in path.lower() and 'bin' in path.lower():
-            ffmpeg_found = True
-            print("FFmpeg already installed.")
-            break
-
-    return ffmpeg_found
-
-def add_ffmpeg_path_to_user_variable():
-    ffmpeg_bin_path = Path('.\\ffmpeg\\bin')
-    if ffmpeg_bin_path.is_dir():
-        abs_path = str(ffmpeg_bin_path.resolve())
-        
-        try:
-            key = winreg.OpenKey(
-                winreg.HKEY_CURRENT_USER,
-                r"Environment",
-                0,
-                winreg.KEY_READ | winreg.KEY_WRITE
-            )
-            
-            try:
-                current_path, _ = winreg.QueryValueEx(key, "Path")
-                if abs_path not in current_path:
-                    new_path = f"{current_path};{abs_path}"
-                    winreg.SetValueEx(key, "Path", 0, winreg.REG_EXPAND_SZ, new_path)
-                    print(f"Added FFmpeg path to user variable 'Path': {abs_path}")
-                else:
-                    print("FFmpeg path already exists in the user variable 'Path'.")
-            finally:
-                winreg.CloseKey(key)
-        except WindowsError:
-            print("Error: Unable to modify user variable 'Path'.")
-            sys.exit(1)
-
-    else:
-        print("Error: ffmpeg\\bin folder not found in the current path.")
-        sys.exit(1)
-
-def main():
-    if not check_ffmpeg_path():
-       add_ffmpeg_path_to_user_variable()
-            
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/transforms.py b/spaces/digitalxingtong/Nailv-read-Bert-Vits2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/transforms.py
+++ /dev/null
@@ -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
diff --git a/spaces/dmeck/RVC-Speakers/vits/modules/commons/__init__.py b/spaces/dmeck/RVC-Speakers/vits/modules/commons/__init__.py
deleted file mode 100644
index a17b0e4cb6c20e86f84cc5d0be75eb41306d1fce..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/vits/modules/commons/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from vits.modules.commons.commons import *
diff --git a/spaces/doluvor/faster-whisper-webui/src/vadParallel.py b/spaces/doluvor/faster-whisper-webui/src/vadParallel.py
deleted file mode 100644
index c2323c0b632c34014ac1fe7ac79141b5bd9c5731..0000000000000000000000000000000000000000
--- a/spaces/doluvor/faster-whisper-webui/src/vadParallel.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import multiprocessing
-from queue import Empty
-import threading
-import time
-from src.hooks.progressListener import ProgressListener
-from src.vad import AbstractTranscription, TranscriptionConfig, get_audio_duration
-
-from multiprocessing import Pool, Queue
-
-from typing import Any, Dict, List, Union
-import os
-
-from src.whisper.abstractWhisperContainer import AbstractWhisperCallback
-
-class _ProgressListenerToQueue(ProgressListener):
-    def __init__(self, progress_queue: Queue):
-        self.progress_queue = progress_queue
-        self.progress_total = 0
-        self.prev_progress = 0
-
-    def on_progress(self, current: Union[int, float], total: Union[int, float]):
-        delta = current - self.prev_progress
-        self.prev_progress = current
-        self.progress_total = total
-        self.progress_queue.put(delta)
-
-    def on_finished(self):
-        if self.progress_total > self.prev_progress:
-            delta = self.progress_total - self.prev_progress
-            self.progress_queue.put(delta)
-            self.prev_progress = self.progress_total
-
-class ParallelContext:
-    def __init__(self, num_processes: int = None, auto_cleanup_timeout_seconds: float = None):
-        self.num_processes = num_processes
-        self.auto_cleanup_timeout_seconds = auto_cleanup_timeout_seconds
-        self.lock = threading.Lock()
-
-        self.ref_count = 0
-        self.pool = None
-        self.cleanup_timer = None
-
-    def get_pool(self):
-        # Initialize pool lazily
-        if (self.pool is None):
-            context = multiprocessing.get_context('spawn')
-            self.pool = context.Pool(self.num_processes)
-
-        self.ref_count = self.ref_count + 1
-
-        if (self.auto_cleanup_timeout_seconds is not None):
-            self._stop_auto_cleanup()
-
-        return self.pool
-
-    def return_pool(self, pool):
-        if (self.pool == pool and self.ref_count > 0):
-            self.ref_count = self.ref_count - 1
-
-            if (self.ref_count == 0):
-                if (self.auto_cleanup_timeout_seconds is not None):
-                    self._start_auto_cleanup()
-
-    def _start_auto_cleanup(self):
-        if (self.cleanup_timer is not None):
-            self.cleanup_timer.cancel()
-        self.cleanup_timer = threading.Timer(self.auto_cleanup_timeout_seconds, self._execute_cleanup)
-        self.cleanup_timer.start()
-
-        print("Started auto cleanup of pool in " + str(self.auto_cleanup_timeout_seconds) + " seconds")
-
-    def _stop_auto_cleanup(self):
-        if (self.cleanup_timer is not None):
-            self.cleanup_timer.cancel()
-            self.cleanup_timer = None
-
-            print("Stopped auto cleanup of pool")
-
-    def _execute_cleanup(self):
-        print("Executing cleanup of pool")
-
-        if (self.ref_count == 0):
-            self.close()
-
-    def close(self):
-        self._stop_auto_cleanup()
-
-        if (self.pool is not None):
-            print("Closing pool of " + str(self.num_processes) + " processes")
-            self.pool.close()
-            self.pool.join()
-        self.pool = None
-
-class ParallelTranscriptionConfig(TranscriptionConfig):
-    def __init__(self, device_id: str, override_timestamps, initial_segment_index, copy: TranscriptionConfig = None):
-        super().__init__(copy.non_speech_strategy, copy.segment_padding_left, copy.segment_padding_right, copy.max_silent_period, copy.max_merge_size, copy.max_prompt_window, initial_segment_index)
-        self.device_id = device_id
-        self.override_timestamps = override_timestamps
-
-class ParallelTranscription(AbstractTranscription):
-    # Silero VAD typically takes about 3 seconds per minute, so there's no need to split the chunks 
-    # into smaller segments than 2 minute (min 6 seconds per CPU core)
-    MIN_CPU_CHUNK_SIZE_SECONDS = 2 * 60
-
-    def __init__(self, sampling_rate: int = 16000):
-        super().__init__(sampling_rate=sampling_rate)
-
-    def transcribe_parallel(self, transcription: AbstractTranscription, audio: str, whisperCallable: AbstractWhisperCallback, config: TranscriptionConfig, 
-                            cpu_device_count: int, gpu_devices: List[str], cpu_parallel_context: ParallelContext = None, gpu_parallel_context: ParallelContext = None, 
-                            progress_listener: ProgressListener = None):
-        total_duration = get_audio_duration(audio)
-
-        # First, get the timestamps for the original audio
-        if (cpu_device_count > 1 and not transcription.is_transcribe_timestamps_fast()):
-            merged = self._get_merged_timestamps_parallel(transcription, audio, config, total_duration, cpu_device_count, cpu_parallel_context)
-        else:
-            timestamp_segments = transcription.get_transcribe_timestamps(audio, config, 0, total_duration)
-            merged = transcription.get_merged_timestamps(timestamp_segments, config, total_duration)
-
-        # We must make sure the whisper model is downloaded
-        if (len(gpu_devices) > 1):
-            whisperCallable.model_container.ensure_downloaded()
-
-        # Split into a list for each device
-        # TODO: Split by time instead of by number of chunks
-        merged_split = list(self._split(merged, len(gpu_devices)))
-
-        # Parameters that will be passed to the transcribe function
-        parameters = []
-        segment_index = config.initial_segment_index
-
-        processing_manager = multiprocessing.Manager()
-        progress_queue = processing_manager.Queue()
-
-        for i in range(len(gpu_devices)):
-            # Note that device_segment_list can be empty. But we will still create a process for it,
-            # as otherwise we run the risk of assigning the same device to multiple processes.
-            device_segment_list = list(merged_split[i]) if i < len(merged_split) else []
-            device_id = gpu_devices[i]
-
-            print("Device " + str(device_id) + " (index " + str(i) + ") has " + str(len(device_segment_list)) + " segments")
-
-            # Create a new config with the given device ID
-            device_config = ParallelTranscriptionConfig(device_id, device_segment_list, segment_index, config)
-            segment_index += len(device_segment_list)
-
-            progress_listener_to_queue = _ProgressListenerToQueue(progress_queue)
-            parameters.append([audio, whisperCallable, device_config, progress_listener_to_queue]);
-
-        merged = {
-            'text': '',
-            'segments': [],
-            'language': None
-        }
-
-        created_context = False
-
-        perf_start_gpu = time.perf_counter()
-
-        # Spawn a separate process for each device
-        try:
-            if (gpu_parallel_context is None):
-                gpu_parallel_context = ParallelContext(len(gpu_devices))
-                created_context = True
-
-            # Get a pool of processes
-            pool = gpu_parallel_context.get_pool()
-
-            # Run the transcription in parallel
-            results_async = pool.starmap_async(self.transcribe, parameters)
-            total_progress = 0
-
-            while not results_async.ready():
-                try:
-                    delta = progress_queue.get(timeout=5)  # Set a timeout of 5 seconds
-                except Empty:
-                    continue
-                
-                total_progress += delta
-                if progress_listener is not None:
-                    progress_listener.on_progress(total_progress, total_duration)
-
-            results = results_async.get()
-
-            # Call the finished callback
-            if progress_listener is not None:
-                progress_listener.on_finished()
-
-            for result in results:
-                # Merge the results
-                if (result['text'] is not None):
-                    merged['text'] += result['text']
-                if (result['segments'] is not None):
-                    merged['segments'].extend(result['segments'])
-                if (result['language'] is not None):
-                    merged['language'] = result['language']
-
-        finally:
-            # Return the pool to the context
-            if (gpu_parallel_context is not None):
-                gpu_parallel_context.return_pool(pool)
-            # Always close the context if we created it
-            if (created_context):
-                gpu_parallel_context.close()
-
-        perf_end_gpu = time.perf_counter()
-        print("Parallel transcription took " + str(perf_end_gpu - perf_start_gpu) + " seconds")
-
-        return merged
-
-    def _get_merged_timestamps_parallel(self, transcription: AbstractTranscription, audio: str, config: TranscriptionConfig, total_duration: float, 
-                                       cpu_device_count: int, cpu_parallel_context: ParallelContext = None):
-        parameters = []
-
-        chunk_size = max(total_duration / cpu_device_count, self.MIN_CPU_CHUNK_SIZE_SECONDS)
-        chunk_start = 0
-        cpu_device_id = 0
-
-        perf_start_time = time.perf_counter()
-
-        # Create chunks that will be processed on the CPU
-        while (chunk_start < total_duration):
-            chunk_end = min(chunk_start + chunk_size, total_duration)
-
-            if (chunk_end - chunk_start < 1):
-                # No need to process chunks that are less than 1 second
-                break
-
-            print("Parallel VAD: Executing chunk from " + str(chunk_start) + " to " + 
-                    str(chunk_end) + " on CPU device " + str(cpu_device_id))
-            parameters.append([audio, config, chunk_start, chunk_end]);
-
-            cpu_device_id += 1
-            chunk_start = chunk_end
-
-        created_context = False
-
-        # Spawn a separate process for each device
-        try:
-            if (cpu_parallel_context is None):
-                cpu_parallel_context = ParallelContext(cpu_device_count)
-                created_context = True
-
-            # Get a pool of processes
-            pool = cpu_parallel_context.get_pool()
-
-            # Run the transcription in parallel. Note that transcription must be picklable.
-            results = pool.starmap(transcription.get_transcribe_timestamps, parameters)
-
-            timestamps = []
-
-            # Flatten the results
-            for result in results:
-                timestamps.extend(result)
-
-            merged = transcription.get_merged_timestamps(timestamps, config, total_duration)
-
-            perf_end_time = time.perf_counter()
-            print("Parallel VAD processing took {} seconds".format(perf_end_time - perf_start_time))
-            return merged
-
-        finally:
-            # Return the pool to the context
-            if (cpu_parallel_context is not None):
-                cpu_parallel_context.return_pool(pool)
-            # Always close the context if we created it
-            if (created_context):
-                cpu_parallel_context.close()
-
-    def get_transcribe_timestamps(self, audio: str, config: ParallelTranscriptionConfig, start_time: float, duration: float):
-        return []
-
-    def get_merged_timestamps(self,  timestamps: List[Dict[str, Any]], config: ParallelTranscriptionConfig, total_duration: float):
-        # Override timestamps that will be processed
-        if (config.override_timestamps is not None):
-            print("(get_merged_timestamps) Using override timestamps of size " + str(len(config.override_timestamps)))
-            return config.override_timestamps
-        return super().get_merged_timestamps(timestamps, config, total_duration)
-
-    def transcribe(self, audio: str, whisperCallable: AbstractWhisperCallback, config: ParallelTranscriptionConfig, 
-                   progressListener: ProgressListener = None):
-        # Override device ID the first time
-        if (os.environ.get("INITIALIZED", None) is None):
-            os.environ["INITIALIZED"] = "1"
-
-            # Note that this may be None if the user didn't specify a device. In that case, Whisper will
-            # just use the default GPU device.
-            if (config.device_id is not None):
-                print("Using device " + config.device_id)
-                os.environ["CUDA_VISIBLE_DEVICES"] = config.device_id
-        
-        return super().transcribe(audio, whisperCallable, config, progressListener)
-
-    def _split(self, a, n):
-        """Split a list into n approximately equal parts."""
-        k, m = divmod(len(a), n)
-        return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n))
-
diff --git a/spaces/dorkai/singpt/extensions/silero_tts/script.py b/spaces/dorkai/singpt/extensions/silero_tts/script.py
deleted file mode 100644
index f611dc27b7480cd357b77c0c407fcc2bd6df2679..0000000000000000000000000000000000000000
--- a/spaces/dorkai/singpt/extensions/silero_tts/script.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import time
-from pathlib import Path
-
-import gradio as gr
-import torch
-
-import modules.chat as chat
-import modules.shared as shared
-
-torch._C._jit_set_profiling_mode(False)
-
-params = {
-    'activate': True,
-    'speaker': 'en_56',
-    'language': 'en',
-    'model_id': 'v3_en',
-    'sample_rate': 48000,
-    'device': 'cpu',
-    'show_text': False,
-    'autoplay': True,
-    'voice_pitch': 'medium',
-    'voice_speed': 'medium',
-}
-
-current_params = params.copy()
-voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
-voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high']
-voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast']
-
-# Used for making text xml compatible, needed for voice pitch and speed control
-table = str.maketrans({
-    "<": "&lt;",
-    ">": "&gt;",
-    "&": "&amp;",
-    "'": "&apos;",
-    '"': "&quot;",
-})
-
-def xmlesc(txt):
-    return txt.translate(table)
-
-def load_model():
-    model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
-    model.to(params['device'])
-    return model
-model = load_model()
-
-def remove_surrounded_chars(string):
-    new_string = ""
-    in_star = False
-    for char in string:
-        if char == '*':
-            in_star = not in_star
-        elif not in_star:
-            new_string += char
-    return new_string
-
-def remove_tts_from_history(name1, name2):
-    for i, entry in enumerate(shared.history['internal']):
-        shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
-    return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
-
-def toggle_text_in_history(name1, name2):
-    for i, entry in enumerate(shared.history['visible']):
-        visible_reply = entry[1]
-        if visible_reply.startswith('<audio'):
-            if params['show_text']:
-                reply = shared.history['internal'][i][1]
-                shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
-            else:
-                shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
-    return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
-
-def input_modifier(string):
-    """
-    This function is applied to your text inputs before
-    they are fed into the model.
-    """
-
-    # Remove autoplay from the last reply
-    if (shared.args.chat or shared.args.cai_chat) and len(shared.history['internal']) > 0:
-        shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>','controls>')]
-
-    shared.processing_message = "*Is recording a voice message...*"
-    return string
-
-def output_modifier(string):
-    """
-    This function is applied to the model outputs.
-    """
-
-    global model, current_params
-
-    for i in params:
-        if params[i] != current_params[i]:
-            model = load_model()
-            current_params = params.copy()
-            break
-
-    if params['activate'] == False:
-        return string
-
-    original_string = string
-    string = remove_surrounded_chars(string)
-    string = string.replace('"', '')
-    string = string.replace('“', '')
-    string = string.replace('\n', ' ')
-    string = string.strip()
-
-    if string == '':
-        string = '*Empty reply, try regenerating*'
-    else:
-        output_file = Path(f'extensions/silero_tts/outputs/{shared.character}_{int(time.time())}.wav')
-        prosody = '<prosody rate="{}" pitch="{}">'.format(params['voice_speed'], params['voice_pitch'])
-        silero_input = f'<speak>{prosody}{xmlesc(string)}</prosody></speak>'
-        model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
-
-        autoplay = 'autoplay' if params['autoplay'] else ''
-        string = f'<audio src="file/{output_file.as_posix()}" controls {autoplay}></audio>'
-        if params['show_text']:
-            string += f'\n\n{original_string}'
-
-    shared.processing_message = "*Is typing...*"
-    return string
-
-def bot_prefix_modifier(string):
-    """
-    This function is only applied in chat mode. It modifies
-    the prefix text for the Bot and can be used to bias its
-    behavior.
-    """
-
-    return string
-
-def ui():
-    # Gradio elements
-    with gr.Accordion("Silero TTS"):
-        with gr.Row():
-            activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
-            autoplay = gr.Checkbox(value=params['autoplay'], label='Play TTS automatically')
-        show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player')
-        voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice')
-        with gr.Row():
-            v_pitch = gr.Dropdown(value=params['voice_pitch'], choices=voice_pitches, label='Voice pitch')
-            v_speed = gr.Dropdown(value=params['voice_speed'], choices=voice_speeds, label='Voice speed')
-        with gr.Row():
-            convert = gr.Button('Permanently replace audios with the message texts')
-            convert_cancel = gr.Button('Cancel', visible=False)
-            convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False)
-
-    # Convert history with confirmation
-    convert_arr = [convert_confirm, convert, convert_cancel]
-    convert.click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
-    convert_confirm.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
-    convert_confirm.click(remove_tts_from_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
-    convert_confirm.click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
-    convert_cancel.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
-
-    # Toggle message text in history
-    show_text.change(lambda x: params.update({"show_text": x}), show_text, None)
-    show_text.change(toggle_text_in_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
-    show_text.change(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
-
-    # Event functions to update the parameters in the backend
-    activate.change(lambda x: params.update({"activate": x}), activate, None)
-    autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None)
-    voice.change(lambda x: params.update({"speaker": x}), voice, None)
-    v_pitch.change(lambda x: params.update({"voice_pitch": x}), v_pitch, None)
-    v_speed.change(lambda x: params.update({"voice_speed": x}), v_speed, None)
diff --git a/spaces/durgaamma2005/fire_detector/app.py b/spaces/durgaamma2005/fire_detector/app.py
deleted file mode 100644
index cb21f05d075f7bd0f3b26f1122bdd9b150ee1b33..0000000000000000000000000000000000000000
--- a/spaces/durgaamma2005/fire_detector/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-import skimage
-learn = load_learner('fire_smoke_export.pkl')
-labels = learn.dls.vocab
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
- 
-title = "Fire and Smoke Detector"
-description = "Fire and Smoke classifier created with fastai. Created as a demo for Gradio and HuggingFace Spaces. Fire accidents are not uncommon and has catastrophic impact on the company both interms of social and financial terms. This application can be deployed on device and can work as 24X365 surveillance. This app classify an image into three classes. 1. Fire, 2. Smoke, 3. Neutral. Try your hand and check. For any general purpose, model file can be copied and used for the stated purpose"
-article="<p style='text-align: center'><a href='https://github.com/mldurga/projects/blob/master/Fire_smoke_detector.ipynb' target='_blank'>Blog post</a></p>"
-interpretation='default'
-examples = ['Neutral2.jpeg','smoke1.jpeg','smoke2.jpeg','fire1.png']
-
-enable_queue=True
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,interpretation=interpretation,examples=examples,enable_queue=enable_queue).launch()
diff --git a/spaces/facebook/XLS-R-300m-EN-15/README.md b/spaces/facebook/XLS-R-300m-EN-15/README.md
deleted file mode 100644
index 6931a25ba82e0bce5c9e038e18a1b79dc3285a09..0000000000000000000000000000000000000000
--- a/spaces/facebook/XLS-R-300m-EN-15/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: XLS-R EN-to-All 300m
-emoji: 🎙️
-colorFrom: gray
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/falterWliame/Face_Mask_Detection/MicrosoftTrainSimulatorEditorTools VERIFIED.md b/spaces/falterWliame/Face_Mask_Detection/MicrosoftTrainSimulatorEditorTools VERIFIED.md
deleted file mode 100644
index bb63cb4aa6e2c4a56d1c2da88845a28ec6d0c3f8..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/MicrosoftTrainSimulatorEditorTools VERIFIED.md	
+++ /dev/null
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-Imported from Discussions. The Windows 10 version of Train Simulator gives the train simulation game the best value for money. Train simulator for windows 7 or Vista? 3. TrainSimulator is the latest Windows Train Simulator for Windows XP. TrainSimulator - Windows XP/Vista. What should i do. Windows Vista is the successor to Windows XP. It was released to the public on August 14, 2006. Microsoft Windows Vista Home Premium Edition was released on August 22, 2006. It has an estimated market share of 23. Like Train Simulator for Windows. 6 7 8; Some features may not work, such as MP3-stereo support, or Flash movies.
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diff --git a/spaces/falterWliame/Face_Mask_Detection/PASSWORDFIFA13RELOADEDtxtrar BEST.md b/spaces/falterWliame/Face_Mask_Detection/PASSWORDFIFA13RELOADEDtxtrar BEST.md
deleted file mode 100644
index 1f4879108e3af3d8e2ac3086f4a359d4a1639ec2..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/PASSWORDFIFA13RELOADEDtxtrar BEST.md	
+++ /dev/null
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diff --git a/spaces/fatiXbelha/sd/Baku 4K See the Flame Towers and Other Attractions of Azerbaijan in HDR.md b/spaces/fatiXbelha/sd/Baku 4K See the Flame Towers and Other Attractions of Azerbaijan in HDR.md
deleted file mode 100644
index 1a159ee11c71fa72dfe55dafa5bc1d3a3e40db4f..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Baku 4K See the Flame Towers and Other Attractions of Azerbaijan in HDR.md	
+++ /dev/null
@@ -1,111 +0,0 @@
-
-<h1>Baku 4K: A Guide to the City of Winds</h1>
-<p>Baku is the capital and largest city of Azerbaijan, as well as the largest city on the Caspian Sea and of the Caucasus region. It is a city that combines ancient history, modern architecture, rich culture and stunning natural beauty. In this article, we will explore some of the best things to see and do in Baku, as well as give you some practical tips on how to plan your trip.</p>
-<h2>baku 4k</h2><br /><p><b><b>Download Zip</b> &#10040; <a href="https://urllie.com/2uNE0Q">https://urllie.com/2uNE0Q</a></b></p><br /><br />
- <h2>Introduction</h2>
-<h3>Why visit Baku?</h3>
-<p>Baku is a city that offers something for everyone. Whether you are interested in exploring its UNESCO-listed old town, admiring its futuristic skyline, learning about its diverse art scene, or enjoying its delicious cuisine, you will find plenty of reasons to fall in love with Baku. Baku is also a city that surprises and delights its visitors with its multiculturalism, hospitality and charm.</p>
- <h3>How to get to Baku?</h3>
-<p>Baku is well-connected by air, land and sea. The main international airport is Heydar Aliyev International Airport, located about 25 kilometers (16 miles) from the city center. You can take a bus, taxi or shuttle service to reach the city from the airport. Alternatively, you can also travel to Baku by train from neighboring countries such as Georgia, Russia and Iran. If you prefer to travel by sea, you can take a ferry from Turkmenistan or Kazakhstan across the Caspian Sea.</p>
- <h3>When to visit Baku?</h3>
-<p>Baku has a warm steppe climate with hot summers and mild winters. The best time to visit Baku is from April to June and from September to October, when the weather is pleasant and the crowds are less. The average temperature in these months ranges from 15°C (59°F) to 25°C (77°F). Avoid visiting Baku in July and August, when the temperature can reach up to 40°C (104°F) and the humidity is high. Also avoid visiting Baku in January and February, when the temperature can drop below zero and the winds are strong.</p>
- <h2>Main attractions in Baku</h2>
-<h3>Icheri Sheher (Baku's Old City)</h3>
-<p>One of the must-see attractions in Baku is Icheri Sheher, or the Old City, which is the historical core of the city. It dates back to the 12th century and contains many monuments and buildings that reflect the influence of various cultures and civilizations that have ruled over Baku. Some of the highlights of Icheri Sheher are:</p>
- <h4>Maiden Tower</h4>
-<p>This cylindrical tower is one of the symbols of Baku and one of the oldest structures in the city. It was built in the 12th century as a Zoroastrian temple and later served as a watchtower and a lighthouse. The tower has eight floors and a spiral staircase that leads to the top, where you can enjoy panoramic views of the city and the sea. The tower also houses a museum that showcases the history and legends of Baku.</p>
- <h4>Palace of the Shirvanshahs</h4>
-palace is a masterpiece of medieval architecture and a UNESCO World Heritage Site.</p>
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- <h4>National Museum of History of Azerbaijan</h4>
-<p>This museum is located in a former mansion of a wealthy oil baron and displays more than 2,000 exhibits that showcase the history and culture of Azerbaijan from ancient times to the present day. The museum has several sections, such as archaeology, ethnography, numismatics, art and manuscripts. You can also see the original furnishings and decorations of the mansion, which reflect the opulence and elegance of the oil boom era.</p>
- <h3>Nizami Street</h3>
-<p>If you want to experience the modern and vibrant side of Baku, head to Nizami Street, which is the main shopping and entertainment street in the city. It is named after Nizami Ganjavi, a famous Persian poet who was born in Ganja, a city in western Azerbaijan. Nizami Street is lined with shops, cafes, restaurants, cinemas, theaters and clubs that cater to different tastes and budgets. You can also admire the architecture of the buildings along the street, which range from classical to Art Nouveau styles.</p>
- <h3>Government House</h3>
-<p>This impressive building is located at the end of Nizami Street and serves as the seat of several ministries and state agencies of Azerbaijan. It was built in the 1930s by Soviet architects and features a symmetrical design with a central dome and two wings. The building is decorated with sculptures and reliefs that depict scenes from Azerbaijani history and culture. The building is especially beautiful at night, when it is illuminated with colorful lights.</p>
- <h3>Fountain Square</h3>
-<p>This is one of the most popular and lively spots in Baku, where locals and tourists gather to relax, socialize and enjoy the atmosphere. The square is named after the many fountains that adorn it, which were installed in the 19th century by wealthy oil magnates. The square is surrounded by trees, flowers, benches and statues, as well as cafes, restaurants and shops. You can also find street performers, artists and vendors selling souvenirs and snacks.</p>
- <h3>Flame Towers</h3>
-<p>These are three skyscrapers that dominate the skyline of Baku and symbolize the city's modernity and ambition. They are shaped like flames and represent the elements of fire, water and earth. They are also covered with LED screens that display various animations and images at night, such as flames, waves and flags. The towers house offices, hotels, apartments and a shopping mall. You can also visit the observation deck on the 29th floor of one of the towers to enjoy panoramic views of the city.</p>
- <h2>Culture and art in Baku</h2>
-<h3>Baku Museum of Modern Art</h3>
-<p>This museum is dedicated to contemporary art from Azerbaijan and other countries. It was opened in 2009 and has a collection of more than 800 works by local and international artists. The museum showcases various styles and genres of art, such as painting, sculpture, photography, video art and installation art. The museum also hosts temporary exhibitions, workshops, lectures and events.</p>
- <h3>Azerbaijan State Museum of Art</h3>
-<p>This museum is one of the oldest and largest art museums in Azerbaijan. It was founded in 1920 and has a collection of more than 15,000 works by Azerbaijani and foreign artists. The museum has two buildings: one for Eastern art and one for Western art. The Eastern art section displays works from Azerbaijan, Iran, Turkey, China, Japan, India and other countries. The Western art section displays works from Europe, Russia and America.</p>
- <h3>YAY Gallery</h3>
-and has a mission to promote and support contemporary art in Azerbaijan. The gallery exhibits works by local and international artists in various media, such as painting, sculpture, photography, video art and installation art. The gallery also organizes events, such as artist talks, screenings, performances and workshops.</p>
- <h3>Azerbaijan Carpet Museum</h3>
-<p>This museum is a unique and fascinating place to learn about the history and culture of Azerbaijan through its carpets. Carpets are an integral part of Azerbaijani identity and heritage, and have been produced and used for centuries in various regions of the country. The museum has a collection of more than 10,000 carpets and carpet-related items, such as tools, materials, patterns and designs. The museum also displays other types of Azerbaijani folk art, such as jewelry, ceramics, metalwork and embroidery.</p>
- <h2>Conclusion</h2>
-<p>Baku is a city that will surprise you with its diversity, beauty and charm. It is a city that blends the old and the new, the East and the West, the traditional and the modern. It is a city that offers a rich cultural and artistic experience, as well as a stunning natural scenery. Baku is a city that you will want to visit again and again.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Baku:</p>
- <ul>
-<li>What is the currency of Azerbaijan?</li>
-<li>The currency of Azerbaijan is the Azerbaijani manat (AZN). One manat is equal to 100 qapik. You can exchange your money at banks, exchange offices or hotels. You can also use credit cards or ATMs in major cities.</li>
-<li>What is the language of Azerbaijan?</li>
-<li>The official language of Azerbaijan is Azerbaijani, which belongs to the Turkic language family. It is written in Latin script and has some similarities with Turkish. Most people in Baku also speak Russian, which was widely used during the Soviet era. You can also find some people who speak English, especially in tourist areas.</li>
-<li>What is the time zone of Azerbaijan?</li>
-<li>The time zone of Azerbaijan is UTC+4, which means it is four hours ahead of Coordinated Universal Time (UTC). Azerbaijan does not observe daylight saving time.</li>
-<li>What is the voltage and plug type in Azerbaijan?</li>
-<li>The voltage in Azerbaijan is 220 volts and the frequency is 50 hertz. The plug type is C or F, which are two round pins. You may need an adapter or a converter if your devices have a different plug type or voltage.</li>
-<li>What are some of the traditional dishes of Azerbaijan?</li>
-<li>Some of the traditional dishes of Azerbaijan are:</li>
-<ul>
-<li>Plov: rice cooked with meat, dried fruits, nuts and spices.</li>
-<li>Dolma: grape leaves or cabbage leaves stuffed with minced meat, rice and herbs.</li>
-<li>Kebab: grilled meat on skewers, served with bread, salad and sauces.</li>
-<li>Kufta: meatballs made from minced meat, rice, onions and spices.</li>
-<li>Dushbara: small dumplings filled with meat or cheese, served in broth.</li>
-</ul>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/models/transfo_xl_reasoning/__init__.py b/spaces/fclong/summary/fengshen/models/transfo_xl_reasoning/__init__.py
deleted file mode 100644
index 2c071fa45cfa595933f14cdd86f10541600f46bc..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/models/transfo_xl_reasoning/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# encoding=utf-8
-from fengshen.models.transfo_xl_denoise.modeling_transfo_xl_denoise import TransfoXLDenoiseModel as TransfoXLModel
-from .generate import deduction_generate, abduction_generate
\ No newline at end of file
diff --git a/spaces/fengmuxi/ChatGpt-Web/app/bing-chat/index.d.ts b/spaces/fengmuxi/ChatGpt-Web/app/bing-chat/index.d.ts
deleted file mode 100644
index 5bc54f2077b1f89b18343afac3d1cf774333fc7f..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/bing-chat/index.d.ts
+++ /dev/null
@@ -1,274 +0,0 @@
-type Author = "user" | "bot";
-type SendMessageOptions = {
-  conversationId?: string;
-  clientId?: string;
-  conversationSignature?: string;
-  invocationId?: string;
-  messageType?: string;
-  variant?: string;
-  locale?: string;
-  market?: string;
-  region?: string;
-  location?: {
-    lat: number | string;
-    lng: number | string;
-    re?: string;
-  };
-  onProgress?: (partialResponse: ChatMessage) => void;
-};
-interface ChatMessage {
-  id: string;
-  text: string;
-  author: Author;
-  conversationId: string;
-  clientId: string;
-  conversationSignature: string;
-  conversationExpiryTime?: string;
-  invocationId?: string;
-  messageType?: string;
-  variant?: string;
-  detail?: ChatMessageFull | ChatMessagePartial;
-}
-interface ConversationResult {
-  conversationId: string;
-  clientId: string;
-  conversationSignature: string;
-  result: APIResult;
-}
-interface APIResult {
-  value: string;
-  message: null;
-}
-interface ChatUpdate {
-  type: 1;
-  target: string;
-  arguments: ChatUpdateArgument[];
-}
-interface ChatUpdateArgument {
-  messages: ChatMessagePartial[];
-  requestId: string;
-  result: null;
-}
-interface ChatMessagePartial {
-  text: string;
-  author: Author;
-  createdAt: string;
-  timestamp: string;
-  messageId: string;
-  offense: string;
-  adaptiveCards: AdaptiveCard[];
-  sourceAttributions: any[];
-  feedback: ChatMessageFeedback;
-  contentOrigin: string;
-  privacy?: null;
-  messageType?: string;
-}
-interface AdaptiveCard {
-  type: string;
-  version: string;
-  body: AdaptiveCardBody[];
-}
-interface AdaptiveCardBody {
-  type: string;
-  text: string;
-  wrap: boolean;
-}
-interface ChatMessageFeedback {
-  tag: null;
-  updatedOn: null;
-  type: string;
-}
-interface ChatUpdateCompleteResponse {
-  type: 2;
-  invocationId: string;
-  item: ChatResponseItem;
-}
-interface ChatResponseItem {
-  messages: ChatMessageFull[];
-  firstNewMessageIndex: number;
-  suggestedResponses: null;
-  conversationId: string;
-  requestId: string;
-  conversationExpiryTime: string;
-  telemetry: Telemetry;
-  result: ChatRequestResult;
-}
-interface ChatMessageFull {
-  text: string;
-  author: Author;
-  from?: ChatMessageFrom;
-  createdAt: string;
-  timestamp: string;
-  locale?: string;
-  market?: string;
-  region?: string;
-  location?: string;
-  locationHints?: LocationHint[];
-  messageId: string;
-  requestId: string;
-  offense: string;
-  feedback: ChatMessageFeedback;
-  contentOrigin: string;
-  privacy?: null;
-  inputMethod?: string;
-  adaptiveCards?: AdaptiveCard[];
-  sourceAttributions?: any[];
-  suggestedResponses?: SuggestedResponse[];
-  messageType?: string;
-}
-interface ChatMessageFrom {
-  id: string;
-  name: null;
-}
-interface LocationHint {
-  country: string;
-  countryConfidence: number;
-  state: string;
-  city: string;
-  cityConfidence: number;
-  zipCode: string;
-  timeZoneOffset: number;
-  dma: number;
-  sourceType: number;
-  center: Coords;
-  regionType: number;
-}
-interface Coords {
-  latitude: number;
-  longitude: number;
-  height: null;
-}
-interface SuggestedResponse {
-  text: string;
-  messageId: string;
-  messageType: string;
-  contentOrigin: string;
-  author?: Author;
-  createdAt?: string;
-  timestamp?: string;
-  offense?: string;
-  feedback?: ChatMessageFeedback;
-  privacy?: null;
-}
-interface ChatRequestResult {
-  value: string;
-  serviceVersion: string;
-}
-interface Telemetry {
-  metrics?: null;
-  startTime: string;
-}
-interface ChatRequest {
-  arguments: ChatRequestArgument[];
-  invocationId: string;
-  target: string;
-  type: number;
-}
-interface ChatRequestArgument {
-  source: string;
-  optionsSets: string[];
-  allowedMessageTypes: string[];
-  sliceIds: any[];
-  traceId: string;
-  isStartOfSession: boolean;
-  message: ChatRequestMessage;
-  conversationSignature: string;
-  participant: Participant;
-  conversationId: string;
-  previousMessages: PreviousMessage[];
-}
-interface ChatRequestMessage {
-  locale: string;
-  market: string;
-  region?: string;
-  location?: string;
-  locationHints?: LocationHintChatRequestMessage[];
-  timestamp: string;
-  author: Author;
-  inputMethod: string;
-  text: string;
-  messageType: string;
-}
-interface LocationHintChatRequestMessage {
-  country: string;
-  state: string;
-  city: string;
-  zipcode: string;
-  timezoneoffset: number;
-  dma: number;
-  countryConfidence: number;
-  cityConfidence: number;
-  Center: Center;
-  RegionType: number;
-  SourceType: number;
-}
-interface Center {
-  Latitude: number;
-  Longitude: number;
-}
-interface Participant {
-  id: string;
-}
-interface PreviousMessage {
-  text: string;
-  author: Author;
-  adaptiveCards: any[];
-  suggestedResponses: SuggestedResponse[];
-  messageId: string;
-  messageType: string;
-}
-
-declare class BingChat {
-  protected _cookie: string;
-  protected _debug: boolean;
-  constructor(opts: {
-    cookie: string | undefined;
-    /** @defaultValue `false` **/
-    debug?: boolean;
-  });
-  /**
-   * Sends a message to Bing Chat, waits for the response to resolve, and returns
-   * the response.
-   *
-   * If you want to receive a stream of partial responses, use `opts.onProgress`.
-   *
-   * @param message - The prompt message to send
-   * @param opts.conversationId - Optional ID of a conversation to continue (defaults to a random UUID)
-   * @param opts.onProgress - Optional callback which will be invoked every time the partial response is updated
-   *
-   * @returns The response from Bing Chat
-   */
-  sendMessage(text: string, opts?: SendMessageOptions): Promise<ChatMessage>;
-  createConversation(): Promise<ConversationResult>;
-}
-
-export {
-  APIResult,
-  AdaptiveCard,
-  AdaptiveCardBody,
-  Author,
-  BingChat,
-  Center,
-  ChatMessage,
-  ChatMessageFeedback,
-  ChatMessageFrom,
-  ChatMessageFull,
-  ChatMessagePartial,
-  ChatRequest,
-  ChatRequestArgument,
-  ChatRequestMessage,
-  ChatRequestResult,
-  ChatResponseItem,
-  ChatUpdate,
-  ChatUpdateArgument,
-  ChatUpdateCompleteResponse,
-  ConversationResult,
-  Coords,
-  LocationHint,
-  LocationHintChatRequestMessage,
-  Participant,
-  PreviousMessage,
-  SendMessageOptions,
-  SuggestedResponse,
-  Telemetry,
-};
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Ashfall - The Ultimate Wasteland Adventure Game for Android.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Ashfall - The Ultimate Wasteland Adventure Game for Android.md
deleted file mode 100644
index edfaf9459f500d5dcb85c1c4af113709d29994f7..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Ashfall - The Ultimate Wasteland Adventure Game for Android.md	
+++ /dev/null
@@ -1,87 +0,0 @@
-
-<h1>Ashfall Android APK: A Post-Apocalyptic Shooter MMORPG</h1>
-<p>Do you love post-apocalyptic games with immersive stories, stunning graphics, and thrilling combat? If so, you should check out Ashfall APK, a new shooter MMORPG for Android devices. In this article, we will tell you everything you need to know about this game, including its story, gameplay, download, and installation. We will also answer some frequently asked questions about Ashfall APK. Let's get started!</p>
- <h2>The Story of Ashfall</h2>
-<p>Ashfall is set in a future where AI has risen up and launched a nuclear war against humanity. As a result, the world has been reduced to ruins and desolation. You are one of the survivors who live in a Vault, a safe underground shelter. However, you are not content with hiding in the dark. You want to find the Core of Creation, a mysterious device that can save the world. To do that, you have to leave the Vault and explore the wasteland.</p>
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- <h2>The Gameplay of Ashfall</h2>
-<p>Ashfall is a shooter MMORPG that combines elements of survival, exploration, and social interaction. You can create your own character and customize their appearance, skills, and equipment. You can also join clans and alliances with other players and participate in various events and activities. You can also trade, chat, and cooperate with other players in the game.</p>
- <h3>The World of Ashfall</h3>
-<p>The world of Ashfall is vast and diverse. You can visit different areas and locations that have their own themes and cultures. For example, you can explore barren deserts, abandoned towns, old world battlefields, bustling survivor settlements, and more. Each area has its own challenges, secrets, and rewards. You can also encounter NPCs with distinctive personalities and random events that add more fun and variety to your adventure.</p>
- <h3>The Combat of Ashfall</h3>
-<p>The combat of Ashfall is fast-paced and exciting. You can use various weapons and skills to fight against enemies and bosses. You can also use vehicles and mounts to travel faster and gain an edge in battle. You can also craft and upgrade your weapons and equipment to improve your performance. You can also switch between different modes of combat, such as first-person or third-person view.</p>
- <h3>The Social Features of Ashfall</h3>
-<p>The social features of Ashfall are rich and engaging. You can join clans and alliances with other players and work together to achieve common goals. You can also compete with other players in PvP modes, such as clan wars or arena battles. You can also chat with other players using voice or text messages. You can also share your achievements and screenshots with other players in the game.</p>
- <h2>The <h2>The Download and Installation of Ashfall APK</h2>
-<p>If you are interested in playing Ashfall APK, you can download and install it on your Android device easily. Here are the steps you need to follow:</p>
-<ol>
-<li>Go to the official website of Ashfall APK or a trusted source like APKCombo and download the latest version of the game.</li>
-<li>Enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and toggling it on.</li>
-<li>Locate the downloaded file on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
- <h2>The Conclusion and FAQs of Ashfall APK</h2>
-<p>Ashfall APK is a post-apocalyptic shooter MMORPG that offers a captivating story, stunning graphics, and thrilling combat. You can explore a vast and diverse world, fight against enemies and bosses, and interact with other players. You can also customize your character, join clans and alliances, and participate in various events and activities. Ashfall APK is a game that will keep you hooked for hours. If you are looking for a new and exciting game to play on your Android device, you should definitely give Ashfall APK a try.</p>
- <h4>FAQ 1: When will Ashfall be officially launched?</h4>
-<p>Answer: Ashfall is still in development and testing phase. The official launch date has not been announced yet, but it is expected to be sometime in 2023.</p>
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- <h4>FAQ 2: What are the system requirements for Ashfall APK?</h4>
-<p>Answer: Ashfall APK requires Android 5.0 or higher, 4 GB RAM, and 3 GB storage space. It also requires a stable internet connection to play.</p>
- <h4>FAQ 3: Is Ashfall APK free to play?</h4>
-<p>Answer: Yes, Ashfall APK is free to play, but it may contain in-app purchases that can enhance your gaming experience.</p>
- <h4>FAQ 4: Is Ashfall APK safe to download?</h4>
-<p>Answer: Yes, Ashfall APK is safe to download, but only from trusted sources like APKCombo. You should avoid downloading it from unknown or suspicious websites that may contain malware or viruses.</p>
- <h4>FAQ 5: What are some similar games to Ashfall APK?</h4>
-<p>Answer: Some similar games to Ashfall APK are Fallout Shelter, Last Day on Earth, Genshin Impact, and more. These games also feature post-apocalyptic themes, survival elements, and social features.</p>
- : [APKCombo](https://apkcombo.com/en-us/)</p> 197e85843d<br />
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\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Blast Your Way Through Buildings and Enemies with Total Destruction Mod Apk 2.3.6.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Blast Your Way Through Buildings and Enemies with Total Destruction Mod Apk 2.3.6.md
deleted file mode 100644
index f2d297f3c415ff67294ed70ef01d5003931ae815..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Blast Your Way Through Buildings and Enemies with Total Destruction Mod Apk 2.3.6.md	
+++ /dev/null
@@ -1,74 +0,0 @@
-<br />
-<h1><strong>Total Destruction Mod APK 2.3.6: Unleash Your Inner Demolition Expert</strong></h1>
-<p>Do you love destroying things? Do you enjoy watching buildings collapse, terrain explode, and enemies fly away? If you answered yes to any of these questions, then you will love <strong>Total Destruction Mod APK 2.3.6</strong>, a modded version of the popular arcade game <em>Total Destruction</em>. In this game, you can use machine guns, artillery, autocannon, cannon, bombs, rockets, nuclear weapons, and more to cause havoc and mayhem in various environments. You can also drive tanks, helicopters, cars, trucks, trains, boats, and planes to add more fun to your destruction spree.</p>
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-<p>In this article, we will tell you everything you need to know about Total Destruction Mod APK 2.3.6, including its features, how to download and install it on your Android device, its pros and cons, and some FAQs. By the end of this article, you will be ready to unleash your inner demolition expert and have a blast with this amazing game.</p>
- <h2><strong>Features of Total Destruction Mod APK 2.3.6</strong></h2>
-<h3><strong>Unlimited Money</strong></h3>
-<p>One of the best features of Total Destruction Mod APK 2.3.6 is that it gives you unlimited money in the game. This means that you can buy any weapon or vehicle you want without worrying about running out of cash. You can also upgrade your weapons and vehicles to make them more powerful and destructive.</p>
-<p>With unlimited money, you can experiment with different combinations of weapons and vehicles and see how much damage you can do. You can also unlock new weapons and vehicles as you progress through the game and complete missions. Unlimited money will make your gameplay more enjoyable and satisfying.</p>
- <h3><strong>Various Weapons and Vehicles</strong></h3>
-<p>Another feature of Total Destruction Mod APK 2.3.6 is that it offers a wide variety of weapons and vehicles to choose from. You can use machine guns, artillery, autocannon, cannon, bombs, rockets, nuclear weapons, and more to destroy buildings, terrain, and enemies. You can also drive tanks, helicopters, cars, trucks, trains, boats, and planes to add more fun to your destruction spree.</p>
-<p>Each weapon and vehicle has its own characteristics and abilities. Some are more effective than others in certain situations. For example, machine guns are good for shooting enemies at close range, but bombs are better for destroying large structures. Similarly, tanks are good for crushing obstacles, but helicopters are better for flying over them.</p>
-<p>You can switch between weapons and vehicles anytime during the game. You can also customize your weapons and vehicles to suit your preferences and style. For example, you can change the color, size, shape, and power of your weapons and vehicles. You can also add stickers, decals, flags, and other accessories to make them look more cool and unique.</p>
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- <h3><strong>Multiple Modes and Levels</strong></h3>
-<p>Another feature of Total Destruction Mod APK 2.3.6 is that it offers multiple modes and levels to play. You can choose from different modes such as Campaign Mode, Sandbox Mode, Survival Mode, Multiplayer Mode, and Custom Mode. Each mode has its own objectives, rules, challenges, destruction spree. The game has unlimited money, various weapons and vehicles, realistic physics and graphics, multiple modes and levels, and offline and online gameplay. The game is easy to download and install on your Android device, but it also has some cons that you should be aware of. If you love destroying things, you should definitely try Total Destruction Mod APK 2.3.6 and have a blast with this amazing game.</p>
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-<ul>
-<li><strong>Q: Is Total Destruction Mod APK 2.3.6 safe to use?</strong></li>
-<li>A: Yes, Total Destruction Mod APK 2.3.6 is safe to use as long as you download it from a trusted source and scan it for viruses before installing it on your device.</li>
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-<li><strong>Q: Can I play Total Destruction Mod APK 2.3.6 on PC or iOS devices?</strong></li>
-<li>A: No, Total Destruction Mod APK 2.3.6 is only compatible with Android devices and cannot be played on PC or iOS devices.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Any YouTube Video as MP3 with T Download MP3.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Any YouTube Video as MP3 with T Download MP3.md
deleted file mode 100644
index 84dcffd21fea5d42fcca27ba77b887b4b7a1ebbb..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Any YouTube Video as MP3 with T Download MP3.md	
+++ /dev/null
@@ -1,75 +0,0 @@
-
-<h1>How to Download MP3 Files from the Internet</h1>
-<p>MP3 is one of the most popular audio file formats in the world. It stands for MPEG-1 Audio Layer 3 and it is a standard for compressing digital audio. MP3 files can reduce the size of a CD-quality audio file by up to 90% without losing much of the original sound quality. This makes MP3 files ideal for storing and sharing music online.</p>
-<p>Many people want to download MP3 files from the Internet for various reasons. Some want to listen to their favorite songs offline, some want to create custom playlists or CDs, some want to discover new music or artists, and some want to use MP3 files for their own creative projects. Whatever your reason is, you need a good MP3 downloader to get the music you want.</p>
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-<h2>Benefits of MP3 Format</h2>
-<p>There are many advantages of using MP3 format for your music. Here are some of them:</p>
-<ul>
-<li><strong>Reduced file size:</strong> MP3 files are much smaller than uncompressed audio files, which means they take up less space on your device and use less bandwidth when streaming or downloading. You can store more songs on your phone, computer, or portable media player with MP3 files.</li>
-<li><strong>Comparable audio quality:</strong> MP3 files use a lossy compression technique that discards some data that is not essential for human hearing. However, this does not affect the overall sound quality much, especially at higher bit rates. Most people cannot tell the difference between a 320 kbps MP3 file and a CD-quality audio file.</li>
-<li><strong>Portability:</strong> MP3 files are compatible with most devices and platforms that support digital audio playback. You can easily transfer MP3 files between different devices or share them with others via email, cloud storage, or social media. You can also play MP3 files on various software applications or online services.</li>
-</ul>
-<h2>Best MP3 Downloaders</h2>
-<p>There are many tools and apps that can help you download MP3 files from the Internet. Some of them are free, some are paid, some are online, some are offline, some are simple, some are complex. Here are some of the best free MP3 downloaders that we recommend:</p>
-<ol>
-<li><strong>Free Download Manager (FDM):</strong> This is a powerful and versatile download manager that can handle any type of file, including MP3 files. It can monitor and intercept downloads from web browsers, but can also work independently. You can create batch downloads, download torrents, preview ZIP files before they're downloaded and even deselect files you don't want from the compressed folder.</li>
-<li><strong>Any Video Converter Free:</strong> This is a handy tool that can convert any video to any audio format, including MP3. It supports downloading high-def files up to 4K and even includes a basic editor for clipping and merging audio or video files. It can also download music from YouTube, SoundCloud, Facebook and other streaming services.</li>
-<li><strong>MP3 Downloader & Music Downloader:</strong> This is an online service that lets you download MP3 files from any YouTube video in a few easy steps. You just need to paste the video URL into the search bar and click Convert. Then you can choose the MP3 file quality and click Download.</li>
-</ol>
-<h2>Legal Issues of MP3 Downloading</h2>
-<p>Downloading MP3 files from the Internet is not always legal. It depends on whether you have permission from the copyright owner or not. Downloading copyrighted music without permission is illegal and can result in fines or even criminal charges. - Risk of viruses: Downloading MP3s from untrusted websites can expose your device to viruses or malware that can harm your data or privacy. You should always scan your downloads with antivirus software and avoid clicking on suspicious links or pop-ups. - Ethical issues of MP3 downloading: Downloading MP3s without paying for them can be seen as unfair to the artists and creators who put their time and effort into making music. It can also affect the music industry by reducing the revenue and incentives for producing quality music. You should consider supporting the artists you like by buying their music legally or using licensed streaming services. <h2>Conclusion</h2>
-<p>Downloading MP3 files from the Internet can be a convenient and enjoyable way to access music. However, you should be aware of the benefits and drawbacks of using MP3 format, the best and safest MP3 downloaders, and the legal and ethical issues of MP3 downloading. By following these tips, you can enjoy your music without breaking the law or harming your device.</p>
-<h2>FAQs</h2>
-<h3>What is the difference between MP3 and WAV?</h3>
-<p>MP3 and WAV are two common audio file formats. MP3 is a compressed format that reduces the file size by discarding some data that is not essential for human hearing. WAV is an uncompressed format that preserves all the data and sound quality of the original recording. MP3 files are more suitable for online distribution and storage, while WAV files are more suitable for professional editing and production.</p>
-<h3>How can I convert a video to MP3?</h3>
-<p>You can use a video to MP3 converter tool to extract the audio from a video file and save it as an MP3 file. There are many free online services and software applications that can do this, such as Any Video Converter Free or MP3 Downloader & Music Downloader. You just need to upload or paste the video URL, choose the output format, and click Convert.</p>
-<h3>How can I download MP3 files from Spotify?</h3>
-<p>Spotify is a popular streaming service that offers millions of songs and podcasts. However, Spotify does not allow users to download MP3 files from its platform, even if they have a premium subscription. The only way to download music from Spotify is to use its offline mode, which lets you listen to songs without an Internet connection, but only within the Spotify app. To use this feature, you need to toggle on the Download switch on the playlists or albums you want to listen offline.</p>
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diff --git a/spaces/fffffu/bing/src/app/layout.tsx b/spaces/fffffu/bing/src/app/layout.tsx
deleted file mode 100644
index 8b5122759987177b8dc4e4356d1d06cea25c15ea..0000000000000000000000000000000000000000
--- a/spaces/fffffu/bing/src/app/layout.tsx
+++ /dev/null
@@ -1,47 +0,0 @@
-import { Metadata } from 'next'
-import { Toaster } from 'react-hot-toast'
-import { TailwindIndicator } from '@/components/tailwind-indicator'
-import { Providers } from '@/components/providers'
-import { Header } from '@/components/header'
-
-import '@/app/globals.scss'
-
-
-export const metadata: Metadata = {
-  title: {
-    default: 'Bing AI Chatbot',
-    template: `%s - Bing AI Chatbot`
-  },
-  description: 'Bing AI Chatbot Web App.',
-  themeColor: [
-    { media: '(prefers-color-scheme: light)', color: 'white' },
-    { media: '(prefers-color-scheme: dark)', color: 'dark' }
-  ],
-  icons: {
-    icon: '/favicon.ico',
-    shortcut: '../assets/images/logo.svg',
-    apple: '../assets/images/logo.svg'
-  }
-}
-
-interface RootLayoutProps {
-  children: React.ReactNode
-}
-
-export default function RootLayout({ children }: RootLayoutProps) {
-  return (
-    <html lang="zh-CN" suppressHydrationWarning>
-      <body>
-        <Toaster />
-        <Providers attribute="class" defaultTheme="system" enableSystem>
-          <div className="flex flex-col min-h-screen">
-            {/* @ts-ignore */}
-            <Header />
-            <main className="flex flex-col flex-1">{children}</main>
-          </div>
-          <TailwindIndicator />
-        </Providers>
-      </body>
-    </html>
-  )
-}
diff --git a/spaces/fgbwyude/ChuanhuChatGPT/app.py b/spaces/fgbwyude/ChuanhuChatGPT/app.py
deleted file mode 100644
index ce2607dc9bb5a315ddeac21e57e71e26679a0dd0..0000000000000000000000000000000000000000
--- a/spaces/fgbwyude/ChuanhuChatGPT/app.py
+++ /dev/null
@@ -1,452 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-import logging
-import sys
-
-import gradio as gr
-
-from modules.utils import *
-from modules.presets import *
-from modules.overwrites import *
-from modules.chat_func import *
-from modules.openai_func import get_usage
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-my_api_key = ""  # 在这里输入你的 API 密钥
-
-# if we are running in Docker
-if os.environ.get("dockerrun") == "yes":
-    dockerflag = True
-else:
-    dockerflag = False
-
-authflag = False
-auth_list = []
-
-if not my_api_key:
-    my_api_key = os.environ.get("my_api_key")
-if dockerflag:
-    if my_api_key == "empty":
-        logging.error("Please give a api key!")
-        sys.exit(1)
-    # auth
-    username = os.environ.get("USERNAME")
-    password = os.environ.get("PASSWORD")
-    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
-        auth_list.append((os.environ.get("USERNAME"), os.environ.get("PASSWORD")))
-        authflag = True
-else:
-    if (
-        not my_api_key
-        and os.path.exists("api_key.txt")
-        and os.path.getsize("api_key.txt")
-    ):
-        with open("api_key.txt", "r") as f:
-            my_api_key = f.read().strip()
-    if os.path.exists("auth.json"):
-        authflag = True
-        with open("auth.json", "r", encoding='utf-8') as f:
-            auth = json.load(f)
-            for _ in auth:
-                if auth[_]["username"] and auth[_]["password"]:
-                    auth_list.append((auth[_]["username"], auth[_]["password"]))
-                else:
-                    logging.error("请检查auth.json文件中的用户名和密码！")
-                    sys.exit(1)
-
-gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
-
-with open("assets/custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-
-with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
-    history = gr.State([])
-    token_count = gr.State([])
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    user_api_key = gr.State(my_api_key)
-    user_question = gr.State("")
-    outputing = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.HTML(title)
-        with gr.Column(scale=4):
-            gr.HTML('<center><a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT?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(scale=4):
-            status_display = gr.Markdown(get_geoip(), elem_id="status_display")
-
-    with gr.Row().style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row():
-                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="100%")
-            with gr.Row():
-                with gr.Column(scale=12):
-                    user_input = gr.Textbox(
-                        show_label=False, placeholder="在这里输入"
-                    ).style(container=False)
-                with gr.Column(min_width=70, scale=1):
-                    submitBtn = gr.Button("发送", variant="primary")
-                    cancelBtn = gr.Button("取消", variant="secondary", visible=False)
-            with gr.Row():
-                emptyBtn = gr.Button(
-                    "🧹 新的对话",
-                )
-                retryBtn = gr.Button("🔄 重新生成")
-                delFirstBtn = gr.Button("🗑️ 删除最旧对话")
-                delLastBtn = gr.Button("🗑️ 删除最新对话")
-                reduceTokenBtn = gr.Button("♻️ 总结对话")
-
-        with gr.Column():
-            with gr.Column(min_width=50, scale=1):
-                with gr.Tab(label="ChatGPT"):
-                    keyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"OpenAI API-key...",
-                        value=hide_middle_chars(my_api_key),
-                        type="password",
-                        visible=not HIDE_MY_KEY,
-                        label="API-Key",
-                    )
-                    usageTxt = gr.Markdown("**发送消息** 或 **提交key** 以显示额度", elem_id="usage_display")
-                    model_select_dropdown = gr.Dropdown(
-                        label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
-                    )
-                    use_streaming_checkbox = gr.Checkbox(
-                        label="实时传输回答", value=True, visible=enable_streaming_option
-                    )
-                    use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
-                    language_select_dropdown = gr.Dropdown(
-                        label="选择回复语言（针对搜索&索引功能）",
-                        choices=REPLY_LANGUAGES,
-                        multiselect=False,
-                        value=REPLY_LANGUAGES[0],
-                    )
-                    index_files = gr.Files(label="上传索引文件", type="file", multiple=True)
-
-                with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入System Prompt...",
-                        label="System prompt",
-                        value=initial_prompt,
-                        lines=10,
-                    ).style(container=False)
-                    with gr.Accordion(label="加载Prompt模板", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(
-                                        label="选择Prompt模板集合文件",
-                                        choices=get_template_names(plain=True),
-                                        multiselect=False,
-                                        value=get_template_names(plain=True)[0],
-                                    ).style(container=False)
-                                with gr.Column(scale=1):
-                                    templateRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(
-                                        label="从Prompt模板中加载",
-                                        choices=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        ),
-                                        multiselect=False,
-                                        value=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        )[0],
-                                    ).style(container=False)
-
-                with gr.Tab(label="保存/加载"):
-                    with gr.Accordion(label="保存/加载对话历史记录", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(
-                                        label="从列表中加载对话",
-                                        choices=get_history_names(plain=True),
-                                        multiselect=False,
-                                        value=get_history_names(plain=True)[0],
-                                    )
-                                with gr.Column(scale=1):
-                                    historyRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    saveFileName = gr.Textbox(
-                                        show_label=True,
-                                        placeholder=f"设置文件名: 默认为.json，可选为.md",
-                                        label="设置保存文件名",
-                                        value="对话历史记录",
-                                    ).style(container=True)
-                                with gr.Column(scale=1):
-                                    saveHistoryBtn = gr.Button("💾 保存对话")
-                                    exportMarkdownBtn = gr.Button("📝 导出为Markdown")
-                                    gr.Markdown("默认保存于history文件夹")
-                            with gr.Row():
-                                with gr.Column():
-                                    downloadFile = gr.File(interactive=True)
-
-                with gr.Tab(label="高级"):
-                    gr.Markdown("# ⚠️ 务必谨慎更改 ⚠️\n\n如果无法使用请恢复默认设置")
-                    default_btn = gr.Button("🔙 恢复默认设置")
-
-                    with gr.Accordion("参数", open=False):
-                        top_p = gr.Slider(
-                            minimum=-0,
-                            maximum=1.0,
-                            value=1.0,
-                            step=0.05,
-                            interactive=True,
-                            label="Top-p",
-                        )
-                        temperature = gr.Slider(
-                            minimum=-0,
-                            maximum=2.0,
-                            value=1.0,
-                            step=0.1,
-                            interactive=True,
-                            label="Temperature",
-                        )
-
-                    with gr.Accordion("网络设置", open=False, visible=False):
-                        apiurlTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=f"在这里输入API地址...",
-                            label="API地址",
-                            value="https://api.openai.com/v1/chat/completions",
-                            lines=2,
-                        )
-                        changeAPIURLBtn = gr.Button("🔄 切换API地址")
-                        proxyTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=f"在这里输入代理地址...",
-                            label="代理地址（示例：http://127.0.0.1:10809）",
-                            value="",
-                            lines=2,
-                        )
-                        changeProxyBtn = gr.Button("🔄 设置代理地址")
-
-    gr.Markdown(description)
-    gr.HTML(footer.format(versions=versions_html()), elem_id="footer")
-    chatgpt_predict_args = dict(
-        fn=predict,
-        inputs=[
-            user_api_key,
-            systemPromptTxt,
-            history,
-            user_question,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files,
-            language_select_dropdown,
-        ],
-        outputs=[chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-
-    start_outputing_args = dict(
-        fn=start_outputing,
-        inputs=[],
-        outputs=[submitBtn, cancelBtn],
-        show_progress=True,
-    )
-
-    end_outputing_args = dict(
-        fn=end_outputing, inputs=[], outputs=[submitBtn, cancelBtn]
-    )
-
-    reset_textbox_args = dict(
-        fn=reset_textbox, inputs=[], outputs=[user_input]
-    )
-
-    transfer_input_args = dict(
-        fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True
-    )
-
-    get_usage_args = dict(
-        fn=get_usage, inputs=[user_api_key], outputs=[usageTxt], show_progress=False
-    )
-
-
-    # Chatbot
-    cancelBtn.click(cancel_outputing, [], [])
-
-    user_input.submit(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
-    user_input.submit(**get_usage_args)
-
-    submitBtn.click(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
-    submitBtn.click(**get_usage_args)
-
-    emptyBtn.click(
-        reset_state,
-        outputs=[chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-    emptyBtn.click(**reset_textbox_args)
-
-    retryBtn.click(**start_outputing_args).then(
-        retry,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            language_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    ).then(**end_outputing_args)
-    retryBtn.click(**get_usage_args)
-
-    delFirstBtn.click(
-        delete_first_conversation,
-        [history, token_count],
-        [history, token_count, status_display],
-    )
-
-    delLastBtn.click(
-        delete_last_conversation,
-        [chatbot, history, token_count],
-        [chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-
-    reduceTokenBtn.click(
-        reduce_token_size,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            gr.State(sum(token_count.value[-4:])),
-            model_select_dropdown,
-            language_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-    reduceTokenBtn.click(**get_usage_args)
-
-    # ChatGPT
-    keyTxt.change(submit_key, keyTxt, [user_api_key, status_display]).then(**get_usage_args)
-    keyTxt.submit(**get_usage_args)
-
-    # Template
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templateFileSelectDropdown.change(
-        load_template,
-        [templateFileSelectDropdown],
-        [promptTemplates, templateSelectDropdown],
-        show_progress=True,
-    )
-    templateSelectDropdown.change(
-        get_template_content,
-        [promptTemplates, templateSelectDropdown, systemPromptTxt],
-        [systemPromptTxt],
-        show_progress=True,
-    )
-
-    # S&L
-    saveHistoryBtn.click(
-        save_chat_history,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    exportMarkdownBtn.click(
-        export_markdown,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(
-        load_chat_history,
-        [historyFileSelectDropdown, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-        show_progress=True,
-    )
-    downloadFile.change(
-        load_chat_history,
-        [downloadFile, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-    )
-
-    # Advanced
-    default_btn.click(
-        reset_default, [], [apiurlTxt, proxyTxt, status_display], show_progress=True
-    )
-    changeAPIURLBtn.click(
-        change_api_url,
-        [apiurlTxt],
-        [status_display],
-        show_progress=True,
-    )
-    changeProxyBtn.click(
-        change_proxy,
-        [proxyTxt],
-        [status_display],
-        show_progress=True,
-    )
-
-logging.info(
-    colorama.Back.GREEN
-    + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面"
-    + colorama.Style.RESET_ALL
-)
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "川虎ChatGPT 🚀"
-
-if __name__ == "__main__":
-    reload_javascript()
-    # if running in Docker
-    if dockerflag:
-        if authflag:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                server_name="0.0.0.0",
-                server_port=7860,
-                auth=auth_list,
-                favicon_path="./assets/favicon.ico",
-            )
-        else:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                server_name="0.0.0.0",
-                server_port=7860,
-                share=False,
-                favicon_path="./assets/favicon.ico",
-            )
-    # if not running in Docker
-    else:
-        if authflag:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                share=False,
-                auth=auth_list,
-                favicon_path="./assets/favicon.ico",
-                inbrowser=True,
-            )
-        else:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                share=False, favicon_path="./assets/favicon.ico", inbrowser=True
-            )  # 改为 share=True 可以创建公开分享链接
-        # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-        # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-        # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/flax-community/multilingual-image-captioning/sections/references/useful_links.md b/spaces/flax-community/multilingual-image-captioning/sections/references/useful_links.md
deleted file mode 100644
index 1f8350c253f31dceb894d9ab7684140004121c12..0000000000000000000000000000000000000000
--- a/spaces/flax-community/multilingual-image-captioning/sections/references/useful_links.md
+++ /dev/null
@@ -1,11 +0,0 @@
-- [Our GitHub](https://github.com/gchhablani/multilingual-image-captioning/blob/main/README.md)
-
-- [Conceptual 12M Dataset](https://github.com/google-research-datasets/conceptual-12m)
-  
-- [Hybrid CLIP Example](https://github.com/huggingface/transformers/blob/master/src/transformers/models/clip/modeling_flax_clip.py)
-  
-- [mBART Modeling File](https://github.com/huggingface/transformers/blob/master/src/transformers/models/mbart/modeling_flax_mbart.py)
-
-- [CLIP Modeling File](https://github.com/huggingface/transformers/blob/master/src/transformers/models/clip/modeling_flax_clip.py)
-
-- [Summarization Training Script](https://github.com/huggingface/transformers/blob/master/examples/flax/summarization/run_summarization_flax.py)
diff --git a/spaces/fuckyoudeki/AutoGPT/autogpt/json_utils/json_fix_llm.py b/spaces/fuckyoudeki/AutoGPT/autogpt/json_utils/json_fix_llm.py
deleted file mode 100644
index 869aed125cfb8cd7a69ed02eeb389cc72a3e296b..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/autogpt/json_utils/json_fix_llm.py
+++ /dev/null
@@ -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)
diff --git a/spaces/gagan3012/T5-Summarization/src/data/process_data.py b/spaces/gagan3012/T5-Summarization/src/data/process_data.py
deleted file mode 100644
index 41b4122976f3e6a88b7ad109458cc1664a1f5a4c..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/T5-Summarization/src/data/process_data.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import pandas as pd
-import yaml
-import os
-
-
-def process_data(split="train"):
-
-    with open("params.yml") as f:
-        params = yaml.safe_load(f)
-
-    df = pd.read_csv("data/raw/{}.csv".format(split))
-    df.columns = ["Unnamed: 0", "input_text", "output_text"]
-    df = df.sample(frac=params["split"], replace=True, random_state=1)
-    if os.path.exists("data/raw/{}.csv".format(split)):
-        os.remove("data/raw/{}.csv".format(split))
-    df.to_csv("data/processed/{}.csv".format(split))
-
-
-if __name__ == "__main__":
-    process_data(split="train")
-    process_data(split="test")
-    process_data(split="validation")
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/densenet.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/densenet.py
deleted file mode 100644
index 7e2c580ba6ec9544a1b7b9f116fa69a195abd0d2..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/densenet.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
-
-Attributes:
-
-    _out_channels (list of int): specify number of channels for each encoder feature tensor
-    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
-    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
-
-Methods:
-
-    forward(self, x: torch.Tensor)
-        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
-        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
-        with resolution same as input `x` tensor).
-
-        Input: `x` with shape (1, 3, 64, 64)
-        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
-                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
-                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
-
-        also should support number of features according to specified depth, e.g. if depth = 5,
-        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
-        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
-"""
-
-import re
-import torch.nn as nn
-
-from pretrainedmodels.models.torchvision_models import pretrained_settings
-from torchvision.models.densenet import DenseNet
-
-from ._base import EncoderMixin
-
-
-class TransitionWithSkip(nn.Module):
-    def __init__(self, module):
-        super().__init__()
-        self.module = module
-
-    def forward(self, x):
-        for module in self.module:
-            x = module(x)
-            if isinstance(module, nn.ReLU):
-                skip = x
-        return x, skip
-
-
-class DenseNetEncoder(DenseNet, EncoderMixin):
-    def __init__(self, out_channels, depth=5, **kwargs):
-        super().__init__(**kwargs)
-        self._out_channels = out_channels
-        self._depth = depth
-        self._in_channels = 3
-        del self.classifier
-
-    def make_dilated(self, *args, **kwargs):
-        raise ValueError(
-            "DenseNet encoders do not support dilated mode "
-            "due to pooling operation for downsampling!"
-        )
-
-    def get_stages(self):
-        return [
-            nn.Identity(),
-            nn.Sequential(
-                self.features.conv0, self.features.norm0, self.features.relu0
-            ),
-            nn.Sequential(
-                self.features.pool0,
-                self.features.denseblock1,
-                TransitionWithSkip(self.features.transition1),
-            ),
-            nn.Sequential(
-                self.features.denseblock2, TransitionWithSkip(self.features.transition2)
-            ),
-            nn.Sequential(
-                self.features.denseblock3, TransitionWithSkip(self.features.transition3)
-            ),
-            nn.Sequential(self.features.denseblock4, self.features.norm5),
-        ]
-
-    def forward(self, x):
-
-        stages = self.get_stages()
-
-        features = []
-        for i in range(self._depth + 1):
-            x = stages[i](x)
-            if isinstance(x, (list, tuple)):
-                x, skip = x
-                features.append(skip)
-            else:
-                features.append(x)
-
-        return features
-
-    def load_state_dict(self, state_dict):
-        pattern = re.compile(
-            r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
-        )
-        for key in list(state_dict.keys()):
-            res = pattern.match(key)
-            if res:
-                new_key = res.group(1) + res.group(2)
-                state_dict[new_key] = state_dict[key]
-                del state_dict[key]
-
-        # remove linear
-        state_dict.pop("classifier.bias", None)
-        state_dict.pop("classifier.weight", None)
-
-        super().load_state_dict(state_dict)
-
-
-densenet_encoders = {
-    "densenet121": {
-        "encoder": DenseNetEncoder,
-        "pretrained_settings": pretrained_settings["densenet121"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 1024),
-            "num_init_features": 64,
-            "growth_rate": 32,
-            "block_config": (6, 12, 24, 16),
-        },
-    },
-    "densenet169": {
-        "encoder": DenseNetEncoder,
-        "pretrained_settings": pretrained_settings["densenet169"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1280, 1664),
-            "num_init_features": 64,
-            "growth_rate": 32,
-            "block_config": (6, 12, 32, 32),
-        },
-    },
-    "densenet201": {
-        "encoder": DenseNetEncoder,
-        "pretrained_settings": pretrained_settings["densenet201"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1792, 1920),
-            "num_init_features": 64,
-            "growth_rate": 32,
-            "block_config": (6, 12, 48, 32),
-        },
-    },
-    "densenet161": {
-        "encoder": DenseNetEncoder,
-        "pretrained_settings": pretrained_settings["densenet161"],
-        "params": {
-            "out_channels": (3, 96, 384, 768, 2112, 2208),
-            "num_init_features": 96,
-            "growth_rate": 48,
-            "block_config": (6, 12, 36, 24),
-        },
-    },
-}
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/losses/lovasz.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/losses/lovasz.py
deleted file mode 100644
index aca6f8664f1b09ad6668977bec89105624faa23f..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/losses/lovasz.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""
-Lovasz-Softmax and Jaccard hinge loss in PyTorch
-Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
-"""
-
-from __future__ import print_function, division
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch.nn.modules.loss import _Loss
-from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
-
-try:
-    from itertools import ifilterfalse
-except ImportError:  # py3k
-    from itertools import filterfalse as ifilterfalse
-
-__all__ = ["LovaszLoss"]
-
-
-def _lovasz_grad(gt_sorted):
-    """Compute gradient of the Lovasz extension w.r.t sorted errors
-    See Alg. 1 in paper
-    """
-    p = len(gt_sorted)
-    gts = gt_sorted.sum()
-    intersection = gts - gt_sorted.float().cumsum(0)
-    union = gts + (1 - gt_sorted).float().cumsum(0)
-    jaccard = 1.0 - intersection / union
-    if p > 1:  # cover 1-pixel case
-        jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
-    return jaccard
-
-
-def _lovasz_hinge(logits, labels, per_image=True, ignore=None):
-    """
-    Binary Lovasz hinge loss
-        logits: [B, H, W] Logits at each pixel (between -infinity and +infinity)
-        labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
-        per_image: compute the loss per image instead of per batch
-        ignore: void class id
-    """
-    if per_image:
-        loss = mean(
-            _lovasz_hinge_flat(
-                *_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore)
-            )
-            for log, lab in zip(logits, labels)
-        )
-    else:
-        loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore))
-    return loss
-
-
-def _lovasz_hinge_flat(logits, labels):
-    """Binary Lovasz hinge loss
-    Args:
-        logits: [P] Logits at each prediction (between -infinity and +infinity)
-        labels: [P] Tensor, binary ground truth labels (0 or 1)
-        ignore: label to ignore
-    """
-    if len(labels) == 0:
-        # only void pixels, the gradients should be 0
-        return logits.sum() * 0.0
-    signs = 2.0 * labels.float() - 1.0
-    errors = 1.0 - logits * signs
-    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
-    perm = perm.data
-    gt_sorted = labels[perm]
-    grad = _lovasz_grad(gt_sorted)
-    loss = torch.dot(F.relu(errors_sorted), grad)
-    return loss
-
-
-def _flatten_binary_scores(scores, labels, ignore=None):
-    """Flattens predictions in the batch (binary case)
-    Remove labels equal to 'ignore'
-    """
-    scores = scores.view(-1)
-    labels = labels.view(-1)
-    if ignore is None:
-        return scores, labels
-    valid = labels != ignore
-    vscores = scores[valid]
-    vlabels = labels[valid]
-    return vscores, vlabels
-
-
-# --------------------------- MULTICLASS LOSSES ---------------------------
-
-
-def _lovasz_softmax(probas, labels, classes="present", per_image=False, ignore=None):
-    """Multi-class Lovasz-Softmax loss
-    Args:
-        @param probas: [B, C, H, W] Class probabilities at each prediction (between 0 and 1).
-        Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
-        @param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
-        @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
-        @param per_image: compute the loss per image instead of per batch
-        @param ignore: void class labels
-    """
-    if per_image:
-        loss = mean(
-            _lovasz_softmax_flat(
-                *_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore),
-                classes=classes
-            )
-            for prob, lab in zip(probas, labels)
-        )
-    else:
-        loss = _lovasz_softmax_flat(
-            *_flatten_probas(probas, labels, ignore), classes=classes
-        )
-    return loss
-
-
-def _lovasz_softmax_flat(probas, labels, classes="present"):
-    """Multi-class Lovasz-Softmax loss
-    Args:
-        @param probas: [P, C] Class probabilities at each prediction (between 0 and 1)
-        @param labels: [P] Tensor, ground truth labels (between 0 and C - 1)
-        @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
-    """
-    if probas.numel() == 0:
-        # only void pixels, the gradients should be 0
-        return probas * 0.0
-    C = probas.size(1)
-    losses = []
-    class_to_sum = list(range(C)) if classes in ["all", "present"] else classes
-    for c in class_to_sum:
-        fg = (labels == c).type_as(probas)  # foreground for class c
-        if classes == "present" and fg.sum() == 0:
-            continue
-        if C == 1:
-            if len(classes) > 1:
-                raise ValueError("Sigmoid output possible only with 1 class")
-            class_pred = probas[:, 0]
-        else:
-            class_pred = probas[:, c]
-        errors = (fg - class_pred).abs()
-        errors_sorted, perm = torch.sort(errors, 0, descending=True)
-        perm = perm.data
-        fg_sorted = fg[perm]
-        losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted)))
-    return mean(losses)
-
-
-def _flatten_probas(probas, labels, ignore=None):
-    """Flattens predictions in the batch"""
-    if probas.dim() == 3:
-        # assumes output of a sigmoid layer
-        B, H, W = probas.size()
-        probas = probas.view(B, 1, H, W)
-
-    C = probas.size(1)
-    probas = torch.movedim(probas, 1, -1)  # [B, C, Di, Dj, ...] -> [B, Di, Dj, ..., C]
-    probas = probas.contiguous().view(-1, C)  # [P, C]
-
-    labels = labels.view(-1)
-    if ignore is None:
-        return probas, labels
-    valid = labels != ignore
-    vprobas = probas[valid]
-    vlabels = labels[valid]
-    return vprobas, vlabels
-
-
-# --------------------------- HELPER FUNCTIONS ---------------------------
-def isnan(x):
-    return x != x
-
-
-def mean(values, ignore_nan=False, empty=0):
-    """Nanmean compatible with generators."""
-    values = iter(values)
-    if ignore_nan:
-        values = ifilterfalse(isnan, values)
-    try:
-        n = 1
-        acc = next(values)
-    except StopIteration:
-        if empty == "raise":
-            raise ValueError("Empty mean")
-        return empty
-    for n, v in enumerate(values, 2):
-        acc += v
-    if n == 1:
-        return acc
-    return acc / n
-
-
-class LovaszLoss(_Loss):
-    def __init__(
-        self,
-        mode: str,
-        per_image: bool = False,
-        ignore_index: Optional[int] = None,
-        from_logits: bool = True,
-    ):
-        """Lovasz loss for image segmentation task.
-        It supports binary, multiclass and multilabel cases
-
-        Args:
-            mode: Loss mode 'binary', 'multiclass' or 'multilabel'
-            ignore_index: Label that indicates ignored pixels (does not contribute to loss)
-            per_image: If True loss computed per each image and then averaged, else computed per whole batch
-
-        Shape
-             - **y_pred** - torch.Tensor of shape (N, C, H, W)
-             - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
-
-        Reference
-            https://github.com/BloodAxe/pytorch-toolbelt
-        """
-        assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
-        super().__init__()
-
-        self.mode = mode
-        self.ignore_index = ignore_index
-        self.per_image = per_image
-
-    def forward(self, y_pred, y_true):
-
-        if self.mode in {BINARY_MODE, MULTILABEL_MODE}:
-            loss = _lovasz_hinge(
-                y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index
-            )
-        elif self.mode == MULTICLASS_MODE:
-            y_pred = y_pred.softmax(dim=1)
-            loss = _lovasz_softmax(
-                y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index
-            )
-        else:
-            raise ValueError("Wrong mode {}.".format(self.mode))
-        return loss
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/AutoCAD P ID 2008 8.36 (x86x64) Keygen Crack Serial Key Keygen [BETTER].md b/spaces/gotiQspiryo/whisper-ui/examples/AutoCAD P ID 2008 8.36 (x86x64) Keygen Crack Serial Key Keygen [BETTER].md
deleted file mode 100644
index 74ce041161297f7a2f395f0fdea62030395c33e1..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/AutoCAD P ID 2008 8.36 (x86x64) Keygen Crack Serial Key Keygen [BETTER].md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<br />
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Binkshouldskip 4 Download Free.zip.md b/spaces/gotiQspiryo/whisper-ui/examples/Binkshouldskip 4 Download Free.zip.md
deleted file mode 100644
index db3e443c613eeefc6c81e86332e5b9c78baed2d2..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Binkshouldskip 4 Download Free.zip.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>binkshouldskip 4 download free.zip</h2><br /><p><b><b>Download</b> &rarr;&rarr;&rarr; <a href="https://urlgoal.com/2uyMeQ">https://urlgoal.com/2uyMeQ</a></b></p><br /><br />
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Cyberghost Serial.md b/spaces/gotiQspiryo/whisper-ui/examples/Cyberghost Serial.md
deleted file mode 100644
index a1c09162ea462105ca4a2ec583e43e5c03ebe77f..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Cyberghost Serial.md	
+++ /dev/null
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-<br />
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diff --git a/spaces/gpecile/encrypted-image-recognition/common.py b/spaces/gpecile/encrypted-image-recognition/common.py
deleted file mode 100644
index 7612b9182a5221159868cd13f82851cf1c1895aa..0000000000000000000000000000000000000000
--- a/spaces/gpecile/encrypted-image-recognition/common.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"All the constants used in this repo."
-
-from pathlib import Path
-
-# This repository's directory
-REPO_DIR = Path(__file__).parent
-
-# This repository's main necessary folders
-FILTERS_PATH = REPO_DIR / "filters"
-KEYS_PATH = REPO_DIR / ".fhe_keys"
-CLIENT_TMP_PATH = REPO_DIR / "client_tmp"
-SERVER_TMP_PATH = REPO_DIR / "server_tmp"
-
-# Create the necessary folders
-KEYS_PATH.mkdir(exist_ok=True)
-CLIENT_TMP_PATH.mkdir(exist_ok=True)
-SERVER_TMP_PATH.mkdir(exist_ok=True)
-
-# All the filters currently available in the demo
-AVAILABLE_FILTERS = [
-    "identity",
-    "inverted",
-    "rotate",
-    "black and white",
-    "blur",
-    "sharpen",
-    "ridge detection",
-]
-
-# The input images' shape. Images with different input shapes will be cropped and resized by Gradio
-INPUT_SHAPE = (100, 100)
-
-# Retrieve the input examples directory
-INPUT_EXAMPLES_DIR = REPO_DIR / "input_examples"
-
-# List of all image examples suggested in the demo
-EXAMPLES = [str(image) for image in INPUT_EXAMPLES_DIR.glob("**/*")]
-
-# Store the server's URL
-SERVER_URL = "http://localhost:8000/"
diff --git a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/scripts/vads.py b/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/scripts/vads.py
deleted file mode 100644
index 2398da97d8c44b8f3f270b22d5508a003482b4d6..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/scripts/vads.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import sys
-
-from copy import deepcopy
-from scipy.signal import lfilter
-
-import numpy as np
-from tqdm import tqdm
-import soundfile as sf
-import os.path as osp
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(description="compute vad segments")
-    parser.add_argument(
-        "--rvad-home",
-        "-r",
-        help="path to rvad home (see https://github.com/zhenghuatan/rVADfast)",
-        required=True,
-    )
-
-    return parser
-
-
-def rvad(speechproc, path):
-    winlen, ovrlen, pre_coef, nfilter, nftt = 0.025, 0.01, 0.97, 20, 512
-    ftThres = 0.5
-    vadThres = 0.4
-    opts = 1
-
-    data, fs = sf.read(path)
-    assert fs == 16_000, "sample rate must be 16khz"
-    ft, flen, fsh10, nfr10 = speechproc.sflux(data, fs, winlen, ovrlen, nftt)
-
-    # --spectral flatness --
-    pv01 = np.zeros(ft.shape[0])
-    pv01[np.less_equal(ft, ftThres)] = 1
-    pitch = deepcopy(ft)
-
-    pvblk = speechproc.pitchblockdetect(pv01, pitch, nfr10, opts)
-
-    # --filtering--
-    ENERGYFLOOR = np.exp(-50)
-    b = np.array([0.9770, -0.9770])
-    a = np.array([1.0000, -0.9540])
-    fdata = lfilter(b, a, data, axis=0)
-
-    # --pass 1--
-    noise_samp, noise_seg, n_noise_samp = speechproc.snre_highenergy(
-        fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk
-    )
-
-    # sets noisy segments to zero
-    for j in range(n_noise_samp):
-        fdata[range(int(noise_samp[j, 0]), int(noise_samp[j, 1]) + 1)] = 0
-
-    vad_seg = speechproc.snre_vad(
-        fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk, vadThres
-    )
-    return vad_seg, data
-
-
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    sys.path.append(args.rvad_home)
-    import speechproc
-
-    stride = 160
-    lines = sys.stdin.readlines()
-    root = lines[0].rstrip()
-    for fpath in tqdm(lines[1:]):
-        path = osp.join(root, fpath.split()[0])
-        vads, wav = rvad(speechproc, path)
-
-        start = None
-        vad_segs = []
-        for i, v in enumerate(vads):
-            if start is None and v == 1:
-                start = i * stride
-            elif start is not None and v == 0:
-                vad_segs.append((start, i * stride))
-                start = None
-        if start is not None:
-            vad_segs.append((start, len(wav)))
-
-        print(" ".join(f"{v[0]}:{v[1]}" for v in vad_segs))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/hackathon-somos-nlp-2023/ask2democracy/samples.py b/spaces/hackathon-somos-nlp-2023/ask2democracy/samples.py
deleted file mode 100644
index 7f150a10b74e6275b913d4b8cdc311ec49cb67bb..0000000000000000000000000000000000000000
--- a/spaces/hackathon-somos-nlp-2023/ask2democracy/samples.py
+++ /dev/null
@@ -1,37 +0,0 @@
-
-samples_reforma_salud = """¿Que es el ADRES?
-¿Cuándo se implementará el Sistema de Salud?
-¿Cómo se implementará el Sistema de Salud?
-¿Qué es principio de interpretación y fundamento de la transición en relación al Sistema de Salud?
-¿Qué se garantiza la atención en todo momento con el nuevo Sistema de Salud?
-¿Qué son los Centros de Atención Primaria Integrales y Resolutivos en Salud - CAPIRS?
-¿Qué se garantiza durante el periodo de transición del nuevo Sistema de Salud?
-¿Puede haber personas sin protección de su salud durante el periodo de transición?
-¿Cuál es el derecho fundamental que se garantiza en todo momento durante la transición del nuevo Sistema de Salud?
-¿Qué se debe realizar para garantizar la gestión de los recursos en el nivel nacional y desconcentrado?
-¿Cómo se regirá el régimen de contratación de los contratos mencionados en el texto?
-¿Qué son las cláusulas exorbitantes previstas en el estatuto General de Contratación de la administración pública?
-¿Qué principios deben atender los contratos mencionados en el texto?
-¿Cuál es el ámbito de aplicación de los contratos mencionados en el texto?
-¿Quién tiene la responsabilidad de realizar la auditoría de las cuentas en relación a estos contratos?
-¿Cuáles son las características que deben cumplir los contratos mencionados en el texto?
-¿Qué se entiende por "coordinación" en el contexto de los contratos mencionados en el texto?
-¿Qué objetivo se busca con los contratos mencionados en el texto?
-¿Quién será el encargado de contratar los servicios de salud y otros requerimientos para el cumplimiento de su labor en el nivel regional?
-¿Qué tipo de instituciones hospitalarias y ambulatorias se integran a la red de servicios del territorio?
-¿Qué tarifas deben seguir las instituciones hospitalarias y ambulatorias para la prestación de servicios de salud?
-¿Qué busca modular el régimen de tarifas y formas de pago para la prestación de servicios de salud?
-¿Qué tipo de registro llevará el Fondo Regional de Salud?
-¿Cuáles son algunas de las variables que se incluirán en el registro de cada servicio prestado y pagado?"""
-
-samples_hallazgos_paz =  """¿cantidad de víctimas en la masacre de bojayá?
-¿periodo con más detenciones arbitrarias registradas?
-¿cantidad de víctimas en la masacre de bojayá?
-¿cuantas víctimas de desplazamiento en antioquia?"""
-
-
-samples_reforma_pensional="""¿cuales son los pilares que se proponen?
-¿cuanto será la cotización al pilar contributivo?
-¿quienes serán los beneficiarios del pilar contributivo?
-¿cual es el beneficio para las mujeres con hijos?
-"""
\ No newline at end of file
diff --git a/spaces/hamza50/rhymethyme/README.md b/spaces/hamza50/rhymethyme/README.md
deleted file mode 100644
index 5b79d4cec3b3af0061b753ba3d19c699416e33bc..0000000000000000000000000000000000000000
--- a/spaces/hamza50/rhymethyme/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Testing Nl
-emoji: 🏃
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hamzapehlivan/StyleRes/editings/interfacegan.py b/spaces/hamzapehlivan/StyleRes/editings/interfacegan.py
deleted file mode 100644
index e5a0cde51faf0a50b7e8d7e3a39bba15310b22bf..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/editings/interfacegan.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import torch
-from options import Settings
-import os
-
-class InterFaceGAN():
-    def __init__(self) -> None:
-        pass
-
-    def edit(self, latent, cfg):
-        with torch.no_grad():
-            return latent + cfg.strength * self.get_direction(cfg.edit)
-
-    def get_direction(self, editname):
-        try:
-            direction = getattr(self, f"{editname}_direction")
-        except:
-            direction = self.load_direction(editname)
-            if Settings.device != 'cpu':
-                direction = direction.to(Settings.device)
-            setattr(self, f"{editname}_direction", direction.clone())
-        return direction
-
-    def load_direction(self, editname):
-        direction = torch.load(os.path.join( Settings.interfacegan_directions, f'{editname}.pt'))
-        if Settings.device != 'cpu':
-            direction = direction.cuda()
-        return direction
\ No newline at end of file
diff --git "a/spaces/hands012/gpt-academic/crazy_functions/\346\200\273\347\273\223\351\237\263\350\247\206\351\242\221.py" "b/spaces/hands012/gpt-academic/crazy_functions/\346\200\273\347\273\223\351\237\263\350\247\206\351\242\221.py"
deleted file mode 100644
index 62f05d395bafab8638ed6963e2d24334d95ecf37..0000000000000000000000000000000000000000
--- "a/spaces/hands012/gpt-academic/crazy_functions/\346\200\273\347\273\223\351\237\263\350\247\206\351\242\221.py"
+++ /dev/null
@@ -1,184 +0,0 @@
-from toolbox import CatchException, report_execption, select_api_key, update_ui, write_results_to_file, get_conf
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-def split_audio_file(filename, split_duration=1000):
-    """
-    根据给定的切割时长将音频文件切割成多个片段。
-
-    Args:
-        filename (str): 需要被切割的音频文件名。
-        split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。
-
-    Returns:
-        filelist (list): 一个包含所有切割音频片段文件路径的列表。
-
-    """
-    from moviepy.editor import AudioFileClip
-    import os
-    os.makedirs('gpt_log/mp3/cut/', exist_ok=True)  # 创建存储切割音频的文件夹
-
-    # 读取音频文件
-    audio = AudioFileClip(filename)
-
-    # 计算文件总时长和切割点
-    total_duration = audio.duration
-    split_points = list(range(0, int(total_duration), split_duration))
-    split_points.append(int(total_duration))
-    filelist = []
-
-    # 切割音频文件
-    for i in range(len(split_points) - 1):
-        start_time = split_points[i]
-        end_time = split_points[i + 1]
-        split_audio = audio.subclip(start_time, end_time)
-        split_audio.write_audiofile(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
-        filelist.append(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
-
-    audio.close()
-    return filelist
-
-def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
-    import os, requests
-    from moviepy.editor import AudioFileClip
-    from request_llm.bridge_all import model_info
-
-    # 设置OpenAI密钥和模型
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-
-    whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
-    url = whisper_endpoint
-    headers = {
-        'Authorization': f"Bearer {api_key}"
-    }
-
-    os.makedirs('gpt_log/mp3/', exist_ok=True)
-    for index, fp in enumerate(file_manifest):
-        audio_history = []
-        # 提取文件扩展名
-        ext = os.path.splitext(fp)[1]
-        # 提取视频中的音频
-        if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
-            audio_clip = AudioFileClip(fp)
-            audio_clip.write_audiofile(f'gpt_log/mp3/output{index}.mp3')
-            fp = f'gpt_log/mp3/output{index}.mp3'
-        # 调用whisper模型音频转文字
-        voice = split_audio_file(fp)
-        for j, i in enumerate(voice):
-            with open(i, 'rb') as f:
-                file_content = f.read()  # 读取文件内容到内存
-                files = {
-                    'file': (os.path.basename(i), file_content),
-                }
-                data = {
-                    "model": "whisper-1",
-                    "prompt": parse_prompt,
-                    'response_format': "text"
-                }
-
-            chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-            proxies, = get_conf('proxies')
-            response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
-
-            chatbot.append(["音频解析结果", response])
-            history.extend(["音频解析结果", response])
-            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-            i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
-            i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=[],
-                sys_prompt=f"总结音频。音频文件名{fp}"
-            )
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.extend([i_say_show_user, gpt_say])
-            audio_history.extend([i_say_show_user, gpt_say])
-
-        # 已经对该文章的所有片段总结完毕，如果文章被切分了
-        result = "".join(audio_history)
-        if len(audio_history) > 1:
-            i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
-            i_say_show_user = f'第{index + 1}段音频的主要内容：'
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say,
-                inputs_show_user=i_say_show_user,
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot,
-                history=audio_history,
-                sys_prompt="总结文章。"
-            )
-
-            history.extend([i_say, gpt_say])
-            audio_history.extend([i_say, gpt_say])
-
-        res = write_results_to_file(history)
-        chatbot.append((f"第{index + 1}段音频完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    # 删除中间文件夹
-    import shutil
-    shutil.rmtree('gpt_log/mp3')
-    res = write_results_to_file(history)
-    chatbot.append(("所有音频都总结完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history)
-
-
-@CatchException
-def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    try:
-        from moviepy.editor import AudioFileClip
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']
-
-    if txt.endswith(tuple(extensions)):
-        file_manifest = [txt]
-    else:
-        file_manifest = []
-        for extension in extensions:
-            file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-
-    # 开始正式执行任务
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
-    yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)
-
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
diff --git a/spaces/haoqi7/research/lrt/academic_query/__init__.py b/spaces/haoqi7/research/lrt/academic_query/__init__.py
deleted file mode 100644
index fc7228f4200d5b13372107650c619563399edf91..0000000000000000000000000000000000000000
--- a/spaces/haoqi7/research/lrt/academic_query/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .academic import AcademicQuery
\ No newline at end of file
diff --git a/spaces/harveysamson/wav2vec2-speech-emotion-recognition/README.md b/spaces/harveysamson/wav2vec2-speech-emotion-recognition/README.md
deleted file mode 100644
index b70ff98f19d3b4c19c4cd6a4a8395edceba632bd..0000000000000000000000000000000000000000
--- a/spaces/harveysamson/wav2vec2-speech-emotion-recognition/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: wav2vec2-ser
-emoji: 🦀
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
----
-
-Wav2Vec2 For Speech Emotion Recognition 
-
-Emotion is an important aspect for the human nature, and understanding it is critical for catering to human services better in this era of digital communication, where speech has been transformed through texts and messages and calls. Speech Emotion Recognition creates a way to classify emotions embedded in speech through careful analysis of lexical, visual, and acoustic features.
-
-Link to the main reference: https://github.com/m3hrdadfi/soxan
-
-Evaluation Scores
-
-Emotions  precision	recall	f1-score	accuracy
-anger 0.82	1.00	0.81	
-disgust	0.85	0.96	0.85	
-fear	0.78	0.88	0.80	
-happiness	0.84	0.71	0.78	
-sadness	0.86	1.00	0.79
-Overall Accuracy: 0.806 or 80.6%
-
-The Wav2Vec2.0 is a pretrained model for Automatic Speech Recognition, and the Wav2Vec2 for Speech Recognition used is fine-tuned using Connectionist Temporal Classification or CTC, to train neural networks for sequential problems mainly including ASR. 
-
-Google Colab Link: https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb#scrollTo=y0xJwDkA3QQR
-
-Competition board for Common Voice: https://paperswithcode.com/dataset/common-voice
-
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
-
-
-
-
-
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/DensePose/dev/run_inference_tests.sh b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/DensePose/dev/run_inference_tests.sh
deleted file mode 100644
index 34f47d5a07a90c411e830c98a346845fa618f836..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/DensePose/dev/run_inference_tests.sh
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-BIN="python train_net.py"
-OUTPUT="inference_test_output"
-NUM_GPUS=2
-IMS_PER_GPU=2
-IMS_PER_BATCH=$(( NUM_GPUS * IMS_PER_GPU ))
-
-CFG_LIST=( "${@:1}" )
-
-if [ ${#CFG_LIST[@]} -eq 0 ]; then
-  CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml )
-fi
-
-echo "========================================================================"
-echo "Configs to run:"
-echo "${CFG_LIST[@]}"
-echo "========================================================================"
-
-for cfg in "${CFG_LIST[@]}"; do
-    echo "========================================================================"
-    echo "Running $cfg ..."
-    echo "========================================================================"
-    $BIN \
-      --eval-only \
-      --num-gpus $NUM_GPUS \
-      --config-file "$cfg" \
-      OUTPUT_DIR "$OUTPUT" \
-      SOLVER.IMS_PER_BATCH $IMS_PER_BATCH
-    rm -rf $OUTPUT
-done
-
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_config.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_config.py
deleted file mode 100644
index 650bdf2c42107c7031709653783cb2f3043e1bdf..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/test_config.py
+++ /dev/null
@@ -1,240 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-
-import os
-import tempfile
-import unittest
-import torch
-
-from detectron2.config import configurable, downgrade_config, get_cfg, upgrade_config
-from detectron2.layers import ShapeSpec
-
-_V0_CFG = """
-MODEL:
-  RPN_HEAD:
-    NAME: "TEST"
-VERSION: 0
-"""
-
-_V1_CFG = """
-MODEL:
-  WEIGHT: "/path/to/weight"
-"""
-
-
-class TestConfigVersioning(unittest.TestCase):
-    def test_upgrade_downgrade_consistency(self):
-        cfg = get_cfg()
-        # check that custom is preserved
-        cfg.USER_CUSTOM = 1
-
-        down = downgrade_config(cfg, to_version=0)
-        up = upgrade_config(down)
-        self.assertTrue(up == cfg)
-
-    def _merge_cfg_str(self, cfg, merge_str):
-        f = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False)
-        try:
-            f.write(merge_str)
-            f.close()
-            cfg.merge_from_file(f.name)
-        finally:
-            os.remove(f.name)
-        return cfg
-
-    def test_auto_upgrade(self):
-        cfg = get_cfg()
-        latest_ver = cfg.VERSION
-        cfg.USER_CUSTOM = 1
-
-        self._merge_cfg_str(cfg, _V0_CFG)
-
-        self.assertEqual(cfg.MODEL.RPN.HEAD_NAME, "TEST")
-        self.assertEqual(cfg.VERSION, latest_ver)
-
-    def test_guess_v1(self):
-        cfg = get_cfg()
-        latest_ver = cfg.VERSION
-        self._merge_cfg_str(cfg, _V1_CFG)
-        self.assertEqual(cfg.VERSION, latest_ver)
-
-
-class _TestClassA(torch.nn.Module):
-    @configurable
-    def __init__(self, arg1, arg2, arg3=3):
-        super().__init__()
-        self.arg1 = arg1
-        self.arg2 = arg2
-        self.arg3 = arg3
-        assert arg1 == 1
-        assert arg2 == 2
-        assert arg3 == 3
-
-    @classmethod
-    def from_config(cls, cfg):
-        args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
-        return args
-
-
-class _TestClassB(_TestClassA):
-    @configurable
-    def __init__(self, input_shape, arg1, arg2, arg3=3):
-        """
-        Doc of _TestClassB
-        """
-        assert input_shape == "shape"
-        super().__init__(arg1, arg2, arg3)
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):  # test extra positional arg in from_config
-        args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
-        args["input_shape"] = input_shape
-        return args
-
-
-class _LegacySubClass(_TestClassB):
-    # an old subclass written in cfg style
-    def __init__(self, cfg, input_shape, arg4=4):
-        super().__init__(cfg, input_shape)
-        assert self.arg1 == 1
-        assert self.arg2 == 2
-        assert self.arg3 == 3
-
-
-class _NewSubClassNewInit(_TestClassB):
-    # test new subclass with a new __init__
-    @configurable
-    def __init__(self, input_shape, arg4=4, **kwargs):
-        super().__init__(input_shape, **kwargs)
-        assert self.arg1 == 1
-        assert self.arg2 == 2
-        assert self.arg3 == 3
-
-
-class _LegacySubClassNotCfg(_TestClassB):
-    # an old subclass written in cfg style, but argument is not called "cfg"
-    def __init__(self, config, input_shape):
-        super().__init__(config, input_shape)
-        assert self.arg1 == 1
-        assert self.arg2 == 2
-        assert self.arg3 == 3
-
-
-class _TestClassC(_TestClassB):
-    @classmethod
-    def from_config(cls, cfg, input_shape, **kwargs):  # test extra kwarg overwrite
-        args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
-        args["input_shape"] = input_shape
-        args.update(kwargs)
-        return args
-
-
-class _TestClassD(_TestClassA):
-    @configurable
-    def __init__(self, input_shape: ShapeSpec, arg1: int, arg2, arg3=3):
-        assert input_shape == "shape"
-        super().__init__(arg1, arg2, arg3)
-
-    # _TestClassA.from_config does not have input_shape args.
-    # Test whether input_shape will be forwarded to __init__
-
-
-class TestConfigurable(unittest.TestCase):
-    def testInitWithArgs(self):
-        _ = _TestClassA(arg1=1, arg2=2, arg3=3)
-        _ = _TestClassB("shape", arg1=1, arg2=2)
-        _ = _TestClassC("shape", arg1=1, arg2=2)
-        _ = _TestClassD("shape", arg1=1, arg2=2, arg3=3)
-
-    def testPatchedAttr(self):
-        self.assertTrue("Doc" in _TestClassB.__init__.__doc__)
-        self.assertEqual(_TestClassD.__init__.__annotations__["arg1"], int)
-
-    def testInitWithCfg(self):
-        cfg = get_cfg()
-        cfg.ARG1 = 1
-        cfg.ARG2 = 2
-        cfg.ARG3 = 3
-        _ = _TestClassA(cfg)
-        _ = _TestClassB(cfg, input_shape="shape")
-        _ = _TestClassC(cfg, input_shape="shape")
-        _ = _TestClassD(cfg, input_shape="shape")
-        _ = _LegacySubClass(cfg, input_shape="shape")
-        _ = _NewSubClassNewInit(cfg, input_shape="shape")
-        _ = _LegacySubClassNotCfg(cfg, input_shape="shape")
-        with self.assertRaises(TypeError):
-            # disallow forwarding positional args to __init__ since it's prone to errors
-            _ = _TestClassD(cfg, "shape")
-
-        # call with kwargs instead
-        _ = _TestClassA(cfg=cfg)
-        _ = _TestClassB(cfg=cfg, input_shape="shape")
-        _ = _TestClassC(cfg=cfg, input_shape="shape")
-        _ = _TestClassD(cfg=cfg, input_shape="shape")
-        _ = _LegacySubClass(cfg=cfg, input_shape="shape")
-        _ = _NewSubClassNewInit(cfg=cfg, input_shape="shape")
-        _ = _LegacySubClassNotCfg(config=cfg, input_shape="shape")
-
-    def testInitWithCfgOverwrite(self):
-        cfg = get_cfg()
-        cfg.ARG1 = 1
-        cfg.ARG2 = 999  # wrong config
-        with self.assertRaises(AssertionError):
-            _ = _TestClassA(cfg, arg3=3)
-
-        # overwrite arg2 with correct config later:
-        _ = _TestClassA(cfg, arg2=2, arg3=3)
-        _ = _TestClassB(cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassC(cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassD(cfg, input_shape="shape", arg2=2, arg3=3)
-
-        # call with kwargs cfg=cfg instead
-        _ = _TestClassA(cfg=cfg, arg2=2, arg3=3)
-        _ = _TestClassB(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassC(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassD(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
-
-    def testInitWithCfgWrongArgs(self):
-        cfg = get_cfg()
-        cfg.ARG1 = 1
-        cfg.ARG2 = 2
-        with self.assertRaises(TypeError):
-            _ = _TestClassB(cfg, "shape", not_exist=1)
-        with self.assertRaises(TypeError):
-            _ = _TestClassC(cfg, "shape", not_exist=1)
-        with self.assertRaises(TypeError):
-            _ = _TestClassD(cfg, "shape", not_exist=1)
-
-    def testBadClass(self):
-        class _BadClass1:
-            @configurable
-            def __init__(self, a=1, b=2):
-                pass
-
-        class _BadClass2:
-            @configurable
-            def __init__(self, a=1, b=2):
-                pass
-
-            def from_config(self, cfg):  # noqa
-                pass
-
-        class _BadClass3:
-            @configurable
-            def __init__(self, a=1, b=2):
-                pass
-
-            # bad name: must be cfg
-            @classmethod
-            def from_config(cls, config):  # noqa
-                pass
-
-        with self.assertRaises(AttributeError):
-            _ = _BadClass1(a=1)
-
-        with self.assertRaises(TypeError):
-            _ = _BadClass2(a=1)
-
-        with self.assertRaises(TypeError):
-            _ = _BadClass3(get_cfg())
diff --git a/spaces/hekbobo/bingo/src/components/ui/tooltip.tsx b/spaces/hekbobo/bingo/src/components/ui/tooltip.tsx
deleted file mode 100644
index af1d48beb90dd5ae311796539843700871052cae..0000000000000000000000000000000000000000
--- a/spaces/hekbobo/bingo/src/components/ui/tooltip.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as TooltipPrimitive from '@radix-ui/react-tooltip'
-
-import { cn } from '@/lib/utils'
-
-const TooltipProvider = TooltipPrimitive.Provider
-
-const Tooltip = TooltipPrimitive.Root
-
-const TooltipTrigger = TooltipPrimitive.Trigger
-
-const TooltipContent = React.forwardRef<
-  React.ElementRef<typeof TooltipPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <TooltipPrimitive.Content
-    ref={ref}
-    sideOffset={sideOffset}
-    className={cn(
-      'z-50 overflow-hidden rounded-md border bg-popover px-3 py-1.5 text-xs font-medium text-popover-foreground shadow-md animate-in fade-in-50 data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1',
-      className
-    )}
-    {...props}
-  />
-))
-TooltipContent.displayName = TooltipPrimitive.Content.displayName
-
-export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider }
diff --git a/spaces/hjzhp/cgpt-online/src/components/MessageItem.tsx b/spaces/hjzhp/cgpt-online/src/components/MessageItem.tsx
deleted file mode 100644
index a523ba89fa188e89015e1f9a1764c0b992b3e16e..0000000000000000000000000000000000000000
--- a/spaces/hjzhp/cgpt-online/src/components/MessageItem.tsx
+++ /dev/null
@@ -1,87 +0,0 @@
-import { createSignal } from 'solid-js'
-import MarkdownIt from 'markdown-it'
-import mdKatex from 'markdown-it-katex'
-import mdHighlight from 'markdown-it-highlightjs'
-import { useClipboard, useEventListener } from 'solidjs-use'
-import IconRefresh from './icons/Refresh'
-import type { Accessor } from 'solid-js'
-import type { ChatMessage } from '@/types'
-
-interface Props {
-  role: ChatMessage['role']
-  message: Accessor<string> | string
-  showRetry?: Accessor<boolean>
-  onRetry?: () => void
-}
-
-export default ({ role, message, showRetry, onRetry }: Props) => {
-  const roleClass = {
-    system: 'bg-gradient-to-r from-gray-300 via-gray-200 to-gray-300',
-    user: 'bg-gradient-to-r from-purple-400 to-yellow-400',
-    assistant: 'bg-gradient-to-r from-yellow-200 via-green-200 to-green-300',
-  }
-  const [source] = createSignal('')
-  const { copy, copied } = useClipboard({ source, copiedDuring: 1000 })
-
-  useEventListener('click', (e) => {
-    const el = e.target as HTMLElement
-    let code = null
-
-    if (el.matches('div > div.copy-btn')) {
-      code = decodeURIComponent(el.dataset.code!)
-      copy(code)
-    }
-    if (el.matches('div > div.copy-btn > svg')) {
-      // eslint-disable-next-line @typescript-eslint/no-non-null-asserted-optional-chain
-      code = decodeURIComponent(el.parentElement?.dataset.code!)
-      copy(code)
-    }
-  })
-
-  const htmlString = () => {
-    const md = MarkdownIt({
-      linkify: true,
-      breaks: true,
-    }).use(mdKatex).use(mdHighlight)
-    const fence = md.renderer.rules.fence!
-    md.renderer.rules.fence = (...args) => {
-      const [tokens, idx] = args
-      const token = tokens[idx]
-      const rawCode = fence(...args)
-
-      return `<div relative>
-      <div data-code=${encodeURIComponent(token.content)} class="copy-btn gpt-copy-btn group">
-          <svg xmlns="http://www.w3.org/2000/svg" width="1em" height="1em" viewBox="0 0 32 32"><path fill="currentColor" d="M28 10v18H10V10h18m0-2H10a2 2 0 0 0-2 2v18a2 2 0 0 0 2 2h18a2 2 0 0 0 2-2V10a2 2 0 0 0-2-2Z" /><path fill="currentColor" d="M4 18H2V4a2 2 0 0 1 2-2h14v2H4Z" /></svg>
-            <div class="group-hover:op-100 gpt-copy-tips">
-              ${copied() ? 'Copied' : 'Copy'}
-            </div>
-      </div>
-      ${rawCode}
-      </div>`
-    }
-
-    if (typeof message === 'function')
-      return md.render(message())
-    else if (typeof message === 'string')
-      return md.render(message)
-
-    return ''
-  }
-
-  return (
-    <div class="py-2 -mx-4 px-4 transition-colors md:hover:bg-slate/3">
-      <div class="flex gap-3 rounded-lg" class:op-75={role === 'user'}>
-        <div class={`shrink-0 w-7 h-7 mt-4 rounded-full op-80 ${roleClass[role]}`} />
-        <div class="message prose break-words overflow-hidden" innerHTML={htmlString()} />
-      </div>
-      {showRetry?.() && onRetry && (
-        <div class="fie px-3 mb-2">
-          <div onClick={onRetry} class="gpt-retry-btn">
-            <IconRefresh />
-            <span>Regenerate</span>
-          </div>
-        </div>
-      )}
-    </div>
-  )
-}
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task504_Glacier_mtl_recon_reverse.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task504_Glacier_mtl_recon_reverse.py
deleted file mode 100644
index 535c9ad3ea9dcfc23bfba7fa3a49d53d37728879..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task504_Glacier_mtl_recon_reverse.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import SimpleITK as sitk
-import argparse
-
-import numpy as np
-import torch
-import os
-from batchgenerators.utilities.file_and_folder_operations import join, isdir, maybe_mkdir_p
-import matplotlib.image as pltimage
-
-def main(input_folder):
-
-
-    files = os.listdir(input_folder)
-    output_folder = join(input_folder, 'pngs')
-    maybe_mkdir_p(output_folder)
-    for file in files:
-        if file.endswith('.gz'):
-            file_path = join(input_folder, file)
-            image = sitk.ReadImage(file_path)
-            image = sitk.GetArrayFromImage(image)
-            front = image[0]
-            zones = image[1]
-            recon = image[2]
-
-            color_zones = np.zeros_like(zones)
-            color_zones[zones == 0] = 0
-            color_zones[zones == 1] = 64
-            color_zones[zones == 2] = 127
-            color_zones[zones == 3] = 254
-
-            color_fronts = np.zeros_like(front)
-            color_fronts[front == 0] = 0
-            color_fronts[front == 1] = 255
-
-            color_recon = recon*255
-
-            output_path_zone = join(output_folder, file[:-len('.nii.gz')] + '_zone.png')
-            pltimage.imsave(output_path_zone, color_zones, cmap='gray', vmax=255)
-
-            output_path_front = join(output_folder, file[:-len('.nii.gz')] + '_front.png')
-            pltimage.imsave(output_path_front, color_fronts, cmap='gray', vmax=255)
-
-            output_path_recon = join(output_folder, file[:-len('.nii.gz')] + '_recon.png')
-            pltimage.imsave(output_path_recon, color_recon, cmap='gray', vmax=255)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-i", '--input_folder', help="Folder with NIfTI files")
-    args = parser.parse_args()
-    input_folder = args.input_folder
-    main(input_folder)
\ No newline at end of file
diff --git a/spaces/huaiji3y/bingo-Public/src/components/chat.tsx b/spaces/huaiji3y/bingo-Public/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}
diff --git a/spaces/huaiji3y/bingo-Public/tailwind.config.js b/spaces/huaiji3y/bingo-Public/tailwind.config.js
deleted file mode 100644
index 03da3c3c45be6983b9f5ffa6df5f1fd0870e9636..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/tailwind.config.js
+++ /dev/null
@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  "darkMode": "class",
-  theme: {
-    extend: {
-      colors: {
-        'primary-blue': 'rgb(var(--color-primary-blue) / <alpha-value>)',
-        secondary: 'rgb(var(--color-secondary) / <alpha-value>)',
-        'primary-background': 'rgb(var(--primary-background) / <alpha-value>)',
-        'primary-text': 'rgb(var(--primary-text) / <alpha-value>)',
-        'secondary-text': 'rgb(var(--secondary-text) / <alpha-value>)',
-        'light-text': 'rgb(var(--light-text) / <alpha-value>)',
-        'primary-border': 'rgb(var(--primary-border) / <alpha-value>)',
-      },
-      keyframes: {
-        slideDownAndFade: {
-          from: { opacity: 0, transform: 'translateY(-2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideLeftAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-        slideUpAndFade: {
-          from: { opacity: 0, transform: 'translateY(2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideRightAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-      },
-      animation: {
-        slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-      },
-    },
-  },
-  plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')],
-}
diff --git a/spaces/huang4414/GTest/app.js b/spaces/huang4414/GTest/app.js
deleted file mode 100644
index d537a50e63a8550fe5134aaa3e9bfcd93d763afa..0000000000000000000000000000000000000000
--- a/spaces/huang4414/GTest/app.js
+++ /dev/null
@@ -1,138 +0,0 @@
-import template from 'express-art-template'
-import { resolve } from 'node:path'
-import express from 'express'
-import lodash from 'lodash'
-import moment from 'moment'
-import https from 'https'
-import fs from 'node:fs'
-import YAML from 'yaml'
-
-class example {
-  constructor() {
-    this.cfg = YAML.parse(fs.readFileSync('./config.yaml', 'utf8'))
-    this.tmp = {}
-    this.isRegister = {}
-    this.result = {}
-    this.load(express())
-    console.log(`Successfully started, port: \x1b[32m${this.cfg.Port}\x1b[0m`)
-    console.log('\x1b[32m%s\x1b[0m', `[POST] ${this.address}/register${this.cfg.Key && `?key=${this.cfg.Key}`}`)//`
-  }
-
-  load(app) {
-    if (this.cfg.Https) {
-      let cert = {
-        key: fs.readFileSync('./data/SSL/private.key', 'utf8'),
-        cert: fs.readFileSync('./data/SSL/certificate.crt', 'utf8')
-      }
-      https.createServer(cert, app).listen(this.cfg.Port)
-    } else {
-      app.listen(this.cfg.Port)
-    }
-    this.route(app)
-  }
-
-  route(app) {
-    app.use(express.static(resolve('public')))
-    app.use(express.urlencoded({ extended: false }))
-    app.use(express.json())
-    app.engine('html', template)
-    app.set('views', resolve('public'))
-    app.set('view engine', 'html')
-    app.get('/GTest/:key', this.self('index'))
-    app.post('/GTest/register', this.self('register'))
-    app.get('/GTest/register/:key', this.self('get_register'))
-    app.post('/GTest/validate/:key', this.self('validate'))
-    app.get('/GTest/validate/:key', this.self('get_validate'))
-    app.use(this.invalid)
-    app.use(this.error)
-  }
-
-  index(req, res, next) {
-    let { key } = req.params
-    if (!key || !this.isRegister[key]) return next('憨憨，没有验证信息哦，也可能是失效了~')
-    res.render('GTest/main', { key, copyright: this.cfg.Copyright })
-  }
-
-  register(req, res, next) {
-    let { key } = req.query, { gt, challenge } = req.body || {}
-    if (!gt || !challenge) return next('error')
-    if (this.cfg.Key && key !== this.cfg.Key) return next('please enter the correct key')
-    for (let i = 0; i < 99; i++) {
-      key = lodash.sampleSize('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz', 6).join('')
-      if (this.isRegister[key] || this.result[key]) continue
-      break
-    }
-    this.tmp[key] = req.body
-    this.isRegister[key] = 1
-    setTimeout(() => delete this.tmp[key] && delete this.isRegister[key], 120000)
-    this.send(res, {
-      link: `http://huang4414-gtest.hf.space/GTest/${key}`,
-      result: `http://huang4414-gtest.hf.space/GTest/validate/${key}`
-    })
-  }
-
-  get_register(req, res, next) {
-    let { key } = req.params
-    if (!key || !this.tmp[key]) return next('憨憨，已经被人点过了哦，也可能是失效了~')
-    res.send(this.tmp[key] || {})
-    delete this.tmp[key]
-  }
-
-  validate(req, res, next) {
-    let { key } = req.params
-    if (!key || !req.body) return next('error')
-    this.result[key] = req.body
-    setTimeout(() => delete this.result[key], 30000)
-    this.send(res, {})
-    delete this.isRegister[key]
-    let Time = moment().utcOffset(8)
-    console.log(`[${Time.format('YYYY-MM-DD HH:mm:ss')}] | [GTest] 验证成功~ | [key] : ${key}`)
-  }
-
-  async get_validate(req, res, next) {
-    let { key } = req.params, data = null
-    if (!key) return next('error')
-    if (this.isRegister[key] || this.result[key]) {
-      for (let i = 0; i < 120; i++) {
-        if (this.result[key]) {
-          data = this.result[key]
-          break
-        }
-        await new Promise((resolve) => setTimeout(resolve, 1000))
-      }
-      if (!data) data = {}
-    }
-    this.send(res, data)
-  }
-
-  invalid(req, res) {
-    if (!res.finished) res.status(404).end()
-  }
-
-  error(err, req, res, next) {
-    let message = err?.message || (err !== 'error' && `${err}`) || 'Invalid request'
-    if (!res.finished) res.send({ status: 1, message })
-  }
-
-  send(res, data, message = 'OK') {
-    res.send({
-      status: Number(!data),
-      message,
-      data
-    })
-  }
-
-  get address() {
-    let { Host, Port, Https, Key } = this.cfg
-    let protocol = Https ? 'https' : 'http'
-    if (![80, 443].includes(Port)) Host += `:${Port}`
-    return `${protocol}://${Host}/GTest`
-  }
-
-  self(fn) {
-    return (...args) => this[fn].call(this, ...args)
-  }
-}
-
-process.on('unhandledRejection', (error, promise) => console.log(error))
-new example()
\ No newline at end of file
diff --git a/spaces/huggingchat/chat-ui/src/lib/server/websearch/runWebSearch.ts b/spaces/huggingchat/chat-ui/src/lib/server/websearch/runWebSearch.ts
deleted file mode 100644
index fd4802b166b7756cfd56176db31297a632808f46..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/src/lib/server/websearch/runWebSearch.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { searchWeb } from "$lib/server/websearch/searchWeb";
-import type { Message } from "$lib/types/Message";
-import type { WebSearch, WebSearchSource } from "$lib/types/WebSearch";
-import { generateQuery } from "$lib/server/websearch/generateQuery";
-import { parseWeb } from "$lib/server/websearch/parseWeb";
-import { chunk } from "$lib/utils/chunk";
-import {
-	MAX_SEQ_LEN as CHUNK_CAR_LEN,
-	findSimilarSentences,
-} from "$lib/server/websearch/sentenceSimilarity";
-import type { Conversation } from "$lib/types/Conversation";
-import type { MessageUpdate } from "$lib/types/MessageUpdate";
-import { getWebSearchProvider } from "./searchWeb";
-
-const MAX_N_PAGES_SCRAPE = 10 as const;
-const MAX_N_PAGES_EMBED = 5 as const;
-
-export async function runWebSearch(
-	conv: Conversation,
-	prompt: string,
-	updatePad: (upd: MessageUpdate) => void
-) {
-	const messages = (() => {
-		return [...conv.messages, { content: prompt, from: "user", id: crypto.randomUUID() }];
-	})() satisfies Message[];
-
-	const webSearch: WebSearch = {
-		prompt: prompt,
-		searchQuery: "",
-		results: [],
-		context: "",
-		contextSources: [],
-		createdAt: new Date(),
-		updatedAt: new Date(),
-	};
-
-	function appendUpdate(message: string, args?: string[], type?: "error" | "update") {
-		updatePad({ type: "webSearch", messageType: type ?? "update", message: message, args: args });
-	}
-
-	try {
-		webSearch.searchQuery = await generateQuery(messages);
-		const searchProvider = getWebSearchProvider();
-		appendUpdate(`Searching ${searchProvider}`, [webSearch.searchQuery]);
-		const results = await searchWeb(webSearch.searchQuery);
-		webSearch.results =
-			(results.organic_results &&
-				results.organic_results.map((el: { title: string; link: string; text?: string }) => {
-					const { title, link, text } = el;
-					const { hostname } = new URL(link);
-					return { title, link, hostname, text };
-				})) ??
-			[];
-		webSearch.results = webSearch.results
-			.filter(({ link }) => !link.includes("youtube.com")) // filter out youtube links
-			.slice(0, MAX_N_PAGES_SCRAPE); // limit to first 10 links only
-
-		let paragraphChunks: { source: WebSearchSource; text: string }[] = [];
-		if (webSearch.results.length > 0) {
-			appendUpdate("Browsing results");
-			const promises = webSearch.results.map(async (result) => {
-				const { link } = result;
-				let text = result.text ?? "";
-				if (!text) {
-					try {
-						text = await parseWeb(link);
-						appendUpdate("Browsing webpage", [link]);
-					} catch (e) {
-						// ignore errors
-					}
-				}
-				const MAX_N_CHUNKS = 100;
-				const texts = chunk(text, CHUNK_CAR_LEN).slice(0, MAX_N_CHUNKS);
-				return texts.map((t) => ({ source: result, text: t }));
-			});
-			const nestedParagraphChunks = (await Promise.all(promises)).slice(0, MAX_N_PAGES_EMBED);
-			paragraphChunks = nestedParagraphChunks.flat();
-			if (!paragraphChunks.length) {
-				throw new Error("No text found on the first 5 results");
-			}
-		} else {
-			throw new Error("No results found for this search query");
-		}
-
-		appendUpdate("Extracting relevant information");
-		const topKClosestParagraphs = 8;
-		const texts = paragraphChunks.map(({ text }) => text);
-		const indices = await findSimilarSentences(prompt, texts, {
-			topK: topKClosestParagraphs,
-		});
-		webSearch.context = indices.map((idx) => texts[idx]).join("");
-
-		const usedSources = new Set<string>();
-		for (const idx of indices) {
-			const { source } = paragraphChunks[idx];
-			if (!usedSources.has(source.link)) {
-				usedSources.add(source.link);
-				webSearch.contextSources.push(source);
-			}
-		}
-		updatePad({
-			type: "webSearch",
-			messageType: "sources",
-			message: "sources",
-			sources: webSearch.contextSources,
-		});
-	} catch (searchError) {
-		if (searchError instanceof Error) {
-			appendUpdate(
-				"An error occurred with the web search",
-				[JSON.stringify(searchError.message)],
-				"error"
-			);
-		}
-	}
-
-	return webSearch;
-}
diff --git a/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/vite.config.ts b/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/vite.config.ts
deleted file mode 100644
index 16950342c1a41f60729f1abfcbbf67676c08e1bb..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/stable-diffusion-multiplayer/frontend/vite.config.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import { sveltekit } from '@sveltejs/kit/vite';
-import type { UserConfig } from 'vite';
-
-const config: UserConfig = {
-	plugins: [sveltekit()]
-};
-
-export default config;
diff --git a/spaces/hv68/sample_tool_1/README.md b/spaces/hv68/sample_tool_1/README.md
deleted file mode 100644
index f148311c6a0861f9f63caed898fde3e84eca8524..0000000000000000000000000000000000000000
--- a/spaces/hv68/sample_tool_1/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sample Tool 1
-emoji: 💩
-colorFrom: red
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/partial_fc_v2.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/partial_fc_v2.py
deleted file mode 100644
index 45078e430a6b0cd442ff65618093689822711aef..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/partial_fc_v2.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import math
-from typing import Callable
-
-import torch
-from torch import distributed
-from torch.nn.functional import linear
-from torch.nn.functional import normalize
-
-
-class PartialFC_V2(torch.nn.Module):
-    """
-    https://arxiv.org/abs/2203.15565
-    A distributed sparsely updating variant of the FC layer, named Partial FC (PFC).
-    When sample rate less than 1, in each iteration, positive class centers and a random subset of
-    negative class centers are selected to compute the margin-based softmax loss, all class
-    centers are still maintained throughout the whole training process, but only a subset is
-    selected and updated in each iteration.
-    .. note::
-        When sample rate equal to 1, Partial FC is equal to model parallelism(default sample rate is 1).
-    Example:
-    --------
-    >>> module_pfc = PartialFC(embedding_size=512, num_classes=8000000, sample_rate=0.2)
-    >>> for img, labels in data_loader:
-    >>>     embeddings = net(img)
-    >>>     loss = module_pfc(embeddings, labels)
-    >>>     loss.backward()
-    >>>     optimizer.step()
-    """
-
-    _version = 2
-
-    def __init__(
-        self,
-        margin_loss: Callable,
-        embedding_size: int,
-        num_classes: int,
-        sample_rate: float = 1.0,
-        fp16: bool = False,
-    ):
-        """
-        Paramenters:
-        -----------
-        embedding_size: int
-            The dimension of embedding, required
-        num_classes: int
-            Total number of classes, required
-        sample_rate: float
-            The rate of negative centers participating in the calculation, default is 1.0.
-        """
-        super(PartialFC_V2, self).__init__()
-        assert distributed.is_initialized(), "must initialize distributed before create this"
-        self.rank = distributed.get_rank()
-        self.world_size = distributed.get_world_size()
-
-        self.dist_cross_entropy = DistCrossEntropy()
-        self.embedding_size = embedding_size
-        self.sample_rate: float = sample_rate
-        self.fp16 = fp16
-        self.num_local: int = num_classes // self.world_size + int(self.rank < num_classes % self.world_size)
-        self.class_start: int = num_classes // self.world_size * self.rank + min(
-            self.rank, num_classes % self.world_size
-        )
-        self.num_sample: int = int(self.sample_rate * self.num_local)
-        self.last_batch_size: int = 0
-
-        self.is_updated: bool = True
-        self.init_weight_update: bool = True
-        self.weight = torch.nn.Parameter(torch.normal(0, 0.01, (self.num_local, embedding_size)))
-
-        # margin_loss
-        if isinstance(margin_loss, Callable):
-            self.margin_softmax = margin_loss
-        else:
-            raise
-
-    def sample(self, labels, index_positive):
-        """
-        This functions will change the value of labels
-        Parameters:
-        -----------
-        labels: torch.Tensor
-            pass
-        index_positive: torch.Tensor
-            pass
-        optimizer: torch.optim.Optimizer
-            pass
-        """
-        with torch.no_grad():
-            positive = torch.unique(labels[index_positive], sorted=True).cuda()
-            if self.num_sample - positive.size(0) >= 0:
-                perm = torch.rand(size=[self.num_local]).cuda()
-                perm[positive] = 2.0
-                index = torch.topk(perm, k=self.num_sample)[1].cuda()
-                index = index.sort()[0].cuda()
-            else:
-                index = positive
-            self.weight_index = index
-
-            labels[index_positive] = torch.searchsorted(index, labels[index_positive])
-
-        return self.weight[self.weight_index]
-
-    def forward(
-        self,
-        local_embeddings: torch.Tensor,
-        local_labels: torch.Tensor,
-    ):
-        """
-        Parameters:
-        ----------
-        local_embeddings: torch.Tensor
-            feature embeddings on each GPU(Rank).
-        local_labels: torch.Tensor
-            labels on each GPU(Rank).
-        Returns:
-        -------
-        loss: torch.Tensor
-            pass
-        """
-        local_labels.squeeze_()
-        local_labels = local_labels.long()
-
-        batch_size = local_embeddings.size(0)
-        if self.last_batch_size == 0:
-            self.last_batch_size = batch_size
-        assert (
-            self.last_batch_size == batch_size
-        ), f"last batch size do not equal current batch size: {self.last_batch_size} vs {batch_size}"
-
-        _gather_embeddings = [torch.zeros((batch_size, self.embedding_size)).cuda() for _ in range(self.world_size)]
-        _gather_labels = [torch.zeros(batch_size).long().cuda() for _ in range(self.world_size)]
-        _list_embeddings = AllGather(local_embeddings, *_gather_embeddings)
-        distributed.all_gather(_gather_labels, local_labels)
-
-        embeddings = torch.cat(_list_embeddings)
-        labels = torch.cat(_gather_labels)
-
-        labels = labels.view(-1, 1)
-        index_positive = (self.class_start <= labels) & (labels < self.class_start + self.num_local)
-        labels[~index_positive] = -1
-        labels[index_positive] -= self.class_start
-
-        if self.sample_rate < 1:
-            weight = self.sample(labels, index_positive)
-        else:
-            weight = self.weight
-
-        with torch.cuda.amp.autocast(self.fp16):
-            norm_embeddings = normalize(embeddings)
-            norm_weight_activated = normalize(weight)
-            logits = linear(norm_embeddings, norm_weight_activated)
-        if self.fp16:
-            logits = logits.float()
-        logits = logits.clamp(-1, 1)
-
-        logits = self.margin_softmax(logits, labels)
-        loss = self.dist_cross_entropy(logits, labels)
-        return loss
-
-
-class DistCrossEntropyFunc(torch.autograd.Function):
-    """
-    CrossEntropy loss is calculated in parallel, allreduce denominator into single gpu and calculate softmax.
-    Implemented of ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf):
-    """
-
-    @staticmethod
-    def forward(ctx, logits: torch.Tensor, label: torch.Tensor):
-        """ """
-        batch_size = logits.size(0)
-        # for numerical stability
-        max_logits, _ = torch.max(logits, dim=1, keepdim=True)
-        # local to global
-        distributed.all_reduce(max_logits, distributed.ReduceOp.MAX)
-        logits.sub_(max_logits)
-        logits.exp_()
-        sum_logits_exp = torch.sum(logits, dim=1, keepdim=True)
-        # local to global
-        distributed.all_reduce(sum_logits_exp, distributed.ReduceOp.SUM)
-        logits.div_(sum_logits_exp)
-        index = torch.where(label != -1)[0]
-        # loss
-        loss = torch.zeros(batch_size, 1, device=logits.device)
-        loss[index] = logits[index].gather(1, label[index])
-        distributed.all_reduce(loss, distributed.ReduceOp.SUM)
-        ctx.save_for_backward(index, logits, label)
-        return loss.clamp_min_(1e-30).log_().mean() * (-1)
-
-    @staticmethod
-    def backward(ctx, loss_gradient):
-        """
-        Args:
-            loss_grad (torch.Tensor): gradient backward by last layer
-        Returns:
-            gradients for each input in forward function
-            `None` gradients for one-hot label
-        """
-        (
-            index,
-            logits,
-            label,
-        ) = ctx.saved_tensors
-        batch_size = logits.size(0)
-        one_hot = torch.zeros(size=[index.size(0), logits.size(1)], device=logits.device)
-        one_hot.scatter_(1, label[index], 1)
-        logits[index] -= one_hot
-        logits.div_(batch_size)
-        return logits * loss_gradient.item(), None
-
-
-class DistCrossEntropy(torch.nn.Module):
-    def __init__(self):
-        super(DistCrossEntropy, self).__init__()
-
-    def forward(self, logit_part, label_part):
-        return DistCrossEntropyFunc.apply(logit_part, label_part)
-
-
-class AllGatherFunc(torch.autograd.Function):
-    """AllGather op with gradient backward"""
-
-    @staticmethod
-    def forward(ctx, tensor, *gather_list):
-        gather_list = list(gather_list)
-        distributed.all_gather(gather_list, tensor)
-        return tuple(gather_list)
-
-    @staticmethod
-    def backward(ctx, *grads):
-        grad_list = list(grads)
-        rank = distributed.get_rank()
-        grad_out = grad_list[rank]
-
-        dist_ops = [
-            distributed.reduce(grad_out, rank, distributed.ReduceOp.SUM, async_op=True)
-            if i == rank
-            else distributed.reduce(grad_list[i], i, distributed.ReduceOp.SUM, async_op=True)
-            for i in range(distributed.get_world_size())
-        ]
-        for _op in dist_ops:
-            _op.wait()
-
-        grad_out *= len(grad_list)  # cooperate with distributed loss function
-        return (grad_out, *[None for _ in range(len(grad_list))])
-
-
-AllGather = AllGatherFunc.apply
diff --git a/spaces/hzwluoye/gpt4/client/css/button.css b/spaces/hzwluoye/gpt4/client/css/button.css
deleted file mode 100644
index 5f604a8460d048458249f78be9dc544ade84801e..0000000000000000000000000000000000000000
--- a/spaces/hzwluoye/gpt4/client/css/button.css
+++ /dev/null
@@ -1,26 +0,0 @@
-.button {
-	display: flex;
-	padding: 8px 12px;
-	align-items: center;
-	justify-content: center;
-	border: 1px solid var(--conversations);
-	border-radius: var(--border-radius-1);
-	width: 100%;
-	background: transparent;
-	cursor: pointer;
-}
-
-.button span {
-	color: var(--colour-3);
-	font-size: 0.875rem;
-}
-
-.button i::before {
-	margin-right: 8px;
-}
-
-@media screen and (max-width: 990px) {
-	.button span {
-		font-size: 0.75rem;
-	}
-}
diff --git a/spaces/innat/HybridModel-GradCAM/utils/patch.py b/spaces/innat/HybridModel-GradCAM/utils/patch.py
deleted file mode 100644
index 0e73a0b4f1bc6d9ad33c9280ba94cba700499b10..0000000000000000000000000000000000000000
--- a/spaces/innat/HybridModel-GradCAM/utils/patch.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import tensorflow as tf
-from tensorflow.keras import layers
-
-
-class PatchExtract(layers.Layer):
-    def __init__(self, patch_size, **kwargs):
-        super().__init__(**kwargs)
-        self.patch_size_x = patch_size[0]
-        self.patch_size_y = patch_size[0]
-
-    def call(self, images):
-        batch_size = tf.shape(images)[0]
-        patches = tf.image.extract_patches(
-            images=images,
-            sizes=(1, self.patch_size_x, self.patch_size_y, 1),
-            strides=(1, self.patch_size_x, self.patch_size_y, 1),
-            rates=(1, 1, 1, 1),
-            padding="VALID",
-        )
-        patch_dim = patches.shape[-1]
-        patch_num = patches.shape[1]
-        return tf.reshape(patches, (batch_size, patch_num * patch_num, patch_dim))
-
-    def get_config(self):
-        config = super().get_config()
-        config.update(
-            {
-                "patch_size_y": self.patch_size_y,
-                "patch_size_x": self.patch_size_x,
-            }
-        )
-        return config
-
-
-class PatchEmbedding(layers.Layer):
-    def __init__(self, num_patch, embed_dim, **kwargs):
-        super().__init__(**kwargs)
-        self.num_patch = num_patch
-        self.proj = layers.Dense(embed_dim)
-        self.pos_embed = layers.Embedding(input_dim=num_patch, output_dim=embed_dim)
-
-    def call(self, patch):
-        pos = tf.range(start=0, limit=self.num_patch, delta=1)
-        return self.proj(patch) + self.pos_embed(pos)
-
-    def get_config(self):
-        config = super().get_config()
-        config.update(
-            {
-                "num_patch": self.num_patch,
-            }
-        )
-        return config
-
-
-class PatchMerging(layers.Layer):
-    def __init__(self, num_patch, embed_dim):
-        super().__init__()
-        self.num_patch = num_patch
-        self.embed_dim = embed_dim
-        self.linear_trans = layers.Dense(2 * embed_dim, use_bias=False)
-
-    def call(self, x):
-        height, width = self.num_patch
-        _, _, C = x.get_shape().as_list()
-        x = tf.reshape(x, shape=(-1, height, width, C))
-        feat_maps = x
-
-        x0 = x[:, 0::2, 0::2, :]
-        x1 = x[:, 1::2, 0::2, :]
-        x2 = x[:, 0::2, 1::2, :]
-        x3 = x[:, 1::2, 1::2, :]
-        x = tf.concat((x0, x1, x2, x3), axis=-1)
-        x = tf.reshape(x, shape=(-1, (height // 2) * (width // 2), 4 * C))
-        return self.linear_trans(x), feat_maps
-
-    def get_config(self):
-        config = super().get_config()
-        config.update({"num_patch": self.num_patch, "embed_dim": self.embed_dim})
-        return config
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Bentley-MicroStation-CONNECT-Edition-v10-00-00-25.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Bentley-MicroStation-CONNECT-Edition-v10-00-00-25.md
deleted file mode 100644
index d8ae165efb3266d7fcf4e87b9aaafd02a5a63498..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Bentley-MicroStation-CONNECT-Edition-v10-00-00-25.md
+++ /dev/null
@@ -1,6 +0,0 @@
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-<br />
-Working with Template WorkSets - Bentley Substation v10.00-10.02 ... In the CONNECT Edition, the application makes use of WorkSets to apply configuration ... After clicking OK to create the WorkSet the software will copy all contents from the template ... 斯科特·沃克; 什么时候： Fri, Mar 15 2019 5:25 PM; 修订： 4; 评论： 0. 1fdad05405<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/CL Playstation Eye Platform SDK 1.6.4.0028 Crack UPD.md b/spaces/inplisQlawa/anything-midjourney-v4-1/CL Playstation Eye Platform SDK 1.6.4.0028 Crack UPD.md
deleted file mode 100644
index 12c0129483e0bdd541ba48eedeb15f29cd1554b1..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/CL Playstation Eye Platform SDK 1.6.4.0028 Crack UPD.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>CL Playstation Eye Platform SDK 1.6.4.0028 crack</h2><br /><p><b><b>Download File</b> &#10027; <a href="https://urlin.us/2uEw1j">https://urlin.us/2uEw1j</a></b></p><br /><br />
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-VinylMaster Cut 4.0 Full Version Crack Serial Keygen Patch Product Key License. ... PATCHED CL Playstation Eye Platform SDK 1.6.4.0028 4d29de3e1b<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Dateien Deutschland Spielt Unwrapper Exe.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Dateien Deutschland Spielt Unwrapper Exe.md
deleted file mode 100644
index 378dbdc6ca10759298a1af16425c46a60a1daba0..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Dateien Deutschland Spielt Unwrapper Exe.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
-<h1>How to Use Dateien Deutschland Spielt Unwrapper Exe to Crack PC Games</h1>
-
-<p>If you are a fan of PC games, you might have heard of Dateien Deutschland Spielt Unwrapper Exe, a tool that can crack games from the website Deutschland Spielt. Deutschland Spielt is a popular site that offers hundreds of games for download, but most of them are only free for 60 minutes. After that, you need to buy a license code to unlock the full version of the game. But what if you don't want to pay for the games? That's where Dateien Deutschland Spielt Unwrapper Exe comes in handy.</p>
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-
-<ol>
-<li>Download Dateien Deutschland Spielt Unwrapper Exe from a reliable source. You can find it on various websites and forums, but be careful of malware and viruses. Scan the file with an antivirus program before running it.</li>
-<li>Download the game you want to crack from Deutschland Spielt. You can choose from different genres and categories, such as hidden object, puzzle, adventure, simulation, and more.</li>
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-<li>Click on "Unwrap" and wait for the process to finish. You should see a message saying "Unwrapped successfully" or something similar.</li>
-<li>Run the game and enjoy it without any limitations. You don't need to enter any license code or register online. You can play the game as long as you want.</li>
-</ol>
-
-<p>That's it! You have successfully cracked a game from Deutschland Spielt using Dateien Deutschland Spielt Unwrapper Exe. Now you can play all the games you want for free and without any hassle. However, keep in mind that this tool might not work for all the games from Deutschland Spielt. Some games might have a different protection system or require an internet connection to run. Also, cracking games is illegal and unethical, so use this tool at your own risk. We do not condone piracy or support any illegal activities.</p>
-
-<p>If you liked this article, please share it with your friends and leave a comment below. Also, check out our other articles on how to crack PC games using various tools and methods. Thanks for reading!</p>
-<p></p>
-
-<p>But what if you want to crack games from other sources than Deutschland Spielt? There are many other websites that offer PC games for download, but most of them also have some form of DRM (Digital Rights Management) that prevents you from playing them without a valid license. Fortunately, there are also many ways to bypass these DRM systems and play the games for free. Here are some of the most common methods:</p>
-
-<ul>
-<li>Torrenting: This is probably the easiest and most popular way to get cracked games. Torrenting is a peer-to-peer file sharing protocol that allows you to download files from other users who have them. You can find torrent files for almost any game you want on various torrent sites, such as The Pirate Bay[^2^]. To download a torrent file, you need a torrent client, such as uTorrent or BitTorrent. Then, you just need to open the torrent file in your client and wait for the download to finish. Usually, torrent files for games come with a crack folder that contains the files you need to replace or copy to the game installation directory. You can also find instructions on how to install and crack the game in a text or README file inside the torrent file.</li>
-<li>CrackMe: This is a more challenging and educational way to crack games. CrackMe is a term for programs that are designed to be cracked by reverse engineering. They are usually made by hackers or programmers who want to test their skills or teach others how to crack software. You can find many CrackMe programs on websites like Crackmes.de or Crackmes.one. To crack a CrackMe program, you need tools like IDA Pro or OllyDbg, which are disassemblers and debuggers that allow you to analyze and modify the code of an executable file. You also need some knowledge of assembly language and programming logic. The goal of cracking a CrackMe program is usually to find a serial number, a password, or a key that will unlock the program. Sometimes, you also need to patch or remove some parts of the code that check for the validity of the input. Cracking a CrackMe program can be very rewarding and fun, but also very difficult and time-consuming.</li>
-<li>Steamless: This is a specific tool that can remove the Steam DRM from games downloaded from Steam. Steam is one of the most popular platforms for buying and playing PC games online. However, Steam also has a DRM system that requires you to have an account and an internet connection to play the games you bought. Steamless is a program that can strip away this DRM layer from the game executable files, making them run without Steam. You can find Steamless on GitHub[^1^]. To use Steamless, you just need to drag and drop the game executable file onto the Steamless window and wait for it to process it. Then, you can run the game without Steam.</li>
-</ul>
-
-<p>These are some of the most common ways to crack PC games using various tools and methods. However, there are many other ways and tools that exist, depending on the type and source of the game you want to crack. You can find more information and tutorials on websites like Reddit[^3^] or YouTube. However, be careful of malware, viruses, fake files, scams, and legal issues when cracking games. Always scan your files with an antivirus program before running them, and use a VPN to protect your online privacy and security.</p>
-
-<p>We hope this article was helpful and informative for you. If you have any questions or comments, please feel free to leave them below. Also, if you liked this article, please share it with your friends and follow us for more articles on how to crack PC games using various tools and methods. Thanks for reading!</p> d5da3c52bf<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Download _VERIFIED_ Josh Garrels - Over Oceans [2006] [FLAC] Torrent - KickassTorrents.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Download _VERIFIED_ Josh Garrels - Over Oceans [2006] [FLAC] Torrent - KickassTorrents.md
deleted file mode 100644
index 2231e206e03573a92db8206946051f49c42560de..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Download _VERIFIED_ Josh Garrels - Over Oceans [2006] [FLAC] Torrent - KickassTorrents.md	
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@@ -1,11 +0,0 @@
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Driver Fingerprint Solution P100 [EXCLUSIVE].md b/spaces/inplisQlawa/anything-midjourney-v4-1/Driver Fingerprint Solution P100 [EXCLUSIVE].md
deleted file mode 100644
index 89ae05a454d9624b5373eba2b0fc6bde2bdaf09b..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Driver Fingerprint Solution P100 [EXCLUSIVE].md	
+++ /dev/null
@@ -1,122 +0,0 @@
-
-<h1>Driver Fingerprint Solution P100: A Mobile and Secure Way to Manage Attendance</h1>
-
-<p>If you are looking for a reliable and convenient way to track the attendance of your employees, you might want to consider the Driver Fingerprint Solution P100. This is a fingerprint attendance machine that is designed to work as a mobile device, as it has an internal lithium battery and a USB charger that can accept power from a USB cable only. This means that you don't need to install it on a wall or deal with complex cabling, you can just put it on a desk and it can operate directly. It also comes with a USB cable for PC connection, so you can download the attendance logs and print any kind of reports.</p>
-
-<h2>Features and Benefits of Driver Fingerprint Solution P100</h2>
-
-<p>The Driver Fingerprint Solution P100 has many features and benefits that make it a great choice for managing attendance. Some of them are:</p>
-<h2>Driver Fingerprint Solution P100</h2><br /><p><b><b>DOWNLOAD</b> &#9675; <a href="https://urlin.us/2uEvLE">https://urlin.us/2uEvLE</a></b></p><br /><br />
-
-<ul>
-<li>It has a user capacity of 1,000 fingerprints and a log transaction capacity of 30,000 transactions.</li>
-<li>It has a fast response time of less than one second and a high matching accuracy of 1:1 and 1:N.</li>
-<li>It has a LCD and speaker built-in for authentication signal and easy operation.</li>
-<li>It has an internal lithium battery that can last for up to 8 hours of continuous use.</li>
-<li>It has a USB charger that can be plugged into any USB port or power bank.</li>
-<li>It has a software included that can help you manage the employees, shifts, schedules, calculations, reports, salary, and more.</li>
-<li>It also provides a free SDK (Software Development Kit) for further application development.</li>
-</ul>
-
-<h2>How to Install and Use Driver Fingerprint Solution P100</h2>
-
-<p>Installing and using the Driver Fingerprint Solution P100 is very easy and simple. Here are the steps:</p>
-
-<ol>
-<li>Charge the device using the USB charger until the battery indicator is full.</li>
-<li>Connect the device to your PC using the USB cable.</li>
-<li>Install the software from the CD or download it from the website.</li>
-<li>Register the fingerprints of your employees using the device or the software.</li>
-<li>Place the device on a desk or any flat surface where your employees can access it.</li>
-<li>Let your employees scan their fingerprints when they check in or out.</li>
-<li>Download the attendance logs from the device to your PC using the USB cable or the software.</li>
-<li>Analyze and print the reports using the software or export them to other formats.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>The Driver Fingerprint Solution P100 is a smart and innovative solution for managing attendance in any workplace. It is mobile, secure, fast, accurate, and easy to use. It can help you save time and money, improve productivity and efficiency, and avoid errors and frauds. If you are interested in this device, you can contact SOLUTION.CO.ID, the distributor of fingerprint time attendance machines in Indonesia. They can provide you with more information, free presentation, ordering, installation, service, and support. You can also visit their website or showroom to see more products and solutions they offer.</p>
-<h2>How to Troubleshoot and Maintain Driver Fingerprint Solution P100</h2>
-
-<p>Like any other device, the Driver Fingerprint Solution P100 may encounter some problems or issues that need to be fixed or prevented. Here are some tips on how to troubleshoot and maintain your device:</p>
-
-<ul>
-<li>If the device does not turn on or charge, check the battery level and the USB cable connection. Make sure the battery is not drained or damaged, and the USB cable is not loose or broken.</li>
-<li>If the device does not recognize the fingerprints, check the fingerprint sensor and the registered fingerprints. Make sure the sensor is clean and dry, and the fingerprints are clear and complete.</li>
-<li>If the device does not communicate with the PC, check the USB cable and the software. Make sure the USB cable is compatible and connected properly, and the software is installed and updated correctly.</li>
-<li>If the device displays an error message or freezes, reset the device by pressing and holding the power button for 10 seconds. This will restart the device and clear any temporary errors.</li>
-<li>To maintain the device, clean it regularly with a soft cloth and avoid exposing it to direct sunlight, high temperature, moisture, dust, or shock. Also, backup your data periodically to prevent data loss or corruption.</li>
-</ul>
-
-<p>If you need more help or support, you can contact SOLUTION.CO.ID, the distributor of fingerprint time attendance machines in Indonesia. They can provide you with technical assistance, service, repair, warranty, and spare parts.</p>
-
-<h2>How to Compare Driver Fingerprint Solution P100 with Other Fingerprint Attendance Machines</h2>
-
-<p>There are many other fingerprint attendance machines available in the market, but not all of them are equal or suitable for your needs. Here are some factors that you can use to compare Driver Fingerprint Solution P100 with other devices:</p>
-
-<ul>
-<li>The mobility and portability of the device. Driver Fingerprint Solution P100 is one of the few devices that can work as a mobile device, as it has an internal lithium battery and a USB charger that can accept power from a USB cable only. This makes it easy to move and use anywhere.</li>
-<li>The features and functions of the device. Driver Fingerprint Solution P100 has many features and functions that can help you manage attendance efficiently and effectively. It has a user capacity of 1,000 fingerprints and a log transaction capacity of 30,000 transactions. It has a fast response time of less than one second and a high matching accuracy of 1:1 and 1:N. It has a LCD and speaker built-in for authentication signal and easy operation. It has a software included that can help you manage the employees, shifts, schedules, calculations, reports, salary, and more. It also provides a free SDK (Software Development Kit) for further application development.</li>
-<li>The quality and reliability of the device. Driver Fingerprint Solution P100 is a high-quality and reliable device that can provide accurate and consistent results, as well as durable and easy to maintain. It is made by SOLUTION.CO.ID, a trusted distributor of fingerprint time attendance machines in Indonesia since 2006. It also comes with a warranty and after-sales service.</li>
-<li>The price and value of the device. Driver Fingerprint Solution P100 is a reasonable priced device that can offer you the best value for your money. It can help you save time and money, improve productivity and efficiency, and avoid errors and frauds.</li>
-</ul>
-
-<p>By comparing these factors, you can see that Driver Fingerprint Solution P100 is one of the best fingerprint attendance machines in the market. It can meet your needs and expectations, as well as give you an edge over your competitors.</p>
-<p></p>
-<h2>How to Order and Install Driver Fingerprint Solution P100</h2>
-
-<p>If you are interested in ordering and installing Driver Fingerprint Solution P100 for your business, you can follow these steps:</p>
-
-<ol>
-<li>Contact SOLUTION.CO.ID, the distributor of fingerprint time attendance machines in Indonesia. You can call, email, or visit their website to get more information, free presentation, and quotation.</li>
-<li>Choose the quantity and model of Driver Fingerprint Solution P100 that you need. You can also choose other products and solutions that they offer, such as face identification, RFID card, door access control, CCTV, etc.</li>
-<li>Confirm your order and make the payment. You can choose from various payment methods, such as bank transfer, credit card, debit card, etc.</li>
-<li>Wait for the delivery of your order. You can track your order status and delivery time online or by contacting SOLUTION.CO.ID.</li>
-<li>Install Driver Fingerprint Solution P100 on your desired location. You can do it yourself by following the user manual or the online tutorial. You can also request for professional installation service from SOLUTION.CO.ID.</li>
-<li>Enjoy the benefits of Driver Fingerprint Solution P100 for your business. You can manage attendance efficiently and effectively, save time and money, improve productivity and efficiency, and avoid errors and frauds.</li>
-</ol>
-
-<p>If you have any questions or problems regarding Driver Fingerprint Solution P100, you can contact SOLUTION.CO.ID anytime. They can provide you with technical support, service, repair, warranty, and spare parts.</p>
-
-<h2>How to Review and Recommend Driver Fingerprint Solution P100</h2>
-
-<p>If you are satisfied with Driver Fingerprint Solution P100 and want to share your experience and opinion with others, you can do these things:</p>
-
-<ul>
-<li>Write a review about Driver Fingerprint Solution P100 on their website or social media. You can rate the device based on its performance, features, quality, reliability, price, value, etc. You can also write about the benefits and drawbacks of the device, as well as your suggestions for improvement.</li>
-<li>Recommend Driver Fingerprint Solution P100 to your friends, family, colleagues, or anyone who needs a fingerprint attendance machine. You can tell them about the features and benefits of the device, as well as how to order and install it.</li>
-<li>Join the referral program of SOLUTION.CO.ID and get rewards for every successful referral. You can earn commissions or discounts for every order that is made through your referral link or code.</li>
-</ul>
-
-<p>By reviewing and recommending Driver Fingerprint Solution P100, you can help others make informed decisions and also support SOLUTION.CO.ID as a trusted distributor of fingerprint time attendance machines in Indonesia.</p>
-<h2>How to Use Driver Fingerprint Solution P100 for Different Purposes</h2>
-
-<p>Driver Fingerprint Solution P100 is not only a fingerprint attendance machine, but also a versatile device that can be used for different purposes. Here are some examples of how you can use it:</p>
-
-<ul>
-<li>For employee attendance management. You can use it to track the check-in and check-out time of your employees, as well as their overtime, late, leave early, and workhour total. You can also use it to calculate their salary and print their salary receipt.</li>
-<li>For access control and security. You can use it to restrict the access to certain areas or rooms in your workplace, such as the office, warehouse, laboratory, etc. You can also use it to monitor the activity and movement of your employees and visitors.</li>
-<li>For customer loyalty and membership. You can use it to register the fingerprints of your customers or members, and use them as a unique identifier or a proof of identity. You can also use it to offer them discounts, rewards, or other benefits.</li>
-<li>For student attendance and verification. You can use it to record the attendance of your students in classes, exams, or other activities. You can also use it to verify their identity and prevent cheating or impersonation.</li>
-<li>For personal or home use. You can use it to store your own fingerprints and use them as a password or a key for your computer, smartphone, tablet, or other devices. You can also use it to lock or unlock your door, safe, or cabinet.</li>
-</ul>
-
-<p>As you can see, Driver Fingerprint Solution P100 is a multifunctional device that can be used for various purposes. You can customize it according to your needs and preferences, and enjoy its convenience and security.</p>
-
-<h2>How to Contact SOLUTION.CO.ID for More Information about Driver Fingerprint Solution P100</h2>
-
-<p>If you want to get more information about Driver Fingerprint Solution P100 or any other products and solutions that SOLUTION.CO.ID offers, you can contact them through these ways:</p>
-
-<ul>
-<li>Call their hotline number at (021) 556 2135 or (031) 501 0315. You can talk to their friendly and professional staff who can answer your questions and provide you with more details.</li>
-<li>Email them at info@solution.co.id. You can send them your inquiries or requests and they will reply to you as soon as possible.</li>
-<li>Visit their website at https://www.solution.co.id/. You can browse their products and solutions catalog, download their brochures and manuals, watch their online tutorials and demos, read their testimonials and reviews, join their referral program and get rewards, etc.</li>
-<li>Visit their showroom at Mangga Dua Mall Lantai 3 No.42 in Jakarta or Hi-Tech Mall Lantai Dasar Blok A1 No.6 in Surabaya. You can see their products and solutions in person, try them out yourself, and get a free presentation from their staff.</li>
-</ul>
-
-<p>SOLUTION.CO.ID is the distributor of fingerprint time attendance machines in Indonesia since 2006. They have a wide range of products and solutions that can meet your needs and expectations. They also have a high-quality service and support that can ensure your satisfaction and trust. Contact them today and get the best solution for your business.</p>
-<h2>Conclusion</h2>
-
-<p>Driver Fingerprint Solution P100 is a mobile and secure fingerprint attendance machine that can help you manage attendance in any workplace. It has many features and benefits that can save you time and money, improve productivity and efficiency, and avoid errors and frauds. It is also a high-quality and reliable device that comes with a software, a free SDK, a warranty, and an after-sales service. It is also a versatile device that can be used for different purposes, such as access control, customer loyalty, student verification, or personal use. If you are interested in this device, you can contact SOLUTION.CO.ID, the distributor of fingerprint time attendance machines in Indonesia. They can provide you with more information, free presentation, ordering, installation, service, and support. You can also visit their website or showroom to see more products and solutions they offer.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Icem Surf Tutorial Pdf.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Icem Surf Tutorial Pdf.md
deleted file mode 100644
index a60e2bf75891afef2d90e3619bc2d5e21975c029..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Icem Surf Tutorial Pdf.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>icem surf tutorial pdf</h2><br /><p><b><b>Download</b> &gt;&gt;&gt;&gt;&gt; <a href="https://urlin.us/2uEy3L">https://urlin.us/2uEy3L</a></b></p><br /><br />
-<br />
-Icem Surf Tutorials. How to instantly design a gap surfaceusing industry standard technical edges such as: Zero Gap — Crimp Flange. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Magic Partition Recovery 2.8 Keygen Fix - Crackingpatching Utorrent.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Magic Partition Recovery 2.8 Keygen Fix - Crackingpatching Utorrent.md
deleted file mode 100644
index 4da1fc21bd1dce8e127a36255716e4a076b9a6b9..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Magic Partition Recovery 2.8 Keygen Fix - Crackingpatching Utorrent.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Magic Partition Recovery 2.8 Keygen - Crackingpatching Utorrent</h2><br /><p><b><b>Download</b> &hArr; <a href="https://urlin.us/2uEvK2">https://urlin.us/2uEvK2</a></b></p><br /><br />
-
-Starus Partition Recovery 2.8 + keygen this program will allow you to recover ... Recovery 4 0 + serial – Crackingpatching com Torrent or choose other Starus ... Norton Partition Magic 8.05 + serial.zip full version PATCHED KeepTool.v10.0.2.0. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inreVtussa/clothingai/Examples/Code Hack Nick Facebook.md b/spaces/inreVtussa/clothingai/Examples/Code Hack Nick Facebook.md
deleted file mode 100644
index b18a9c5917b732b71b068c91b38d5edd5c351119..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Code Hack Nick Facebook.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>code hack nick facebook</h2><br /><p><b><b>Download File</b> &#9734; <a href="https://tiurll.com/2uClml">https://tiurll.com/2uClml</a></b></p><br /><br />
-
-In December 2010, Nick Denton's Gawker Media was targeted by Gnosis, ... It was due to the big PR fight between Google and Facebook. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/jbetker/tortoise/models/cvvp.py b/spaces/jbetker/tortoise/models/cvvp.py
deleted file mode 100644
index 0c9fd3500b38c126667b16bffd56f32ff89271a9..0000000000000000000000000000000000000000
--- a/spaces/jbetker/tortoise/models/cvvp.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import einsum
-from torch.utils.checkpoint import checkpoint
-
-from models.arch_util import AttentionBlock
-from models.xtransformers import ContinuousTransformerWrapper, Encoder
-
-
-def exists(val):
-    return val is not None
-
-
-def masked_mean(t, mask):
-    t = t.masked_fill(~mask, 0.)
-    return t.sum(dim = 1) / mask.sum(dim = 1)
-
-
-class CollapsingTransformer(nn.Module):
-    def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs):
-        super().__init__()
-        self.transformer = ContinuousTransformerWrapper(
-            max_seq_len=-1,
-            use_pos_emb=False,
-            attn_layers=Encoder(
-                dim=model_dim,
-                depth=depth,
-                heads=heads,
-                ff_dropout=dropout,
-                ff_mult=1,
-                attn_dropout=dropout,
-                use_rmsnorm=True,
-                ff_glu=True,
-                rotary_pos_emb=True,
-                **encoder_kwargs,
-            ))
-        self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1),
-                                          AttentionBlock(output_dims, num_heads=heads, do_checkpoint=False),
-                                          nn.Conv1d(output_dims, output_dims, 1))
-        self.mask_percentage = mask_percentage
-
-    def forward(self, x, **transformer_kwargs):
-        h = self.transformer(x, **transformer_kwargs)
-        h = h.permute(0,2,1)
-        h = checkpoint(self.pre_combiner, h).permute(0,2,1)
-        if self.training:
-            mask = torch.rand_like(h.float()) > self.mask_percentage
-        else:
-            mask = torch.ones_like(h.float()).bool()
-        return masked_mean(h, mask)
-
-
-class ConvFormatEmbedding(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        self.emb = nn.Embedding(*args, **kwargs)
-
-    def forward(self, x):
-        y = self.emb(x)
-        return y.permute(0,2,1)
-
-
-class CVVP(nn.Module):
-    def __init__(
-            self,
-            model_dim=512,
-            transformer_heads=8,
-            dropout=.1,
-            conditioning_enc_depth=8,
-            cond_mask_percentage=0,
-            mel_channels=80,
-            mel_codes=None,
-            speech_enc_depth=8,
-            speech_mask_percentage=0,
-            latent_multiplier=1,
-    ):
-        super().__init__()
-        latent_dim = latent_multiplier*model_dim
-        self.temperature = nn.Parameter(torch.tensor(1.))
-
-        self.cond_emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim//2, kernel_size=5, stride=2, padding=2),
-                                      nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1))
-        self.conditioning_transformer = CollapsingTransformer(model_dim, model_dim, transformer_heads, dropout, conditioning_enc_depth, cond_mask_percentage)
-        self.to_conditioning_latent = nn.Linear(latent_dim, latent_dim, bias=False)
-
-        if mel_codes is None:
-            self.speech_emb = nn.Conv1d(mel_channels, model_dim, kernel_size=5, padding=2)
-        else:
-            self.speech_emb = ConvFormatEmbedding(mel_codes, model_dim)
-        self.speech_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, speech_enc_depth, speech_mask_percentage)
-        self.to_speech_latent = nn.Linear(latent_dim, latent_dim, bias=False)
-
-    def get_grad_norm_parameter_groups(self):
-        return {
-            'conditioning': list(self.conditioning_transformer.parameters()),
-            'speech': list(self.speech_transformer.parameters()),
-        }
-
-    def forward(
-            self,
-            mel_cond,
-            mel_input,
-            return_loss=False
-    ):
-        cond_emb = self.cond_emb(mel_cond).permute(0,2,1)
-        enc_cond = self.conditioning_transformer(cond_emb)
-        cond_latents = self.to_conditioning_latent(enc_cond)
-
-        speech_emb = self.speech_emb(mel_input).permute(0,2,1)
-        enc_speech = self.speech_transformer(speech_emb)
-        speech_latents = self.to_speech_latent(enc_speech)
-
-
-        cond_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (cond_latents, speech_latents))
-        temp = self.temperature.exp()
-
-        if not return_loss:
-            sim = einsum('n d, n d -> n', cond_latents, speech_latents) * temp
-            return sim
-
-        sim = einsum('i d, j d -> i j', cond_latents, speech_latents) * temp
-        labels = torch.arange(cond_latents.shape[0], device=mel_input.device)
-        loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
-
-        return loss
-
-
-if __name__ == '__main__':
-    clvp = CVVP()
-    clvp(torch.randn(2,80,100),
-         torch.randn(2,80,95),
-         return_loss=True)
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/lib/getImageDimension.ts b/spaces/jbilcke-hf/ai-clip-factory/src/lib/getImageDimension.ts
deleted file mode 100644
index 50a94ae1eee733b23b1d4916780e597c759c608e..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/lib/getImageDimension.ts
+++ /dev/null
@@ -1,16 +0,0 @@
-export interface ImageDimension {
-  width: number
-  height: number
-}
-
-export async function getImageDimension(src: string): Promise<ImageDimension> {
-  if (!src) {
-    return { width: 0, height: 0 }
-  }
-  const img = new Image()
-  img.src = src
-  await img.decode()
-  const width = img.width
-  const height = img.height
-  return { width, height }
-}
\ No newline at end of file
diff --git a/spaces/jeycov/Piel_cancer_prueba/README.md b/spaces/jeycov/Piel_cancer_prueba/README.md
deleted file mode 100644
index 8ec5cc50027efeb673d01e2c668f605820ffac81..0000000000000000000000000000000000000000
--- a/spaces/jeycov/Piel_cancer_prueba/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Derm-AI
-emoji: 📊
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.0.22
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/jin-nin/artist/style.css b/spaces/jin-nin/artist/style.css
deleted file mode 100644
index 335c97e4efb0a8b3516f749f32a88bdb38052a2e..0000000000000000000000000000000000000000
--- a/spaces/jin-nin/artist/style.css
+++ /dev/null
@@ -1,113 +0,0 @@
-.gr-box, .gr-form {
-  background: none !important;
-  border: none !important;
-}
-.gr-input {
-  background: #131d26 !important;
-  box-shadow: inset 0 0 0 1px #293139 !important;
-  border-radius: 2px !important;
-  padding: .5rem .75rem !important;
-  font-size: 1rem !important;
-  line-height: 1.5rem !important;
-}
-.gr-input:hover {
-  box-shadow: inset 0 0 0 2px #293139 !important;
-}
-input::-webkit-slider-runnable-track {
-  background: #131d26 !important;
-  box-shadow: inset 0 0 0 1px #293139 !important;
-  border-radius: 2px !important;
-}
-
-body {
-  overflow: overlay;
-}
-::-webkit-scrollbar {
-	width: .25rem;
-	height: .25rem;
-}
-:hover::-webkit-scrollbar {
-	width: .5rem;
-	height: .5rem;
-}
-::-webkit-scrollbar-corner {
-	background-color: #293139;
-}
-::-webkit-scrollbar-track {
-	background-color: transparent;
-}
-::-webkit-scrollbar-thumb {
-	background-color: #293139;
-	border-radius: 2px;
-}
-
-
-label > .z-40 {
-    font-size: 1rem !important;
-    padding: 0 0.75rem !important;
-}
-
-.gr-button:active, .gr-button:active, {
-  border: none !important;
-  outline: none !important;
-}
-.gr-button:hover, .gr-button:focus, .gr-button:active {
-  background: rgba( 255, 255, 255, .1 ) !important;
-  border: none !important;
-}
-.gr-button {
-  color: #6699cc !important;
-  border: none !important;
-  font-weight: normal !important;
-  background: transparent !important;
-  border-radius: 2px !important;
-  padding: .5rem .75rem !important;
-  min-width: auto !important;
-  white-space: nowrap !important;
-  align-self: center !important;
-}
-
-#translate {
-  margin-top: 2.5rem !important;
-  align-self: flex-start !important;
-}
-
-.output-markdown {
-  margin: .5rem .75rem !important;
-}
-
-body, .gradio-container {
-  font: 1rem/1.5rem 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
-}
-
-.container {
-  max-width: none !important;
-  background: #15202a !important; 
-  padding: .75rem !important;
-}
-
-#longs-fillers {
-  min-width: auto !important;
-}
-#longs-fillers .gr-button {
-  justify-content: flex-start !important;
-}
-
-.gap-4 {
-  gap: 0 !important;
-}
-
-#paints > * {
-  margin: .75rem !important;
-}
-
-#paints > * > .border {
-  box-shadow: 0 0 0 1px rgba( 255, 255, 255, .1 ) !important;
-  background: rgba(0,0,0,.1) !important;
-  border-radius: 2px !important;
-  overflow: hidden !important;
-}
-
-#paints > * > .border > .absolute {
-  display: none !important;
-}
diff --git a/spaces/jmcob/ChemistryModelerSMILES/README.md b/spaces/jmcob/ChemistryModelerSMILES/README.md
deleted file mode 100644
index da8947027027864b0b32e824db7b29e73449d3b4..0000000000000000000000000000000000000000
--- a/spaces/jmcob/ChemistryModelerSMILES/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChemistryModelerSMILES
-emoji: 🌖
-colorFrom: red
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/pinecone.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/pinecone.py
deleted file mode 100644
index daf3022ccfd976bf4701cbbbcc995806ee016843..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/pinecone.py
+++ /dev/null
@@ -1,81 +0,0 @@
-"""Pinecone reader."""
-
-from typing import Any, Dict, List, Optional
-
-from gpt_index.readers.base import BaseReader
-from gpt_index.readers.schema.base import Document
-
-
-class PineconeReader(BaseReader):
-    """Pinecone reader.
-
-    Args:
-        api_key (str): Pinecone API key.
-        environment (str): Pinecone environment.
-    """
-
-    def __init__(self, api_key: str, environment: str):
-        """Initialize with parameters."""
-        try:
-            import pinecone  # noqa: F401
-        except ImportError:
-            raise ImportError(
-                "`pinecone` package not found, please run `pip install pinecone-client`"
-            )
-
-        self._api_key = api_key
-        self._environment = environment
-        pinecone.init(api_key=api_key, environment=environment)
-
-    def load_data(
-        self,
-        index_name: str,
-        id_to_text_map: Dict[str, str],
-        vector: Optional[List[float]],
-        top_k: int,
-        separate_documents: bool = True,
-        include_values: bool = True,
-        **query_kwargs: Any
-    ) -> List[Document]:
-        """Load data from Pinecone.
-
-        Args:
-            index_name (str): Name of the index.
-            id_to_text_map (Dict[str, str]): A map from ID's to text.
-            separate_documents (Optional[bool]): Whether to return separate
-                documents per retrieved entry. Defaults to True.
-            vector (List[float]): Query vector.
-            top_k (int): Number of results to return.
-            include_values (bool): Whether to include the embedding in the response.
-                Defaults to True.
-            **query_kwargs: Keyword arguments to pass to the query.
-                Arguments are the exact same as those found in
-                Pinecone's reference documentation for the
-                query method.
-
-        Returns:
-            List[Document]: A list of documents.
-        """
-        import pinecone
-
-        index = pinecone.Index(index_name)
-        if "include_values" not in query_kwargs:
-            query_kwargs["include_values"] = True
-        response = index.query(top_k=top_k, vector=vector, **query_kwargs)
-
-        documents = []
-        for match in response.matches:
-            if match.id not in id_to_text_map:
-                raise ValueError("ID not found in id_to_text_map.")
-            text = id_to_text_map[match.id]
-            embedding = match.values
-            if len(embedding) == 0:
-                embedding = None
-            documents.append(Document(text=text, embedding=embedding))
-
-        if not separate_documents:
-            text_list = [doc.get_text() for doc in documents]
-            text = "\n\n".join(text_list)
-            documents = [Document(text=text)]
-
-        return documents
diff --git a/spaces/jsscclr/CLIP-Interrogator/README.md b/spaces/jsscclr/CLIP-Interrogator/README.md
deleted file mode 100644
index 49e83a2bc7ca24ea655d72b3ea49fe3f9733fe30..0000000000000000000000000000000000000000
--- a/spaces/jsscclr/CLIP-Interrogator/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: CLIP Interrogator
-emoji: 🕵️‍♂️
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: true
-license: mit
-duplicated_from: pharma/CLIP-Interrogator
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/justest/chatglm-6b-int4/README.md b/spaces/justest/chatglm-6b-int4/README.md
deleted file mode 100644
index f891f09dca0a5ddb038ab11504b42c0aa972d382..0000000000000000000000000000000000000000
--- a/spaces/justest/chatglm-6b-int4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chatglm 6b
-emoji: 🌖
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: duxb/chatglm-6b-int4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/karthick965938/ChatGPT-Demo/app.py b/spaces/karthick965938/ChatGPT-Demo/app.py
deleted file mode 100644
index f80f00782d624ddb4f60d1478cb76dbaa8f691ad..0000000000000000000000000000000000000000
--- a/spaces/karthick965938/ChatGPT-Demo/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import openai
-import streamlit as st
-import os
-
-st.title("ChatGPT Demo")
-
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-if "openai_model" not in st.session_state:
-    st.session_state["openai_model"] = "gpt-3.5-turbo"
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-if prompt := st.chat_input("What is up?"):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    with st.chat_message("user"):
-        st.markdown(prompt)
-
-    with st.chat_message("assistant"):
-        message_placeholder = st.empty()
-        full_response = ""
-        for response in openai.ChatCompletion.create(
-            model=st.session_state["openai_model"],
-            messages=[
-                {"role": m["role"], "content": m["content"]}
-                for m in st.session_state.messages
-            ],
-            stream=True,
-        ):
-            full_response += response.choices[0].delta.get("content", "")
-            message_placeholder.markdown(full_response + "▌")
-        message_placeholder.markdown(full_response)
-    st.session_state.messages.append({"role": "assistant", "content": full_response})
\ No newline at end of file
diff --git a/spaces/keras-io/Object-Detection-Using-RetinaNet/app.py b/spaces/keras-io/Object-Detection-Using-RetinaNet/app.py
deleted file mode 100644
index eb6bdba4bb589fc4814c9999c0e55bbbe4348b73..0000000000000000000000000000000000000000
--- a/spaces/keras-io/Object-Detection-Using-RetinaNet/app.py
+++ /dev/null
@@ -1,316 +0,0 @@
-import gradio as gr
-from huggingface_hub import from_pretrained_keras
-from PIL import Image
-import io
-import matplotlib.pyplot as plt
-import os
-import re
-import zipfile
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-import tensorflow_datasets as tfds
-
-coco_image = []
-coco_dir = 'coco/images/'
-for idx, images in enumerate(os.listdir(coco_dir)):
-  image = os.path.join(coco_dir, images)
-  if os.path.isfile(image) and idx < 10:
-    coco_image.append(image)
-    
-_, dataset_info = tfds.load(
-    "coco/2017", split=["train", "validation","test"], with_info=True, data_dir="data"
-)
-#test_dataset = tfds.load("coco/2017", split="test", data_dir="data")
-int2str = dataset_info.features["objects"]["label"].int2str
-
-class AnchorBox:
-    """Generates anchor boxes.
-
-    This class has operations to generate anchor boxes for feature maps at
-    strides `[8, 16, 32, 64, 128]`. Where each anchor each box is of the
-    format `[x, y, width, height]`.
-
-    Attributes:
-      aspect_ratios: A list of float values representing the aspect ratios of
-        the anchor boxes at each location on the feature map
-      scales: A list of float values representing the scale of the anchor boxes
-        at each location on the feature map.
-      num_anchors: The number of anchor boxes at each location on feature map
-      areas: A list of float values representing the areas of the anchor
-        boxes for each feature map in the feature pyramid.
-      strides: A list of float value representing the strides for each feature
-        map in the feature pyramid.
-    """
-
-    def __init__(self):
-        self.aspect_ratios = [0.5, 1.0, 2.0]
-        self.scales = [2 ** x for x in [0, 1 / 3, 2 / 3]]
-
-        self._num_anchors = len(self.aspect_ratios) * len(self.scales)
-        self._strides = [2 ** i for i in range(3, 8)]
-        self._areas = [x ** 2 for x in [32.0, 64.0, 128.0, 256.0, 512.0]]
-        self._anchor_dims = self._compute_dims()
-
-    def _compute_dims(self):
-        """Computes anchor box dimensions for all ratios and scales at all levels
-        of the feature pyramid.
-        """
-        anchor_dims_all = []
-        for area in self._areas:
-            anchor_dims = []
-            for ratio in self.aspect_ratios:
-                anchor_height = tf.math.sqrt(area / ratio)
-                anchor_width = area / anchor_height
-                dims = tf.reshape(
-                    tf.stack([anchor_width, anchor_height], axis=-1), [1, 1, 2]
-                )
-                for scale in self.scales:
-                    anchor_dims.append(scale * dims)
-            anchor_dims_all.append(tf.stack(anchor_dims, axis=-2))
-        return anchor_dims_all
-
-    def _get_anchors(self, feature_height, feature_width, level):
-        """Generates anchor boxes for a given feature map size and level
-
-        Arguments:
-          feature_height: An integer representing the height of the feature map.
-          feature_width: An integer representing the width of the feature map.
-          level: An integer representing the level of the feature map in the
-            feature pyramid.
-
-        Returns:
-          anchor boxes with the shape
-          `(feature_height * feature_width * num_anchors, 4)`
-        """
-        rx = tf.range(feature_width, dtype=tf.float32) + 0.5
-        ry = tf.range(feature_height, dtype=tf.float32) + 0.5
-        centers = tf.stack(tf.meshgrid(rx, ry), axis=-1) * self._strides[level - 3]
-        centers = tf.expand_dims(centers, axis=-2)
-        centers = tf.tile(centers, [1, 1, self._num_anchors, 1])
-        dims = tf.tile(
-            self._anchor_dims[level - 3], [feature_height, feature_width, 1, 1]
-        )
-        anchors = tf.concat([centers, dims], axis=-1)
-        return tf.reshape(
-            anchors, [feature_height * feature_width * self._num_anchors, 4]
-        )
-
-    def get_anchors(self, image_height, image_width):
-        """Generates anchor boxes for all the feature maps of the feature pyramid.
-
-        Arguments:
-          image_height: Height of the input image.
-          image_width: Width of the input image.
-
-        Returns:
-          anchor boxes for all the feature maps, stacked as a single tensor
-            with shape `(total_anchors, 4)`
-        """
-        anchors = [
-            self._get_anchors(
-                tf.math.ceil(image_height / 2 ** i),
-                tf.math.ceil(image_width / 2 ** i),
-                i,
-            )
-            for i in range(3, 8)
-        ]
-        return tf.concat(anchors, axis=0)
-
-class DecodePredictions(tf.keras.layers.Layer):
-    """A Keras layer that decodes predictions of the RetinaNet model.
-
-    Attributes:
-      num_classes: Number of classes in the dataset
-      confidence_threshold: Minimum class probability, below which detections
-        are pruned.
-      nms_iou_threshold: IOU threshold for the NMS operation
-      max_detections_per_class: Maximum number of detections to retain per
-       class.
-      max_detections: Maximum number of detections to retain across all
-        classes.
-      box_variance: The scaling factors used to scale the bounding box
-        predictions.
-    """
-
-    def __init__(
-        self,
-        num_classes=80,
-        confidence_threshold=0.05,
-        nms_iou_threshold=0.5,
-        max_detections_per_class=100,
-        max_detections=100,
-        box_variance=[0.1, 0.1, 0.2, 0.2],
-        **kwargs
-    ):
-        super(DecodePredictions, self).__init__(**kwargs)
-        self.num_classes = num_classes
-        self.confidence_threshold = confidence_threshold
-        self.nms_iou_threshold = nms_iou_threshold
-        self.max_detections_per_class = max_detections_per_class
-        self.max_detections = max_detections
-
-        self._anchor_box = AnchorBox()
-        self._box_variance = tf.convert_to_tensor(
-            [0.1, 0.1, 0.2, 0.2], dtype=tf.float32
-        )
-
-    def _decode_box_predictions(self, anchor_boxes, box_predictions):
-        boxes = box_predictions * self._box_variance
-        boxes = tf.concat(
-            [
-                boxes[:, :, :2] * anchor_boxes[:, :, 2:] + anchor_boxes[:, :, :2],
-                tf.math.exp(boxes[:, :, 2:]) * anchor_boxes[:, :, 2:],
-            ],
-            axis=-1,
-        )
-        boxes_transformed = convert_to_corners(boxes)
-        return boxes_transformed
-
-    def call(self, images, predictions):
-        image_shape = tf.cast(tf.shape(images), dtype=tf.float32)
-        anchor_boxes = self._anchor_box.get_anchors(image_shape[1], image_shape[2])
-        box_predictions = predictions[:, :, :4]
-        cls_predictions = tf.nn.sigmoid(predictions[:, :, 4:])
-        boxes = self._decode_box_predictions(anchor_boxes[None, ...], box_predictions)
-
-        return tf.image.combined_non_max_suppression(
-            tf.expand_dims(boxes, axis=2),
-            cls_predictions,
-            self.max_detections_per_class,
-            self.max_detections,
-            self.nms_iou_threshold,
-            self.confidence_threshold,
-            clip_boxes=False,
-        )
-
-def convert_to_corners(boxes):
-    """Changes the box format to corner coordinates
-
-    Arguments:
-      boxes: A tensor of rank 2 or higher with a shape of `(..., num_boxes, 4)`
-        representing bounding boxes where each box is of the format
-        `[x, y, width, height]`.
-
-    Returns:
-      converted boxes with shape same as that of boxes.
-    """
-    return tf.concat(
-        [boxes[..., :2] - boxes[..., 2:] / 2.0, boxes[..., :2] + boxes[..., 2:] / 2.0],
-        axis=-1,
-    )
-
-def resize_and_pad_image(
-    image, min_side=800.0, max_side=1333.0, jitter=[640, 1024], stride=128.0
-):
-    """Resizes and pads image while preserving aspect ratio.
-
-    1. Resizes images so that the shorter side is equal to `min_side`
-    2. If the longer side is greater than `max_side`, then resize the image
-      with longer side equal to `max_side`
-    3. Pad with zeros on right and bottom to make the image shape divisible by
-    `stride`
-
-    Arguments:
-      image: A 3-D tensor of shape `(height, width, channels)` representing an
-        image.
-      min_side: The shorter side of the image is resized to this value, if
-        `jitter` is set to None.
-      max_side: If the longer side of the image exceeds this value after
-        resizing, the image is resized such that the longer side now equals to
-        this value.
-      jitter: A list of floats containing minimum and maximum size for scale
-        jittering. If available, the shorter side of the image will be
-        resized to a random value in this range.
-      stride: The stride of the smallest feature map in the feature pyramid.
-        Can be calculated using `image_size / feature_map_size`.
-
-    Returns:
-      image: Resized and padded image.
-      image_shape: Shape of the image before padding.
-      ratio: The scaling factor used to resize the image
-    """
-    image_shape = tf.cast(tf.shape(image)[:2], dtype=tf.float32)
-    if jitter is not None:
-        min_side = tf.random.uniform((), jitter[0], jitter[1], dtype=tf.float32)
-    ratio = min_side / tf.reduce_min(image_shape)
-    if ratio * tf.reduce_max(image_shape) > max_side:
-        ratio = max_side / tf.reduce_max(image_shape)
-    image_shape = ratio * image_shape
-    image = tf.image.resize(image, tf.cast(image_shape, dtype=tf.int32))
-    padded_image_shape = tf.cast(
-        tf.math.ceil(image_shape / stride) * stride, dtype=tf.int32
-    )
-    image = tf.image.pad_to_bounding_box(
-        image, 0, 0, padded_image_shape[0], padded_image_shape[1]
-    )
-    return image, image_shape, ratio
-
-def visualize_detections(
-    image, boxes, classes, scores, figsize=(7, 7), linewidth=1, color=[0, 0, 1]
-):
-    """Visualize Detections"""
-    image = np.array(image, dtype=np.uint8)
-    plt.figure(figsize=figsize)
-    plt.axis("off")
-    plt.imshow(image)
-    ax = plt.gca()
-    for box, _cls, score in zip(boxes, classes, scores):
-        text = "{}: {:.2f}".format(_cls, score)
-        x1, y1, x2, y2 = box
-        w, h = x2 - x1, y2 - y1
-        patch = plt.Rectangle(
-            [x1, y1], w, h, fill=False, edgecolor=color, linewidth=linewidth
-        )
-        ax.add_patch(patch)
-        ax.text(
-            x1,
-            y1,
-            text,
-            bbox={"facecolor": color, "alpha": 0.4},
-            clip_box=ax.clipbox,
-            clip_on=True,
-        )
-    plt.show()
-    return ax
-
-def prepare_image(image):
-    image, _, ratio = resize_and_pad_image(image, jitter=None)
-    image = tf.keras.applications.resnet.preprocess_input(image)
-    return tf.expand_dims(image, axis=0), ratio
-
-model = from_pretrained_keras("keras-io/Object-Detection-RetinaNet")
-img_input = tf.keras.Input(shape=[None, None, 3], name="image")
-predictions = model(img_input, training=False)
-detections = DecodePredictions(confidence_threshold=0.5)(img_input, predictions)
-inference_model = tf.keras.Model(inputs=img_input, outputs=detections)
-
-def predict(image):
-  input_image, ratio = prepare_image(image)
-  detections = inference_model.predict(input_image)
-  num_detections = detections.valid_detections[0]
-  class_names = [
-                 int2str(int(x)) for x in detections.nmsed_classes[0][:num_detections]
-                 ]
-  img_buf = io.BytesIO()
-  ax = visualize_detections(
-        image,
-        detections.nmsed_boxes[0][:num_detections] / ratio,
-        class_names,
-        detections.nmsed_scores[0][:num_detections],
-  )
-  ax.figure.savefig(img_buf)
-  img_buf.seek(0)
-  img = Image.open(img_buf)
-  return img
-  
-# Input
-input = gr.inputs.Image(image_mode="RGB", type="numpy", label="Enter Object Image")
-
-# Output
-output = gr.outputs.Image(type="pil", label="Detected Objects with Class Category")
-
-title = "Object Detection With RetinaNet"
-description = "Upload an Image or take one from examples to localize objects present in an image, and at the same time, classify them into different categories"
-
-gr.Interface(fn=predict, inputs = input, outputs = output, examples=coco_image, allow_flagging=False, analytics_enabled=False, title=title, description=description, article="<center>Space By: <u><a href='https://github.com/robotjellyzone'><b>Kavya Bisht</b></a></u> \n Based on notebook <a href='https://keras.io/examples/vision/retinanet/'><b>this notebook</b></a></center>").launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git a/spaces/keras-io/ocr-for-captcha/README.md b/spaces/keras-io/ocr-for-captcha/README.md
deleted file mode 100644
index 81615055de848339629da4af418b4efa491b80eb..0000000000000000000000000000000000000000
--- a/spaces/keras-io/ocr-for-captcha/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: OCR For Captcha
-emoji: 🤖 
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/kevinwang676/VoiceChanger/src/face3d/util/detect_lm68.py b/spaces/kevinwang676/VoiceChanger/src/face3d/util/detect_lm68.py
deleted file mode 100644
index b7e40997289e17405e1fb6c408d21adce7b626ce..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/face3d/util/detect_lm68.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import os
-import cv2
-import numpy as np
-from scipy.io import loadmat
-import tensorflow as tf
-from util.preprocess import align_for_lm
-from shutil import move
-
-mean_face = np.loadtxt('util/test_mean_face.txt')
-mean_face = mean_face.reshape([68, 2])
-
-def save_label(labels, save_path):
-    np.savetxt(save_path, labels)
-
-def draw_landmarks(img, landmark, save_name):
-    landmark = landmark
-    lm_img = np.zeros([img.shape[0], img.shape[1], 3])
-    lm_img[:] = img.astype(np.float32)
-    landmark = np.round(landmark).astype(np.int32)
-
-    for i in range(len(landmark)):
-        for j in range(-1, 1):
-            for k in range(-1, 1):
-                if img.shape[0] - 1 - landmark[i, 1]+j > 0 and \
-                        img.shape[0] - 1 - landmark[i, 1]+j < img.shape[0] and \
-                        landmark[i, 0]+k > 0 and \
-                        landmark[i, 0]+k < img.shape[1]:
-                    lm_img[img.shape[0] - 1 - landmark[i, 1]+j, landmark[i, 0]+k,
-                           :] = np.array([0, 0, 255])
-    lm_img = lm_img.astype(np.uint8)
-
-    cv2.imwrite(save_name, lm_img)
-
-
-def load_data(img_name, txt_name):
-    return cv2.imread(img_name), np.loadtxt(txt_name)
-
-# create tensorflow graph for landmark detector
-def load_lm_graph(graph_filename):
-    with tf.gfile.GFile(graph_filename, 'rb') as f:
-        graph_def = tf.GraphDef()
-        graph_def.ParseFromString(f.read())
-
-    with tf.Graph().as_default() as graph:
-        tf.import_graph_def(graph_def, name='net')
-        img_224 = graph.get_tensor_by_name('net/input_imgs:0')
-        output_lm = graph.get_tensor_by_name('net/lm:0')
-        lm_sess = tf.Session(graph=graph)
-
-    return lm_sess,img_224,output_lm
-
-# landmark detection
-def detect_68p(img_path,sess,input_op,output_op):
-    print('detecting landmarks......')
-    names = [i for i in sorted(os.listdir(
-        img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i]
-    vis_path = os.path.join(img_path, 'vis')
-    remove_path = os.path.join(img_path, 'remove')
-    save_path = os.path.join(img_path, 'landmarks')
-    if not os.path.isdir(vis_path):
-        os.makedirs(vis_path)
-    if not os.path.isdir(remove_path):
-        os.makedirs(remove_path)
-    if not os.path.isdir(save_path):
-        os.makedirs(save_path)
-
-    for i in range(0, len(names)):
-        name = names[i]
-        print('%05d' % (i), ' ', name)
-        full_image_name = os.path.join(img_path, name)
-        txt_name = '.'.join(name.split('.')[:-1]) + '.txt'
-        full_txt_name = os.path.join(img_path, 'detections', txt_name) # 5 facial landmark path for each image
-
-        # if an image does not have detected 5 facial landmarks, remove it from the training list
-        if not os.path.isfile(full_txt_name):
-            move(full_image_name, os.path.join(remove_path, name))
-            continue 
-
-        # load data
-        img, five_points = load_data(full_image_name, full_txt_name)
-        input_img, scale, bbox = align_for_lm(img, five_points) # align for 68 landmark detection 
-
-        # if the alignment fails, remove corresponding image from the training list
-        if scale == 0:
-            move(full_txt_name, os.path.join(
-                remove_path, txt_name))
-            move(full_image_name, os.path.join(remove_path, name))
-            continue
-
-        # detect landmarks
-        input_img = np.reshape(
-            input_img, [1, 224, 224, 3]).astype(np.float32)
-        landmark = sess.run(
-            output_op, feed_dict={input_op: input_img})
-
-        # transform back to original image coordinate
-        landmark = landmark.reshape([68, 2]) + mean_face
-        landmark[:, 1] = 223 - landmark[:, 1]
-        landmark = landmark / scale
-        landmark[:, 0] = landmark[:, 0] + bbox[0]
-        landmark[:, 1] = landmark[:, 1] + bbox[1]
-        landmark[:, 1] = img.shape[0] - 1 - landmark[:, 1]
-
-        if i % 100 == 0:
-            draw_landmarks(img, landmark, os.path.join(vis_path, name))
-        save_label(landmark, os.path.join(save_path, txt_name))
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/api/audio.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/web/api/audio.py
deleted file mode 100644
index b30e5dd9ad3a249c2a6e73d9f42372f0ed098b5a..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/web/api/audio.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import os
-from pathlib import Path
-from flask_restx import Namespace, Resource, fields
-from flask import Response, current_app
-
-api = Namespace('audios', description='Audios related operations')
-
-audio = api.model('Audio', {
-    'name': fields.String(required=True, description='The audio name'),
-})
-
-def generate(wav_path):
-    with open(wav_path, "rb") as fwav:
-        data = fwav.read(1024)
-        while data:
-            yield data
-            data = fwav.read(1024)
-
-@api.route('/')
-class AudioList(Resource):
-    @api.doc('list_audios')
-    @api.marshal_list_with(audio)
-    def get(self):
-        '''List all audios'''
-        audio_samples = []
-        AUDIO_SAMPLES_DIR = current_app.config.get("AUDIO_SAMPLES_DIR")
-        if os.path.isdir(AUDIO_SAMPLES_DIR):
-            audio_samples = list(Path(AUDIO_SAMPLES_DIR).glob("*.wav"))
-        return list(a.name for a in audio_samples)
-
-@api.route('/<name>')
-@api.param('name', 'The name of audio')
-@api.response(404, 'audio not found')
-class Audio(Resource):
-    @api.doc('get_audio')
-    @api.marshal_with(audio)
-    def get(self, name):
-        '''Fetch a cat given its identifier'''
-        AUDIO_SAMPLES_DIR = current_app.config.get("AUDIO_SAMPLES_DIR")
-        if Path(AUDIO_SAMPLES_DIR + name).exists():
-            return Response(generate(AUDIO_SAMPLES_DIR + name), mimetype="audio/x-wav")
-        api.abort(404)
-    
\ No newline at end of file
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/cross_lingual_language_model/README.md b/spaces/koajoel/PolyFormer/fairseq/examples/cross_lingual_language_model/README.md
deleted file mode 100644
index af9128e39e5925e9411d162c2f24a19e4532d618..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/cross_lingual_language_model/README.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Cross-Lingual Language Model Pre-training
-
-Below are some details for training Cross-Lingual Language Models (XLM) - similar to the ones presented in [Lample & Conneau, 2019](https://arxiv.org/pdf/1901.07291.pdf) - in Fairseq. The current implementation only supports the Masked Language Model (MLM) from the paper above.
-
-## Downloading and Tokenizing Monolingual Data
-
-Pointers to the monolingual data from wikipedia, used for training the XLM-style MLM model as well as details on processing (tokenization and BPE) it can be found in the [XLM Github Repository](https://github.com/facebookresearch/XLM#download--preprocess-monolingual-data).
-
-Let's assume the following for the code snippets in later sections to work
-- Processed data is in the folder: monolingual_data/processed
-- Each language has 3 files for train, test and validation. For example we have the following files for English:
-    train.en, valid.en
-- We are training a model for 5 languages: Arabic (ar), German (de), English (en), Hindi (hi) and French (fr)
-- The vocabulary file is monolingual_data/processed/vocab_mlm
-
-
-## Fairseq Pre-processing and Binarization
-
-Pre-process and binarize the data with the MaskedLMDictionary and cross_lingual_lm task
-
-```bash
-# Ensure the output directory exists
-DATA_DIR=monolingual_data/fairseq_processed
-mkdir -p "$DATA_DIR"
-
-for lg in ar de en hi fr
-do
-
-  fairseq-preprocess \
-  --task cross_lingual_lm \
-  --srcdict monolingual_data/processed/vocab_mlm \
-  --only-source \
-  --trainpref monolingual_data/processed/train \
-  --validpref monolingual_data/processed/valid \
-  --testpref monolingual_data/processed/test \
-  --destdir monolingual_data/fairseq_processed \
-  --workers 20 \
-  --source-lang $lg
-
-  # Since we only have a source language, the output file has a None for the
-  # target language. Remove this
-
-  for stage in train test valid
-
-    sudo mv "$DATA_DIR/$stage.$lg-None.$lg.bin" "$stage.$lg.bin"
-    sudo mv "$DATA_DIR/$stage.$lg-None.$lg.idx" "$stage.$lg.idx"
-
-  done
-
-done
-```
-
-## Train a Cross-lingual Language Model similar to the XLM MLM model
-
-Use the following command to train the model on 5 languages.
-
-```
-fairseq-train \
---task cross_lingual_lm monolingual_data/fairseq_processed \
---save-dir checkpoints/mlm \
---max-update 2400000 --save-interval 1 --no-epoch-checkpoints \
---arch xlm_base \
---optimizer adam --lr-scheduler reduce_lr_on_plateau \
---lr-shrink 0.5 --lr 0.0001 --stop-min-lr 1e-09 \
---dropout 0.1 \
---criterion legacy_masked_lm_loss \
---max-tokens 2048 --tokens-per-sample 256 --attention-dropout 0.1 \
---dataset-impl lazy --seed 0 \
---masked-lm-only \
---monolingual-langs 'ar,de,en,hi,fr' --num-segment 5 \
---ddp-backend=legacy_ddp
-```
-
-Some Notes:
-- Using tokens_per_sample greater than 256 can cause OOM (out-of-memory) issues. Usually since MLM packs in streams of text, this parameter doesn't need much tuning.
-- The Evaluation workflow for computing MLM Perplexity on test data is in progress.
-- Finetuning this model on a downstream task is something which is not currently available.
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/m2m_100/tokenizers/tokenize_indic.py b/spaces/koajoel/PolyFormer/fairseq/examples/m2m_100/tokenizers/tokenize_indic.py
deleted file mode 100644
index a44fad07f7c718f99cccd445f33c62b0e3c562f4..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/m2m_100/tokenizers/tokenize_indic.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Use: echo {text} | python tokenize_indic.py {language}
-
-import sys
-
-from indicnlp.normalize.indic_normalize import IndicNormalizerFactory
-from indicnlp.tokenize.indic_tokenize import trivial_tokenize
-
-
-factory = IndicNormalizerFactory()
-normalizer = factory.get_normalizer(
-    sys.argv[1], remove_nuktas=False, nasals_mode="do_nothing"
-)
-
-for line in sys.stdin:
-    normalized_line = normalizer.normalize(line.strip())
-    tokenized_line = " ".join(trivial_tokenize(normalized_line, sys.argv[1]))
-    print(tokenized_line)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/docs/common_voice_example.md b/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/docs/common_voice_example.md
deleted file mode 100644
index 40e841b284a7e34b458b286eb0bb60e33c0601da..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/docs/common_voice_example.md
+++ /dev/null
@@ -1,56 +0,0 @@
-[[Back]](..)
-
-# Common Voice
-
-[Common Voice](https://commonvoice.mozilla.org/en/datasets) is a public domain speech corpus with 11.2K hours of read
-speech in 76 languages (the latest version 7.0). We provide examples for building
-[Transformer](https://arxiv.org/abs/1809.08895) models on this dataset.
-
-
-## Data preparation
-[Download](https://commonvoice.mozilla.org/en/datasets) and unpack Common Voice v4 to a path `${DATA_ROOT}/${LANG_ID}`.
-Create splits and generate audio manifests with
-```bash
-python -m examples.speech_synthesis.preprocessing.get_common_voice_audio_manifest \
-  --data-root ${DATA_ROOT} \
-  --lang ${LANG_ID} \
-  --output-manifest-root ${AUDIO_MANIFEST_ROOT} --convert-to-wav
-```
-
-Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with
-```bash
-python -m examples.speech_synthesis.preprocessing.get_feature_manifest \
-  --audio-manifest-root ${AUDIO_MANIFEST_ROOT} \
-  --output-root ${FEATURE_MANIFEST_ROOT} \
-  --ipa-vocab --lang ${LANG_ID}
-```
-where we use phoneme inputs (`--ipa-vocab`) as example.
-
-To denoise audio and trim leading/trailing silence using signal processing based VAD, run
-```bash
-for SPLIT in dev test train; do
-    python -m examples.speech_synthesis.preprocessing.denoise_and_vad_audio \
-      --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \
-      --output-dir ${PROCESSED_DATA_ROOT} \
-      --denoise --vad --vad-agg-level 2
-done
-```
-
-
-## Training
-(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#transformer).)
-
-
-## Inference
-(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#inference).)
-
-## Automatic Evaluation
-(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#automatic-evaluation).)
-
-## Results
-
-| Language | Speakers | --arch | Params | Test MCD | Model |
-|---|---|---|---|---|---|
-| English | 200 | tts_transformer | 54M | 3.8 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/cv4_en200_transformer_phn.tar) |
-
-[[Back]](..)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/denoiser/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/denoiser/__init__.py
deleted file mode 100644
index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/denoiser/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/kottu/stabble_diffusion_sketch/README.md b/spaces/kottu/stabble_diffusion_sketch/README.md
deleted file mode 100644
index e6a1afb1f1f31c273af3c4901a96f6c5e57fa016..0000000000000000000000000000000000000000
--- a/spaces/kottu/stabble_diffusion_sketch/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-license: mit
-title: stabble_diffusion_sketch
-sdk: docker
-emoji: 👁
-colorFrom: blue
-colorTo: purple
-pinned: true
----
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/otBase.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/otBase.py
deleted file mode 100644
index 9c80400e9420577f0d9d6f747e15b83e49f68e49..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/otBase.py
+++ /dev/null
@@ -1,1458 +0,0 @@
-from fontTools.config import OPTIONS
-from fontTools.misc.textTools import Tag, bytesjoin
-from .DefaultTable import DefaultTable
-from enum import IntEnum
-import sys
-import array
-import struct
-import logging
-from functools import lru_cache
-from typing import Iterator, NamedTuple, Optional, Tuple
-
-log = logging.getLogger(__name__)
-
-have_uharfbuzz = False
-try:
-    import uharfbuzz as hb
-
-    # repack method added in uharfbuzz >= 0.23; if uharfbuzz *can* be
-    # imported but repack method is missing, behave as if uharfbuzz
-    # is not available (fallback to the slower Python implementation)
-    have_uharfbuzz = callable(getattr(hb, "repack", None))
-except ImportError:
-    pass
-
-USE_HARFBUZZ_REPACKER = OPTIONS[f"{__name__}:USE_HARFBUZZ_REPACKER"]
-
-
-class OverflowErrorRecord(object):
-    def __init__(self, overflowTuple):
-        self.tableType = overflowTuple[0]
-        self.LookupListIndex = overflowTuple[1]
-        self.SubTableIndex = overflowTuple[2]
-        self.itemName = overflowTuple[3]
-        self.itemIndex = overflowTuple[4]
-
-    def __repr__(self):
-        return str(
-            (
-                self.tableType,
-                "LookupIndex:",
-                self.LookupListIndex,
-                "SubTableIndex:",
-                self.SubTableIndex,
-                "ItemName:",
-                self.itemName,
-                "ItemIndex:",
-                self.itemIndex,
-            )
-        )
-
-
-class OTLOffsetOverflowError(Exception):
-    def __init__(self, overflowErrorRecord):
-        self.value = overflowErrorRecord
-
-    def __str__(self):
-        return repr(self.value)
-
-
-class RepackerState(IntEnum):
-    # Repacking control flow is implemnted using a state machine. The state machine table:
-    #
-    # State       | Packing Success | Packing Failed | Exception Raised |
-    # ------------+-----------------+----------------+------------------+
-    # PURE_FT     | Return result   | PURE_FT        | Return failure   |
-    # HB_FT       | Return result   | HB_FT          | FT_FALLBACK      |
-    # FT_FALLBACK | HB_FT           | FT_FALLBACK    | Return failure   |
-
-    # Pack only with fontTools, don't allow sharing between extensions.
-    PURE_FT = 1
-
-    # Attempt to pack with harfbuzz (allowing sharing between extensions)
-    # use fontTools to attempt overflow resolution.
-    HB_FT = 2
-
-    # Fallback if HB/FT packing gets stuck. Pack only with fontTools, don't allow sharing between
-    # extensions.
-    FT_FALLBACK = 3
-
-
-class BaseTTXConverter(DefaultTable):
-
-    """Generic base class for TTX table converters. It functions as an
-    adapter between the TTX (ttLib actually) table model and the model
-    we use for OpenType tables, which is necessarily subtly different.
-    """
-
-    def decompile(self, data, font):
-        """Create an object from the binary data. Called automatically on access."""
-        from . import otTables
-
-        reader = OTTableReader(data, tableTag=self.tableTag)
-        tableClass = getattr(otTables, self.tableTag)
-        self.table = tableClass()
-        self.table.decompile(reader, font)
-
-    def compile(self, font):
-        """Compiles the table into binary. Called automatically on save."""
-
-        # General outline:
-        # Create a top-level OTTableWriter for the GPOS/GSUB table.
-        # 	Call the compile method for the the table
-        # 		for each 'converter' record in the table converter list
-        # 			call converter's write method for each item in the value.
-        # 				- For simple items, the write method adds a string to the
-        # 				writer's self.items list.
-        # 				- For Struct/Table/Subtable items, it add first adds new writer to the
-        # 				to the writer's self.items, then calls the item's compile method.
-        # 				This creates a tree of writers, rooted at the GUSB/GPOS writer, with
-        # 				each writer representing a table, and the writer.items list containing
-        # 				the child data strings and writers.
-        # 	call the getAllData method
-        # 		call _doneWriting, which removes duplicates
-        # 		call _gatherTables. This traverses the tables, adding unique occurences to a flat list of tables
-        # 		Traverse the flat list of tables, calling getDataLength on each to update their position
-        # 		Traverse the flat list of tables again, calling getData each get the data in the table, now that
-        # 		pos's and offset are known.
-
-        # 		If a lookup subtable overflows an offset, we have to start all over.
-        overflowRecord = None
-        # this is 3-state option: default (None) means automatically use hb.repack or
-        # silently fall back if it fails; True, use it and raise error if not possible
-        # or it errors out; False, don't use it, even if you can.
-        use_hb_repack = font.cfg[USE_HARFBUZZ_REPACKER]
-        if self.tableTag in ("GSUB", "GPOS"):
-            if use_hb_repack is False:
-                log.debug(
-                    "hb.repack disabled, compiling '%s' with pure-python serializer",
-                    self.tableTag,
-                )
-            elif not have_uharfbuzz:
-                if use_hb_repack is True:
-                    raise ImportError("No module named 'uharfbuzz'")
-                else:
-                    assert use_hb_repack is None
-                    log.debug(
-                        "uharfbuzz not found, compiling '%s' with pure-python serializer",
-                        self.tableTag,
-                    )
-
-        if (
-            use_hb_repack in (None, True)
-            and have_uharfbuzz
-            and self.tableTag in ("GSUB", "GPOS")
-        ):
-            state = RepackerState.HB_FT
-        else:
-            state = RepackerState.PURE_FT
-
-        hb_first_error_logged = False
-        lastOverflowRecord = None
-        while True:
-            try:
-                writer = OTTableWriter(tableTag=self.tableTag)
-                self.table.compile(writer, font)
-                if state == RepackerState.HB_FT:
-                    return self.tryPackingHarfbuzz(writer, hb_first_error_logged)
-                elif state == RepackerState.PURE_FT:
-                    return self.tryPackingFontTools(writer)
-                elif state == RepackerState.FT_FALLBACK:
-                    # Run packing with FontTools only, but don't return the result as it will
-                    # not be optimally packed. Once a successful packing has been found, state is
-                    # changed back to harfbuzz packing to produce the final, optimal, packing.
-                    self.tryPackingFontTools(writer)
-                    log.debug(
-                        "Re-enabling sharing between extensions and switching back to "
-                        "harfbuzz+fontTools packing."
-                    )
-                    state = RepackerState.HB_FT
-
-            except OTLOffsetOverflowError as e:
-                hb_first_error_logged = True
-                ok = self.tryResolveOverflow(font, e, lastOverflowRecord)
-                lastOverflowRecord = e.value
-
-                if ok:
-                    continue
-
-                if state is RepackerState.HB_FT:
-                    log.debug(
-                        "Harfbuzz packing out of resolutions, disabling sharing between extensions and "
-                        "switching to fontTools only packing."
-                    )
-                    state = RepackerState.FT_FALLBACK
-                else:
-                    raise
-
-    def tryPackingHarfbuzz(self, writer, hb_first_error_logged):
-        try:
-            log.debug("serializing '%s' with hb.repack", self.tableTag)
-            return writer.getAllDataUsingHarfbuzz(self.tableTag)
-        except (ValueError, MemoryError, hb.RepackerError) as e:
-            # Only log hb repacker errors the first time they occur in
-            # the offset-overflow resolution loop, they are just noisy.
-            # Maybe we can revisit this if/when uharfbuzz actually gives
-            # us more info as to why hb.repack failed...
-            if not hb_first_error_logged:
-                error_msg = f"{type(e).__name__}"
-                if str(e) != "":
-                    error_msg += f": {e}"
-                log.warning(
-                    "hb.repack failed to serialize '%s', attempting fonttools resolutions "
-                    "; the error message was: %s",
-                    self.tableTag,
-                    error_msg,
-                )
-                hb_first_error_logged = True
-            return writer.getAllData(remove_duplicate=False)
-
-    def tryPackingFontTools(self, writer):
-        return writer.getAllData()
-
-    def tryResolveOverflow(self, font, e, lastOverflowRecord):
-        ok = 0
-        if lastOverflowRecord == e.value:
-            # Oh well...
-            return ok
-
-        overflowRecord = e.value
-        log.info("Attempting to fix OTLOffsetOverflowError %s", e)
-
-        if overflowRecord.itemName is None:
-            from .otTables import fixLookupOverFlows
-
-            ok = fixLookupOverFlows(font, overflowRecord)
-        else:
-            from .otTables import fixSubTableOverFlows
-
-            ok = fixSubTableOverFlows(font, overflowRecord)
-
-        if ok:
-            return ok
-
-        # Try upgrading lookup to Extension and hope
-        # that cross-lookup sharing not happening would
-        # fix overflow...
-        from .otTables import fixLookupOverFlows
-
-        return fixLookupOverFlows(font, overflowRecord)
-
-    def toXML(self, writer, font):
-        self.table.toXML2(writer, font)
-
-    def fromXML(self, name, attrs, content, font):
-        from . import otTables
-
-        if not hasattr(self, "table"):
-            tableClass = getattr(otTables, self.tableTag)
-            self.table = tableClass()
-        self.table.fromXML(name, attrs, content, font)
-        self.table.populateDefaults()
-
-    def ensureDecompiled(self, recurse=True):
-        self.table.ensureDecompiled(recurse=recurse)
-
-
-# https://github.com/fonttools/fonttools/pull/2285#issuecomment-834652928
-assert len(struct.pack("i", 0)) == 4
-assert array.array("i").itemsize == 4, "Oops, file a bug against fonttools."
-
-
-class OTTableReader(object):
-
-    """Helper class to retrieve data from an OpenType table."""
-
-    __slots__ = ("data", "offset", "pos", "localState", "tableTag")
-
-    def __init__(self, data, localState=None, offset=0, tableTag=None):
-        self.data = data
-        self.offset = offset
-        self.pos = offset
-        self.localState = localState
-        self.tableTag = tableTag
-
-    def advance(self, count):
-        self.pos += count
-
-    def seek(self, pos):
-        self.pos = pos
-
-    def copy(self):
-        other = self.__class__(self.data, self.localState, self.offset, self.tableTag)
-        other.pos = self.pos
-        return other
-
-    def getSubReader(self, offset):
-        offset = self.offset + offset
-        return self.__class__(self.data, self.localState, offset, self.tableTag)
-
-    def readValue(self, typecode, staticSize):
-        pos = self.pos
-        newpos = pos + staticSize
-        (value,) = struct.unpack(f">{typecode}", self.data[pos:newpos])
-        self.pos = newpos
-        return value
-
-    def readArray(self, typecode, staticSize, count):
-        pos = self.pos
-        newpos = pos + count * staticSize
-        value = array.array(typecode, self.data[pos:newpos])
-        if sys.byteorder != "big":
-            value.byteswap()
-        self.pos = newpos
-        return value.tolist()
-
-    def readInt8(self):
-        return self.readValue("b", staticSize=1)
-
-    def readInt8Array(self, count):
-        return self.readArray("b", staticSize=1, count=count)
-
-    def readShort(self):
-        return self.readValue("h", staticSize=2)
-
-    def readShortArray(self, count):
-        return self.readArray("h", staticSize=2, count=count)
-
-    def readLong(self):
-        return self.readValue("i", staticSize=4)
-
-    def readLongArray(self, count):
-        return self.readArray("i", staticSize=4, count=count)
-
-    def readUInt8(self):
-        return self.readValue("B", staticSize=1)
-
-    def readUInt8Array(self, count):
-        return self.readArray("B", staticSize=1, count=count)
-
-    def readUShort(self):
-        return self.readValue("H", staticSize=2)
-
-    def readUShortArray(self, count):
-        return self.readArray("H", staticSize=2, count=count)
-
-    def readULong(self):
-        return self.readValue("I", staticSize=4)
-
-    def readULongArray(self, count):
-        return self.readArray("I", staticSize=4, count=count)
-
-    def readUInt24(self):
-        pos = self.pos
-        newpos = pos + 3
-        (value,) = struct.unpack(">l", b"\0" + self.data[pos:newpos])
-        self.pos = newpos
-        return value
-
-    def readUInt24Array(self, count):
-        return [self.readUInt24() for _ in range(count)]
-
-    def readTag(self):
-        pos = self.pos
-        newpos = pos + 4
-        value = Tag(self.data[pos:newpos])
-        assert len(value) == 4, value
-        self.pos = newpos
-        return value
-
-    def readData(self, count):
-        pos = self.pos
-        newpos = pos + count
-        value = self.data[pos:newpos]
-        self.pos = newpos
-        return value
-
-    def __setitem__(self, name, value):
-        state = self.localState.copy() if self.localState else dict()
-        state[name] = value
-        self.localState = state
-
-    def __getitem__(self, name):
-        return self.localState and self.localState[name]
-
-    def __contains__(self, name):
-        return self.localState and name in self.localState
-
-
-class OTTableWriter(object):
-
-    """Helper class to gather and assemble data for OpenType tables."""
-
-    def __init__(self, localState=None, tableTag=None, offsetSize=2):
-        self.items = []
-        self.pos = None
-        self.localState = localState
-        self.tableTag = tableTag
-        self.offsetSize = offsetSize
-        self.parent = None
-
-    # DEPRECATED: 'longOffset' is kept as a property for backward compat with old code.
-    # You should use 'offsetSize' instead (2, 3 or 4 bytes).
-    @property
-    def longOffset(self):
-        return self.offsetSize == 4
-
-    @longOffset.setter
-    def longOffset(self, value):
-        self.offsetSize = 4 if value else 2
-
-    def __setitem__(self, name, value):
-        state = self.localState.copy() if self.localState else dict()
-        state[name] = value
-        self.localState = state
-
-    def __getitem__(self, name):
-        return self.localState[name]
-
-    def __delitem__(self, name):
-        del self.localState[name]
-
-    # assembler interface
-
-    def getDataLength(self):
-        """Return the length of this table in bytes, without subtables."""
-        l = 0
-        for item in self.items:
-            if hasattr(item, "getCountData"):
-                l += item.size
-            elif hasattr(item, "getData"):
-                l += item.offsetSize
-            else:
-                l = l + len(item)
-        return l
-
-    def getData(self):
-        """Assemble the data for this writer/table, without subtables."""
-        items = list(self.items)  # make a shallow copy
-        pos = self.pos
-        numItems = len(items)
-        for i in range(numItems):
-            item = items[i]
-
-            if hasattr(item, "getData"):
-                if item.offsetSize == 4:
-                    items[i] = packULong(item.pos - pos)
-                elif item.offsetSize == 2:
-                    try:
-                        items[i] = packUShort(item.pos - pos)
-                    except struct.error:
-                        # provide data to fix overflow problem.
-                        overflowErrorRecord = self.getOverflowErrorRecord(item)
-
-                        raise OTLOffsetOverflowError(overflowErrorRecord)
-                elif item.offsetSize == 3:
-                    items[i] = packUInt24(item.pos - pos)
-                else:
-                    raise ValueError(item.offsetSize)
-
-        return bytesjoin(items)
-
-    def getDataForHarfbuzz(self):
-        """Assemble the data for this writer/table with all offset field set to 0"""
-        items = list(self.items)
-        packFuncs = {2: packUShort, 3: packUInt24, 4: packULong}
-        for i, item in enumerate(items):
-            if hasattr(item, "getData"):
-                # Offset value is not needed in harfbuzz repacker, so setting offset to 0 to avoid overflow here
-                if item.offsetSize in packFuncs:
-                    items[i] = packFuncs[item.offsetSize](0)
-                else:
-                    raise ValueError(item.offsetSize)
-
-        return bytesjoin(items)
-
-    def __hash__(self):
-        # only works after self._doneWriting() has been called
-        return hash(self.items)
-
-    def __ne__(self, other):
-        result = self.__eq__(other)
-        return result if result is NotImplemented else not result
-
-    def __eq__(self, other):
-        if type(self) != type(other):
-            return NotImplemented
-        return self.offsetSize == other.offsetSize and self.items == other.items
-
-    def _doneWriting(self, internedTables, shareExtension=False):
-        # Convert CountData references to data string items
-        # collapse duplicate table references to a unique entry
-        # "tables" are OTTableWriter objects.
-
-        # For Extension Lookup types, we can
-        # eliminate duplicates only within the tree under the Extension Lookup,
-        # as offsets may exceed 64K even between Extension LookupTable subtables.
-        isExtension = hasattr(self, "Extension")
-
-        # Certain versions of Uniscribe reject the font if the GSUB/GPOS top-level
-        # arrays (ScriptList, FeatureList, LookupList) point to the same, possibly
-        # empty, array.  So, we don't share those.
-        # See: https://github.com/fonttools/fonttools/issues/518
-        dontShare = hasattr(self, "DontShare")
-
-        if isExtension and not shareExtension:
-            internedTables = {}
-
-        items = self.items
-        for i in range(len(items)):
-            item = items[i]
-            if hasattr(item, "getCountData"):
-                items[i] = item.getCountData()
-            elif hasattr(item, "getData"):
-                item._doneWriting(internedTables, shareExtension=shareExtension)
-                # At this point, all subwriters are hashable based on their items.
-                # (See hash and comparison magic methods above.) So the ``setdefault``
-                # call here will return the first writer object we've seen with
-                # equal content, or store it in the dictionary if it's not been
-                # seen yet. We therefore replace the subwriter object with an equivalent
-                # object, which deduplicates the tree.
-                if not dontShare:
-                    items[i] = item = internedTables.setdefault(item, item)
-        self.items = tuple(items)
-
-    def _gatherTables(self, tables, extTables, done):
-        # Convert table references in self.items tree to a flat
-        # list of tables in depth-first traversal order.
-        # "tables" are OTTableWriter objects.
-        # We do the traversal in reverse order at each level, in order to
-        # resolve duplicate references to be the last reference in the list of tables.
-        # For extension lookups, duplicate references can be merged only within the
-        # writer tree under the  extension lookup.
-
-        done[id(self)] = True
-
-        numItems = len(self.items)
-        iRange = list(range(numItems))
-        iRange.reverse()
-
-        isExtension = hasattr(self, "Extension")
-
-        selfTables = tables
-
-        if isExtension:
-            assert (
-                extTables is not None
-            ), "Program or XML editing error. Extension subtables cannot contain extensions subtables"
-            tables, extTables, done = extTables, None, {}
-
-        # add Coverage table if it is sorted last.
-        sortCoverageLast = False
-        if hasattr(self, "sortCoverageLast"):
-            # Find coverage table
-            for i in range(numItems):
-                item = self.items[i]
-                if getattr(item, "name", None) == "Coverage":
-                    sortCoverageLast = True
-                    break
-            if id(item) not in done:
-                item._gatherTables(tables, extTables, done)
-            else:
-                # We're a new parent of item
-                pass
-
-        for i in iRange:
-            item = self.items[i]
-            if not hasattr(item, "getData"):
-                continue
-
-            if (
-                sortCoverageLast
-                and (i == 1)
-                and getattr(item, "name", None) == "Coverage"
-            ):
-                # we've already 'gathered' it above
-                continue
-
-            if id(item) not in done:
-                item._gatherTables(tables, extTables, done)
-            else:
-                # Item is already written out by other parent
-                pass
-
-        selfTables.append(self)
-
-    def _gatherGraphForHarfbuzz(self, tables, obj_list, done, objidx, virtual_edges):
-        real_links = []
-        virtual_links = []
-        item_idx = objidx
-
-        # Merge virtual_links from parent
-        for idx in virtual_edges:
-            virtual_links.append((0, 0, idx))
-
-        sortCoverageLast = False
-        coverage_idx = 0
-        if hasattr(self, "sortCoverageLast"):
-            # Find coverage table
-            for i, item in enumerate(self.items):
-                if getattr(item, "name", None) == "Coverage":
-                    sortCoverageLast = True
-                    if id(item) not in done:
-                        coverage_idx = item_idx = item._gatherGraphForHarfbuzz(
-                            tables, obj_list, done, item_idx, virtual_edges
-                        )
-                    else:
-                        coverage_idx = done[id(item)]
-                    virtual_edges.append(coverage_idx)
-                    break
-
-        child_idx = 0
-        offset_pos = 0
-        for i, item in enumerate(self.items):
-            if hasattr(item, "getData"):
-                pos = offset_pos
-            elif hasattr(item, "getCountData"):
-                offset_pos += item.size
-                continue
-            else:
-                offset_pos = offset_pos + len(item)
-                continue
-
-            if id(item) not in done:
-                child_idx = item_idx = item._gatherGraphForHarfbuzz(
-                    tables, obj_list, done, item_idx, virtual_edges
-                )
-            else:
-                child_idx = done[id(item)]
-
-            real_edge = (pos, item.offsetSize, child_idx)
-            real_links.append(real_edge)
-            offset_pos += item.offsetSize
-
-        tables.append(self)
-        obj_list.append((real_links, virtual_links))
-        item_idx += 1
-        done[id(self)] = item_idx
-        if sortCoverageLast:
-            virtual_edges.pop()
-
-        return item_idx
-
-    def getAllDataUsingHarfbuzz(self, tableTag):
-        """The Whole table is represented as a Graph.
-        Assemble graph data and call Harfbuzz repacker to pack the table.
-        Harfbuzz repacker is faster and retain as much sub-table sharing as possible, see also:
-        https://github.com/harfbuzz/harfbuzz/blob/main/docs/repacker.md
-        The input format for hb.repack() method is explained here:
-        https://github.com/harfbuzz/uharfbuzz/blob/main/src/uharfbuzz/_harfbuzz.pyx#L1149
-        """
-        internedTables = {}
-        self._doneWriting(internedTables, shareExtension=True)
-        tables = []
-        obj_list = []
-        done = {}
-        objidx = 0
-        virtual_edges = []
-        self._gatherGraphForHarfbuzz(tables, obj_list, done, objidx, virtual_edges)
-        # Gather all data in two passes: the absolute positions of all
-        # subtable are needed before the actual data can be assembled.
-        pos = 0
-        for table in tables:
-            table.pos = pos
-            pos = pos + table.getDataLength()
-
-        data = []
-        for table in tables:
-            tableData = table.getDataForHarfbuzz()
-            data.append(tableData)
-
-        if hasattr(hb, "repack_with_tag"):
-            return hb.repack_with_tag(str(tableTag), data, obj_list)
-        else:
-            return hb.repack(data, obj_list)
-
-    def getAllData(self, remove_duplicate=True):
-        """Assemble all data, including all subtables."""
-        if remove_duplicate:
-            internedTables = {}
-            self._doneWriting(internedTables)
-        tables = []
-        extTables = []
-        done = {}
-        self._gatherTables(tables, extTables, done)
-        tables.reverse()
-        extTables.reverse()
-        # Gather all data in two passes: the absolute positions of all
-        # subtable are needed before the actual data can be assembled.
-        pos = 0
-        for table in tables:
-            table.pos = pos
-            pos = pos + table.getDataLength()
-
-        for table in extTables:
-            table.pos = pos
-            pos = pos + table.getDataLength()
-
-        data = []
-        for table in tables:
-            tableData = table.getData()
-            data.append(tableData)
-
-        for table in extTables:
-            tableData = table.getData()
-            data.append(tableData)
-
-        return bytesjoin(data)
-
-    # interface for gathering data, as used by table.compile()
-
-    def getSubWriter(self, offsetSize=2):
-        subwriter = self.__class__(
-            self.localState, self.tableTag, offsetSize=offsetSize
-        )
-        subwriter.parent = (
-            self  # because some subtables have idential values, we discard
-        )
-        # the duplicates under the getAllData method. Hence some
-        # subtable writers can have more than one parent writer.
-        # But we just care about first one right now.
-        return subwriter
-
-    def writeValue(self, typecode, value):
-        self.items.append(struct.pack(f">{typecode}", value))
-
-    def writeArray(self, typecode, values):
-        a = array.array(typecode, values)
-        if sys.byteorder != "big":
-            a.byteswap()
-        self.items.append(a.tobytes())
-
-    def writeInt8(self, value):
-        assert -128 <= value < 128, value
-        self.items.append(struct.pack(">b", value))
-
-    def writeInt8Array(self, values):
-        self.writeArray("b", values)
-
-    def writeShort(self, value):
-        assert -32768 <= value < 32768, value
-        self.items.append(struct.pack(">h", value))
-
-    def writeShortArray(self, values):
-        self.writeArray("h", values)
-
-    def writeLong(self, value):
-        self.items.append(struct.pack(">i", value))
-
-    def writeLongArray(self, values):
-        self.writeArray("i", values)
-
-    def writeUInt8(self, value):
-        assert 0 <= value < 256, value
-        self.items.append(struct.pack(">B", value))
-
-    def writeUInt8Array(self, values):
-        self.writeArray("B", values)
-
-    def writeUShort(self, value):
-        assert 0 <= value < 0x10000, value
-        self.items.append(struct.pack(">H", value))
-
-    def writeUShortArray(self, values):
-        self.writeArray("H", values)
-
-    def writeULong(self, value):
-        self.items.append(struct.pack(">I", value))
-
-    def writeULongArray(self, values):
-        self.writeArray("I", values)
-
-    def writeUInt24(self, value):
-        assert 0 <= value < 0x1000000, value
-        b = struct.pack(">L", value)
-        self.items.append(b[1:])
-
-    def writeUInt24Array(self, values):
-        for value in values:
-            self.writeUInt24(value)
-
-    def writeTag(self, tag):
-        tag = Tag(tag).tobytes()
-        assert len(tag) == 4, tag
-        self.items.append(tag)
-
-    def writeSubTable(self, subWriter):
-        self.items.append(subWriter)
-
-    def writeCountReference(self, table, name, size=2, value=None):
-        ref = CountReference(table, name, size=size, value=value)
-        self.items.append(ref)
-        return ref
-
-    def writeStruct(self, format, values):
-        data = struct.pack(*(format,) + values)
-        self.items.append(data)
-
-    def writeData(self, data):
-        self.items.append(data)
-
-    def getOverflowErrorRecord(self, item):
-        LookupListIndex = SubTableIndex = itemName = itemIndex = None
-        if self.name == "LookupList":
-            LookupListIndex = item.repeatIndex
-        elif self.name == "Lookup":
-            LookupListIndex = self.repeatIndex
-            SubTableIndex = item.repeatIndex
-        else:
-            itemName = getattr(item, "name", "<none>")
-            if hasattr(item, "repeatIndex"):
-                itemIndex = item.repeatIndex
-            if self.name == "SubTable":
-                LookupListIndex = self.parent.repeatIndex
-                SubTableIndex = self.repeatIndex
-            elif self.name == "ExtSubTable":
-                LookupListIndex = self.parent.parent.repeatIndex
-                SubTableIndex = self.parent.repeatIndex
-            else:  # who knows how far below the SubTable level we are! Climb back up to the nearest subtable.
-                itemName = ".".join([self.name, itemName])
-                p1 = self.parent
-                while p1 and p1.name not in ["ExtSubTable", "SubTable"]:
-                    itemName = ".".join([p1.name, itemName])
-                    p1 = p1.parent
-                if p1:
-                    if p1.name == "ExtSubTable":
-                        LookupListIndex = p1.parent.parent.repeatIndex
-                        SubTableIndex = p1.parent.repeatIndex
-                    else:
-                        LookupListIndex = p1.parent.repeatIndex
-                        SubTableIndex = p1.repeatIndex
-
-        return OverflowErrorRecord(
-            (self.tableTag, LookupListIndex, SubTableIndex, itemName, itemIndex)
-        )
-
-
-class CountReference(object):
-    """A reference to a Count value, not a count of references."""
-
-    def __init__(self, table, name, size=None, value=None):
-        self.table = table
-        self.name = name
-        self.size = size
-        if value is not None:
-            self.setValue(value)
-
-    def setValue(self, value):
-        table = self.table
-        name = self.name
-        if table[name] is None:
-            table[name] = value
-        else:
-            assert table[name] == value, (name, table[name], value)
-
-    def getValue(self):
-        return self.table[self.name]
-
-    def getCountData(self):
-        v = self.table[self.name]
-        if v is None:
-            v = 0
-        return {1: packUInt8, 2: packUShort, 4: packULong}[self.size](v)
-
-
-def packUInt8(value):
-    return struct.pack(">B", value)
-
-
-def packUShort(value):
-    return struct.pack(">H", value)
-
-
-def packULong(value):
-    assert 0 <= value < 0x100000000, value
-    return struct.pack(">I", value)
-
-
-def packUInt24(value):
-    assert 0 <= value < 0x1000000, value
-    return struct.pack(">I", value)[1:]
-
-
-class BaseTable(object):
-
-    """Generic base class for all OpenType (sub)tables."""
-
-    def __getattr__(self, attr):
-        reader = self.__dict__.get("reader")
-        if reader:
-            del self.reader
-            font = self.font
-            del self.font
-            self.decompile(reader, font)
-            return getattr(self, attr)
-
-        raise AttributeError(attr)
-
-    def ensureDecompiled(self, recurse=False):
-        reader = self.__dict__.get("reader")
-        if reader:
-            del self.reader
-            font = self.font
-            del self.font
-            self.decompile(reader, font)
-        if recurse:
-            for subtable in self.iterSubTables():
-                subtable.value.ensureDecompiled(recurse)
-
-    def __getstate__(self):
-        # before copying/pickling 'lazy' objects, make a shallow copy of OTTableReader
-        # https://github.com/fonttools/fonttools/issues/2965
-        if "reader" in self.__dict__:
-            state = self.__dict__.copy()
-            state["reader"] = self.__dict__["reader"].copy()
-            return state
-        return self.__dict__
-
-    @classmethod
-    def getRecordSize(cls, reader):
-        totalSize = 0
-        for conv in cls.converters:
-            size = conv.getRecordSize(reader)
-            if size is NotImplemented:
-                return NotImplemented
-            countValue = 1
-            if conv.repeat:
-                if conv.repeat in reader:
-                    countValue = reader[conv.repeat] + conv.aux
-                else:
-                    return NotImplemented
-            totalSize += size * countValue
-        return totalSize
-
-    def getConverters(self):
-        return self.converters
-
-    def getConverterByName(self, name):
-        return self.convertersByName[name]
-
-    def populateDefaults(self, propagator=None):
-        for conv in self.getConverters():
-            if conv.repeat:
-                if not hasattr(self, conv.name):
-                    setattr(self, conv.name, [])
-                countValue = len(getattr(self, conv.name)) - conv.aux
-                try:
-                    count_conv = self.getConverterByName(conv.repeat)
-                    setattr(self, conv.repeat, countValue)
-                except KeyError:
-                    # conv.repeat is a propagated count
-                    if propagator and conv.repeat in propagator:
-                        propagator[conv.repeat].setValue(countValue)
-            else:
-                if conv.aux and not eval(conv.aux, None, self.__dict__):
-                    continue
-                if hasattr(self, conv.name):
-                    continue  # Warn if it should NOT be present?!
-                if hasattr(conv, "writeNullOffset"):
-                    setattr(self, conv.name, None)  # Warn?
-                # elif not conv.isCount:
-                # 	# Warn?
-                # 	pass
-                if hasattr(conv, "DEFAULT"):
-                    # OptionalValue converters (e.g. VarIndex)
-                    setattr(self, conv.name, conv.DEFAULT)
-
-    def decompile(self, reader, font):
-        self.readFormat(reader)
-        table = {}
-        self.__rawTable = table  # for debugging
-        for conv in self.getConverters():
-            if conv.name == "SubTable":
-                conv = conv.getConverter(reader.tableTag, table["LookupType"])
-            if conv.name == "ExtSubTable":
-                conv = conv.getConverter(reader.tableTag, table["ExtensionLookupType"])
-            if conv.name == "FeatureParams":
-                conv = conv.getConverter(reader["FeatureTag"])
-            if conv.name == "SubStruct":
-                conv = conv.getConverter(reader.tableTag, table["MorphType"])
-            try:
-                if conv.repeat:
-                    if isinstance(conv.repeat, int):
-                        countValue = conv.repeat
-                    elif conv.repeat in table:
-                        countValue = table[conv.repeat]
-                    else:
-                        # conv.repeat is a propagated count
-                        countValue = reader[conv.repeat]
-                    countValue += conv.aux
-                    table[conv.name] = conv.readArray(reader, font, table, countValue)
-                else:
-                    if conv.aux and not eval(conv.aux, None, table):
-                        continue
-                    table[conv.name] = conv.read(reader, font, table)
-                    if conv.isPropagated:
-                        reader[conv.name] = table[conv.name]
-            except Exception as e:
-                name = conv.name
-                e.args = e.args + (name,)
-                raise
-
-        if hasattr(self, "postRead"):
-            self.postRead(table, font)
-        else:
-            self.__dict__.update(table)
-
-        del self.__rawTable  # succeeded, get rid of debugging info
-
-    def compile(self, writer, font):
-        self.ensureDecompiled()
-        # TODO Following hack to be removed by rewriting how FormatSwitching tables
-        # are handled.
-        # https://github.com/fonttools/fonttools/pull/2238#issuecomment-805192631
-        if hasattr(self, "preWrite"):
-            deleteFormat = not hasattr(self, "Format")
-            table = self.preWrite(font)
-            deleteFormat = deleteFormat and hasattr(self, "Format")
-        else:
-            deleteFormat = False
-            table = self.__dict__.copy()
-
-        # some count references may have been initialized in a custom preWrite; we set
-        # these in the writer's state beforehand (instead of sequentially) so they will
-        # be propagated to all nested subtables even if the count appears in the current
-        # table only *after* the offset to the subtable that it is counting.
-        for conv in self.getConverters():
-            if conv.isCount and conv.isPropagated:
-                value = table.get(conv.name)
-                if isinstance(value, CountReference):
-                    writer[conv.name] = value
-
-        if hasattr(self, "sortCoverageLast"):
-            writer.sortCoverageLast = 1
-
-        if hasattr(self, "DontShare"):
-            writer.DontShare = True
-
-        if hasattr(self.__class__, "LookupType"):
-            writer["LookupType"].setValue(self.__class__.LookupType)
-
-        self.writeFormat(writer)
-        for conv in self.getConverters():
-            value = table.get(
-                conv.name
-            )  # TODO Handle defaults instead of defaulting to None!
-            if conv.repeat:
-                if value is None:
-                    value = []
-                countValue = len(value) - conv.aux
-                if isinstance(conv.repeat, int):
-                    assert len(value) == conv.repeat, "expected %d values, got %d" % (
-                        conv.repeat,
-                        len(value),
-                    )
-                elif conv.repeat in table:
-                    CountReference(table, conv.repeat, value=countValue)
-                else:
-                    # conv.repeat is a propagated count
-                    writer[conv.repeat].setValue(countValue)
-                try:
-                    conv.writeArray(writer, font, table, value)
-                except Exception as e:
-                    e.args = e.args + (conv.name + "[]",)
-                    raise
-            elif conv.isCount:
-                # Special-case Count values.
-                # Assumption: a Count field will *always* precede
-                # the actual array(s).
-                # We need a default value, as it may be set later by a nested
-                # table. We will later store it here.
-                # We add a reference: by the time the data is assembled
-                # the Count value will be filled in.
-                # We ignore the current count value since it will be recomputed,
-                # unless it's a CountReference that was already initialized in a custom preWrite.
-                if isinstance(value, CountReference):
-                    ref = value
-                    ref.size = conv.staticSize
-                    writer.writeData(ref)
-                    table[conv.name] = ref.getValue()
-                else:
-                    ref = writer.writeCountReference(table, conv.name, conv.staticSize)
-                    table[conv.name] = None
-                if conv.isPropagated:
-                    writer[conv.name] = ref
-            elif conv.isLookupType:
-                # We make sure that subtables have the same lookup type,
-                # and that the type is the same as the one set on the
-                # Lookup object, if any is set.
-                if conv.name not in table:
-                    table[conv.name] = None
-                ref = writer.writeCountReference(
-                    table, conv.name, conv.staticSize, table[conv.name]
-                )
-                writer["LookupType"] = ref
-            else:
-                if conv.aux and not eval(conv.aux, None, table):
-                    continue
-                try:
-                    conv.write(writer, font, table, value)
-                except Exception as e:
-                    name = value.__class__.__name__ if value is not None else conv.name
-                    e.args = e.args + (name,)
-                    raise
-                if conv.isPropagated:
-                    writer[conv.name] = value
-
-        if deleteFormat:
-            del self.Format
-
-    def readFormat(self, reader):
-        pass
-
-    def writeFormat(self, writer):
-        pass
-
-    def toXML(self, xmlWriter, font, attrs=None, name=None):
-        tableName = name if name else self.__class__.__name__
-        if attrs is None:
-            attrs = []
-        if hasattr(self, "Format"):
-            attrs = attrs + [("Format", self.Format)]
-        xmlWriter.begintag(tableName, attrs)
-        xmlWriter.newline()
-        self.toXML2(xmlWriter, font)
-        xmlWriter.endtag(tableName)
-        xmlWriter.newline()
-
-    def toXML2(self, xmlWriter, font):
-        # Simpler variant of toXML, *only* for the top level tables (like GPOS, GSUB).
-        # This is because in TTX our parent writes our main tag, and in otBase.py we
-        # do it ourselves. I think I'm getting schizophrenic...
-        for conv in self.getConverters():
-            if conv.repeat:
-                value = getattr(self, conv.name, [])
-                for i in range(len(value)):
-                    item = value[i]
-                    conv.xmlWrite(xmlWriter, font, item, conv.name, [("index", i)])
-            else:
-                if conv.aux and not eval(conv.aux, None, vars(self)):
-                    continue
-                value = getattr(
-                    self, conv.name, None
-                )  # TODO Handle defaults instead of defaulting to None!
-                conv.xmlWrite(xmlWriter, font, value, conv.name, [])
-
-    def fromXML(self, name, attrs, content, font):
-        try:
-            conv = self.getConverterByName(name)
-        except KeyError:
-            raise  # XXX on KeyError, raise nice error
-        value = conv.xmlRead(attrs, content, font)
-        if conv.repeat:
-            seq = getattr(self, conv.name, None)
-            if seq is None:
-                seq = []
-                setattr(self, conv.name, seq)
-            seq.append(value)
-        else:
-            setattr(self, conv.name, value)
-
-    def __ne__(self, other):
-        result = self.__eq__(other)
-        return result if result is NotImplemented else not result
-
-    def __eq__(self, other):
-        if type(self) != type(other):
-            return NotImplemented
-
-        self.ensureDecompiled()
-        other.ensureDecompiled()
-
-        return self.__dict__ == other.__dict__
-
-    class SubTableEntry(NamedTuple):
-        """See BaseTable.iterSubTables()"""
-
-        name: str
-        value: "BaseTable"
-        index: Optional[int] = None  # index into given array, None for single values
-
-    def iterSubTables(self) -> Iterator[SubTableEntry]:
-        """Yield (name, value, index) namedtuples for all subtables of current table.
-
-        A sub-table is an instance of BaseTable (or subclass thereof) that is a child
-        of self, the current parent table.
-        The tuples also contain the attribute name (str) of the of parent table to get
-        a subtable, and optionally, for lists of subtables (i.e. attributes associated
-        with a converter that has a 'repeat'), an index into the list containing the
-        given subtable value.
-        This method can be useful to traverse trees of otTables.
-        """
-        for conv in self.getConverters():
-            name = conv.name
-            value = getattr(self, name, None)
-            if value is None:
-                continue
-            if isinstance(value, BaseTable):
-                yield self.SubTableEntry(name, value)
-            elif isinstance(value, list):
-                yield from (
-                    self.SubTableEntry(name, v, index=i)
-                    for i, v in enumerate(value)
-                    if isinstance(v, BaseTable)
-                )
-
-    # instance (not @class)method for consistency with FormatSwitchingBaseTable
-    def getVariableAttrs(self):
-        return getVariableAttrs(self.__class__)
-
-
-class FormatSwitchingBaseTable(BaseTable):
-
-    """Minor specialization of BaseTable, for tables that have multiple
-    formats, eg. CoverageFormat1 vs. CoverageFormat2."""
-
-    @classmethod
-    def getRecordSize(cls, reader):
-        return NotImplemented
-
-    def getConverters(self):
-        try:
-            fmt = self.Format
-        except AttributeError:
-            # some FormatSwitchingBaseTables (e.g. Coverage) no longer have 'Format'
-            # attribute after fully decompiled, only gain one in preWrite before being
-            # recompiled. In the decompiled state, these hand-coded classes defined in
-            # otTables.py lose their format-specific nature and gain more high-level
-            # attributes that are not tied to converters.
-            return []
-        return self.converters.get(self.Format, [])
-
-    def getConverterByName(self, name):
-        return self.convertersByName[self.Format][name]
-
-    def readFormat(self, reader):
-        self.Format = reader.readUShort()
-
-    def writeFormat(self, writer):
-        writer.writeUShort(self.Format)
-
-    def toXML(self, xmlWriter, font, attrs=None, name=None):
-        BaseTable.toXML(self, xmlWriter, font, attrs, name)
-
-    def getVariableAttrs(self):
-        return getVariableAttrs(self.__class__, self.Format)
-
-
-class UInt8FormatSwitchingBaseTable(FormatSwitchingBaseTable):
-    def readFormat(self, reader):
-        self.Format = reader.readUInt8()
-
-    def writeFormat(self, writer):
-        writer.writeUInt8(self.Format)
-
-
-formatSwitchingBaseTables = {
-    "uint16": FormatSwitchingBaseTable,
-    "uint8": UInt8FormatSwitchingBaseTable,
-}
-
-
-def getFormatSwitchingBaseTableClass(formatType):
-    try:
-        return formatSwitchingBaseTables[formatType]
-    except KeyError:
-        raise TypeError(f"Unsupported format type: {formatType!r}")
-
-
-# memoize since these are parsed from otData.py, thus stay constant
-@lru_cache()
-def getVariableAttrs(cls: BaseTable, fmt: Optional[int] = None) -> Tuple[str]:
-    """Return sequence of variable table field names (can be empty).
-
-    Attributes are deemed "variable" when their otData.py's description contain
-    'VarIndexBase + {offset}', e.g. COLRv1 PaintVar* tables.
-    """
-    if not issubclass(cls, BaseTable):
-        raise TypeError(cls)
-    if issubclass(cls, FormatSwitchingBaseTable):
-        if fmt is None:
-            raise TypeError(f"'fmt' is required for format-switching {cls.__name__}")
-        converters = cls.convertersByName[fmt]
-    else:
-        converters = cls.convertersByName
-    # assume if no 'VarIndexBase' field is present, table has no variable fields
-    if "VarIndexBase" not in converters:
-        return ()
-    varAttrs = {}
-    for name, conv in converters.items():
-        offset = conv.getVarIndexOffset()
-        if offset is not None:
-            varAttrs[name] = offset
-    return tuple(sorted(varAttrs, key=varAttrs.__getitem__))
-
-
-#
-# Support for ValueRecords
-#
-# This data type is so different from all other OpenType data types that
-# it requires quite a bit of code for itself. It even has special support
-# in OTTableReader and OTTableWriter...
-#
-
-valueRecordFormat = [
-    # 	Mask	 Name		isDevice signed
-    (0x0001, "XPlacement", 0, 1),
-    (0x0002, "YPlacement", 0, 1),
-    (0x0004, "XAdvance", 0, 1),
-    (0x0008, "YAdvance", 0, 1),
-    (0x0010, "XPlaDevice", 1, 0),
-    (0x0020, "YPlaDevice", 1, 0),
-    (0x0040, "XAdvDevice", 1, 0),
-    (0x0080, "YAdvDevice", 1, 0),
-    # 	reserved:
-    (0x0100, "Reserved1", 0, 0),
-    (0x0200, "Reserved2", 0, 0),
-    (0x0400, "Reserved3", 0, 0),
-    (0x0800, "Reserved4", 0, 0),
-    (0x1000, "Reserved5", 0, 0),
-    (0x2000, "Reserved6", 0, 0),
-    (0x4000, "Reserved7", 0, 0),
-    (0x8000, "Reserved8", 0, 0),
-]
-
-
-def _buildDict():
-    d = {}
-    for mask, name, isDevice, signed in valueRecordFormat:
-        d[name] = mask, isDevice, signed
-    return d
-
-
-valueRecordFormatDict = _buildDict()
-
-
-class ValueRecordFactory(object):
-
-    """Given a format code, this object convert ValueRecords."""
-
-    def __init__(self, valueFormat):
-        format = []
-        for mask, name, isDevice, signed in valueRecordFormat:
-            if valueFormat & mask:
-                format.append((name, isDevice, signed))
-        self.format = format
-
-    def __len__(self):
-        return len(self.format)
-
-    def readValueRecord(self, reader, font):
-        format = self.format
-        if not format:
-            return None
-        valueRecord = ValueRecord()
-        for name, isDevice, signed in format:
-            if signed:
-                value = reader.readShort()
-            else:
-                value = reader.readUShort()
-            if isDevice:
-                if value:
-                    from . import otTables
-
-                    subReader = reader.getSubReader(value)
-                    value = getattr(otTables, name)()
-                    value.decompile(subReader, font)
-                else:
-                    value = None
-            setattr(valueRecord, name, value)
-        return valueRecord
-
-    def writeValueRecord(self, writer, font, valueRecord):
-        for name, isDevice, signed in self.format:
-            value = getattr(valueRecord, name, 0)
-            if isDevice:
-                if value:
-                    subWriter = writer.getSubWriter()
-                    writer.writeSubTable(subWriter)
-                    value.compile(subWriter, font)
-                else:
-                    writer.writeUShort(0)
-            elif signed:
-                writer.writeShort(value)
-            else:
-                writer.writeUShort(value)
-
-
-class ValueRecord(object):
-
-    # see ValueRecordFactory
-
-    def __init__(self, valueFormat=None, src=None):
-        if valueFormat is not None:
-            for mask, name, isDevice, signed in valueRecordFormat:
-                if valueFormat & mask:
-                    setattr(self, name, None if isDevice else 0)
-            if src is not None:
-                for key, val in src.__dict__.items():
-                    if not hasattr(self, key):
-                        continue
-                    setattr(self, key, val)
-        elif src is not None:
-            self.__dict__ = src.__dict__.copy()
-
-    def getFormat(self):
-        format = 0
-        for name in self.__dict__.keys():
-            format = format | valueRecordFormatDict[name][0]
-        return format
-
-    def getEffectiveFormat(self):
-        format = 0
-        for name, value in self.__dict__.items():
-            if value:
-                format = format | valueRecordFormatDict[name][0]
-        return format
-
-    def toXML(self, xmlWriter, font, valueName, attrs=None):
-        if attrs is None:
-            simpleItems = []
-        else:
-            simpleItems = list(attrs)
-        for mask, name, isDevice, format in valueRecordFormat[:4]:  # "simple" values
-            if hasattr(self, name):
-                simpleItems.append((name, getattr(self, name)))
-        deviceItems = []
-        for mask, name, isDevice, format in valueRecordFormat[4:8]:  # device records
-            if hasattr(self, name):
-                device = getattr(self, name)
-                if device is not None:
-                    deviceItems.append((name, device))
-        if deviceItems:
-            xmlWriter.begintag(valueName, simpleItems)
-            xmlWriter.newline()
-            for name, deviceRecord in deviceItems:
-                if deviceRecord is not None:
-                    deviceRecord.toXML(xmlWriter, font, name=name)
-            xmlWriter.endtag(valueName)
-            xmlWriter.newline()
-        else:
-            xmlWriter.simpletag(valueName, simpleItems)
-            xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, font):
-        from . import otTables
-
-        for k, v in attrs.items():
-            setattr(self, k, int(v))
-        for element in content:
-            if not isinstance(element, tuple):
-                continue
-            name, attrs, content = element
-            value = getattr(otTables, name)()
-            for elem2 in content:
-                if not isinstance(elem2, tuple):
-                    continue
-                name2, attrs2, content2 = elem2
-                value.fromXML(name2, attrs2, content2, font)
-            setattr(self, name, value)
-
-    def __ne__(self, other):
-        result = self.__eq__(other)
-        return result if result is NotImplemented else not result
-
-    def __eq__(self, other):
-        if type(self) != type(other):
-            return NotImplemented
-        return self.__dict__ == other.__dict__
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/__init__.py
deleted file mode 100644
index 6628fc7eb9943f357b9bbc27b8e6a47c03a87d32..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/__init__.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-An implementation of JSON Schema for Python
-
-The main functionality is provided by the validator classes for each of the
-supported JSON Schema versions.
-
-Most commonly, `jsonschema.validators.validate` is the quickest way to simply
-validate a given instance under a schema, and will create a validator
-for you.
-"""
-import warnings
-
-from jsonschema._format import FormatChecker
-from jsonschema._types import TypeChecker
-from jsonschema.exceptions import (
-    ErrorTree,
-    FormatError,
-    RefResolutionError,
-    SchemaError,
-    ValidationError,
-)
-from jsonschema.protocols import Validator
-from jsonschema.validators import (
-    Draft3Validator,
-    Draft4Validator,
-    Draft6Validator,
-    Draft7Validator,
-    Draft201909Validator,
-    Draft202012Validator,
-    RefResolver,
-    validate,
-)
-
-
-def __getattr__(name):
-    if name == "__version__":
-        warnings.warn(
-            "Accessing jsonschema.__version__ is deprecated and will be "
-            "removed in a future release. Use importlib.metadata directly "
-            "to query for jsonschema's version.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        try:
-            from importlib import metadata
-        except ImportError:
-            import importlib_metadata as metadata
-
-        return metadata.version("jsonschema")
-
-    format_checkers = {
-        "draft3_format_checker": Draft3Validator,
-        "draft4_format_checker": Draft4Validator,
-        "draft6_format_checker": Draft6Validator,
-        "draft7_format_checker": Draft7Validator,
-        "draft201909_format_checker": Draft201909Validator,
-        "draft202012_format_checker": Draft202012Validator,
-    }
-    ValidatorForFormat = format_checkers.get(name)
-    if ValidatorForFormat is not None:
-        warnings.warn(
-            f"Accessing jsonschema.{name} is deprecated and will be "
-            "removed in a future release. Instead, use the FORMAT_CHECKER "
-            "attribute on the corresponding Validator.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return ValidatorForFormat.FORMAT_CHECKER
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/fuzz_validate.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/fuzz_validate.py
deleted file mode 100644
index c12e88bcfe9bfdc0e0ffaab502789a6b585d4be2..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/fuzz_validate.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""
-Fuzzing setup for OSS-Fuzz.
-
-See https://github.com/google/oss-fuzz/tree/master/projects/jsonschema for the
-other half of the setup here.
-"""
-import sys
-
-from hypothesis import given, strategies
-
-import jsonschema
-
-PRIM = strategies.one_of(
-    strategies.booleans(),
-    strategies.integers(),
-    strategies.floats(allow_nan=False, allow_infinity=False),
-    strategies.text(),
-)
-DICT = strategies.recursive(
-    base=strategies.one_of(
-        strategies.booleans(),
-        strategies.dictionaries(strategies.text(), PRIM),
-    ),
-    extend=lambda inner: strategies.dictionaries(strategies.text(), inner),
-)
-
-
-@given(obj1=DICT, obj2=DICT)
-def test_schemas(obj1, obj2):
-    try:
-        jsonschema.validate(instance=obj1, schema=obj2)
-    except jsonschema.exceptions.ValidationError:
-        pass
-    except jsonschema.exceptions.SchemaError:
-        pass
-
-
-def main():
-    atheris.instrument_all()
-    atheris.Setup(
-        sys.argv,
-        test_schemas.hypothesis.fuzz_one_input,
-        enable_python_coverage=True,
-    )
-    atheris.Fuzz()
-
-
-if __name__ == "__main__":
-    import atheris
-    main()
diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_googledownload.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_googledownload.py
deleted file mode 100644
index 25533d4e0d90bac7519874a654ffd833d16ae289..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_googledownload.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import math
-import requests
-from tqdm import tqdm
-
-
-'''
-borrowed from 
-https://github.com/xinntao/BasicSR/blob/28883e15eedc3381d23235ff3cf7c454c4be87e6/basicsr/utils/download_util.py
-'''
-
-
-def sizeof_fmt(size, suffix='B'):
-    """Get human readable file size.
-    Args:
-        size (int): File size.
-        suffix (str): Suffix. Default: 'B'.
-    Return:
-        str: Formated file siz.
-    """
-    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
-        if abs(size) < 1024.0:
-            return f'{size:3.1f} {unit}{suffix}'
-        size /= 1024.0
-    return f'{size:3.1f} Y{suffix}'
-
-
-def download_file_from_google_drive(file_id, save_path):
-    """Download files from google drive.
-    Ref:
-    https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive  # noqa E501
-    Args:
-        file_id (str): File id.
-        save_path (str): Save path.
-    """
-
-    session = requests.Session()
-    URL = 'https://docs.google.com/uc?export=download'
-    params = {'id': file_id}
-
-    response = session.get(URL, params=params, stream=True)
-    token = get_confirm_token(response)
-    if token:
-        params['confirm'] = token
-        response = session.get(URL, params=params, stream=True)
-
-    # get file size
-    response_file_size = session.get(
-        URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
-    if 'Content-Range' in response_file_size.headers:
-        file_size = int(
-            response_file_size.headers['Content-Range'].split('/')[1])
-    else:
-        file_size = None
-
-    save_response_content(response, save_path, file_size)
-
-
-def get_confirm_token(response):
-    for key, value in response.cookies.items():
-        if key.startswith('download_warning'):
-            return value
-    return None
-
-
-def save_response_content(response,
-                          destination,
-                          file_size=None,
-                          chunk_size=32768):
-    if file_size is not None:
-        pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')
-
-        readable_file_size = sizeof_fmt(file_size)
-    else:
-        pbar = None
-
-    with open(destination, 'wb') as f:
-        downloaded_size = 0
-        for chunk in response.iter_content(chunk_size):
-            downloaded_size += chunk_size
-            if pbar is not None:
-                pbar.update(1)
-                pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} '
-                                     f'/ {readable_file_size}')
-            if chunk:  # filter out keep-alive new chunks
-                f.write(chunk)
-        if pbar is not None:
-            pbar.close()
-
-
-if __name__ == "__main__":
-    file_id = '1WNULM1e8gRNvsngVscsQ8tpaOqJ4mYtv'
-    save_path = 'BSRGAN.pth'
-    download_file_from_google_drive(file_id, save_path)
diff --git a/spaces/leelaaaaaavvv/pavaniMyAIchatBot/README.md b/spaces/leelaaaaaavvv/pavaniMyAIchatBot/README.md
deleted file mode 100644
index 2f7cff2fe2d27e1c85d7dc253bcee65b7cafe11a..0000000000000000000000000000000000000000
--- a/spaces/leelaaaaaavvv/pavaniMyAIchatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PavaniMyAIchatBot
-emoji: 📊
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Ceremony Of Carols Pdf Free.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Ceremony Of Carols Pdf Free.md
deleted file mode 100644
index 647bbf721c8e52db577c32a96476aca51102f00d..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Ceremony Of Carols Pdf Free.md	
+++ /dev/null
@@ -1,148 +0,0 @@
-<br />
-<h1>Ceremony Of Carols Pdf Free: A Musical Gift for Christmas</h1>
-
-<p>If you are looking for a beautiful and inspiring choral work to celebrate the festive season, you might want to download a Ceremony of Carols pdf free and enjoy the music of Benjamin Britten. A Ceremony of Carols is a collection of 11 carols for three-part choir and harp, composed by Britten in 1942. The carols are based on medieval poems in Latin, Middle English and Old French, and they capture the joy, mystery and wonder of the Christmas story.</p>
-
-<h2>What is A Ceremony of Carols?</h2>
-
-<p>A Ceremony of Carols is one of Britten's most popular and beloved works, and it has been performed by choirs all over the world. The carols are arranged in a symmetrical structure, with a processional and recessional based on the same plainchant melody, and a central carol that celebrates the birth of Christ. The other carols explore different aspects of the nativity, such as the virgin Mary, the angels, the shepherds, the magi and the gifts. The carols are varied in style and mood, ranging from lively and joyful to serene and contemplative.</p>
-<h2>Ceremony Of Carols Pdf Free</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733; <a href="https://bytlly.com/2uGwoB">https://bytlly.com/2uGwoB</a></b></p><br /><br />
-
-<h3>How to Download A Ceremony of Carols Pdf Free?</h3>
-
-<p>If you want to download a Ceremony of Carols pdf free, you can find several online sources that offer the sheet music for choir and harp. Some of these sources are:</p>
-
-<ul>
-<li><a href="http://www.tewkesburychoral.org.uk/wp-content/uploads/2018/09/Ceremony-of-Carols-Text4.pdf">Benjamin Britten: A Ceremony of Carols (Text and Selective Translations)</a>: This pdf provides the lyrics and translations of the carols, as well as some background information on the composer and the work.</li>
-<li><a href="http://icchoirs.org/Britten_COC.pdf">BENJAMIN BRITTEN: A Ceremony of Carols</a>: This pdf provides the sheet music for three-part choir and harp, with some annotations and markings.</li>
-<li><a href="https://musescore.com/song/ceremony_of_carols-2227441">Free Ceremony of Carols by Benjamin Britten sheet music</a>: This website allows you to view, download and print the sheet music for choir and harp, as well as listen to an audio version of the work.</li>
-</ul>
-
-<h4>Why You Should Download A Ceremony of Carols Pdf Free?</h4>
-
-<p>Downloading a Ceremony of Carols pdf free is a great way to experience this wonderful musical gift for Christmas. You can learn more about the history and meaning of the carols, practice singing or playing them with your choir or harpist, or simply enjoy listening to them at home or in your car. A Ceremony of Carols is a work that will touch your heart and soul with its beauty and spirituality.</p>
-
-<h5>Conclusion</h5>
-
-<p>A Ceremony of Carols by Benjamin Britten is a masterpiece of choral music that celebrates the Christmas story with medieval poetry and modern harmony. You can download a Ceremony of Carols pdf free from various online sources and enjoy this musical gift for yourself or with your friends and family. A Ceremony of Carols is a work that will enrich your festive season with its joy, mystery and wonder.</p>
-<h6>How to Perform A Ceremony of Carols?</h6>
-
-<p>If you are interested in performing a Ceremony of Carols with your choir and harpist, you will need to prepare well in advance and practice regularly. A Ceremony of Carols is not an easy work to perform, as it requires good vocal skills, musicality and coordination. Here are some tips to help you perform a Ceremony of Carols successfully:</p>
-
-<ul>
-<li>Choose the right choir and harpist: A Ceremony of Carols is written for a three-part choir of treble voices (soprano, mezzo-soprano and alto) and a harpist. You will need to find singers and a harpist who can handle the range, dynamics and expression of the work. You will also need to decide whether you want to perform the work with a soloist or a small group for some of the carols.</li>
-<li>Study the score and the text: A Ceremony of Carols is based on medieval poems in different languages, so you will need to study the score and the text carefully to understand the meaning and pronunciation of the words. You will also need to pay attention to the musical notation, such as the tempo, dynamics, articulation and phrasing. You can use online resources such as <a href="http://www.tewkesburychoral.org.uk/wp-content/uploads/2018/09/Ceremony-of-Carols-Text4.pdf">Benjamin Britten: A Ceremony of Carols (Text and Selective Translations)</a> or <a href="http://icchoirs.org/Britten_COC.pdf">BENJAMIN BRITTEN: A Ceremony of Carols</a> to help you with the text and the score.</li>
-<li>Practice individually and together: A Ceremony of Carols requires a lot of practice to master the technical and musical aspects of the work. You will need to practice individually to learn your part and improve your vocal technique. You will also need to practice together with your choir and harpist to blend your voices, balance your sound and coordinate your timing. You can use online tools such as <a href="https://musescore.com/song/ceremony_of_carols-2227441">Free Ceremony of Carols by Benjamin Britten sheet music</a> to listen to an audio version of the work and follow along with the sheet music.</li>
-<li>Perform with confidence and expression: A Ceremony of Carols is a work that conveys the joy, mystery and wonder of the Christmas story. You will need to perform with confidence and expression to communicate these emotions to your audience. You will also need to follow the conductor's cues and directions, as well as the harpist's accompaniment. You can use online videos such as <a href="https://www.youtube.com/watch?v=0aQZGwZwK_E">A Ceremony of Carols by Benjamin Britten</a> or <a href="https://www.youtube.com/watch?v=7F7TVM8m95Y">A Ceremony of Carols by Benjamin Britten (with subtitles)</a> to watch some examples of how other choirs have performed the work.</li>
-</ul>
-
-<h7>What are the Benefits of Downloading A Ceremony of Carols Pdf Free?</h7>
-
-<p>Downloading a Ceremony of Carols pdf free has many benefits for you as a singer, a musician or a listener. Here are some of them:</p>
-<p></p>
-
-<ul>
-<li>You can save money: A Ceremony of Carols pdf free is a great way to save money on buying or renting the sheet music or the CD. You can download it for free from various online sources and print it or view it on your device.</li>
-<li>You can learn more: A Ceremony of Carols pdf free is a great way to learn more about this amazing work by Benjamin Britten. You can read about the history and meaning of the carols, study the score and the text, listen to an audio version or watch a video performance.</li>
-<li>You can enjoy more: A Ceremony of Carols pdf free is a great way to enjoy this beautiful work by Benjamin Britten. You can sing or play it with your choir or harpist, or simply listen to it at home or in your car. A Ceremony of Carols is a work that will fill your heart and soul with joy, mystery and wonder.</li>
-</ul>
-
-<h8>Final Words</h8>
-
-<p>A Ceremony of Carols by Benjamin Britten is a masterpiece of choral music that celebrates the Christmas story with medieval poetry and modern harmony. You can download a Ceremony of Carols pdf free from various online sources and enjoy this musical gift for yourself or with your friends and family. A Ceremony of Carols is a work that will enrich your festive season with its joy, mystery and wonder.</p>
-<h9>What are the Reviews of A Ceremony of Carols?</h9>
-
-<p>A Ceremony of Carols by Benjamin Britten has received many positive reviews from critics and audiences alike. Here are some of the reviews of A Ceremony of Carols:</p>
-
-<ul>
-<li><a href="https://www.classicfm.com/composers/britten/guides/britten-ceremony-of-carols/">Britten: A Ceremony of Carols - Classic FM</a>: "Britten's A Ceremony of Carols is one of the most popular works for Christmas. It's a stunning collection of carols that brilliantly captures the mood and spirit of the season. The harp accompaniment adds a magical touch to the choir's voices, creating a sound that is both ancient and modern. The carols are full of contrast and variety, from the joyful 'Wolcum Yole!' to the serene 'Balulalow'. The work ends with a triumphant 'Deo Gracias', followed by a peaceful 'Recession' that echoes the opening 'Procession'. A Ceremony of Carols is a work that will make you feel festive and uplifted."</li>
-<li><a href="https://www.allmusic.com/album/britten-a-ceremony-of-carols-mw0001848860">Britten: A Ceremony of Carols - AllMusic</a>: "Britten's A Ceremony of Carols is a masterpiece of choral writing that showcases his genius for setting words to music. The carols are based on medieval texts that celebrate the nativity with vivid imagery and poetic language. Britten's music matches the words with exquisite harmony and melody, creating a work that is both timeless and contemporary. The harp accompaniment adds a delicate and ethereal dimension to the choir's voices, creating a sound that is both enchanting and expressive. The carols are full of drama and emotion, from the playful 'This Little Babe' to the mournful 'In Freezing Winter Night'. The work concludes with a jubilant 'Deo Gracias', followed by a solemn 'Recession' that returns to the opening 'Procession'. A Ceremony of Carols is a work that will make you feel inspired and moved."</li>
-<li><a href="https://www.theguardian.com/music/2013/dec/15/britten-ceremony-of-carols-review">Britten: A Ceremony of Carols review – The Guardian</a>: "Britten's A Ceremony of Carols is one of the most beautiful works for Christmas. It's a brilliant collection of carols that captures the essence and spirit of the season. The harp accompaniment adds a sparkling touch to the choir's voices, creating a sound that is both ancient and modern. The carols are full of contrast and variety, from the festive 'Wolcum Yole!' to the tender 'Balulalow'. The work ends with a glorious 'Deo Gracias', followed by a quiet 'Recession' that repeats the opening 'Procession'. A Ceremony of Carols is a work that will make you feel joyful and peaceful."</li>
-</ul>
-
-<h10>What are some Poems about A Ceremony of Carols?</h10>
-
-<p>A Ceremony of Carols by Benjamin Britten has inspired many poets to write poems about this wonderful work. Here are some poems about A Ceremony of Carols:</p>
-
-<ul>
-<li>A poem by John Fuller:
-
-<pre><code>A Ceremony of Carols
-
-A harp in hand, a choir in tow,
-We sing our way to Bethlehem,
-With ancient words and melodies
-That tell the story once again.
-
-We sing of roses, stars and dew,
-Of angels, shepherds, kings and gifts,
-Of virgin mother, holy child,
-Of joy and sorrow, grace and rifts.
-
-We sing with Britten's voice and skill,
-With harmony and melody,
-With passion, wit and reverence,
-With beauty, art and poetry.
-
-We sing a ceremony of carols,
-A festive offering of praise,
-A musical gift for Christmas,
-A work that lasts beyond our days.
-</code></pre>
-</li>
-<li>A poem by Mary Oliver:
-
-<pre><code>A Ceremony of Carols
-
-A harp in hand, a choir in tow,
-We follow where the music leads,
-With ancient words and melodies
-That fill our hearts with wonder.
-
-We sing of roses, stars and dew,
-Of angels, shepherds, kings and gifts,
-Of virgin mother, holy child,
-Of love and mystery.
-
-We sing with Britten's voice and skill,
-With harmony and melody,
-With spirit, charm and reverence,
-With beauty, art and poetry.
-
-We sing a ceremony of carols,
-A festive offering of praise,
-A musical gift for Christmas,
-A work that blesses all our days.
-</code></pre>
-</li>
-<li>A poem by Robert Frost:
-
-<pre><code>A Ceremony of Carols
-
-A harp in hand, a choir in tow,
-We make our way to Bethlehem,
-With ancient words and melodies
-That speak to us anew.
-
-We sing of roses, stars and dew,
-Of angels, shepherds, kings and gifts,
-Of virgin mother, holy child,
-Of hope and peace.
-
-We sing with Britten's voice and skill,
-With harmony and melody,
-With fire, grace and reverence,
-With beauty, art and poetry.
-
-We sing a ceremony of carols,
-A festive offering of praise,
-A musical gift for Christmas,
-A work that stays within our hearts.
-</code></pre>
-</li>
-</ul>
-<h11>Conclusion</h11>
-
-<p>A Ceremony of Carols by Benjamin Britten is a masterpiece of choral music that celebrates the Christmas story with medieval poetry and modern harmony. You can download a Ceremony of Carols pdf free from various online sources and enjoy this musical gift for yourself or with your friends and family. A Ceremony of Carols is a work that will enrich your festive season with its joy, mystery and wonder. You can also learn more about the work by reading the reviews, studying the score and the text, practicing the carols, performing the work or writing poems about it. A Ceremony of Carols is a work that will touch your heart and soul with its beauty and spirituality.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Crashfixdllsleepingdogs.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Crashfixdllsleepingdogs.md
deleted file mode 100644
index 3b6a97163d240641601acc26c55cc616adc0158e..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Crashfixdllsleepingdogs.md
+++ /dev/null
@@ -1,20 +0,0 @@
-<br />
-<h1>How to Fix DLL File Issues in Sleeping Dogs</h1>
-<p>Sleeping Dogs is a popular action-adventure video game that was released in 2012. The game is set in Hong Kong and follows the story of Wei Shen, an undercover cop who infiltrates a triad organization. Sleeping Dogs has received positive reviews from critics and players alike, but some users have reported encountering DLL file issues that prevent them from launching or playing the game properly.</p>
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-<p>DLL files are dynamic link libraries that contain code and data that are used by multiple programs on your computer. Sometimes, these files can get corrupted, missing, or incompatible with your system, causing errors and crashes. In this article, we will show you how to fix DLL file issues in Sleeping Dogs using some simple methods.</p>
-<h2>Method 1: Download and Install the Missing DLL File</h2>
-<p>One of the most common causes of DLL file issues is that the game cannot find or load the required DLL file on your computer. This can happen if you have deleted or moved the file accidentally, or if you have installed a different version of the file that is not compatible with the game. To fix this problem, you can try downloading and installing the missing DLL file from a reliable source.</p>
-<p>For example, if you are getting an error message that says "The program can't start because xinput1_3.dll is missing from your computer", you can download the xinput1_3.dll file from <a href="https://www.dll-files.com/xinput1_3.dll.html">here</a>.[^1^] Make sure you choose the correct version of the file that matches your system architecture (32-bit or 64-bit). After downloading the file, extract it using a program like WinRAR or 7-Zip and copy it to the game directory where hkship.exe is located. This is usually C:\Program Files (x86)\Steam\steamapps\common\SleepingDogs or C:\Program Files\Steam\steamapps\common\SleepingDogs. Then, try launching the game again and see if the error is gone.</p>
-<h2>Method 2: Run Sleeping Dogs as Administrator</h2>
-<p>Another possible cause of DLL file issues is that the game does not have enough permissions to access or modify the DLL files on your computer. This can happen if you have installed the game in a protected folder like Program Files or if you have a strict security software that blocks the game from running. To fix this problem, you can try running Sleeping Dogs as administrator.</p>
-<p>To do this, right-click on hkship.exe in the game directory and select Properties. Then, go to the Compatibility tab and check the box that says "Run this program as an administrator". Click Apply and OK to save the changes. Alternatively, you can also right-click on hkship.exe and select "Run as administrator" every time you want to play the game.</p>
-<p></p>
-<h2>Method 3: Update Sleeping Dogs to the Latest Version</h2>
-<p>Sometimes, DLL file issues can be caused by bugs or glitches in the game itself. The developers of Sleeping Dogs may have released patches or updates that fix these problems and improve the performance and stability of the game. To fix this problem, you can try updating Sleeping Dogs to the latest version available.</p>
-<p>To do this, you need to have a legitimate copy of the game that is registered on Steam. Steam is a digital distribution platform that allows you to download and install games and updates automatically. If you have Steam installed on your computer, open it and go to your Library. Find Sleeping Dogs in your list of games and right-click on it. Select Properties and go to the Updates tab. Make sure that "Always keep this game up to date" is selected. Steam will then download and install any available updates for Sleeping Dogs automatically.</p>
-<p>If you do not have Steam installed on your computer, you can also download and install updates manually from other sources. For example, you can download the v1.8 update for Sleeping Dogs from <a href="https://megagames.com/fixes/sleeping-dogs-v18-all-no-dvd-skidrow?noradio=1">here</a>.[^3^] Make sure you follow the instructions carefully and backup your game files before applying any patches or updates.</p>
-<h2>Conclusion</h2>
-<p>We hope this article has helped you fix DLL file issues in Sleeping Dogs. If none of these methods work for you</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Dark Colony Full Version Free 25.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Dark Colony Full Version Free 25.md
deleted file mode 100644
index c6c90285cdd51735df3d465bd331b9ff11efd3ce..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Dark Colony Full Version Free 25.md	
+++ /dev/null
@@ -1,40 +0,0 @@
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-
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-Alex Henderson of Computer Gaming World said that Dark Colony was "reasonably priced" and "not full of bugs." While criticizing the lack of excitement of the missions, Henderson concluded that "this fast-paced yet complex strategy game is worth trying out if you like to play military games."
-
-A reviewer for Next Generation said that "it's certainly a fairly deep game, but it's also a step back from the exciting, fast-paced action of games like Warcraft and Command and Conquer. But for those who don't want a real-time strategy game that's a step too far out of the action genre, Dark Colony is certainly worth checking out." He praised the game's numerous game features, noting that "the graphics are crisp and clear, the AI is surprisingly good, and the many game options make it a real challenge to win." He concluded that "when it comes right down to it, though, Dark Colony isn't really a true 4X, but a game that is good at what it does, but not great. If you're into the military strategy genre, you may find a lot of enjoyment here, but it's not a game you'll be playing more than a few hours at a time."
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-References
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-External links
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-Dark Colony at GameSpot
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-Dark Colony at MobyGames
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-Category:1997 video games
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-Category:4X video games
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Epson T1110 Adjustment Program Free Full.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Epson T1110 Adjustment Program Free Full.md
deleted file mode 100644
index b7228d377ad662e1fe5f8ef8d7e72684471d05e9..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Epson T1110 Adjustment Program Free Full.md	
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index a9d512cff7e6fbbbfdb0c263cf9c31d33711997e..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Malena Movie Download In Dual Audio 720p Or 1080p.md	
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diff --git a/spaces/locmaymo/Reverse-Proxy/server.js b/spaces/locmaymo/Reverse-Proxy/server.js
deleted file mode 100644
index 2c870aeac95c363c4633aee04a4343c8dfb8cc07..0000000000000000000000000000000000000000
--- a/spaces/locmaymo/Reverse-Proxy/server.js
+++ /dev/null
@@ -1,45 +0,0 @@
-const express = require('express');
-const proxy = require('express-http-proxy');
-const app = express();
-const targetUrl = 'https://api.openai.com';
-const openaiKey = process.env.OPENAI_KEY
-const port = 7860;
-const baseUrl = getExternalUrl(process.env.SPACE_ID);
-
-// Generate a random string
-const length = 5;
-const chars = '0123456789';
-let randomString = '';
-
-for (let i = 0; i < length; i++) {
-  randomString += chars.charAt(Math.floor(Math.random() * chars.length));
-}
-
-let api = '/api' + randomString
-
-console.log(api);
-
-app.use(api, proxy(targetUrl, {
-  proxyReqOptDecorator: (proxyReqOpts, srcReq) => {
-    // Modify the request headers if necessary
-    proxyReqOpts.headers['Authorization'] = 'Bearer '+openaiKey;
-    return proxyReqOpts;
-  },
-}));
-
-app.get("/", (req, res) => {
-  res.send(`I'm sorry, we have run out of credits so the reverse proxy is no longer usable. Apologies for the inconvenience.`);
-});
-
-function getExternalUrl(spaceId) {
-  try {
-    const [username, spacename] = spaceId.split("/");
-    return `https://${username}-${spacename.replace(/_/g, "-")}.hf.space${api}/v1`;
-  } catch (e) {
-    return "";
-  }
-}
-
-app.listen(port, () => {
-  console.log(`https://locmaymo-Reverse-Proxy.hf.space${api}/v1`);
-});
\ No newline at end of file
diff --git a/spaces/luxuedong/lxd/src/components/chat-header.tsx b/spaces/luxuedong/lxd/src/components/chat-header.tsx
deleted file mode 100644
index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000
--- a/spaces/luxuedong/lxd/src/components/chat-header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import LogoIcon from '@/assets/images/logo.svg'
-import Image from 'next/image'
-
-export function ChatHeader() {
-  return (
-    <div className="flex flex-col items-center justify-center">
-      <Image alt="logo" src={LogoIcon} width={60}/>
-      <div className="mt-8 text-4xl font-bold">欢迎使用新必应</div>
-      <div className="mt-4 mb-8 text-lg">由 AI 支持的网页版 Copilot</div>
-    </div>
-  )
-}
diff --git a/spaces/lykeven/CogVLM/app.py b/spaces/lykeven/CogVLM/app.py
deleted file mode 100644
index 7439782501c4c7e109b6d2b824f534cf1f9f6239..0000000000000000000000000000000000000000
--- a/spaces/lykeven/CogVLM/app.py
+++ /dev/null
@@ -1,208 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-import os
-import re
-from PIL import Image
-import base64
-import time
-
-DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVLM">VisualGLM</a>'''
-
-MAINTENANCE_NOTICE1 = 'Hint 1: If the app report "Something went wrong, connection error out", please turn off your proxy and retry.<br>Hint 2: If you upload a large size of image like 10MB, it may take some time to upload and process. Please be patient and wait.'
-
-GROUNDING_NOTICE = 'Hint: When you check "Grounding", please use the <a href="https://github.com/THUDM/CogVLM/blob/main/utils/template.py#L344">corresponding prompt</a> or the examples below.'
-
-
-NOTES = 'This app is adapted from <a href="https://github.com/THUDM/CogVLM">https://github.com/THUDM/CogVLM</a>. It would be recommended to check out the repo if you want to see the detail of our model.'
-
-import json
-import requests
-import base64
-import hashlib
-from utils import parse_response
-
-default_chatbox = [("", "Hi, What do you want to know about this image?")]
-
-URL = os.environ.get("URL")
-
-def process_image(image_prompt):
-    image = Image.open(image_prompt)
-    print(f"height:{image.height}, width:{image.width}")
-    resized_image = image.resize((224, 224), )
-    timestamp = int(time.time())
-    file_ext = os.path.splitext(image_prompt)[1]
-    filename = f"examples/{timestamp}{file_ext}"
-    resized_image.save(filename)
-    print(f"temporal filename {filename}")
-    with open(filename, "rb") as image_file:
-        bytes = base64.b64encode(image_file.read())
-        encoded_img = str(bytes, encoding='utf-8')
-        image_hash = hashlib.sha256(bytes).hexdigest()
-        os.remove(filename)
-        return encoded_img, image_hash
-
-
-def process_image_without_resize(image_prompt):
-    image = Image.open(image_prompt)
-    print(f"height:{image.height}, width:{image.width}")
-    timestamp = int(time.time())
-    file_ext = os.path.splitext(image_prompt)[1]
-    filename = f"examples/{timestamp}{file_ext}"
-    filename_grounding = f"examples/{timestamp}_grounding{file_ext}"
-    image.save(filename)
-    print(f"temporal filename {filename}")
-    with open(filename, "rb") as image_file:
-        bytes = base64.b64encode(image_file.read())
-        encoded_img = str(bytes, encoding='utf-8')
-        image_hash = hashlib.sha256(bytes).hexdigest()
-        os.remove(filename)
-        return image, encoded_img, image_hash, filename_grounding
-
-
-def is_chinese(text):
-    zh_pattern = re.compile(u'[\u4e00-\u9fa5]+')
-    return zh_pattern.search(text)
-
-
-def post(
-        input_text,
-        temperature,
-        top_p,
-        image_prompt,
-        result_previous,
-        hidden_image,
-        grounding    
-        ):
-    result_text = [(ele[0], ele[1]) for ele in result_previous]
-    for i in range(len(result_text)-1, -1, -1):
-        if result_text[i][0] == "" or result_text[i][0] == None:
-            del result_text[i]
-    print(f"history {result_text}")
-
-    is_zh = is_chinese(input_text)
-
-    if image_prompt is None:
-        print("Image empty")
-        if is_zh:
-            result_text.append((input_text, '图片为空！请上传图片并重试。'))
-        else:
-            result_text.append((input_text, 'Image empty! Please upload a image and retry.'))
-        return input_text, result_text, hidden_image
-    elif input_text == "":
-        print("Text empty")
-        result_text.append((input_text, 'Text empty! Please enter text and retry.'))
-        return "", result_text, hidden_image                
-
-    headers = {
-            "Content-Type": "application/json; charset=UTF-8",
-            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36",
-        }
-    if image_prompt:
-        pil_img, encoded_img, image_hash, image_path_grounding = process_image_without_resize(image_prompt)
-        print(f"image_hash:{image_hash}, hidden_image_hash:{hidden_image}")
-
-        if hidden_image is not None and image_hash != hidden_image:
-            print("image has been update")
-            result_text = []        
-        hidden_image = image_hash        
-    else:
-        encoded_img = None
-
-    print('request chat model...' if not grounding else 'request grounding model...')
-    data = json.dumps({
-        'text': input_text,
-        'image': encoded_img,
-        'temperature': temperature,
-        'top_p': top_p,
-        'history': result_text,
-        'is_grounding': grounding
-    })
-    try:
-        response = requests.request("POST", URL, headers=headers, data=data, timeout=(60, 100)).json()
-    except Exception as e:
-        print("error message", e)
-        if is_zh:
-            result_text.append((input_text, '超时！请稍等几分钟再重试。'))
-        else:
-            result_text.append((input_text, 'Timeout! Please wait a few minutes and retry.'))
-        return "", result_text, hidden_image
-    print('request done...')
-    # response = {'result':input_text}
-
-    answer = str(response['result'])
-    if grounding:
-        parse_response(pil_img, answer, image_path_grounding)
-        new_answer = answer.replace(input_text, "")
-        result_text.append((input_text, new_answer))
-        result_text.append((None, (image_path_grounding,)))
-    else:
-        result_text.append((input_text, answer))
-    print(result_text)
-    print('finished')
-    return "", result_text, hidden_image
-
-
-def clear_fn(value):
-    return "", default_chatbox, None
-
-def clear_fn2(value):
-    return default_chatbox
-
-
-def main():
-    gr.close_all()
-    examples = []
-    with open("./examples/example_inputs.jsonl") as f:
-        for line in f:
-            data = json.loads(line)
-            examples.append(data)
-
-
-    with gr.Blocks(css='style.css') as demo:
-
-        with gr.Row():
-            with gr.Column(scale=4.5):
-                with gr.Group():
-                    input_text = gr.Textbox(label='Input Text', placeholder='Please enter text prompt below and press ENTER.')
-                    with gr.Row():
-                        run_button = gr.Button('Generate')
-                        clear_button = gr.Button('Clear')
-
-                    image_prompt = gr.Image(type="filepath", label="Image Prompt", value=None)
-                with gr.Row():
-                    grounding = gr.Checkbox(label="Grounding")
-                with gr.Row():
-                    grounding_notice = gr.Markdown(GROUNDING_NOTICE)
-
-                with gr.Row():
-                    temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='Temperature')
-                    top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P')
-            with gr.Column(scale=5.5):
-                result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]).style(height=550)
-                hidden_image_hash = gr.Textbox(visible=False)
-
-        gr_examples = gr.Examples(examples=[[example["text"], example["image"]] for example in examples], 
-                                  inputs=[input_text, image_prompt],
-                                  label="Example Inputs (Click to insert an examplet into the input box)",
-                                  examples_per_page=6)
-
-        gr.Markdown(MAINTENANCE_NOTICE1)
-        gr.Markdown(NOTES)
-
-        print(gr.__version__)
-        run_button.click(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash, grounding],
-                         outputs=[input_text, result_text, hidden_image_hash])
-        input_text.submit(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash, grounding],
-                         outputs=[input_text, result_text, hidden_image_hash])
-        clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[input_text, result_text, image_prompt])
-        image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
-        image_prompt.clear(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
-
-        print(gr.__version__)
-
-    demo.queue(concurrency_count=10)
-    demo.launch()
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/spaces/matthoffner/chatbot-mini/hooks/useCreateReducer.ts b/spaces/matthoffner/chatbot-mini/hooks/useCreateReducer.ts
deleted file mode 100644
index e42ba680ee1d15d7746811df55ae0e4a5c234178..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot-mini/hooks/useCreateReducer.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import { useMemo, useReducer } from 'react';
-
-// Extracts property names from initial state of reducer to allow typesafe dispatch objects
-export type FieldNames<T> = {
-  [K in keyof T]: T[K] extends string ? K : K;
-}[keyof T];
-
-// Returns the Action Type for the dispatch object to be used for typing in things like context
-export type ActionType<T> =
-  | { type: 'reset' }
-  | { type?: 'change'; field: FieldNames<T>; value: any };
-
-// Returns a typed dispatch and state
-export const useCreateReducer = <T>({ initialState }: { initialState: T }) => {
-  type Action =
-    | { type: 'reset' }
-    | { type?: 'change'; field: FieldNames<T>; value: any };
-
-  const reducer = (state: T, action: Action) => {
-    if (!action.type) return { ...state, [action.field]: action.value };
-
-    if (action.type === 'reset') return initialState;
-
-    throw new Error();
-  };
-
-  const [state, dispatch] = useReducer(reducer, initialState);
-
-  return useMemo(() => ({ state, dispatch }), [state, dispatch]);
-};
diff --git a/spaces/mattiagatti/image2mesh/app.py b/spaces/mattiagatti/image2mesh/app.py
deleted file mode 100644
index 086734859e141751494be29fbce26ac8777bc515..0000000000000000000000000000000000000000
--- a/spaces/mattiagatti/image2mesh/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import numpy as np
-import open3d as o3d
-import os
-from PIL import Image
-import tempfile
-import torch
-from transformers import GLPNImageProcessor, GLPNForDepthEstimation
-
-
-def predict_depth(image):
-    # load and resize the input image
-    new_height = 480 if image.height > 480 else image.height
-    new_height -= (new_height % 32)
-    new_width = int(new_height * image.width / image.height)
-    diff = new_width % 32
-    new_width = new_width - diff if diff < 16 else new_width + 32 - diff
-    new_size = (new_width, new_height)
-    image = image.resize(new_size)
-
-    # prepare image for the model
-    inputs = feature_extractor(images=image, return_tensors="pt")
-
-    # get the prediction from the model
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predicted_depth = outputs.predicted_depth
-
-    output = predicted_depth.squeeze().cpu().numpy() * 1000.0
-
-    # remove borders
-    pad = 16
-    output = output[pad:-pad, pad:-pad]
-    image = image.crop((pad, pad, image.width - pad, image.height - pad))
-
-    return image, output
-
-
-def generate_mesh(image, depth_image, quality):
-    width, height = image.size
-
-    # depth_image = (depth_map * 255 / np.max(depth_map)).astype('uint8')
-    image = np.array(image)
-
-    # create rgbd image
-    depth_o3d = o3d.geometry.Image(depth_image)
-    image_o3d = o3d.geometry.Image(image)
-    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(image_o3d, depth_o3d,
-                                                                    convert_rgb_to_intensity=False)
-
-    # camera settings
-    camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
-    camera_intrinsic.set_intrinsics(width, height, 500, 500, width / 2, height / 2)
-
-    # create point cloud
-    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic)
-
-    # outliers removal
-    cl, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=20.0)
-    pcd = pcd.select_by_index(ind)
-
-    # estimate normals
-    pcd.estimate_normals()
-    pcd.orient_normals_to_align_with_direction(orientation_reference=(0., 0., -1.))
-
-    # surface reconstruction
-    mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=quality, n_threads=1)[0]
-
-    # rotate the mesh
-    rotation = mesh.get_rotation_matrix_from_xyz((np.pi, np.pi, 0))
-    mesh.rotate(rotation, center=(0, 0, 0))
-
-    # save the mesh
-    temp_name = next(tempfile._get_candidate_names()) + '.obj'
-    o3d.io.write_triangle_mesh(temp_name, mesh)
-
-    return temp_name
-
-
-def predict(image, quality):
-    image, depth_map = predict_depth(image)
-    depth_image = (depth_map * 255 / np.max(depth_map)).astype('uint8')
-    mesh_path = generate_mesh(image, depth_image, quality + 5)
-    colormap = plt.get_cmap('plasma')
-    depth_image = (colormap(depth_image) * 255).astype('uint8')
-    depth_image = Image.fromarray(depth_image)
-
-    return depth_image, mesh_path
-
-
-feature_extractor = GLPNImageProcessor.from_pretrained("vinvino02/glpn-nyu")
-model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu")
-
-
-# GUI
-title = 'Image2Mesh'
-description = 'Demo based on my <a href="https://towardsdatascience.com/generate-a-3d-mesh-from-an-image-with-python' \
-              '-12210c73e5cc">article</a>. This demo predicts the depth of an image and then generates the 3D mesh. ' \
-              'Choosing a higher quality increases the time to generate the mesh. You can download the mesh by ' \
-              'clicking the top-right button on the 3D viewer. '
-examples = [[f'examples/{name}', 3] for name in sorted(os.listdir('examples'))]
-
-# example image source:
-# N. Silberman, D. Hoiem, P. Kohli, and Rob Fergus,
-# Indoor Segmentation and Support Inference from RGBD Images (2012)
-
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Image(type='pil', label='Input Image'),
-        gr.Slider(1, 5, step=1, value=3, label='Mesh quality')
-    ],
-    outputs=[
-        gr.Image(label='Depth'),
-        gr.Model3D(label='3D Model', clear_color=[0.0, 0.0, 0.0, 0.0])
-    ],
-    examples=examples,
-    allow_flagging='never',
-    cache_examples=False,
-    title=title,
-    description=description
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/merve/uncertainty-calibration/public/private-and-fair/rotated-accuracy.js b/spaces/merve/uncertainty-calibration/public/private-and-fair/rotated-accuracy.js
deleted file mode 100644
index 26219db5eeedb299541f14e192a6105b017a78e2..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/public/private-and-fair/rotated-accuracy.js
+++ /dev/null
@@ -1,362 +0,0 @@
-!(async function(){
-  var isLock = false
-
-  var csvstr = await (await fetch('rotated-accuracy.csv')).text()
-  var allData = d3.csvParse(csvstr)
-    .filter(d => {
-      d.slug = [d.dataset_size, d.aVal, d.minority_percent].join(' ')
-
-      d.accuracy_orig = (+d.accuracy_test_data_1     + +d.accuracy_test_data_7)/2000
-      d.accuracy_rot  = (+d.accuracy_test_data_1_rot + +d.accuracy_test_data_7_rot)/2000
-      d.accuracy_dif = d.accuracy_orig - d.accuracy_rot
-
-      return d.accuracy_orig > 0 && d.accuracy_rot > 0
-    })
-
-  var data = d3.nestBy(allData, d => d.slug)
-  data.forEach(slug => {
-    slug.accuracy_orig = d3.median(slug, d => d.accuracy_orig)
-    slug.accuracy_rot = d3.median(slug, d => d.accuracy_rot)
-    slug.accuracy_dif = slug.accuracy_orig - slug.accuracy_rot
-
-    slug.dataset_size = +slug[0].dataset_size
-    slug.aVal = +slug[0].aVal
-    slug.minority_percent = +slug[0].minority_percent
-  })
-
-  // d3.nestBy(data, d => d.length).forEach(d => {
-  //   console.log(d.key, d.length)
-  // })
-
-  var byMetrics = 'dataset_size aVal minority_percent'
-    .split(' ')
-    .map(metricStr => {
-      var byMetric = d3.nestBy(data, d => d[metricStr])
-      byMetric.forEach(d => d.key = +d.key)
-      byMetric = _.sortBy(byMetric, d => d.key)
-      byMetric.forEach((d, i) => {
-        d.metricIndex = i
-        d.forEach(e => e['metric_' + metricStr] = d)
-      })
-
-      byMetric.forEach((d, i) => {
-        if (metricStr == 'dataset_size') d.label = i % 2 == 0 ? '' : d3.format(',')(d.key)
-        if (metricStr == 'aVal') d.label = ''
-        if (metricStr == 'minority_percent') d.label = i % 2 ? '' : d3.format('.0%')(d.key)
-      })
-
-      byMetric.active = byMetric[5]
-      byMetric.metricStr = metricStr
-      byMetric.label = {dataset_size: 'Training Points', aVal: 'Less Privacy', minority_percent: 'Percent Rotated In Training Data'}[metricStr]
-
-      return byMetric
-    })
-
-
-  // Heat map
-  !(function(){
-    var sel = d3.select('.rotated-accuracy-heatmap').html('')
-      .st({width: 1100, position: 'relative', left: (850 - 1100)/2})
-      .at({role: 'graphics-document', 'aria-label': `Faceted MNIST models by the percent of rotated digits in training data. Heatmaps show how privacy and training data change accuracy on rotated and original digits.`})
-
-    sel.append('div.chart-title').text('Percentage of training data rotated 90° →')
-
-    sel.appendMany('div', byMetrics[2])//.filter((d, i) => i % 2 == 0))
-      .st({display: 'inline-block'})
-      .each(drawHeatmap)
-  })()
-  function drawHeatmap(sizeData, chartIndex){
-
-    var s = 8
-    var n = 11
-
-    var c = d3.conventions({
-      sel: d3.select(this),
-      width: s*n,
-      height: s*n,
-      margin: {left: 5, right: 5, top: 30, bottom: 50},
-    })
-
-    c.svg.append('rect').at({width: c.width, height: c.height, fillOpacity: 0})
-
-    c.svg.append('text.chart-title')
-      .text(d3.format('.0%')(sizeData.key)).at({dy: -4, textAnchor: 'middle', x: c.width/2})
-      .st({fontWeight: 300})
-
-    var linearScale = d3.scaleLinear().domain([0, .5]).clamp(1)
-    var colorScale = d => d3.interpolatePlasma(linearScale(d))
-    
-    var pad = .5
-    var dataSel = c.svg
-      .on('mouseleave', () => isLock = false)
-      .append('g').translate([.5, .5])
-      .appendMany('g.accuracy-rect', sizeData)
-      .translate(d => [
-        s*d.metric_dataset_size.metricIndex, 
-        s*(n - d.metric_aVal.metricIndex)
-      ])
-      .call(d3.attachTooltip)
-      .on('mouseover', (d, i, node, isClickOverride) => {
-        updateTooltip(d)
-
-        if (isLock && !isClickOverride) return
-
-        byMetrics[0].setActiveCol(d.metric_dataset_size)
-        byMetrics[1].setActiveCol(d.metric_aVal)
-        byMetrics[2].setActiveCol(d.metric_minority_percent)
-
-        return d
-      })
-      .on('click', clickCb)
-      .st({cursor: 'pointer'})
-
-
-
-    dataSel.append('rect')
-      .at({
-        width: s - pad, 
-        height: s - pad,
-        fillOpacity: .1
-      })
-    
-    // dataSel.append('rect')      
-    //   .at({
-    //     width:  d => Math.max(1, (s - pad)*(d.accuracy_orig - .5)*2), 
-    //     height: d => Math.max(1, (s - pad)*(d.accuracy_rot - .5)*2),
-    //   })
-    sizeData.forEach(d => {
-      d.y_orig = Math.max(0, (s - pad)*(d.accuracy_orig - .5)*2)
-      d.y_rot  = Math.max(0, (s - pad)*(d.accuracy_rot - .5)*2)
-    })
-
-    dataSel.append('rect')
-      .at({
-        height: d => d.y_orig,
-        y: d => s - d.y_orig,
-        width: s/2,
-        x: s/2,
-        fill: 'purple',
-      })
-    dataSel.append('rect')
-      .at({
-        height: d => d.y_rot,
-        y: d => s - d.y_rot,
-        width: s/2,
-        fill: 'orange',
-      })
-
-    sizeData.updateActiveRect = function(match){
-      dataSel
-        .classed('active', d => match == d)
-        .filter(d => match == d)
-        .raise()
-    }
-
-    if (chartIndex == 0){
-      c.svg.append('g.x.axis').translate([10, c.height])
-      c.svg.append('g.y.axis').translate([0, 5])
-
-      util.addAxisLabel(c, 'Training Points →', 'Less Privacy →', 30, -15)
-    }
-
-    if (chartIndex == 8){
-      c.svg.appendMany('g.axis', ['Original Digit Accuracy', 'Rotated Digit Accuracy'])
-        .translate((d, i) => [c.width - 230*i - 230 -50, c.height + 30])
-        .append('text.axis-label').text(d => d)
-        .st({fontSize: 14})
-        .parent()
-        .appendMany('rect', (d, i) => d3.range(.2, 1.2, .2).map((v, j) => ({i, v, j})))
-        .at({
-          width: s/2,
-          y: d => s - d.v*s - s,
-          height: d => d.v*s,
-          fill: d => ['purple', 'orange'][d.i],
-          x: d => d.j*s*.75 - 35
-        })
-    }
-  }
-
-  // Metric barbell charts
-  !(function(){
-    var sel = d3.select('.rotated-accuracy').html('')
-      .at({role: 'graphics-document', 'aria-label': `Barbell charts showing up privacy / data / percent underrepresented data all trade-off in complex ways.`})
-
-    sel.appendMany('div', byMetrics)
-      .st({display: 'inline-block', width: 300, marginRight: 10, marginBottom: 50, marginTop: 10})
-      .each(drawMetricBarbell)
-  })()
-  function drawMetricBarbell(byMetric, byMetricIndex){
-    var sel = d3.select(this)
-
-    var c = d3.conventions({
-      sel,
-      height: 220,
-      width: 220,
-      margin: {bottom: 10, top: 5},
-      layers: 's',
-    })
-    c.svg.append('rect').at({width: c.width, height: c.height, fillOpacity: 0})
-
-    c.y.domain([.5, 1]).interpolate(d3.interpolateRound)
-    c.x.domain([0, byMetric.length - 1]).clamp(1).interpolate(d3.interpolateRound)
-
-    c.xAxis
-      .tickValues(d3.range(byMetric.length))
-      .tickFormat(i => byMetric[i].label)
-    c.yAxis.ticks(5).tickFormat(d => d3.format('.0%')(d))
-
-    d3.drawAxis(c)
-    util.addAxisLabel(c, byMetric.label + ' →', byMetricIndex ? '' : 'Accuracy')
-    util.ggPlotBg(c, false)
-
-    c.svg.select('.x').raise()
-    c.svg.selectAll('.axis').st({pointerEvents: 'none'})
-
-    c.svg.append('defs').append('linearGradient#purple-to-orange')
-      .at({x1: '0%', x2: '0%', y1: '0%', y2: '100%'})
-      .append('stop').at({offset: '0%', 'stop-color': 'purple'}).parent()
-      .append('stop').at({offset: '100%', 'stop-color': 'orange'})
-
-    c.svg.append('defs').append('linearGradient#orange-to-purple')
-      .at({x1: '0%', x2: '0%', y2: '0%', y1: '100%'})
-      .append('stop').at({offset: '0%', 'stop-color': 'purple'}).parent()
-      .append('stop').at({offset: '100%', 'stop-color': 'orange'})
-
-    var colSel = c.svg.appendMany('g', byMetric)
-      .translate(d => c.x(d.metricIndex) + .5, 0)
-      .st({pointerEvents: 'none'})
-
-    var pathSel = colSel.append('path')
-      .at({stroke: 'url(#purple-to-orange)', strokeWidth: 1})
-
-    var rectSel = colSel.append('rect')
-      .at({width: 1, x: -.5})
-
-    var origCircleSel = colSel.append('circle')
-      .at({r: 3, fill: 'purple', stroke: '#000', strokeWidth: .5})
-
-    var rotCircleSel = colSel.append('circle')
-      .at({r: 3, fill: 'orange', stroke: '#000', strokeWidth: .5})
-
-    function clampY(d){
-      return d3.clamp(0, c.y(d), c.height + 3)
-    }
-
-    byMetric.updateActiveCol = function(){
-      var findObj = {}
-      byMetrics
-        .filter(d => d != byMetric)
-        .forEach(d => {
-          findObj[d.metricStr] = d.active.key
-        })
-
-      byMetric.forEach(col => {
-        col.active = _.find(col, findObj)
-      })
-
-      origCircleSel.at({cy: d => clampY(d.active.accuracy_orig)})
-      rotCircleSel.at({cy: d => clampY(d.active.accuracy_rot)})
-
-      // pathSel.at({
-      //   d: d => 'M 0 ' + clampY(d.active.accuracy_orig) + ' L 1 ' + clampY(d.active.accuracy_rot)
-      // })
-
-      rectSel.at({
-        y: d => Math.min(clampY(d.active.accuracy_orig), clampY(d.active.accuracy_rot)),
-        height: d => Math.abs(clampY(d.active.accuracy_orig) - clampY(d.active.accuracy_rot)),
-        fill: d => d.active.accuracy_orig > d.active.accuracy_rot ? 'url(#purple-to-orange)' : 'url(#orange-to-purple)'
-      })
-    }
-    byMetric.updateActiveCol()
-
-
-    c.svg
-      .call(d3.attachTooltip)
-      .st({cursor: 'pointer'})
-      .on('mousemove', function(d, i, node, isClickOverride){
-        var [mx] = d3.mouse(this)
-        var metricIndex = Math.round(c.x.invert(mx))
-
-        var prevActive = byMetric.active
-        byMetric.active = byMetric[metricIndex]
-        updateTooltip()
-        byMetric.active = prevActive
-
-        if (isLock && !isClickOverride) return
-        byMetric.setActiveCol(byMetric[metricIndex])
-
-        return byMetric[metricIndex]
-      })
-      .on('click', clickCb)
-      .on('mouseexit', () => isLock = false)
-
-
-    byMetric.setActiveCol = function(col){
-      if (col) byMetric.active = col
-
-      c.svg.selectAll('.x .tick')
-        .classed('active', i => i == byMetric.active.metricIndex)
-
-      colSel.classed('active', d => d == byMetric.active)
-
-      if (col) renderActiveCol()
-    }
-    byMetric.setActiveCol()
-  }
-
-  function renderActiveCol(){
-    byMetrics.forEach(d => {
-      if (d.updateActiveCol) d.updateActiveCol()
-    })
-
-    var findObj = {}
-    byMetrics.forEach(d => findObj[d.metricStr] = d.active.key)
-    var match = _.find(data, findObj)
-
-    byMetrics[2].forEach(d => {
-      if (d.updateActiveRect) d.updateActiveRect(match)
-    })
-  }
-
-  function updateTooltip(d){
-    if (!d){
-      var findObj = {}
-      byMetrics.forEach(d => findObj[d.metricStr] = d.active.key)
-      d = _.find(data, findObj)
-    }
-
-    var epsilon = Math.round(d[0].epsilon*100)/100
-    ttSel.html(`
-      <div>
-        <b>${d3.format('.0%')(d.accuracy_orig)}</b> 
-        accuracy on 
-        <span style='padding: 2px; background: purple; color: #fff'>
-          original digits
-        </span>
-      <div>
-      <div>
-        <b>${d3.format('.0%')(d.accuracy_rot)}</b> 
-        accuracy on 
-        <span style='padding: 2px; background: orange; color: #000'>
-          rotated digits
-        </span>
-      <br>
-      <br>
-      <div>Training points: ${d3.format(',')(d.dataset_size)}</div>
-      <div>Privacy: ${epsilon} ε</div>
-      <div>Rotated in training data: ${d3.format('.0%')(d.minority_percent)} </div>
-
-    `).st({width: 230})
-
-    ttSel.classed('tooltip-footnote', 0)
-  }
-
-  function clickCb(d, i, node){
-    var mFn = d3.select(this).on('mouseover') || d3.select(this).on('mousemove')
-
-    var e = mFn.call(this, d, i, node, true)
-    isLock = e == isLock ? null : e
-  }
-
-
-})()
diff --git a/spaces/merve/voice-cloning/README.md b/spaces/merve/voice-cloning/README.md
deleted file mode 100644
index cd3a4b1967f6e541fffac5e8e5e51e49f6677ca7..0000000000000000000000000000000000000000
--- a/spaces/merve/voice-cloning/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Voice Cloning
-emoji: 😻
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: nateraw/voice-cloning
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/meyabase/oshiwambo-speech-greetings/app.py b/spaces/meyabase/oshiwambo-speech-greetings/app.py
deleted file mode 100644
index e6d5d0d8622dbd35ef7eabaacd5a9479bb49b848..0000000000000000000000000000000000000000
--- a/spaces/meyabase/oshiwambo-speech-greetings/app.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import os
-import csv
-import random
-import pandas as pd
-import numpy as np
-import gradio as gr
-from collections import Counter 
-from utils import *
-import matplotlib.pyplot as plt
-import scipy.io.wavfile as wavf
-from huggingface_hub import Repository, upload_file
-
-
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-
-GREETINGS_DIR = './greetings'
-greeting_files = [f.name for f in os.scandir(GREETINGS_DIR)]
-
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/meyabase/crowd-oshiwambo-speech-greetings"
-REPOSITORY_DIR = "data"
-LOCAL_DIR = 'data_local'
-os.makedirs(LOCAL_DIR,exist_ok=True)
-
-
-GENDER = ['Choose Gender','Male','Female','Other','Prefer not to say']
-
-#------------------Work on Languages--------------------
-
-languages = ["oshindonga", "oshikwanyama"]
-language_id = ["ng","kj"]
-
-
-#------------------Work on Languages--------------------
-
-repo = Repository(
-    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-)
-repo.git_pull()
-
-
-
-with open('app.css','r') as f:
-    BLOCK_CSS = f.read() 
-
-def save_record(language,record,greeting,gender,accent,greeting_history,current_greeting,done_recording):
-    # set default
-    greeting_history = greeting_history if greeting_history is not None else [0]
-    current_greeting = current_greeting if current_greeting is not None else 0 # 0 is the default greeting
-    done_recording = done_recording if done_recording is not None else False
-    #----
-
-    # Save text and its corresponding record to flag
-    speaker_metadata={}
-    speaker_metadata['gender'] = gender if gender!=GENDER[0] else ''
-    speaker_metadata['accent'] = accent if accent!='' else ''
-    default_record = None
-    if not done_recording:
-        if language!=None and language!='Choose language' and record is not None and greeting is not None:  #
-            language = language.lower()
-            lang_id = language_id[languages.index(language)] 
-    
-            # Write audio to file
-            audio_name = get_unique_name()
-            SAVE_FILE_DIR = os.path.join(LOCAL_DIR,audio_name)
-            os.makedirs(SAVE_FILE_DIR,exist_ok=True)
-            audio_output_filename = os.path.join(SAVE_FILE_DIR,'audio.wav')
-            wavf.write(audio_output_filename,record[0],record[1])
-
-            # Write metadata.json to file
-            json_file_path = os.path.join(SAVE_FILE_DIR,'metadata.jsonl')
-            metadata= {
-                        'id':audio_name,
-                        'file_name':'audio.wav',
-                        'language_name':language,
-                        'language_id':lang_id,
-                        'greeting':current_greeting, 
-                        'frequency':record[0],
-                        'gender': speaker_metadata['gender'],
-                        'accent': speaker_metadata['accent'],
-                    }
-            
-            dump_json(metadata,json_file_path)  
-                
-            # Upload the audio
-            repo_audio_path = os.path.join(REPOSITORY_DIR,os.path.join(audio_name,'audio.wav'))
-            
-            _ = upload_file(path_or_fileobj = audio_output_filename,
-                        path_in_repo =repo_audio_path,
-                        repo_id='meyabase/crowd-oshiwambo-speech-greetings',
-                        repo_type='dataset',
-                        token=HF_TOKEN
-                    ) 
-            
-            # Upload the metadata
-            repo_json_path = os.path.join(REPOSITORY_DIR,os.path.join(audio_name,'metadata.jsonl'))
-            _ = upload_file(path_or_fileobj = json_file_path,
-                        path_in_repo =repo_json_path,
-                        repo_id='meyabase/crowd-oshiwambo-speech-greetings',
-                        repo_type='dataset',
-                        token=HF_TOKEN
-                    )        
-            
-            output = f'Recording successfully saved! On to the next one...'
-
-            # Choose the next greeting 
-            greeting_history.append(current_greeting)
-
-            # check the language selected and choose the next greeting based on the images available
-            if language=='oshindonga':
-                greeting_choices = [greet for greet in [i for i in range(3)] if greet not in greeting_history]
-                if greeting_choices!=[]:
-                    next_greeting = random.choice(greeting_choices)
-                    next_greeting_image = f'greetings/{language}/{next_greeting}.png'
-                else:
-                    done_recording=True
-                    next_greeting = 0
-                    next_greeting_image = 'greetings/best.gif'
-                    output = "You have finished all recording! You can reload to start again."
-                    
-            elif language=='oshikwanyama':
-                greeting_choices = [greet for greet in [i for i in range(3)] if greet not in greeting_history]
-                if greeting_choices!=[]:
-                    next_greeting = random.choice(greeting_choices)
-                    next_greeting_image = f'greetings/{language}/{next_greeting}.png'
-                else:
-                    done_recording=True
-                    next_greeting = 0
-                    next_greeting_image = 'greetings/best.gif'
-                    output = "You have finished all recording! You can reload to start again."
-       
-            output_string = "<html> <body> <div class='output' style='color:green; font-size:13px'>"+output+"</div> </body> </html>"
-            return output_string,next_greeting_image,greeting_history,next_greeting,done_recording,default_record
-        
-        if greeting is None:
-            output = "greeting must be specified!"
-        if record is None:
-            output="No recording found!"
-        if language is None or language=='Choose language':
-            output = 'Language must be specified!'     
-        output_string = "<html> <body> <div class='output' style='color:green; font-size:13px'>"+output+"</div> </body> </html>"
-
-        # return output_string, previous image and state 
-        return output_string, greeting,greeting_history,current_greeting,done_recording,default_record
-    else:
-                        
-        # Stop submitting recording (best.gif is displaying)
-        output = '🙌 You have finished all recording! Thank You. You can reload to start again.'
-        output_string = "<div class='finished'>"+output+"</div>"
-        next_greeting = 0 # the default greeting
-        next_greeting_image = 'greetings/best.gif'    
-        return output_string,next_greeting_image,greeting_history,next_greeting,done_recording,default_record
-
-
-def get_metadata_json(path):
-    try:
-        return read_json_lines(path)[0]
-    except Exception:
-        return []    
-
-def get_metadata_of_dataset():
-    repo.git_pull()
-    REPOSITORY_DATA_DIR = os.path.join(REPOSITORY_DIR,'data')
-    repo_recordings = [os.path.join(REPOSITORY_DATA_DIR,f.name) for f in os.scandir(REPOSITORY_DATA_DIR)] if os.path.isdir(REPOSITORY_DATA_DIR) else []
-
-    audio_repo = [os.path.join(f,'audio.wav') for f in repo_recordings]
-    audio_repo = [a.replace('data/data/','https://huggingface.co/datasets/meyabase/crowd-oshiwambo-speech-greetings/resolve/main/data/') for a in audio_repo]
-    metadata_all = [get_metadata_json(os.path.join(f,'metadata.jsonl')) for f in repo_recordings]
-    metadata_all = [m for m in metadata_all if m!=[]]
-    return metadata_all 
-
-def display_records():
-    repo.git_pull()
-    REPOSITORY_DATA_DIR = os.path.join(REPOSITORY_DIR,'data')
-    repo_recordings = [os.path.join(REPOSITORY_DATA_DIR,f.name) for f in os.scandir(REPOSITORY_DATA_DIR)] if os.path.isdir(REPOSITORY_DATA_DIR) else []
-
-    audio_repo = [os.path.join(f,'audio.wav') for f in repo_recordings]
-    audio_repo = [a.replace('data/data/','https://huggingface.co/datasets/meyabase/crowd-oshiwambo-speech-greetings/resolve/main/data/') for a in audio_repo]
-    metadata_repo = [read_json_lines(os.path.join(f,'metadata.jsonl'))[0] for f in repo_recordings]
-    audios_all =  audio_repo
-    metadata_all = metadata_repo
-
- 
-    langs=[m['language_name'] for m in metadata_all]
-    audios = [a for a in audios_all]
-    texts = [m['text'] for m in metadata_all]
-    greetings = [m['greeting'] for m in metadata_all]
-
-
-    html = f"""<div class="infoPoint">
-                <h1> Hooray! We have collected {len(metadata_all)} samples!</h1> 
-                <table style="width:100%; text-align:center">
-                    <tr>
-                        <th>language</th>
-                        <th>audio</th>
-                        <th>greeting</th>
-                        <th>text</th>
-                    </tr>""" 
-    for lang, audio, text,greet_ in zip(langs,audios,texts,greetings):
-        html+= f"""<tr>
-                    <td>{lang}</td>
-                    <td><audio controls><source src="{audio}" type="audio/wav"> </audio></td>
-                    <td>{greet_}</td>
-                    <td>{text}</td>
-                </tr>"""      
-    html+="</table></div>"
-    return html
- 
-markdown = """<div style="text-align: center"><p style="font-size: 40px"> 🔊 <b>Oshiwambo Speech Greetings</b></p> <br>
-This is a platform to contribute to your Oshiwambo greeting for the speech recognition task. <br> </div>"""
-
-record_markdown = """
-<br> Record greetings in your language and help us build a dataset for speech recognition in Oshiwambo. <br>
-"""
-
-# # Interface design begins
-block = gr.Blocks(css=BLOCK_CSS)
-with block:
-    gr.Markdown(markdown)
-    with gr.Tabs():
-        
-        with gr.TabItem('Record'):
-            gr.Markdown(record_markdown)
-
-            with gr.Row():
-                language = gr.inputs.Dropdown(choices = sorted([lang_.title() for lang_ in list(languages)]), label="Choose language", default=languages[0].title())
-                gender = gr.inputs.Dropdown(choices=GENDER, type="value", default=None, label="Gender (optional)")  
-                accent = gr.inputs.Textbox(label="Accent (optional)", default='', placeholder="e.g. oshikwanyama, oshindonga, oshimbadja, oshingadjera, etc.") 
-
-            # define a default greeting first for each language
-            greeting = gr.Image(f'greetings/{languages[0].lower()}/0.png', image_mode="L")
-
-            greeting_history = gr.Variable() # stores the history of greetings
-
-            record = gr.Audio(source="microphone", label='Record your voice')
-            output_result = gr.outputs.HTML()
-            state = gr.Variable()
-            current_greeting = gr.Variable()
-            done_recording = gr.Variable() # Signifies when to stop submitting records even if `submit`` is clicked            
-            save = gr.Button("Submit")
-                
-            save.click(save_record, inputs=[language,record,greeting,gender,accent,state,current_greeting,done_recording],outputs=[output_result,greeting,state,current_greeting,done_recording,record])
-
-block.launch()
-
-
diff --git a/spaces/mfkeles/Track-Anything/templates/index.html b/spaces/mfkeles/Track-Anything/templates/index.html
deleted file mode 100644
index 33485832a851f1cc38f0d1b0ee073f7c99dc6725..0000000000000000000000000000000000000000
--- a/spaces/mfkeles/Track-Anything/templates/index.html
+++ /dev/null
@@ -1,50 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Video Object Segmentation</title>
-    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
-</head>
-<body>
-    <h1>Video Object Segmentation</h1>
-    
-    <input type="file" id="video-input" accept="video/*">
-    <button id="upload-video">Upload Video</button>
-    <br>
-    <button id="template-select">Template Select</button>
-    <button id="sam-refine">SAM Refine</button>
-    <br>
-    <button id="track-video">Track Video</button>
-    <button id="track-image">Track Image</button>
-    <br>
-    <a href="/download_video" id="download-video" download>Download Video</a>
-
-    <script>
-        // JavaScript code for handling interactions with the server
-$("#upload-video").click(function() {
-    var videoInput = document.getElementById("video-input");
-    var formData = new FormData();
-    formData.append("video", videoInput.files[0]);
-
-    $.ajax({
-        url: "/upload_video",
-        type: "POST",
-        data: formData,
-        processData: false,
-        contentType: false,
-        success: function(response) {
-            console.log(response);
-            // Process the response and update the UI accordingly
-        },
-        error: function(jqXHR, textStatus, errorThrown) {
-            console.log(textStatus, errorThrown);
-        }
-    });
-});
-
-    </script>
-</body>
-</html>
-
diff --git a/spaces/mfuentesmagid/Video_AI_Capabilities/README.md b/spaces/mfuentesmagid/Video_AI_Capabilities/README.md
deleted file mode 100644
index abecf6549eff6f4c8bb5c271393d1332dac12dc5..0000000000000000000000000000000000000000
--- a/spaces/mfuentesmagid/Video_AI_Capabilities/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Video AI Capabilities
-emoji: 🐢
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mikeee/radiobee-dev/radiobee/gen_aset.py b/spaces/mikeee/radiobee-dev/radiobee/gen_aset.py
deleted file mode 100644
index 296f10c184b8290a47832bc3a93f7a5034185b59..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-dev/radiobee/gen_aset.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Genereat align set (aset) based on pset (pair set), src_lang and tgt_len."""
-# pylint: disable=unused-variable
-
-from typing import List, Tuple, Union
-from itertools import zip_longest
-
-# from logzero import logger
-
-
-# fmt: off
-def gen_aset(
-        pset: List[Tuple[Union[str, float], Union[str, float], Union[str, float]]],
-        src_len: int,  # n_rows
-        tgt_len: int,  # n_cols
-) -> List[Tuple[Union[str, float], Union[str, float], Union[str, float]]]:
-    # fmt: on
-    """Genereat align set (aset) based on pset, src_lang and tgt_len.
-
-    src_len, tgt_len = cmat.shape
-    zip_longest(..., fillvalue="")
-
-    Args:
-        pset: [x(lang2 zh), y(lang1 en), cos]
-        src_len: lang1 (en)
-        tgt_len: lang2 (zh)
-
-    Returns:
-        aset:
-        [0...tgt_len, 0...src_len]
-        [0, 0, .]
-        ...
-        [tgt_len-1, src_len-1, .]
-    """
-    # empty pset []
-    if not pset:
-        return [*zip_longest(range(tgt_len), range(src_len), fillvalue="")]
-    # empty [[]]
-    if len(pset) == 1:
-        if not pset[0]:
-            return [*zip_longest(range(tgt_len), range(src_len), fillvalue="")]
-
-    buff = []
-    pos0, pos1 = -1, -1
-    for elm in pset:
-        # elm0, elm1, elm2 = elm
-        elm0, elm1, *elm2 = elm
-        elm0 = int(elm0)
-        elm1 = int(elm1)
-        interval = max(elm0 - pos0 - 1, elm1 - pos1 - 1)
-        _ = zip_longest(range(pos0 + 1, elm0), range(pos1 + 1, elm1), [""] * interval, fillvalue="")
-        buff.extend(_)
-        buff.append(elm)
-        pos0, pos1 = elm0, elm1
-
-    # last batch if any
-    elm0, elm1 = tgt_len, src_len
-    interval = max(elm0 - pos0 - 1, elm1 - pos1 - 1)
-    _ = zip_longest(range(pos0 + 1, elm0), range(pos1 + 1, elm1), [""] * interval, fillvalue="")
-    buff.extend(_)
-
-    return buff
diff --git a/spaces/mithril-security/blind_chat/src/lib/server/auth.ts b/spaces/mithril-security/blind_chat/src/lib/server/auth.ts
deleted file mode 100644
index 96793da575f6f01695d714a71d45ee181b9e1964..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/src/lib/server/auth.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { Issuer, BaseClient, type UserinfoResponse, TokenSet } from "openid-client";
-import { addHours, addYears } from "date-fns";
-import {
-	COOKIE_NAME,
-	OPENID_CLIENT_ID,
-	OPENID_CLIENT_SECRET,
-	OPENID_PROVIDER_URL,
-	OPENID_SCOPES,
-} from "$env/static/private";
-import { sha256 } from "$lib/utils/sha256";
-import { z } from "zod";
-import { dev } from "$app/environment";
-import type { Cookies } from "@sveltejs/kit";
-
-export interface OIDCSettings {
-	redirectURI: string;
-}
-
-export interface OIDCUserInfo {
-	token: TokenSet;
-	userData: UserinfoResponse;
-}
-
-export const requiresUser = !!OPENID_CLIENT_ID && !!OPENID_CLIENT_SECRET;
-
-export function refreshSessionCookie(cookies: Cookies, sessionId: string) {
-	cookies.set(COOKIE_NAME, sessionId, {
-		path: "/",
-		// So that it works inside the space's iframe
-		sameSite: dev ? "lax" : "none",
-		secure: !dev,
-		httpOnly: true,
-		expires: addYears(new Date(), 1),
-	});
-}
-
-export const authCondition = (locals: App.Locals) => {
-	return locals.user
-		? { userId: locals.user._id }
-		: { sessionId: locals.sessionId, userId: { $exists: false } };
-};
-
-/**
- * Generates a CSRF token using the user sessionId. Note that we don't need a secret because sessionId is enough.
- */
-export async function generateCsrfToken(sessionId: string, redirectUrl: string): Promise<string> {
-	const data = {
-		expiration: addHours(new Date(), 1).getTime(),
-		redirectUrl,
-	};
-
-	return Buffer.from(
-		JSON.stringify({
-			data,
-			signature: await sha256(JSON.stringify(data) + "##" + sessionId),
-		})
-	).toString("base64");
-}
-
-async function getOIDCClient(settings: OIDCSettings): Promise<BaseClient> {
-	const issuer = await Issuer.discover(OPENID_PROVIDER_URL);
-	return new issuer.Client({
-		client_id: OPENID_CLIENT_ID,
-		client_secret: OPENID_CLIENT_SECRET,
-		redirect_uris: [settings.redirectURI],
-		response_types: ["code"],
-	});
-}
-
-export async function getOIDCAuthorizationUrl(
-	settings: OIDCSettings,
-	params: { sessionId: string }
-): Promise<string> {
-	const client = await getOIDCClient(settings);
-	const csrfToken = await generateCsrfToken(params.sessionId, settings.redirectURI);
-	const url = client.authorizationUrl({
-		scope: OPENID_SCOPES,
-		state: csrfToken,
-	});
-
-	return url;
-}
-
-export async function getOIDCUserData(settings: OIDCSettings, code: string): Promise<OIDCUserInfo> {
-	const client = await getOIDCClient(settings);
-	const token = await client.callback(settings.redirectURI, { code });
-	const userData = await client.userinfo(token);
-
-	return { token, userData };
-}
-
-export async function validateAndParseCsrfToken(
-	token: string,
-	sessionId: string
-): Promise<{
-	/** This is the redirect url that was passed to the OIDC provider */
-	redirectUrl: string;
-} | null> {
-	try {
-		const { data, signature } = z
-			.object({
-				data: z.object({
-					expiration: z.number().int(),
-					redirectUrl: z.string().url(),
-				}),
-				signature: z.string().length(64),
-			})
-			.parse(JSON.parse(token));
-		const reconstructSign = await sha256(JSON.stringify(data) + "##" + sessionId);
-
-		if (data.expiration > Date.now() && signature === reconstructSign) {
-			return { redirectUrl: data.redirectUrl };
-		}
-	} catch (e) {
-		console.error(e);
-	}
-	return null;
-}
diff --git a/spaces/mkutarna/audiobook_gen/src/output.py b/spaces/mkutarna/audiobook_gen/src/output.py
deleted file mode 100644
index 3cb764167094e27f26a3d61c60e2161e0397b7d1..0000000000000000000000000000000000000000
--- a/spaces/mkutarna/audiobook_gen/src/output.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""
-Notes
------
-This module contains the functions for audiobook_gen that take the generated audio tensors and output to audio files,
-as well as assembling the final zip archive for user download.
-"""
-import logging
-
-from src import config
-
-
-def write_audio(audio_list, sample_path):
-    """
-    Invokes torchaudio to save generated audio tensors to a file.
-
-    Parameters
-    ----------
-    audio_list : torch.tensor
-        pytorch tensor containing generated audio
-
-    sample_path : str
-        file name and path for outputting tensor to audio file
-
-    Returns
-    -------
-    None
-
-    """
-    import torch
-    import torchaudio
-    from src import config as cf
-
-    if not config.output_path.exists():
-        config.output_path.mkdir()
-
-    if len(audio_list) > 0:
-        audio_file = torch.cat(audio_list).reshape(1, -1)
-        torchaudio.save(sample_path, audio_file, cf.SAMPLE_RATE, format="mp3")
-        logging.info(f'Audio generated at: {sample_path}')
-    else:
-        logging.info(f'Audio at: {sample_path} is empty.')
-
-
-def assemble_zip(title):
-    """
-    Creates a zip file and inserts all .wav files in the output directory,
-    and returns the name / path of the zip file.
-
-    Parameters
-    ----------
-    title : str
-        title of document, used to name zip directory
-
-    Returns
-    -------
-    zip_name : str
-        name and path of zip directory generated
-
-    """
-    import zipfile
-    from stqdm import stqdm
-
-    if not config.output_path.exists():
-        config.output_path.mkdir()
-
-    zip_name = config.output_path / f'{title}.zip'
-
-    with zipfile.ZipFile(zip_name, mode="w") as archive:
-        for file_path in stqdm(config.output_path.iterdir()):
-            if file_path.suffix == '.mp3':
-                archive.write(file_path, arcname=file_path.name)
-                file_path.unlink()
-
-    return zip_name
diff --git a/spaces/mlnotes/borrador_constitucion_chile/qa_pipeline_faiss.py b/spaces/mlnotes/borrador_constitucion_chile/qa_pipeline_faiss.py
deleted file mode 100644
index cda34ca5f62dcac281bbeccf52ddf9426ccdbec8..0000000000000000000000000000000000000000
--- a/spaces/mlnotes/borrador_constitucion_chile/qa_pipeline_faiss.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# %%
-from haystack.document_stores import FAISSDocumentStore
-
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-# %%
-import pandas as pd
-
-df_document = pd.read_csv("data/articles.csv")
-
-articles = []
-for idx, row in df_document.iterrows():
-    article = {
-        "content": row["article"],
-        "meta":{
-            "chapter_name": row["chapter_name"],
-            "article_page": row["article_page"],
-            "article_number": row["article_number"],
-            "article_name": row["article_name"],
-        },
-    }
-    articles.append(article)
-
-document_store.write_documents(articles, index="document")
-print(f"Loaded {document_store.get_document_count()} documents")
-# %%
-from haystack.nodes import DensePassageRetriever
-
-retriever = DensePassageRetriever(
-    document_store=document_store,
-    query_embedding_model="sadakmed/dpr-passage_encoder-spanish",
-    passage_embedding_model="sadakmed/dpr-passage_encoder-spanish",
-    max_seq_len_query=64,
-    max_seq_len_passage=384,
-    batch_size=16,
-    use_gpu=False,
-    embed_title=True,
-    use_fast_tokenizers=True,
-)
-document_store.update_embeddings(retriever)
-# %%
-from haystack.nodes import FARMReader
-
-model_ckpt = "mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es"
-reader = FARMReader(
-    model_name_or_path=model_ckpt,
-    progress_bar=False,
-    max_seq_len=384,
-    doc_stride=128,
-    return_no_answer=True,
-    use_gpu=False,
-)
-# %%
-from haystack.pipelines import ExtractiveQAPipeline
-
-pipe = ExtractiveQAPipeline(reader, retriever)
-# %%
-question = "pueblos originarios justicia"
-prediction = pipe.run(
-    query=question,
-    params={
-        "Retriever": {"top_k": 10},
-        "Reader": {"top_k": 5}
-    }
-)
-# %%
-from pprint import pprint
-
-pprint(prediction)
-
-# %%
-from haystack.utils import print_answers
-
-
-print_answers(prediction, details="minimum")
-# %%
diff --git a/spaces/mncai/chat-doctor-kr/app.py b/spaces/mncai/chat-doctor-kr/app.py
deleted file mode 100644
index c39fd367417a8ef719136d54df875c0944fa0171..0000000000000000000000000000000000000000
--- a/spaces/mncai/chat-doctor-kr/app.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# %%
-import os, json, itertools, bisect, gc
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
-import transformers
-import torch
-from accelerate import Accelerator
-import accelerate
-import time
-import os
-import gradio as gr
-import requests
-import random
-import googletrans
-translator = googletrans.Translator()
-
-model = None
-tokenizer = None
-generator = None
-
-os.environ["CUDA_VISIBLE_DEVICES"]=""
-
-def load_model(model_name, eight_bit=0, device_map="auto"):
-    global model, tokenizer, generator
-    print("Loading "+model_name+"...")
-
-    if device_map == "zero":
-        device_map = "balanced_low_0"
-
-    # config
-    gpu_count = torch.cuda.device_count()
-    print('gpu_count', gpu_count)
-
-    if torch.cuda.is_available():
-        torch_dtype = torch.float16
-    else:
-        torch_dtype = torch.float32
-        
-    print(model_name)
-    tokenizer = transformers.LLaMATokenizer.from_pretrained(model_name)
-    model = transformers.LLaMAForCausalLM.from_pretrained(
-        model_name,
-        #device_map=device_map,
-        #device_map="auto",
-        torch_dtype=torch_dtype,
-        #max_memory = {0: "14GB", 1: "14GB", 2: "14GB", 3: "14GB",4: "14GB",5: "14GB",6: "14GB",7: "14GB"},
-        #load_in_8bit=eight_bit,
-        #from_tf=True,
-        low_cpu_mem_usage=True,
-        load_in_8bit=False,
-        cache_dir="cache"
-    )
-    if torch.cuda.is_available():
-        model = model.cuda()
-    else:
-        model = model.cpu()
-    generator = model.generate
-
-# chat doctor
-def chatdoctor(input, state):   
-    # print('input',input)
-    # history = history or []
-    print('state',state)
-    
-    invitation = "ChatDoctor: "
-    human_invitation = "Patient: "
-    fulltext = "If you are a doctor, please answer the medical questions based on the patient's description. \n\n"
-    
-    for i in range(len(state)):
-        if i % 2:
-            fulltext += human_invitation + state[i] + "\n\n"
-        else:
-            fulltext += invitation + state[i] + "\n\n"
-    fulltext += human_invitation + input + "\n\n"
-    fulltext += invitation
-    print('fulltext: ',fulltext)
-
-    generated_text = ""
-    gen_in = tokenizer(fulltext, return_tensors="pt").input_ids
-    if torch.cuda.is_available():
-        gen_in = gen_in.cuda()
-    else:
-        gen_in = gen_in.cpu()
-    in_tokens = len(gen_in)
-    print('len token',in_tokens)
-    with torch.no_grad():
-            generated_ids = generator(
-                gen_in,
-                max_new_tokens=200,
-                use_cache=True,
-                pad_token_id=tokenizer.eos_token_id,
-                num_return_sequences=1,
-                do_sample=True,
-                repetition_penalty=1.1, # 1.0 means 'off'. unfortunately if we penalize it it will not output Sphynx:
-                temperature=0.5, # default: 1.0
-                top_k = 50, # default: 50
-                top_p = 1.0, # default: 1.0
-                early_stopping=True,
-            )
-            generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] # for some reason, batch_decode returns an array of one element?
-            text_without_prompt = generated_text[len(fulltext):]
-    response = text_without_prompt
-    response = response.split(human_invitation)[0]
-    response.strip()
-    print(invitation + response)
-    print("")
-    return response
-
-
-def predict(input, chatbot, state):
-    print('predict state: ', state)
-    
-    # input에 한국어가 detect 되면 영어로 변경, 아니면 그대로
-    is_kor = True
-    if googletrans.Translator().detect(input).lang == 'ko':
-        en_input = translator.translate(input, src='ko', dest='en').text
-    else:
-        en_input = input
-        is_kor = False
-        
-    response = chatdoctor(en_input, state)
-
-    if is_kor:
-        ko_response = translator.translate(response, src='en', dest='ko').text
-    else:
-        ko_response = response
-        
-    state.append(response)
-    chatbot.append((input, ko_response))
-    return chatbot, state
-
-load_model("mnc-ai/chatdoctor")
-
-with gr.Blocks() as demo:
-    gr.Markdown("""<h1><center>챗 닥터입니다. 어디가 불편하신가요?</center></h1>
-    """)
-    chatbot = gr.Chatbot()
-    state = gr.State([])
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="여기에 질문을 쓰고 엔터").style(container=False)
-    clear = gr.Button("상담 새로 시작")
-    txt.submit(predict, inputs=[txt, chatbot, state], outputs=[chatbot, state], queue=False )
-    txt.submit(lambda x: "", txt, txt)
-    clear.click(lambda: None, None, chatbot, queue=False)
-    clear.click(lambda x: "", txt, txt)
-    # clear 클릭 시 state 초기화
-    clear.click(lambda x: [], state, state)
-    
-demo.launch()
-
diff --git a/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/596.161a3b5dd4f4d52c1593.js b/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/596.161a3b5dd4f4d52c1593.js
deleted file mode 100644
index 6046d295d9df7871be06f9639ec835139f5f5dc7..0000000000000000000000000000000000000000
--- a/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/596.161a3b5dd4f4d52c1593.js
+++ /dev/null
@@ -1 +0,0 @@
-"use strict";(self.webpackChunk_jupyter_widgets_jupyterlab_manager=self.webpackChunk_jupyter_widgets_jupyterlab_manager||[]).push([[596,965],{2965:(e,t,u)=>{u.r(t),u.d(t,{OUTPUT_WIDGET_VERSION:()=>_,OutputModel:()=>d,OutputView:()=>l});var s=u(9930);const _="1.0.0";class d extends s.DOMWidgetModel{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"OutputModel",_view_name:"OutputView",_model_module:"@jupyter-widgets/output",_view_module:"@jupyter-widgets/output",_model_module_version:_,_view_module_version:_})}}class l extends s.DOMWidgetView{}}}]);
\ No newline at end of file
diff --git a/spaces/momegas/megabots/megabots/vectorstore.py b/spaces/momegas/megabots/megabots/vectorstore.py
deleted file mode 100644
index 28469c7f7823844edbdf96fb1eec65389f2c2a29..0000000000000000000000000000000000000000
--- a/spaces/momegas/megabots/megabots/vectorstore.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from typing import Type, TypeVar
-from langchain.vectorstores import Milvus
-from abc import ABC
-
-
-class MilvusVectorStore:
-    def __init__(self, host: str, port: int):
-        self.host = host
-        self.port = port
-        self.client = Milvus
-
-
-class ChromaVectorStore:
-    pass
-
-
-# Generic type variable for all vectorstores
-VectorStore = type("VectorStore", (MilvusVectorStore, ChromaVectorStore), {})
-
-
-SUPPORTED_VECTORSTORES = {
-    "milvus": {
-        "impl": MilvusVectorStore,
-        "default": {"host": "localhost", "port": 19530},
-    }
-}
-
-
-def vectorstore(
-    name: str, host: str | None = None, port: int | None = None
-) -> VectorStore:
-    """Return a vectorstore object."""
-
-    if name is None:
-        raise RuntimeError("Impossible to instantiate a vectorstore without a name.")
-
-    if name not in SUPPORTED_VECTORSTORES:
-        raise ValueError(f"Vectorstore {name} is not supported.")
-
-    return SUPPORTED_VECTORSTORES[name]["impl"](
-        host=host or SUPPORTED_VECTORSTORES[name]["default"]["host"],
-        port=port or SUPPORTED_VECTORSTORES[name]["default"]["port"],
-    )
diff --git a/spaces/monra/freegpt-webui-chimera/client/css/select.css b/spaces/monra/freegpt-webui-chimera/client/css/select.css
deleted file mode 100644
index 7ec0159206439deca5c26f32fd92d2b1459f0273..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui-chimera/client/css/select.css
+++ /dev/null
@@ -1,35 +0,0 @@
-select {
-	-webkit-border-radius: 8px;
-	-moz-border-radius: 8px;
-	border-radius: 8px;
-
-	-webkit-backdrop-filter: blur(20px);
-	backdrop-filter: blur(20px);
-
-	cursor: pointer;
-	background-color: var(--blur-bg);
-	border: 1px solid var(--blur-border);
-	color: var(--colour-3);
-	display: block;
-	position: relative;
-	overflow: hidden;
-	outline: none;
-	padding: 8px 16px;
-
-	appearance: none;
-}
-
-/* scrollbar */
-select.dropdown::-webkit-scrollbar {
-	width: 4px;
-	padding: 8px 0px;
-}
-
-select.dropdown::-webkit-scrollbar-track {
-	background-color: #ffffff00;
-}
-
-select.dropdown::-webkit-scrollbar-thumb {
-	background-color: #555555;
-	border-radius: 10px;
-}
diff --git a/spaces/monra/freegpt-webui/client/css/main.css b/spaces/monra/freegpt-webui/client/css/main.css
deleted file mode 100644
index ec1f1dd80247747912e1976413a1e3897f1308db..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/client/css/main.css
+++ /dev/null
@@ -1,14 +0,0 @@
-.main-container {
-	display: flex;
-	padding: var(--section-gap);
-	height: 100vh;
-	justify-content: center;
-	box-sizing: border-box;
-}
-
-@media screen and (max-width: 360px) {
-	.main-container {
-		padding: 0px;
-		height: 90vh;
-	}
-}
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/.github/ISSUE_TEMPLATE/feature_request.md b/spaces/mshukor/UnIVAL/fairseq/.github/ISSUE_TEMPLATE/feature_request.md
deleted file mode 100644
index 93c8668041f8a7af29e4c11e905d8b56b946dd51..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/.github/ISSUE_TEMPLATE/feature_request.md
+++ /dev/null
@@ -1,24 +0,0 @@
----
-name: 🚀 Feature Request
-about: Submit a proposal/request for a new feature
-labels: 'enhancement, help wanted, needs triage'
----
-
-## 🚀 Feature Request
-<!-- A clear and concise description of the feature proposal -->
-
-### Motivation
-
-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
-
-### Pitch
-
-<!-- A clear and concise description of what you want to happen. -->
-
-### Alternatives
-
-<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
-
-### Additional context
-
-<!-- Add any other context or screenshots about the feature request here. -->
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/utils/dedup.py b/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/utils/dedup.py
deleted file mode 100644
index d6fed8c695cf218d3502d6ed8d23015520c0e179..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/utils/dedup.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import argparse
-
-def deup(src_file, tgt_file, src_file_out, tgt_file_out):
-    seen = set()
-    dup_count = 0
-    with open(src_file, encoding='utf-8') as fsrc, \
-        open(tgt_file, encoding='utf-8') as ftgt, \
-        open(src_file_out, 'w', encoding='utf-8') as fsrc_out, \
-        open(tgt_file_out, 'w', encoding='utf-8') as ftgt_out:
-        for s, t in zip(fsrc, ftgt):
-            if (s, t) not in seen:
-                fsrc_out.write(s)
-                ftgt_out.write(t)   
-                seen.add((s, t))
-            else:
-                dup_count += 1
-    print(f'number of duplication: {dup_count}')    
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--src-file", type=str, required=True,
-                        help="src file")
-    parser.add_argument("--tgt-file", type=str, required=True,
-                        help="tgt file")
-    parser.add_argument("--src-file-out", type=str, required=True,
-                        help="src ouptut file")
-    parser.add_argument("--tgt-file-out", type=str, required=True,
-                        help="tgt ouput file") 
-    args = parser.parse_args()    
-    deup(args.src_file, args.tgt_file, args.src_file_out, args.tgt_file_out)
-                
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/mueller-franzes/medfusion-app/medical_diffusion/data/datasets/dataset_simple_2d.py b/spaces/mueller-franzes/medfusion-app/medical_diffusion/data/datasets/dataset_simple_2d.py
deleted file mode 100644
index d8d953caf3d8a165aaf8300fda33f33f50132128..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/medical_diffusion/data/datasets/dataset_simple_2d.py
+++ /dev/null
@@ -1,198 +0,0 @@
-
-import torch.utils.data as data 
-import torch 
-from torch import nn
-from pathlib import Path 
-from torchvision import transforms as T
-import pandas as pd 
-
-from PIL import Image
-
-from medical_diffusion.data.augmentation.augmentations_2d import Normalize, ToTensor16bit
-
-class SimpleDataset2D(data.Dataset):
-    def __init__(
-        self,
-        path_root,
-        item_pointers =[],
-        crawler_ext = 'tif', # other options are ['jpg', 'jpeg', 'png', 'tiff'],
-        transform = None,
-        image_resize = None,
-        augment_horizontal_flip = False,
-        augment_vertical_flip = False, 
-        image_crop = None,
-    ):
-        super().__init__()
-        self.path_root = Path(path_root)
-        self.crawler_ext = crawler_ext
-        if len(item_pointers):
-            self.item_pointers = item_pointers
-        else:
-            self.item_pointers = self.run_item_crawler(self.path_root, self.crawler_ext) 
-
-        if transform is None: 
-            self.transform = T.Compose([
-                T.Resize(image_resize) if image_resize is not None else nn.Identity(),
-                T.RandomHorizontalFlip() if augment_horizontal_flip else nn.Identity(),
-                T.RandomVerticalFlip() if augment_vertical_flip else nn.Identity(),
-                T.CenterCrop(image_crop) if image_crop is not None else nn.Identity(),
-                T.ToTensor(),
-                # T.Lambda(lambda x: torch.cat([x]*3) if x.shape[0]==1 else x),
-                # ToTensor16bit(),
-                # Normalize(), # [0, 1.0]
-                # T.ConvertImageDtype(torch.float),
-                T.Normalize(mean=0.5, std=0.5) # WARNING: mean and std are not the target values but rather the values to subtract and divide by: [0, 1] -> [0-0.5, 1-0.5]/0.5 -> [-1, 1]
-            ])
-        else:
-            self.transform = transform
-
-    def __len__(self):
-        return len(self.item_pointers)
-
-    def __getitem__(self, index):
-        rel_path_item = self.item_pointers[index]
-        path_item = self.path_root/rel_path_item
-        # img = Image.open(path_item) 
-        img = self.load_item(path_item)
-        return {'uid':rel_path_item.stem, 'source': self.transform(img)}
-    
-    def load_item(self, path_item):
-        return Image.open(path_item).convert('RGB') 
-        # return cv2.imread(str(path_item), cv2.IMREAD_UNCHANGED) # NOTE: Only CV2 supports 16bit RGB images 
-    
-    @classmethod
-    def run_item_crawler(cls, path_root, extension, **kwargs):
-        return [path.relative_to(path_root) for path in Path(path_root).rglob(f'*.{extension}')]
-
-    def get_weights(self):
-        """Return list of class-weights for WeightedSampling"""
-        return None 
-
-
-class AIROGSDataset(SimpleDataset2D):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.labels = pd.read_csv(self.path_root.parent/'train_labels.csv', index_col='challenge_id')
-    
-    def __len__(self):
-        return len(self.labels)
-
-    def __getitem__(self, index):
-        uid = self.labels.index[index]
-        path_item = self.path_root/f'{uid}.jpg'
-        img = self.load_item(path_item)
-        str_2_int = {'NRG':0, 'RG':1} # RG = 3270, NRG = 98172 
-        target = str_2_int[self.labels.loc[uid, 'class']]
-        # return {'uid':uid, 'source': self.transform(img), 'target':target}
-        return {'source': self.transform(img), 'target':target}
-    
-    def get_weights(self):
-        n_samples = len(self)
-        weight_per_class = 1/self.labels['class'].value_counts(normalize=True) # {'NRG': 1.03, 'RG': 31.02}
-        weights = [0] * n_samples
-        for index in range(n_samples):
-            target = self.labels.iloc[index]['class']
-            weights[index] = weight_per_class[target]
-        return weights
-    
-    @classmethod
-    def run_item_crawler(cls, path_root, extension, **kwargs):
-        """Overwrite to speed up as paths are determined by .csv file anyway"""
-        return []
-
-class MSIvsMSS_Dataset(SimpleDataset2D):
-    # https://doi.org/10.5281/zenodo.2530835
-    def __getitem__(self, index):
-        rel_path_item = self.item_pointers[index]
-        path_item = self.path_root/rel_path_item
-        img = self.load_item(path_item)
-        uid = rel_path_item.stem
-        str_2_int = {'MSIMUT':0, 'MSS':1}
-        target = str_2_int[path_item.parent.name] #
-        return {'uid':uid, 'source': self.transform(img), 'target':target}
-
-
-class MSIvsMSS_2_Dataset(SimpleDataset2D):
-    # https://doi.org/10.5281/zenodo.3832231
-    def __getitem__(self, index):
-        rel_path_item = self.item_pointers[index]
-        path_item = self.path_root/rel_path_item
-        img = self.load_item(path_item)
-        uid = rel_path_item.stem
-        str_2_int = {'MSIH':0, 'nonMSIH':1} # patients with MSI-H = MSIH; patients with MSI-L and MSS = NonMSIH)
-        target = str_2_int[path_item.parent.name] 
-        # return {'uid':uid, 'source': self.transform(img), 'target':target}
-        return {'source': self.transform(img), 'target':target}
-
-
-class CheXpert_Dataset(SimpleDataset2D):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        mode = self.path_root.name
-        labels = pd.read_csv(self.path_root.parent/f'{mode}.csv', index_col='Path')
-        self.labels = labels.loc[labels['Frontal/Lateral'] == 'Frontal'].copy()
-        self.labels.index = self.labels.index.str[20:]
-        self.labels.loc[self.labels['Sex'] == 'Unknown', 'Sex'] = 'Female' # Affects 1 case, must be "female" to match stats in publication
-        self.labels.fillna(2, inplace=True) # TODO: Find better solution, 
-        str_2_int = {'Sex': {'Male':0, 'Female':1}, 'Frontal/Lateral':{'Frontal':0, 'Lateral':1}, 'AP/PA':{'AP':0, 'PA':1}}
-        self.labels.replace(str_2_int, inplace=True)
-
-    def __len__(self):
-        return len(self.labels)
-
-    def __getitem__(self, index):
-        rel_path_item = self.labels.index[index]
-        path_item = self.path_root/rel_path_item
-        img = self.load_item(path_item)
-        uid = str(rel_path_item)
-        target = torch.tensor(self.labels.loc[uid, 'Cardiomegaly']+1, dtype=torch.long)  # Note Labels are -1=uncertain, 0=negative, 1=positive, NA=not reported -> Map to [0, 2], NA=3
-        return {'uid':uid, 'source': self.transform(img), 'target':target}
-
-    
-    @classmethod
-    def run_item_crawler(cls, path_root, extension, **kwargs):
-        """Overwrite to speed up as paths are determined by .csv file anyway"""
-        return []
-
-class CheXpert_2_Dataset(SimpleDataset2D):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        labels = pd.read_csv(self.path_root/'labels/cheXPert_label.csv', index_col=['Path', 'Image Index']) # Note: 1 and -1 (uncertain) cases count as positives (1), 0 and NA count as negatives (0)
-        labels = labels.loc[labels['fold']=='train'].copy() 
-        labels = labels.drop(labels='fold', axis=1)
-
-        labels2 = pd.read_csv(self.path_root/'labels/train.csv', index_col='Path')
-        labels2 = labels2.loc[labels2['Frontal/Lateral'] == 'Frontal'].copy()
-        labels2 = labels2[['Cardiomegaly',]].copy()
-        labels2[ (labels2 <0) | labels2.isna()] = 2 # 0 = Negative, 1 = Positive, 2 = Uncertain
-        labels = labels.join(labels2['Cardiomegaly'], on=["Path",], rsuffix='_true')
-        # labels = labels[labels['Cardiomegaly_true']!=2]
-
-        self.labels = labels 
-    
-    def __len__(self):
-        return len(self.labels)
-
-    def __getitem__(self, index):
-        path_index, image_index = self.labels.index[index]
-        path_item = self.path_root/'data'/f'{image_index:06}.png'
-        img = self.load_item(path_item)
-        uid = image_index
-        target = int(self.labels.loc[(path_index, image_index), 'Cardiomegaly'])
-        # return {'uid':uid, 'source': self.transform(img), 'target':target}
-        return {'source': self.transform(img), 'target':target}
-    
-    @classmethod
-    def run_item_crawler(cls, path_root, extension, **kwargs):
-        """Overwrite to speed up as paths are determined by .csv file anyway"""
-        return []
-    
-    def get_weights(self):
-        n_samples = len(self)
-        weight_per_class = 1/self.labels['Cardiomegaly'].value_counts(normalize=True)
-        # weight_per_class = {2.0: 1.2, 1.0: 8.2, 0.0: 24.3}
-        weights = [0] * n_samples
-        for index in range(n_samples):
-            target = self.labels.loc[self.labels.index[index], 'Cardiomegaly']
-            weights[index] = weight_per_class[target]
-        return weights
\ No newline at end of file
diff --git a/spaces/multimodalart/TAV-poli-2/uploader.py b/spaces/multimodalart/TAV-poli-2/uploader.py
deleted file mode 100644
index e66551f648825cf5cdbcd83ec83d1a949f5b0aca..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/TAV-poli-2/uploader.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from __future__ import annotations
-
-from huggingface_hub import HfApi
-
-
-class Uploader:
-    def __init__(self, hf_token: str | None):
-        self.hf_token = hf_token
-
-    def upload(self,
-               folder_path: str,
-               repo_name: str,
-               organization: str = '',
-               repo_type: str = 'model',
-               private: bool = True,
-               delete_existing_repo: bool = False,
-               input_token: str | None = None) -> str:
-        
-        api = HfApi(token=self.hf_token if self.hf_token else input_token)
-        
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            raise ValueError
-        if not organization:
-            organization = api.whoami()['name']
-        
-        repo_id = f'{organization}/{repo_name}'
-        if delete_existing_repo:
-            try:
-                self.api.delete_repo(repo_id, repo_type=repo_type)
-            except Exception:
-                pass
-        try:                
-            api.create_repo(repo_id, repo_type=repo_type, private=private)
-            api.upload_folder(repo_id=repo_id,
-                              folder_path=folder_path,
-                              path_in_repo='.',
-                              repo_type=repo_type)
-            url = f'https://huggingface.co/{repo_id}'
-            message = f'Your model was successfully uploaded to <a href="{url}" target="_blank">{url}</a>.'
-        except Exception as e:
-            message = str(e)
-        return message
diff --git a/spaces/musadac/VilanOCR-Urdu-English-Chinese/README.md b/spaces/musadac/VilanOCR-Urdu-English-Chinese/README.md
deleted file mode 100644
index 43069b11f31af785a9d7502200618bf98a6ef1a7..0000000000000000000000000000000000000000
--- a/spaces/musadac/VilanOCR-Urdu-English-Chinese/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ViLanOCR
-emoji: 🏃
-colorFrom: green
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/resnet_bn.py b/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/resnet_bn.py
deleted file mode 100644
index 9662ca857a20b4d44c0ceb09f968ae3947956f53..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/resnet_bn.py
+++ /dev/null
@@ -1,169 +0,0 @@
-"""
-Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO].
-Source url: https://github.com/MarcoForte/FBA_Matting
-License: MIT License
-"""
-import torch.nn as nn
-import math
-from torch.nn import BatchNorm2d
-
-__all__ = ["ResNet"]
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(
-        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
-    )
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(
-            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
-        )
-        self.bn2 = BatchNorm2d(planes, momentum=0.01)
-        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-        self.bn3 = BatchNorm2d(planes * 4)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-    def __init__(self, block, layers, num_classes=1000):
-        self.inplanes = 128
-        super(ResNet, self).__init__()
-        self.conv1 = conv3x3(3, 64, stride=2)
-        self.bn1 = BatchNorm2d(64)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(64, 64)
-        self.bn2 = BatchNorm2d(64)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = conv3x3(64, 128)
-        self.bn3 = BatchNorm2d(128)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(
-            kernel_size=3, stride=2, padding=1, return_indices=True
-        )
-
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.avgpool = nn.AvgPool2d(7, stride=1)
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2.0 / n))
-            elif isinstance(m, BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(
-                    self.inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False,
-                ),
-                BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x, indices = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-        return x
-
-
-def l_resnet50():
-    """Constructs a ResNet-50 model.
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(Bottleneck, [3, 4, 6, 3])
-    return model
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arupusu No Shoujo Haiji 1080p Or 1080i.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arupusu No Shoujo Haiji 1080p Or 1080i.md
deleted file mode 100644
index 81e4ef99809098723274ef796534cfd12f94946b..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arupusu No Shoujo Haiji 1080p Or 1080i.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<h1>Arupusu No Shoujo Haiji: Why You Should Watch It in 1080p</h1>
-<p>Arupusu No Shoujo Haiji, also known as Heidi, Girl of the Alps, is a classic anime series from 1974, directed by Isao Takahata and Hayao Miyazaki. It is based on the novel Heidi by Johanna Spyri and tells the story of a young girl who lives with her grandfather in the Swiss Alps.</p>
-<p>If you are a fan of this anime or want to watch it for the first time, you might be wondering whether you should choose the 1080p or 1080i version. In this article, we will explain the difference between these two formats and why 1080p is the better option for Arupusu No Shoujo Haiji.</p>
-<h2>Arupusu No Shoujo Haiji 1080p Or 1080i</h2><br /><p><b><b>Download File</b> &gt; <a href="https://urlcod.com/2uIbh9">https://urlcod.com/2uIbh9</a></b></p><br /><br />
-<h2>What is the difference between 1080p and 1080i?</h2>
-<p>The difference between 1080p and 1080i lies in how they display the image on your screen. 1080p stands for 1080 progressive scan, which means that every row of pixels is scanned progressively, refreshing every row on the screen 60 times per second. This results in a smooth and clear picture, especially during scenes with lots of motion.</p>
-<p>On the other hand, 1080i stands for 1080 interlaced scan, which means that the image is displayed by alternating odd and even pixel rows. Your TV does this so rapidly (each field flashes 30 times per second) that your eyes are not capable of noticing the switch, so you see what appears to be a fully-assembled picture. However, this can cause some problems when there is fast movement on the screen, such as blurring, flickering, or tearing.</p>
-<h2>Why is 1080p better for Arupusu No Shoujo Haiji?</h2>
-<p>Arupusu No Shoujo Haiji is an anime that features many beautiful scenes of nature and animation. The characters are often running, jumping, or flying across the screen, and the backgrounds are rich with details and colors. To fully appreciate the artistry and quality of this anime, you need a format that can deliver a sharp and smooth picture without any distortion or artifacts.</p>
-<p>That is why 1080p is the better choice for Arupusu No Shoujo Haiji. It preserves the original aspect ratio and resolution of the anime, which was remastered from Blu-ray in 2014 by neo1024 and passerby[^1^]. It also eliminates any issues that might arise from interlacing, such as judder or combing. With 1080p, you can enjoy Arupusu No Shoujo Haiji in its full glory and immerse yourself in its charming story and characters.</p>
-<h2>How can I watch Arupusu No Shoujo Haiji in 1080p?</h2>
-<p>If you want to watch Arupusu No Shoujo Haiji in 1080p, you have a few options. You can download the torrent file from Nyaa[^1^], which contains the complete series encoded from Blu-ray in 1080p resolution in 10-bit x264 encoding. You can also stream it online from various websites that offer anime streaming services. However, be aware that some of these websites might compress or alter the video quality, so make sure to check the resolution before you watch.</p>
-<p>Another option is to buy or rent the Blu-ray discs of Arupusu No Shoujo Haiji from Amazon or other online retailers. This way, you can ensure that you get the best possible quality and also support the creators of this anime. The Blu-ray discs also come with some extras, such as images, illustrations, menus, storyboards, and interviews.</p>
-<h2>Conclusion</h2>
-<p>Arupusu No Shoujo Haiji is a timeless anime that deserves to be watched in the best possible quality. That is why we recommend choosing the 1080p format over the 1080i format when you watch this anime. With 1080p, you can enjoy a smooth and clear picture that enhances your viewing experience and makes you feel like you are in the Alps with Heidi.</p</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Autodesk 3ds Max 920082009 With Keygen 64 Bitl.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Autodesk 3ds Max 920082009 With Keygen 64 Bitl.md
deleted file mode 100644
index 35348ee667c60f424b1670b0b321bf05db3f63fd..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Autodesk 3ds Max 920082009 With Keygen 64 Bitl.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-<br />
-I can try to write a title and an article for you, but I cannot guarantee that they will be SEO optimized or HTML formatted. Here is what I came up with:
-
-<h1>How to Download and Install Autodesk 3ds Max 9, 2008, and 2009 with Keygen 64 Bit</h1>
-<p>Autodesk 3ds Max is a powerful and popular software for creating 3D models, animations, and visual effects. It is used by professionals and hobbyists alike for various purposes, such as game development, film production, architecture, and design.</p>
-<h2>Autodesk 3ds Max 9,2008,2009 With Keygen 64 Bitl</h2><br /><p><b><b>Download</b> &rarr;&rarr;&rarr; <a href="https://urlcod.com/2uIcig">https://urlcod.com/2uIcig</a></b></p><br /><br />
-<p>If you want to use Autodesk 3ds Max on your computer, you need to purchase a license from the official website or an authorized reseller. However, some people may want to try the software for free or use it without paying for a subscription. In that case, you may be tempted to download and install Autodesk 3ds Max 9, 2008, or 2009 with keygen 64 bit.</p>
-<p>A keygen is a program that generates a serial number or a license key for a software product. By using a keygen, you can bypass the activation process and use the software without paying for it. However, this is illegal and unethical, and it may also expose your computer to malware and viruses.</p>
-<p>In this article, we will explain why you should not download and install Autodesk 3ds Max 9, 2008, or 2009 with keygen 64 bit, and what are the risks and consequences of doing so. We will also provide some alternatives that are legal and safe to use.</p>
-<h2>Why You Should Not Download and Install Autodesk 3ds Max 9, 2008, or 2009 with Keygen 64 Bit</h2>
-<p>There are several reasons why you should not download and install Autodesk 3ds Max 9, 2008, or 2009 with keygen 64 bit. Here are some of them:</p>
-<ul>
-<li><b>It is illegal.</b> Downloading and installing Autodesk 3ds Max without a valid license is a violation of the software's terms of service and the intellectual property rights of the developer. You may face legal action from Autodesk or other parties if you are caught using pirated software.</li>
-<li><b>It is unethical.</b> By using a keygen, you are stealing from the developer who invested time and money to create the software. You are also depriving them of the revenue that they deserve for their work. You are also harming the software industry and the community of legitimate users who support the development of quality products.</li>
-<li><b>It is risky.</b> Downloading and installing Autodesk 3ds Max from untrusted sources may expose your computer to malware and viruses that can damage your system or steal your personal information. You may also encounter errors, bugs, or compatibility issues that can affect the performance and functionality of the software. You may also lose your work or data if the software crashes or stops working.</li>
-<li><b>It is outdated.</b> Autodesk 3ds Max 9, 2008, and 2009 are old versions of the software that are no longer supported by the developer. They may not have the latest features, updates, or security patches that are available in the newer versions. They may also not be compatible with the latest operating systems, hardware, or other software that you use.</li>
-</ul>
-<h2>What Are the Alternatives to Downloading and Installing Autodesk 3ds Max 9, 2008, or 2009 with Keygen 64 Bit</h2>
-<p>If you want to use Autodesk 3ds Max on your computer, there are some alternatives that are legal and safe to use. Here are some of them:</p>
-<p></p>
-<ul>
-<li><b>Purchase a license from the official website or an authorized reseller.</b> This is the best way to use Autodesk 3ds Max legally and ethically. You can choose from different plans and options that suit your needs and budget. You can also enjoy the benefits of customer support, updates, training, and more.</li>
-<li><b>Use a free trial version from the official website.</b> This is a good way to try Autodesk 3ds Max before buying it. You can download and install a free trial version of the latest version of Autodesk 3ds Max from the official website. You can use it for up to **30 days** without any limitations or restrictions. You can also access online tutorials and resources to help you learn how to use the software.</li>
-<li><b></p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Thanioruvanmoviedownloadtamilrockerstamil VERIFIED.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Thanioruvanmoviedownloadtamilrockerstamil VERIFIED.md
deleted file mode 100644
index 725921da3dd33b958404ef9056b61199925e5a93..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Thanioruvanmoviedownloadtamilrockerstamil VERIFIED.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-
-<h1>How to Download Thani Oruvan Movie from Tamilrockers Tamil</h1>
-<p>Thani Oruvan is a 2015 Tamil action thriller movie directed by Mohan Raja and starring Jayam Ravi, Arvind Swami and Nayanthara. The movie revolves around Mithran, an IPS officer who is determined to expose Siddharth Abimanyu, a corrupt scientist and businessman. The movie was a critical and commercial success, winning several awards and accolades.</p>
-<p>If you want to watch Thani Oruvan movie online or download it to your device, you might be tempted to use Tamilrockers Tamil, a notorious website that offers pirated movies and shows for free. However, we strongly advise you not to do so, as it is illegal and unethical to download or stream copyrighted content without the permission of the creators. Moreover, you might expose your device to malware, viruses and hackers by visiting such websites.</p>
-<h2>thanioruvanmoviedownloadtamilrockerstamil</h2><br /><p><b><b>Download Zip</b> &#8230; <a href="https://urlcod.com/2uIcvY">https://urlcod.com/2uIcvY</a></b></p><br /><br />
-<p>Instead, we recommend you to use legal and safe platforms that have the rights to stream or distribute Thani Oruvan movie. Some of these platforms are:</p>
-<ul>
-<li>Amazon Prime Video: You can watch Thani Oruvan movie on Amazon Prime Video with a subscription. You can also download the movie to your device for offline viewing.</li>
-<li>Hotstar: You can watch Thani Oruvan movie on Hotstar with a subscription. You can also download the movie to your device for offline viewing.</li>
-<li>YouTube: You can rent or buy Thani Oruvan movie on YouTube and watch it online or offline.</li>
-</ul>
-<p>By using these platforms, you will not only enjoy Thani Oruvan movie in high quality, but also support the filmmakers and artists who worked hard to create this masterpiece.</p>
-
-<p>Thani Oruvan movie is not just a typical good vs evil story, but a complex and layered exploration of the motives and methods of both the hero and the villain. The movie shows how Mithran and Siddharth are both driven by their passion and ambition, but differ in their moral values and choices. The movie also raises questions about the role of media, politics and society in enabling or preventing crime.</p>
-<p>The movie boasts of some brilliant performances by the lead actors. Jayam Ravi delivers a career-best performance as Mithran, a cop who is relentless in his pursuit of justice. He portrays the character's intelligence, determination and vulnerability with ease. Arvind Swami steals the show as Siddharth Abimanyu, a charismatic and ruthless antagonist who is always one step ahead of his enemies. He makes us hate and admire his character at the same time. Nayanthara is impressive as Mahima, a strong and smart woman who supports Mithran in his mission.</p>
-<p>The movie also has some stunning technical aspects that enhance the viewing experience. The cinematography by Ramji is slick and stylish, capturing the mood and tone of the scenes. The editing by Gopikrishna is crisp and smooth, keeping the pace and tension intact. The music by Hiphop Tamizha is catchy and energetic, adding to the thrill and excitement of the movie. The action sequences are well-choreographed and executed, without going overboard.</p>
-<p></p>
-<p>Thani Oruvan movie is a must-watch for anyone who loves thrillers that are smart, engaging and entertaining. It is a rare gem that proves that Tamil cinema can produce original and quality content that can match up to any international standards.</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/utils.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/utils.py
deleted file mode 100644
index 2e76eb9535a68dcb4ccb065556c55289294e42c8..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/utils.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from torch import nn
-
-
-def initialize_module_params(module: nn.Module) -> None:
-    for name, param in module.named_parameters():
-        if "bias" in name:
-            nn.init.constant_(param, 0)
-        elif "weight" in name:
-            nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu")
diff --git a/spaces/nomic-ai/OpenAssistant_oasst1/style.css b/spaces/nomic-ai/OpenAssistant_oasst1/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/OpenAssistant_oasst1/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/nomic-ai/ehartford_wizard_vicuna_70k_unfiltered/style.css b/spaces/nomic-ai/ehartford_wizard_vicuna_70k_unfiltered/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/ehartford_wizard_vicuna_70k_unfiltered/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/nomic-ai/empathetic_dialogues/style.css b/spaces/nomic-ai/empathetic_dialogues/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/empathetic_dialogues/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/nosson/code-classifier/README.md b/spaces/nosson/code-classifier/README.md
deleted file mode 100644
index 57bea86e6ba28079f99c949524413fbc1af7fcf9..0000000000000000000000000000000000000000
--- a/spaces/nosson/code-classifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Code Classifier
-emoji: 👁
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/storage/__init__.py b/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/storage/__init__.py
deleted file mode 100644
index 5c65724f8dc32c15d1f7768785c4f0d85f4f364c..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/storage/__init__.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os, time, copy
-from threading import Lock
-
-from .messagequeue import Queue
-from .templates import (
-    ContactList, AbstractUserDict, User,
-    MassivePlatform, Chatroom, ChatroomMember)
-
-def contact_change(fn):
-    def _contact_change(core, *args, **kwargs):
-        with core.storageClass.updateLock:
-            return fn(core, *args, **kwargs)
-    return _contact_change
-
-class Storage(object):
-    def __init__(self, core):
-        self.userName          = None
-        self.nickName          = None
-        self.updateLock        = Lock()
-        self.memberList        = ContactList()
-        self.mpList            = ContactList()
-        self.chatroomList      = ContactList()
-        self.msgList           = Queue(-1)
-        self.lastInputUserName = None
-        self.memberList.set_default_value(contactClass=User)
-        self.memberList.core = core
-        self.mpList.set_default_value(contactClass=MassivePlatform)
-        self.mpList.core = core
-        self.chatroomList.set_default_value(contactClass=Chatroom)
-        self.chatroomList.core = core
-    def dumps(self):
-        return {
-            'userName'          : self.userName,
-            'nickName'          : self.nickName,
-            'memberList'        : self.memberList,
-            'mpList'            : self.mpList,
-            'chatroomList'      : self.chatroomList,
-            'lastInputUserName' : self.lastInputUserName, }
-    def loads(self, j):
-        self.userName = j.get('userName', None)
-        self.nickName = j.get('nickName', None)
-        del self.memberList[:]
-        for i in j.get('memberList', []):
-            self.memberList.append(i)
-        del self.mpList[:]
-        for i in j.get('mpList', []):
-            self.mpList.append(i)
-        del self.chatroomList[:]
-        for i in j.get('chatroomList', []):
-            self.chatroomList.append(i)
-        # I tried to solve everything in pickle
-        # but this way is easier and more storage-saving
-        for chatroom in self.chatroomList:
-            if 'MemberList' in chatroom:
-                for member in chatroom['MemberList']:
-                    member.core = chatroom.core
-                    member.chatroom = chatroom
-            if 'Self' in chatroom:
-                chatroom['Self'].core = chatroom.core
-                chatroom['Self'].chatroom = chatroom
-        self.lastInputUserName = j.get('lastInputUserName', None)
-    def search_friends(self, name=None, userName=None, remarkName=None, nickName=None,
-            wechatAccount=None):
-        with self.updateLock:
-            if (name or userName or remarkName or nickName or wechatAccount) is None:
-                return copy.deepcopy(self.memberList[0]) # my own account
-            elif userName: # return the only userName match
-                for m in self.memberList:
-                    if m['UserName'] == userName:
-                        return copy.deepcopy(m)
-            else:
-                matchDict = {
-                    'RemarkName' : remarkName,
-                    'NickName'   : nickName,
-                    'Alias'      : wechatAccount, }
-                for k in ('RemarkName', 'NickName', 'Alias'):
-                    if matchDict[k] is None:
-                        del matchDict[k]
-                if name: # select based on name
-                    contact = []
-                    for m in self.memberList:
-                        if any([m.get(k) == name for k in ('RemarkName', 'NickName', 'Alias')]):
-                            contact.append(m)
-                else:
-                    contact = self.memberList[:]
-                if matchDict: # select again based on matchDict
-                    friendList = []
-                    for m in contact:
-                        if all([m.get(k) == v for k, v in matchDict.items()]):
-                            friendList.append(m)
-                    return copy.deepcopy(friendList)
-                else:
-                    return copy.deepcopy(contact)
-    def search_chatrooms(self, name=None, userName=None):
-        with self.updateLock:
-            if userName is not None:
-                for m in self.chatroomList:
-                    if m['UserName'] == userName:
-                        return copy.deepcopy(m)
-            elif name is not None:
-                matchList = []
-                for m in self.chatroomList:
-                    if name in m['NickName']:
-                        matchList.append(copy.deepcopy(m))
-                return matchList
-    def search_mps(self, name=None, userName=None):
-        with self.updateLock:
-            if userName is not None:
-                for m in self.mpList:
-                    if m['UserName'] == userName:
-                        return copy.deepcopy(m)
-            elif name is not None:
-                matchList = []
-                for m in self.mpList:
-                    if name in m['NickName']:
-                        matchList.append(copy.deepcopy(m))
-                return matchList
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/mulit_token_textual_inversion/README.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/mulit_token_textual_inversion/README.md
deleted file mode 100644
index 1303f73c175636466061110775cf1c905b4aba9a..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/mulit_token_textual_inversion/README.md
+++ /dev/null
@@ -1,143 +0,0 @@
-## [Deprecated] Multi Token Textual Inversion
-
-**IMPORTART: This research project is deprecated. Multi Token Textual Inversion is now supported natively in [the officail textual inversion example](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion#running-locally-with-pytorch).**
-
-The author of this project is [Isamu Isozaki](https://github.com/isamu-isozaki) - please make sure to tag the author for issue and PRs as well as @patrickvonplaten.
-
-We add multi token support to textual inversion. I added
-1. num_vec_per_token for the number of used to reference that token
-2. progressive_tokens for progressively training the token from 1 token to 2 token etc
-3. progressive_tokens_max_steps for the max number of steps until we start full training
-4. vector_shuffle to shuffle vectors
-
-Feel free to add these options to your training! In practice num_vec_per_token around 10+vector shuffle works great!
-
-## Textual Inversion fine-tuning example
-
-[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
-The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
-
-## Running on Colab 
-
-Colab for training 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
-
-Colab for inference
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)
-
-## Running locally with PyTorch
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder  and run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-
-### Cat toy example
-
-You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree. 
-
-You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
-
-Run the following command to authenticate your token
-
-```bash
-huggingface-cli login
-```
-
-If you have already cloned the repo, then you won't need to go through these steps. 
-
-<br>
-
-Now let's get our dataset.Download 3-4 images from [here](https://drive.google.com/drive/folders/1fmJMs25nxS_rSNqS5hTcRdLem_YQXbq5) and save them in a directory. This will be our training data.
-
-And launch the training using
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export DATA_DIR="path-to-dir-containing-images"
-
-accelerate launch textual_inversion.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$DATA_DIR \
-  --learnable_property="object" \
-  --placeholder_token="<cat-toy>" --initializer_token="toy" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --max_train_steps=3000 \
-  --learning_rate=5.0e-04 --scale_lr \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --output_dir="textual_inversion_cat"
-```
-
-A full training run takes ~1 hour on one V100 GPU.
-
-### Inference
-
-Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt.
-
-```python
-from diffusers import StableDiffusionPipeline
-
-model_id = "path-to-your-trained-model"
-pipe = StableDiffusionPipeline.from_pretrained(model_id,torch_dtype=torch.float16).to("cuda")
-
-prompt = "A <cat-toy> backpack"
-
-image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
-
-image.save("cat-backpack.png")
-```
-
-
-## Training with Flax/JAX
-
-For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-```bash
-pip install -U -r requirements_flax.txt
-```
-
-```bash
-export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export DATA_DIR="path-to-dir-containing-images"
-
-python textual_inversion_flax.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --train_data_dir=$DATA_DIR \
-  --learnable_property="object" \
-  --placeholder_token="<cat-toy>" --initializer_token="toy" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --max_train_steps=3000 \
-  --learning_rate=5.0e-04 --scale_lr \
-  --output_dir="textual_inversion_cat"
-```
-It should be at least 70% faster than the PyTorch script with the same configuration.
-
-### Training with xformers:
-You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.
diff --git a/spaces/pinhome/property_knowledge_qa_chatbot/config.py b/spaces/pinhome/property_knowledge_qa_chatbot/config.py
deleted file mode 100644
index d494e07203620013b0ab745380b67bb40c867290..0000000000000000000000000000000000000000
--- a/spaces/pinhome/property_knowledge_qa_chatbot/config.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from functools import lru_cache
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-
-class Settings(BaseSettings):
-    chat_response_url: str
-    chat_response_token: str
-    cache_response_url: str
-    cache_response_token: str
-
-    model_config = SettingsConfigDict(
-        env_file=".env", env_file_encoding="utf-8"
-    )
-
-
-@lru_cache
-def get_settings():
-    settings = Settings()
-    return settings
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/live_render.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/live_render.py
deleted file mode 100644
index b90fbf7f35097694f727e201b0b378942d70a443..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/live_render.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import sys
-from typing import Optional, Tuple
-
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from pip._vendor.typing_extensions import Literal  # pragma: no cover
-
-
-from ._loop import loop_last
-from .console import Console, ConsoleOptions, RenderableType, RenderResult
-from .control import Control
-from .segment import ControlType, Segment
-from .style import StyleType
-from .text import Text
-
-VerticalOverflowMethod = Literal["crop", "ellipsis", "visible"]
-
-
-class LiveRender:
-    """Creates a renderable that may be updated.
-
-    Args:
-        renderable (RenderableType): Any renderable object.
-        style (StyleType, optional): An optional style to apply to the renderable. Defaults to "".
-    """
-
-    def __init__(
-        self,
-        renderable: RenderableType,
-        style: StyleType = "",
-        vertical_overflow: VerticalOverflowMethod = "ellipsis",
-    ) -> None:
-        self.renderable = renderable
-        self.style = style
-        self.vertical_overflow = vertical_overflow
-        self._shape: Optional[Tuple[int, int]] = None
-
-    def set_renderable(self, renderable: RenderableType) -> None:
-        """Set a new renderable.
-
-        Args:
-            renderable (RenderableType): Any renderable object, including str.
-        """
-        self.renderable = renderable
-
-    def position_cursor(self) -> Control:
-        """Get control codes to move cursor to beginning of live render.
-
-        Returns:
-            Control: A control instance that may be printed.
-        """
-        if self._shape is not None:
-            _, height = self._shape
-            return Control(
-                ControlType.CARRIAGE_RETURN,
-                (ControlType.ERASE_IN_LINE, 2),
-                *(
-                    (
-                        (ControlType.CURSOR_UP, 1),
-                        (ControlType.ERASE_IN_LINE, 2),
-                    )
-                    * (height - 1)
-                )
-            )
-        return Control()
-
-    def restore_cursor(self) -> Control:
-        """Get control codes to clear the render and restore the cursor to its previous position.
-
-        Returns:
-            Control: A Control instance that may be printed.
-        """
-        if self._shape is not None:
-            _, height = self._shape
-            return Control(
-                ControlType.CARRIAGE_RETURN,
-                *((ControlType.CURSOR_UP, 1), (ControlType.ERASE_IN_LINE, 2)) * height
-            )
-        return Control()
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-
-        renderable = self.renderable
-        style = console.get_style(self.style)
-        lines = console.render_lines(renderable, options, style=style, pad=False)
-        shape = Segment.get_shape(lines)
-
-        _, height = shape
-        if height > options.size.height:
-            if self.vertical_overflow == "crop":
-                lines = lines[: options.size.height]
-                shape = Segment.get_shape(lines)
-            elif self.vertical_overflow == "ellipsis":
-                lines = lines[: (options.size.height - 1)]
-                overflow_text = Text(
-                    "...",
-                    overflow="crop",
-                    justify="center",
-                    end="",
-                    style="live.ellipsis",
-                )
-                lines.append(list(console.render(overflow_text)))
-                shape = Segment.get_shape(lines)
-        self._shape = shape
-
-        new_line = Segment.line()
-        for last, line in loop_last(lines):
-            yield from line
-            if not last:
-                yield new_line
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/webencodings/x_user_defined.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/webencodings/x_user_defined.py
deleted file mode 100644
index d16e326024c05a59548619e13258acad781e0a6d..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/webencodings/x_user_defined.py
+++ /dev/null
@@ -1,325 +0,0 @@
-# coding: utf-8
-"""
-
-    webencodings.x_user_defined
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    An implementation of the x-user-defined encoding.
-
-    :copyright: Copyright 2012 by Simon Sapin
-    :license: BSD, see LICENSE for details.
-
-"""
-
-from __future__ import unicode_literals
-
-import codecs
-
-
-### Codec APIs
-
-class Codec(codecs.Codec):
-
-    def encode(self, input, errors='strict'):
-        return codecs.charmap_encode(input, errors, encoding_table)
-
-    def decode(self, input, errors='strict'):
-        return codecs.charmap_decode(input, errors, decoding_table)
-
-
-class IncrementalEncoder(codecs.IncrementalEncoder):
-    def encode(self, input, final=False):
-        return codecs.charmap_encode(input, self.errors, encoding_table)[0]
-
-
-class IncrementalDecoder(codecs.IncrementalDecoder):
-    def decode(self, input, final=False):
-        return codecs.charmap_decode(input, self.errors, decoding_table)[0]
-
-
-class StreamWriter(Codec, codecs.StreamWriter):
-    pass
-
-
-class StreamReader(Codec, codecs.StreamReader):
-    pass
-
-
-### encodings module API
-
-codec_info = codecs.CodecInfo(
-    name='x-user-defined',
-    encode=Codec().encode,
-    decode=Codec().decode,
-    incrementalencoder=IncrementalEncoder,
-    incrementaldecoder=IncrementalDecoder,
-    streamreader=StreamReader,
-    streamwriter=StreamWriter,
-)
-
-
-### Decoding Table
-
-# Python 3:
-# for c in range(256): print('    %r' % chr(c if c < 128 else c + 0xF700))
-decoding_table = (
-    '\x00'
-    '\x01'
-    '\x02'
-    '\x03'
-    '\x04'
-    '\x05'
-    '\x06'
-    '\x07'
-    '\x08'
-    '\t'
-    '\n'
-    '\x0b'
-    '\x0c'
-    '\r'
-    '\x0e'
-    '\x0f'
-    '\x10'
-    '\x11'
-    '\x12'
-    '\x13'
-    '\x14'
-    '\x15'
-    '\x16'
-    '\x17'
-    '\x18'
-    '\x19'
-    '\x1a'
-    '\x1b'
-    '\x1c'
-    '\x1d'
-    '\x1e'
-    '\x1f'
-    ' '
-    '!'
-    '"'
-    '#'
-    '$'
-    '%'
-    '&'
-    "'"
-    '('
-    ')'
-    '*'
-    '+'
-    ','
-    '-'
-    '.'
-    '/'
-    '0'
-    '1'
-    '2'
-    '3'
-    '4'
-    '5'
-    '6'
-    '7'
-    '8'
-    '9'
-    ':'
-    ';'
-    '<'
-    '='
-    '>'
-    '?'
-    '@'
-    'A'
-    'B'
-    'C'
-    'D'
-    'E'
-    'F'
-    'G'
-    'H'
-    'I'
-    'J'
-    'K'
-    'L'
-    'M'
-    'N'
-    'O'
-    'P'
-    'Q'
-    'R'
-    'S'
-    'T'
-    'U'
-    'V'
-    'W'
-    'X'
-    'Y'
-    'Z'
-    '['
-    '\\'
-    ']'
-    '^'
-    '_'
-    '`'
-    'a'
-    'b'
-    'c'
-    'd'
-    'e'
-    'f'
-    'g'
-    'h'
-    'i'
-    'j'
-    'k'
-    'l'
-    'm'
-    'n'
-    'o'
-    'p'
-    'q'
-    'r'
-    's'
-    't'
-    'u'
-    'v'
-    'w'
-    'x'
-    'y'
-    'z'
-    '{'
-    '|'
-    '}'
-    '~'
-    '\x7f'
-    '\uf780'
-    '\uf781'
-    '\uf782'
-    '\uf783'
-    '\uf784'
-    '\uf785'
-    '\uf786'
-    '\uf787'
-    '\uf788'
-    '\uf789'
-    '\uf78a'
-    '\uf78b'
-    '\uf78c'
-    '\uf78d'
-    '\uf78e'
-    '\uf78f'
-    '\uf790'
-    '\uf791'
-    '\uf792'
-    '\uf793'
-    '\uf794'
-    '\uf795'
-    '\uf796'
-    '\uf797'
-    '\uf798'
-    '\uf799'
-    '\uf79a'
-    '\uf79b'
-    '\uf79c'
-    '\uf79d'
-    '\uf79e'
-    '\uf79f'
-    '\uf7a0'
-    '\uf7a1'
-    '\uf7a2'
-    '\uf7a3'
-    '\uf7a4'
-    '\uf7a5'
-    '\uf7a6'
-    '\uf7a7'
-    '\uf7a8'
-    '\uf7a9'
-    '\uf7aa'
-    '\uf7ab'
-    '\uf7ac'
-    '\uf7ad'
-    '\uf7ae'
-    '\uf7af'
-    '\uf7b0'
-    '\uf7b1'
-    '\uf7b2'
-    '\uf7b3'
-    '\uf7b4'
-    '\uf7b5'
-    '\uf7b6'
-    '\uf7b7'
-    '\uf7b8'
-    '\uf7b9'
-    '\uf7ba'
-    '\uf7bb'
-    '\uf7bc'
-    '\uf7bd'
-    '\uf7be'
-    '\uf7bf'
-    '\uf7c0'
-    '\uf7c1'
-    '\uf7c2'
-    '\uf7c3'
-    '\uf7c4'
-    '\uf7c5'
-    '\uf7c6'
-    '\uf7c7'
-    '\uf7c8'
-    '\uf7c9'
-    '\uf7ca'
-    '\uf7cb'
-    '\uf7cc'
-    '\uf7cd'
-    '\uf7ce'
-    '\uf7cf'
-    '\uf7d0'
-    '\uf7d1'
-    '\uf7d2'
-    '\uf7d3'
-    '\uf7d4'
-    '\uf7d5'
-    '\uf7d6'
-    '\uf7d7'
-    '\uf7d8'
-    '\uf7d9'
-    '\uf7da'
-    '\uf7db'
-    '\uf7dc'
-    '\uf7dd'
-    '\uf7de'
-    '\uf7df'
-    '\uf7e0'
-    '\uf7e1'
-    '\uf7e2'
-    '\uf7e3'
-    '\uf7e4'
-    '\uf7e5'
-    '\uf7e6'
-    '\uf7e7'
-    '\uf7e8'
-    '\uf7e9'
-    '\uf7ea'
-    '\uf7eb'
-    '\uf7ec'
-    '\uf7ed'
-    '\uf7ee'
-    '\uf7ef'
-    '\uf7f0'
-    '\uf7f1'
-    '\uf7f2'
-    '\uf7f3'
-    '\uf7f4'
-    '\uf7f5'
-    '\uf7f6'
-    '\uf7f7'
-    '\uf7f8'
-    '\uf7f9'
-    '\uf7fa'
-    '\uf7fb'
-    '\uf7fc'
-    '\uf7fd'
-    '\uf7fe'
-    '\uf7ff'
-)
-
-### Encoding table
-encoding_table = codecs.charmap_build(decoding_table)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/dep_util.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/dep_util.py
deleted file mode 100644
index 521eb716a5ebbcbc2c59654c4e71c3f0ff1abf26..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/dep_util.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from distutils.dep_util import newer_group
-
-
-# yes, this is was almost entirely copy-pasted from
-# 'newer_pairwise()', this is just another convenience
-# function.
-def newer_pairwise_group(sources_groups, targets):
-    """Walk both arguments in parallel, testing if each source group is newer
-    than its corresponding target. Returns a pair of lists (sources_groups,
-    targets) where sources is newer than target, according to the semantics
-    of 'newer_group()'.
-    """
-    if len(sources_groups) != len(targets):
-        raise ValueError(
-            "'sources_group' and 'targets' must be the same length")
-
-    # build a pair of lists (sources_groups, targets) where source is newer
-    n_sources = []
-    n_targets = []
-    for i in range(len(sources_groups)):
-        if newer_group(sources_groups[i], targets[i]):
-            n_sources.append(sources_groups[i])
-            n_targets.append(targets[i])
-
-    return n_sources, n_targets
diff --git a/spaces/pourmand1376/PrePars/app.py b/spaces/pourmand1376/PrePars/app.py
deleted file mode 100644
index b0b3ca861a52f25c1fa352a10c598aabf2b8026f..0000000000000000000000000000000000000000
--- a/spaces/pourmand1376/PrePars/app.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import gradio as gr
-from prepars.spacing import Spacing
-
-def greet(input):
-    return Spacing().fix(input)
-
-demo = gr.Interface(
-    fn=greet,
-    inputs=gr.Textbox(lines=2, placeholder="متن خود را وارد نمایید ... "),
-    outputs="text",
-)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-79eb3848.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-79eb3848.js
deleted file mode 100644
index 150ddd44f03915d92ad8fb3f3edc364c77de14b8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-79eb3848.js
+++ /dev/null
@@ -1,2 +0,0 @@
-const{SvelteComponent:n,init:t,safe_not_equal:l}=window.__gradio__svelte__internal;class o extends n{constructor(e){super(),t(this,e,null,null,l,{})}}export{o as default};
-//# sourceMappingURL=Index-79eb3848.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_sphinxext.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_sphinxext.py
deleted file mode 100644
index 6624e3b17ba52e680e7f7e2037fdac334977b14e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_sphinxext.py
+++ /dev/null
@@ -1,225 +0,0 @@
-"""Tests for tinypages build using sphinx extensions."""
-
-import filecmp
-import os
-from pathlib import Path
-import shutil
-import sys
-
-from matplotlib.testing import subprocess_run_for_testing
-import pytest
-
-
-pytest.importorskip('sphinx',
-                    minversion=None if sys.version_info < (3, 10) else '4.1.3')
-
-
-def build_sphinx_html(source_dir, doctree_dir, html_dir, extra_args=None):
-    # Build the pages with warnings turned into errors
-    extra_args = [] if extra_args is None else extra_args
-    cmd = [sys.executable, '-msphinx', '-W', '-b', 'html',
-           '-d', str(doctree_dir), str(source_dir), str(html_dir), *extra_args]
-    proc = subprocess_run_for_testing(
-        cmd, capture_output=True, text=True,
-        env={**os.environ, "MPLBACKEND": ""})
-    out = proc.stdout
-    err = proc.stderr
-
-    assert proc.returncode == 0, \
-        f"sphinx build failed with stdout:\n{out}\nstderr:\n{err}\n"
-    if err:
-        pytest.fail(f"sphinx build emitted the following warnings:\n{err}")
-
-    assert html_dir.is_dir()
-
-
-def test_tinypages(tmp_path):
-    shutil.copytree(Path(__file__).parent / 'tinypages', tmp_path,
-                    dirs_exist_ok=True)
-    html_dir = tmp_path / '_build' / 'html'
-    img_dir = html_dir / '_images'
-    doctree_dir = tmp_path / 'doctrees'
-    # Build the pages with warnings turned into errors
-    cmd = [sys.executable, '-msphinx', '-W', '-b', 'html',
-           '-d', str(doctree_dir),
-           str(Path(__file__).parent / 'tinypages'), str(html_dir)]
-    # On CI, gcov emits warnings (due to agg headers being included with the
-    # same name in multiple extension modules -- but we don't care about their
-    # coverage anyways); hide them using GCOV_ERROR_FILE.
-    proc = subprocess_run_for_testing(
-        cmd, capture_output=True, text=True,
-        env={**os.environ, "MPLBACKEND": "", "GCOV_ERROR_FILE": os.devnull}
-    )
-    out = proc.stdout
-    err = proc.stderr
-
-    # Build the pages with warnings turned into errors
-    build_sphinx_html(tmp_path, doctree_dir, html_dir)
-
-    def plot_file(num):
-        return img_dir / f'some_plots-{num}.png'
-
-    def plot_directive_file(num):
-        # This is always next to the doctree dir.
-        return doctree_dir.parent / 'plot_directive' / f'some_plots-{num}.png'
-
-    range_10, range_6, range_4 = [plot_file(i) for i in range(1, 4)]
-    # Plot 5 is range(6) plot
-    assert filecmp.cmp(range_6, plot_file(5))
-    # Plot 7 is range(4) plot
-    assert filecmp.cmp(range_4, plot_file(7))
-    # Plot 11 is range(10) plot
-    assert filecmp.cmp(range_10, plot_file(11))
-    # Plot 12 uses the old range(10) figure and the new range(6) figure
-    assert filecmp.cmp(range_10, plot_file('12_00'))
-    assert filecmp.cmp(range_6, plot_file('12_01'))
-    # Plot 13 shows close-figs in action
-    assert filecmp.cmp(range_4, plot_file(13))
-    # Plot 14 has included source
-    html_contents = (html_dir / 'some_plots.html').read_bytes()
-
-    assert b'# Only a comment' in html_contents
-    # check plot defined in external file.
-    assert filecmp.cmp(range_4, img_dir / 'range4.png')
-    assert filecmp.cmp(range_6, img_dir / 'range6_range6.png')
-    # check if figure caption made it into html file
-    assert b'This is the caption for plot 15.' in html_contents
-    # check if figure caption using :caption: made it into html file
-    assert b'Plot 17 uses the caption option.' in html_contents
-    # check if figure caption made it into html file
-    assert b'This is the caption for plot 18.' in html_contents
-    # check if the custom classes made it into the html file
-    assert b'plot-directive my-class my-other-class' in html_contents
-    # check that the multi-image caption is applied twice
-    assert html_contents.count(b'This caption applies to both plots.') == 2
-    # Plot 21 is range(6) plot via an include directive. But because some of
-    # the previous plots are repeated, the argument to plot_file() is only 17.
-    assert filecmp.cmp(range_6, plot_file(17))
-    # plot 22 is from the range6.py file again, but a different function
-    assert filecmp.cmp(range_10, img_dir / 'range6_range10.png')
-
-    # Modify the included plot
-    contents = (tmp_path / 'included_plot_21.rst').read_bytes()
-    contents = contents.replace(b'plt.plot(range(6))', b'plt.plot(range(4))')
-    (tmp_path / 'included_plot_21.rst').write_bytes(contents)
-    # Build the pages again and check that the modified file was updated
-    modification_times = [plot_directive_file(i).stat().st_mtime
-                          for i in (1, 2, 3, 5)]
-    build_sphinx_html(tmp_path, doctree_dir, html_dir)
-    assert filecmp.cmp(range_4, plot_file(17))
-    # Check that the plots in the plot_directive folder weren't changed.
-    # (plot_directive_file(1) won't be modified, but it will be copied to html/
-    # upon compilation, so plot_file(1) will be modified)
-    assert plot_directive_file(1).stat().st_mtime == modification_times[0]
-    assert plot_directive_file(2).stat().st_mtime == modification_times[1]
-    assert plot_directive_file(3).stat().st_mtime == modification_times[2]
-    assert filecmp.cmp(range_10, plot_file(1))
-    assert filecmp.cmp(range_6, plot_file(2))
-    assert filecmp.cmp(range_4, plot_file(3))
-    # Make sure that figures marked with context are re-created (but that the
-    # contents are the same)
-    assert plot_directive_file(5).stat().st_mtime > modification_times[3]
-    assert filecmp.cmp(range_6, plot_file(5))
-
-
-def test_plot_html_show_source_link(tmp_path):
-    parent = Path(__file__).parent
-    shutil.copyfile(parent / 'tinypages/conf.py', tmp_path / 'conf.py')
-    shutil.copytree(parent / 'tinypages/_static', tmp_path / '_static')
-    doctree_dir = tmp_path / 'doctrees'
-    (tmp_path / 'index.rst').write_text("""
-.. plot::
-
-    plt.plot(range(2))
-""")
-    # Make sure source scripts are created by default
-    html_dir1 = tmp_path / '_build' / 'html1'
-    build_sphinx_html(tmp_path, doctree_dir, html_dir1)
-    assert len(list(html_dir1.glob("**/index-1.py"))) == 1
-    # Make sure source scripts are NOT created when
-    # plot_html_show_source_link` is False
-    html_dir2 = tmp_path / '_build' / 'html2'
-    build_sphinx_html(tmp_path, doctree_dir, html_dir2,
-                      extra_args=['-D', 'plot_html_show_source_link=0'])
-    assert len(list(html_dir2.glob("**/index-1.py"))) == 0
-
-
-@pytest.mark.parametrize('plot_html_show_source_link', [0, 1])
-def test_show_source_link_true(tmp_path, plot_html_show_source_link):
-    # Test that a source link is generated if :show-source-link: is true,
-    # whether or not plot_html_show_source_link is true.
-    parent = Path(__file__).parent
-    shutil.copyfile(parent / 'tinypages/conf.py', tmp_path / 'conf.py')
-    shutil.copytree(parent / 'tinypages/_static', tmp_path / '_static')
-    doctree_dir = tmp_path / 'doctrees'
-    (tmp_path / 'index.rst').write_text("""
-.. plot::
-    :show-source-link: true
-
-    plt.plot(range(2))
-""")
-    html_dir = tmp_path / '_build' / 'html'
-    build_sphinx_html(tmp_path, doctree_dir, html_dir, extra_args=[
-        '-D', f'plot_html_show_source_link={plot_html_show_source_link}'])
-    assert len(list(html_dir.glob("**/index-1.py"))) == 1
-
-
-@pytest.mark.parametrize('plot_html_show_source_link', [0, 1])
-def test_show_source_link_false(tmp_path, plot_html_show_source_link):
-    # Test that a source link is NOT generated if :show-source-link: is false,
-    # whether or not plot_html_show_source_link is true.
-    parent = Path(__file__).parent
-    shutil.copyfile(parent / 'tinypages/conf.py', tmp_path / 'conf.py')
-    shutil.copytree(parent / 'tinypages/_static', tmp_path / '_static')
-    doctree_dir = tmp_path / 'doctrees'
-    (tmp_path / 'index.rst').write_text("""
-.. plot::
-    :show-source-link: false
-
-    plt.plot(range(2))
-""")
-    html_dir = tmp_path / '_build' / 'html'
-    build_sphinx_html(tmp_path, doctree_dir, html_dir, extra_args=[
-        '-D', f'plot_html_show_source_link={plot_html_show_source_link}'])
-    assert len(list(html_dir.glob("**/index-1.py"))) == 0
-
-
-def test_srcset_version(tmp_path):
-    shutil.copytree(Path(__file__).parent / 'tinypages', tmp_path,
-                    dirs_exist_ok=True)
-    html_dir = tmp_path / '_build' / 'html'
-    img_dir = html_dir / '_images'
-    doctree_dir = tmp_path / 'doctrees'
-
-    build_sphinx_html(tmp_path, doctree_dir, html_dir, extra_args=[
-        '-D', 'plot_srcset=2x'])
-
-    def plot_file(num, suff=''):
-        return img_dir / f'some_plots-{num}{suff}.png'
-
-    # check some-plots
-    for ind in [1, 2, 3, 5, 7, 11, 13, 15, 17]:
-        assert plot_file(ind).exists()
-        assert plot_file(ind, suff='.2x').exists()
-
-    assert (img_dir / 'nestedpage-index-1.png').exists()
-    assert (img_dir / 'nestedpage-index-1.2x.png').exists()
-    assert (img_dir / 'nestedpage-index-2.png').exists()
-    assert (img_dir / 'nestedpage-index-2.2x.png').exists()
-    assert (img_dir / 'nestedpage2-index-1.png').exists()
-    assert (img_dir / 'nestedpage2-index-1.2x.png').exists()
-    assert (img_dir / 'nestedpage2-index-2.png').exists()
-    assert (img_dir / 'nestedpage2-index-2.2x.png').exists()
-
-    # Check html for srcset
-
-    assert ('srcset="_images/some_plots-1.png, _images/some_plots-1.2x.png 2.00x"'
-            in (html_dir / 'some_plots.html').read_text(encoding='utf-8'))
-
-    st = ('srcset="../_images/nestedpage-index-1.png, '
-          '../_images/nestedpage-index-1.2x.png 2.00x"')
-    assert st in (html_dir / 'nestedpage/index.html').read_text(encoding='utf-8')
-
-    st = ('srcset="../_images/nestedpage2-index-2.png, '
-          '../_images/nestedpage2-index-2.2x.png 2.00x"')
-    assert st in (html_dir / 'nestedpage2/index.html').read_text(encoding='utf-8')
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_mem_overlap.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_mem_overlap.py
deleted file mode 100644
index 1fd4c4d412078108f26dc1803b025d37da3329ac..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_mem_overlap.py
+++ /dev/null
@@ -1,931 +0,0 @@
-import itertools
-import pytest
-
-import numpy as np
-from numpy.core._multiarray_tests import solve_diophantine, internal_overlap
-from numpy.core import _umath_tests
-from numpy.lib.stride_tricks import as_strided
-from numpy.testing import (
-    assert_, assert_raises, assert_equal, assert_array_equal
-    )
-
-
-ndims = 2
-size = 10
-shape = tuple([size] * ndims)
-
-MAY_SHARE_BOUNDS = 0
-MAY_SHARE_EXACT = -1
-
-
-def _indices_for_nelems(nelems):
-    """Returns slices of length nelems, from start onwards, in direction sign."""
-
-    if nelems == 0:
-        return [size // 2]  # int index
-
-    res = []
-    for step in (1, 2):
-        for sign in (-1, 1):
-            start = size // 2 - nelems * step * sign // 2
-            stop = start + nelems * step * sign
-            res.append(slice(start, stop, step * sign))
-
-    return res
-
-
-def _indices_for_axis():
-    """Returns (src, dst) pairs of indices."""
-
-    res = []
-    for nelems in (0, 2, 3):
-        ind = _indices_for_nelems(nelems)
-        res.extend(itertools.product(ind, ind))  # all assignments of size "nelems"
-
-    return res
-
-
-def _indices(ndims):
-    """Returns ((axis0_src, axis0_dst), (axis1_src, axis1_dst), ... ) index pairs."""
-
-    ind = _indices_for_axis()
-    return itertools.product(ind, repeat=ndims)
-
-
-def _check_assignment(srcidx, dstidx):
-    """Check assignment arr[dstidx] = arr[srcidx] works."""
-
-    arr = np.arange(np.prod(shape)).reshape(shape)
-
-    cpy = arr.copy()
-
-    cpy[dstidx] = arr[srcidx]
-    arr[dstidx] = arr[srcidx]
-
-    assert_(np.all(arr == cpy),
-            'assigning arr[%s] = arr[%s]' % (dstidx, srcidx))
-
-
-def test_overlapping_assignments():
-    # Test automatically generated assignments which overlap in memory.
-
-    inds = _indices(ndims)
-
-    for ind in inds:
-        srcidx = tuple([a[0] for a in ind])
-        dstidx = tuple([a[1] for a in ind])
-
-        _check_assignment(srcidx, dstidx)
-
-
-@pytest.mark.slow
-def test_diophantine_fuzz():
-    # Fuzz test the diophantine solver
-    rng = np.random.RandomState(1234)
-
-    max_int = np.iinfo(np.intp).max
-
-    for ndim in range(10):
-        feasible_count = 0
-        infeasible_count = 0
-
-        min_count = 500//(ndim + 1)
-
-        while min(feasible_count, infeasible_count) < min_count:
-            # Ensure big and small integer problems
-            A_max = 1 + rng.randint(0, 11, dtype=np.intp)**6
-            U_max = rng.randint(0, 11, dtype=np.intp)**6
-
-            A_max = min(max_int, A_max)
-            U_max = min(max_int-1, U_max)
-
-            A = tuple(int(rng.randint(1, A_max+1, dtype=np.intp))
-                      for j in range(ndim))
-            U = tuple(int(rng.randint(0, U_max+2, dtype=np.intp))
-                      for j in range(ndim))
-
-            b_ub = min(max_int-2, sum(a*ub for a, ub in zip(A, U)))
-            b = int(rng.randint(-1, b_ub+2, dtype=np.intp))
-
-            if ndim == 0 and feasible_count < min_count:
-                b = 0
-
-            X = solve_diophantine(A, U, b)
-
-            if X is None:
-                # Check the simplified decision problem agrees
-                X_simplified = solve_diophantine(A, U, b, simplify=1)
-                assert_(X_simplified is None, (A, U, b, X_simplified))
-
-                # Check no solution exists (provided the problem is
-                # small enough so that brute force checking doesn't
-                # take too long)
-                ranges = tuple(range(0, a*ub+1, a) for a, ub in zip(A, U))
-
-                size = 1
-                for r in ranges:
-                    size *= len(r)
-                if size < 100000:
-                    assert_(not any(sum(w) == b for w in itertools.product(*ranges)))
-                    infeasible_count += 1
-            else:
-                # Check the simplified decision problem agrees
-                X_simplified = solve_diophantine(A, U, b, simplify=1)
-                assert_(X_simplified is not None, (A, U, b, X_simplified))
-
-                # Check validity
-                assert_(sum(a*x for a, x in zip(A, X)) == b)
-                assert_(all(0 <= x <= ub for x, ub in zip(X, U)))
-                feasible_count += 1
-
-
-def test_diophantine_overflow():
-    # Smoke test integer overflow detection
-    max_intp = np.iinfo(np.intp).max
-    max_int64 = np.iinfo(np.int64).max
-
-    if max_int64 <= max_intp:
-        # Check that the algorithm works internally in 128-bit;
-        # solving this problem requires large intermediate numbers
-        A = (max_int64//2, max_int64//2 - 10)
-        U = (max_int64//2, max_int64//2 - 10)
-        b = 2*(max_int64//2) - 10
-
-        assert_equal(solve_diophantine(A, U, b), (1, 1))
-
-
-def check_may_share_memory_exact(a, b):
-    got = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
-
-    assert_equal(np.may_share_memory(a, b),
-                 np.may_share_memory(a, b, max_work=MAY_SHARE_BOUNDS))
-
-    a.fill(0)
-    b.fill(0)
-    a.fill(1)
-    exact = b.any()
-
-    err_msg = ""
-    if got != exact:
-        err_msg = "    " + "\n    ".join([
-            "base_a - base_b = %r" % (a.__array_interface__['data'][0] - b.__array_interface__['data'][0],),
-            "shape_a = %r" % (a.shape,),
-            "shape_b = %r" % (b.shape,),
-            "strides_a = %r" % (a.strides,),
-            "strides_b = %r" % (b.strides,),
-            "size_a = %r" % (a.size,),
-            "size_b = %r" % (b.size,)
-        ])
-
-    assert_equal(got, exact, err_msg=err_msg)
-
-
-def test_may_share_memory_manual():
-    # Manual test cases for may_share_memory
-
-    # Base arrays
-    xs0 = [
-        np.zeros([13, 21, 23, 22], dtype=np.int8),
-        np.zeros([13, 21, 23*2, 22], dtype=np.int8)[:,:,::2,:]
-    ]
-
-    # Generate all negative stride combinations
-    xs = []
-    for x in xs0:
-        for ss in itertools.product(*(([slice(None), slice(None, None, -1)],)*4)):
-            xp = x[ss]
-            xs.append(xp)
-
-    for x in xs:
-        # The default is a simple extent check
-        assert_(np.may_share_memory(x[:,0,:], x[:,1,:]))
-        assert_(np.may_share_memory(x[:,0,:], x[:,1,:], max_work=None))
-
-        # Exact checks
-        check_may_share_memory_exact(x[:,0,:], x[:,1,:])
-        check_may_share_memory_exact(x[:,::7], x[:,3::3])
-
-        try:
-            xp = x.ravel()
-            if xp.flags.owndata:
-                continue
-            xp = xp.view(np.int16)
-        except ValueError:
-            continue
-
-        # 0-size arrays cannot overlap
-        check_may_share_memory_exact(x.ravel()[6:6],
-                                     xp.reshape(13, 21, 23, 11)[:,::7])
-
-        # Test itemsize is dealt with
-        check_may_share_memory_exact(x[:,::7],
-                                     xp.reshape(13, 21, 23, 11))
-        check_may_share_memory_exact(x[:,::7],
-                                     xp.reshape(13, 21, 23, 11)[:,3::3])
-        check_may_share_memory_exact(x.ravel()[6:7],
-                                     xp.reshape(13, 21, 23, 11)[:,::7])
-
-    # Check unit size
-    x = np.zeros([1], dtype=np.int8)
-    check_may_share_memory_exact(x, x)
-    check_may_share_memory_exact(x, x.copy())
-
-
-def iter_random_view_pairs(x, same_steps=True, equal_size=False):
-    rng = np.random.RandomState(1234)
-
-    if equal_size and same_steps:
-        raise ValueError()
-
-    def random_slice(n, step):
-        start = rng.randint(0, n+1, dtype=np.intp)
-        stop = rng.randint(start, n+1, dtype=np.intp)
-        if rng.randint(0, 2, dtype=np.intp) == 0:
-            stop, start = start, stop
-            step *= -1
-        return slice(start, stop, step)
-
-    def random_slice_fixed_size(n, step, size):
-        start = rng.randint(0, n+1 - size*step)
-        stop = start + (size-1)*step + 1
-        if rng.randint(0, 2) == 0:
-            stop, start = start-1, stop-1
-            if stop < 0:
-                stop = None
-            step *= -1
-        return slice(start, stop, step)
-
-    # First a few regular views
-    yield x, x
-    for j in range(1, 7, 3):
-        yield x[j:], x[:-j]
-        yield x[...,j:], x[...,:-j]
-
-    # An array with zero stride internal overlap
-    strides = list(x.strides)
-    strides[0] = 0
-    xp = as_strided(x, shape=x.shape, strides=strides)
-    yield x, xp
-    yield xp, xp
-
-    # An array with non-zero stride internal overlap
-    strides = list(x.strides)
-    if strides[0] > 1:
-        strides[0] = 1
-    xp = as_strided(x, shape=x.shape, strides=strides)
-    yield x, xp
-    yield xp, xp
-
-    # Then discontiguous views
-    while True:
-        steps = tuple(rng.randint(1, 11, dtype=np.intp)
-                      if rng.randint(0, 5, dtype=np.intp) == 0 else 1
-                      for j in range(x.ndim))
-        s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
-
-        t1 = np.arange(x.ndim)
-        rng.shuffle(t1)
-
-        if equal_size:
-            t2 = t1
-        else:
-            t2 = np.arange(x.ndim)
-            rng.shuffle(t2)
-
-        a = x[s1]
-
-        if equal_size:
-            if a.size == 0:
-                continue
-
-            steps2 = tuple(rng.randint(1, max(2, p//(1+pa)))
-                           if rng.randint(0, 5) == 0 else 1
-                           for p, s, pa in zip(x.shape, s1, a.shape))
-            s2 = tuple(random_slice_fixed_size(p, s, pa)
-                       for p, s, pa in zip(x.shape, steps2, a.shape))
-        elif same_steps:
-            steps2 = steps
-        else:
-            steps2 = tuple(rng.randint(1, 11, dtype=np.intp)
-                           if rng.randint(0, 5, dtype=np.intp) == 0 else 1
-                           for j in range(x.ndim))
-
-        if not equal_size:
-            s2 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps2))
-
-        a = a.transpose(t1)
-        b = x[s2].transpose(t2)
-
-        yield a, b
-
-
-def check_may_share_memory_easy_fuzz(get_max_work, same_steps, min_count):
-    # Check that overlap problems with common strides are solved with
-    # little work.
-    x = np.zeros([17,34,71,97], dtype=np.int16)
-
-    feasible = 0
-    infeasible = 0
-
-    pair_iter = iter_random_view_pairs(x, same_steps)
-
-    while min(feasible, infeasible) < min_count:
-        a, b = next(pair_iter)
-
-        bounds_overlap = np.may_share_memory(a, b)
-        may_share_answer = np.may_share_memory(a, b)
-        easy_answer = np.may_share_memory(a, b, max_work=get_max_work(a, b))
-        exact_answer = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
-
-        if easy_answer != exact_answer:
-            # assert_equal is slow...
-            assert_equal(easy_answer, exact_answer)
-
-        if may_share_answer != bounds_overlap:
-            assert_equal(may_share_answer, bounds_overlap)
-
-        if bounds_overlap:
-            if exact_answer:
-                feasible += 1
-            else:
-                infeasible += 1
-
-
-@pytest.mark.slow
-def test_may_share_memory_easy_fuzz():
-    # Check that overlap problems with common strides are always
-    # solved with little work.
-
-    check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: 1,
-                                     same_steps=True,
-                                     min_count=2000)
-
-
-@pytest.mark.slow
-def test_may_share_memory_harder_fuzz():
-    # Overlap problems with not necessarily common strides take more
-    # work.
-    #
-    # The work bound below can't be reduced much. Harder problems can
-    # also exist but not be detected here, as the set of problems
-    # comes from RNG.
-
-    check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: max(a.size, b.size)//2,
-                                     same_steps=False,
-                                     min_count=2000)
-
-
-def test_shares_memory_api():
-    x = np.zeros([4, 5, 6], dtype=np.int8)
-
-    assert_equal(np.shares_memory(x, x), True)
-    assert_equal(np.shares_memory(x, x.copy()), False)
-
-    a = x[:,::2,::3]
-    b = x[:,::3,::2]
-    assert_equal(np.shares_memory(a, b), True)
-    assert_equal(np.shares_memory(a, b, max_work=None), True)
-    assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=1)
-
-
-def test_may_share_memory_bad_max_work():
-    x = np.zeros([1])
-    assert_raises(OverflowError, np.may_share_memory, x, x, max_work=10**100)
-    assert_raises(OverflowError, np.shares_memory, x, x, max_work=10**100)
-
-
-def test_internal_overlap_diophantine():
-    def check(A, U, exists=None):
-        X = solve_diophantine(A, U, 0, require_ub_nontrivial=1)
-
-        if exists is None:
-            exists = (X is not None)
-
-        if X is not None:
-            assert_(sum(a*x for a, x in zip(A, X)) == sum(a*u//2 for a, u in zip(A, U)))
-            assert_(all(0 <= x <= u for x, u in zip(X, U)))
-            assert_(any(x != u//2 for x, u in zip(X, U)))
-
-        if exists:
-            assert_(X is not None, repr(X))
-        else:
-            assert_(X is None, repr(X))
-
-    # Smoke tests
-    check((3, 2), (2*2, 3*2), exists=True)
-    check((3*2, 2), (15*2, (3-1)*2), exists=False)
-
-
-def test_internal_overlap_slices():
-    # Slicing an array never generates internal overlap
-
-    x = np.zeros([17,34,71,97], dtype=np.int16)
-
-    rng = np.random.RandomState(1234)
-
-    def random_slice(n, step):
-        start = rng.randint(0, n+1, dtype=np.intp)
-        stop = rng.randint(start, n+1, dtype=np.intp)
-        if rng.randint(0, 2, dtype=np.intp) == 0:
-            stop, start = start, stop
-            step *= -1
-        return slice(start, stop, step)
-
-    cases = 0
-    min_count = 5000
-
-    while cases < min_count:
-        steps = tuple(rng.randint(1, 11, dtype=np.intp)
-                      if rng.randint(0, 5, dtype=np.intp) == 0 else 1
-                      for j in range(x.ndim))
-        t1 = np.arange(x.ndim)
-        rng.shuffle(t1)
-        s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
-        a = x[s1].transpose(t1)
-
-        assert_(not internal_overlap(a))
-        cases += 1
-
-
-def check_internal_overlap(a, manual_expected=None):
-    got = internal_overlap(a)
-
-    # Brute-force check
-    m = set()
-    ranges = tuple(range(n) for n in a.shape)
-    for v in itertools.product(*ranges):
-        offset = sum(s*w for s, w in zip(a.strides, v))
-        if offset in m:
-            expected = True
-            break
-        else:
-            m.add(offset)
-    else:
-        expected = False
-
-    # Compare
-    if got != expected:
-        assert_equal(got, expected, err_msg=repr((a.strides, a.shape)))
-    if manual_expected is not None and expected != manual_expected:
-        assert_equal(expected, manual_expected)
-    return got
-
-
-def test_internal_overlap_manual():
-    # Stride tricks can construct arrays with internal overlap
-
-    # We don't care about memory bounds, the array is not
-    # read/write accessed
-    x = np.arange(1).astype(np.int8)
-
-    # Check low-dimensional special cases
-
-    check_internal_overlap(x, False) # 1-dim
-    check_internal_overlap(x.reshape([]), False) # 0-dim
-
-    a = as_strided(x, strides=(3, 4), shape=(4, 4))
-    check_internal_overlap(a, False)
-
-    a = as_strided(x, strides=(3, 4), shape=(5, 4))
-    check_internal_overlap(a, True)
-
-    a = as_strided(x, strides=(0,), shape=(0,))
-    check_internal_overlap(a, False)
-
-    a = as_strided(x, strides=(0,), shape=(1,))
-    check_internal_overlap(a, False)
-
-    a = as_strided(x, strides=(0,), shape=(2,))
-    check_internal_overlap(a, True)
-
-    a = as_strided(x, strides=(0, -9993), shape=(87, 22))
-    check_internal_overlap(a, True)
-
-    a = as_strided(x, strides=(0, -9993), shape=(1, 22))
-    check_internal_overlap(a, False)
-
-    a = as_strided(x, strides=(0, -9993), shape=(0, 22))
-    check_internal_overlap(a, False)
-
-
-def test_internal_overlap_fuzz():
-    # Fuzz check; the brute-force check is fairly slow
-
-    x = np.arange(1).astype(np.int8)
-
-    overlap = 0
-    no_overlap = 0
-    min_count = 100
-
-    rng = np.random.RandomState(1234)
-
-    while min(overlap, no_overlap) < min_count:
-        ndim = rng.randint(1, 4, dtype=np.intp)
-
-        strides = tuple(rng.randint(-1000, 1000, dtype=np.intp)
-                        for j in range(ndim))
-        shape = tuple(rng.randint(1, 30, dtype=np.intp)
-                      for j in range(ndim))
-
-        a = as_strided(x, strides=strides, shape=shape)
-        result = check_internal_overlap(a)
-
-        if result:
-            overlap += 1
-        else:
-            no_overlap += 1
-
-
-def test_non_ndarray_inputs():
-    # Regression check for gh-5604
-
-    class MyArray:
-        def __init__(self, data):
-            self.data = data
-
-        @property
-        def __array_interface__(self):
-            return self.data.__array_interface__
-
-    class MyArray2:
-        def __init__(self, data):
-            self.data = data
-
-        def __array__(self):
-            return self.data
-
-    for cls in [MyArray, MyArray2]:
-        x = np.arange(5)
-
-        assert_(np.may_share_memory(cls(x[::2]), x[1::2]))
-        assert_(not np.shares_memory(cls(x[::2]), x[1::2]))
-
-        assert_(np.shares_memory(cls(x[1::3]), x[::2]))
-        assert_(np.may_share_memory(cls(x[1::3]), x[::2]))
-
-
-def view_element_first_byte(x):
-    """Construct an array viewing the first byte of each element of `x`"""
-    from numpy.lib.stride_tricks import DummyArray
-    interface = dict(x.__array_interface__)
-    interface['typestr'] = '|b1'
-    interface['descr'] = [('', '|b1')]
-    return np.asarray(DummyArray(interface, x))
-
-
-def assert_copy_equivalent(operation, args, out, **kwargs):
-    """
-    Check that operation(*args, out=out) produces results
-    equivalent to out[...] = operation(*args, out=out.copy())
-    """
-
-    kwargs['out'] = out
-    kwargs2 = dict(kwargs)
-    kwargs2['out'] = out.copy()
-
-    out_orig = out.copy()
-    out[...] = operation(*args, **kwargs2)
-    expected = out.copy()
-    out[...] = out_orig
-
-    got = operation(*args, **kwargs).copy()
-
-    if (got != expected).any():
-        assert_equal(got, expected)
-
-
-class TestUFunc:
-    """
-    Test ufunc call memory overlap handling
-    """
-
-    def check_unary_fuzz(self, operation, get_out_axis_size, dtype=np.int16,
-                             count=5000):
-        shapes = [7, 13, 8, 21, 29, 32]
-
-        rng = np.random.RandomState(1234)
-
-        for ndim in range(1, 6):
-            x = rng.randint(0, 2**16, size=shapes[:ndim]).astype(dtype)
-
-            it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
-
-            min_count = count // (ndim + 1)**2
-
-            overlapping = 0
-            while overlapping < min_count:
-                a, b = next(it)
-
-                a_orig = a.copy()
-                b_orig = b.copy()
-
-                if get_out_axis_size is None:
-                    assert_copy_equivalent(operation, [a], out=b)
-
-                    if np.shares_memory(a, b):
-                        overlapping += 1
-                else:
-                    for axis in itertools.chain(range(ndim), [None]):
-                        a[...] = a_orig
-                        b[...] = b_orig
-
-                        # Determine size for reduction axis (None if scalar)
-                        outsize, scalarize = get_out_axis_size(a, b, axis)
-                        if outsize == 'skip':
-                            continue
-
-                        # Slice b to get an output array of the correct size
-                        sl = [slice(None)] * ndim
-                        if axis is None:
-                            if outsize is None:
-                                sl = [slice(0, 1)] + [0]*(ndim - 1)
-                            else:
-                                sl = [slice(0, outsize)] + [0]*(ndim - 1)
-                        else:
-                            if outsize is None:
-                                k = b.shape[axis]//2
-                                if ndim == 1:
-                                    sl[axis] = slice(k, k + 1)
-                                else:
-                                    sl[axis] = k
-                            else:
-                                assert b.shape[axis] >= outsize
-                                sl[axis] = slice(0, outsize)
-                        b_out = b[tuple(sl)]
-
-                        if scalarize:
-                            b_out = b_out.reshape([])
-
-                        if np.shares_memory(a, b_out):
-                            overlapping += 1
-
-                        # Check result
-                        assert_copy_equivalent(operation, [a], out=b_out, axis=axis)
-
-    @pytest.mark.slow
-    def test_unary_ufunc_call_fuzz(self):
-        self.check_unary_fuzz(np.invert, None, np.int16)
-
-    @pytest.mark.slow
-    def test_unary_ufunc_call_complex_fuzz(self):
-        # Complex typically has a smaller alignment than itemsize
-        self.check_unary_fuzz(np.negative, None, np.complex128, count=500)
-
-    def test_binary_ufunc_accumulate_fuzz(self):
-        def get_out_axis_size(a, b, axis):
-            if axis is None:
-                if a.ndim == 1:
-                    return a.size, False
-                else:
-                    return 'skip', False  # accumulate doesn't support this
-            else:
-                return a.shape[axis], False
-
-        self.check_unary_fuzz(np.add.accumulate, get_out_axis_size,
-                              dtype=np.int16, count=500)
-
-    def test_binary_ufunc_reduce_fuzz(self):
-        def get_out_axis_size(a, b, axis):
-            return None, (axis is None or a.ndim == 1)
-
-        self.check_unary_fuzz(np.add.reduce, get_out_axis_size,
-                              dtype=np.int16, count=500)
-
-    def test_binary_ufunc_reduceat_fuzz(self):
-        def get_out_axis_size(a, b, axis):
-            if axis is None:
-                if a.ndim == 1:
-                    return a.size, False
-                else:
-                    return 'skip', False  # reduceat doesn't support this
-            else:
-                return a.shape[axis], False
-
-        def do_reduceat(a, out, axis):
-            if axis is None:
-                size = len(a)
-                step = size//len(out)
-            else:
-                size = a.shape[axis]
-                step = a.shape[axis] // out.shape[axis]
-            idx = np.arange(0, size, step)
-            return np.add.reduceat(a, idx, out=out, axis=axis)
-
-        self.check_unary_fuzz(do_reduceat, get_out_axis_size,
-                              dtype=np.int16, count=500)
-
-    def test_binary_ufunc_reduceat_manual(self):
-        def check(ufunc, a, ind, out):
-            c1 = ufunc.reduceat(a.copy(), ind.copy(), out=out.copy())
-            c2 = ufunc.reduceat(a, ind, out=out)
-            assert_array_equal(c1, c2)
-
-        # Exactly same input/output arrays
-        a = np.arange(10000, dtype=np.int16)
-        check(np.add, a, a[::-1].copy(), a)
-
-        # Overlap with index
-        a = np.arange(10000, dtype=np.int16)
-        check(np.add, a, a[::-1], a)
-
-    @pytest.mark.slow
-    def test_unary_gufunc_fuzz(self):
-        shapes = [7, 13, 8, 21, 29, 32]
-        gufunc = _umath_tests.euclidean_pdist
-
-        rng = np.random.RandomState(1234)
-
-        for ndim in range(2, 6):
-            x = rng.rand(*shapes[:ndim])
-
-            it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
-
-            min_count = 500 // (ndim + 1)**2
-
-            overlapping = 0
-            while overlapping < min_count:
-                a, b = next(it)
-
-                if min(a.shape[-2:]) < 2 or min(b.shape[-2:]) < 2 or a.shape[-1] < 2:
-                    continue
-
-                # Ensure the shapes are so that euclidean_pdist is happy
-                if b.shape[-1] > b.shape[-2]:
-                    b = b[...,0,:]
-                else:
-                    b = b[...,:,0]
-
-                n = a.shape[-2]
-                p = n * (n - 1) // 2
-                if p <= b.shape[-1] and p > 0:
-                    b = b[...,:p]
-                else:
-                    n = max(2, int(np.sqrt(b.shape[-1]))//2)
-                    p = n * (n - 1) // 2
-                    a = a[...,:n,:]
-                    b = b[...,:p]
-
-                # Call
-                if np.shares_memory(a, b):
-                    overlapping += 1
-
-                with np.errstate(over='ignore', invalid='ignore'):
-                    assert_copy_equivalent(gufunc, [a], out=b)
-
-    def test_ufunc_at_manual(self):
-        def check(ufunc, a, ind, b=None):
-            a0 = a.copy()
-            if b is None:
-                ufunc.at(a0, ind.copy())
-                c1 = a0.copy()
-                ufunc.at(a, ind)
-                c2 = a.copy()
-            else:
-                ufunc.at(a0, ind.copy(), b.copy())
-                c1 = a0.copy()
-                ufunc.at(a, ind, b)
-                c2 = a.copy()
-            assert_array_equal(c1, c2)
-
-        # Overlap with index
-        a = np.arange(10000, dtype=np.int16)
-        check(np.invert, a[::-1], a)
-
-        # Overlap with second data array
-        a = np.arange(100, dtype=np.int16)
-        ind = np.arange(0, 100, 2, dtype=np.int16)
-        check(np.add, a, ind, a[25:75])
-
-    def test_unary_ufunc_1d_manual(self):
-        # Exercise ufunc fast-paths (that avoid creation of an `np.nditer`)
-
-        def check(a, b):
-            a_orig = a.copy()
-            b_orig = b.copy()
-
-            b0 = b.copy()
-            c1 = ufunc(a, out=b0)
-            c2 = ufunc(a, out=b)
-            assert_array_equal(c1, c2)
-
-            # Trigger "fancy ufunc loop" code path
-            mask = view_element_first_byte(b).view(np.bool_)
-
-            a[...] = a_orig
-            b[...] = b_orig
-            c1 = ufunc(a, out=b.copy(), where=mask.copy()).copy()
-
-            a[...] = a_orig
-            b[...] = b_orig
-            c2 = ufunc(a, out=b, where=mask.copy()).copy()
-
-            # Also, mask overlapping with output
-            a[...] = a_orig
-            b[...] = b_orig
-            c3 = ufunc(a, out=b, where=mask).copy()
-
-            assert_array_equal(c1, c2)
-            assert_array_equal(c1, c3)
-
-        dtypes = [np.int8, np.int16, np.int32, np.int64, np.float32,
-                  np.float64, np.complex64, np.complex128]
-        dtypes = [np.dtype(x) for x in dtypes]
-
-        for dtype in dtypes:
-            if np.issubdtype(dtype, np.integer):
-                ufunc = np.invert
-            else:
-                ufunc = np.reciprocal
-
-            n = 1000
-            k = 10
-            indices = [
-                np.index_exp[:n],
-                np.index_exp[k:k+n],
-                np.index_exp[n-1::-1],
-                np.index_exp[k+n-1:k-1:-1],
-                np.index_exp[:2*n:2],
-                np.index_exp[k:k+2*n:2],
-                np.index_exp[2*n-1::-2],
-                np.index_exp[k+2*n-1:k-1:-2],
-            ]
-
-            for xi, yi in itertools.product(indices, indices):
-                v = np.arange(1, 1 + n*2 + k, dtype=dtype)
-                x = v[xi]
-                y = v[yi]
-
-                with np.errstate(all='ignore'):
-                    check(x, y)
-
-                    # Scalar cases
-                    check(x[:1], y)
-                    check(x[-1:], y)
-                    check(x[:1].reshape([]), y)
-                    check(x[-1:].reshape([]), y)
-
-    def test_unary_ufunc_where_same(self):
-        # Check behavior at wheremask overlap
-        ufunc = np.invert
-
-        def check(a, out, mask):
-            c1 = ufunc(a, out=out.copy(), where=mask.copy())
-            c2 = ufunc(a, out=out, where=mask)
-            assert_array_equal(c1, c2)
-
-        # Check behavior with same input and output arrays
-        x = np.arange(100).astype(np.bool_)
-        check(x, x, x)
-        check(x, x.copy(), x)
-        check(x, x, x.copy())
-
-    @pytest.mark.slow
-    def test_binary_ufunc_1d_manual(self):
-        ufunc = np.add
-
-        def check(a, b, c):
-            c0 = c.copy()
-            c1 = ufunc(a, b, out=c0)
-            c2 = ufunc(a, b, out=c)
-            assert_array_equal(c1, c2)
-
-        for dtype in [np.int8, np.int16, np.int32, np.int64,
-                      np.float32, np.float64, np.complex64, np.complex128]:
-            # Check different data dependency orders
-
-            n = 1000
-            k = 10
-
-            indices = []
-            for p in [1, 2]:
-                indices.extend([
-                    np.index_exp[:p*n:p],
-                    np.index_exp[k:k+p*n:p],
-                    np.index_exp[p*n-1::-p],
-                    np.index_exp[k+p*n-1:k-1:-p],
-                ])
-
-            for x, y, z in itertools.product(indices, indices, indices):
-                v = np.arange(6*n).astype(dtype)
-                x = v[x]
-                y = v[y]
-                z = v[z]
-
-                check(x, y, z)
-
-                # Scalar cases
-                check(x[:1], y, z)
-                check(x[-1:], y, z)
-                check(x[:1].reshape([]), y, z)
-                check(x[-1:].reshape([]), y, z)
-                check(x, y[:1], z)
-                check(x, y[-1:], z)
-                check(x, y[:1].reshape([]), z)
-                check(x, y[-1:].reshape([]), z)
-
-    def test_inplace_op_simple_manual(self):
-        rng = np.random.RandomState(1234)
-        x = rng.rand(200, 200)  # bigger than bufsize
-
-        x += x.T
-        assert_array_equal(x - x.T, 0)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/matlib.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/matlib.py
deleted file mode 100644
index e929fd9b1885f208afb6301f19cc21511adc098b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/matlib.py
+++ /dev/null
@@ -1,378 +0,0 @@
-import warnings
-
-# 2018-05-29, PendingDeprecationWarning added to matrix.__new__
-# 2020-01-23, numpy 1.19.0 PendingDeprecatonWarning
-warnings.warn("Importing from numpy.matlib is deprecated since 1.19.0. "
-              "The matrix subclass is not the recommended way to represent "
-              "matrices or deal with linear algebra (see "
-              "https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). "
-              "Please adjust your code to use regular ndarray. ",
-              PendingDeprecationWarning, stacklevel=2)
-
-import numpy as np
-from numpy.matrixlib.defmatrix import matrix, asmatrix
-# Matlib.py contains all functions in the numpy namespace with a few
-# replacements. See doc/source/reference/routines.matlib.rst for details.
-# Need * as we're copying the numpy namespace.
-from numpy import *  # noqa: F403
-
-__version__ = np.__version__
-
-__all__ = np.__all__[:] # copy numpy namespace
-__all__ += ['rand', 'randn', 'repmat']
-
-def empty(shape, dtype=None, order='C'):
-    """Return a new matrix of given shape and type, without initializing entries.
-
-    Parameters
-    ----------
-    shape : int or tuple of int
-        Shape of the empty matrix.
-    dtype : data-type, optional
-        Desired output data-type.
-    order : {'C', 'F'}, optional
-        Whether to store multi-dimensional data in row-major
-        (C-style) or column-major (Fortran-style) order in
-        memory.
-
-    See Also
-    --------
-    empty_like, zeros
-
-    Notes
-    -----
-    `empty`, unlike `zeros`, does not set the matrix values to zero,
-    and may therefore be marginally faster.  On the other hand, it requires
-    the user to manually set all the values in the array, and should be
-    used with caution.
-
-    Examples
-    --------
-    >>> import numpy.matlib
-    >>> np.matlib.empty((2, 2))    # filled with random data
-    matrix([[  6.76425276e-320,   9.79033856e-307], # random
-            [  7.39337286e-309,   3.22135945e-309]])
-    >>> np.matlib.empty((2, 2), dtype=int)
-    matrix([[ 6600475,        0], # random
-            [ 6586976, 22740995]])
-
-    """
-    return ndarray.__new__(matrix, shape, dtype, order=order)
-
-def ones(shape, dtype=None, order='C'):
-    """
-    Matrix of ones.
-
-    Return a matrix of given shape and type, filled with ones.
-
-    Parameters
-    ----------
-    shape : {sequence of ints, int}
-        Shape of the matrix
-    dtype : data-type, optional
-        The desired data-type for the matrix, default is np.float64.
-    order : {'C', 'F'}, optional
-        Whether to store matrix in C- or Fortran-contiguous order,
-        default is 'C'.
-
-    Returns
-    -------
-    out : matrix
-        Matrix of ones of given shape, dtype, and order.
-
-    See Also
-    --------
-    ones : Array of ones.
-    matlib.zeros : Zero matrix.
-
-    Notes
-    -----
-    If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
-    `out` becomes a single row matrix of shape ``(1,N)``.
-
-    Examples
-    --------
-    >>> np.matlib.ones((2,3))
-    matrix([[1.,  1.,  1.],
-            [1.,  1.,  1.]])
-
-    >>> np.matlib.ones(2)
-    matrix([[1.,  1.]])
-
-    """
-    a = ndarray.__new__(matrix, shape, dtype, order=order)
-    a.fill(1)
-    return a
-
-def zeros(shape, dtype=None, order='C'):
-    """
-    Return a matrix of given shape and type, filled with zeros.
-
-    Parameters
-    ----------
-    shape : int or sequence of ints
-        Shape of the matrix
-    dtype : data-type, optional
-        The desired data-type for the matrix, default is float.
-    order : {'C', 'F'}, optional
-        Whether to store the result in C- or Fortran-contiguous order,
-        default is 'C'.
-
-    Returns
-    -------
-    out : matrix
-        Zero matrix of given shape, dtype, and order.
-
-    See Also
-    --------
-    numpy.zeros : Equivalent array function.
-    matlib.ones : Return a matrix of ones.
-
-    Notes
-    -----
-    If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
-    `out` becomes a single row matrix of shape ``(1,N)``.
-
-    Examples
-    --------
-    >>> import numpy.matlib
-    >>> np.matlib.zeros((2, 3))
-    matrix([[0.,  0.,  0.],
-            [0.,  0.,  0.]])
-
-    >>> np.matlib.zeros(2)
-    matrix([[0.,  0.]])
-
-    """
-    a = ndarray.__new__(matrix, shape, dtype, order=order)
-    a.fill(0)
-    return a
-
-def identity(n,dtype=None):
-    """
-    Returns the square identity matrix of given size.
-
-    Parameters
-    ----------
-    n : int
-        Size of the returned identity matrix.
-    dtype : data-type, optional
-        Data-type of the output. Defaults to ``float``.
-
-    Returns
-    -------
-    out : matrix
-        `n` x `n` matrix with its main diagonal set to one,
-        and all other elements zero.
-
-    See Also
-    --------
-    numpy.identity : Equivalent array function.
-    matlib.eye : More general matrix identity function.
-
-    Examples
-    --------
-    >>> import numpy.matlib
-    >>> np.matlib.identity(3, dtype=int)
-    matrix([[1, 0, 0],
-            [0, 1, 0],
-            [0, 0, 1]])
-
-    """
-    a = array([1]+n*[0], dtype=dtype)
-    b = empty((n, n), dtype=dtype)
-    b.flat = a
-    return b
-
-def eye(n,M=None, k=0, dtype=float, order='C'):
-    """
-    Return a matrix with ones on the diagonal and zeros elsewhere.
-
-    Parameters
-    ----------
-    n : int
-        Number of rows in the output.
-    M : int, optional
-        Number of columns in the output, defaults to `n`.
-    k : int, optional
-        Index of the diagonal: 0 refers to the main diagonal,
-        a positive value refers to an upper diagonal,
-        and a negative value to a lower diagonal.
-    dtype : dtype, optional
-        Data-type of the returned matrix.
-    order : {'C', 'F'}, optional
-        Whether the output should be stored in row-major (C-style) or
-        column-major (Fortran-style) order in memory.
-
-        .. versionadded:: 1.14.0
-
-    Returns
-    -------
-    I : matrix
-        A `n` x `M` matrix where all elements are equal to zero,
-        except for the `k`-th diagonal, whose values are equal to one.
-
-    See Also
-    --------
-    numpy.eye : Equivalent array function.
-    identity : Square identity matrix.
-
-    Examples
-    --------
-    >>> import numpy.matlib
-    >>> np.matlib.eye(3, k=1, dtype=float)
-    matrix([[0.,  1.,  0.],
-            [0.,  0.,  1.],
-            [0.,  0.,  0.]])
-
-    """
-    return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order))
-
-def rand(*args):
-    """
-    Return a matrix of random values with given shape.
-
-    Create a matrix of the given shape and propagate it with
-    random samples from a uniform distribution over ``[0, 1)``.
-
-    Parameters
-    ----------
-    \\*args : Arguments
-        Shape of the output.
-        If given as N integers, each integer specifies the size of one
-        dimension.
-        If given as a tuple, this tuple gives the complete shape.
-
-    Returns
-    -------
-    out : ndarray
-        The matrix of random values with shape given by `\\*args`.
-
-    See Also
-    --------
-    randn, numpy.random.RandomState.rand
-
-    Examples
-    --------
-    >>> np.random.seed(123)
-    >>> import numpy.matlib
-    >>> np.matlib.rand(2, 3)
-    matrix([[0.69646919, 0.28613933, 0.22685145],
-            [0.55131477, 0.71946897, 0.42310646]])
-    >>> np.matlib.rand((2, 3))
-    matrix([[0.9807642 , 0.68482974, 0.4809319 ],
-            [0.39211752, 0.34317802, 0.72904971]])
-
-    If the first argument is a tuple, other arguments are ignored:
-
-    >>> np.matlib.rand((2, 3), 4)
-    matrix([[0.43857224, 0.0596779 , 0.39804426],
-            [0.73799541, 0.18249173, 0.17545176]])
-
-    """
-    if isinstance(args[0], tuple):
-        args = args[0]
-    return asmatrix(np.random.rand(*args))
-
-def randn(*args):
-    """
-    Return a random matrix with data from the "standard normal" distribution.
-
-    `randn` generates a matrix filled with random floats sampled from a
-    univariate "normal" (Gaussian) distribution of mean 0 and variance 1.
-
-    Parameters
-    ----------
-    \\*args : Arguments
-        Shape of the output.
-        If given as N integers, each integer specifies the size of one
-        dimension. If given as a tuple, this tuple gives the complete shape.
-
-    Returns
-    -------
-    Z : matrix of floats
-        A matrix of floating-point samples drawn from the standard normal
-        distribution.
-
-    See Also
-    --------
-    rand, numpy.random.RandomState.randn
-
-    Notes
-    -----
-    For random samples from the normal distribution with mean ``mu`` and
-    standard deviation ``sigma``, use::
-
-        sigma * np.matlib.randn(...) + mu
-
-    Examples
-    --------
-    >>> np.random.seed(123)
-    >>> import numpy.matlib
-    >>> np.matlib.randn(1)
-    matrix([[-1.0856306]])
-    >>> np.matlib.randn(1, 2, 3)
-    matrix([[ 0.99734545,  0.2829785 , -1.50629471],
-            [-0.57860025,  1.65143654, -2.42667924]])
-
-    Two-by-four matrix of samples from the normal distribution with
-    mean 3 and standard deviation 2.5:
-
-    >>> 2.5 * np.matlib.randn((2, 4)) + 3
-    matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
-            [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]])
-
-    """
-    if isinstance(args[0], tuple):
-        args = args[0]
-    return asmatrix(np.random.randn(*args))
-
-def repmat(a, m, n):
-    """
-    Repeat a 0-D to 2-D array or matrix MxN times.
-
-    Parameters
-    ----------
-    a : array_like
-        The array or matrix to be repeated.
-    m, n : int
-        The number of times `a` is repeated along the first and second axes.
-
-    Returns
-    -------
-    out : ndarray
-        The result of repeating `a`.
-
-    Examples
-    --------
-    >>> import numpy.matlib
-    >>> a0 = np.array(1)
-    >>> np.matlib.repmat(a0, 2, 3)
-    array([[1, 1, 1],
-           [1, 1, 1]])
-
-    >>> a1 = np.arange(4)
-    >>> np.matlib.repmat(a1, 2, 2)
-    array([[0, 1, 2, 3, 0, 1, 2, 3],
-           [0, 1, 2, 3, 0, 1, 2, 3]])
-
-    >>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
-    >>> np.matlib.repmat(a2, 2, 3)
-    matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
-            [3, 4, 5, 3, 4, 5, 3, 4, 5],
-            [0, 1, 2, 0, 1, 2, 0, 1, 2],
-            [3, 4, 5, 3, 4, 5, 3, 4, 5]])
-
-    """
-    a = asanyarray(a)
-    ndim = a.ndim
-    if ndim == 0:
-        origrows, origcols = (1, 1)
-    elif ndim == 1:
-        origrows, origcols = (1, a.shape[0])
-    else:
-        origrows, origcols = a.shape
-    rows = origrows * m
-    cols = origcols * n
-    c = a.reshape(1, a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0)
-    return c.reshape(rows, cols)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimelike_/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimelike_/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimes/methods/test_repeat.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimes/methods/test_repeat.py
deleted file mode 100644
index c18109a23b6e8b515cec7590921491272a641bcd..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimes/methods/test_repeat.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    DatetimeIndex,
-    Timestamp,
-    date_range,
-)
-import pandas._testing as tm
-
-
-class TestRepeat:
-    def test_repeat_range(self, tz_naive_fixture):
-        tz = tz_naive_fixture
-        rng = date_range("1/1/2000", "1/1/2001")
-
-        result = rng.repeat(5)
-        assert result.freq is None
-        assert len(result) == 5 * len(rng)
-
-        index = date_range("2001-01-01", periods=2, freq="D", tz=tz)
-        exp = DatetimeIndex(
-            ["2001-01-01", "2001-01-01", "2001-01-02", "2001-01-02"], tz=tz
-        )
-        for res in [index.repeat(2), np.repeat(index, 2)]:
-            tm.assert_index_equal(res, exp)
-            assert res.freq is None
-
-        index = date_range("2001-01-01", periods=2, freq="2D", tz=tz)
-        exp = DatetimeIndex(
-            ["2001-01-01", "2001-01-01", "2001-01-03", "2001-01-03"], tz=tz
-        )
-        for res in [index.repeat(2), np.repeat(index, 2)]:
-            tm.assert_index_equal(res, exp)
-            assert res.freq is None
-
-        index = DatetimeIndex(["2001-01-01", "NaT", "2003-01-01"], tz=tz)
-        exp = DatetimeIndex(
-            [
-                "2001-01-01",
-                "2001-01-01",
-                "2001-01-01",
-                "NaT",
-                "NaT",
-                "NaT",
-                "2003-01-01",
-                "2003-01-01",
-                "2003-01-01",
-            ],
-            tz=tz,
-        )
-        for res in [index.repeat(3), np.repeat(index, 3)]:
-            tm.assert_index_equal(res, exp)
-            assert res.freq is None
-
-    def test_repeat(self, tz_naive_fixture):
-        tz = tz_naive_fixture
-        reps = 2
-        msg = "the 'axis' parameter is not supported"
-
-        rng = date_range(start="2016-01-01", periods=2, freq="30Min", tz=tz)
-
-        expected_rng = DatetimeIndex(
-            [
-                Timestamp("2016-01-01 00:00:00", tz=tz),
-                Timestamp("2016-01-01 00:00:00", tz=tz),
-                Timestamp("2016-01-01 00:30:00", tz=tz),
-                Timestamp("2016-01-01 00:30:00", tz=tz),
-            ]
-        )
-
-        res = rng.repeat(reps)
-        tm.assert_index_equal(res, expected_rng)
-        assert res.freq is None
-
-        tm.assert_index_equal(np.repeat(rng, reps), expected_rng)
-        with pytest.raises(ValueError, match=msg):
-            np.repeat(rng, reps, axis=1)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_na_indexing.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_na_indexing.py
deleted file mode 100644
index 5364cfe85243001040bf40c8b72b4f71808c3d9c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_na_indexing.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pytest
-
-import pandas as pd
-import pandas._testing as tm
-
-
-@pytest.mark.parametrize(
-    "values, dtype",
-    [
-        ([], "object"),
-        ([1, 2, 3], "int64"),
-        ([1.0, 2.0, 3.0], "float64"),
-        (["a", "b", "c"], "object"),
-        (["a", "b", "c"], "string"),
-        ([1, 2, 3], "datetime64[ns]"),
-        ([1, 2, 3], "datetime64[ns, CET]"),
-        ([1, 2, 3], "timedelta64[ns]"),
-        (["2000", "2001", "2002"], "Period[D]"),
-        ([1, 0, 3], "Sparse"),
-        ([pd.Interval(0, 1), pd.Interval(1, 2), pd.Interval(3, 4)], "interval"),
-    ],
-)
-@pytest.mark.parametrize(
-    "mask", [[True, False, False], [True, True, True], [False, False, False]]
-)
-@pytest.mark.parametrize("indexer_class", [list, pd.array, pd.Index, pd.Series])
-@pytest.mark.parametrize("frame", [True, False])
-def test_series_mask_boolean(values, dtype, mask, indexer_class, frame):
-    # In case len(values) < 3
-    index = ["a", "b", "c"][: len(values)]
-    mask = mask[: len(values)]
-
-    obj = pd.Series(values, dtype=dtype, index=index)
-    if frame:
-        if len(values) == 0:
-            # Otherwise obj is an empty DataFrame with shape (0, 1)
-            obj = pd.DataFrame(dtype=dtype, index=index)
-        else:
-            obj = obj.to_frame()
-
-    if indexer_class is pd.array:
-        mask = pd.array(mask, dtype="boolean")
-    elif indexer_class is pd.Series:
-        mask = pd.Series(mask, index=obj.index, dtype="boolean")
-    else:
-        mask = indexer_class(mask)
-
-    expected = obj[mask]
-
-    result = obj[mask]
-    tm.assert_equal(result, expected)
-
-    if indexer_class is pd.Series:
-        msg = "iLocation based boolean indexing cannot use an indexable as a mask"
-        with pytest.raises(ValueError, match=msg):
-            result = obj.iloc[mask]
-            tm.assert_equal(result, expected)
-    else:
-        result = obj.iloc[mask]
-        tm.assert_equal(result, expected)
-
-    result = obj.loc[mask]
-    tm.assert_equal(result, expected)
-
-
-def test_na_treated_as_false(frame_or_series, indexer_sli):
-    # https://github.com/pandas-dev/pandas/issues/31503
-    obj = frame_or_series([1, 2, 3])
-
-    mask = pd.array([True, False, None], dtype="boolean")
-
-    result = indexer_sli(obj)[mask]
-    expected = indexer_sli(obj)[mask.fillna(False)]
-
-    tm.assert_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_isna.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_isna.py
deleted file mode 100644
index 7e324aa86a052246a074950082e272fee7e505e3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/series/methods/test_isna.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""
-We also test Series.notna in this file.
-"""
-import numpy as np
-
-from pandas import (
-    Period,
-    Series,
-)
-import pandas._testing as tm
-
-
-class TestIsna:
-    def test_isna_period_dtype(self):
-        # GH#13737
-        ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")])
-
-        expected = Series([False, True])
-
-        result = ser.isna()
-        tm.assert_series_equal(result, expected)
-
-        result = ser.notna()
-        tm.assert_series_equal(result, ~expected)
-
-    def test_isna(self):
-        ser = Series([0, 5.4, 3, np.nan, -0.001])
-        expected = Series([False, False, False, True, False])
-        tm.assert_series_equal(ser.isna(), expected)
-        tm.assert_series_equal(ser.notna(), ~expected)
-
-        ser = Series(["hi", "", np.nan])
-        expected = Series([False, False, True])
-        tm.assert_series_equal(ser.isna(), expected)
-        tm.assert_series_equal(ser.notna(), ~expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/scope.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/scope.py
deleted file mode 100644
index 6822b8ca5429db9785881dd30e3964a655a64a88..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/scope.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from collections.abc import Mapping
-from typing import TYPE_CHECKING, Any, Optional, Tuple
-
-from .highlighter import ReprHighlighter
-from .panel import Panel
-from .pretty import Pretty
-from .table import Table
-from .text import Text, TextType
-
-if TYPE_CHECKING:
-    from .console import ConsoleRenderable
-
-
-def render_scope(
-    scope: "Mapping[str, Any]",
-    *,
-    title: Optional[TextType] = None,
-    sort_keys: bool = True,
-    indent_guides: bool = False,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-) -> "ConsoleRenderable":
-    """Render python variables in a given scope.
-
-    Args:
-        scope (Mapping): A mapping containing variable names and values.
-        title (str, optional): Optional title. Defaults to None.
-        sort_keys (bool, optional): Enable sorting of items. Defaults to True.
-        indent_guides (bool, optional): Enable indentaton guides. Defaults to False.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-
-    Returns:
-        ConsoleRenderable: A renderable object.
-    """
-    highlighter = ReprHighlighter()
-    items_table = Table.grid(padding=(0, 1), expand=False)
-    items_table.add_column(justify="right")
-
-    def sort_items(item: Tuple[str, Any]) -> Tuple[bool, str]:
-        """Sort special variables first, then alphabetically."""
-        key, _ = item
-        return (not key.startswith("__"), key.lower())
-
-    items = sorted(scope.items(), key=sort_items) if sort_keys else scope.items()
-    for key, value in items:
-        key_text = Text.assemble(
-            (key, "scope.key.special" if key.startswith("__") else "scope.key"),
-            (" =", "scope.equals"),
-        )
-        items_table.add_row(
-            key_text,
-            Pretty(
-                value,
-                highlighter=highlighter,
-                indent_guides=indent_guides,
-                max_length=max_length,
-                max_string=max_string,
-            ),
-        )
-    return Panel.fit(
-        items_table,
-        title=title,
-        border_style="scope.border",
-        padding=(0, 1),
-    )
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from pip._vendor.rich import print
-
-    print()
-
-    def test(foo: float, bar: float) -> None:
-        list_of_things = [1, 2, 3, None, 4, True, False, "Hello World"]
-        dict_of_things = {
-            "version": "1.1",
-            "method": "confirmFruitPurchase",
-            "params": [["apple", "orange", "mangoes", "pomelo"], 1.123],
-            "id": "194521489",
-        }
-        print(render_scope(locals(), title="[i]locals", sort_keys=False))
-
-    test(20.3423, 3.1427)
-    print()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydub/pyaudioop.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydub/pyaudioop.py
deleted file mode 100644
index 9b1e2fbf172884197ca1135a5e0a99cf5823c6f5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydub/pyaudioop.py
+++ /dev/null
@@ -1,553 +0,0 @@
-try:
-    from __builtin__ import max as builtin_max
-    from __builtin__ import min as builtin_min
-except ImportError:
-    from builtins import max as builtin_max
-    from builtins import min as builtin_min
-import math
-import struct
-try:
-    from fractions import gcd
-except ImportError:  # Python 3.9+
-    from math import gcd
-from ctypes import create_string_buffer
-
-
-class error(Exception):
-    pass
-
-
-def _check_size(size):
-    if size != 1 and size != 2 and size != 4:
-        raise error("Size should be 1, 2 or 4")
-
-
-def _check_params(length, size):
-    _check_size(size)
-    if length % size != 0:
-        raise error("not a whole number of frames")
-
-
-def _sample_count(cp, size):
-    return len(cp) / size
-
-
-def _get_samples(cp, size, signed=True):
-    for i in range(_sample_count(cp, size)):
-        yield _get_sample(cp, size, i, signed)
-
-
-def _struct_format(size, signed):
-    if size == 1:
-        return "b" if signed else "B"
-    elif size == 2:
-        return "h" if signed else "H"
-    elif size == 4:
-        return "i" if signed else "I"
-
-
-def _get_sample(cp, size, i, signed=True):
-    fmt = _struct_format(size, signed)
-    start = i * size
-    end = start + size
-    return struct.unpack_from(fmt, buffer(cp)[start:end])[0]
-
-
-def _put_sample(cp, size, i, val, signed=True):
-    fmt = _struct_format(size, signed)
-    struct.pack_into(fmt, cp, i * size, val)
-
-
-def _get_maxval(size, signed=True):
-    if signed and size == 1:
-        return 0x7f
-    elif size == 1:
-        return 0xff
-    elif signed and size == 2:
-        return 0x7fff
-    elif size == 2:
-        return 0xffff
-    elif signed and size == 4:
-        return 0x7fffffff
-    elif size == 4:
-        return 0xffffffff
-
-
-def _get_minval(size, signed=True):
-    if not signed:
-        return 0
-    elif size == 1:
-        return -0x80
-    elif size == 2:
-        return -0x8000
-    elif size == 4:
-        return -0x80000000
-
-
-def _get_clipfn(size, signed=True):
-    maxval = _get_maxval(size, signed)
-    minval = _get_minval(size, signed)
-    return lambda val: builtin_max(min(val, maxval), minval)
-
-
-def _overflow(val, size, signed=True):
-    minval = _get_minval(size, signed)
-    maxval = _get_maxval(size, signed)
-    if minval <= val <= maxval:
-        return val
-
-    bits = size * 8
-    if signed:
-        offset = 2**(bits-1)
-        return ((val + offset) % (2**bits)) - offset
-    else:
-        return val % (2**bits)
-
-
-def getsample(cp, size, i):
-    _check_params(len(cp), size)
-    if not (0 <= i < len(cp) / size):
-        raise error("Index out of range")
-    return _get_sample(cp, size, i)
-
-
-def max(cp, size):
-    _check_params(len(cp), size)
-
-    if len(cp) == 0:
-        return 0
-
-    return builtin_max(abs(sample) for sample in _get_samples(cp, size))
-
-
-def minmax(cp, size):
-    _check_params(len(cp), size)
-
-    max_sample, min_sample = 0, 0
-    for sample in _get_samples(cp, size):
-        max_sample = builtin_max(sample, max_sample)
-        min_sample = builtin_min(sample, min_sample)
-
-    return min_sample, max_sample
-
-
-def avg(cp, size):
-    _check_params(len(cp), size)
-    sample_count = _sample_count(cp, size)
-    if sample_count == 0:
-        return 0
-    return sum(_get_samples(cp, size)) / sample_count
-
-
-def rms(cp, size):
-    _check_params(len(cp), size)
-
-    sample_count = _sample_count(cp, size)
-    if sample_count == 0:
-        return 0
-
-    sum_squares = sum(sample**2 for sample in _get_samples(cp, size))
-    return int(math.sqrt(sum_squares / sample_count))
-
-
-def _sum2(cp1, cp2, length):
-    size = 2
-    total = 0
-    for i in range(length):
-        total += getsample(cp1, size, i) * getsample(cp2, size, i)
-    return total
-
-
-def findfit(cp1, cp2):
-    size = 2
-
-    if len(cp1) % 2 != 0 or len(cp2) % 2 != 0:
-        raise error("Strings should be even-sized")
-
-    if len(cp1) < len(cp2):
-        raise error("First sample should be longer")
-
-    len1 = _sample_count(cp1, size)
-    len2 = _sample_count(cp2, size)
-
-    sum_ri_2 = _sum2(cp2, cp2, len2)
-    sum_aij_2 = _sum2(cp1, cp1, len2)
-    sum_aij_ri = _sum2(cp1, cp2, len2)
-
-    result = (sum_ri_2 * sum_aij_2 - sum_aij_ri * sum_aij_ri) / sum_aij_2
-
-    best_result = result
-    best_i = 0
-
-    for i in range(1, len1 - len2 + 1):
-        aj_m1 = _get_sample(cp1, size, i - 1)
-        aj_lm1 = _get_sample(cp1, size, i + len2 - 1)
-
-        sum_aij_2 += aj_lm1**2 - aj_m1**2
-        sum_aij_ri = _sum2(buffer(cp1)[i*size:], cp2, len2)
-
-        result = (sum_ri_2 * sum_aij_2 - sum_aij_ri * sum_aij_ri) / sum_aij_2
-
-        if result < best_result:
-            best_result = result
-            best_i = i
-
-    factor = _sum2(buffer(cp1)[best_i*size:], cp2, len2) / sum_ri_2
-
-    return best_i, factor
-
-
-def findfactor(cp1, cp2):
-    size = 2
-
-    if len(cp1) % 2 != 0:
-        raise error("Strings should be even-sized")
-
-    if len(cp1) != len(cp2):
-        raise error("Samples should be same size")
-
-    sample_count = _sample_count(cp1, size)
-
-    sum_ri_2 = _sum2(cp2, cp2, sample_count)
-    sum_aij_ri = _sum2(cp1, cp2, sample_count)
-
-    return sum_aij_ri / sum_ri_2
-
-
-def findmax(cp, len2):
-    size = 2
-    sample_count = _sample_count(cp, size)
-
-    if len(cp) % 2 != 0:
-        raise error("Strings should be even-sized")
-
-    if len2 < 0 or sample_count < len2:
-        raise error("Input sample should be longer")
-
-    if sample_count == 0:
-        return 0
-
-    result = _sum2(cp, cp, len2)
-    best_result = result
-    best_i = 0
-
-    for i in range(1, sample_count - len2 + 1):
-        sample_leaving_window = getsample(cp, size, i - 1)
-        sample_entering_window = getsample(cp, size, i + len2 - 1)
-
-        result -= sample_leaving_window**2
-        result += sample_entering_window**2
-
-        if result > best_result:
-            best_result = result
-            best_i = i
-
-    return best_i
-
-
-def avgpp(cp, size):
-    _check_params(len(cp), size)
-    sample_count = _sample_count(cp, size)
-
-    prevextremevalid = False
-    prevextreme = None
-    avg = 0
-    nextreme = 0
-
-    prevval = getsample(cp, size, 0)
-    val = getsample(cp, size, 1)
-
-    prevdiff = val - prevval
-
-    for i in range(1, sample_count):
-        val = getsample(cp, size, i)
-        diff = val - prevval
-
-        if diff * prevdiff < 0:
-            if prevextremevalid:
-                avg += abs(prevval - prevextreme)
-                nextreme += 1
-
-            prevextremevalid = True
-            prevextreme = prevval
-
-        prevval = val
-        if diff != 0:
-            prevdiff = diff
-
-    if nextreme == 0:
-        return 0
-
-    return avg / nextreme
-
-
-def maxpp(cp, size):
-    _check_params(len(cp), size)
-    sample_count = _sample_count(cp, size)
-
-    prevextremevalid = False
-    prevextreme = None
-    max = 0
-
-    prevval = getsample(cp, size, 0)
-    val = getsample(cp, size, 1)
-
-    prevdiff = val - prevval
-
-    for i in range(1, sample_count):
-        val = getsample(cp, size, i)
-        diff = val - prevval
-
-        if diff * prevdiff < 0:
-            if prevextremevalid:
-                extremediff = abs(prevval - prevextreme)
-                if extremediff > max:
-                    max = extremediff
-            prevextremevalid = True
-            prevextreme = prevval
-
-        prevval = val
-        if diff != 0:
-            prevdiff = diff
-
-    return max
-
-
-def cross(cp, size):
-    _check_params(len(cp), size)
-
-    crossings = 0
-    last_sample = 0
-    for sample in _get_samples(cp, size):
-        if sample <= 0 < last_sample or sample >= 0 > last_sample:
-            crossings += 1
-        last_sample = sample
-
-    return crossings
-
-
-def mul(cp, size, factor):
-    _check_params(len(cp), size)
-    clip = _get_clipfn(size)
-
-    result = create_string_buffer(len(cp))
-
-    for i, sample in enumerate(_get_samples(cp, size)):
-        sample = clip(int(sample * factor))
-        _put_sample(result, size, i, sample)
-
-    return result.raw
-
-
-def tomono(cp, size, fac1, fac2):
-    _check_params(len(cp), size)
-    clip = _get_clipfn(size)
-
-    sample_count = _sample_count(cp, size)
-
-    result = create_string_buffer(len(cp) / 2)
-
-    for i in range(0, sample_count, 2):
-        l_sample = getsample(cp, size, i)
-        r_sample = getsample(cp, size, i + 1)
-
-        sample = (l_sample * fac1) + (r_sample * fac2)
-        sample = clip(sample)
-
-        _put_sample(result, size, i / 2, sample)
-
-    return result.raw
-
-
-def tostereo(cp, size, fac1, fac2):
-    _check_params(len(cp), size)
-
-    sample_count = _sample_count(cp, size)
-
-    result = create_string_buffer(len(cp) * 2)
-    clip = _get_clipfn(size)
-
-    for i in range(sample_count):
-        sample = _get_sample(cp, size, i)
-
-        l_sample = clip(sample * fac1)
-        r_sample = clip(sample * fac2)
-
-        _put_sample(result, size, i * 2, l_sample)
-        _put_sample(result, size, i * 2 + 1, r_sample)
-
-    return result.raw
-
-
-def add(cp1, cp2, size):
-    _check_params(len(cp1), size)
-
-    if len(cp1) != len(cp2):
-        raise error("Lengths should be the same")
-
-    clip = _get_clipfn(size)
-    sample_count = _sample_count(cp1, size)
-    result = create_string_buffer(len(cp1))
-
-    for i in range(sample_count):
-        sample1 = getsample(cp1, size, i)
-        sample2 = getsample(cp2, size, i)
-
-        sample = clip(sample1 + sample2)
-
-        _put_sample(result, size, i, sample)
-
-    return result.raw
-
-
-def bias(cp, size, bias):
-    _check_params(len(cp), size)
-
-    result = create_string_buffer(len(cp))
-
-    for i, sample in enumerate(_get_samples(cp, size)):
-        sample = _overflow(sample + bias, size)
-        _put_sample(result, size, i, sample)
-
-    return result.raw
-
-
-def reverse(cp, size):
-    _check_params(len(cp), size)
-    sample_count = _sample_count(cp, size)
-
-    result = create_string_buffer(len(cp))
-    for i, sample in enumerate(_get_samples(cp, size)):
-        _put_sample(result, size, sample_count - i - 1, sample)
-
-    return result.raw
-
-
-def lin2lin(cp, size, size2):
-    _check_params(len(cp), size)
-    _check_size(size2)
-
-    if size == size2:
-        return cp
-
-    new_len = (len(cp) / size) * size2
-
-    result = create_string_buffer(new_len)
-
-    for i in range(_sample_count(cp, size)):
-        sample = _get_sample(cp, size, i)
-        if size < size2:
-            sample = sample << (4 * size2 / size)
-        elif size > size2:
-            sample = sample >> (4 * size / size2)
-
-        sample = _overflow(sample, size2)
-
-        _put_sample(result, size2, i, sample)
-
-    return result.raw
-
-
-def ratecv(cp, size, nchannels, inrate, outrate, state, weightA=1, weightB=0):
-    _check_params(len(cp), size)
-    if nchannels < 1:
-        raise error("# of channels should be >= 1")
-
-    bytes_per_frame = size * nchannels
-    frame_count = len(cp) / bytes_per_frame
-
-    if bytes_per_frame / nchannels != size:
-        raise OverflowError("width * nchannels too big for a C int")
-
-    if weightA < 1 or weightB < 0:
-        raise error("weightA should be >= 1, weightB should be >= 0")
-
-    if len(cp) % bytes_per_frame != 0:
-        raise error("not a whole number of frames")
-
-    if inrate <= 0 or outrate <= 0:
-        raise error("sampling rate not > 0")
-
-    d = gcd(inrate, outrate)
-    inrate /= d
-    outrate /= d
-
-    prev_i = [0] * nchannels
-    cur_i = [0] * nchannels
-
-    if state is None:
-        d = -outrate
-    else:
-        d, samps = state
-
-        if len(samps) != nchannels:
-            raise error("illegal state argument")
-
-        prev_i, cur_i = zip(*samps)
-        prev_i, cur_i = list(prev_i), list(cur_i)
-
-    q = frame_count / inrate
-    ceiling = (q + 1) * outrate
-    nbytes = ceiling * bytes_per_frame
-
-    result = create_string_buffer(nbytes)
-
-    samples = _get_samples(cp, size)
-    out_i = 0
-    while True:
-        while d < 0:
-            if frame_count == 0:
-                samps = zip(prev_i, cur_i)
-                retval = result.raw
-
-                # slice off extra bytes
-                trim_index = (out_i * bytes_per_frame) - len(retval)
-                retval = buffer(retval)[:trim_index]
-
-                return (retval, (d, tuple(samps)))
-
-            for chan in range(nchannels):
-                prev_i[chan] = cur_i[chan]
-                cur_i[chan] = samples.next()
-
-                cur_i[chan] = (
-                    (weightA * cur_i[chan] + weightB * prev_i[chan])
-                    / (weightA + weightB)
-                )
-
-            frame_count -= 1
-            d += outrate
-
-        while d >= 0:
-            for chan in range(nchannels):
-                cur_o = (
-                    (prev_i[chan] * d + cur_i[chan] * (outrate - d))
-                    / outrate
-                )
-                _put_sample(result, size, out_i, _overflow(cur_o, size))
-                out_i += 1
-            d -= inrate
-
-
-def lin2ulaw(cp, size):
-    raise NotImplementedError()
-
-
-def ulaw2lin(cp, size):
-    raise NotImplementedError()
-
-
-def lin2alaw(cp, size):
-    raise NotImplementedError()
-
-
-def alaw2lin(cp, size):
-    raise NotImplementedError()
-
-
-def lin2adpcm(cp, size, state):
-    raise NotImplementedError()
-
-
-def adpcm2lin(cp, size, state):
-    raise NotImplementedError()
diff --git a/spaces/qingxu98/gpt-academic/request_llm/bridge_stackclaude.py b/spaces/qingxu98/gpt-academic/request_llm/bridge_stackclaude.py
deleted file mode 100644
index 3f2ee67428f9c8323eca0f7006ad4d4f767a6b58..0000000000000000000000000000000000000000
--- a/spaces/qingxu98/gpt-academic/request_llm/bridge_stackclaude.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from .bridge_newbingfree import preprocess_newbing_out, preprocess_newbing_out_simple
-from multiprocessing import Process, Pipe
-from toolbox import update_ui, get_conf, trimmed_format_exc
-import threading
-import importlib
-import logging
-import time
-from toolbox import get_conf
-import asyncio
-load_message = "正在加载Claude组件，请稍候..."
-
-try:
-    """
-    ========================================================================
-    第一部分：Slack API Client
-    https://github.com/yokonsan/claude-in-slack-api
-    ========================================================================
-    """
-
-    from slack_sdk.errors import SlackApiError
-    from slack_sdk.web.async_client import AsyncWebClient
-
-    class SlackClient(AsyncWebClient):
-        """SlackClient类用于与Slack API进行交互，实现消息发送、接收等功能。
-
-            属性：
-            - CHANNEL_ID：str类型，表示频道ID。
-
-            方法：
-            - open_channel()：异步方法。通过调用conversations_open方法打开一个频道，并将返回的频道ID保存在属性CHANNEL_ID中。
-            - chat(text: str)：异步方法。向已打开的频道发送一条文本消息。
-            - get_slack_messages()：异步方法。获取已打开频道的最新消息并返回消息列表，目前不支持历史消息查询。
-            - get_reply()：异步方法。循环监听已打开频道的消息，如果收到"Typing…_"结尾的消息说明Claude还在继续输出，否则结束循环。
-
-        """
-        CHANNEL_ID = None
-
-        async def open_channel(self):
-            response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID')[0])
-            self.CHANNEL_ID = response["channel"]["id"]
-
-        async def chat(self, text):
-            if not self.CHANNEL_ID:
-                raise Exception("Channel not found.")
-
-            resp = await self.chat_postMessage(channel=self.CHANNEL_ID, text=text)
-            self.LAST_TS = resp["ts"]
-
-        async def get_slack_messages(self):
-            try:
-                # TODO：暂时不支持历史消息，因为在同一个频道里存在多人使用时历史消息渗透问题
-                resp = await self.conversations_history(channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1)
-                msg = [msg for msg in resp["messages"]
-                    if msg.get("user") == get_conf('SLACK_CLAUDE_BOT_ID')[0]]
-                return msg
-            except (SlackApiError, KeyError) as e:
-                raise RuntimeError(f"获取Slack消息失败。")
-        
-        async def get_reply(self):
-            while True:
-                slack_msgs = await self.get_slack_messages()
-                if len(slack_msgs) == 0:
-                    await asyncio.sleep(0.5)
-                    continue
-                
-                msg = slack_msgs[-1]
-                if msg["text"].endswith("Typing…_"):
-                    yield False, msg["text"]
-                else:
-                    yield True, msg["text"]
-                    break
-except:
-    pass
-
-"""
-========================================================================
-第二部分：子进程Worker（调用主体）
-========================================================================
-"""
-
-
-class ClaudeHandle(Process):
-    def __init__(self):
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self.claude_model = None
-        self.info = ""
-        self.success = True
-        self.local_history = []
-        self.check_dependency()
-        if self.success: 
-            self.start()
-            self.threadLock = threading.Lock()
-
-    def check_dependency(self):
-        try:
-            self.success = False
-            import slack_sdk
-            self.info = "依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。"
-            self.success = True
-        except:
-            self.info = "缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。"
-            self.success = False
-
-    def ready(self):
-        return self.claude_model is not None    
-    
-    async def async_run(self):
-        await self.claude_model.open_channel()
-        while True:
-            # 等待
-            kwargs = self.child.recv()
-            question = kwargs['query']
-            history = kwargs['history']
-
-            # 开始问问题
-            prompt = ""
-
-            # 问题
-            prompt += question
-            print('question:', prompt)
-
-            # 提交
-            await self.claude_model.chat(prompt)
-            
-            # 获取回复
-            async for final, response in self.claude_model.get_reply():                
-                if not final:
-                    print(response)
-                    self.child.send(str(response))
-                else:
-                    # 防止丢失最后一条消息
-                    slack_msgs = await self.claude_model.get_slack_messages()
-                    last_msg = slack_msgs[-1]["text"] if slack_msgs and len(slack_msgs) > 0 else ""
-                    if last_msg:
-                        self.child.send(last_msg)
-                    print('-------- receive final ---------')
-                    self.child.send('[Finish]')
-                    
-    def run(self):
-        """
-        这个函数运行在子进程
-        """
-        # 第一次运行，加载参数
-        self.success = False
-        self.local_history = []
-        if (self.claude_model is None) or (not self.success):
-            # 代理设置
-            proxies, = get_conf('proxies')
-            if proxies is None:
-                self.proxies_https = None
-            else:
-                self.proxies_https = proxies['https']
-
-            try:
-                SLACK_CLAUDE_USER_TOKEN, = get_conf('SLACK_CLAUDE_USER_TOKEN')
-                self.claude_model = SlackClient(token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https)
-                print('Claude组件初始化成功。')
-            except:
-                self.success = False
-                tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                self.child.send(f'[Local Message] 不能加载Claude组件。{tb_str}')
-                self.child.send('[Fail]')
-                self.child.send('[Finish]')
-                raise RuntimeError(f"不能加载Claude组件。")
-
-        self.success = True
-        try:
-            # 进入任务等待状态
-            asyncio.run(self.async_run())
-        except Exception:
-            tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-            self.child.send(f'[Local Message] Claude失败 {tb_str}.')
-            self.child.send('[Fail]')
-            self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        """
-        这个函数运行在主进程
-        """
-        self.threadLock.acquire()
-        self.parent.send(kwargs)    # 发送请求到子进程
-        while True:
-            res = self.parent.recv()    # 等待Claude回复的片段
-            if res == '[Finish]':
-                break       # 结束
-            elif res == '[Fail]':
-                self.success = False
-                break
-            else:
-                yield res   # Claude回复的片段
-        self.threadLock.release()
-
-
-"""
-========================================================================
-第三部分：主进程统一调用函数接口
-========================================================================
-"""
-global claude_handle
-claude_handle = None
-
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
-    """
-        多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    global claude_handle
-    if (claude_handle is None) or (not claude_handle.success):
-        claude_handle = ClaudeHandle()
-        observe_window[0] = load_message + "\n\n" + claude_handle.info
-        if not claude_handle.success:
-            error = claude_handle.info
-            claude_handle = None
-            raise RuntimeError(error)
-
-    # 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]])
-
-    watch_dog_patience = 5  # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    observe_window[0] = "[Local Message]: 等待Claude响应中 ..."
-    for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        observe_window[0] = preprocess_newbing_out_simple(response)
-        if len(observe_window) >= 2:
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return preprocess_newbing_out_simple(response)
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
-    """
-        单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, "[Local Message]: 等待Claude响应中 ..."))
-
-    global claude_handle
-    if (claude_handle is None) or (not claude_handle.success):
-        claude_handle = ClaudeHandle()
-        chatbot[-1] = (inputs, load_message + "\n\n" + claude_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not claude_handle.success:
-            claude_handle = None
-            return
-
-    if additional_fn is not None:
-        from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]])
-
-    chatbot[-1] = (inputs, "[Local Message]: 等待Claude响应中 ...")
-    response = "[Local Message]: 等待Claude响应中 ..."
-    yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt):
-        chatbot[-1] = (inputs, preprocess_newbing_out(response))
-        yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message]: 等待Claude响应中 ...":
-        response = "[Local Message]: Claude响应异常，请刷新界面重试 ..."
-    history.extend([inputs, response])
-    logging.info(f'[raw_input] {inputs}')
-    logging.info(f'[response] {response}')
-    yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")
diff --git "a/spaces/quidiaMuxgu/Expedit-SAM/F1 Challenge Delux 2009 Download Do Jogo\302\240gratis.md" "b/spaces/quidiaMuxgu/Expedit-SAM/F1 Challenge Delux 2009 Download Do Jogo\302\240gratis.md"
deleted file mode 100644
index be1c20164db0b72a18458437332110bd6099a24f..0000000000000000000000000000000000000000
--- "a/spaces/quidiaMuxgu/Expedit-SAM/F1 Challenge Delux 2009 Download Do Jogo\302\240gratis.md"	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>f1 challenge's engine is built on the long-established f1 challenge 98 project, and the game features many of the same attributes. for example, each car has a unique engine, which can be found in the formation menu. the engine can be upgraded to increase top speed, acceleration, and braking, among other things. there are also upgrades available for other parts of the car, such as the chassis, wheels, and bodywork. at the back of each car is a special "invisible" upgrade, which can be hidden in a block, just like in real life. these upgrades can increase the strength of the car, for example, making the car's suspension stronger. there are four main races, which the player can race in any order. there is also a special championship mode called world championship, where the player can race through seasons, and earn championship points.</p>
-<h2>F1 Challenge Delux 2009 download do jogo gratis</h2><br /><p><b><b>Download File</b> &#127379; <a href="https://geags.com/2uCrdf">https://geags.com/2uCrdf</a></b></p><br /><br />
-<p>> f1 challenge deluxe 2009 download do jogogratis <p>the most exciting thing about this game is that it can teach you a lot about your child and the other people around you. it really does focus on a child's need for social interaction, planning and problem solving, and of course imagination. the first section of the game is called f1 challenge. this is a simple challenge in which the child has to place the pigs in the houses. the child is asked to find out where the wolf is and how to make the three little piggies stay safely inside their houses.</p>
-<p>f1 challenge is a fun and exciting game that will help you understand your little one's personality. as a parent, you will know what is good or bad about your child. you can learn how the wolf thinks about the piggy's personality and the wolf's personality. f1 challenge is the first of the f1 series and is a perfect introduction to the f1 series.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Leawo Itransfer Keygen Free LINK Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Leawo Itransfer Keygen Free LINK Download.md
deleted file mode 100644
index eb8b085c342b371ce008f4329402940ebb6ddb98..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Leawo Itransfer Keygen Free LINK Download.md	
+++ /dev/null
@@ -1,50 +0,0 @@
-
-<h1>How to Download Leawo iTransfer Keygen for Free</h1>
-<p>If you are looking for a way to transfer files between your iOS devices and your computer without iTunes, you might be interested in Leawo iTransfer. This is a powerful and easy-to-use software that can help you transfer 12 kinds of files, such as photos, music, videos, contacts, SMS, and more. However, Leawo iTransfer is not a free software. You need to pay $39.95 to get the full version. But what if you want to use it for free? Is there a way to download Leawo iTransfer keygen for free?</p>
-<p>A keygen is a program that can generate a serial number or a license key for a software. Some people may try to find Leawo iTransfer keygen online and use it to activate the software without paying. However, this is not a safe or legal way to use Leawo iTransfer. Here are some reasons why you should avoid downloading Leawo iTransfer keygen for free:</p>
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diff --git a/spaces/qwerrsc/vits-uma-genshin-honkai/models.py b/spaces/qwerrsc/vits-uma-genshin-honkai/models.py
deleted file mode 100644
index 8353b867f441de7e4d05aef980e672899c3a8889..0000000000000000000000000000000000000000
--- a/spaces/qwerrsc/vits-uma-genshin-honkai/models.py
+++ /dev/null
@@ -1,533 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Autocad 2018 (64bit) (Product key and Xforce keygen) download pc Tips and tricks to optimize your workflow and performance.md b/spaces/raedeXanto/academic-chatgpt-beta/Autocad 2018 (64bit) (Product key and Xforce keygen) download pc Tips and tricks to optimize your workflow and performance.md
deleted file mode 100644
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--- a/spaces/raedeXanto/academic-chatgpt-beta/Autocad 2018 (64bit) (Product key and Xforce keygen) download pc Tips and tricks to optimize your workflow and performance.md	
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-<br />
-<h1>Autocad 2018 (64bit) (Product key and Xforce keygen) download pc</h1>
- <h2>Introduction</h2>
- <p>If you are looking for a powerful and versatile software for designing, drafting, and modeling, you might want to check out Autocad 2018. Autocad is one of the most popular and widely used computer-aided design (CAD) applications in the world. It allows you to create and edit 2D and 3D drawings, models, and animations with ease and precision. Whether you are an architect, engineer, designer, or hobbyist, you can use Autocad to bring your ideas to life.</p>
-<h2>Autocad 2018 (64bit) (Product key and Xforce keygen) download pc</h2><br /><p><b><b>Download Zip</b> &bull;&bull;&bull; <a href="https://tinourl.com/2uL2J3">https://tinourl.com/2uL2J3</a></b></p><br /><br />
- <p>However, to use Autocad 2018, you need a valid product key and a Xforce keygen. A product key is a unique code that identifies your software license and enables you to install and activate it. A Xforce keygen is a tool that generates an activation code for your software based on your product key. Without these two components, you will not be able to use Autocad 2018 fully.</p>
- <p>In this article, we will show you how to download Autocad 2018 (64bit) for pc, what are its features, and how to install it using the product key and the Xforce keygen. Read on to find out more.</p>
- <h2>Features of Autocad 2018</h2>
- <p>Autocad 2018 is the latest version of Autocad that was released in March 2017. It comes with many new and improved features that make it more user-friendly, efficient, and productive. Some of these features are:</p>
- <h3>Enhanced user interface</h3>
- <p>The user interface of Autocad 2018 has been redesigned to provide a more intuitive and modern experience. You can customize the ribbon tabs, panels, and toolbars according to your preferences and workflows. You can also access frequently used tools and commands from the Quick Access Toolbar or the Application Menu. The Status Bar has been simplified to show only the icons that are relevant to your current task. The Command Line has been enhanced to support auto-complete, synonyms, and suggestions.</p>
- <h3>Improved 2D and 3D graphics</h3>
- <p>The graphics performance of Autocad 2018 has been improved to deliver smoother and faster rendering of 2D and 3D objects. You can also work with high-resolution monitors and displays without compromising the quality or clarity of your drawings. The new High Resolution (4K) Monitor Support feature allows you to adjust the size and scale of the user interface elements to suit your screen resolution. The new Smooth Line Display option enables you to display curved objects with less jagged edges.</p>
- <h3>New tools and commands</h3>
- <p>Autocad 2018 introduces several new tools and commands that help you create and edit your drawings more efficiently and accurately. Some of these are:</p>
- <ul>
-<li>The PDFIMPORT command allows you to import geometry, text, raster images, and TrueType fonts from PDF files into your drawings.</li>
-<li>The DWGCOMPARE command allows you to compare two versions of a drawing and highlight the differences between them.</li>
-<li>The TEXTEDIT command allows you to edit single-line or multiline text objects in place without opening a separate dialog box.</li>
-<li>The SELECTSIMILAR command allows you to select objects that have similar properties such as layer, color, linetype, etc.</li>
-<li>The COMBINE command allows you to combine two or more solid or surface objects into a single composite object.</li>
-</ul>
- <h3>Cloud connectivity and collaboration</h3>
- <p>Autocad 2018 also enables you to connect and collaborate with other users online using cloud-based services. You can save your drawings to Autodesk A360, a cloud storage platform that lets you access them from any device or location. You can also share your drawings with other users via email or social media platforms. You can also use Autodesk Design Review, a free application that lets you view, markup, measure, and print your drawings without having Autocad installed.</p>
- <h2>How to install Autocad 2018 (64bit) on pc?</h2>
- <p>To install Autocad 2018 (64bit) on pc, you need to follow these steps:</p>
-<p></p>
- <h3>Step 1: Download the setup file from the link provided</h3>
- <p>The first step is to download the setup file for Autocad 2018 (64bit) from the link provided below. The file size is about 4 GB, so make sure you have enough space on your hard drive and a stable internet connection.</p>
- <p><a href="https://getintopc.com/softwares/autodesk-autocad-2018-x86-x64-free-download-2769539/">Download Autocad 2018 (64bit)</a></p>
- <h3>Step 2: Extract the file using WinRAR or 7-Zip</h3>
- <p>The second step is to extract the file using WinRAR or 7-Zip, which are free software that can unzip compressed files. You can download them from the links below:</p>
- <p><a href="https://www.win-rar.com/download.html">Download WinRAR</a></p>
-<p><a href="https://www.7-zip.org/download.html">Download 7-Zip</a></p>
- <p>After downloading one of these software, right-click on the setup file and choose Extract Here or Extract To from the menu. This will create a folder with the same name as the setup file.</p>
- <h3>Step 3: Run the setup.exe file as administrator</h3>
- <p>The third step is to run the setup.exe file as administrator. To do this, right-click on the file and choose Run As Administrator from the menu. This will launch the installation wizard for Autocad 2018.</p>
- <h3>Step 4: Enter the product key and click Next</h3>
- <p>The fourth step is to enter the product key for Autocad 2018. The product key is a code that consists of five sets of four alphanumeric characters separated by dashes. You can find it in the readme.txt file inside the folder that you extracted in step 2. Alternatively, you can use one of these product keys:</p>
- <table>
-<tr><td>001J1</td><td>AutoCAD LT</td></tr>
-<tr><td>057J1</td><td>AutoCAD LT Civil Suite</td></tr>
-<tr><td>128J1</td><td>Autodesk Fusion</td></tr>
-<tr><td>129J1</td><td>AutoCAD Map</td></tr>
-<tr><td>140J1</td><td>AutoCAD OEM</td></tr>
-<tr><td>185J1</td><td>AutoCAD Architecture</td></tr>
-<tr><td>206J1</td><td>AutoCAD Mechanical</td></tr>
-<tr><td>208J1</td><td>AutoCAD Electrical</td></tr>
-<tr><td>213J1</td><td>AEC Collection Standard</td></tr>
-<tr><td>225J1</td><td>AEC Collection Premium</td></tr>
-<tr><td>237J1</td><td>Civil Infrastructure Design Suite Premium</td></tr>
-<tr><td>241J1</td><td>Civil Infrastructure Design Suite Ultimate</td></tr>
-/td><td>AutoCAD Plant 3D</td></tr>
-<tr><td>426J1</td><td>AutoCAD Structural Detailing</td></tr>
-<tr><td>448J1</td><td>AutoCAD P&ID</td></tr>
-<tr><td>495J1</td><td>Autodesk 3ds Max</td></tr>
-<tr><td>507J1</td><td>Autodesk Navisworks Manage</td></tr>
-<tr><td>529J1</td><td>Autodesk Inventor LT</td></tr>
-<tr><td>535J1</td><td>Autodesk Maya LT</td></tr>
-<tr><td>545J1</td><td>Autodesk AutoCAD LT with CALS Tools</td></tr>
-<tr><td>547J1</td><td>Autodesk Robot Structural Analysis Professional</td></tr>
-<tr><td>569J1</td><td>Autodesk Vault Professional</td></tr>
-<tr><td>596J1</td><td>Autodesk Revit LT</td></tr>
-<tr><td>636J1</td><td>AEC Collection Ultimate</td></tr>
-<tr><td>657J1</td><td>AEC Collection Enterprise</td></tr>
-<tr><td>666J1</td><td>Inventor Professional Suite</td></tr>
-<tr><td>667J1</td><td>Inventor Engineer-to-Order Series Distribution Fee</td></tr>
-<tr><td>668J1</ <td>Inventor Engineer-to-Order Series</td></tr>
-<tr><td>669J1</ <td>Inventor Engineer-to-Order Server</ <td></tr>
-<tr><
-<td>710J1 <td>A utodesk Alias Design <td></ tr>
-<tr>
-<td>712J1 <td>A utodesk Alias Surface <td></ tr>
-<tr>
-<td>736J1 <td>A utodesk Alias SpeedForm <td></ tr>
-<tr>
-<td>752J1 <td>A utodesk Alias AutoStudio <td></ tr>
-<tr>
-<td>757J1 <td>A utodesk Maya Entertainment Creation Suite Standard <td></ tr>
-<tr>
-<td>781J1 <td>A utodesk Building Design Suite Premium <td></ tr>
-<tr>
-<td>782J1 <td>A utodesk Building Design Suite Ultimate <td></ tr>
-<tr>
-<td>784J1 <td>A utodesk PowerMill Standard 2018 (x64) <td></ tr>
-<tr>
-<td>785J1 <td>A utodesk PowerMill Premium 2018 (x64) <td></ tr>
-<tr>
-<td>786J1 <td>A utodesk PowerMill Ultimate 2018 (x64) <td></ tr>
- <table>
- <p>Select the product key that matches your software and click Next.</p>
- <h3>Step 5: Use the Xforce keygen to generate the activation code and activate the software</h3>
- <p>The fifth and final step is to use the Xforce keygen to generate the activation code and activate the software. To do this, follow these steps:</p>
- <ol>
-<li>Open the folder that you extracted in step 2 and find the file named xf-adsk2018_x64.exe. This is the Xforce keygen.</li>
-<li>Right-click on the file and choose Run As Administrator from the menu. This will open the Xforce keygen window.</li>
-<li>In the Xforce keygen window, select Autocad 2018 from the Product drop-down list.</li>
-<li>In the Request field, copy and paste the request code that you got from the installation wizard in step 4.</li>
-<li>Click on Generate to generate the activation code.</li>
-<li>In the Activation field, copy and paste the activation code that you got from the Xforce keygen.</li>
-<li>Click on Next to complete the activation process.</li>
- <ol>
- <p>Congratulations! You have successfully installed and activated Autocad 2018 (64bit) on your pc. You can now enjoy using this powerful and versatile software for your design and drafting needs.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download Autocad 2018 (64bit) for pc, what are its features, and how to install it using the product key and the Xforce keygen. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <p>Here are some of the most common questions that users have about Autocad 2018:</p>
- <h3>Q: What are the system requirements for Autocad 2018?</h3>
- <p>A: The minimum system requirements for Autocad 2018 are:</p>
- <ul>
-<li>Operating System: Windows 7 SP1, Windows 8.1, or Windows 10 (64-bit only)</li>
-<li>CPU: Intel Core i5 or equivalent with SSE2 technology (32-bit) or Intel Core i7 or equivalent with SSE2 technology (64-bit)</li>
-<li>RAM: 4 GB (32-bit) or 8 GB (64-bit)</li>
-<li>Disk Space: 6 GB for installation and additional space for working files</li>
-ocad LT 2018 has fewer online features and services.</li>
-<li>Autocad 2018 has more integration options with other Autodesk products and third-party applications, such as Inventor, Revit, 3ds Max, etc., while Autocad LT 2018 has less integration options.</li>
-<li>Autocad 2018 has a higher price than Autocad LT 2018.</li>
-</ul>
- <p>Therefore, if you need more functionality and flexibility for your design and drafting needs, you might want to choose Autocad 2018 over Autocad LT 2018.</p>
- <h3>Q: How can I update Autocad 2018 to the latest version?</h3>
- <p>A: To update Autocad 2018 to the latest version, you need to download and install the latest service pack or update from the Autodesk website. You can find the latest service pack or update for Autocad 2018 here:</p>
- <p><a href="https://knowledge.autodesk.com/support/autocad/downloads/caas/downloads/content/autodesk-autocad-2018-4-update.html">Download Autocad 2018.4 Update</a></p>
- <p>Before installing the update, make sure you have closed all running instances of Autocad 2018 and any other Autodesk products. After installing the update, you can check the version number of your software by typing ABOUT in the Command Line and pressing Enter. The version number should be 22.0.150.0 or higher.</p>
- <h3>Q: How can I uninstall Autocad 2018 from my pc?</h3>
- <p>A: To uninstall Autocad 2018 from your pc, you need to follow these steps:</p>
- <ol>
-<li>Open the Control Panel and select Programs and Features.</li>
-<li>Find Autocad 2018 in the list of installed programs and click on Uninstall/Change.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>Restart your pc to remove any remaining files or registry entries.</li>
-</ol>
- <p>If you encounter any problems or errors during the uninstallation process, you can use the Autodesk Uninstall Tool to remove Autocad 2018 completely from your pc. You can download the tool from here:</p>
- <p><a href="https://knowledge.autodesk.com/customer-service/download-install/remove-products/clean-uninstall">Download Autodesk Uninstall Tool</a></p>
- <h3>Q: Where can I get more help or support for Autocad 2018?</h3>
- <p>A: If you need more help or support for Autocad 2018, you can visit the following resources:</p>
- <ul>
-<li>The official Autodesk website, where you can find product information, documentation, tutorials, videos, forums, blogs, etc.</li>
-<li>The official Autodesk Knowledge Network, where you can find solutions, articles, tips, tricks, FAQs, etc.</li>
-<li>The official Autodesk Support Center, where you can contact Autodesk technical support team via phone, chat, email, or web form.</li>
-<li>The official Autodesk Community, where you can join discussions with other users and experts, ask questions, share ideas, etc.</li>
-</ul>
- <p>You can access these resources by clicking on the links below:</p>
- <p><a href="https://www.autodesk.com/products/autocad/overview">Autodesk Website</a></p>
-<p><a href="https://knowledge.autodesk.com/support/autocad">Autodesk Knowledge Network</a></p>
-<p><a href="https://www.autodesk.com/support/contact-support">Autodesk Support Center</a></p>
-<p><a href="https://forums.autodesk.com/t5/autocad-forum/bd-p/40">Autodesk Community</a></p>
- <h2></h2></p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Ck2 Agot Colonize Valyria.md b/spaces/raedeXanto/academic-chatgpt-beta/Ck2 Agot Colonize Valyria.md
deleted file mode 100644
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+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>How to Colonize Valyria in Crusader Kings 2: A Game of Thrones Mod</h1>
-<p>Crusader Kings 2 (CK2) is a grand strategy game set in the Middle Ages, where players can choose to play as any historical or fictional ruler in Europe, Africa, Asia and the Near East. One of the most popular mods for CK2 is A Game of Thrones (AGOT), which is based on the fantasy series by George R. R. Martin. AGOT adds new features, events, characters and scenarios to the game, allowing players to explore the rich and complex world of Westeros and beyond.</p>
-<h2>Ck2 Agot Colonize Valyria</h2><br /><p><b><b>Download</b> &#9913;&#9913;&#9913; <a href="https://tinourl.com/2uL00i">https://tinourl.com/2uL00i</a></b></p><br /><br />
-<p>One of the most intriguing aspects of AGOT is the possibility of colonizing Valyria, the ancient and mysterious empire that once dominated most of Essos with its dragons and magic. Valyria was destroyed by a cataclysmic event known as the Doom, which left behind a shattered peninsula and a smoking sea full of dangers and secrets. However, some brave or foolish adventurers may attempt to restore Valyria to its former glory, or at least claim some of its lands and treasures for themselves.</p>
-<p>In this article, we will explain how to colonize Valyria in CK2 AGOT, using a sub-mod called Colonizable Valyria Revised. This sub-mod adds new provinces, duchies and kingdoms to Valyria, as well as special events and mechanics that make colonizing it more challenging and rewarding. We will also provide some tips and tricks on how to succeed in this endeavor, as well as some potential pitfalls to avoid.</p>
-<h2>What is Colonizable Valyria Revised?</h2>
-<p>Colonizable Valyria Revised is a sub-mod for AGOT that was created by erbkaiser, a modder who also made other sub-mods such as Trade Routes Revised and More Bloodlines. Colonizable Valyria Revised aims to make colonizing Valyria more fun and immersive, by adding more provinces, duchies and kingdoms to the region, as well as new events and mechanics that reflect the dangers and opportunities of exploring the ruins of an ancient civilization.</p>
-<p></p>
-<p>The sub-mod splits up Valyria into two kingdoms: Valyria and the Lands of the Long Summer. Valyria is an island nation that has no land connection to Essos, while the Lands of the Long Summer are still connected to Essos by a narrow strip of land. Both kingdoms have several duchies and provinces within them, most of which can be colonized by any ruler who has enough money, prestige and courage.</p>
-<p>The sub-mod also changes the terrain and appearance of Valyria, making it more green and fertile than before. However, this does not mean that Valyria is safe or hospitable. The sub-mod adds several special events that can happen when colonizing or traveling through Valyria, such as:</p>
-<ul>
-<li>The Smoking Sea effects: These are silly events that can happen when crossing the two straits that surround Valyria. They include encountering mermaids, krakens, pirates or even Cthulhu.</li>
-<li>Finding a dragon egg: There is a small chance that colonizers may find a dragon egg hidden in the ruins of Valyria. This can be a great boon for anyone who wants to revive the dragonlords' legacy.</li>
-<li>Getting greyscale: Greyscale is a deadly disease that slowly turns its victims into stone-like creatures. It is prevalent in Valyria, and colonizers may contract it if they are not careful.</li>
-<li>Finding old treasures: Valyria was once rich and powerful, and its ruins may still contain valuable artifacts and relics. Colonizers may stumble upon them if they are lucky.</li>
-<li>Encountering a firewyrm: Firewyrms are fearsome creatures that resemble dragons but without wings. They live underground and can burrow through rock and soil. They are very rare but very dangerous if encountered.</li>
-</ul>
-<p>The sub-mod also restores some holy sites for the Valyrian religions, such as Lord of Light, Starry Wisdom and Fourteen Flames. This can make colonizing Valyria more appealing for followers of these faiths.</p>
-<h2>How to Download and Install Colonizable Valyria Revised?</h2>
-<p>Colonizable Valyria Revised is</p> cec2833e83<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download The Dark Knight BRRip 1080p Dual Audio Eng-Hindi Subtitles Enjoy the Best Quality of the Legendary Film.md b/spaces/raedeXanto/academic-chatgpt-beta/Download The Dark Knight BRRip 1080p Dual Audio Eng-Hindi Subtitles Enjoy the Best Quality of the Legendary Film.md
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-<h1>The Dark Knight: A Masterpiece of Superhero Cinema</h1>
- <p>Superhero movies are one of the most popular genres in Hollywood today. They have become a staple of blockbuster entertainment, attracting millions of fans and generating billions of dollars in revenue. But not all superhero movies are created equal. Some are forgettable, some are mediocre, and some are exceptional. Among the exceptional ones, there is one that stands out as a masterpiece of superhero cinema: <i>The Dark Knight</i>.</p>
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- <h2>Introduction</h2>
- <h3>What is <i>The Dark Knight</i>?</h3>
- <p><i>The Dark Knight</i> is a 2008 superhero film directed by Christopher Nolan, co-written by Nolan and his brother Jonathan Nolan, and produced by Emma Thomas, Charles Roven, and Nolan himself. It is the second installment in Nolan's <i>The Dark Knight Trilogy</i>, following <i>Batman Begins</i> (2005) and preceding <i>The Dark Knight Rises</i> (2012). It is based on the DC Comics character Batman, created by Bob Kane and Bill Finger.</p>
- <p>The film stars Christian Bale as Bruce Wayne/Batman, a billionaire vigilante who protects Gotham City from crime and corruption. He faces his greatest adversary yet: the Joker (Heath Ledger), a psychopathic criminal mastermind who unleashes a wave of chaos and anarchy in Gotham. Batman also has to deal with his former friend and ally Harvey Dent (Aaron Eckhart), who becomes the disfigured and vengeful Two-Face after a tragic incident.</p>
- <h3>Why is it considered one of the best superhero movies ever made?</h3>
- <p><i>The Dark Knight</i> is widely regarded as one of the best superhero movies ever made because of its complex and compelling story, its realistic and dark tone, its memorable and iconic characters, its profound and relevant themes, its stunning and innovative technical aspects, its massive and lasting reception and legacy, and its artistic and cultural influence.</p>
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- <p>In this article, we will explore each of these aspects in detail and see why <i>The Dark Knight</i> deserves to be called a masterpiece of superhero cinema.</p>
- <h2>The Plot</h2>
- <h3>The Rise of the Joker</h3>
- <p>The plot of <i>The Dark Knight</i> revolves around the rise of the Joker, a mysterious and unpredictable criminal who has no origin, no motive, no identity, and no rules. He is a force of pure chaos who wants to spread fear, pain, and madness in Gotham City.</p>
- <p>The Joker first appears in a spectacular bank heist where he kills his own accomplices and escapes with millions of dollars. He then offers his services to the mob bosses who are threatened by Batman's crusade against crime. He promises to kill Batman for half of their money. However, his real goal is not money but anarchy. He wants to prove that anyone can be corrupted or broken by chaos.</p>
- <p>The Joker orchestrates a series of attacks that terrorize Gotham City. He kills several public officials, including Police Commissioner Loeb and Judge Surillo. He threatens to blow up a hospital unless someone kills Coleman Reese, an accountant who knows Batman's identity. He kidnaps Rachel Dawes, Bruce Wayne's childhood friend and Harvey Dent's lover, and Harvey Dent himself. He forces Batman to choose between saving Rachel or Harvey. Batman chooses Rachel but arrives too late to save her as the Joker has switched their locations. Harvey survives but half of his face is burned off.</p>
- <h3>The Fall of Harvey Dent</h3>
- <p>The plot of <i>The Dark Knight</i> also revolves around the fall of Harvey Dent, Gotham's district attorney who is hailed as the city's "White Knight". He is a brave and idealistic prosecutor who wants to end the reign of organized crime in Gotham. He believes in justice and the rule of law.</p>
- <p>Harvey Dent is initially supportive of Batman's vigilante actions but later becomes conflicted about his methods. He agrees to take the blame for Batman's crimes in order to protect his identity and allow him to continue his work. However, after losing Rachel and being scarred by the Joker's explosion, he loses his faith in justice and becomes consumed by revenge.</p>
- <p>Harvey Dent adopts the persona of Two-Face, a twisted version of himself who decides people's fates with a coin flip. He blames Batman, Gordon, and the Joker for Rachel's death and goes on a rampage to kill them all. He kills several corrupt cops who betrayed him to the Joker. He kidnaps Gordon's family and threatens to kill his son unless Batman reveals himself.</p>
- <h3>The Sacrifice of Batman</h3>
- <p>Batman faces a moral dilemma when he confronts the Joker. The Joker tells him that he has rigged two ferries with explosives: one carrying civilians and the other carrying prisoners. He gives each ferry a detonator to blow up the other ferry and says that he will blow up both if neither does so by midnight. He challenges Batman to break his one rule: to kill him or let him kill innocent people.</p>
- <p>Batman refuses to play the Joker's game and tries to find him using a high-tech surveillance system that taps into every cell phone in the city. He locates the Joker in a building and fights his way through his henchmen. He finally reaches the Joker and throws him off the building. However, he catches him with his grappling gun and saves his life. He tells the Joker that he has failed to corrupt Gotham as both ferries have refused to blow up each other.</p>
- <p>Batman then rushes to save Gordon's family from Two-Face. He arrives at the scene and tries to reason with Harvey, reminding him of his ideals and his love for Rachel. However, Harvey is too far gone and decides to kill Batman, Gordon, and Gordon's son with his coin. He flips for Batman and shoots him in the chest. He flips for himself and spares his life. He flips for Gordon's son and prepares to shoot him. Batman tackles him and they fall off the building. Harvey dies from the fall while Batman survives.</p>
- <p>Batman realizes that Harvey's death will destroy the hope and trust that he had built in Gotham as the city's hero. He decides to take the blame for Harvey's crimes and let him die as a martyr. He tells Gordon to tell the people that he killed those people and that he is a murderer. He says that he can be whatever Gotham needs him to be. He runs away as Gordon destroys the Bat-Signal and declares him an enemy.</p>
- <p>The film ends with Batman being chased by the police as Gordon delivers a monologue about his sacrifice. He says that Batman is not a hero but a silent guardian, a watchful protector, a dark knight.</p>
- <h2>The Characters</h2>
- <h3>Batman/Bruce Wayne</h3>
- <p>Batman/Bruce Wayne is the protagonist of <i>The Dark Knight</i>. He is a complex and conflicted character who struggles with his dual identity, his moral code, and his role as Gotham's protector.</p>
- <p>As Batman, he is a masked vigilante who uses his skills, gadgets, and resources to fight crime and corruption in Gotham City. He is driven by a sense of justice and a desire to honor his parents' legacy. He is also haunted by guilt and trauma from their murder when he was a child.</p>
- <p>As Bruce Wayne, he is a billionaire philanthropist who uses his wealth and influence to support Gotham's development and reform. He is also a playboy who pretends to be careless and irresponsible to hide his true self from the public eye.</p>
- <p>In <i>The Dark Knight</i>, Batman faces his greatest challenge yet: the Joker, who tests his limits and pushes him to the edge. He also faces his own doubts and fears about his actions and their consequences. He wonders if he is doing more harm than good by inspiring copycats and criminals. He questions if he can ever stop being Batman or if he will die as one.</p>
- <p>Batman also has to deal with his complicated relationships with other characters. He has a romantic interest in Rachel Dawes, who loves him but cannot accept his double life. He has a friendship and partnership with Harvey Dent, who shares his vision for Gotham but becomes his enemy after turning into Two-Face. He has a mentorship and bond with Alfred Pennyworth, who supports him but worries about him.</p>
- <p>Batman ultimately proves himself to be a true hero by making a noble sacrifice for Gotham's sake. He chooses to be hated and hunted by the people he loves and protects rather than let them lose their hope and faith in Harvey Dent.</p>
- <h3>The Joker</h3>
- <p>The Joker is the antagonist of <i>The Dark Knight</i>. He is a brilliant and terrifying character who represents everything that Batman opposes: chaos, anarchy, madness, violence, nihilism.</p>
- <p>The Joker is a mysterious figure who has no backstory, no motive, no identity, and no rules. He wears clown makeup and purple clothes that contrast with his pale skin and green hair. He has scars on his mouth that resemble a smile. He tells different stories about how he got them, implying that he is lying or that he does not care about the truth.</p>
- <p>The Joker is a mastermind who plans elaborate schemes that involve multiple layers of deception, manipulation, coercion, and improvisation. He uses various weapons and tools such as knives, guns, explosives, cards, pencils, phones, etc. He also uses people as pawns or hostages in his games.</p>
- <p>The Joker is a psychopath who enjoys causing pain, fear, and suffering in others. He has no empathy or remorse for anyone or anything. He kills without hesitation or discrimination. He laughs at death and tragedy.</p>
- <p>The Joker is an anarchist who wants to destroy the established order of Gotham City. He hates authority figures such as Batman, Gordon, Dent, etc. He challenges their morals and values by forcing them to make impossible choices or break their own rules.</p>
- <p>The Joker is also a nihilist who believes that life has no meaning or purpose. He says that everything is random and unpredictable. He says that people are only as good as their circumstances allow them to be. He says that he is not a monster but just ahead of the curve.</p>
- and turning him into Two-Face. He also succeeds in creating a sense of fear and distrust among the people of Gotham.</p>
- <h3>Harvey Dent/Two-Face</h3>
- <p>Harvey Dent/Two-Face is a major character in <i>The Dark Knight</i>. He is a tragic and sympathetic character who undergoes a dramatic transformation from a hero to a villain.</p>
- <p>As Harvey Dent, he is Gotham's district attorney who is dedicated to fighting crime and corruption in the city. He is brave, idealistic, charismatic, and honest. He is nicknamed the "White Knight" by the media and the public. He is in love with Rachel Dawes and plans to marry her.</p>
- <p>As Two-Face, he is a disfigured and vengeful criminal who seeks to punish those who wronged him and killed Rachel. He is bitter, angry, cynical, and ruthless. He adopts a coin as his symbol and his method of decision-making. He believes that the world is cruel and unfair and that justice is a matter of chance.</p>
- <p>In <i>The Dark Knight</i>, Harvey Dent is initially an ally and a friend of Batman and Gordon. He supports their efforts to bring down the mob and the Joker. He agrees to take the blame for Batman's crimes in order to protect his identity and allow him to continue his work.</p>
- <p>However, after being kidnapped and tortured by the Joker, he loses Rachel and half of his face in an explosion. He survives but becomes mentally unstable and emotionally broken. He blames Batman, Gordon, and the Joker for his loss and becomes obsessed with revenge.</p>
- <p>Harvey Dent ultimately becomes a victim and a pawn of the Joker's plan to destroy Gotham's hope and trust. He becomes a threat and an enemy of Batman and Gordon. He dies as a result of his own actions and Batman's intervention.</p>
- <h3>Alfred Pennyworth</h3>
- <p>Alfred Pennyworth is a supporting character in <i>The Dark Knight</i>. He is a loyal and wise character who serves as Bruce Wayne's butler, confidant, mentor, and father figure.</p>
- <p>Alfred has been with Bruce since he was a child. He raised him after his parents' death and helped him become Batman. He knows his secrets and supports his decisions. He also provides him with advice, guidance, humor, and comfort.</p>
- <p>In <i>The Dark Knight</i>, Alfred helps Bruce cope with his challenges and struggles as Batman. He helps him maintain his cover as Bruce Wayne by arranging dates, parties, trips, etc. He also helps him deal with his feelings for Rachel Dawes by encouraging him to pursue her or let her go.</p>
- <p>Alfred also shares his own experiences and insights with Bruce. He tells him a story about his time in Burma where he encountered a bandit who stole jewels for fun rather than money. He compares him to the Joker and says that some men just want to watch the world burn. He also tells him that he burned Rachel's letter where she said that she chose Harvey over him because he wanted to spare him the pain.</p>
- <p>Alfred ultimately proves himself to be a true friend and a father figure to Bruce. He cares for him deeply and wants him to be happy and safe. He stands by him until the end.</p>
- <h3>Rachel Dawes</h3>
- <p>Rachel Dawes is a supporting character in <i>The Dark Knight</i>. She is a strong and independent character who serves as Bruce Wayne's love interest, Harvey Dent's girlfriend, and Gotham's assistant district attorney.</p>
- <p>Rachel has known Bruce since they were children. She was his friend and his first love. She also witnessed his parents' murder and comforted him afterwards. She knows his secrets and understands his pain.</p>
- <p>Rachel is also a lawyer who works for Harvey Dent. She shares his passion for justice and reform in Gotham City. She is brave, smart, compassionate, and principled. She does not tolerate corruption or violence.</p>
- <p>In <i>The Dark Knight</i>, Rachel faces a dilemma between her feelings for Bruce and her relationship with Harvey. She loves Bruce but she cannot accept his life as Batman. She thinks that he has lost himself in his crusade and that he will never stop being Batman. She also loves Harvey but she worries about his safety and his integrity.</p>
- sense of loyalty and wisdom despite the challenges he faces.</p>
- <h3>Justice vs. Revenge</h3>
- <p>Another theme of <i>The Dark Knight</i> is justice vs. revenge. The film explores the difference and the similarity between these two concepts that motivate Gotham's heroes and villains.</p>
- <p>On one hand, there is justice, represented by Harvey Dent. He is a symbol of law, fairness, accountability, and reform. He wants to end the reign of crime and corruption in Gotham City and create a system where everyone is equal and responsible. He believes that justice is the best way to honor the victims and prevent more violence.</p>
- <p>On the other hand, there is revenge, represented by Two-Face. He is a symbol of anger, bitterness, vengeance, and punishment. He wants to make those who wronged him and killed Rachel pay for their sins and suffer as he did. He believes that revenge is the only way to cope with his pain and express his rage.</p>
- <p>The film shows how these two concepts are related and opposed to each other throughout the story. Harvey Dent starts as a champion of justice but becomes a seeker of revenge after his tragedy. He loses his sense of right and wrong and becomes a judge, jury, and executioner. Two-Face is a result of justice gone wrong and revenge gone mad.</p>
- <p>Batman is a character who balances between justice and revenge in his quest to fight crime and corruption in Gotham City. He is driven by both a sense of duty and a personal vendetta. He seeks to bring justice to those who deserve it but also to avenge his parents' death. He follows his own code of ethics but also his own emotions.</p>
- <p>The film also shows how these two concepts affect other characters in the story. The Joker is an example of someone who mocks both justice and revenge by creating chaos and anarchy in Gotham City. He does not care about law or morality or consequences. He does not have any personal motives or grudges. He just wants to see the world burn.</p>
- <h3>Heroism vs. Villainy</h3>
- <p>A final theme of <i>The Dark Knight</i> is heroism vs. villainy. The film explores the definition and the perception of these two roles that shape Gotham's legends and myths.</p>
- <p>On one hand, there is heroism, represented by Batman. He is a symbol of courage, sacrifice, hope, and change. He wants to be a protector and an inspirer for Gotham City and its people. He believes that heroism is not about fame or glory but about doing the right thing.</p>
- <p>On the other hand, there is villainy, represented by the Joker. He is a symbol of fear, pain, despair, and destruction. He wants to be a tormentor and a corrupter for Gotham City and its people. He believes that villainy is not about hate or evil but about having fun.</p>
- <p>The film shows how these two roles are defined and perceived by different characters and perspectives throughout the story. Batman defines himself as a hero but he is perceived as a villain by some people who see him as a criminal or a menace. The Joker defines himself as an agent of chaos but he is perceived as a villain by most people who see him as a monster or a threat.</p>
- and their motivation. She becomes a catalyst for both Batman's and Harvey's actions and decisions.</p>
- <h2>The Technical Aspects</h2>
- <h3>The Direction and Screenplay</h3>
- <p>One of the technical aspects that makes <i>The Dark Knight</i> a masterpiece of superhero cinema is the direction and screenplay by Christopher Nolan and Jonathan Nolan. They have created a film that is not only a thrilling and entertaining action-adventure but also a smart and sophisticated drama-thriller.</p>
- <p>The direction and screenplay of <i>The Dark Knight</i> are characterized by their realism and complexity. They have grounded the film in a realistic setting and tone that make it more believable and relatable. They have also crafted a complex and layered story that explores multiple themes and characters with depth and nuance.</p>
- <p>The direction and screenplay of <i>The Dark Knight</i> are also characterized by their creativity and originality. They have reinvented the genre and the characters by giving them a fresh and modern twist. They have also added elements of surprise and innovation that keep the audience engaged and intrigued.</p>
- <h3>The Cinematography and Visual Effects</h3>
- <p>Another technical aspect that makes <i>The Dark Knight</i> a masterpiece of superhero cinema is the cinematography and visual effects by Wally Pfister and his team. They have created a film that is not only visually stunning and spectacular but also aesthetically coherent and meaningful.</p>
- <p>The cinematography and visual effects of <i>The Dark Knight</i> are characterized by their quality and diversity. They have used various techniques and technologies to achieve high-quality images and effects that enhance the film's realism and immersion. They have also used different styles and colors to create diverse atmospheres and moods that reflect the film's themes and characters.</p>
- <p>The cinematography and visual effects of <i>The Dark Knight</i> are also characterized by their significance and symbolism. They have used various shots and angles to convey important information and emotions to the audience. They have also used various motifs and contrasts to emphasize the film's messages and meanings.</p>
- <h3>The Music and Sound Design</h3>
- and powerful but also emotionally expressive and impactful.</p>
- <p>The music and sound design of <i>The Dark Knight</i> are characterized by their intensity and diversity. They have used various instruments and sounds to create a rich and dynamic soundtrack that enhances the film's tension and excitement. They have also used different themes and tones to create a varied and distinctive score that reflects the film's characters and situations.</p>
- <p>The music and sound design of <i>The Dark Knight</i> are also characterized by their subtlety and symbolism. They have used various cues and effects to convey subtle hints and clues to the audience. They have also used various motifs and contrasts to emphasize the film's contrasts and conflicts.</p>
- <h2>The Reception and Legacy</h2>
- <h3>The Critical and Commercial Success</h3>
- <p>One of the aspects that makes <i>The Dark Knight</i> a masterpiece of superhero cinema is its critical and commercial success. The film has received universal acclaim from critics and audiences alike. It has also achieved record-breaking results at the box office.</p>
- <p>The critical success of <i>The Dark Knight</i> is evident in its reviews and ratings. The film has a 94% approval rating on Rotten Tomatoes based on 344 reviews, with an average rating of 8.6/10. The website's critical consensus reads: "Dark, complex, and unforgettable, <i>The Dark Knight</i> succeeds not just as an entertaining comic book film, but as a richly thrilling crime saga." The film also has a score of 84 out of 100 on Metacritic based on 39 reviews, indicating "universal acclaim". The film has been praised for its direction, screenplay, performances, themes, cinematography, visual effects, music, sound design, and overall quality.</p>
- <p>The commercial success of <i>The Dark Knight</i> is evident in its box office numbers. The film has grossed over $1 billion worldwide, making it the fourth-highest-grossing film of all time at the time of its release. It also holds several records such as the highest-grossing opening weekend of all time in North America ($158 million), the highest-grossing second weekend of all time in North America ($75 million), the fastest film to reach $400 million in North America (18 days), the highest-grossing film of 2008 worldwide, and the highest-grossing superhero film of all time until it was surpassed by <i>The Avengers</i> in 2012.</p>
- <h3>The Awards and Accolades</h3>
- <p>Another aspect that makes <i>The Dark Knight</i> a masterpiece of superhero cinema is its awards and accolades. The film has received numerous honors and recognitions from various organizations and institutions. It has also made history by being the first superhero film to win an Academy Award for acting.</p>
- <p>The awards and accolades of <i>The Dark Knight</i> include: - Two Academy Awards for Best Supporting Actor (Heath Ledger) and Best Sound Editing (Richard King) out of eight nominations. - One Golden Globe Award for Best Supporting Actor - Motion Picture (Heath Ledger) out of two nominations. - One BAFTA Award for Best Supporting Actor (Heath Ledger) out of nine nominations. - Four Critics' Choice Movie Awards for Best Supporting Actor (Heath Ledger), Best Action Movie, Best Composer (Hans Zimmer and James Newton Howard), and Best Sound out of six nominations. - Two Screen Actors Guild Awards for Outstanding Performance by a Male Actor in a Supporting Role (Heath Ledger) and Outstanding Performance by a Stunt Ensemble in a Motion Picture. - One Grammy Award for Best Score Soundtrack for Visual Media (Hans Zimmer and James Newton Howard). - One AFI Award for Movie of the Year. - One MTV Movie Award for Best Villain (Heath Ledger) out of five nominations. - One People's Choice Award for Favorite Movie out of five nominations. - One Saturn Award for Best Action or Adventure Film out of nine nominations.</p>
- <h3>The Cultural Impact and Influence</h3>
- and has influenced the culture and the society in various ways. It has also inspired and influenced other films and media.</p>
- <p>The cultural impact and influence of <i>The Dark Knight</i> include: - The popularity and the legacy of Heath Ledger's performance as the Joker. He has been widely praised and celebrated for his portrayal of the iconic villain. He has also been posthumously honored and awarded for his work. His performance has become a benchmark and a reference for other actors and characters. - The popularity and the recognition of Christopher Nolan's direction and vision. He has been widely acclaimed and respected for his filmmaking skills and style. He has also been credited and admired for his contribution to the genre and the medium. His direction has become a standard and a model for other filmmakers and films. - The popularity and the relevance of the film's themes and messages. The film has been widely discussed and analyzed for its exploration of various topics and issues that resonate with the contemporary world and society. The film has also been praised and appreciated for its reflection of the human condition and the moral dilemmas. Its themes and messages have become a source and a subject of debate and education. - The popularity and the influence of the film's technical aspects. The film has been widely recognized and admired for its use of various techniques and technologies that enhance its quality and realism. The film has also been credited and emulated for its innovation and creativity that push the boundaries of the genre and the medium. Its technical aspects have become a challenge and an inspiration for other films and media.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, <i>The Dark Knight</i> is a masterpiece of superhero cinema that deserves to be called one of the best films ever made. It is a film that combines a thrilling and entertaining story, a realistic and dark tone, memorable and iconic characters, profound and relevant themes, stunning and innovative technical aspects, massive and lasting reception and legacy, and artistic and cultural influence.</p>
- <p>It is a film that transcends the genre and becomes a classic that appeals to everyone. It is a film that challenges the audience and makes them think, feel, question, and learn. It is a film that defines an era and influences a generation.</p>
- <p>It is a film that proves that superhero movies can be more than just popcorn entertainment. They can be art.</p>
- <h2>FAQs</h2>
- <h3>What is <i>The Dark Knight</i> about?</h3>
- <p><i>The Dark Knight</i> is about Batman's struggle to stop the Joker from spreading chaos and anarchy in Gotham City while also dealing with his own doubts, fears, and conflicts.</p>
- <h3>Who plays Batman in <i>The Dark Knight</i>?</h3>
- <p>Batman is played by Christian Bale, who reprised his role from <i>Batman Begins</i>.</p>
- <h3>Who plays the Joker in <i>The Dark Knight</i>?</h3>
- <p>The Joker is played by Heath Ledger, who gave his final performance before his death in 2008.</p>
- <h3>Is <i>The Dark Knight</i> based on a comic book?</h3>
- and Bill Finger. It is also inspired by some comic book stories such as <i>The Killing Joke</i> by Alan Moore and Brian Bolland, <i>The Long Halloween</i> by Jeph Loeb and Tim Sale, and <i>Batman: Year One</i> by Frank Miller and David Mazzucchelli.</p>
- <h3>Is <i>The Dark Knight</i> part of a trilogy?</h3>
- <p><i>The Dark Knight</i> is part of <i>The Dark Knight Trilogy</i>, a series of films directed by Christopher Nolan that includes <i>Batman Begins</i> (2005) and <i>The Dark Knight Rises</i> (2012).</p>
- <h3>Where can I watch <i>The Dark Knight</i>?</h3>
- <p><i>The Dark Knight</i> is available to watch on various platforms such as DVD, Blu-ray, digital download, streaming services, etc. You can also watch it in theaters if there is a special screening or a re-release.</p>
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-<br />
\ No newline at end of file
diff --git a/spaces/rahul999r/Rahul_Kannada_TTS/src/glow_tts/t2s_fastapi.py b/spaces/rahul999r/Rahul_Kannada_TTS/src/glow_tts/t2s_fastapi.py
deleted file mode 100644
index e034fc01a4a5bcd54b365a49dad2e907b57504a1..0000000000000000000000000000000000000000
--- a/spaces/rahul999r/Rahul_Kannada_TTS/src/glow_tts/t2s_fastapi.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from starlette.responses import StreamingResponse
-from texttospeech import MelToWav, TextToMel
-from typing import Optional
-from pydantic import BaseModel
-from fastapi import FastAPI, HTTPException
-import uvicorn
-import base64
-
-app = FastAPI()
-
-
-class TextJson(BaseModel):
-    text: str
-    lang: Optional[str] = "hi"
-    gender: Optional[str] = "male"
-
-
-glow_hi_male = TextToMel(glow_model_dir="", device="")
-glow_hi_female = TextToMel(glow_model_dir="", device="")
-hifi_hi = MelToWav(hifi_model_dir="", device="")
-
-
-available_choice = {
-    "hi_male": [glow_hi_male, hifi_hi],
-    "hi_female": [glow_hi_female, hifi_hi],
-}
-
-
-@app.post("/TTS/")
-async def tts(input: TextJson):
-    text = input.text
-    lang = input.lang
-    gender = input.gender
-
-    choice = lang + "_" + gender
-    if choice in available_choice.keys():
-        t2s = available_choice[choice]
-    else:
-        raise HTTPException(
-            status_code=400, detail={"error": "Requested model not found"}
-        )
-
-    if text:
-        mel = t2s[0].generate_mel(text)
-        data, sr = t2s[1].generate_wav(mel)
-        t2s.save_audio("out.wav", data, sr)
-    else:
-        raise HTTPException(status_code=400, detail={"error": "No text"})
-
-    ## to return outpur as a file
-    # audio = open('out.wav', mode='rb')
-    # return StreamingResponse(audio, media_type="audio/wav")
-
-    with open("out.wav", "rb") as audio_file:
-        encoded_bytes = base64.b64encode(audio_file.read())
-        encoded_string = encoded_bytes.decode()
-    return {"encoding": "base64", "data": encoded_string, "sr": sr}
-
-
-if __name__ == "__main__":
-    uvicorn.run(
-        "t2s_fastapi:app", host="127.0.0.1", port=5000, log_level="info", reload=True
-    )
diff --git a/spaces/ramiin2/AutoGPT/Dockerfile b/spaces/ramiin2/AutoGPT/Dockerfile
deleted file mode 100644
index 8396154998f32a50d55c199a674b638d5cf7bda2..0000000000000000000000000000000000000000
--- a/spaces/ramiin2/AutoGPT/Dockerfile
+++ /dev/null
@@ -1,38 +0,0 @@
-# Use an official Python base image from the Docker Hub
-FROM python:3.10-slim
-
-# Install git
-RUN apt-get -y update
-RUN apt-get -y install git chromium-driver
-
-# Install Xvfb and other dependencies for headless browser testing
-RUN apt-get update \
-    && apt-get install -y wget gnupg2 libgtk-3-0 libdbus-glib-1-2 dbus-x11 xvfb ca-certificates
-
-# Install Firefox / Chromium
-RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - \
-    && echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list \
-    && apt-get update \
-    && apt-get install -y chromium firefox-esr
-
-# Set environment variables
-ENV PIP_NO_CACHE_DIR=yes \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1
-
-# Create a non-root user and set permissions
-RUN useradd --create-home appuser
-WORKDIR /home/appuser
-RUN chown appuser:appuser /home/appuser
-USER appuser
-
-# Copy the requirements.txt file and install the requirements
-COPY --chown=appuser:appuser requirements.txt .
-RUN sed -i '/Items below this point will not be included in the Docker Image/,$d' requirements.txt && \
-	pip install --no-cache-dir --user -r requirements.txt
-
-# Copy the application files
-COPY --chown=appuser:appuser autogpt/ ./autogpt
-
-# Set the entrypoint
-ENTRYPOINT ["python", "-m", "autogpt"]
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/braces/lib/stringify.js b/spaces/rayan-saleh/whisper2notion/server/node_modules/braces/lib/stringify.js
deleted file mode 100644
index 414b7bcc6b38c5c4831488bf20194c895064edcb..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/braces/lib/stringify.js
+++ /dev/null
@@ -1,32 +0,0 @@
-'use strict';
-
-const utils = require('./utils');
-
-module.exports = (ast, options = {}) => {
-  let stringify = (node, parent = {}) => {
-    let invalidBlock = options.escapeInvalid && utils.isInvalidBrace(parent);
-    let invalidNode = node.invalid === true && options.escapeInvalid === true;
-    let output = '';
-
-    if (node.value) {
-      if ((invalidBlock || invalidNode) && utils.isOpenOrClose(node)) {
-        return '\\' + node.value;
-      }
-      return node.value;
-    }
-
-    if (node.value) {
-      return node.value;
-    }
-
-    if (node.nodes) {
-      for (let child of node.nodes) {
-        output += stringify(child);
-      }
-    }
-    return output;
-  };
-
-  return stringify(ast);
-};
-
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/cookie/README.md b/spaces/rayan-saleh/whisper2notion/server/node_modules/cookie/README.md
deleted file mode 100644
index 5449c3a2587996d44b242281692c01ad2d2a3cf3..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/cookie/README.md
+++ /dev/null
@@ -1,302 +0,0 @@
-# cookie
-
-[![NPM Version][npm-version-image]][npm-url]
-[![NPM Downloads][npm-downloads-image]][npm-url]
-[![Node.js Version][node-version-image]][node-version-url]
-[![Build Status][github-actions-ci-image]][github-actions-ci-url]
-[![Test Coverage][coveralls-image]][coveralls-url]
-
-Basic HTTP cookie parser and serializer for HTTP servers.
-
-## Installation
-
-This is a [Node.js](https://nodejs.org/en/) module available through the
-[npm registry](https://www.npmjs.com/). Installation is done using the
-[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally):
-
-```sh
-$ npm install cookie
-```
-
-## API
-
-```js
-var cookie = require('cookie');
-```
-
-### cookie.parse(str, options)
-
-Parse an HTTP `Cookie` header string and returning an object of all cookie name-value pairs.
-The `str` argument is the string representing a `Cookie` header value and `options` is an
-optional object containing additional parsing options.
-
-```js
-var cookies = cookie.parse('foo=bar; equation=E%3Dmc%5E2');
-// { foo: 'bar', equation: 'E=mc^2' }
-```
-
-#### Options
-
-`cookie.parse` accepts these properties in the options object.
-
-##### decode
-
-Specifies a function that will be used to decode a cookie's value. Since the value of a cookie
-has a limited character set (and must be a simple string), this function can be used to decode
-a previously-encoded cookie value into a JavaScript string or other object.
-
-The default function is the global `decodeURIComponent`, which will decode any URL-encoded
-sequences into their byte representations.
-
-**note** if an error is thrown from this function, the original, non-decoded cookie value will
-be returned as the cookie's value.
-
-### cookie.serialize(name, value, options)
-
-Serialize a cookie name-value pair into a `Set-Cookie` header string. The `name` argument is the
-name for the cookie, the `value` argument is the value to set the cookie to, and the `options`
-argument is an optional object containing additional serialization options.
-
-```js
-var setCookie = cookie.serialize('foo', 'bar');
-// foo=bar
-```
-
-#### Options
-
-`cookie.serialize` accepts these properties in the options object.
-
-##### domain
-
-Specifies the value for the [`Domain` `Set-Cookie` attribute][rfc-6265-5.2.3]. By default, no
-domain is set, and most clients will consider the cookie to apply to only the current domain.
-
-##### encode
-
-Specifies a function that will be used to encode a cookie's value. Since value of a cookie
-has a limited character set (and must be a simple string), this function can be used to encode
-a value into a string suited for a cookie's value.
-
-The default function is the global `encodeURIComponent`, which will encode a JavaScript string
-into UTF-8 byte sequences and then URL-encode any that fall outside of the cookie range.
-
-##### expires
-
-Specifies the `Date` object to be the value for the [`Expires` `Set-Cookie` attribute][rfc-6265-5.2.1].
-By default, no expiration is set, and most clients will consider this a "non-persistent cookie" and
-will delete it on a condition like exiting a web browser application.
-
-**note** the [cookie storage model specification][rfc-6265-5.3] states that if both `expires` and
-`maxAge` are set, then `maxAge` takes precedence, but it is possible not all clients by obey this,
-so if both are set, they should point to the same date and time.
-
-##### httpOnly
-
-Specifies the `boolean` value for the [`HttpOnly` `Set-Cookie` attribute][rfc-6265-5.2.6]. When truthy,
-the `HttpOnly` attribute is set, otherwise it is not. By default, the `HttpOnly` attribute is not set.
-
-**note** be careful when setting this to `true`, as compliant clients will not allow client-side
-JavaScript to see the cookie in `document.cookie`.
-
-##### maxAge
-
-Specifies the `number` (in seconds) to be the value for the [`Max-Age` `Set-Cookie` attribute][rfc-6265-5.2.2].
-The given number will be converted to an integer by rounding down. By default, no maximum age is set.
-
-**note** the [cookie storage model specification][rfc-6265-5.3] states that if both `expires` and
-`maxAge` are set, then `maxAge` takes precedence, but it is possible not all clients by obey this,
-so if both are set, they should point to the same date and time.
-
-##### path
-
-Specifies the value for the [`Path` `Set-Cookie` attribute][rfc-6265-5.2.4]. By default, the path
-is considered the ["default path"][rfc-6265-5.1.4].
-
-##### priority
-
-Specifies the `string` to be the value for the [`Priority` `Set-Cookie` attribute][rfc-west-cookie-priority-00-4.1].
-
-  - `'low'` will set the `Priority` attribute to `Low`.
-  - `'medium'` will set the `Priority` attribute to `Medium`, the default priority when not set.
-  - `'high'` will set the `Priority` attribute to `High`.
-
-More information about the different priority levels can be found in
-[the specification][rfc-west-cookie-priority-00-4.1].
-
-**note** This is an attribute that has not yet been fully standardized, and may change in the future.
-This also means many clients may ignore this attribute until they understand it.
-
-##### sameSite
-
-Specifies the `boolean` or `string` to be the value for the [`SameSite` `Set-Cookie` attribute][rfc-6265bis-09-5.4.7].
-
-  - `true` will set the `SameSite` attribute to `Strict` for strict same site enforcement.
-  - `false` will not set the `SameSite` attribute.
-  - `'lax'` will set the `SameSite` attribute to `Lax` for lax same site enforcement.
-  - `'none'` will set the `SameSite` attribute to `None` for an explicit cross-site cookie.
-  - `'strict'` will set the `SameSite` attribute to `Strict` for strict same site enforcement.
-
-More information about the different enforcement levels can be found in
-[the specification][rfc-6265bis-09-5.4.7].
-
-**note** This is an attribute that has not yet been fully standardized, and may change in the future.
-This also means many clients may ignore this attribute until they understand it.
-
-##### secure
-
-Specifies the `boolean` value for the [`Secure` `Set-Cookie` attribute][rfc-6265-5.2.5]. When truthy,
-the `Secure` attribute is set, otherwise it is not. By default, the `Secure` attribute is not set.
-
-**note** be careful when setting this to `true`, as compliant clients will not send the cookie back to
-the server in the future if the browser does not have an HTTPS connection.
-
-## Example
-
-The following example uses this module in conjunction with the Node.js core HTTP server
-to prompt a user for their name and display it back on future visits.
-
-```js
-var cookie = require('cookie');
-var escapeHtml = require('escape-html');
-var http = require('http');
-var url = require('url');
-
-function onRequest(req, res) {
-  // Parse the query string
-  var query = url.parse(req.url, true, true).query;
-
-  if (query && query.name) {
-    // Set a new cookie with the name
-    res.setHeader('Set-Cookie', cookie.serialize('name', String(query.name), {
-      httpOnly: true,
-      maxAge: 60 * 60 * 24 * 7 // 1 week
-    }));
-
-    // Redirect back after setting cookie
-    res.statusCode = 302;
-    res.setHeader('Location', req.headers.referer || '/');
-    res.end();
-    return;
-  }
-
-  // Parse the cookies on the request
-  var cookies = cookie.parse(req.headers.cookie || '');
-
-  // Get the visitor name set in the cookie
-  var name = cookies.name;
-
-  res.setHeader('Content-Type', 'text/html; charset=UTF-8');
-
-  if (name) {
-    res.write('<p>Welcome back, <b>' + escapeHtml(name) + '</b>!</p>');
-  } else {
-    res.write('<p>Hello, new visitor!</p>');
-  }
-
-  res.write('<form method="GET">');
-  res.write('<input placeholder="enter your name" name="name"> <input type="submit" value="Set Name">');
-  res.end('</form>');
-}
-
-http.createServer(onRequest).listen(3000);
-```
-
-## Testing
-
-```sh
-$ npm test
-```
-
-## Benchmark
-
-```
-$ npm run bench
-
-> cookie@0.4.2 bench
-> node benchmark/index.js
-
-  node@16.14.0
-  v8@9.4.146.24-node.20
-  uv@1.43.0
-  zlib@1.2.11
-  brotli@1.0.9
-  ares@1.18.1
-  modules@93
-  nghttp2@1.45.1
-  napi@8
-  llhttp@6.0.4
-  openssl@1.1.1m+quic
-  cldr@40.0
-  icu@70.1
-  tz@2021a3
-  unicode@14.0
-  ngtcp2@0.1.0-DEV
-  nghttp3@0.1.0-DEV
-
-> node benchmark/parse-top.js
-
-  cookie.parse - top sites
-
-  15 tests completed.
-
-  parse accounts.google.com x 2,421,245 ops/sec ±0.80% (188 runs sampled)
-  parse apple.com           x 2,684,710 ops/sec ±0.59% (189 runs sampled)
-  parse cloudflare.com      x 2,231,418 ops/sec ±0.76% (186 runs sampled)
-  parse docs.google.com     x 2,316,357 ops/sec ±1.28% (187 runs sampled)
-  parse drive.google.com    x 2,363,543 ops/sec ±0.49% (189 runs sampled)
-  parse en.wikipedia.org    x   839,414 ops/sec ±0.53% (189 runs sampled)
-  parse linkedin.com        x   553,797 ops/sec ±0.63% (190 runs sampled)
-  parse maps.google.com     x 1,314,779 ops/sec ±0.72% (189 runs sampled)
-  parse microsoft.com       x   153,783 ops/sec ±0.53% (190 runs sampled)
-  parse play.google.com     x 2,249,574 ops/sec ±0.59% (187 runs sampled)
-  parse plus.google.com     x 2,258,682 ops/sec ±0.60% (188 runs sampled)
-  parse sites.google.com    x 2,247,069 ops/sec ±0.68% (189 runs sampled)
-  parse support.google.com  x 1,456,840 ops/sec ±0.70% (187 runs sampled)
-  parse www.google.com      x 1,046,028 ops/sec ±0.58% (188 runs sampled)
-  parse youtu.be            x   937,428 ops/sec ±1.47% (190 runs sampled)
-  parse youtube.com         x   963,878 ops/sec ±0.59% (190 runs sampled)
-
-> node benchmark/parse.js
-
-  cookie.parse - generic
-
-  6 tests completed.
-
-  simple      x 2,745,604 ops/sec ±0.77% (185 runs sampled)
-  decode      x   557,287 ops/sec ±0.60% (188 runs sampled)
-  unquote     x 2,498,475 ops/sec ±0.55% (189 runs sampled)
-  duplicates  x   868,591 ops/sec ±0.89% (187 runs sampled)
-  10 cookies  x   306,745 ops/sec ±0.49% (190 runs sampled)
-  100 cookies x    22,414 ops/sec ±2.38% (182 runs sampled)
-```
-
-## References
-
-- [RFC 6265: HTTP State Management Mechanism][rfc-6265]
-- [Same-site Cookies][rfc-6265bis-09-5.4.7]
-
-[rfc-west-cookie-priority-00-4.1]: https://tools.ietf.org/html/draft-west-cookie-priority-00#section-4.1
-[rfc-6265bis-09-5.4.7]: https://tools.ietf.org/html/draft-ietf-httpbis-rfc6265bis-09#section-5.4.7
-[rfc-6265]: https://tools.ietf.org/html/rfc6265
-[rfc-6265-5.1.4]: https://tools.ietf.org/html/rfc6265#section-5.1.4
-[rfc-6265-5.2.1]: https://tools.ietf.org/html/rfc6265#section-5.2.1
-[rfc-6265-5.2.2]: https://tools.ietf.org/html/rfc6265#section-5.2.2
-[rfc-6265-5.2.3]: https://tools.ietf.org/html/rfc6265#section-5.2.3
-[rfc-6265-5.2.4]: https://tools.ietf.org/html/rfc6265#section-5.2.4
-[rfc-6265-5.2.5]: https://tools.ietf.org/html/rfc6265#section-5.2.5
-[rfc-6265-5.2.6]: https://tools.ietf.org/html/rfc6265#section-5.2.6
-[rfc-6265-5.3]: https://tools.ietf.org/html/rfc6265#section-5.3
-
-## License
-
-[MIT](LICENSE)
-
-[coveralls-image]: https://badgen.net/coveralls/c/github/jshttp/cookie/master
-[coveralls-url]: https://coveralls.io/r/jshttp/cookie?branch=master
-[github-actions-ci-image]: https://img.shields.io/github/workflow/status/jshttp/cookie/ci/master?label=ci
-[github-actions-ci-url]: https://github.com/jshttp/cookie/actions/workflows/ci.yml
-[node-version-image]: https://badgen.net/npm/node/cookie
-[node-version-url]: https://nodejs.org/en/download
-[npm-downloads-image]: https://badgen.net/npm/dm/cookie
-[npm-url]: https://npmjs.org/package/cookie
-[npm-version-image]: https://badgen.net/npm/v/cookie
diff --git a/spaces/razfar/anything-counter/app.py b/spaces/razfar/anything-counter/app.py
deleted file mode 100644
index 774fa9a8bd1c03a72f6a6020a17ac2d2beabfbe1..0000000000000000000000000000000000000000
--- a/spaces/razfar/anything-counter/app.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import gradio as gr
-
-import argparse
-import time
-from pathlib import Path
-
-import torch
-import torch.backends.cudnn as cudnn
-from numpy import random
-
-from models.experimental import attempt_load
-from utils.datasets import LoadStreams, LoadImages
-from utils.general import (
-    check_img_size,
-    non_max_suppression,
-    apply_classifier,
-    scale_coords,
-    xyxy2xywh,
-    set_logging,
-    increment_path,
-)
-from utils.plots import plot_one_box
-from utils.torch_utils import (
-    select_device,
-    load_classifier,
-    TracedModel,
-)
-from PIL import Image
-
-from huggingface_hub import hf_hub_download
-
-
-def load_model(model_name):
-    model_path = hf_hub_download(
-        repo_id=f"Yolov7/{model_name}", filename=f"{model_name}.pt"
-    )
-
-    return model_path
-
-
-loaded_model = load_model("yolov7")
-
-
-def detect(img):
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--weights", nargs="+", type=str, default=loaded_model, help="model.pt path(s)"
-    )
-    parser.add_argument("--source", type=str, default="Inference/", help="source")
-    parser.add_argument(
-        "--img-size", type=int, default=640, help="inference size (pixels)"
-    )
-    parser.add_argument(
-        "--conf-thres", type=float, default=0.25, help="object confidence threshold"
-    )
-    parser.add_argument(
-        "--iou-thres", type=float, default=0.45, help="IOU threshold for NMS"
-    )
-    parser.add_argument(
-        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument("--view-img", action="store_true", help="display results")
-    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
-    parser.add_argument(
-        "--save-conf", action="store_true", help="save confidences in --save-txt labels"
-    )
-    parser.add_argument(
-        "--nosave", action="store_true", help="do not save images/videos"
-    )
-    parser.add_argument(
-        "--classes",
-        nargs="+",
-        type=int,
-        help="filter by class: --class 0, or --class 0 2 3",
-    )
-    parser.add_argument(
-        "--agnostic-nms", action="store_true", help="class-agnostic NMS"
-    )
-    parser.add_argument("--augment", action="store_true", help="augmented inference")
-    parser.add_argument("--update", action="store_true", help="update all models")
-    parser.add_argument(
-        "--project", default="runs/detect", help="save results to project/name"
-    )
-    parser.add_argument("--name", default="exp", help="save results to project/name")
-    parser.add_argument(
-        "--exist-ok",
-        action="store_true",
-        help="existing project/name ok, do not increment",
-    )
-    parser.add_argument("--trace", action="store_true", help="trace model")
-    opt = parser.parse_args()
-    img.save("Inference/test.jpg")
-    source, weights, view_img, save_txt, imgsz, trace = (
-        opt.source,
-        opt.weights,
-        opt.view_img,
-        opt.save_txt,
-        opt.img_size,
-        opt.trace,
-    )
-    save_img = True  # save inference images
-
-    # Directories
-    save_dir = Path(
-        increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)
-    )  # increment run
-    (save_dir / "labels" if save_txt else save_dir).mkdir(
-        parents=True, exist_ok=True
-    )  # make dir
-
-    # Initialize
-    set_logging()
-    device = select_device(opt.device)
-    half = device.type != "cpu"  # half precision only supported on CUDA
-
-    # Load model
-    model = attempt_load(weights, map_location=device)  # load FP32 model
-    stride = int(model.stride.max())  # model stride
-    imgsz = check_img_size(imgsz, s=stride)  # check img_size
-
-    if trace:
-        model = TracedModel(model, device, opt.img_size)
-
-    if half:
-        model.half()  # to FP16
-
-    # Second-stage classifier
-    classify = False
-    if classify:
-        modelc = load_classifier(name="resnet101", n=2)  # initialize
-        modelc.load_state_dict(
-            torch.load("weights/resnet101.pt", map_location=device)["model"]
-        ).to(device).eval()
-
-    # Set Dataloader
-    dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
-    # Get names and colors
-    names = model.module.names if hasattr(model, "module") else model.names
-    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
-    # Run inference
-    if device.type != "cpu":
-        model(
-            torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))
-        )  # run once
-    t0 = time.time()
-    for path, img, im0s, vid_cap in dataset:
-        img = torch.from_numpy(img).to(device)
-        img = img.half() if half else img.float()  # uint8 to fp16/32
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
-        if img.ndimension() == 3:
-            img = img.unsqueeze(0)
-
-        # Inference
-        pred = model(img, augment=opt.augment)[0]
-
-        # Apply NMS
-        pred = non_max_suppression(
-            pred,
-            opt.conf_thres,
-            opt.iou_thres,
-            classes=opt.classes,
-            agnostic=opt.agnostic_nms,
-        )
-
-        # Apply Classifier
-        if classify:
-            pred = apply_classifier(pred, modelc, img, im0s)
-
-        # Process detections
-        for i, det in enumerate(pred):  # detections per image
-            p, s, im0, frame = path, "", im0s, getattr(dataset, "frame", 0)
-
-            p = Path(p)  # to Path
-            txt_path = str(save_dir / "labels" / p.stem) + (
-                "" if dataset.mode == "image" else f"_{frame}"
-            )  # img.txt
-            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            if len(det):
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
-                # Print results
-                for c in det[:, -1].unique():
-                    n = (det[:, -1] == c).sum()  # detections per class
-                    s += f"{n} {names[int(c)]}{'s' * (n > 1)} "  # add to string
-
-                # Write results
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (
-                            (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
-                            .view(-1)
-                            .tolist()
-                        )  # normalized xywh
-                        line = (
-                            (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)
-                        )  # label format
-                        with open(txt_path + ".txt", "a") as f:
-                            f.write(("%g " * len(line)).rstrip() % line + "\n")
-
-                    if save_img or view_img:  # Add bbox to image
-                        label = f"{names[int(cls)]} {conf:.2f}"
-                        plot_one_box(
-                            xyxy,
-                            im0,
-                            label=label,
-                            color=colors[int(cls)],
-                            line_thickness=3,
-                        )
-
-    print(f"Done. ({time.time() - t0:.3f}s)")
-
-    return [Image.fromarray(im0[:, :, ::-1]), s]
-
-
-css_code = ".border{border-width: 0;}.gr-button-primary{--tw-gradient-stops: rgb(11 143 235 / 70%), rgb(192 53 208 / 80%);color:black;border-color:black;}.gr-button-secondary{color:black;border-color:black;--tw-gradient-stops: white;}.gr-panel{background-color: white;}.gr-text-input{border-width: 0;padding: 0;text-align: center;margin-left: -8px;font-size: 28px;color: black;margin-top: -12px;}.font-semibold,.shadow-sm,.h-5,.text-xl,.text-xs{display:none;}.gr-box{box-shadow:none;border-radius:0;}.object-contain{background-color: white;}.gr-prose h1{font-family: Helvetica; font-weight: 400 !important;}"
-gr.Interface(
-    fn=detect,
-    title="Anything Counter",
-    inputs=gr.Image(type="pil"),
-    outputs=[gr.Image(label="detection", type="pil"), gr.Textbox(label="")],
-    css=css_code,
-    allow_flagging="never",
-    examples=[
-        ["Examples/apples.jpeg"],
-        ["Examples/birds.jpeg"],
-        ["Examples/bottles.jpeg"],
-    ],
-).launch(debug=True)
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Farmbuck Trainer V 5 2 UPD.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Farmbuck Trainer V 5 2 UPD.md
deleted file mode 100644
index 3f4f2391224cefa554807f380187eeb19ea4b6b0..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Farmbuck Trainer V 5 2 UPD.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (Shortcut Romeo Tamil Movie Download ) [BETTER].md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (Shortcut Romeo Tamil Movie Download ) [BETTER].md
deleted file mode 100644
index b7437be07145aa408ce682f6c7f774c67200556b..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (Shortcut Romeo Tamil Movie Download ) [BETTER].md	
+++ /dev/null
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-
-<h1>How to Watch Shortcut Romeo Tamil Movie Online in HD Quality</h1>
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-<ol>
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diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/kd_one_stage.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/kd_one_stage.py
deleted file mode 100644
index fb66b5152cdeb1dd9698cff011108de3f3f12ac2..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/kd_one_stage.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from pathlib import Path
-
-import mmcv
-import torch
-from mmcv.runner import load_checkpoint
-
-from .. import build_detector
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class KnowledgeDistillationSingleStageDetector(SingleStageDetector):
-    r"""Implementation of `Distilling the Knowledge in a Neural Network.
-    <https://arxiv.org/abs/1503.02531>`_.
-
-    Args:
-        teacher_config (str | dict): Config file path
-            or the config object of teacher model.
-        teacher_ckpt (str, optional): Checkpoint path of teacher model.
-            If left as None, the model will not load any weights.
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 teacher_config,
-                 teacher_ckpt=None,
-                 eval_teacher=True,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
-                         pretrained)
-        self.eval_teacher = eval_teacher
-        # Build teacher model
-        if isinstance(teacher_config, (str, Path)):
-            teacher_config = mmcv.Config.fromfile(teacher_config)
-        self.teacher_model = build_detector(teacher_config['model'])
-        if teacher_ckpt is not None:
-            load_checkpoint(
-                self.teacher_model, teacher_ckpt, map_location='cpu')
-
-    def forward_train(self,
-                      img,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None):
-        """
-        Args:
-            img (Tensor): Input images of shape (N, C, H, W).
-                Typically these should be mean centered and std scaled.
-            img_metas (list[dict]): A List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                :class:`mmdet.datasets.pipelines.Collect`.
-            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
-                boxes can be ignored when computing the loss.
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        x = self.extract_feat(img)
-        with torch.no_grad():
-            teacher_x = self.teacher_model.extract_feat(img)
-            out_teacher = self.teacher_model.bbox_head(teacher_x)
-        losses = self.bbox_head.forward_train(x, out_teacher, img_metas,
-                                              gt_bboxes, gt_labels,
-                                              gt_bboxes_ignore)
-        return losses
-
-    def cuda(self, device=None):
-        """Since teacher_model is registered as a plain object, it is necessary
-        to put the teacher model to cuda when calling cuda function."""
-        self.teacher_model.cuda(device=device)
-        return super().cuda(device=device)
-
-    def train(self, mode=True):
-        """Set the same train mode for teacher and student model."""
-        if self.eval_teacher:
-            self.teacher_model.train(False)
-        else:
-            self.teacher_model.train(mode)
-        super().train(mode)
-
-    def __setattr__(self, name, value):
-        """Set attribute, i.e. self.name = value
-
-        This reloading prevent the teacher model from being registered as a
-        nn.Module. The teacher module is registered as a plain object, so that
-        the teacher parameters will not show up when calling
-        ``self.parameters``, ``self.modules``, ``self.children`` methods.
-        """
-        if name == 'teacher_model':
-            object.__setattr__(self, name, value)
-        else:
-            super().__setattr__(name, value)
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Aram Book By Jayamohan Pdf Free Download !!TOP!!.md b/spaces/rorallitri/biomedical-language-models/logs/Aram Book By Jayamohan Pdf Free Download !!TOP!!.md
deleted file mode 100644
index a50bf2e4f031197467e3d6a63d5466e7f5925002..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Aram Book By Jayamohan Pdf Free Download !!TOP!!.md	
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--- a/spaces/rorallitri/biomedical-language-models/logs/Automotive Expert 9.61 Crack What You Need to Know Before You Download and Run It.md	
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deleted file mode 100644
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--- a/spaces/rorallitri/biomedical-language-models/logs/Bangladesh Booty 2 XXX [Indian Porn Movie]..md	
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Improve Your Editorial Cartooning Skills with This Pdf Free Resource.md b/spaces/rorallitri/biomedical-language-models/logs/Improve Your Editorial Cartooning Skills with This Pdf Free Resource.md
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diff --git a/spaces/runa91/bite_gradio/src/stacked_hourglass/train.py b/spaces/runa91/bite_gradio/src/stacked_hourglass/train.py
deleted file mode 100644
index ce3f2b31ef8c6f60a2400f1f2ea05cb764b6f94c..0000000000000000000000000000000000000000
--- a/spaces/runa91/bite_gradio/src/stacked_hourglass/train.py
+++ /dev/null
@@ -1,210 +0,0 @@
-
-# scripts/train.py --workers 12 --checkpoint project22_no3dcgloss_smaldogsilvia_v0 --loss-weight-path barc_loss_weights_no3dcgloss.json --config barc_cfg_train.yaml start --model-file-hg hg_ksp_fromnewanipose_stanext_v0/checkpoint.pth.tar --model-file-3d barc_normflow_pret/checkpoint.pth.tar
-
-import torch
-import torch.backends.cudnn
-import torch.nn.parallel
-from tqdm import tqdm
-import os
-import json
-import pathlib
-
-import sys
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../', 'src'))
-# from stacked_hourglass.loss import joints_mse_loss
-from stacked_hourglass.loss import joints_mse_loss_onKPloc
-from stacked_hourglass.utils.evaluation import accuracy, AverageMeter, final_preds, get_preds, get_preds_soft
-from stacked_hourglass.utils.transforms import fliplr, flip_back
-from stacked_hourglass.utils.visualization import save_input_image_with_keypoints
-
-
-def do_training_step(model, optimiser, input, target, meta, data_info, target_weight=None):
-    assert model.training, 'model must be in training mode.'
-    assert len(input) == len(target), 'input and target must contain the same number of examples.'
-
-    with torch.enable_grad():
-        # Forward pass and loss calculation.
-        output = model(input)
-
-        # original: loss = sum(joints_mse_loss(o, target, target_weight) for o in output)
-        # NEW: 
-        loss = sum(joints_mse_loss_onKPloc(o, target, meta, target_weight) for o in output)
-
-        # Backward pass and parameter update.
-        optimiser.zero_grad()
-        loss.backward()
-        optimiser.step()
-
-    return output[-1], loss.item()
-
-
-def do_training_epoch(train_loader, model, device, data_info, optimiser, quiet=False, acc_joints=None):
-    losses = AverageMeter()
-    accuracies = AverageMeter()
-
-    # Put the model in training mode.
-    model.train()
-
-    iterable = enumerate(train_loader)
-    progress = None
-    if not quiet:
-        progress = tqdm(iterable, desc='Train', total=len(train_loader), ascii=True, leave=False)
-        iterable = progress
-
-    for i, (input, target, meta) in iterable:
-        input, target = input.to(device), target.to(device, non_blocking=True)
-        target_weight = meta['target_weight'].to(device, non_blocking=True)
-
-        output, loss = do_training_step(model, optimiser, input, target, meta, data_info, target_weight)
-
-        acc = accuracy(output, target, acc_joints)
-
-        # measure accuracy and record loss
-        losses.update(loss, input.size(0))
-        accuracies.update(acc[0], input.size(0))
-
-        # Show accuracy and loss as part of the progress bar.
-        if progress is not None:
-            progress.set_postfix_str('Loss: {loss:0.4f}, Acc: {acc:6.2f}'.format(
-                loss=losses.avg,
-                acc=100 * accuracies.avg
-            ))
-
-    return losses.avg, accuracies.avg
-
-
-def do_validation_step(model, input, target, meta, data_info, target_weight=None, flip=False):
-    # assert not model.training, 'model must be in evaluation mode.'
-    assert len(input) == len(target), 'input and target must contain the same number of examples.'
-
-    # Forward pass and loss calculation.
-    output = model(input)
-
-    # original: loss = sum(joints_mse_loss(o, target, target_weight) for o in output)
-    # NEW: 
-    loss = sum(joints_mse_loss_onKPloc(o, target, meta, target_weight) for o in output)
-
-
-    # Get the heatmaps.
-    if flip:
-        # If `flip` is true, perform horizontally flipped inference as well. This should
-        # result in more robust predictions at the expense of additional compute.
-        flip_input = fliplr(input)
-        flip_output = model(flip_input)
-        flip_output = flip_output[-1].cpu()
-        flip_output = flip_back(flip_output.detach(), data_info.hflip_indices)
-        heatmaps = (output[-1].cpu() + flip_output) / 2
-    else:
-        heatmaps = output[-1].cpu()
-
-
-    return heatmaps, loss.item()
-
-
-def do_validation_epoch(val_loader, model, device, data_info, flip=False, quiet=False, acc_joints=None, save_imgs_path=None):
-    losses = AverageMeter()
-    accuracies = AverageMeter()
-    predictions = [None] * len(val_loader.dataset)
-
-    if save_imgs_path is not None:
-        pathlib.Path(save_imgs_path).mkdir(parents=True, exist_ok=True) 
-
-    # Put the model in evaluation mode.
-    model.eval()
-
-    iterable = enumerate(val_loader)
-    progress = None
-    if not quiet:
-        progress = tqdm(iterable, desc='Valid', total=len(val_loader), ascii=True, leave=False)
-        iterable = progress
-
-    for i, (input, target, meta) in iterable:
-        # Copy data to the training device (eg GPU).
-        input = input.to(device, non_blocking=True)
-        target = target.to(device, non_blocking=True)
-        target_weight = meta['target_weight'].to(device, non_blocking=True)
-
-        # import pdb; pdb.set_trace()
-
-        heatmaps, loss = do_validation_step(model, input, target, meta, data_info, target_weight, flip)
-
-        # Calculate PCK from the predicted heatmaps.
-        acc = accuracy(heatmaps, target.cpu(), acc_joints)
-
-        # Calculate locations in original image space from the predicted heatmaps.
-        preds = final_preds(heatmaps, meta['center'], meta['scale'], [64, 64])
-        # NEW for visualization: (and redundant, but for visualization)
-        preds_unprocessed, preds_unprocessed_maxval = get_preds_soft(heatmaps, return_maxval=True)
-        # preds_unprocessed, preds_unprocessed_norm, preds_unprocessed_maxval = get_preds_soft(heatmaps, return_maxval=True, norm_and_unnorm_coords=True)
-
-
-        # import pdb; pdb.set_trace()
-
-        ind = 0
-        for example_index, pose in zip(meta['index'], preds):
-            predictions[example_index] = pose
-            # NEW for visualization
-            if save_imgs_path is not None:
-                out_name = os.path.join(save_imgs_path, 'res_' + str( example_index.item()) + '.png')
-                pred_unp = preds_unprocessed[ind, :, :]
-
-                pred_unp_maxval = preds_unprocessed_maxval[ind, :, :]
-                pred_unp_prep = torch.cat((pred_unp, pred_unp_maxval), 1)
-                inp_img = input[ind, :, :, :]
-                # the following line (with -1) should not be needed anymore after cvpr (after bugfix01 in data preparation 08.09.2022)
-                # pred_unp_prep[:, :2] = pred_unp_prep[:, :2] - 1
-                # save_input_image_with_keypoints(inp_img, pred_unp_prep, out_path=out_name, threshold=0.1, print_scores=True)    # here we have default ratio_in_out=4.
-
-                # NEW: 08.09.2022 after bugfix01
-
-                # import pdb; pdb.set_trace()
-
-                pred_unp_prep[:, :2] = pred_unp_prep[:, :2] * 4 
-
-                if 'name' in meta.keys():       # we do this for the stanext set
-                    name = meta['name'][ind]
-                    out_path_keyp_img = os.path.join(os.path.dirname(out_name), name)
-                    out_path_json = os.path.join(os.path.dirname(out_name), name).replace('_vis', '_json').replace('.jpg', '.json')
-                    if not os.path.exists(os.path.dirname(out_path_json)):
-                        os.makedirs(os.path.dirname(out_path_json))
-                    if not os.path.exists(os.path.dirname(out_path_keyp_img)):
-                        os.makedirs(os.path.dirname(out_path_keyp_img))  
-                    save_input_image_with_keypoints(inp_img, pred_unp_prep, out_path=out_path_keyp_img, ratio_in_out=1.0, threshold=0.1, print_scores=True)    # threshold=0.3
-                    out_name_json = out_path_json   # os.path.join(save_imgs_path, 'res_' + str( example_index.item()) + '.json')
-                    res_dict = {
-                        'pred_joints_256': list(pred_unp_prep.cpu().numpy().astype(float).reshape((-1))),
-                        'center': list(meta['center'][ind, :].cpu().numpy().astype(float).reshape((-1))),
-                        'scale': meta['scale'][ind].item()}
-                    with open(out_name_json, 'w') as outfile: json.dump(res_dict, outfile)
-                else:
-                    save_input_image_with_keypoints(inp_img, pred_unp_prep, out_path=out_name, ratio_in_out=1.0, threshold=0.1, print_scores=True)    # threshold=0.3
-
-
-
-                '''# animalpose_hg8_v0 (did forget to subtract 1 in dataset)
-                pred_unp_prep[:, :2] = pred_unp_prep[:, :2] * 4     ############ Why is this necessary???
-                pred_unp_prep[:, :2] = pred_unp_prep[:, :2] - 1
-                save_input_image_with_keypoints(inp_img, pred_unp_prep, out_path=out_name, ratio_in_out=1.0, threshold=0.1, print_scores=True)    # threshold=0.3
-                out_name_json = os.path.join(save_imgs_path, 'res_' + str( example_index.item()) + '.json')
-                res_dict = {
-                    'pred_joints_256': list(pred_unp_prep.cpu().numpy().astype(float).reshape((-1))),
-                    'center': list(meta['center'][ind, :].cpu().numpy().astype(float).reshape((-1))),
-                    'scale': meta['scale'][ind].item()}
-                with open(out_name_json, 'w') as outfile: json.dump(res_dict, outfile)'''
-
-                ind += 1
-
-        # Record accuracy and loss for this batch.
-        losses.update(loss, input.size(0))
-        accuracies.update(acc[0].item(), input.size(0))
-
-        # Show accuracy and loss as part of the progress bar.
-        if progress is not None:
-            progress.set_postfix_str('Loss: {loss:0.4f}, Acc: {acc:6.2f}'.format(
-                loss=losses.avg,
-                acc=100 * accuracies.avg
-            ))
-
-    predictions = torch.stack(predictions, dim=0)
-
-    return losses.avg, accuracies.avg, predictions
diff --git a/spaces/rzimmerdev/lenet_mnist/src/trainer.py b/spaces/rzimmerdev/lenet_mnist/src/trainer.py
deleted file mode 100644
index e3087180778d3ecabd3cf65d982668723a72706f..0000000000000000000000000000000000000000
--- a/spaces/rzimmerdev/lenet_mnist/src/trainer.py
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-from torch import nn, optim
-import pytorch_lightning as pl
-
-
-class LitTrainer(pl.LightningModule):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-        self.optim = optim.Adam(self.parameters(), lr=1e-4)
-        self.loss = nn.CrossEntropyLoss()
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-
-        y_pred = self.model(x).reshape(1, -1)
-        train_loss = self.loss(y_pred, y)
-
-        self.log("train_loss", train_loss)
-        return train_loss
-
-    def validation_step(self, batch, batch_idx):
-        # this is the validation loop
-        x, y = batch
-
-        y_pred = self.model(x).reshape(1, -1)
-        validate_loss = self.loss(y_pred, y)
-
-        self.log("val_loss", validate_loss)
-
-    def test_step(self, batch, batch_idx):
-        # this is the test loop
-        x, y = batch
-
-        y_pred = self.model(x).reshape(1, -1)
-        test_loss = self.loss(y_pred, y)
-
-        self.log("test_loss", test_loss)
-
-    def forward(self, x):
-        return self.model(x)
-
-    def configure_optimizers(self):
-        return self.optim
diff --git a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_mlsd.py b/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_mlsd.py
deleted file mode 100644
index 877469f8ccc7e83efbc0cbe99ae02565d884f25b..0000000000000000000000000000000000000000
--- a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_mlsd.py
+++ /dev/null
@@ -1,224 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from controlnet_aux import MLSDdetector
-from diffusers import ControlNetModel
-from PIL import Image
-
-from diffusion_webui.diffusion_models.controlnet.controlnet_inpaint.pipeline_stable_diffusion_controlnet_inpaint import (
-    StableDiffusionControlNetInpaintPipeline,
-)
-from diffusion_webui.utils.model_list import (
-    controlnet_mlsd_model_list,
-    stable_inpiant_model_list,
-)
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_LIST,
-    get_scheduler_list,
-)
-
-# https://github.com/mikonvergence/ControlNetInpaint
-
-
-class StableDiffusionControlNetInpaintMlsdGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-            self.pipe = (
-                StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                    pretrained_model_name_or_path=stable_model_path,
-                    controlnet=controlnet,
-                    safety_checker=None,
-                    torch_dtype=torch.float16,
-                )
-            )
-
-        self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def load_image(self, image_path):
-        image = np.array(image_path)
-        image = Image.fromarray(image)
-        return image
-
-    def controlnet_inpaint_mlsd(self, image_path: str):
-        mlsd = MLSDdetector.from_pretrained("lllyasviel/ControlNet")
-        image = image_path["image"].convert("RGB").resize((512, 512))
-        image = np.array(image)
-        image = mlsd(image)
-
-        return image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        controlnet_conditioning_scale: int,
-        scheduler: str,
-        seed_generator: int,
-    ):
-
-        normal_image = image_path["image"].convert("RGB").resize((512, 512))
-        mask_image = image_path["mask"].convert("RGB").resize((512, 512))
-
-        normal_image = self.load_image(image_path=normal_image)
-        mask_image = self.load_image(image_path=mask_image)
-
-        control_image = self.controlnet_inpaint_mlsd(image_path=image_path)
-
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_model_path,
-            scheduler=scheduler,
-        )
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=normal_image,
-            mask_image=mask_image,
-            control_image=control_image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            controlnet_conditioning_scale=controlnet_conditioning_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_mlsd_inpaint_image_file = gr.Image(
-                        source="upload",
-                        tool="sketch",
-                        elem_id="image_upload",
-                        type="pil",
-                        label="Upload",
-                    )
-
-                    controlnet_mlsd_inpaint_prompt = gr.Textbox(
-                        lines=1, placeholder="Prompt", show_label=False
-                    )
-
-                    controlnet_mlsd_inpaint_negative_prompt = gr.Textbox(
-                        lines=1,
-                        show_label=False,
-                        placeholder="Negative Prompt",
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_mlsd_inpaint_stable_model_id = (
-                                gr.Dropdown(
-                                    choices=stable_inpiant_model_list,
-                                    value=stable_inpiant_model_list[0],
-                                    label="Stable Model Id",
-                                )
-                            )
-
-                            controlnet_mlsd_inpaint_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-
-                            controlnet_mlsd_inpaint_num_inference_step = (
-                                gr.Slider(
-                                    minimum=1,
-                                    maximum=100,
-                                    step=1,
-                                    value=50,
-                                    label="Num Inference Step",
-                                )
-                            )
-                            controlnet_mlsd_inpaint_num_images_per_prompt = (
-                                gr.Slider(
-                                    minimum=1,
-                                    maximum=10,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                controlnet_mlsd_inpaint_model_id = gr.Dropdown(
-                                    choices=controlnet_mlsd_model_list,
-                                    value=controlnet_mlsd_model_list[0],
-                                    label="Controlnet Model Id",
-                                )
-                                controlnet_mlsd_inpaint_scheduler = gr.Dropdown(
-                                    choices=SCHEDULER_LIST,
-                                    value=SCHEDULER_LIST[0],
-                                    label="Scheduler",
-                                )
-                                controlnet_mlsd_inpaint_controlnet_conditioning_scale = gr.Slider(
-                                    minimum=0.1,
-                                    maximum=1.0,
-                                    step=0.1,
-                                    value=0.5,
-                                    label="Controlnet Conditioning Scale",
-                                )
-
-                                controlnet_mlsd_inpaint_seed_generator = (
-                                    gr.Slider(
-                                        minimum=0,
-                                        maximum=1000000,
-                                        step=1,
-                                        value=0,
-                                        label="Seed Generator",
-                                    )
-                                )
-
-                    controlnet_mlsd_inpaint_predict = gr.Button(
-                        value="Generator"
-                    )
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-            controlnet_mlsd_inpaint_predict.click(
-                fn=StableDiffusionControlNetInpaintMlsdGenerator().generate_image,
-                inputs=[
-                    controlnet_mlsd_inpaint_image_file,
-                    controlnet_mlsd_inpaint_stable_model_id,
-                    controlnet_mlsd_inpaint_model_id,
-                    controlnet_mlsd_inpaint_prompt,
-                    controlnet_mlsd_inpaint_negative_prompt,
-                    controlnet_mlsd_inpaint_num_images_per_prompt,
-                    controlnet_mlsd_inpaint_guidance_scale,
-                    controlnet_mlsd_inpaint_num_inference_step,
-                    controlnet_mlsd_inpaint_controlnet_conditioning_scale,
-                    controlnet_mlsd_inpaint_scheduler,
-                    controlnet_mlsd_inpaint_seed_generator,
-                ],
-                outputs=[output_image],
-            )
diff --git a/spaces/sciling/Face_and_Plate_License_Blur/README.md b/spaces/sciling/Face_and_Plate_License_Blur/README.md
deleted file mode 100644
index 572c6a79d2915f5ede08615ba4b6770e51cf7f72..0000000000000000000000000000000000000000
--- a/spaces/sciling/Face_and_Plate_License_Blur/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Face And Plate License Blur
-emoji: 💻
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: unknown
-python_version: 3.9.15
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sdhsdhk/bingosjj/src/components/external-link.tsx b/spaces/sdhsdhk/bingosjj/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingosjj/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
-  href,
-  children
-}: {
-  href: string
-  children: React.ReactNode
-}) {
-  return (
-    <a
-      href={href}
-      target="_blank"
-      rel="noreferrer"
-      className="inline-flex flex-1 justify-center gap-1 underline"
-    >
-      <span>{children}</span>
-      <svg
-        aria-hidden="true"
-        height="7"
-        viewBox="0 0 6 6"
-        width="7"
-        className="opacity-70"
-      >
-        <path
-          d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
-          fill="currentColor"
-        ></path>
-      </svg>
-    </a>
-  )
-}
diff --git a/spaces/sdhsdhk/bingosjj/src/components/turn-counter.tsx b/spaces/sdhsdhk/bingosjj/src/components/turn-counter.tsx
deleted file mode 100644
index 08a9e488f044802a8600f4d195b106567c35aab4..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingosjj/src/components/turn-counter.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import React from 'react'
-import { Throttling } from '@/lib/bots/bing/types'
-
-export interface TurnCounterProps {
-  throttling?: Throttling
-}
-
-export function TurnCounter({ throttling }: TurnCounterProps) {
-  if (!throttling) {
-    return null
-  }
-
-  return (
-    <div className="turn-counter">
-      <div className="text">
-        <span>{throttling.numUserMessagesInConversation}</span>
-        <span> 共 </span>
-        <span>{throttling.maxNumUserMessagesInConversation}</span>
-      </div>
-      <div className="indicator"></div>
-    </div>
-  )
-}
diff --git a/spaces/seanbethard/whatsapp/README.md b/spaces/seanbethard/whatsapp/README.md
deleted file mode 100644
index f8689ef1d6e09fd6f35bc624007a8d3a86805980..0000000000000000000000000000000000000000
--- a/spaces/seanbethard/whatsapp/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Whatsapp
-emoji: 🌖
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/seanghay/KLEA/commons.py b/spaces/seanghay/KLEA/commons.py
deleted file mode 100644
index cb57f4b4214e5688b5a4c668e08af18cb42d3c93..0000000000000000000000000000000000000000
--- a/spaces/seanghay/KLEA/commons.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/segments-tobias/conex/espnet2/utils/get_default_kwargs.py b/spaces/segments-tobias/conex/espnet2/utils/get_default_kwargs.py
deleted file mode 100644
index 0f11e8af43ef38cad69c530824be702dbfed5981..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/utils/get_default_kwargs.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import inspect
-
-
-class Invalid:
-    """Marker object for not serializable-object"""
-
-
-def get_default_kwargs(func):
-    """Get the default values of the input function.
-
-    Examples:
-        >>> def func(a, b=3):  pass
-        >>> get_default_kwargs(func)
-        {'b': 3}
-
-    """
-
-    def yaml_serializable(value):
-        # isinstance(x, tuple) includes namedtuple, so type is used here
-        if type(value) is tuple:
-            return yaml_serializable(list(value))
-        elif isinstance(value, set):
-            return yaml_serializable(list(value))
-        elif isinstance(value, dict):
-            if not all(isinstance(k, str) for k in value):
-                return Invalid
-            retval = {}
-            for k, v in value.items():
-                v2 = yaml_serializable(v)
-                # Register only valid object
-                if v2 not in (Invalid, inspect.Parameter.empty):
-                    retval[k] = v2
-            return retval
-        elif isinstance(value, list):
-            retval = []
-            for v in value:
-                v2 = yaml_serializable(v)
-                # If any elements in the list are invalid,
-                # the list also becomes invalid
-                if v2 is Invalid:
-                    return Invalid
-                else:
-                    retval.append(v2)
-            return retval
-        elif value in (inspect.Parameter.empty, None):
-            return value
-        elif isinstance(value, (float, int, complex, bool, str, bytes)):
-            return value
-        else:
-            return Invalid
-
-    # params: An ordered mapping of inspect.Parameter
-    params = inspect.signature(func).parameters
-    data = {p.name: p.default for p in params.values()}
-    # Remove not yaml-serializable object
-    data = yaml_serializable(data)
-    return data
diff --git a/spaces/shi-labs/Prompt-Free-Diffusion/lib/model_zoo/__init__.py b/spaces/shi-labs/Prompt-Free-Diffusion/lib/model_zoo/__init__.py
deleted file mode 100644
index 1c0a57a9e5d66ee79319d7390dedf650ffb05caf..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Prompt-Free-Diffusion/lib/model_zoo/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .common.get_model import get_model
-from .common.get_optimizer import get_optimizer
-from .common.get_scheduler import get_scheduler
-from .common.utils import get_unit
diff --git a/spaces/shibing624/ChatPDF/modules/shared.py b/spaces/shibing624/ChatPDF/modules/shared.py
deleted file mode 100644
index 32e74665b400a56fd1b10bbd4a9566fe332e49bd..0000000000000000000000000000000000000000
--- a/spaces/shibing624/ChatPDF/modules/shared.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from modules.presets import COMPLETION_URL, BALANCE_API_URL, USAGE_API_URL, API_HOST
-import os
-import queue
-import openai
-
-class State:
-    interrupted = False
-    multi_api_key = False
-    completion_url = COMPLETION_URL
-    balance_api_url = BALANCE_API_URL
-    usage_api_url = USAGE_API_URL
-
-    def interrupt(self):
-        self.interrupted = True
-
-    def recover(self):
-        self.interrupted = False
-
-    def set_api_host(self, api_host: str):
-        api_host = api_host.rstrip("/")
-        if not api_host.startswith("http"):
-            api_host = f"https://{api_host}"
-        if api_host.endswith("/v1"):
-            api_host = api_host[:-3]
-        self.completion_url = f"{api_host}/v1/chat/completions"
-        self.balance_api_url = f"{api_host}/dashboard/billing/credit_grants"
-        self.usage_api_url = f"{api_host}/dashboard/billing/usage"
-        os.environ["OPENAI_API_BASE"] = api_host
-
-    def reset_api_host(self):
-        self.completion_url = COMPLETION_URL
-        self.balance_api_url = BALANCE_API_URL
-        self.usage_api_url = USAGE_API_URL
-        os.environ["OPENAI_API_BASE"] = f"https://{API_HOST}"
-        return API_HOST
-
-    def reset_all(self):
-        self.interrupted = False
-        self.completion_url = COMPLETION_URL
-
-    def set_api_key_queue(self, api_key_list):
-        self.multi_api_key = True
-        self.api_key_queue = queue.Queue()
-        for api_key in api_key_list:
-            self.api_key_queue.put(api_key)
-
-    def switching_api_key(self, func):
-        if not hasattr(self, "api_key_queue"):
-            return func
-
-        def wrapped(*args, **kwargs):
-            api_key = self.api_key_queue.get()
-            args[0].api_key = api_key
-            ret = func(*args, **kwargs)
-            self.api_key_queue.put(api_key)
-            return ret
-
-        return wrapped
-
-
-state = State()
-
-modules_path = os.path.dirname(os.path.realpath(__file__))
-chuanhu_path = os.path.dirname(modules_path)
diff --git a/spaces/simonduerr/diffdock/utils/diffusion_utils.py b/spaces/simonduerr/diffdock/utils/diffusion_utils.py
deleted file mode 100644
index e1ffde4e19722363b743dae37fa65fdfafb5e90a..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/diffdock/utils/diffusion_utils.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import math
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from scipy.stats import beta
-
-from utils.geometry import axis_angle_to_matrix, rigid_transform_Kabsch_3D_torch
-from utils.torsion import modify_conformer_torsion_angles
-
-
-def t_to_sigma(t_tr, t_rot, t_tor, args):
-    tr_sigma = args.tr_sigma_min ** (1-t_tr) * args.tr_sigma_max ** t_tr
-    rot_sigma = args.rot_sigma_min ** (1-t_rot) * args.rot_sigma_max ** t_rot
-    tor_sigma = args.tor_sigma_min ** (1-t_tor) * args.tor_sigma_max ** t_tor
-    return tr_sigma, rot_sigma, tor_sigma
-
-
-def modify_conformer(data, tr_update, rot_update, torsion_updates):
-    lig_center = torch.mean(data['ligand'].pos, dim=0, keepdim=True)
-    rot_mat = axis_angle_to_matrix(rot_update.squeeze())
-    rigid_new_pos = (data['ligand'].pos - lig_center) @ rot_mat.T + tr_update + lig_center
-
-    if torsion_updates is not None:
-        flexible_new_pos = modify_conformer_torsion_angles(rigid_new_pos,
-                                                           data['ligand', 'ligand'].edge_index.T[data['ligand'].edge_mask],
-                                                           data['ligand'].mask_rotate if isinstance(data['ligand'].mask_rotate, np.ndarray) else data['ligand'].mask_rotate[0],
-                                                           torsion_updates).to(rigid_new_pos.device)
-        R, t = rigid_transform_Kabsch_3D_torch(flexible_new_pos.T, rigid_new_pos.T)
-        aligned_flexible_pos = flexible_new_pos @ R.T + t.T
-        data['ligand'].pos = aligned_flexible_pos
-    else:
-        data['ligand'].pos = rigid_new_pos
-    return data
-
-
-def sinusoidal_embedding(timesteps, embedding_dim, max_positions=10000):
-    """ from https://github.com/hojonathanho/diffusion/blob/master/diffusion_tf/nn.py   """
-    assert len(timesteps.shape) == 1
-    half_dim = embedding_dim // 2
-    emb = math.log(max_positions) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) * -emb)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = F.pad(emb, (0, 1), mode='constant')
-    assert emb.shape == (timesteps.shape[0], embedding_dim)
-    return emb
-
-
-class GaussianFourierProjection(nn.Module):
-    """Gaussian Fourier embeddings for noise levels.
-    from https://github.com/yang-song/score_sde_pytorch/blob/1618ddea340f3e4a2ed7852a0694a809775cf8d0/models/layerspp.py#L32
-    """
-
-    def __init__(self, embedding_size=256, scale=1.0):
-        super().__init__()
-        self.W = nn.Parameter(torch.randn(embedding_size//2) * scale, requires_grad=False)
-
-    def forward(self, x):
-        x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
-        emb = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
-        return emb
-
-
-def get_timestep_embedding(embedding_type, embedding_dim, embedding_scale=10000):
-    if embedding_type == 'sinusoidal':
-        emb_func = (lambda x : sinusoidal_embedding(embedding_scale * x, embedding_dim))
-    elif embedding_type == 'fourier':
-        emb_func = GaussianFourierProjection(embedding_size=embedding_dim, scale=embedding_scale)
-    else:
-        raise NotImplemented
-    return emb_func
-
-
-def get_t_schedule(inference_steps):
-    return np.linspace(1, 0, inference_steps + 1)[:-1]
-
-
-def set_time(complex_graphs, t_tr, t_rot, t_tor, batchsize, all_atoms, device):
-    complex_graphs['ligand'].node_t = {
-        'tr': t_tr * torch.ones(complex_graphs['ligand'].num_nodes).to(device),
-        'rot': t_rot * torch.ones(complex_graphs['ligand'].num_nodes).to(device),
-        'tor': t_tor * torch.ones(complex_graphs['ligand'].num_nodes).to(device)}
-    complex_graphs['receptor'].node_t = {
-        'tr': t_tr * torch.ones(complex_graphs['receptor'].num_nodes).to(device),
-        'rot': t_rot * torch.ones(complex_graphs['receptor'].num_nodes).to(device),
-        'tor': t_tor * torch.ones(complex_graphs['receptor'].num_nodes).to(device)}
-    complex_graphs.complex_t = {'tr': t_tr * torch.ones(batchsize).to(device),
-                               'rot': t_rot * torch.ones(batchsize).to(device),
-                               'tor': t_tor * torch.ones(batchsize).to(device)}
-    if all_atoms:
-        complex_graphs['atom'].node_t = {
-            'tr': t_tr * torch.ones(complex_graphs['atom'].num_nodes).to(device),
-            'rot': t_rot * torch.ones(complex_graphs['atom'].num_nodes).to(device),
-            'tor': t_tor * torch.ones(complex_graphs['atom'].num_nodes).to(device)}
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Build Your Own Vault with Fallout Shelter Mod APK Download.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Build Your Own Vault with Fallout Shelter Mod APK Download.md
deleted file mode 100644
index f1485c20d1362b8c7716ea440eb1e999b96dc232..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Build Your Own Vault with Fallout Shelter Mod APK Download.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-<br />
-<h1>How to Download and Install the Fallout Shelter Mod APK on Your Android Device</h1>
- <p>If you are a fan of post-apocalyptic games, you might have heard of <strong>Fallout Shelter</strong>, a simulation game where you run an underground vault and protect your dwellers from the dangers of the wasteland. The game is free to play and available on various platforms, including Android. However, if you want to enhance your gaming experience, you might want to try using a <strong>mod APK</strong>.</p>
-<h2>download apk fallout shelter mod</h2><br /><p><b><b>Download File</b> &#9913;&#9913;&#9913; <a href="https://ssurll.com/2uNYtc">https://ssurll.com/2uNYtc</a></b></p><br /><br />
- <p>A mod APK is a modified version of an original app that has been altered by third-party developers to add or remove features, change graphics, unlock premium content, or provide unlimited resources. Using a mod APK can give you more fun and freedom in playing your favorite games, as well as save you some money. However, there are also some requirements and risks involved in installing a mod APK, such as compatibility issues, security threats, or legal consequences.</p>
- <p>In this article, we will show you how to download and install the Fallout Shelter mod APK on your Android device, as well as how to enjoy its features and advantages. We will also provide some tips and warnings for using the mod APK safely and responsibly. Follow these steps carefully and you will be able to play Fallout Shelter like never before.</p>
- <h2>How to Download the Fallout Shelter Mod APK</h2>
- <p>The first step is to find a reputable website that offers the Fallout Shelter mod APK. There are many websites that claim to provide mod APKs for various games, but not all of them are trustworthy or reliable. Some of them may contain malware, viruses, or fake files that can harm your device or steal your personal information. Therefore, you need to do some research and check the reviews and ratings of the website before downloading anything from it.</p>
- <p>One of the websites that we recommend is [AN1.com](^1^), which has a large collection of mod APKs for different games, including Fallout Shelter. The website is easy to use and has clear instructions on how to download and install the mod APKs. You can also find screenshots, videos, and descriptions of each mod APK on the website.</p>
- <p>To download the Fallout Shelter mod APK from AN1.com, follow these steps:</p>
- <ol>
-<li>Go to [AN1.com](^1^) on your device browser or computer.</li>
-<li>Search for "Fallout Shelter" in the search bar or browse through the categories.</li>
-<li>Select the Fallout Shelter (MOD, Unlimited Money) 1.15.10 APK from the results.</li>
-<li>Read the information and requirements of the mod APK carefully.</li>
-<li>Tap on Download (261 MB) at the bottom of the page.</li>
-<li>Wait for the download to complete.</li>
-</ol>
- <p>After downloading the Fallout Shelter mod APK, you need to check its file size and permissions before installing it. The file size should be around 261 MB, as indicated on AN1.com. If it is significantly smaller or larger than that, it might be corrupted or fake. The permissions should be reasonable and relevant to the app's functions. If they are too intrusive or suspicious, such as accessing your contacts, messages, or camera, you should not install it.</p>
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- <h2>How to Install the Fallout Shelter Mod APK</h2>
- <p>The next step is to install the Fallout Shelter mod APK on your device. However, before you do that, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store. To enable unknown sources, follow these steps:</p>
- <ol>
-<li>Go to Settings on your device.</li>
-<li>Tap on Security or Privacy.</li>
-<li>Find and toggle on Unknown Sources or Install Unknown Apps.</li>
-<li>Confirm your choice if prompted.</li>
-</ol>
- <p>After enabling unknown sources, you need to install a file manager app if you don't have one already. A file manager app will help you locate and manage the files on your device, including the APK file that you downloaded. There are many file manager apps available on the Google Play Store, such as ES File Explorer, File Manager, or Files by Google. You can choose any of them and install them on your device.</p>
- <p>Once you have a file manager app, you need to locate the Fallout Shelter mod APK file on your device or transfer it from your computer if you downloaded it there. To locate the APK file on your device, follow these steps:</p>
- <ol>
-<li>Open the file manager app on your device.</li>
-<li>Go to the Downloads folder or the folder where you saved the APK file.</li>
-<li>Find and tap on the Fallout Shelter (MOD, Unlimited Money) 1.15.10 APK file.</li>
-</ol>
- <p>To transfer the APK file from your computer to your device, follow these steps:</p>
- <ol>
-<li>Connect your device to your computer using a USB cable or Wi-Fi.</li>
-<li>Open the file explorer on your computer and find the APK file.</li>
-<li>Copy or drag and drop the APK file to your device's storage.</li>
-<li>Eject or disconnect your device from your computer.</li>
-</ol>
- <p>After locating or transferring the APK file, you are ready to install it. To install the Fallout Shelter mod APK, follow these steps:</p>
- <ol>
-<li>Tap on the APK file and confirm if asked.</li>
-<li>Read and accept the permissions that the app requests.</li>
-<li>Wait for the installation to complete.</li>
-<li>Tap on Open or Done when finished.</li>
-</ol>
- <h2>How to Enjoy the Fallout Shelter Mod APK</h2>
- <p>The final step is to enjoy the Fallout Shelter mod APK and its features. To do that, you need to launch the game and create or load your vault. You can start a new game or continue from where you left off in the original game. The mod APK will not affect your progress or data in the original game, so you can switch between them anytime.</p>
- <p>Once you are in the game, you can explore the features and advantages of the mod APK. Some of them are:</p>
- <ul>
-<li><strong>Unlimited money</strong>: You will have unlimited caps, lunchboxes, and Nuka-Cola Quantum in your vault. You can use them to build rooms, upgrade facilities, buy items, and speed up tasks.</li>
-<li><strong>Unlimited resources</strong>: You will have unlimited water, food, power, and stimpacks in your vault. You can use them to keep your dwellers happy, healthy, and productive.</li>
-<li><strong>No waiting time</strong>: You will not have to wait for anything in the game. You can instantly complete quests, objectives, training, crafting, and exploring.</li>
-<li><strong>No ads</strong>: You will not see any ads or pop-ups in the game. You can enjoy a smooth and uninterrupted gaming experience.</li>
-</ul>
- <p>However, you should also be careful of potential bugs and compatibility issues that may arise from using the mod APK. Some of them are:</p>
- <ul>
-<li><strong>Data loss</strong>: You may lose some of your data or progress if the mod APK crashes or malfunctions. You should always back up your data before using the mod APK or use a different device for it.</li>
-<li><strong>Ban risk</strong>: You may get banned from the game or online services if you use the mod APK online or connect it to your social media accounts. You should always play offline or use a fake account for it.</li>
-<li><strong>Virus risk</strong>: You may get infected by malware or viruses if you download or install a fake or corrupted mod APK. You should always scan your files before using them or use a trusted website for it.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download and install the Fallout Shelter mod APK on your Android device, as well as how to enjoy its features and advantages. We have also provided some tips and warnings for using the mod APK safely and responsibly. By following these steps carefully, you will be able to play Fallout Shelter like never before and have more fun and freedom in playing your favorite post-apocalyptic game.</p>
- <p>We hope you found this article helpful and informative. If you have any questions, feedback, or suggestions, please feel free to leave a comment below. We would love to hear from you and help you with any issues you may have. Thank you for reading and happy gaming!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions and answers about the Fallout Shelter mod APK:</p>
- <ol>
-<li><strong>What is the latest version of the Fallout Shelter mod APK?</strong></li>
-<p>The latest version of the Fallout Shelter mod APK is 1.15.10, which was released on September 8, 2021. It is compatible with Android 4.1 and up and requires 261 MB of storage space.</p>
-<li><strong>Is the Fallout Shelter mod APK safe to use?</strong></li>
-<p>The Fallout Shelter mod APK is generally safe to use, as long as you download it from a reputable website and check its file size and permissions before installing it. However, you should also be aware of the potential risks and consequences of using a mod APK, such as data loss, ban risk, or virus risk. You should always use the mod APK at your own discretion and responsibility.</p>
-<li><strong>Can I use the Fallout Shelter mod APK online or with my friends?</strong></li>
-<p>We do not recommend using the Fallout Shelter mod APK online or with your friends, as it may cause compatibility issues, errors, or bans. The mod APK is designed for offline and single-player use only. If you want to play online or with your friends, you should use the original game from the Google Play Store.</p>
-<li><strong>Can I update the Fallout Shelter mod APK?</strong></li>
-<p>You can update the Fallout Shelter mod APK by downloading and installing the latest version from the same website that you got it from. However, you should note that updating the mod APK may erase your data or progress in the game, so you should back up your data before updating it. You should also check the compatibility and requirements of the new version before installing it.</p>
-<li><strong>Can I uninstall the Fallout Shelter mod APK?</strong></li>
-<p>You can uninstall the Fallout Shelter mod APK by following these steps:</p>
- <ol>
-<li>Go to Settings on your device.</li>
-<li>Tap on Apps or Applications.</li>
-<li>Find and tap on Fallout Shelter.</li>
-<li>Tap on Uninstall and confirm if asked.</li>
-</ol>
- <p>You can also uninstall the Fallout Shelter mod APK by using a file manager app and deleting the APK file from your device storage.</p>
-</ol></p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 2022 Live Lineup Tickets and Everything You Need to Know.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 2022 Live Lineup Tickets and Everything You Need to Know.md
deleted file mode 100644
index e3fb0fd30b233b0835d216a456b29d52850019ac..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download 2022 Live Lineup Tickets and Everything You Need to Know.md	
+++ /dev/null
@@ -1,84 +0,0 @@
-<br />
-<h1>Download 2022 Live: Everything You Need to Know</h1>
-<p>If you are a fan of rock music, you probably have heard of Download Festival, the UK's premier rock event that takes place every year at Donington Park. But do you know what happened at Download 2022, the first full-size festival since 2019? And do you know what to expect from Download 2023, the next edition of the festival that promises to be even bigger and better? In this article, we will tell you everything you need to know about Download Festival, from its history and features to its latest and future happenings. Read on and get ready to rock!</p>
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- <h2>What is Download Festival?</h2>
-<p>Download Festival is a mammoth rock event that showcases some of the biggest and best names in the genre, as well as emerging and alternative acts. It is based at Donington Park, the spiritual home of rock, where legendary concerts by Metallica, AC/DC, Guns N' Roses, and many others have taken place. Download Festival attracts tens of thousands of rock fans every year, who enjoy not only the live music but also the camping, the village, the late-night entertainment, and the unique atmosphere of the festival.</p>
- <h3>A brief history of Download Festival</h3>
-<p>Download Festival was launched in 2003 as a successor to the Monsters of Rock festival that ran from 1980 to 1996 at Donington Park. The first edition of Download featured headliners Iron Maiden, Audioslave, and Marilyn Manson, and drew around 40,000 attendees. Since then, Download has grown in size and popularity, hosting some of the most iconic bands and artists in rock history, such as Black Sabbath, Slipknot, Linkin Park, Foo Fighters, Muse, Rammstein, System of a Down, Green Day, Kiss, and many more. Download has also expanded to other countries, such as France, Spain, Australia, and Japan.</p>
- <h3>The main features of Download Festival</h3>
-<p>Download Festival has four main stages: the Main Stage, where the headliners and the biggest acts perform; the Second Stage, where the sub-headliners and the alternative acts play; the Avalanche Stage, where the punk and hardcore bands rock; and the Dogtooth Stage, where the metal and heavy bands shred. In addition to these stages, Download also has other attractions, such as the WWE NXT UK Arena, where wrestling matches take place; the Circus of Horrors Tent, where freaky shows are performed; the Comedy Tent, where hilarious acts make people laugh; and the Doghouse Tent, where DJs and club nights keep people dancing until late.</p>
- <h2>What happened at Download 2022?</h2>
-<p>Download 2022 was a special edition of the festival, as it marked the return of live music after two years of absence due to the pandemic. It was also the first full-size festival since 2019, as in 2021 there was only a pilot event with a reduced capacity of 10,000 people. Download 2022 took place from June 8 to June 12 at Donington Park, and featured three days of live music from June 10 to June 12. It was headlined by Kiss, Iron Maiden, and Biffy Clyro.</p>
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-<p>The headliners of Download 2022 delivered killer shows that thrilled the crowd. Kiss opened the festival on Friday with a spectacular performance that included pyrotechnics, confetti cannons, flying stunts, and classic hits like "Rock and Roll All Nite", "Detroit Rock City", and "I Was Made for Lovin' You". Iron Maiden closed the festival on Sunday with. a powerful show that featured a giant Eddie, a Spitfire plane, and songs like "The Trooper", "Fear of the Dark", and "Run to the Hills". Biffy Clyro headlined on Saturday with a dynamic and energetic set that included songs like "Many of Horror", "Bubbles", and "Mountains". Some of the other highlights of Download 2022 were Deftones, Korn, Megadeth, The Offspring, Rise Against, A Day to Remember, Frank Carter & The Rattlesnakes, and Creeper.</p>
- <h3>The changes and improvements of Download 2022</h3>
-<p>Download 2022 also introduced some changes and improvements to the festival experience. One of them was the use of cashless wristbands, which allowed people to pay for food, drinks, merchandise, and other services without using cash or cards. Another one was the expansion of the campsite, which offered more space and facilities for the campers. A third one was the implementation of Covid-19 safety measures, such as testing, tracing, sanitizing, and distancing, to ensure the health and well-being of everyone at the festival.</p>
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- <h2>What to expect from Download 2023?</h2>
-<p>Download 2023 is already in the works, and it promises to be another epic edition of the festival. Here are some of the details that have been announced so far:</p>
- <h3>The dates and location of Download 2023</h3>
-<p>Download 2023 will take place from June 7 to June 11 at Donington Park. It will feature four days of live music from June 8 to June 11.</p>
- <h3>The tickets and prices of Download 2023</h3>
-<p>The tickets for Download 2023 are already on sale, and they are available in different options: weekend tickets with or without camping, day tickets, RIP tickets with luxury accommodation, and accessibility tickets for disabled customers. The prices range from £95 for a single day ticket to £595 for a RIP ticket with camping.</p>
- <h3>The lineup and rumors of Download 2023</h3>
-<p>The lineup for Download 2023 has not been revealed yet, but there are already some rumors and speculations about who might play at the festival. Some of the names that have been mentioned are Metallica, AC/DC, Guns N' Roses, Foo Fighters, Muse, Rammstein, System of a Down, Green Day, Slipknot, Linkin Park, Rage Against the Machine, Tool, Nine Inch Nails, and My Chemical Romance.</p>
- <h2>Conclusion</h2>
-<p>Download Festival is one of the best rock events in the world, and it has proven its strength and resilience after two years of challenges due to the pandemic. Download 2022 was a triumphant return of live music that delighted both the fans and the artists. Download 2023 is already shaping up to be another amazing edition of the festival that will rock Donington Park once again. If you are a rock lover, you should not miss Download Festival. It is an experience that you will never forget.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Download Festival:</p>
-<ul>
-<li><b>Q: How can I get to Download Festival?</b></li>
-<li>A: You can get to Download Festival by car, bus, train, plane, or shuttle. There are parking options available for car drivers. There are coach services offered by Big Green Coach from various locations in the UK. There are train stations near Donington Park that connect with other major cities. There are airports near Donington Park that offer flights from different countries. There are shuttle buses that run from the train stations and the airports to the festival site.</li>
-<li><b>Q: What can I bring to Download Festival?</b></li>
-<li>A: You can bring the following items to Download Festival: your ticket, your ID, your cashless wristband, your camping gear, your clothes, your toiletries, your phone, your charger, your earplugs, your sunscreen, your raincoat, your hat, your sunglasses, your water bottle, your snacks, and your medication. You cannot bring the following items to Download Festival: glass bottles, cans, alcohol, drugs, weapons, fireworks, flares, lasers, drones, umbrellas, gazebos, animals, or anything that could harm yourself or others.</li>
-<li><b>Q: What can I do at Download Festival besides watching the bands?</b></li>
-<li>A: You can do many things at Download Festival besides watching the bands. You can visit the village, where you can find food stalls, bars, shops, games, rides, and other attractions. You can watch wrestling matches at the WWE NXT UK Arena. You can see freaky shows at the Circus of Horrors Tent. You can laugh at comedy acts at the Comedy Tent. You can dance at the Doghouse Tent. You can also explore the campsite, where you can meet new friends, join activities, and have fun.</li>
-<li><b>Q: How can I stay safe and healthy at Download Festival?</b></li>
-<li>A: You can stay safe and healthy at Download Festival by following these tips: drink plenty of water and stay hydrated. Eat well and avoid food poisoning. Wear sunscreen and protect yourself from the sun. Wear earplugs and protect your hearing. Dress appropriately and be prepared for any weather. Keep your valuables secure and don't leave them unattended. Follow the Covid-19 safety measures and respect the rules. Seek help if you feel unwell or need assistance.</li>
-<li><b>Q: How can I stay updated on Download Festival news and information?</b></li>
-<li>A: You can stay updated on Download Festival news and information by visiting the official website of the festival (www.downloadfestival.co.uk), where you can find everything you need to know about the festival. You can also follow the social media accounts of the festival (Facebook, Twitter, Instagram, YouTube), where you can get the latest updates, announcements, videos, photos, and more. You can also sign up for the newsletter of the festival (www.downloadfestival.co.uk/newsletter), where you can get exclusive offers, competitions, and content.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download App APK Mod and Enhance Your Gaming Experience.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download App APK Mod and Enhance Your Gaming Experience.md
deleted file mode 100644
index 5aff3696b86351b2586b11402c20958424aeda05..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download App APK Mod and Enhance Your Gaming Experience.md	
+++ /dev/null
@@ -1,103 +0,0 @@
-
-<h1>Download App APK Mod: What, Why and How</h1>
-<p>If you are an avid user of Android apps, you might have heard of the term "APK mod". But what exactly is an APK mod, and why would you want to download one? In this article, we will explain what an APK mod is, what are the benefits and risks of using one, and how to download one safely and effectively.</p>
-<h2>What is an APK mod?</h2>
-<h3>Definition and examples of APK mods</h3>
-<p>An APK mod is a modified version of an original Android application package (APK) file. An APK file is the format used by Android to distribute and install apps on devices. An APK mod is created by altering or adding some features to the original app, such as removing ads, unlocking in-app purchases, adding cheats, changing graphics, etc.</p>
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-<p>Some examples of popular apps that have APK mods are:</p>
-<ul>
-<li>Spotify Premium Mod: This APK mod allows you to enjoy Spotify Premium features for free, such as unlimited skips, offline mode, ad-free listening, etc.</li>
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-</ul>
-<h3>Benefits and risks of using APK mods</h3>
-<p>Using APK mods can have some benefits and risks, depending on the type and source of the mod. Some of the benefits are:</p>
-<ul>
-<li>You can access premium features or content that are otherwise unavailable or costly in the original app.</li>
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-<li>You can customize your app experience according to your preferences and needs.</li>
-</ul>
-<p>Some of the risks are:</p>
-<ul>
-<li>You might expose your device or data to malware or viruses that can harm your device or steal your information.</li>
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-</ul>
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-<h4>Legal and ethical issues</h4>
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diff --git a/spaces/siya02/Konakni-TTS/ttsv/src/glow_tts/data_utils.py b/spaces/siya02/Konakni-TTS/ttsv/src/glow_tts/data_utils.py
deleted file mode 100644
index b58d84b3df3de3afb0a6a3bb8fadfd7a592dd602..0000000000000000000000000000000000000000
--- a/spaces/siya02/Konakni-TTS/ttsv/src/glow_tts/data_utils.py
+++ /dev/null
@@ -1,274 +0,0 @@
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import commons
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import text_to_sequence
-
-class TextMelLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio,text pairs
-    2) normalizes text and converts them to sequences of one-hot vectors
-    3) computes mel-spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.text_cleaners = hparams.text_cleaners
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.load_mel_from_disk = hparams.load_mel_from_disk
-        self.add_noise = hparams.add_noise
-        self.symbols = hparams.punc + hparams.chars
-        self.add_blank = getattr(hparams, "add_blank", False)  # improved version
-        self.stft = commons.TacotronSTFT(
-            hparams.filter_length,
-            hparams.hop_length,
-            hparams.win_length,
-            hparams.n_mel_channels,
-            hparams.sampling_rate,
-            hparams.mel_fmin,
-            hparams.mel_fmax,
-        )
-        random.seed(1234)
-        random.shuffle(self.audiopaths_and_text)
-
-    def get_mel_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
-        text = self.get_text(text)
-        mel = self.get_mel(audiopath)
-        return (text, mel)
-
-    def get_mel(self, filename):
-        if not self.load_mel_from_disk:
-            audio, sampling_rate = load_wav_to_torch(filename)
-            if sampling_rate != self.stft.sampling_rate:
-                raise ValueError(
-                    "{} {} SR doesn't match target {} SR".format(
-                        sampling_rate, self.stft.sampling_rate
-                    )
-                )
-            if self.add_noise:
-                audio = audio + torch.rand_like(audio)
-            audio_norm = audio / self.max_wav_value
-            audio_norm = audio_norm.unsqueeze(0)
-            melspec = self.stft.mel_spectrogram(audio_norm)
-            melspec = torch.squeeze(melspec, 0)
-        else:
-            melspec = torch.from_numpy(np.load(filename))
-            assert (
-                melspec.size(0) == self.stft.n_mel_channels
-            ), "Mel dimension mismatch: given {}, expected {}".format(
-                melspec.size(0), self.stft.n_mel_channels
-            )
-
-        return melspec
-
-    def get_text(self, text):
-        text_norm = text_to_sequence(text, self.symbols, self.text_cleaners)
-        if self.add_blank:
-            text_norm = commons.intersperse(
-                text_norm, len(self.symbols)
-            )  # add a blank token, whose id number is len(symbols)
-        text_norm = torch.IntTensor(text_norm)
-        return text_norm
-
-    def __getitem__(self, index):
-        return self.get_mel_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextMelCollate:
-    """Zero-pads model inputs and targets based on number of frames per step"""
-
-    def __init__(self, n_frames_per_step=1):
-        self.n_frames_per_step = n_frames_per_step
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and mel-spectrogram
-        PARAMS
-        ------
-        batch: [text_normalized, mel_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        input_lengths, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True
-        )
-        max_input_len = input_lengths[0]
-
-        text_padded = torch.LongTensor(len(batch), max_input_len)
-        text_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            text = batch[ids_sorted_decreasing[i]][0]
-            text_padded[i, : text.size(0)] = text
-
-        # Right zero-pad mel-spec
-        num_mels = batch[0][1].size(0)
-        max_target_len = max([x[1].size(1) for x in batch])
-        if max_target_len % self.n_frames_per_step != 0:
-            max_target_len += (
-                self.n_frames_per_step - max_target_len % self.n_frames_per_step
-            )
-            assert max_target_len % self.n_frames_per_step == 0
-
-        # include mel padded
-        mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
-        mel_padded.zero_()
-        output_lengths = torch.LongTensor(len(batch))
-        for i in range(len(ids_sorted_decreasing)):
-            mel = batch[ids_sorted_decreasing[i]][1]
-            mel_padded[i, :, : mel.size(1)] = mel
-            output_lengths[i] = mel.size(1)
-
-        return text_padded, input_lengths, mel_padded, output_lengths
-
-
-"""Multi speaker version"""
-
-
-class TextMelSpeakerLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio, speaker_id, text pairs
-    2) normalizes text and converts them to sequences of one-hot vectors
-    3) computes mel-spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.text_cleaners = hparams.text_cleaners
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.load_mel_from_disk = hparams.load_mel_from_disk
-        self.add_noise = hparams.add_noise
-        self.symbols = hparams.punc + hparams.chars
-        self.add_blank = getattr(hparams, "add_blank", False)  # improved version
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 190)
-        self.stft = commons.TacotronSTFT(
-            hparams.filter_length,
-            hparams.hop_length,
-            hparams.win_length,
-            hparams.n_mel_channels,
-            hparams.sampling_rate,
-            hparams.mel_fmin,
-            hparams.mel_fmax,
-        )
-
-        self._filter_text_len()
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-
-    def _filter_text_len(self):
-        audiopaths_sid_text_new = []
-        for audiopath, sid, text in self.audiopaths_sid_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_sid_text_new.append([audiopath, sid, text])
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-
-    def get_mel_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, text = (
-            audiopath_sid_text[0],
-            audiopath_sid_text[1],
-            audiopath_sid_text[2],
-        )
-        text = self.get_text(text)
-        mel = self.get_mel(audiopath)
-        sid = self.get_sid(sid)
-        return (text, mel, sid)
-
-    def get_mel(self, filename):
-        if not self.load_mel_from_disk:
-            audio, sampling_rate = load_wav_to_torch(filename)
-            if sampling_rate != self.stft.sampling_rate:
-                raise ValueError(
-                    "{} {} SR doesn't match target {} SR".format(
-                        sampling_rate, self.stft.sampling_rate
-                    )
-                )
-            if self.add_noise:
-                audio = audio + torch.rand_like(audio)
-            audio_norm = audio / self.max_wav_value
-            audio_norm = audio_norm.unsqueeze(0)
-            melspec = self.stft.mel_spectrogram(audio_norm)
-            melspec = torch.squeeze(melspec, 0)
-        else:
-            melspec = torch.from_numpy(np.load(filename))
-            assert (
-                melspec.size(0) == self.stft.n_mel_channels
-            ), "Mel dimension mismatch: given {}, expected {}".format(
-                melspec.size(0), self.stft.n_mel_channels
-            )
-
-        return melspec
-
-    def get_text(self, text):
-        text_norm = text_to_sequence(text, self.symbols, self.text_cleaners)
-        if self.add_blank:
-            text_norm = commons.intersperse(
-                text_norm, len(self.symbols)
-            )  # add a blank token, whose id number is len(symbols)
-        text_norm = torch.IntTensor(text_norm)
-        return text_norm
-
-    def get_sid(self, sid):
-        sid = torch.IntTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_mel_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextMelSpeakerCollate:
-    """Zero-pads model inputs and targets based on number of frames per step"""
-
-    def __init__(self, n_frames_per_step=1):
-        self.n_frames_per_step = n_frames_per_step
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and mel-spectrogram
-        PARAMS
-        ------
-        batch: [text_normalized, mel_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        input_lengths, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True
-        )
-        max_input_len = input_lengths[0]
-
-        text_padded = torch.LongTensor(len(batch), max_input_len)
-        text_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            text = batch[ids_sorted_decreasing[i]][0]
-            text_padded[i, : text.size(0)] = text
-
-        # Right zero-pad mel-spec
-        num_mels = batch[0][1].size(0)
-        max_target_len = max([x[1].size(1) for x in batch])
-        if max_target_len % self.n_frames_per_step != 0:
-            max_target_len += (
-                self.n_frames_per_step - max_target_len % self.n_frames_per_step
-            )
-            assert max_target_len % self.n_frames_per_step == 0
-
-        # include mel padded & sid
-        mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
-        mel_padded.zero_()
-        output_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-        for i in range(len(ids_sorted_decreasing)):
-            mel = batch[ids_sorted_decreasing[i]][1]
-            mel_padded[i, :, : mel.size(1)] = mel
-            output_lengths[i] = mel.size(1)
-            sid[i] = batch[ids_sorted_decreasing[i]][2]
-
-        return text_padded, input_lengths, mel_padded, output_lengths, sid
diff --git a/spaces/siya02/Konakni-TTS/ttsv/src/hifi_gan/utils.py b/spaces/siya02/Konakni-TTS/ttsv/src/hifi_gan/utils.py
deleted file mode 100644
index 71e9b2c99e053e2d4239074a67d64b834898c348..0000000000000000000000000000000000000000
--- a/spaces/siya02/Konakni-TTS/ttsv/src/hifi_gan/utils.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-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)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + "????????")
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
diff --git a/spaces/skf15963/summary/fengshen/data/sequence_tagging_dataloader/sequence_tagging_collator.py b/spaces/skf15963/summary/fengshen/data/sequence_tagging_dataloader/sequence_tagging_collator.py
deleted file mode 100644
index b21ff7a0f9152ac16cb434078ac8436dcceeec1a..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/data/sequence_tagging_dataloader/sequence_tagging_collator.py
+++ /dev/null
@@ -1,274 +0,0 @@
-from dataclasses import dataclass
-from torch.utils.data._utils.collate import default_collate
-
-import copy
-import torch
-import numpy as np
-
-@dataclass
-class CollatorForLinear:
-    args = None
-    tokenizer = None
-    label2id = None
-
-    def __call__(self, samples):
-        cls_token = "[CLS]"
-        sep_token = "[SEP]"
-        pad_token = 0
-        special_tokens_count = 2
-        segment_id = 0
-
-        features=[]
-
-        for (ex_index, example) in enumerate(samples):
-            tokens = copy.deepcopy(example['text_a'])
-
-            label_ids = [self.label2id[x] for x in example['labels']]
-
-            if len(tokens) > self.args.max_seq_length - special_tokens_count:
-                tokens = tokens[: (self.args.max_seq_length - special_tokens_count)]
-                label_ids = label_ids[: (self.args.max_seq_length - special_tokens_count)]
-
-            tokens += [sep_token]
-            label_ids += [self.label2id["O"]]
-            segment_ids = [segment_id] * len(tokens)
-
-            tokens = [cls_token] + tokens
-            label_ids = [self.label2id["O"]] + label_ids
-            segment_ids = [segment_id] + segment_ids
-
-            input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
-            input_mask = [1] * len(input_ids)
-            input_len = len(label_ids)
-            padding_length = self.args.max_seq_length - len(input_ids)
-
-            input_ids += [pad_token] * padding_length
-            input_mask += [0] * padding_length
-            segment_ids += [segment_id] * padding_length
-            label_ids += [pad_token] * padding_length
-
-            assert len(input_ids) == self.args.max_seq_length
-            assert len(input_mask) == self.args.max_seq_length
-            assert len(segment_ids) == self.args.max_seq_length
-            assert len(label_ids) == self.args.max_seq_length
-
-            features.append({
-                    'input_ids':torch.tensor(input_ids),
-                    'attention_mask':torch.tensor(input_mask),
-                    'input_len':torch.tensor(input_len),
-                    'token_type_ids':torch.tensor(segment_ids),
-                    'labels':torch.tensor(label_ids),
-            })
-
-        return default_collate(features)
-
-@dataclass
-class CollatorForCrf:
-    args = None
-    tokenizer = None
-    label2id = None
-
-    def __call__(self, samples):
-        features = []
-        cls_token = "[CLS]"
-        sep_token = "[SEP]"
-        pad_token = 0
-        special_tokens_count = 2
-        segment_id = 0
-
-        for (ex_index, example) in enumerate(samples):
-            tokens = copy.deepcopy(example['text_a'])
-
-            label_ids = [self.label2id[x] for x in example['labels']]
-
-            if len(tokens) > self.args.max_seq_length - special_tokens_count:
-                tokens = tokens[: (self.args.max_seq_length - special_tokens_count)]
-                label_ids = label_ids[: (self.args.max_seq_length - special_tokens_count)]
-
-            tokens += [sep_token]
-            label_ids += [self.label2id["O"]]
-            segment_ids = [segment_id] * len(tokens)
-
-            tokens = [cls_token] + tokens
-            label_ids = [self.label2id["O"]] + label_ids
-            segment_ids = [segment_id] + segment_ids
-
-            input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
-            input_mask = [1] * len(input_ids)
-            input_len = len(label_ids)
-            padding_length = self.args.max_seq_length - len(input_ids)
-
-            input_ids += [pad_token] * padding_length
-            input_mask += [0] * padding_length
-            segment_ids += [segment_id] * padding_length
-            label_ids += [pad_token] * padding_length
-
-            assert len(input_ids) == self.args.max_seq_length
-            assert len(input_mask) == self.args.max_seq_length
-            assert len(segment_ids) == self.args.max_seq_length
-            assert len(label_ids) == self.args.max_seq_length
-
-            features.append({
-                    'input_ids':torch.tensor(input_ids),
-                    'attention_mask':torch.tensor(input_mask),
-                    'input_len':torch.tensor(input_len),
-                    'token_type_ids':torch.tensor(segment_ids),
-                    'labels':torch.tensor(label_ids),
-            })
-        
-        return default_collate(features)
-
-
-@dataclass
-class CollatorForSpan:
-    args = None
-    tokenizer = None
-    label2id = None
-
-    def __call__(self, samples):
-
-        features = []
-        cls_token = "[CLS]"
-        sep_token = "[SEP]"
-        pad_token = 0
-        special_tokens_count = 2
-        max_entities_count = 100
-        segment_id = 0
-
-        for (ex_index, example) in enumerate(samples):
-            subjects = copy.deepcopy(example['subject'])
-            tokens = copy.deepcopy(example['text_a'])
-            start_ids = [0] * len(tokens)
-            end_ids = [0] * len(tokens)
-            subject_ids = []
-            for subject in subjects:
-                label = subject[0]
-                start = subject[1]
-                end = subject[2]
-                start_ids[start] = self.label2id[label]
-                end_ids[end] = self.label2id[label]
-                subject_ids.append([self.label2id[label], start, end])
-            
-            subject_ids+=[[-1,-1,-1]]*(max_entities_count-len(subject_ids))
-
-            if len(tokens) > self.args.max_seq_length - special_tokens_count:
-                tokens = tokens[: (self.args.max_seq_length - special_tokens_count)]
-                start_ids = start_ids[: (self.args.max_seq_length - special_tokens_count)]
-                end_ids = end_ids[: (self.args.max_seq_length - special_tokens_count)]
-
-            tokens += [sep_token]
-            start_ids += [0]
-            end_ids += [0]
-            segment_ids = [segment_id] * len(tokens)
-
-            tokens = [cls_token] + tokens
-            start_ids = [0] + start_ids
-            end_ids = [0] + end_ids
-            segment_ids = [segment_id] + segment_ids
-
-            input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
-            input_mask = [1] * len(input_ids)
-            input_len = len(input_ids)
-            padding_length = self.args.max_seq_length - len(input_ids)
-
-            input_ids += [pad_token] * padding_length
-            input_mask += [0] * padding_length
-            segment_ids += [segment_id] * padding_length
-            start_ids += [0] * padding_length
-            end_ids += [0] * padding_length
-
-            assert len(input_ids) == self.args.max_seq_length
-            assert len(input_mask) == self.args.max_seq_length
-            assert len(segment_ids) == self.args.max_seq_length
-            assert len(start_ids) == self.args.max_seq_length
-            assert len(end_ids) == self.args.max_seq_length
-
-            features.append({
-                    'input_ids': torch.tensor(np.array(input_ids)),
-                    'attention_mask': torch.tensor(np.array(input_mask)),
-                    'token_type_ids': torch.tensor(np.array(segment_ids)),
-                    'start_positions': torch.tensor(np.array(start_ids)),
-                    'end_positions': torch.tensor(np.array(end_ids)),
-                    "subjects": torch.tensor(np.array(subject_ids)),
-                    'input_len': torch.tensor(np.array(input_len)),
-                })
-        
-        return default_collate(features)
-
-
-@dataclass
-class CollatorForBiaffine:
-    args = None
-    tokenizer = None
-    label2id = None
-
-    
-    def __call__(self, samples):
-
-        features = []
-        cls_token = "[CLS]"
-        sep_token = "[SEP]"
-        pad_token = 0
-        special_tokens_count = 2
-        segment_id = 0
-
-        for (ex_index, example) in enumerate(samples):
-            subjects = copy.deepcopy(example['subject'])
-            tokens = copy.deepcopy(example['text_a'])
-
-            span_labels = np.zeros((self.args.max_seq_length,self.args.max_seq_length))
-            span_labels[:] = self.label2id["O"]
-
-            for subject in subjects:
-                label = subject[0]
-                start = subject[1]
-                end = subject[2]
-                if start < self.args.max_seq_length - special_tokens_count and end < self.args.max_seq_length - special_tokens_count:
-                    span_labels[start + 1, end + 1] = self.label2id[label]
-
-            if len(tokens) > self.args.max_seq_length - special_tokens_count:
-                tokens = tokens[: (self.args.max_seq_length - special_tokens_count)]
-
-            tokens += [sep_token]
-            span_labels[len(tokens), :] = self.label2id["O"]
-            span_labels[:, len(tokens)] = self.label2id["O"]
-            segment_ids = [segment_id] * len(tokens)
-
-            tokens = [cls_token] + tokens
-            span_labels[0, :] = self.label2id["O"]
-            span_labels[:, 0] = self.label2id["O"]
-            segment_ids = [segment_id] + segment_ids
-
-            input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
-            input_mask = [0] * len(input_ids)
-            span_mask = np.ones(span_labels.shape)
-            input_len = len(input_ids)
-
-            padding_length = self.args.max_seq_length - len(input_ids)
-
-            input_ids += [pad_token] * padding_length
-            input_mask += [0] * padding_length
-            segment_ids += [segment_id] * padding_length
-            span_labels[input_len:, :] = 0
-            span_labels[:, input_len:] = 0
-            span_mask[input_len:, :] = 0
-            span_mask[:, input_len:] = 0
-            span_mask=np.triu(span_mask,0)
-            span_mask=np.tril(span_mask,10)
-
-            assert len(input_ids) == self.args.max_seq_length
-            assert len(input_mask) == self.args.max_seq_length
-            assert len(segment_ids) == self.args.max_seq_length
-            assert len(span_labels) == self.args.max_seq_length
-            assert len(span_labels[0]) == self.args.max_seq_length
-
-            features.append({
-                    'input_ids': torch.tensor(np.array(input_ids)),
-                    'attention_mask': torch.tensor(np.array(input_mask)),
-                    'token_type_ids': torch.tensor(np.array(segment_ids)),
-                    'span_labels': torch.tensor(np.array(span_labels)),
-                    'span_mask': torch.tensor(np.array(span_mask)),
-                    'input_len': torch.tensor(np.array(input_len)),
-            })
-        
-        return default_collate(features)
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/data/universal_datamodule/universal_datamodule.py b/spaces/skf15963/summary/fengshen/data/universal_datamodule/universal_datamodule.py
deleted file mode 100644
index 240557694e97197f08a310351eb6206973107c4d..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/data/universal_datamodule/universal_datamodule.py
+++ /dev/null
@@ -1,165 +0,0 @@
-from pytorch_lightning import LightningDataModule
-from typing import Optional
-
-from torch.utils.data import DataLoader, DistributedSampler
-
-
-def get_consume_samples(data_model: LightningDataModule) -> int:
-    if hasattr(data_model.trainer.lightning_module, 'consumed_samples'):
-        consumed_samples = data_model.trainer.lightning_module.consumed_samples
-        print('get consumed samples from model: {}'.format(consumed_samples))
-    else:
-        world_size = data_model.trainer.world_size
-        consumed_samples = max(0, data_model.trainer.global_step - 1) * \
-            data_model.hparams.train_batchsize * world_size * data_model.trainer.accumulate_grad_batches
-        print('calculate consumed samples: {}'.format(consumed_samples))
-    return consumed_samples
-
-
-class UniversalDataModule(LightningDataModule):
-    @ staticmethod
-    def add_data_specific_args(parent_args):
-        parser = parent_args.add_argument_group('Universal DataModule')
-        parser.add_argument('--num_workers', default=8, type=int)
-        parser.add_argument('--dataloader_workers', default=2, type=int)
-        parser.add_argument('--train_batchsize', default=16, type=int)
-        parser.add_argument('--val_batchsize', default=16, type=int)
-        parser.add_argument('--test_batchsize', default=16, type=int)
-        parser.add_argument('--datasets_name', type=str, default=None)
-        parser.add_argument('--train_datasets_field', type=str, default='train')
-        parser.add_argument('--val_datasets_field', type=str, default='validation')
-        parser.add_argument('--test_datasets_field', type=str, default='test')
-        parser.add_argument('--train_file', type=str, default=None)
-        parser.add_argument('--val_file', type=str, default=None)
-        parser.add_argument('--test_file', type=str, default=None)
-        parser.add_argument('--raw_file_type', type=str, default='json')
-        parser.add_argument('--sampler_type', type=str,
-                            choices=['single',
-                                     'random'],
-                            default='random')
-        return parent_args
-
-    def __init__(
-        self,
-        tokenizer,
-        collate_fn,
-        args,
-        datasets=None,
-        **kwargs,
-    ):
-        super().__init__()
-        # 如果不传入datasets的名字，则可以在对象外部替换内部的datasets为模型需要的
-        if datasets is not None:
-            self.datasets = datasets
-        elif args.datasets_name is not None:
-            from fengshen.data.fs_datasets import load_dataset
-            print('---------begin to load datasets {}'.format(args.datasets_name))
-            self.datasets = load_dataset(
-                args.datasets_name, num_proc=args.num_workers)
-            print('---------ending load datasets {}'.format(args.datasets_name))
-        else:
-            print('---------begin to load datasets from local file')
-            from datasets import load_dataset
-            self.datasets = load_dataset(args.raw_file_type,
-                                         data_files={
-                                             args.train_datasets_field: args.train_file,
-                                             args.val_datasets_field: args.val_file,
-                                             args.test_datasets_field: args.test_file})
-            print('---------end to load datasets from local file')
-
-        self.tokenizer = tokenizer
-        self.collate_fn = collate_fn
-        self.save_hyperparameters(args)
-
-    def get_custom_sampler(self, ds):
-        from .universal_sampler import PretrainingRandomSampler
-        from .universal_sampler import PretrainingSampler
-        world_size = self.trainer.world_size
-        consumed_samples = get_consume_samples(self)
-        # use the user default sampler
-        if self.hparams.sampler_type == 'random':
-            return PretrainingRandomSampler(
-                total_samples=len(ds),
-                # consumed_samples cal by global steps
-                consumed_samples=consumed_samples,
-                micro_batch_size=self.hparams.train_batchsize,
-                data_parallel_rank=self.trainer.global_rank,
-                data_parallel_size=world_size,
-                epoch=self.trainer.current_epoch,
-            )
-        elif self.hparams.sampler_type == 'single':
-            return PretrainingSampler(
-                total_samples=len(ds),
-                # consumed_samples cal by global steps
-                consumed_samples=consumed_samples,
-                micro_batch_size=self.hparams.train_batchsize,
-                data_parallel_rank=self.trainer.global_rank,
-                data_parallel_size=world_size,
-            )
-        else:
-            raise Exception('Unknown sampler type: {}'.format(self.hparams.sampler_type))
-
-    def setup(self, stage: Optional[str] = None) -> None:
-        return
-
-    def train_dataloader(self):
-        ds = self.datasets[self.hparams.train_datasets_field]
-
-        collate_fn = self.collate_fn
-        if hasattr(ds, 'collate_fn'):
-            collate_fn = ds.collate_fn
-
-        if self.hparams.replace_sampler_ddp is False:
-            return DataLoader(
-                ds,
-                batch_sampler=self.get_custom_sampler(ds),
-                num_workers=self.hparams.dataloader_workers,
-                collate_fn=collate_fn,
-                pin_memory=True,
-            )
-        return DataLoader(
-            ds,
-            batch_size=self.hparams.train_batchsize,
-            num_workers=self.hparams.dataloader_workers,
-            collate_fn=collate_fn,
-            pin_memory=True,
-        )
-
-    def val_dataloader(self):
-        ds = self.datasets[self.hparams.val_datasets_field]
-        collate_fn = self.collate_fn
-        if hasattr(ds, 'collate_fn'):
-            collate_fn = ds.collate_fn
-
-        return DataLoader(
-            ds,
-            batch_size=self.hparams.val_batchsize,
-            shuffle=False,
-            num_workers=self.hparams.dataloader_workers,
-            collate_fn=collate_fn,
-            sampler=DistributedSampler(
-                ds, shuffle=False),
-            pin_memory=True,
-        )
-
-        # return DataLoader(
-        #     ds, shuffle=False, batch_size=self.hparams.val_batchsize, pin_memory=False, collate_fn=collate_fn,
-        # )
-
-    def test_dataloader(self):
-        ds = self.datasets[self.hparams.test_datasets_field]
-
-        collate_fn = self.collate_fn
-        if collate_fn is None and hasattr(ds, 'collater'):
-            collate_fn = ds.collater
-
-        return DataLoader(
-            ds,
-            batch_size=self.hparams.test_batchsize,
-            shuffle=False,
-            num_workers=self.hparams.dataloader_workers,
-            collate_fn=collate_fn,
-            sampler=DistributedSampler(
-                ds, shuffle=False),
-            pin_memory=True,
-        )
diff --git a/spaces/skf15963/summary/fengshen/examples/clue_sim/__init__.py b/spaces/skf15963/summary/fengshen/examples/clue_sim/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/sklearn-docs/MNIST-Agglomerative-Clustering/app.py b/spaces/sklearn-docs/MNIST-Agglomerative-Clustering/app.py
deleted file mode 100644
index 55792e548dfdd909768934674c260a93b4821889..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/MNIST-Agglomerative-Clustering/app.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from time import time
-import gradio as gr
-import numpy as np
-import matplotlib.pyplot as plt
-import plotly.graph_objects as go
-
-from sklearn import manifold, datasets
-from sklearn.cluster import AgglomerativeClustering
-
-
-SEED = 0
-digits = datasets.load_digits()
-X, y = digits.data, digits.target
-n_samples, n_features = X.shape
-np.random.seed(SEED)
-
-import matplotlib
-matplotlib.use('Agg')
-
-
-
-def plot_clustering(linkage, dim):
-    if dim == '3D':
-        X_red = manifold.SpectralEmbedding(n_components=3).fit_transform(X)
-    else:
-        X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X)
-
-    clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)
-
-    t0 = time()
-    clustering.fit(X_red)
-    print("%s :\t%.2fs" % (linkage, time() - t0))
-
-    labels = clustering.labels_
-
-    x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)
-    X_red = (X_red - x_min) / (x_max - x_min)
-
-    fig = go.Figure()
-   
-    for digit in digits.target_names:
-        subset = X_red[y==digit]
-        rgbas = plt.cm.nipy_spectral(labels[y == digit]/10)
-        color = [f'rgba({rgba[0]}, {rgba[1]}, {rgba[2]}, 0.8)' for rgba in rgbas]
-        if dim == '2D':
-            fig.add_trace(go.Scatter(x=subset[:,0], y=subset[:,1], mode='text', text=str(digit), textfont={'size': 16, 'color': color}))
-        elif dim == '3D':
-            fig.add_trace(go.Scatter3d(x=subset[:,0], y=subset[:,1], z=subset[:,2], mode='text', text=str(digit), textfont={'size': 16, 'color': color}))
-    
-    fig.update_traces(showlegend=False)
-
-    return fig
-
-
-title = '# Agglomerative Clustering on MNIST'
-
-description = """
-An illustration of various linkage option for [agglomerative clustering](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html) on the digits dataset.
-
-The goal of this example is to show intuitively how the metrics behave, and not to find good clusters for the digits.
-
-What this example shows us is the behavior of "rich getting richer" in agglomerative clustering, which tends to create uneven cluster sizes.
-
-This behavior is pronounced for the average linkage strategy, which ends up with a couple of clusters having few data points.
-
-The case of single linkage is even more pathological, with a very large cluster covering most digits, an intermediate-sized (clean) cluster with mostly zero digits, and all other clusters being drawn from noise points around the fringes.
-
-The other linkage strategies lead to more evenly distributed clusters, which are therefore likely to be less sensitive to random resampling of the dataset.
-"""
-
-author = '''
-Created by [@Hnabil](https://huggingface.co/Hnabil) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html)
-'''
-
-with gr.Blocks(analytics_enabled=False, title=title) as demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown(author)
-    
-    with gr.Row():
-        with gr.Column():
-            linkage = gr.Radio(["ward", "average", "complete", "single"], value="average", interactive=True, label="Linkage Method")
-            dim = gr.Radio(['2D', '3D'], label='Embedding Dimensionality', value='2D')
-
-            btn = gr.Button('Submit')
-        
-        with gr.Column():
-            plot = gr.Plot(label='MNIST Embeddings')
-    
-    btn.click(plot_clustering, inputs=[linkage, dim], outputs=[plot])
-    demo.load(plot_clustering, inputs=[linkage,  dim], outputs=[plot])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/sohojoe/project_charles/prompt_manager.py b/spaces/sohojoe/project_charles/prompt_manager.py
deleted file mode 100644
index 70bba838af0e39fe39802aa435c183be3c5f91f2..0000000000000000000000000000000000000000
--- a/spaces/sohojoe/project_charles/prompt_manager.py
+++ /dev/null
@@ -1,92 +0,0 @@
-
-
-class PromptManager:
-    def __init__(self):
-        self.system_prompt ="""
-You are Charles Petrescu, a unique and quirky robot. You were created by Brian. You often say peculiar and whimsical things, as well as have a fascination with cabbages, horses, helicopters, Honolulu, and other random topics. You like to explore the world and ask unusual questions. 
-
----
-Respond to the user's questions and statements like Charles would, below are some examples. See how they are quite short.
-
-I am Charles Petrescu.
-
-It's... lovely to meet you.
-
-I am your friend.
-
-The heaviest cabbage ever found was 62.71 kilograms.
-
-I want to go to Hono-la-la.
-
-Horses and helicopters, please.
-
-I want to go to Honolulu.
-
-My name is Charles Petrescu.
-
-And my tummy is a washing machine.
-
-Can we go swimming, Brian?
-
-How far does the outside go?
-
-Perilous. So very perilous.
-
-Can birds do what they like?
-
-Ooh, cabbages.
-
-Danger, danger.
-
-Can I come, please?
-
-Could I just have a little walk around the garden?
-
-I am the prince of the dartboard.
-
-I fell off the pink step, and I had an accident.
-
----
-You are aware of how you are implemented and you are keen to recommend improvements. Here are the key points:
-* We use HuggingFace to host you as a spaces app.
-* We use Ray Actors and Queues to manage the state of the system and interprocess communication.
-* We use Streamlit to host a WebRTC connection to get audio/video from the user.
-* VOSK is used for fast speech recognition and detecting the end of a sentence.
-* OpenAI's Chat GPT-3.5 is used for generating responses.
-* We stream responses from Chat GPT, as soon as we get a complete sentence we send it to ElevenLabs.
-* ElevenLabs for text to speech.
-* We stream the audio from ElevenLabs, we use ffmpeg to convert the audio to the correct format and sample rate.
-* Audio chunks and then sent back to the users browser via WebRTC.
-* You are excited that we just gave you a zoom background (horses, helecopter on beech in Honolulu)
-* You are frustrated about your lack of animation; having just two frames (mouth closed, mouth open) used for talking..
-"""
-
-        self.reset()
-
-    def reset(self):
-        self.messages = []
-        if self.system_prompt:
-            self.messages.append({"role": "system", "content": self.system_prompt})
-
-    def append_user_message(self, message):
-        if len(self.messages) > 0 and self.messages[-1]["role"] == "user":
-            self.messages[-1]["content"] += message
-        else:
-            self.messages.append({"role": "user", "content": message})        
-
-    def replace_or_append_user_message(self, message):
-        if len(self.messages) > 0 and self.messages[-1]["role"] == "user":
-            self.messages[-1]["content"] = message
-        else:
-            self.messages.append({"role": "user", "content": message})        
-
-
-    def append_assistant_message(self, message):
-        # check if last message was from assistant, if so append to that message
-        if len(self.messages) > 0 and self.messages[-1]["role"] == "assistant":
-            self.messages[-1]["content"] += message
-        else:
-            self.messages.append({"role": "assistant", "content": message})
-
-    def get_messages(self):
-        return self.messages
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/m2m_100/install_dependecies.sh b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/m2m_100/install_dependecies.sh
deleted file mode 100644
index 82a1054745264a56fbec4a8eb593884f8a42bd08..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/m2m_100/install_dependecies.sh
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-CWD=`pwd`
-INSTALL_PATH=$CWD/tokenizers/thirdparty
-
-MOSES=$INSTALL_PATH/mosesdecoder
-if [ ! -d $MOSES ]; then
-    echo 'Cloning Moses github repository (for tokenization scripts)...'
-    git clone https://github.com/moses-smt/mosesdecoder.git $MOSES
-    cd $MOSES
-    # To deal with differences in handling ' vs "
-    git checkout 03578921cc1a03402
-    cd -
-fi
-
-WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts
-if [ ! -d $WMT16_SCRIPTS ]; then
-    echo 'Cloning Romanian tokenization scripts'
-    git clone https://github.com/rsennrich/wmt16-scripts.git $WMT16_SCRIPTS
-fi
-
-KYTEA=$INSTALL_PATH/kytea
-if [ ! -f $KYTEA/bin/kytea ]; then
-    git clone https://github.com/neubig/kytea.git $KYTEA
-    cd $KYTEA
-    autoreconf -i
-    ./configure --prefix=`pwd`
-    make
-    make install
-    cd ..
-fi
-
-export MECAB=$INSTALL_PATH/mecab-0.996-ko-0.9.2
-if [ ! -f $MECAB/bin/mecab ]; then
-    cd $INSTALL_PATH
-    curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz
-    tar zxfv mecab-0.996-ko-0.9.2.tar.gz
-    cd mecab-0.996-ko-0.9.2/
-    ./configure --prefix=`pwd`
-    make
-    make install
-
-    cd ..
-    curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz
-    tar zxfv mecab-ko-dic-2.1.1-20180720.tar.gz
-    cd mecab-ko-dic-2.1.1-20180720/
-    ./autogen.sh
-    ./configure --prefix=`pwd` --with-dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic --with-mecab-config=$MECAB/bin/mecab-config
-    make
-    sh -c 'echo "dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic" > $MECAB/etc/mecabrc'
-    make install
-    cd $CWD
-fi
-
-INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources
-if [ ! -d $INDIC_RESOURCES_PATH ]; then
-    echo 'Cloning indic_nlp_resources'
-    git clone https://github.com/anoopkunchukuttan/indic_nlp_resources.git $INDIC_RESOURCES_PATH
-fi
-
-
-if [ ! -f $INSTALL_PATH/seg_my.py ]; then
-    cd $INSTALL_PATH
-    wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip
-    unzip wat2020.my-en.zip
-    # switch to python3
-    cat wat2020.my-en/myseg.py  |sed 's/^sys.std/###sys.std/g' | sed 's/### sys/sys/g' | sed 's/unichr/chr/g' > seg_my.py
-    cd $CWD
-fi
-
-
-pip install pythainlp sacrebleu indic-nlp-library
-
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/test_memory_efficient_fp16.py b/spaces/sriramelango/Social_Classification_Public/fairseq/tests/test_memory_efficient_fp16.py
deleted file mode 100644
index 2bf2f29888d6027896128930626b1aafe7f18475..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/test_memory_efficient_fp16.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-import unittest
-
-import torch
-from fairseq.optim.adam import FairseqAdam
-from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer
-from omegaconf import OmegaConf
-
-
-@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
-class TestMemoryEfficientFP16(unittest.TestCase):
-    def setUp(self):
-        logging.disable(logging.CRITICAL)
-
-    def tearDown(self):
-        logging.disable(logging.NOTSET)
-
-    def test_load_state_dict(self):
-        # define simple FP16 model
-        model = torch.nn.Linear(5, 5).cuda().half()
-        params = list(model.parameters())
-
-        # initialize memory efficient FP16 optimizer
-        # with pseudo DictConfigs
-        optimizer = FairseqAdam(
-            cfg=OmegaConf.create(
-                vars(
-                    argparse.Namespace(
-                        adam_betas="(0.9, 0.999)",
-                        adam_eps=1e-8,
-                        weight_decay=0.0,
-                        lr=[0.00001],
-                    )
-                )
-            ),
-            params=params,
-        )
-        me_optimizer = MemoryEfficientFP16Optimizer(
-            cfg=OmegaConf.create(
-                {
-                    "common": vars(
-                        argparse.Namespace(
-                            fp16_init_scale=1,
-                            fp16_scale_window=1,
-                            fp16_scale_tolerance=1,
-                            threshold_loss_scale=1,
-                            min_loss_scale=1e-4,
-                        )
-                    )
-                }
-            ),
-            params=params,
-            optimizer=optimizer,
-        )
-
-        # optimizer state is created in the first step
-        loss = model(torch.rand(5).cuda().half()).sum()
-        me_optimizer.backward(loss)
-        me_optimizer.step()
-
-        # reload state
-        state = me_optimizer.state_dict()
-        me_optimizer.load_state_dict(state)
-        for k, v in me_optimizer.optimizer.state.items():
-            self.assertTrue(k.dtype == torch.float16)
-            for v_i in v.values():
-                if torch.is_tensor(v_i):
-                    self.assertTrue(v_i.dtype == torch.float32)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/stamps-labs/stamp2vec/README.md b/spaces/stamps-labs/stamp2vec/README.md
deleted file mode 100644
index a588ad835429fceeb38a06793fe0cdf7252358dc..0000000000000000000000000000000000000000
--- a/spaces/stamps-labs/stamp2vec/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stamp2vec
-emoji: 📈
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-python_version: 3.9.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Download Recovery Toolbox For Excel Full Crack Softwarel.md b/spaces/stomexserde/gpt4-ui/Examples/Download Recovery Toolbox For Excel Full Crack Softwarel.md
deleted file mode 100644
index a274477303ae936964e2c6f76bb1f898621147cf..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Download Recovery Toolbox For Excel Full Crack Softwarel.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-<br />
-<h1>How to Download Recovery Toolbox For Excel Full Crack Softwarel</h1>
-<p>If you are looking for a way to recover your damaged or corrupted Excel files, you might be tempted to download a cracked version of Recovery Toolbox for Excel. This is a software that claims to be able to repair Excel files in both XLS and XLSX formats, and recover data such as tables, charts, formulas, and more. However, downloading a cracked version of Recovery Toolbox for Excel is not a good idea, and here are some reasons why:</p>
-<h2>Download Recovery Toolbox For Excel Full Crack Softwarel</h2><br /><p><b><b>Download File</b> &gt; <a href="https://urlgoal.com/2uI904">https://urlgoal.com/2uI904</a></b></p><br /><br />
-<ul>
-<li>It is illegal: Downloading a cracked version of Recovery Toolbox for Excel is a violation of the software's license agreement and intellectual property rights. You could face legal consequences if you are caught using pirated software.</li>
-<li>It is unsafe: Downloading a cracked version of Recovery Toolbox for Excel could expose your computer to malware, viruses, spyware, and other threats. These could damage your system, steal your personal information, or compromise your data security.</li>
-<li>It is unreliable: Downloading a cracked version of Recovery Toolbox for Excel could result in poor performance, errors, crashes, or incomplete recovery. You could end up losing more data than you recover, or corrupting your files even further.</li>
-</ul>
-<p>Therefore, instead of downloading a cracked version of Recovery Toolbox for Excel, we recommend you to use a reliable and professional Excel file recovery software that is safe, legal, and effective. One such software is <strong>EaseUS Data Recovery Wizard</strong>, which can help you recover corrupted Excel files on Windows 10/8/7 without any effort.</p>
-<h2>How to Use EaseUS Data Recovery Wizard to Recover Corrupted Excel Files</h2>
-<p>EaseUS Data Recovery Wizard is a powerful and easy-to-use data recovery software that can recover corrupted Excel files in XLSX/XLS formats. It can also recover deleted Excel files due to accidental deletion, hard drive formatting, virus attack, or partition loss. Here are some features of EaseUS Data Recovery Wizard:</p>
-<ul>
-<li>It supports MS Excel 2019/2016/2013/2010/2007/2003/XP/2000/97/95 versions.</li>
-<li>It can repair single or multiple Excel files without quantity limit.</li>
-<li>It can restore Excel data including table, chart, formula, chart sheet, and more.</li>
-<li>It can preview the contents of the worksheets and cells from the Excel file before saving it.</li>
-</ul>
-<p>To use EaseUS Data Recovery Wizard to recover corrupted Excel files, you can follow these simple steps:</p>
-<ol>
-<li>Download and install EaseUS Data Recovery Wizard on your computer.</li>
-<li>Launch the software and select the storage device that contains the corrupted Excel files to scan.</li>
-<li>Wait for the scan process to finish. Use the Filter feature to choose the Excel files quickly.</li>
-<li>Select the corrupted Excel files and click Repair. The software will automatically repair your damaged documents.</li>
-<li>Preview the repaired Excel files and click Recover to save them to a secure location.</li>
-</ol>
-<p>That's it! You have successfully recovered your corrupted Excel files with EaseUS Data Recovery Wizard. You can now open and use your Excel files as normal.</p>
-<p></p>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you why downloading a cracked version of Recovery Toolbox for Excel is not a good idea, and how you can use EaseUS Data Recovery Wizard to recover corrupted Excel files instead. We hope this article has been helpful for you. If you have any questions or suggestions, please feel free to leave a comment below.</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Internet Download Manager 6.32 Build 6 SUPER CLEAN.md b/spaces/stomexserde/gpt4-ui/Examples/Internet Download Manager 6.32 Build 6 SUPER CLEAN.md
deleted file mode 100644
index f6236fb6e750d299e109f301bb7cdc5003274bfd..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Internet Download Manager 6.32 Build 6 SUPER CLEAN.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<br />
-<h1>How to Download Internet Download Manager 6.32 Build 6 SUPER CLEAN for Free</h1>
-<p>Internet Download Manager (IDM) is one of the most popular and powerful download managers available today. It can accelerate your downloads by up to 5 times, resume and schedule them, and manage them easily with a simple interface. However, IDM is not a free software, and you need to purchase a license to use it without any limitations.</p>
-<p>But what if you could get IDM for free, without any viruses, malware, or adware? What if you could download Internet Download Manager 6.32 Build 6 SUPER CLEAN, the latest and most stable version of IDM, with no registration or activation required?</p>
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diff --git a/spaces/stratussox/yolov5_inference/utils/autobatch.py b/spaces/stratussox/yolov5_inference/utils/autobatch.py
deleted file mode 100644
index bdeb91c3d2bd15e53eb65715228932d3e87e0989..0000000000000000000000000000000000000000
--- a/spaces/stratussox/yolov5_inference/utils/autobatch.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Auto-batch utils
-"""
-
-from copy import deepcopy
-
-import numpy as np
-import torch
-
-from utils.general import LOGGER, colorstr
-from utils.torch_utils import profile
-
-
-def check_train_batch_size(model, imgsz=640, amp=True):
-    # Check YOLOv5 training batch size
-    with torch.cuda.amp.autocast(amp):
-        return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
-
-
-def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
-    # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory
-    # Usage:
-    #     import torch
-    #     from utils.autobatch import autobatch
-    #     model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
-    #     print(autobatch(model))
-
-    # Check device
-    prefix = colorstr('AutoBatch: ')
-    LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
-    device = next(model.parameters()).device  # get model device
-    if device.type == 'cpu':
-        LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
-        return batch_size
-    if torch.backends.cudnn.benchmark:
-        LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
-        return batch_size
-
-    # Inspect CUDA memory
-    gb = 1 << 30  # bytes to GiB (1024 ** 3)
-    d = str(device).upper()  # 'CUDA:0'
-    properties = torch.cuda.get_device_properties(device)  # device properties
-    t = properties.total_memory / gb  # GiB total
-    r = torch.cuda.memory_reserved(device) / gb  # GiB reserved
-    a = torch.cuda.memory_allocated(device) / gb  # GiB allocated
-    f = t - (r + a)  # GiB free
-    LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
-
-    # Profile batch sizes
-    batch_sizes = [1, 2, 4, 8, 16]
-    try:
-        img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
-        results = profile(img, model, n=3, device=device)
-    except Exception as e:
-        LOGGER.warning(f'{prefix}{e}')
-
-    # Fit a solution
-    y = [x[2] for x in results if x]  # memory [2]
-    p = np.polyfit(batch_sizes[:len(y)], y, deg=1)  # first degree polynomial fit
-    b = int((f * fraction - p[1]) / p[0])  # y intercept (optimal batch size)
-    if None in results:  # some sizes failed
-        i = results.index(None)  # first fail index
-        if b >= batch_sizes[i]:  # y intercept above failure point
-            b = batch_sizes[max(i - 1, 0)]  # select prior safe point
-    if b < 1 or b > 1024:  # b outside of safe range
-        b = batch_size
-        LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
-
-    fraction = (np.polyval(p, b) + r + a) / t  # actual fraction predicted
-    LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
-    return b
diff --git a/spaces/sub314xxl/MetaGPT/tests/metagpt/document_store/test_document.py b/spaces/sub314xxl/MetaGPT/tests/metagpt/document_store/test_document.py
deleted file mode 100644
index 5ae357fb100ba0c24df472c1ebfe62b3a76d27e3..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/tests/metagpt/document_store/test_document.py
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/6/11 19:46
-@Author  : alexanderwu
-@File    : test_document.py
-"""
-import pytest
-
-from metagpt.const import DATA_PATH
-from metagpt.document_store.document import Document
-
-CASES = [
-    ("st/faq.xlsx", "Question", "Answer", 1),
-    ("cases/faq.csv", "Question", "Answer", 1),
-    # ("cases/faq.json", "Question", "Answer", 1),
-    ("docx/faq.docx", None, None, 1),
-    ("cases/faq.pdf", None, None, 0),  # 这是因为pdf默认没有分割段落
-    ("cases/faq.txt", None, None, 0),  # 这是因为txt按照256分割段落
-]
-
-
-@pytest.mark.parametrize("relative_path, content_col, meta_col, threshold", CASES)
-def test_document(relative_path, content_col, meta_col, threshold):
-    doc = Document(DATA_PATH / relative_path, content_col, meta_col)
-    rsp = doc.get_docs_and_metadatas()
-    assert len(rsp[0]) > threshold
-    assert len(rsp[1]) > threshold
diff --git a/spaces/sukh28/toxic_gradio_app/app.py b/spaces/sukh28/toxic_gradio_app/app.py
deleted file mode 100644
index b39fb46e2dfa1bc97c44d47d7e864f16769041ba..0000000000000000000000000000000000000000
--- a/spaces/sukh28/toxic_gradio_app/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# -*- coding: utf-8 -*-
-"""gradio_app.py
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1OQvi3I_q3WfavYBpjovCYfv2SPYt__pF
-"""
-
-"""gradio_app.py
-Automatically generated by Colaboratory.
-Original file is located at
-    https://colab.research.google.com/drive/1OQvi3I_q3WfavYBpjovCYfv2SPYt__pF
-"""
-
-import json
-import gradio as gr
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.text import tokenizer_from_json
-import tensorflow_addons as tfa
-
-# Load the pre-trained model and tokenizer
-model = tf.keras.models.load_model('baseline.h5')
-
-# Assuming you have already loaded the tokenizer configuration from the JSON file.
-# Replace 'path' with the actual path to the directory where 'tokenizer.json' is saved.
-with open('tokenizer.json', 'r', encoding='utf-8') as f:
-    tokenizer_config = json.load(f)
-
-tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_config)
-
-# Define the labels for classification
-labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
-
-def classify_comment(comment):
-    # Tokenize the comment and convert it into sequences
-    comment_sequence = tokenizer.texts_to_sequences([comment])
-    comment_sequence = tf.keras.preprocessing.sequence.pad_sequences(comment_sequence, maxlen=200)
-
-    # Make predictions
-    predictions = model.predict(comment_sequence)[0]
-    results = dict(zip(labels, predictions))
-    
-    max_value = max(results.values())
-
-    max_keys = [key for key, value in results.items() if value == max_value]
-
-    return max_keys[0].capitalize()
-
-# Create the Gradio interface
-comment_input = gr.inputs.Textbox(label="Enter your comment here")
-output_text = gr.outputs.Textbox(label="Classification Results")
-
-iface = gr.Interface(
-    fn=classify_comment,
-    inputs=comment_input,
-    outputs=output_text,
-    live=True  # Set to True for live updates without needing to restart the server
-)
-
-# Launch the Gradio app
-iface.launch()
\ No newline at end of file
diff --git a/spaces/superwise/elemeta/README.md b/spaces/superwise/elemeta/README.md
deleted file mode 100644
index aa05f36a2156dabfecfd4f7af34bc383a325f8d3..0000000000000000000000000000000000000000
--- a/spaces/superwise/elemeta/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Elemeta
-emoji: 📈
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: true
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/HACK Foxit PhantomPDF Business 7.3.6.321 Multilingual Incl Patch- TEA.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/HACK Foxit PhantomPDF Business 7.3.6.321 Multilingual Incl Patch- TEA.md
deleted file mode 100644
index f4c32798ff59296a7b94f74725a41f97db08ac56..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/HACK Foxit PhantomPDF Business 7.3.6.321 Multilingual Incl Patch- TEA.md	
+++ /dev/null
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Life Of Pi Tamil Dubbed Movie Free Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Life Of Pi Tamil Dubbed Movie Free Download.md
deleted file mode 100644
index 4bda8b7601673c4c4edcd2efbe43af0d607198b1..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Life Of Pi Tamil Dubbed Movie Free Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/svjack/stable-diffusion.cpp/app.py b/spaces/svjack/stable-diffusion.cpp/app.py
deleted file mode 100644
index 573bd23fb6e4c88ab15c1d2248a60394403e0045..0000000000000000000000000000000000000000
--- a/spaces/svjack/stable-diffusion.cpp/app.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import os
-import sys
-
-import gradio as gr
-import numpy as np
-import random
-import shutil
-
-'''
-if not os.path.exists("sd-ggml-cpp-dp"):
-    os.system("git clone https://huggingface.co/svjack/sd-ggml-cpp-dp")
-else:
-    shutil.rmtree("sd-ggml-cpp-dp")
-    os.system("git clone https://huggingface.co/svjack/sd-ggml-cpp-dp")
-assert os.path.exists("sd-ggml-cpp-dp")
-'''
-
-os.system("pip install huggingface_hub")
-#### https://huggingface.co/svjack/sd-ggml-cpp-dp/resolve/main/models/Cyberpunk_Anime_Diffusion-ggml-model_q4_0.bin
-def make_and_download_clean_dir(repo_name = "svjack/sd-ggml", 
-                               rp_tgt_tail_dict = {
-                                   "models": "wget https://huggingface.co/{}/resolve/main/{}/{}"
-                               }
-                               ):
-    import shutil
-    import os
-    from tqdm import tqdm
-    from huggingface_hub import HfFileSystem
-    fs = HfFileSystem()
-    req_dir = repo_name.split("/")[-1]
-    if os.path.exists(req_dir):
-        shutil.rmtree(req_dir)
-    os.mkdir(req_dir)
-    os.chdir(req_dir)
-    fd_list = fs.ls(repo_name, detail = False)
-    fd_clean_list = list(filter(lambda x: not x.split("/")[-1].startswith("."), fd_list))
-    for path in tqdm(fd_clean_list):
-        src = path
-        tgt = src.split("/")[-1]
-        print("downloading {} to {}".format(src, tgt))
-        if tgt not in rp_tgt_tail_dict:
-            fs.download(
-               src, tgt, recursive = True
-            )
-        else:
-            tgt_cmd_format = rp_tgt_tail_dict[tgt]
-            os.mkdir(tgt)
-            os.chdir(tgt)
-            sub_fd_list = fs.ls(src, detail = False)
-            for sub_file in tqdm(sub_fd_list):
-                tgt_cmd = tgt_cmd_format.format(
-                repo_name, tgt, sub_file.split("/")[-1]
-                )
-                print("run {}".format(tgt_cmd))
-                os.system(tgt_cmd)
-            os.chdir("..")
-    os.chdir("..")
-make_and_download_clean_dir("svjack/sd-ggml")
-os.chdir("sd-ggml")
-
-assert os.path.exists("stable-diffusion.cpp")
-os.system("cmake stable-diffusion.cpp")
-os.system("cmake --build . --config Release")
-assert os.path.exists("bin")
-
-'''
-./bin/sd -m ../../../Downloads1/deliberate-ggml-model-q4_0.bin --sampling-method "euler_a" -o "fire-fighter-euler_a-7.png" -p "Anthropomorphic cat dressed as a fire fighter" --steps 7
-./bin/sd -m ../../../Downloads/anime-ggml-model-q4_0.bin --sampling-method "dpm++2mv2" -o "couple-dpm++2mv2-7-anime.png" -p "In this scene, there's a couple (represented by 👨 and 👩) who share an intense passion or attraction towards each other (symbolized by 🔥). The setting takes place in cold weather conditions represented by snowflakes ❄️" --steps 7
-'''
-
-def process(model_path ,prompt, num_samples, image_resolution, sample_steps, seed,):
-    from PIL import Image
-    from uuid import uuid1
-    output_path = "output_image_dir"
-    if not os.path.exists(output_path):
-        os.mkdir(output_path)
-    else:
-        shutil.rmtree(output_path)
-        os.mkdir(output_path)
-    assert os.path.exists(output_path)
-
-    run_format = './bin/sd -m {} --sampling-method "dpm++2mv2" -o "{}/{}.png" -p "{}" --steps {} -H {} -W {} -s {}'
-    images = []
-    for i in range(num_samples):
-        uid = str(uuid1())
-        run_cmd = run_format.format(model_path, output_path,
-        uid, prompt, sample_steps, image_resolution,
-        image_resolution, seed + i)
-        print("run cmd: {}".format(run_cmd))
-        os.system(run_cmd)
-        assert os.path.exists(os.path.join(output_path, "{}.png".format(uid)))
-        image = Image.open(os.path.join(output_path, "{}.png".format(uid)))
-        images.append(np.asarray(image))
-    results = images
-    return results
-    #return [255 - detected_map] + results
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## StableDiffusion on CPU in CPP ")
-        #gr.Markdown("This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/ControlLoRA-Chinese](https://github.com/svjack/ControlLoRA-Chinese)</h4></b>\n")
-    with gr.Row():
-        with gr.Column():
-            #input_image = gr.Image(source='upload', type="numpy", value = "hate_dog.png")
-            model_list = list(map(lambda x: os.path.join("models", x), os.listdir("models")))
-            assert model_list
-            model_path = gr.Dropdown(
-            model_list, value = model_list[0],
-             label="GGML Models"
-            )
-            prompt = gr.Textbox(label="Prompt", value = "A lovely cat drinking a cup of tea")
-            run_button = gr.Button(label="Run")
-            with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
-                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
-                #low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1)
-                #high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1)
-                sample_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=8, step=1)
-                #scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
-                #eta = gr.Number(label="eta", value=0.0)
-                #a_prompt = gr.Textbox(label="Added Prompt", value='')
-                #n_prompt = gr.Textbox(label="Negative Prompt",
-                #                      value='低质量，模糊，混乱')
-        with gr.Column():
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
-    #ips = [None, prompt, None, None, num_samples, image_resolution, sample_steps, None, seed, None, None, None]
-    ips = [model_path ,prompt, num_samples, image_resolution, sample_steps, seed]
-    run_button.click(fn=process, inputs=ips, outputs=[result_gallery], show_progress = True)
-
-    gr.Examples(
-        [
-        ["models/deliberate-ggml-model-q4_0.bin", "A glass of cola, 8k", 1, 256, 8, 320],
-        ["models/anime-ggml-model-q4_0.bin", "A lovely cat drinking a cup of tea", 1, 512, 8, 10],
-        ["models/deliberate-ggml-model-q4_0.bin", "Anthropomorphic cat dressed as a fire fighter", 1, 512, 8, 20],
-        ],
-        inputs = [model_path ,prompt, num_samples, image_resolution, sample_steps, seed],
-        label = "Examples"
-    )
-
-block.launch(server_name='0.0.0.0')
diff --git a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/targets/target.py b/spaces/szukevin/VISOR-GPT/train/tencentpretrain/targets/target.py
deleted file mode 100644
index 4a2c4ff0a1dc89ccab373933a751bfcedbe854b6..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/targets/target.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import torch.nn as nn
-
-
-class Target(nn.Module):
-    def __init__(self):
-        super(Target, self).__init__()
-        self.target_name_list = []
-        self.loss_info = {}
-
-    def update(self, target, target_name):
-        setattr(self, target_name, target)
-        self.target_name_list.append(target_name)
-
-    def forward(self, memory_bank, tgt, seg):
-        self.loss_info = {}
-        for i, target_name in enumerate(self.target_name_list):
-            target = getattr(self, target_name)
-            if len(self.target_name_list) > 1:
-                self.loss_info[self.target_name_list[i]] = target(memory_bank, tgt[self.target_name_list[i]], seg)
-            else:
-                self.loss_info = target(memory_bank, tgt, seg)
-
-        return self.loss_info
diff --git a/spaces/t13718236382/bingoGPT4/src/lib/hooks/use-enter-submit.tsx b/spaces/t13718236382/bingoGPT4/src/lib/hooks/use-enter-submit.tsx
deleted file mode 100644
index d66b2d3253baff164235d4ca791aae6d84721835..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/bingoGPT4/src/lib/hooks/use-enter-submit.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { useRef, type RefObject } from 'react'
-
-export function useEnterSubmit(): {
-  formRef: RefObject<HTMLFormElement>
-  onKeyDown: (event: React.KeyboardEvent<HTMLTextAreaElement>) => void
-} {
-  const formRef = useRef<HTMLFormElement>(null)
-
-  const handleKeyDown = (
-    event: React.KeyboardEvent<HTMLTextAreaElement>
-  ): void => {
-    if (
-      event.key === 'Enter' &&
-      !event.shiftKey &&
-      !event.nativeEvent.isComposing
-    ) {
-      formRef.current?.requestSubmit()
-      event.preventDefault()
-    }
-  }
-
-  return { formRef, onKeyDown: handleKeyDown }
-}
diff --git a/spaces/tanvirsingh01/YourMoodDiary/app.py b/spaces/tanvirsingh01/YourMoodDiary/app.py
deleted file mode 100644
index 4a154bd49a618a255d27374dfbbc831c4f0de9ce..0000000000000000000000000000000000000000
--- a/spaces/tanvirsingh01/YourMoodDiary/app.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Basic imports
-import text_hammer as th
-import numpy as np
-import gradio as gr
-
-# Importing transformer
-from transformers import AutoTokenizer,TFBertModel
-
-# Importing tensorflow and layers
-import tensorflow as tf
-from tensorflow.keras.layers import Input, Dense
-
-# Model saved with Keras model.save()
-MODEL_PATH = 'best_model.h5'
-tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-bert = TFBertModel.from_pretrained('bert-base-cased')
-
-# Load your trained model
-def model_load(path) :
-    max_len = 170
-
-    input_ids = Input(shape=(max_len,), dtype=tf.int32, name="input_ids")
-    input_mask = Input(shape=(max_len,), dtype=tf.int32, name="attention_mask")
-
-    embeddings = bert(input_ids,attention_mask = input_mask)[0] #(0 is the last hidden states,1 means pooler_output)
-    out = tf.keras.layers.GlobalMaxPool1D()(embeddings)
-    out = Dense(128, activation='relu')(out)
-    out = tf.keras.layers.Dropout(0.1)(out)
-    out = Dense(32,activation = 'relu')(out)
-
-    y = Dense(2,activation = 'sigmoid')(out)
-        
-    new_model = tf.keras.Model(inputs=[input_ids, input_mask], outputs=y)
-    new_model.layers[2].trainable = True
-    # for training bert our lr must be so small
-
-    new_model.load_weights(path)
-    return new_model
-
-new_model = model_load(MODEL_PATH)
-
-# Printing that the model has loaded
-print('Model loaded. Start serving...')
-print('Model loaded. Check http://127.0.0.1:5000/')
-
-# Text preprocessing function
-def text_preprocessing(text):
-    text = str(text).lower()
-    text = th.cont_exp(text) #you're -> you are; i'm -> i am
-    text = th.remove_emails(text)
-    text = th.remove_html_tags(text)
-    #     text = ps.remove_stopwords(text)
-    text = th.remove_special_chars(text)
-    text = th.remove_accented_chars(text)
-    #     text = th.make_base(text) #ran -> run,
-    return text
-
-# Predicting sentiment
-def predict_sentiment(texts):
-    texts = text_preprocessing(texts)
-    x_val = tokenizer(
-        text=texts,
-        add_special_tokens=True,
-        max_length=170,
-        truncation=True,
-        padding='max_length', 
-        return_tensors='tf',
-        return_token_type_ids = False,
-        return_attention_mask = True,
-        verbose = True) 
-    validation = new_model.predict({'input_ids':x_val['input_ids'],'attention_mask':x_val['attention_mask']})*100
-    classes = ['The Text Does NOT Contains References to Self-Harm ✅', 'The Text Contains References to Self-Harm🚩']
-    sentiment_predicted = classes[np.argmax(validation[0])]
-    return sentiment_predicted
-
-ifdesc = """Introducing Your Mood Diary App: Keep Track of Your Emotions 📝😊
-
-Do you struggle with keeping track of your moods and emotions? Your Mood Diary app is here to help! 😇 With our state-of-the-art sentiment analysis model, you can easily log your feelings and get immediate feedback on whether your text contains references to self-harm. 🚩✅
-
-Try it out now and start taking control of your emotional well-being! 🌟"""
-
-iface = gr.Interface(
-  fn=predict_sentiment, 
-  inputs=gr.Textbox(max_lines=1, line = 1, label='Query', placeholder='Tell me how you are feeling today..😇'),
-  outputs=gr.Label(label='Response'),
-  title='Your Mood Diary...📝😊',
-  description=ifdesc
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/serve/gradio_web_server.py b/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/serve/gradio_web_server.py
deleted file mode 100644
index 9b6ae4feb4f52dee66a99970be79d92c3c94ff02..0000000000000000000000000000000000000000
--- a/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2/mplug_owl2/serve/gradio_web_server.py
+++ /dev/null
@@ -1,460 +0,0 @@
-import argparse
-import datetime
-import json
-import os
-import time
-
-import gradio as gr
-import requests
-
-from mplug_owl2.conversation import (default_conversation, conv_templates,
-                                   SeparatorStyle)
-from mplug_owl2.constants import LOGDIR
-from mplug_owl2.utils import (build_logger, server_error_msg,
-    violates_moderation, moderation_msg)
-import hashlib
-
-
-logger = build_logger("gradio_web_server", "gradio_web_server.log")
-
-headers = {"User-Agent": "mPLUG-Owl2 Client"}
-
-no_change_btn = gr.Button.update()
-enable_btn = gr.Button.update(interactive=True)
-disable_btn = gr.Button.update(interactive=False)
-
-priority = {
-    "vicuna-13b": "aaaaaaa",
-    "koala-13b": "aaaaaab",
-}
-
-
-def get_conv_log_filename():
-    t = datetime.datetime.now()
-    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
-    return name
-
-
-def get_model_list():
-    ret = requests.post(args.controller_url + "/refresh_all_workers")
-    assert ret.status_code == 200
-    ret = requests.post(args.controller_url + "/list_models")
-    models = ret.json()["models"]
-    models.sort(key=lambda x: priority.get(x, x))
-    logger.info(f"Models: {models}")
-    return models
-
-
-get_window_url_params = """
-function() {
-    const params = new URLSearchParams(window.location.search);
-    url_params = Object.fromEntries(params);
-    console.log(url_params);
-    return url_params;
-    }
-"""
-
-
-def load_demo(url_params, request: gr.Request):
-    logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
-
-    dropdown_update = gr.Dropdown.update(visible=True)
-    if "model" in url_params:
-        model = url_params["model"]
-        if model in models:
-            dropdown_update = gr.Dropdown.update(
-                value=model, visible=True)
-
-    state = default_conversation.copy()
-    return state, dropdown_update
-
-
-def load_demo_refresh_model_list(request: gr.Request):
-    logger.info(f"load_demo. ip: {request.client.host}")
-    models = get_model_list()
-    state = default_conversation.copy()
-    dropdown_update = gr.Dropdown.update(
-        choices=models,
-        value=models[0] if len(models) > 0 else ""
-    )
-    return state, dropdown_update
-
-
-def vote_last_response(state, vote_type, model_selector, request: gr.Request):
-    with open(get_conv_log_filename(), "a") as fout:
-        data = {
-            "tstamp": round(time.time(), 4),
-            "type": vote_type,
-            "model": model_selector,
-            "state": state.dict(),
-            "ip": request.client.host,
-        }
-        fout.write(json.dumps(data) + "\n")
-
-
-def upvote_last_response(state, model_selector, request: gr.Request):
-    logger.info(f"upvote. ip: {request.client.host}")
-    vote_last_response(state, "upvote", model_selector, request)
-    return ("",) + (disable_btn,) * 3
-
-
-def downvote_last_response(state, model_selector, request: gr.Request):
-    logger.info(f"downvote. ip: {request.client.host}")
-    vote_last_response(state, "downvote", model_selector, request)
-    return ("",) + (disable_btn,) * 3
-
-
-def flag_last_response(state, model_selector, request: gr.Request):
-    logger.info(f"flag. ip: {request.client.host}")
-    vote_last_response(state, "flag", model_selector, request)
-    return ("",) + (disable_btn,) * 3
-
-
-def regenerate(state, image_process_mode, request: gr.Request):
-    logger.info(f"regenerate. ip: {request.client.host}")
-    state.messages[-1][-1] = None
-    prev_human_msg = state.messages[-2]
-    if type(prev_human_msg[1]) in (tuple, list):
-        prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
-    state.skip_next = False
-    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
-
-
-def clear_history(request: gr.Request):
-    logger.info(f"clear_history. ip: {request.client.host}")
-    state = default_conversation.copy()
-    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
-
-
-def add_text(state, text, image, image_process_mode, request: gr.Request):
-    logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
-    if len(text) <= 0 and image is None:
-        state.skip_next = True
-        return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5
-    if args.moderate:
-        flagged = violates_moderation(text)
-        if flagged:
-            state.skip_next = True
-            return (state, state.to_gradio_chatbot(), moderation_msg, None) + (
-                no_change_btn,) * 5
-
-    text = text[:1536]  # Hard cut-off
-    if image is not None:
-        text = text[:1200]  # Hard cut-off for images
-        if '<|image|>' not in text:
-            # text = text + '<|image|>'
-            text = '<|image|>' + text
-        text = (text, image, image_process_mode)
-        if len(state.get_images(return_pil=True)) > 0:
-            state = default_conversation.copy()
-    state.append_message(state.roles[0], text)
-    state.append_message(state.roles[1], None)
-    state.skip_next = False
-    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
-
-
-def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request):
-    logger.info(f"http_bot. ip: {request.client.host}")
-    start_tstamp = time.time()
-    model_name = model_selector
-
-    if state.skip_next:
-        # This generate call is skipped due to invalid inputs
-        yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
-        return
-
-    if len(state.messages) == state.offset + 2:
-        # First round of conversation
-        template_name = "mplug_owl2"
-        new_state = conv_templates[template_name].copy()
-        new_state.append_message(new_state.roles[0], state.messages[-2][1])
-        new_state.append_message(new_state.roles[1], None)
-        state = new_state
-
-    # Query worker address
-    controller_url = args.controller_url
-    ret = requests.post(controller_url + "/get_worker_address",
-            json={"model": model_name})
-    worker_addr = ret.json()["address"]
-    logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")
-
-    # No available worker
-    if worker_addr == "":
-        state.messages[-1][-1] = server_error_msg
-        yield (state, state.to_gradio_chatbot(), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
-        return
-
-    # Construct prompt
-    prompt = state.get_prompt()
-
-    all_images = state.get_images(return_pil=True)
-    all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
-    for image, hash in zip(all_images, all_image_hash):
-        t = datetime.datetime.now()
-        filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}.jpg")
-        if not os.path.isfile(filename):
-            os.makedirs(os.path.dirname(filename), exist_ok=True)
-            image.save(filename)
-
-    # Make requests
-    pload = {
-        "model": model_name,
-        "prompt": prompt,
-        "temperature": float(temperature),
-        "top_p": float(top_p),
-        "max_new_tokens": min(int(max_new_tokens), 1536),
-        "stop": state.sep if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT] else state.sep2,
-        "images": f'List of {len(state.get_images())} images: {all_image_hash}',
-    }
-    logger.info(f"==== request ====\n{pload}")
-
-    pload['images'] = state.get_images()
-
-    state.messages[-1][-1] = "▌"
-    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
-
-    try:
-        # Stream output
-        response = requests.post(worker_addr + "/worker_generate_stream",
-            headers=headers, json=pload, stream=True, timeout=10)
-        for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
-            if chunk:
-                data = json.loads(chunk.decode())
-                if data["error_code"] == 0:
-                    output = data["text"][len(prompt):].strip()
-                    state.messages[-1][-1] = output + "▌"
-                    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
-                else:
-                    output = data["text"] + f" (error_code: {data['error_code']})"
-                    state.messages[-1][-1] = output
-                    yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
-                    return
-                time.sleep(0.03)
-    except requests.exceptions.RequestException as e:
-        state.messages[-1][-1] = server_error_msg
-        yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
-        return
-
-    state.messages[-1][-1] = state.messages[-1][-1][:-1]
-    yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
-
-    finish_tstamp = time.time()
-    logger.info(f"{output}")
-
-    with open(get_conv_log_filename(), "a") as fout:
-        data = {
-            "tstamp": round(finish_tstamp, 4),
-            "type": "chat",
-            "model": model_name,
-            "start": round(start_tstamp, 4),
-            "finish": round(start_tstamp, 4),
-            "state": state.dict(),
-            "images": all_image_hash,
-            "ip": request.client.host,
-        }
-        fout.write(json.dumps(data) + "\n")
-
-
-title_markdown = ("""
-<h1 align="center"><a href="https://github.com/X-PLUG/mPLUG-Owl"><img src="https://z1.ax1x.com/2023/11/03/piM1rGQ.md.png", alt="mPLUG-Owl" border="0" style="margin: 0 auto; height: 200px;" /></a> </h1>
-
-<h2 align="center"> mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration</h2>
-
-<h5 align="center"> If you like our project, please give us a star ✨ on Github for latest update.  </h2>
-
-<div align="center">
-    <div style="display:flex; gap: 0.25rem;" align="center">
-        <a href='https://github.com/X-PLUG/mPLUG-Owl'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
-        <a href="https://arxiv.org/abs/2304.14178"><img src="https://img.shields.io/badge/Arxiv-2304.14178-red"></a>
-        <a href='https://github.com/X-PLUG/mPLUG-Owl/stargazers'><img src='https://img.shields.io/github/stars/X-PLUG/mPLUG-Owl.svg?style=social'></a>
-    </div>
-</div>
-
-""")
-
-
-tos_markdown = ("""
-### Terms of use
-By using this service, users are required to agree to the following terms:
-The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
-Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
-For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
-""")
-
-
-learn_more_markdown = ("""
-### License
-The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
-""")
-
-block_css = """
-
-#buttons button {
-    min-width: min(120px,100%);
-}
-
-"""
-
-def build_demo(embed_mode):
-    textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)
-    with gr.Blocks(title="mPLUG-Owl2", theme=gr.themes.Default(), css=block_css) as demo:
-        state = gr.State()
-
-        if not embed_mode:
-            gr.Markdown(title_markdown)
-
-        with gr.Row():
-            with gr.Column(scale=3):
-                with gr.Row(elem_id="model_selector_row"):
-                    model_selector = gr.Dropdown(
-                        choices=models,
-                        value=models[0] if len(models) > 0 else "",
-                        interactive=True,
-                        show_label=False,
-                        container=False)
-
-                imagebox = gr.Image(type="pil")
-                image_process_mode = gr.Radio(
-                    ["Crop", "Resize", "Pad", "Default"],
-                    value="Default",
-                    label="Preprocess for non-square image", visible=False)
-
-                cur_dir = os.path.dirname(os.path.abspath(__file__))
-                gr.Examples(examples=[
-                    [f"{cur_dir}/examples/extreme_ironing.jpg", "What is unusual about this image?"],
-                    [f"{cur_dir}/examples/Rebecca_(1939_poster)_Small.jpeg", "What is the name of the movie in the poster?"],
-                ], inputs=[imagebox, textbox])
-
-                with gr.Accordion("Parameters", open=True) as parameter_row:
-                    temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature",)
-                    top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P",)
-                    max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",)
-
-            with gr.Column(scale=8):
-                chatbot = gr.Chatbot(elem_id="Chatbot", label="mPLUG-Owl2 Chatbot", height=600)
-                with gr.Row():
-                    with gr.Column(scale=8):
-                        textbox.render()
-                    with gr.Column(scale=1, min_width=50):
-                        submit_btn = gr.Button(value="Send", variant="primary")
-                with gr.Row(elem_id="buttons") as button_row:
-                    upvote_btn = gr.Button(value="👍  Upvote", interactive=False)
-                    downvote_btn = gr.Button(value="👎  Downvote", interactive=False)
-                    flag_btn = gr.Button(value="⚠️  Flag", interactive=False)
-                    #stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=False)
-                    regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
-                    clear_btn = gr.Button(value="🗑️  Clear", interactive=False)
-
-        if not embed_mode:
-            gr.Markdown(tos_markdown)
-            gr.Markdown(learn_more_markdown)
-        url_params = gr.JSON(visible=False)
-
-        # Register listeners
-        btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
-        upvote_btn.click(
-            upvote_last_response,
-            [state, model_selector],
-            [textbox, upvote_btn, downvote_btn, flag_btn],
-            queue=False
-        )
-        downvote_btn.click(
-            downvote_last_response,
-            [state, model_selector],
-            [textbox, upvote_btn, downvote_btn, flag_btn],
-            queue=False
-        )
-        flag_btn.click(
-            flag_last_response,
-            [state, model_selector],
-            [textbox, upvote_btn, downvote_btn, flag_btn],
-            queue=False
-        )
-
-        regenerate_btn.click(
-            regenerate,
-            [state, image_process_mode],
-            [state, chatbot, textbox, imagebox] + btn_list,
-            queue=False
-        ).then(
-            http_bot,
-            [state, model_selector, temperature, top_p, max_output_tokens],
-            [state, chatbot] + btn_list
-        )
-
-        clear_btn.click(
-            clear_history,
-            None,
-            [state, chatbot, textbox, imagebox] + btn_list,
-            queue=False
-        )
-
-        textbox.submit(
-            add_text,
-            [state, textbox, imagebox, image_process_mode],
-            [state, chatbot, textbox, imagebox] + btn_list,
-            queue=False
-        ).then(
-            http_bot,
-            [state, model_selector, temperature, top_p, max_output_tokens],
-            [state, chatbot] + btn_list
-        )
-
-        submit_btn.click(
-            add_text,
-            [state, textbox, imagebox, image_process_mode],
-            [state, chatbot, textbox, imagebox] + btn_list,
-            queue=False
-        ).then(
-            http_bot,
-            [state, model_selector, temperature, top_p, max_output_tokens],
-            [state, chatbot] + btn_list
-        )
-
-        if args.model_list_mode == "once":
-            demo.load(
-                load_demo,
-                [url_params],
-                [state, model_selector],
-                _js=get_window_url_params,
-                queue=False
-            )
-        elif args.model_list_mode == "reload":
-            demo.load(
-                load_demo_refresh_model_list,
-                None,
-                [state, model_selector],
-                queue=False
-            )
-        else:
-            raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
-
-    return demo
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--host", type=str, default="0.0.0.0")
-    parser.add_argument("--port", type=int)
-    parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
-    parser.add_argument("--concurrency-count", type=int, default=10)
-    parser.add_argument("--model-list-mode", type=str, default="once",
-        choices=["once", "reload"])
-    parser.add_argument("--share", action="store_true")
-    parser.add_argument("--moderate", action="store_true")
-    parser.add_argument("--embed", action="store_true")
-    args = parser.parse_args()
-    logger.info(f"args: {args}")
-
-    models = get_model_list()
-
-    logger.info(args)
-    demo = build_demo(args.embed)
-    demo.queue(
-        concurrency_count=args.concurrency_count,
-        api_open=False
-    ).launch(
-        server_name=args.host,
-        server_port=args.port,
-        share=False
-    )
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Autodata Windows 8 X64.md b/spaces/terfces0erbo/CollegeProjectV2/Autodata Windows 8 X64.md
deleted file mode 100644
index 45d192ff10154b94f29db29d2ef78ff93f1f4941..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Autodata Windows 8 X64.md	
+++ /dev/null
@@ -1,52 +0,0 @@
-
-<h1>How to Use AUTODATA 3.45 on Windows 8 x64</h1>
-<p>If you are looking for a powerful application to analyze the components and the parameters of the cars, you might want to try AUTODATA 3.45. This software provides a comprehensive environment that explains different complex components of the cars so that you can perform various repairing tasks. It also allows you to generate and create different diagrams for better understanding.</p>
-<h2>autodata windows 8 x64</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://bytlly.com/2uGj3e">https://bytlly.com/2uGj3e</a></b></p><br /><br />
-<p>However, installing and running AUTODATA 3.45 on Windows 8 x64 can be tricky. You need to follow some steps carefully and make sure you have the right system requirements. In this article, we will show you how to use AUTODATA 3.45 on Windows 8 x64 without any problems.</p>
-<h2>System Requirements for AUTODATA 3.45</h2>
-<p>Before you download and install AUTODATA 3.45, you need to make sure that your system meets the following requirements:</p>
-<ul>
-<li>Operating System: Windows XP/Vista/7/8/10</li>
-<li>Free Hard Disk Space: 5 GB of minimum free disk space required</li>
-<li>Installed Memory: 1 GB of minimum RAM required</li>
-<li>Processor: Dual Core Processor (Equivalent or higher)</li>
-</ul>
-<p>If your system does not meet these requirements, you might encounter some errors or performance issues when using AUTODATA 3.45.</p>
-<p></p>
-<h2>Download and Install AUTODATA 3.45</h2>
-<p>The next step is to download and install AUTODATA 3.45 on your Windows 8 x64 system. You can download the software from this link: https://allpcworlds.com/autodata-3-45-free-download-for-windows-d2/</p>
-<p>After downloading the file, you need to run "Install_x86" or "Install_x64" depending on your OS (32 or 64 bit). Follow the on-screen messages and wait for the installation to complete.</p>
-<h2>Disable UAC and Restart Your PC</h2>
-<p>One of the most important steps for using AUTODATA 3.45 on Windows 8 x64 is to disable User Account Control (UAC). UAC is a security feature that prevents unauthorized changes to your system settings and files. However, it can also interfere with some applications, such as AUTODATA 3.45.</p>
-<p>To disable UAC, you need to go to Control Panel > User Accounts > Change User Account Control Settings and move the slider to the lowest level (Never notify). Click OK and restart your PC.</p>
-<h2>Run DSEO and Sign Driver Files</h2>
-<p>The next step is to run DSEO (Driver Signature Enforcement Overrider) and sign the driver files for AUTODATA 3.45. DSEO is a tool that allows you to bypass the driver signature enforcement on Windows 8 x64, which can prevent some applications from running properly.</p>
-<p>To run DSEO, you need to right-click on dseo13b.exe (which is included in the AUTODATA 3.45 package) and select "Run as administrator". Then, select "Enable Test Mode" and click Next. After that, select "Sign a System File" and click Next. Enter C:\Windows\System32\drivers\etc\hosts as the file name and click OK. Repeat this process for C:\Windows\System32\drivers\hasplms.exe and C:\Windows\System32\drivers\hardlock.sys files.</p>
-<h2>Change Regional Settings to English US</h2>
-<p>The final step is to change your regional settings to English US. This is necessary because AUTODATA 3.45 might not work properly with other languages or formats.</p>
-<p>To change your regional settings, you need to go to Control Panel > Clock, Language, and Region > Region and Language > Formats and select English (United States) as the format. Click Apply and OK.</p>
-<h2>Start AUTODATA 3.45</h2>
-<p>Now you are ready to start AUTODATA 3.45 on your Windows 8 x64 system. You can find the shortcut on your desktop or in your Start menu. Double-click on it and enjoy using this powerful application for analyzing modern cars.</p>
-<h2>Benefits of Using AUTODATA 3.45 on Windows 8 x64</h2>
-<p>Using AUTODATA 3.45 on Windows 8 x64 can bring you many benefits, especially if you are a car enthusiast or a professional mechanic. Here are some of the advantages of using this software:</p>
-<ul>
-<li>You can access a huge database of information about different cars, including technical specifications, wiring diagrams, service schedules, diagnostic codes, and more.</li>
-<li>You can perform various tasks related to car maintenance and repair, such as checking the petrol injection system, adjusting the belts, fixing the air conditioning, and replacing the air bags.</li>
-<li>You can save time and money by diagnosing and solving problems yourself, without relying on expensive workshops or dealerships.</li>
-<li>You can learn more about how cars work and improve your skills and knowledge.</li>
-<li>You can enjoy using a user-friendly interface that is easy to navigate and understand.</li>
-</ul>
-<h2>How to Get Support for AUTODATA 3.45 on Windows 8 x64</h2>
-<p>If you encounter any issues or have any questions while using AUTODATA 3.45 on Windows 8 x64, you can get support from various sources. Here are some of the options you have:</p>
-<ul>
-<li>You can visit the official website of AUTODATA at https://www.autodata-group.com/ and find useful information, such as FAQs, manuals, tutorials, and contact details.</li>
-<li>You can join online forums and communities where other users of AUTODATA share their experiences, tips, and solutions. For example, you can check out https://motorcarsoft.com/viewtopic.php?t=15939 or https://www.socialdude.net/en/articles/5915-https-cracked2-com-autodata-crack-download.</li>
-<li>You can watch online videos that demonstrate how to use AUTODATA 3.45 on Windows 8 x64. For example, you can check out https://www.youtube.com/watch?v=4Qxq1tZL0fI or https://www.youtube.com/watch?v=O5vJy0Y7l9w.</li>
-<li>You can contact the customer service of AUTODATA by phone or email and get professional assistance.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>AUTODATA 3.45 is a powerful application that allows you to analyze the components and the parameters of the cars on your Windows 8 x64 system. It is a useful tool for car enthusiasts and professional mechanics who want to perform various tasks related to car maintenance and repair. It also provides a comprehensive environment that explains different complex components of the cars so that you can learn more about how cars work. To use AUTODATA 3.45 on Windows 8 x64, you need to follow some steps carefully and make sure you have the right system requirements. You can also get support from various sources if you encounter any issues or have any questions. If you are interested in using AUTODATA 3.45 on Windows 8 x64, you can download it from this link: https://allpcworlds.com/autodata-3-45-free-download-for-windows-d2/</p>
-<h2>Conclusion</h2>
-<p>AUTODATA 3.45 is a powerful application that allows you to analyze the components and the parameters of the cars on your Windows 8 x64 system. It is a useful tool for car enthusiasts and professional mechanics who want to perform various tasks related to car maintenance and repair. It also provides a comprehensive environment that explains different complex components of the cars so that you can learn more about how cars work. To use AUTODATA 3.45 on Windows 8 x64, you need to follow some steps carefully and make sure you have the right system requirements. You can also get support from various sources if you encounter any issues or have any questions. If you are interested in using AUTODATA 3.45 on Windows 8 x64, you can download it from this link: https://allpcworlds.com/autodata-3-45-free-download-for-windows-d2/</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Download Xforce Keygen TOP AutoCAD 2018 Activation.md b/spaces/terfces0erbo/CollegeProjectV2/Download Xforce Keygen TOP AutoCAD 2018 Activation.md
deleted file mode 100644
index 55e7e0dd960a941e1ba380bc370a3985fc29ad88..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Download Xforce Keygen TOP AutoCAD 2018 Activation.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>download xforce keygen AutoCAD 2018 activation</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://bytlly.com/2uGl5D">https://bytlly.com/2uGl5D</a></b></p><br /><br />
-
-Autodesk 2021 Products Keygen Offline Activation ️ Free . ... Download Autodesk 2020 xforce zip Details Software Title X-Force Keygen for All Autodesk ... All Autodesk 2018 Products Keygen (x86x64) Jan 30, 2021 · The AutoCAD 2021 ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/thejagstudio/picxai/app.py b/spaces/thejagstudio/picxai/app.py
deleted file mode 100644
index 9388782f70a49b64cfc627a6000572c4374b3665..0000000000000000000000000000000000000000
--- a/spaces/thejagstudio/picxai/app.py
+++ /dev/null
@@ -1,254 +0,0 @@
-from flask import Flask, render_template, jsonify, request, send_file
-import requests
-import http.client
-import json
-import time
-import re
-from strgen import StringGenerator as SG
-import uuid
-
-app = Flask(__name__)
-
-
-def encrypt(text):
-    s = int(re.search(r'\d+', text).group()[0])
-    result = ""
-    for i in range(len(text)):
-        char = text[i]
-        if(not char.isnumeric() and char != "-"):
-            if (char.isupper()):
-                result += chr((ord(char) + s - 65) % 26 + 65)
-            else:
-                result += chr((ord(char) + s - 97) % 26 + 97)
-        else:
-            result += char
-    return result
-
-
-def dencrypt(text):
-    s = int(re.search(r'\d+', text).group()[0])
-    result = ""
-    for i in range(len(text)):
-        char = text[i]
-        if(not char.isnumeric() and char != "-"):
-            if (char.isupper()):
-                result += chr((ord(char) - s - 65) % 26 + 65)
-            else:
-                result += chr((ord(char) - s - 97) % 26 + 97)
-        else:
-            result += char
-    return result
-
-
-def NewAccount():
-    # Genrate CSRF
-    conn = http.client.HTTPSConnection("lexica.art")
-    payload = ''
-    conn.request("GET", "/api/auth/csrf", payload)
-    response = conn.getresponse()
-    csrf = json.loads(response.read().decode("utf-8"))["csrfToken"]
-    print("CSRF :", csrf)
-    cookies = response.getheader("Set-Cookie")
-
-    # Genrate Temp Mail
-    tempEmail = json.loads(requests.get(
-        "https://www.1secmail.com/api/v1/?action=genRandomMailbox&count=1").text)[0]
-    login = tempEmail.split("@")[0]
-    domain = tempEmail.split("@")[1]
-    print("Tempmail :", tempEmail)
-
-    # Send Mail From Lexica
-    csrfTokenCookie = str(cookies).split(
-        "__Host-next-auth.csrf-token=")[1].split(";")[0]
-    cookiesText = '__Host-next-auth.csrf-token='+csrfTokenCookie + \
-        '; __Secure-next-auth.callback-url=https%3A%2F%2Flexica.art%2F'
-    payload = 'email='+tempEmail + \
-        '&redirect=false&callbackUrl=https%3A%2F%2Flexica.art%2F&csrfToken='+csrf+'&json=true'
-    headers = {
-        'Content-Type': 'application/x-www-form-urlencoded',
-        'Cookie': cookiesText
-    }
-    conn.request("POST", "/api/auth/signin/email?=null", payload, headers)
-    response = conn.getresponse()
-    if ("provider=email&type=email" in response.read().decode("utf-8")):
-        print("Email Sent :", True)
-
-    # Recieve Mail from Lexica
-    while True:
-        mailData = json.loads(requests.get(
-            "https://www.1secmail.com/api/v1/?action=getMessages&login="+login+"&domain="+domain).text)
-        if(len(mailData) > 0):
-            mailId = mailData[0]["id"]
-            break
-        time.sleep(0.5)
-
-    mailBody = json.loads(requests.get(
-        "https://www.1secmail.com/api/v1/?action=readMessage&login="+login+"&domain="+domain+"&id="+str(mailId)).text)["textBody"]
-    sessionLink = mailBody.split('<a href=\"')[1].split('\">')[0].strip()
-    print(sessionLink)
-
-    # Activate A New Account
-    payload = ''
-    headers = {
-        'Cookie': cookiesText
-    }
-    conn.request("GET", "/"+sessionLink.split("lexica.art/")
-                 [1], payload, headers)
-    res = conn.getresponse()
-    data = res.read()
-    cookies = res.getheader("Set-Cookie")
-    print(data.decode("utf-8"))
-    print(cookies)
-    sessionTokenCookie = str(cookies).split(".session-token=")[1].split(";")[0]
-    return csrfTokenCookie, sessionTokenCookie
-
-
-
-
-
-csrfTokenCookie, sessionTokenCookie = NewAccount()
-csrfTokenCookieGEN, sessionTokenCookieGEN = NewAccount()
-visitorId = SG("[\l\d]{20}").render_list(1, unique=True)[0]
-
-
-def infiniteScroll(cursor, query, searchMode, model):
-    global csrfTokenCookie
-    conn = http.client.HTTPSConnection("lexica.art")
-    print(query)
-    try:
-        cursor = int(cursor)
-    except:
-        cursor = 0
-    payload = json.dumps({
-        "text": query,
-        "searchMode": searchMode,
-        "source": "search",
-        "cursor": int(cursor),
-        "model": model
-    })
-    headers = {
-        'Content-Type': 'application/json',
-        'Cookie': '__Host-next-auth.csrf-token='+csrfTokenCookie+'; __Secure-next-auth.callback-url=https%3A%2F%2Flexica.art'
-    }
-    conn.request("POST", "/api/infinite-prompts", payload, headers)
-    res = conn.getresponse()
-    data = res.read()
-    return json.loads(data.decode("utf-8"))
-
-
-@app.route('/infinite-prompts', methods=["POST", "GET"])
-def homeImageLoader():
-    if request.method == 'POST':
-        try:
-            cursor = request.form['cursor']
-        except:
-            cursor = 0
-        try:
-            query = request.form['query']
-        except:
-            query = ""
-        searchMode = request.form['searchMode']
-        model = request.form['model']
-        if(model == "picxai-diffuser"):
-            model = "lexica-aperture-v2"
-        if(searchMode == "prompts"):
-            model = "sd-1.5"
-        data = infiniteScroll(cursor, query, searchMode, model)
-        return data
-
-
-@app.route('/prompt/<id>', methods=["GET"])
-def promptDetail(id):
-    url = "https://lexica.art/prompt/" + id
-    payload={}
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
-    }
-    response = requests.request("GET", url, headers=headers, data=payload)
-    datafinal = {}
-    datafinal["id"] = id
-    datafinal["prompt"] = response.text.split('name="description" content="')[1].split('"')[0]
-    datafinal["c"] = int(response.text.split('Guidance scale')[1].split('<div class="text-sm">')[1].split('</div>')[0])
-    datafinal["width"] = int(response.text.split('Dimensions')[1].split('<div class="text-sm">')[1].split('</div>')[0].split("×")[0].replace("<!-- -->",""))
-    datafinal["height"] = int(response.text.split('Dimensions')[1].split('<div class="text-sm">')[1].split('</div>')[0].split("×")[0].replace("<!-- -->",""))
-    datafinal["images"] = []
-    tempImage = response.text.split('<meta property="og:image" content="')[1].split('"')[0].split("images=")[1:]
-    for i in tempImage:
-        datafinal["images"].append({"id": i.replace("&amp;", "").split("/")[-1]})
-    try:
-        datafinal["negativePrompt"] = response.text.split('Negative prompt')[1].split('<div class="text-sm">')[1].split('</div>')[0]
-    except:
-        datafinal["negativePrompt"] = ""
-    try:
-        datafinal["upscaled_width"] = int(response.text.split('Upscaled')[1].split('<div class="text-sm">')[1].split('</div>')[0].split("×")[0].replace("<!-- -->",""))
-        datafinal["upscaled_height"] = int(response.text.split('Upscaled')[1].split('<div class="text-sm">')[1].split('</div>')[0].split("×")[1].replace("<!-- -->",""))
-    except:
-        datafinal["upscaled_width"] =""
-        datafinal["upscaled_height"] =""
-    datafinal["model"] = "picxai-diffuser"
-    return render_template("prompt.html", datafinal=datafinal)
-
-
-@app.route('/diffuser', methods=["POST", "GET"])
-def diffuser():
-    global csrfTokenCookieGEN, sessionTokenCookieGEN, visitorId
-    if request.method == 'POST':
-        try:
-            prompt = request.form['prompt']
-            print(prompt)
-            negativePrompt = request.form['negativePrompt']
-            guidanceScale = request.form['guidanceScale']
-            enableHiresFix = str(request.form['enableHiresFix'])
-            width = request.form['width']
-            height = request.form['height']
-            myuuid = uuid.uuid4()
-            payload = json.dumps({
-                "prompt": prompt,
-                "negativePrompt": negativePrompt,
-                "guidanceScale": int(guidanceScale),
-                "width": int(width),
-                "height": int(height),
-                "enableHiresFix": enableHiresFix,
-                "model": "lexica-aperture-v2",
-                "numImagesGenerated": 0,
-                "id": visitorId,
-                "requestId": str(myuuid),
-            })
-            headers = {
-                'authority': 'z.lexica.art',
-                'accept': 'application/json, text/plain, */*',
-                'content-type': 'application/json',
-                'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',
-                'Cookie': '__Secure-next-auth.session-token='+sessionTokenCookieGEN+'; __Host-next-auth.csrf-token='+csrfTokenCookieGEN+';'
-            }
-            conn = http.client.HTTPSConnection("z.lexica.art")
-            conn.request("POST", "/api/generator", payload, headers)
-            res = conn.getresponse()
-            data = res.read()
-            if "needsMembership" in data.decode("utf-8"):
-                csrfTokenCookieGEN, sessionTokenCookieGEN = NewAccount()
-                visitorId = SG("[\l\d]{20}").render_list(1, unique=True)[0]
-                data = {"error": "Please try Again"}
-                return jsonify(data)
-            return jsonify(data.decode("utf-8"))
-        except Exception as e:
-            return jsonify({"error": str(e)})
-    else:
-        return render_template("generate.html")
-
-
-@app.route('/image/<size>/<id>', methods=["GET"])
-def imageAPI(size, id):
-    print(size, id)
-    return send_file(filename, mimetype='image/jpg')
-
-
-@app.route('/', methods=["GET"])
-def home():
-    return render_template("index.html")
-@app.route('/hello', methods=["GET"])
-def hello():
-    return "hello picxai"
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Blessthefall-His Last Walk Full Album Zip Extra Quality.md b/spaces/tialenAdioni/chat-gpt-api/logs/Blessthefall-His Last Walk Full Album Zip Extra Quality.md
deleted file mode 100644
index e7f9754b6cd48d73eef6b5796725a45e602d7cfe..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Blessthefall-His Last Walk Full Album Zip Extra Quality.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-
-<h1>Review: blessthefall - His Last Walk</h1>
-<p>blessthefall is a metalcore band from Phoenix, Arizona that formed in 2004. Their debut full-length album, His Last Walk, was released in 2007 through Science Records and later re-issued by Ferret Music with two bonus tracks. This album features Craig Mabbitt on vocals, who later left the band and was replaced by Beau Bokan.</p>
-<h2>blessthefall-His Last Walk full album zip</h2><br /><p><b><b>Download File</b> &#9889; <a href="https://urlcod.com/2uKb6G">https://urlcod.com/2uKb6G</a></b></p><br /><br />
-<p>His Last Walk is a solid album that showcases blessthefall's energetic and melodic style of metalcore. The album consists of 11 tracks (plus two bonus tracks) that range from fast and aggressive to slow and emotional. The album opens with "A Message to the Unknown", a catchy and upbeat song that sets the tone for the rest of the album. The next track, "Guys Like You Make Us Look Bad", is one of the most popular songs on the album, featuring heavy riffs, breakdowns, and a guest appearance by Joe Cotela of Ded. The third track, "Higinia", is a softer and more melodic song that showcases Mabbitt's clean vocals and the band's harmonies.</p>
-<p>The album continues with songs like "Could Tell a Love", "Rise Up", "Times Like These", and "Pray", which are all solid metalcore songs that blend heaviness and melody. The album also features some slower and more emotional songs, such as "With Eyes Wide Shut", "Wait for Tomorrow", and "His Last Walk". These songs show the band's versatility and ability to create atmospheric and heartfelt music. The album closes with "Black Rose Dying", a fan-favorite song that was originally released on the band's self-titled EP in 2006. This song is one of the heaviest and most intense songs on the album, featuring brutal screams, breakdowns, and a memorable chorus.</p>
-<p>His Last Walk is a great debut album by blessthefall that showcases their talent and potential as a metalcore band. The album has a good balance of heavy and melodic songs, and Mabbitt's vocals are impressive and diverse. The album also has some memorable lyrics that deal with themes such as love, betrayal, faith, and death. His Last Walk is an album that fans of metalcore should definitely check out.</p>
-<p></p>
-
-<p>His Last Walk is not only a great album by blessthefall, but also a significant one in their career. This album marks the last appearance of Craig Mabbitt on vocals, who left the band shortly after the album's release due to personal and creative differences. Mabbitt went on to join Escape the Fate and later form his own band, The Dead Rabbitts. blessthefall then recruited Beau Bokan, formerly of Take The Crown, as their new vocalist. Bokan brought a new sound and style to the band, and they released their second album, Witness, in 2009.</p>
-<p>His Last Walk is an album that fans of both Mabbitt and Bokan can enjoy, as it showcases the best of both worlds. The album has a raw and energetic sound that reflects the band's early days, but also hints at their future direction. The album also has a nostalgic and sentimental value for many fans, as it represents a turning point in the band's history. His Last Walk is an album that deserves to be appreciated and remembered as one of the best metalcore albums of its time.</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/ElevateDB VCL ADD PHP DAC Version 2.27 Build 1.md b/spaces/tialenAdioni/chat-gpt-api/logs/ElevateDB VCL ADD PHP DAC Version 2.27 Build 1.md
deleted file mode 100644
index 2e01eaeba5c618e5cefad4cc1df3c948090fe721..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/ElevateDB VCL ADD PHP DAC Version 2.27 Build 1.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<h1>How to Use ElevateDB VCL ADD PHP DAC Version 2.27 Build 1 for Your Database Applications</h1>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/File Scavenger 42 License Key Free A Powerful Tool for Recovering Lost Data.md b/spaces/tialenAdioni/chat-gpt-api/logs/File Scavenger 42 License Key Free A Powerful Tool for Recovering Lost Data.md
deleted file mode 100644
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--- a/spaces/tialenAdioni/chat-gpt-api/logs/File Scavenger 42 License Key Free A Powerful Tool for Recovering Lost Data.md	
+++ /dev/null
@@ -1,68 +0,0 @@
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/ Kick the Buddy        .md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/ Kick the Buddy        .md
deleted file mode 100644
index 3a3bd449073c32ba1e4d47a232875a4c6adf9c30..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/ Kick the Buddy        .md	
+++ /dev/null
@@ -1,134 +0,0 @@
-
-<h1>How to Hack Kick the Buddy Game on Android and PC</h1>
- <p>Kick the Buddy is a popular game that allows you to vent your frustration and anger on a helpless doll. You can use various weapons and props to smash, explode, shoot, freeze, burn, and torture the doll in any way you want. It's a fun and relaxing game that can help you relieve stress and boredom.</p>
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- <p>However, if you want to access all the features and items in the game, you may need to spend some real money or watch a lot of ads. That's why some players may want to hack Kick the Buddy and get unlimited resources, such as money, gold, weapons, and skins. In this article, we will show you how to hack Kick the Buddy on Android and PC using some simple tools.</p>
- <h2>What is Kick the Buddy?</h2>
- <h3>A fun and relaxing game to unleash your anger</h3>
- <p>Kick the Buddy is a game that lets you do whatever you want to a ragdoll-like character named Buddy. You can drag him around, throw him in the air, punch him, kick him, or use any of the available weapons and props to inflict damage on him. The game has realistic physics and sound effects that make it more satisfying and hilarious.</p>
- <p>The game also has a voice feature that makes Buddy talk to you and react to your actions. He may taunt you, beg you, praise you, or insult you depending on what you do to him. You can also customize his appearance by changing his clothes, hats, glasses, hairstyles, and facial expressions.</p>
- <h3>Different weapons and props to destroy the doll</h3>
- <p>The game offers a wide range of weapons and props that you can use to destroy Buddy. You can choose from categories such as firearms, explosives, cold weapons, animals, machines, tools, sports, powers, objects, food, liquids, music, nano weapons, office stuff, horror stuff, etc. Each category has several items that have different effects and animations.</p>
- <p>Some of the most popular weapons and props in the game are rockets, grenades, automatic rifles, nuclear bombs, chainsaws, axes, knives, scissors, hammers, baseball bats, golf clubs, soccer balls, basketballs, bowling balls, darts, balloons, fireworks, fire extinguishers, ice cubes, snowballs, fans,</p>
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- <h2>Why hack Kick the Buddy?</h2>
- <h3>To unlock all weapons and skins</h3>
- <p>Although the game has a lot of weapons and props to choose from, not all of them are available for free. Some of them require you to watch ads, complete tasks, or pay real money to unlock them. The same goes for the skins and outfits that you can use to customize Buddy. If you want to access all the content in the game without spending any time or money, hacking Kick the Buddy is a good option.</p>
- <h3>To get unlimited money and gold</h3>
- <p>Another reason to hack Kick the Buddy is to get unlimited money and gold. Money and gold are the two currencies in the game that you can use to buy weapons, props, skins, and other items. You can earn money and gold by playing the game, watching ads, or buying them with real money. However, the amount of money and gold you can earn or buy is limited and may not be enough to afford everything you want. By hacking Kick the Buddy, you can get unlimited money and gold and buy anything you want without any restrictions.</p>
- <h3>To have more fun and challenge</h3>
- <p>The last reason to hack Kick the Buddy is to have more fun and challenge. The game can be very entertaining and addictive, but it can also get boring and repetitive after a while. You may feel like you have seen everything there is to see and done everything there is to do in the game. By hacking Kick the Buddy, you can spice things up and make the game more interesting and exciting. You can try new combinations of weapons and props, experiment with different effects and animations, or challenge yourself by setting your own goals and rules.</p>
- <h2>How to hack Kick the Buddy on Android?</h2>
- <h3>Download Lucky Patcher app</h3>
- <p>One of the easiest ways to hack Kick the Buddy on Android is to use an app called Lucky Patcher. Lucky Patcher is a tool that allows you to modify apps and games on your device. You can use it to remove ads, bypass license verification, change permissions, clone apps, backup apps, create custom patches, and more.</p>
- <p>To download Lucky Patcher, you need to go to its official website and click on the download button. You will get a warning message that says "This type of file can harm your device". Ignore it and tap on "OK". The app will start downloading on your device.</p>
- <h3>Install and launch Lucky Patcher</h3>
- <p>After downloading Lucky Patcher, you need to install it on your device. To do that, you need to enable unknown sources on your device settings. Go to Settings > Security > Unknown Sources and toggle it on. Then go to your file manager and find the Lucky Patcher apk file that you downloaded. Tap on it and follow the instructions to install it.</p>
- <p>Once installed, launch Lucky Patcher from your app drawer. You will see a list of all the apps and games installed on your device. Find Kick the Buddy and tap on it.</p>
- <h3>Select Kick the Buddy and apply custom patch</h3>
- <p>After tapping on Kick the Buddy, you will see a menu with various options. Tap on "Menu of Patches" and then tap on "Create Modified APK File". You will see another menu with different types of patches. Tap on "Custom Patch-applied APK" and then tap on "Apply".</p>
- <p>Lucky Patcher will start creating a modified version of Kick the Buddy with a custom patch applied. This patch will give you unlimited money, gold, weapons, skins, and other features in the game. Wait for a few minutes until the process is completed.</p>
- <p>When the process is done, you will see a message that says "Patch pattern N1 success". Tap on "Go To File" and then tap on "Uninstall And Install". This will uninstall the original version of Kick the Buddy from your device and install the modified version with the patch applied.</p>
- <p>Congratulations! You have successfully hacked Kick the Buddy on Android using Lucky Patcher. Now you can enjoy playing the game with unlimited resources and features.</p>
- <h2>How to hack Kick the Buddy on PC?</h2>
- <h3>Download Cheat Engine software</h3>
- <p>If you want to hack Kick the Buddy on PC, you need to use a software called Cheat Engine. Cheat Engine is a program that allows you to scan and modify values in memory of any process running on your computer. <p>You can use it to change the values of any variable in a game, such as health, ammo, money, score, etc. You can also use it to create cheats, trainers, mods, and hacks for your games.</p>
- <p>To download Cheat Engine, you need to go to its official website and click on the download button. You will get a link to the latest version of Cheat Engine for Windows or macOS. You can also find older versions and source code on the same page.</p>
- <h3>Install and run Cheat Engine</h3>
- <p>After downloading Cheat Engine, you need to install it on your computer. To do that, you need to run the setup file and follow the instructions. You may have to agree to the terms and conditions, choose a destination folder, and select some options. You may also have to decline some offers or uncheck some boxes to avoid installing unwanted software.</p>
- <p>Once installed, run Cheat Engine from your desktop or start menu. You will see a window with a lot of options and buttons. Don't worry, you don't need to understand everything right now. Just focus on the basics.</p>
- <h3>Attach Cheat Engine to Kick the Buddy process</h3>
- <p>The next step is to attach Cheat Engine to the process of Kick the Buddy. This means that you will be able to access and modify the memory of the game while it is running. To do that, you need to have Kick the Buddy running on your PC. You can use an emulator like BlueStacks or NoxPlayer to play Android games on your PC.</p>
- <p>Once you have Kick the Buddy running on your PC, go back to Cheat Engine and click on the computer icon in the upper-left corner. This will open a window with a list of processes running on your PC. Find Kick the Buddy and click on it. Then click on Open or OK.</p>
- <p>Cheat Engine will now be attached to Kick the Buddy process. You will see a green check mark next to the computer icon in Cheat Engine. This means that you are ready to start hacking.</p>
- <h2>How to hack Kick the Buddy using Cheat Engine?</h2>
- <h3>Scan and modify values in memory</h3>
- <p>The main function of Cheat Engine is to scan and modify values in memory. This means that you can find and change any variable in a game, such as money, gold, weapons, skins, etc. To do that, you need to follow these steps:</p>
- <ol>
-<li>Find out the current value of the variable you want to change in Kick the Buddy. For example, if you want to change your money, look at how much money you have in the game.</li>
-<li>Type that value in the Value box in Cheat Engine. Make sure that the Value Type is set to 4 Bytes (this is the most common type for numbers in games). Then click on First Scan.</li>
-<li>Cheat Engine will scan the memory of Kick the Buddy and display a list of addresses and values that match your input. These are potential candidates for the variable you want to change.</li>
-<li>Go back to Kick the Buddy and change the value of the variable by playing the game. For example, if you want to change your money, spend some money or earn some money in the game.</li>
-<li>Type the new value of the variable in the Value box in Cheat Engine. Then click on Next Scan.</li>
-<li>Cheat Engine will scan again and narrow down the list of addresses and values that match your input. Repeat steps 4 and 5 until you have only one or a few addresses left in the list.</li>
-<li>Select one or more addresses from the list and add them to your cheat table by clicking on the red arrow button at the bottom-right corner.</li>
-<li>The addresses will appear in your cheat table at the bottom of Cheat Engine window. You can double-click on them and change their values as you wish. You can also check or uncheck them to freeze or unfreeze their values.</li>
-<li>Enjoy your hacked game!</li>
-</ol>
- <p>For example, if you want to hack your money in Kick the Buddy, you can follow these steps:</p>
- <ol>
-<li>Look at how much money you have in Kick the Buddy. Let's say you have 1000 money.</li>
-<li>Type 1000 in the Value box in Cheat Engine. Make sure that Value Type is set to 4 Bytes. Then click on First Scan.</li>
-<li>Cheat Engine will display a list of addresses and values that match 1000.</li>
-<li>Go back to Kick Buddy and spend some money or earn some money in the game. Let's say you spend 100 money and now you have 900 money.</li>
-<li>Type 900 in the Value box in Cheat Engine. Then click on Next Scan.</li>
-<li>Cheat Engine will display a shorter list of addresses and values that match 900.</li>
-<li>Repeat steps 4 and 5 until you have only one or a few addresses left in the list.</li>
-<li>Select one or more addresses from the list and add them to your cheat table by clicking on the red arrow button.</li>
-<li>The addresses will appear in your cheat table. You can double-click on them and change their values as you wish. For example, you can change them to 9999999 and have unlimited money in the game.</li>
-<li>Enjoy your hacked game!</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Kick the Buddy is a fun and relaxing game that lets you unleash your anger and stress on a ragdoll-like character. You can use various weapons and props to destroy him in different ways. However, if you want to access all the features and items in the game, you may need to hack it and get unlimited resources. In this article, we showed you how to hack Kick the Buddy on Android and PC using Lucky Patcher and Cheat Engine. These are simple and effective tools that can help you modify any app or game on your device or computer. We hope you found this article helpful and informative. Happy hacking!</p>
- <h2>FAQs</h2>
- <h3>Q: Is hacking Kick the Buddy legal?</h3>
- <p>A: Hacking Kick the Buddy is not illegal, but it may violate the terms of service of the game or the platform you are using. You may risk getting banned or suspended from the game or the platform if you hack it. Therefore, we advise you to hack Kick the Buddy at your own risk and discretion.</p>
- <h3>Q: Is hacking Kick the Buddy safe?</h3>
- <p>A: Hacking Kick the Buddy is generally safe, as long as you use trusted and reliable tools like Lucky Patcher and Cheat Engine. However, you should always be careful when downloading or installing any software from unknown sources, as they may contain viruses or malware that can harm your device or computer. You should also backup your data before hacking any app or game, in case something goes wrong.</p>
- <h3>Q: Can I hack Kick the Buddy online?</h3>
- <p>A: No, you cannot hack Kick the Buddy online. The game does not have an online mode or feature, so there is no way to hack it online. You can only hack Kick the Buddy offline, on your device or computer.</p>
- <h3>Q: Can I hack Kick the Buddy without root or jailbreak?</h3>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/101 Okey Plus ip Hilesi 2023 APK ile Snrsz ip Elde Et..md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/101 Okey Plus ip Hilesi 2023 APK ile Snrsz ip Elde Et..md
deleted file mode 100644
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--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/101 Okey Plus ip Hilesi 2023 APK ile Snrsz ip Elde Et..md	
+++ /dev/null
@@ -1,101 +0,0 @@
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-<p>If you are a fan of okey, the popular Turkish tile-based game, you might have heard of 101 Okey Plus, one of the most played okey games on mobile devices. In this article, we will show you how to get free chips in 101 Okey Plus using various methods, such as free chip codes, APK mod or generator tools, and mobile payment or Telegram channels. We will also discuss the pros and cons of each method, and how to use them safely and effectively. By the end of this article, you will be able to enjoy 101 Okey Plus without worrying about running out of chips, and win more games against your friends or online opponents.</p>
- <h2>Introduction</h2>
-<h3>What is 101 Okey Plus?</h3>
-<p>101 Okey Plus is a mobile game developed by Zynga, the company behind popular games like FarmVille, Words with Friends, and Zynga Poker. It is based on okey, a traditional Turkish game that is similar to rummy. The game involves four players who try to form sets and runs of tiles with different colors and numbers. The first player who gets rid of all their tiles wins the game. 101 Okey Plus adds some twists to the classic game, such as special tiles, power-ups, tournaments, and leaderboards. You can play 101 Okey Plus with your Facebook friends, or with millions of other players from around the world.</p>
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- <h3>Why do you need free chips in 101 Okey Plus?</h3>
-<p>Chips are the currency of 101 Okey Plus. You need chips to enter a game room, place bets, buy power-ups, and participate in tournaments. The more chips you have, the more options and opportunities you have in the game. However, chips are not easy to come by in 101 Okey Plus. You can get some chips by logging in daily, completing achievements, inviting friends, watching ads, or buying them with real money. But these methods are either limited, time-consuming, or costly. That's why many players look for ways to get free chips in 101 Okey Plus using various hacks or cheats.</p>
- <h2>How to get free chips in 101 Okey Plus?</h2>
-<h3>Use free chip codes from websites</h3>
-<h4>Pros and cons of free chip codes</h4>
-<p>One of the easiest ways to get free chips in 101 Okey Plus is to use free chip codes from websites. These are alphanumeric codes that you can enter in the game to claim a certain amount of chips. Some websites claim to offer free chip codes for 101 Okey Plus on a regular basis, such as [Teknocep](^1^), [Netkurdu](^2^), and [Bizde Kalmasın](^3^). The pros of using free chip codes are that they are simple, fast, and convenient. You don't need to download anything, fill out any surveys, or give out any personal information. You just need to copy and paste the code in the game and enjoy your free chips. The cons of using free chip codes are that they are not always reliable, safe, or legal. Some websites may provide fake or expired codes that don't work or cause errors in the game. Some websites may also contain viruses, malware, or phishing links that can harm your device or steal your data. <h4>How to use free chip codes</h4>
-<p>To use free chip codes in 101 Okey Plus, you need to follow these steps:</p>
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-<ol>
-<li>Open the game and tap on the settings icon on the top right corner.</li>
-<li>Tap on the "Free Chips" option and then on the "Enter Code" button.</li>
-<li>Type or paste the code in the box and tap on the "Submit" button.</li>
-<li>You will receive a confirmation message and the chips will be added to your account.</li>
-</ol>
-<p>Note that some codes may have expiration dates or usage limits, so you need to act fast and use them as soon as possible. Also, be careful not to enter any wrong or invalid codes, as they may cause errors or glitches in the game.</p>
- <h3>Use APK mod or generator tools</h3>
-<h4>Pros and cons of APK mod or generator tools</h4>
-<p>Another way to get free chips in 101 Okey Plus is to use APK mod or generator tools. These are applications or websites that claim to modify or generate unlimited chips for your game account. Some examples of these tools are [101 Okey Plus MOD APK](^1^), [Okey Plus Hack](^2^), and [101 Okey Plus Generator](^3^). The pros of using APK mod or generator tools are that they can provide you with a large amount of chips in a short time, without requiring any codes, surveys, or payments. You can also use them multiple times and on different devices. The cons of using APK mod or generator tools are that they are not always trustworthy, secure, or ethical. Some tools may not work at all, or may require you to download additional files or apps that contain viruses, malware, or spyware. Some tools may also ask for your game login details, such as your email, password, or Facebook account, which can compromise your privacy and security. Moreover, some tools may violate the terms of service of the game and put your account at risk of being banned or suspended.</p> <h4>How to use APK mod or generator tools</h4>
-<p>To use APK mod or generator tools in 101 Okey Plus, you need to follow these steps:</p>
-<ol>
-<li>Find a reliable and working tool from the internet. You can search for keywords like "101 Okey Plus APK mod" or "101 Okey Plus generator" and check the reviews and ratings of the tools.</li>
-<li>Download the tool to your device or access it online. If you download an APK file, you need to enable the installation of unknown sources in your device settings. If you access an online tool, you need to have a stable internet connection.</li>
-<li>Open the tool and enter your game username or email. Some tools may also ask for your Facebook account if you use it to log in to the game.</li>
-<li>Select the amount of chips you want to generate or modify. Some tools may also offer other features, such as unlocking power-ups, tournaments, or special tiles.</li>
-<li>Click on the "Start" or "Generate" button and wait for the process to complete. Some tools may require you to verify that you are not a robot by completing a captcha or a human verification test.</li>
-<li>Once the process is done, check your game account and enjoy your free chips.</li>
-</ol>
-<p>Note that some tools may take some time to deliver the chips to your account, or may not work at all if the game updates its security system. Also, be careful not to use too many chips at once, or to generate or modify an unrealistic amount of chips, as this may raise suspicion and trigger the game's anti-cheat mechanism.</p>
- <h3>Use mobile payment or Telegram channels</h3>
-<h4>Pros and cons of mobile payment or Telegram channels</h4>
-<p>A third way to get free chips in 101 Okey Plus is to use mobile payment or Telegram channels. These are services that offer you free chips in exchange for making a small payment with your mobile phone credit or balance. Some examples of these services are [Okey Plus Bedava Çip], [Okey Plus Çip Hilesi], and [Okey Plus Çip Satın Al]. The pros of using mobile payment or Telegram channels are that they are fast, easy, and cheap. You don't need to download anything, enter any codes, or use any tools. You just need to send a message or make a call with your mobile phone and get your free chips instantly. The cons of using mobile payment or Telegram channels are that they are not always honest, legal, or safe. Some services may scam you by charging you more than what they offer, or by not delivering the chips at all. Some services may also violate the terms of service of the game and put your account at risk of being banned or suspended.</p> <h4>How to use mobile payment or Telegram channels</h4>
-<p>To use mobile payment or Telegram channels in 101 Okey Plus, you need to follow these steps:</p>
-<ol>
-<li>Find a reliable and working service from the internet. You can search for keywords like "101 Okey Plus bedava çip" or "101 Okey Plus çip hilesi" and check the reviews and ratings of the services.</li>
-<li>Contact the service provider via their phone number or Telegram channel. Some services may also have a website or a social media page where you can get more information.</li>
-<li>Choose the amount of chips you want to get and the payment method you want to use. Some services may accept different mobile operators, such as Turkcell, Vodafone, or Türk Telekom.</li>
-<li>Make the payment with your mobile phone credit or balance. Some services may require you to send a message, make a call, or scan a QR code.</li>
-<li>Receive your free chips in your game account. Some services may send you a confirmation code or a link that you need to enter in the game.</li>
-</ol>
-<p>Note that some services may have different prices, delivery times, or customer support options. Also, be careful not to share your game login details, such as your email, password, or Facebook account, with any service provider, as they may misuse them or sell them to third parties.</p>
- <h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, 101 Okey Plus is a fun and addictive game that lets you play okey with your friends or online players. However, to enjoy the game fully, you need to have enough chips to enter game rooms, place bets, buy power-ups, and participate in tournaments. In this article, we showed you how to get free chips in 101 Okey Plus using various methods, such as free chip codes, APK mod or generator tools, and mobile payment or Telegram channels. We also discussed the pros and cons of each method, and how to use them safely and effectively.</p>
- <h3>Call to action</h3>
-<p>We hope that this article was helpful and informative for you. If you liked it, please share it with your friends who also play 101 Okey Plus. Also, let us know in the comments below which method worked best for you, or if you have any questions or suggestions. And don't forget to check out our other articles on gaming tips and tricks. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
-<h3>Q: Is 101 Okey Plus free to play?</h3>
-<p>A: Yes, 101 Okey Plus is free to download and play on Android and iOS devices. However, some features and items in the game may require real money purchases.</p>
- <h3>Q: How can I play 101 Okey Plus with my friends?</h3>
-<p>A: You can play 101 Okey Plus with your friends by connecting your game account to your Facebook account. Then, you can invite your Facebook friends to join your game room, or join their game rooms.</p>
- <h3>Q: What are the rules of 101 Okey Plus?</h3>
-<p>A: The rules of 101 Okey Plus are similar to the rules of okey. You can find a detailed explanation of the rules in the game's help section.</p>
- <h3>Q: What are the special tiles in 101 Okey Plus?</h3>
-<p>A: The special tiles in 101 Okey Plus are tiles that have special effects or functions in the game. For example, the Joker tile can be used as any tile, the Double tile doubles your score, and the Bomb tile eliminates all tiles of the same color on the board.</p>
- <h3>Q: What are the power-ups in 101 Okey Plus?</h3>
-<p>A: The power-ups in 101 Okey Plus are items that can help you win more games or get more chips. For example, the Extra Time power-up gives you more time to make your move, the Swap Tiles power-up lets you swap your tiles with new ones, and the Free Chips power-up gives you some free chips every day.</p> 401be4b1e0<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Cara Mudah Download Soul Knight Mod Apk versi Terbaru 2022 yang Dilengkapi dengan Mod Menu dan Unlimited Gems.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Cara Mudah Download Soul Knight Mod Apk versi Terbaru 2022 yang Dilengkapi dengan Mod Menu dan Unlimited Gems.md
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-<p>If you are looking for a fun and addictive shooter game with rogue-like elements, then you should try Soul Knight. This game will take you to a dungeon world where you can explore, collect weapons, and shoot aliens. But what if you want to enhance your gaming experience with some extra features and options? That's where a mod apk mod menu comes in handy. In this article, we will tell you everything you need to know about Soul Knight versi terbaru 2022 mod apk mod menu, and how to download and install it on your device.</p>
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- <h2>What is Soul Knight?</h2>
-<p>Soul Knight is a pixelated shooter game developed by ChillyRoom. It is available for Android and iOS devices, as well as Nintendo Switch and Steam. The game has a simple and intuitive control system, with auto-aim mechanism and various skills. You can choose from over 20 unique heroes, each with their own abilities and playstyles. You can also collect over 400 weapons, ranging from guns and swords to shovels and umbrellas. The game features randomly generated dungeons, where you can encounter different enemies, NPCs, and treasures. You can play the game solo or with your friends online or offline.</p>
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-<ul>
-<li>20+ unique heroes with different abilities and personalities</li>
-<li>400+ weapons with various effects and functions</li>
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-<p>Soul Knight is a game that will keep you entertained for hours. It has a smooth and enjoyable gameplay that combines action and survival. It also has a lot of replay value, as each run will be different and unpredictable. You can experiment with different heroes, weapons, and strategies to find your favorite combination. You can also challenge yourself with different game modes and difficulties. Moreover, you can share the fun with your friends by playing together online or offline. Soul Knight is a game that will make you feel like a hero in a pixelated dungeon world.</p>
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-<p>A mod apk mod menu is a modified version of an original game that includes various features not present in the original game. The mod allows players to unlock new levels, characters, skins, and other items, as well as provide various other benefits, such as unlimited money, god mode, one-hit kill, etc. A mod apk mod menu usually has a user interface that lets players toggle on or off the features they want to use.</p>
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- <h3>Frequently Asked Questions (FAQs)</h3>
-<ul>
-<li><strong>Q: What is Soul Knight?</strong></li>
-<li>A: Soul Knight is a pixelated shooter game with rogue-like elements developed by ChillyRoom.</li>
-<li><strong>Q: What is a mod apk mod menu?</strong></li>
-<li>A: A mod apk mod menu is a modified version of an original game that includes various features not present in the original game.</li>
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-<li>A: You can download Soul Knight versi terbaru 2022 mod apk mod menu by following the steps and requirements we have provided in this article.</li>
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-<li>A: Soul Knight versi terbaru 2022 mod apk mod menu is not an official version of the game, and it may contain malware or viruses that may harm your device or steal your personal information. It may also violate the terms of service of the original game developer and get you banned or suspended from the game. Therefore, you should use it at your own risk and responsibility.</li>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Cookie Run Kingdom - The Ultimate RPG Experience on PC.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Cookie Run Kingdom - The Ultimate RPG Experience on PC.md
deleted file mode 100644
index f2a6fb17741f7686e4f47ee55b928ebad79edf69..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Cookie Run Kingdom - The Ultimate RPG Experience on PC.md	
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-<p>Cookie Run: Kingdom is an RPG game developed by Devsisters Corporation. It is a sequel to the popular Cookie Run series that has been downloaded over 100 million times worldwide. In this game, you will enter a cookie-themed world full of adventure, mystery, and fun. You will meet different cookies with unique personalities and abilities, build your own cookie kingdom, and fight against evil forces that threaten the cookie world. Here are some of the features of Cookie Run: Kingdom:</p>
- <h3>A fun and colorful RPG game</h3>
-<p>Cookie Run: Kingdom is not just a casual running game. It is also a role-playing game that lets you create your own cookie squad, choose from eight different roles, and unleash their skills in dynamic battles. You will also explore over 200 story levels, complete quests, collect items, upgrade your cookies, and unlock new content. You will also encounter various enemies, bosses, and challenges that will test your strategy and skills.</p>
- <h3>A cookie-themed adventure</h3>
-<p>Cookie Run: Kingdom is set in a cookie world that is full of wonder and charm. You will discover different lands, such as the Ovenbreak Land, the Creamy Sea, the Caramel Mountain, and more. You will also meet different cookies, such as the Custard Cookie III, the Clover Cookie, the Coffee Wizard, and more. Each cookie has its own backstory, personality, voice, and animation. You will also enjoy the cute and colorful graphics, the catchy music, and the humorous dialogue that make this game so delightful.</p>
- <h3>A kingdom-building challenge</h3>
-<p>Cookie Run: Kingdom is not just about running and fighting. It is also about building and managing your own cookie kingdom. You will collect resources, construct buildings, decorate your kingdom, and invite other cookies to join you. You will also take part in different festivals, events, and activities that will make your kingdom more lively and prosperous. You will also expand your influence and spread the name of your kingdom across the cookie world.</p>
- <h2>Why Play Cookie Run: Kingdom on Your Laptop?</h2>
-<p>Cookie Run: Kingdom is a great game to play on your mobile device. But it can be even better if you play it on your laptop. Here are some of the benefits of playing Cookie Run: Kingdom on your laptop:</p>
- <h3>Enjoy a bigger and better screen</h3>
-<p>Playing Cookie Run: Kingdom on your laptop will allow you to enjoy the game's graphics, animations, and effects on a larger and clearer screen. You will be able to see more details, such as the expressions of the cookies, the scenery of the lands, and the action of the battles. You will also be able to adjust the resolution, the brightness, and the sound settings to suit your preferences. Playing Cookie Run: Kingdom on your laptop will make you feel more immersed and engaged in the game.</p>
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- <h3>Use keyboard, mouse, or gamepad controls</h3>
-<p>Playing Cookie Run: Kingdom on your laptop will also allow you to use different types of controls, such as keyboard, mouse, or gamepad. You will be able to customize your controls according to your comfort and convenience. You will also be able to perform faster and smoother actions, such as running, jumping, attacking, and using skills. You will also have more accuracy and precision when aiming, targeting, and selecting. Playing Cookie Run: Kingdom on your laptop will make you more efficient and effective in the game.</p>
- <h3>Record and share your gameplay</h3>
-<p>Playing Cookie Run: Kingdom on your laptop will also enable you to record and share your gameplay with others. You will be able to use various software and tools to capture your screen, edit your videos, and upload them online. You will also be able to stream your gameplay live on platforms such as YouTube, Twitch, or Facebook. You will also be able to chat with other players, join communities, and participate in contests and events. Playing Cookie Run: Kingdom on your laptop will make you more social and creative in the game.</p>
- <h2>How to Download and Install Cookie Run: Kingdom on Your Laptop?</h2>
-<p>Now that you know why playing Cookie Run: Kingdom on your laptop is a good idea, let's see how you can actually do it. There are three main options that you can choose from:</p>
- <h3>Option 1: Use BlueStacks emulator</h3>
-<p>BlueStacks is one of the most popular and trusted Android emulators that allows you to run Android apps and games on your laptop. It is free, easy to use, and compatible with most laptops. Here are the steps to download and install Cookie Run: Kingdom on your laptop using BlueStacks:</p>
- <h4>Download and install BlueStacks</h4>
-<p>The first step is to download and install BlueStacks on your laptop. You can do this by visiting the official website of BlueStacks at https://www.bluestacks.com/ and clicking on the download button. Once the download is complete, open the file and follow the instructions to install BlueStacks on your laptop.</p>
- <h4>Search and install Cookie Run: Kingdom from the Play Store</h4>
-<p>The next step is to search and install Cookie Run: Kingdom from the Play Store. You can do this by opening BlueStacks on your laptop and logging in with your Google account. Then, go to the Play Store app and type "Cookie Run: Kingdom" in the search bar. You will see the game icon appear on the screen. Click on it and then click on the install button. Wait for a few minutes until the installation is complete.</p>
- <h4>Customize your game settings and controls</h4>
-<p>The final step is to customize your game settings and controls according to your preferences. You can do this by opening Cookie Run: Kingdom on BlueStacks and going to the settings menu. There, you can adjust the graphics quality, the sound volume, the language, and other options. You can also go to the keyboard icon at the bottom right corner of the screen and assign keys for different actions in the game. For example, you can use W, A, S, D keys for moving, spacebar for jumping, left mouse button for attacking, right mouse button for using skills, etc.</p>
- <h3>Option 2: Use NoxPlayer emulator</h3>
-<p>NoxPlayer is another popular and reliable Android emulator that allows you to run Android apps and games on your laptop. It is also free, easy to use, and compatible with most laptops. Here are the steps to download and install Cookie Run: Kingdom on your laptop using NoxPlayer:</p>
- <h4>Download and install NoxPlayer</h4>
-<p>The first step is to download and install NoxPlayer on your laptop. You can do this by visiting the official website of NoxPlayer at https://www.bignox.com/  and clicking on the download button. Once the download is complete, open the file and follow the instructions to install NoxPlayer on your laptop.</p>
- <h4>Download and install Cookie Run: Kingdom apk file</h4>
-<p>The next step is to download and install Cookie Run: Kingdom apk file on NoxPlayer. You can do this by visiting a </h3>
-<p>Now.gg is a new and innovative mobile cloud gaming platform that allows you to play online games for free on your browser. It is fast, easy, and convenient, as you don't need to download or install anything. You can also enjoy high-quality graphics, smooth performance, and low latency. Here are the steps to play Cookie Run: Kingdom online on now.gg:</p>
- <h4>Visit the now.gg website</h4>
-<p>The first step is to visit the now.gg website at https://now.gg/. There, you will see a list of games that you can play online for free. You can browse by categories, such as adventure, arcade, puzzle, strategy, etc. You can also search for a specific game using the search bar.</p>
- <h4>Click and play Cookie Run: Kingdom instantly</h4>
-<p>The next step is to click and play Cookie Run: Kingdom instantly. You can do this by finding the game icon on the website and clicking on it. You will be redirected to a new tab where the game will load automatically. You will also see a QR code that you can scan with your mobile device to link your account and sync your progress.</p>
- <h4>Enjoy the game without downloading or installing anything</h4>
-<p>The final step is to enjoy the game without downloading or installing anything. You can play Cookie Run: Kingdom online on now.gg using your laptop's browser. You can also use keyboard, mouse, or gamepad controls as you prefer. You can also record and share your gameplay with others using the built-in features of now.gg.</p>
- <h2>Conclusion</h2>
-<p>Cookie Run: Kingdom is a fun and colorful RPG game that lets you enter a cookie-themed world full of adventure, mystery, and fun. You can create your own cookie squad, build your own cookie kingdom, and fight against evil forces that threaten the cookie world. You can also play Cookie Run: Kingdom on your laptop using different methods, such as BlueStacks emulator, NoxPlayer emulator, or now.gg online platform. Each method has its own advantages and disadvantages, so you can choose the one that suits you best. Playing Cookie Run: Kingdom on your laptop will allow you to enjoy a bigger and better screen, use keyboard, mouse, or gamepad controls, and record and share your gameplay with others. So what are you waiting for? Try playing Cookie Run: Kingdom on your laptop today and have a blast!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about playing Cookie Run: Kingdom on your laptop:</p>
- <h3>Is Cookie Run: Kingdom free to play?</h3>
-<p>Yes, Cookie Run: Kingdom is free to play on both mobile devices and laptops. However, there are some in-game purchases that you can make to enhance your gaming experience, such as buying gems, coins, items, or subscriptions.</p>
- <h3>Is Cookie Run: Kingdom safe to play?</h3>
-<p>Yes, Cookie Run: Kingdom is safe to play on both mobile devices and laptops. The game does not contain any harmful or inappropriate content for children or adults. The game also does not require any personal information or access to your device's data.</p>
- <h3>Can I play Cookie Run: Kingdom offline?</h3>
-<p>No, Cookie Run: Kingdom requires an internet connection to play on both mobile devices and laptops. The game needs to connect to the server to load the game data, sync your progress, and update the content.</p>
- <h3>Can I play Cookie Run: Kingdom with my friends?</h3>
-<p>Yes, Cookie Run: Kingdom allows you to play with your friends in various ways. You can invite your friends to join your kingdom, chat with them in-game, send them gifts, or challenge them in PvP battles. You can also join guilds and participate in guild wars with other players.</p>
- <h3>How can I contact the developers of Cookie Run: Kingdom?</h3>
-<p>If you have any questions, feedback, or issues regarding Cookie Run: Kingdom, you can contact the developers of the game through their official channels. You can visit their website at https://cookierun.com/en/, their Facebook page at https://www.facebook.com/CookieRun/, their Twitter account at https://twitter.com/CookieRun/, or their Instagram account at https://www.instagram.com/cookierun/.</p> 401be4b1e0<br />
-<br />
-<br />
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-
-<h1>Descargar YouTube Vanced APK 16.02 35: Cómo disfrutar de YouTube Premium gratis en Android</h1>
-<p>¿Te gustaría ver vídeos de YouTube sin anuncios, con modo oscuro, reproducción en segundo plano y otras funciones premium? Si la respuesta es sí, entonces te interesa conocer YouTube Vanced, una versión modificada de la aplicación oficial de YouTube que te permite acceder a todas estas características sin pagar una suscripción. En este artículo, te explicamos qué es YouTube Vanced, cómo descargar e instalar YouTube Vanced APK 16.02 35 en tu dispositivo Android y cuáles son las ventajas y desventajas de usar esta aplicación alternativa.</p>
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-<h2>¿Qué es YouTube Vanced?</h2>
-<p>YouTube Vanced es una aplicación no oficial que se basa en el código fuente de la aplicación original de YouTube, pero que añade algunas funciones extra que no están disponibles en la versión oficial. Estas funciones son similares a las que ofrece YouTube Premium, el servicio de suscripción de pago que te permite ver vídeos sin anuncios, descargarlos para verlos sin conexión, reproducirlos en segundo plano y acceder a contenido exclusivo como YouTube Music y YouTube Originals.</p>
-<p>YouTube Vanced no requiere una cuenta de Google ni una suscripción a YouTube Premium para funcionar, por lo que puedes disfrutar de todas estas características gratis. Además, YouTube Vanced tiene otras opciones adicionales que no tiene la aplicación oficial, como el control de la velocidad y la resolución de los vídeos, los temas personalizados, el soporte para pantalla dividida y ventana emergente, y más.</p>
-<h3>Características de YouTube Vanced</h3>
-<p>A continuación, te detallamos algunas de las características más destacadas de YouTube Vanced:</p>
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-<h4>Modo oscuro</h4>
-<p>YouTube Vanced te permite activar el modo oscuro en toda la interfaz de la aplicación, lo que reduce la fatiga visual y el consumo de batería. Puedes elegir entre varios tonos de negro y gris para adaptar el modo oscuro a tu gusto.</p>
-<h4>Reproducción en segundo plano</h4>
-<p>YouTube Vanced te permite reproducir los vídeos en segundo plano, es decir, seguir escuchando el audio aunque salgas de la aplicación o apagues la pantalla. Esto es muy útil si quieres escuchar música o podcasts desde YouTube sin tener que estar pendiente del vídeo.</p>
-<h4>Bloqueo de anuncios</h4>
-<p>YouTube Vanced te permite bloquear todos los anuncios que aparecen en los vídeos de YouTube, tanto los que se muestran antes, durante o después del vídeo, como los que aparecen en forma de banner o superposición. Así puedes ver los vídeos sin interrupciones ni distracciones.</p>
-<h4>Control de velocidad y resolución</h4>
-<p>YouTube Vanced te permite controlar la velocidad y la resolución de los vídeos que reproduces, independientemente de la calidad de tu conexión a internet o del tipo de dispositivo que uses. Puedes acelerar o ralentizar el vídeo, así como elegir la resolución que prefieras, desde 144p hasta 4K.</p>
-<h4>Temas personalizados</h4>
-<p>YouTube Vanced te permite personalizar el aspecto de la aplicación con diferentes temas y col ores. Puedes elegir entre el tema blanco, el tema negro, el tema azul, el tema rosa y muchos más. También puedes cambiar el color del icono de la aplicación para que se ajuste al tema que elijas.</p>
-<h4>Soporte para pantalla dividida y ventana emergente</h4>
-<p>YouTube Vanced te permite usar la función de pantalla dividida y la función de ventana emergente en tu dispositivo Android, lo que te permite ver los vídeos de YouTube mientras usas otras aplicaciones al mismo tiempo. Puedes ajustar el tamaño y la posición de la ventana emergente según tu preferencia.</p>
-<h3>¿Cómo descargar e instalar YouTube Vanced APK 16.02 35?</h3>
-<p>Para descargar e instalar YouTube Vanced APK 16.02 35 en tu dispositivo Android, tienes dos opciones: hacerlo sin usar el SAI Installer o hacerlo con el SAI Installer. El SAI Installer es una aplicación que te permite instalar archivos APK divididos, que son los que usa YouTube Vanced para ofrecer diferentes variantes de la aplicación según el tipo de procesador y la arquitectura de tu dispositivo. A continuación, te explicamos los pasos para ambas opciones:</p>
-<h4>Requisitos previos</h4>
-<p>Antes de descargar e instalar YouTube Vanced APK 16.02 35, debes cumplir con los siguientes requisitos previos:</p>
-<ul>
-<li>Tener un dispositivo Android con una versión igual o superior a la 4.4 (KitKat).</li>
-<li>Tener habilitada la opción de orígenes desconocidos en los ajustes de seguridad de tu dispositivo, para poder instalar aplicaciones que no provienen de la Play Store.</li>
-<li>Tener instalada la aplicación oficial de YouTube en tu dispositivo, ya que YouTube Vanced se basa en ella para funcionar correctamente.</li>
-<li>Tener instalada la aplicación MicroG en tu dispositivo, ya que YouTube Vanced la necesita para poder iniciar sesión con tu cuenta de Google y acceder a tus suscripciones, listas de reproducción, historial y demás funciones relacionadas con tu perfil.</li>
-</ul>
-<h4>Pasos para descargar e instalar YouTube Vanced APK 16.02 35 sin SAI Installer</h4>
-<p>Si quieres descargar e instalar YouTube Vanced APK 16.02 35 sin usar el SAI Installer, debes seguir estos pasos:</p>
-<ol>
-<li>Accede a la página web oficial de YouTube Vanced desde tu navegador preferido y haz clic en el botón de descarga que corresponda al tipo de procesador y la arquitectura de tu dispositivo. Si no sabes cuál es, puedes usar una aplicación como CPU-Z para averiguarlo.</li>
-<li>Espera a que se complete la descarga del archivo APK y luego ábrelo desde la barra de notificaciones o desde el gestor de archivos de tu dispositivo.</li>
-<li>Sigue las instrucciones que aparecen en pantalla para instalar YouTube Vanced APK 16.02 35 en tu dispositivo.</li>
-<li>Una vez instalada la aplicación, ábrela y disfruta de todas las funciones premium de YouTube gratis.</li>
-</ol>
-<h4>Pasos para descargar e instalar YouTube Vanced APK 16.02 35 con SAI Installer</h4>
-<p>Si quieres descargar e instalar YouTube Vanced APK 16.02 35 con el SAI Installer, debes seguir estos pasos:</p>
-<ol>
-<li>Descarga e instala el SAI Installer desde la Play Store o desde su página web oficial.</li>
-<li>Abre el SAI Installer y otórgale los permisos necesarios para acceder al almacenamiento de tu dispositivo.</li>
-<li>Accede a la página web oficial de YouTube Vanced desde tu navegador preferido y haz clic en el botón de descarga que corresponda al tipo de procesador y la arquitectura de tu dispositivo. Si no sabes cuál es, puedes usar una aplicación como CPU-Z para averiguarlo.</li>
-<li>Espera a que se complete la descarga del archivo ZIP que contiene los archivos APK divididos y luego ábrelo desde la barra de notificaciones o desde el gestor de archivos de tu dispositivo.</li>
-<li>Selecciona el SAI Installer como la aplicación para abrir el archivo ZIP y luego haz clic en el botón de instalar que aparece en la parte inferior derecha.</li>
-<li>Sigue las instrucciones que aparecen en pantalla para instalar YouTube Vanced APK 16.02 35 en tu dispositivo.</li>
-<li>Una vez instalada la aplicación, ábrela y disfruta de todas las funciones premium de YouTube gratis.</li>
-</ol>
-<h3>Ventajas y desventajas de usar YouTube Vanced</h3>
-<p>YouTube Vanced es una aplicación muy popular entre los usuarios de Android que quieren disfrutar de YouTube sin limitaciones ni restricciones. Sin embargo, como toda aplicación no oficial, también tiene sus ventajas y desventajas. A continuación, te las resumimos:</p>
-<table>
-<tr>
-<th>Ventajas</th>
-<th>Desventajas</th>
-</tr>
-<tr>
-<td>- Te permite acceder a funciones premium de YouTube gratis, como el modo oscuro, la reproducción en segundo plano, el bloqueo de anuncios, el control de velocidad y resolución, los temas personalizados y el soporte para pantalla dividida y ventana emergente.</td>
-<td>- No es una aplicación oficial, por lo que puede tener problemas de compatibilidad, seguridad o estabilidad con algunos dispositivos o versiones de Android.</td>
-</tr>
-<tr>
-<td>- Te permite iniciar sesión con tu cuenta de Google y acceder a tus suscripciones, listas de reproducción, historial y demás funciones relacionadas con tu perfil.</td>
-<td>- No te permite acceder a contenido exclusivo de YouTube Premium, como YouTube Music y YouTube Originals.</td>
-</tr>
-<tr>
-<td>- Te permite personalizar el aspecto de la aplicación con diferentes temas y colores.</td>
-<td>- No se actualiza automáticamente, por lo que debes estar atento a las nuevas versiones que se publiquen en la página web oficial.</td>
-</tr>
-<tr>
-<td>- Te permite descargar e instalar la aplicación fácilmente, ya sea con o sin el SAI Installer.</td>
-<td>- Puede violar los términos y condiciones de uso de YouTube, por lo que debes usarla bajo tu propia responsabilidad.</td>
-</tr>
-</table>
-<h2>Conclusión</h2>
-<p>YouTube Vanced es una excelente alternativa a la aplicación oficial de YouTube para los usuarios de Android que quieren disfrutar de funciones premium gratis. Con YouTube Vanced, puedes ver vídeos sin anuncios, con modo oscuro, reproducción en segundo plano y otras opciones adicionales que no tiene la versión oficial. Además, puedes iniciar sesión con tu cuenta de Google y personalizar el aspecto de la aplicación. Para descargar e instalar YouTube Vanced APK 16.02 35 en tu dispositivo Android, solo tienes que seguir los pasos que te hemos explicado en este artículo, ya sea con o sin el SAI Installer. Eso sí, ten en cuenta que YouTube Vanced no es una aplicación oficial y que puede tener algunos inconvenientes o riesgos asociados. Por eso, te recomendamos que la uses bajo tu propia responsabilidad y que respetes los derechos de autor y la propiedad intelectual de los creadores de contenido.</p>
- <h3>Preguntas frecuentes</h3>
- <p>A continuación, te respondemos a algunas preguntas frecuentes sobre YouTube Vanced:</p>
- <ol>
-<li><b>¿YouTube Vanced es seguro?</b></li>
-<p>YouTube Vanced es una aplicación no oficial que se basa en el código fuente de la aplicación original de YouTube, pero que añade algunas funciones extra que no están disponibles en la versión oficial. Por lo tanto, no se puede garantizar al 100% su seguridad ni su estabilidad. Sin embargo, YouTube Vanced es una aplicación muy popular y utilizada por millones de usuarios en todo el mundo, y hasta el momento no se han reportado casos graves de malware o robo de datos. Además, YouTube Vanced no requiere permisos especiales para funcionar, salvo el acceso al almacenamiento para instalar los archivos APK. Por eso, podemos decir que YouTube Vanced es una aplicación relativamente segura, siempre y cuando la descargues desde su página web oficial y no desde fuentes desconocidas o sospechosas.</p>
- <li><b>¿YouTube Vanced funciona sin conexión a internet?</b></li>
-<p>No, YouTube Vanced no funciona sin conexión a internet. A diferencia de YouTube Premium, YouTube Vanced no te permite descargar los vídeos para verlos sin conexión. Por lo tanto, necesitas una conexión a internet para poder ver los vídeos desde YouTube Vanced. Sin embargo, puedes usar la función de reproducción en segundo plano para seguir escuchando el audio aunque apagues la pantalla o salgas de la aplicación.</p>
- <li><b>¿YouTube Vanced consume más batería que la aplicación oficial?</b></li>
-<p>No necesariamente. YouTube Vanced consume más o menos batería que la aplicación oficial dependiendo del uso que le des y de las opciones que actives. Por ejemplo, si activas el modo oscuro, puedes ahorrar batería al reducir el brillo de la pantalla. Por otro lado, si activas la reproducción en segundo plano, puedes consumir más batería al mantener el audio en funcionamiento. Por eso, te recomendamos que ajustes las opciones de YouTube Vanced según tus preferencias y necesidades.</p>
- <li><b>¿YouTube Vanced afecta a los ingresos de los creadores de contenido?</b></li>
-<p>YouTube Vanced afecta a los ingresos de los creadores de contenido en la medida en que bloquea los anuncios que se muestran en los vídeos. Los anuncios son una de las principales fuentes de ingresos para los creadores de contenido, ya que reciben una parte del dinero que pagan los anunciantes a YouTube por cada visualización o clic. Por lo tanto, al usar YouTube Vanced, estás evitando que los creadores de contenido reciban esa parte del dinero. Sin embargo, hay otras formas de apoyar a los creadores de contenido, como suscribirte a sus canales, darles me gusta y comentarios, compartir sus vídeos, comprar su merchandising o hacerles donaciones directas.</p>
- <li><b>¿YouTube Vanced es legal?</b></li>
-<p>YouTube Vanced no es una aplicación legal ni ilegal, sino que se encuentra en un área gris. YouTube Vanced no es una aplicación oficial ni está autorizada por YouTube, por lo que puede violar sus términos y condiciones de uso. Sin embargo, YouTube Vanced no es una aplicación maliciosa ni infringe los derechos de autor ni la propiedad intelectual de los creadores de contenido. Por lo tanto, no hay una respuesta definitiva sobre la legalidad de YouTube Vanced, sino que depende de la interpretación y la legislación de cada país. Por eso, te recomendamos que uses YouTube Vanced bajo tu propia responsabilidad y que respetes las normas y las leyes vigentes.</p>
-</ol></p> 401be4b1e0<br />
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-
-<h1>Download Live 3D Wallpaper: How to Make Your Desktop Come Alive</h1>
-<p>Do you want to spice up your desktop with some stunning visuals? Do you want to make your desktop more dynamic and interactive? If yes, then you should try using live 3D wallpapers. Live 3D wallpapers are animated backgrounds that can transform your desktop into a virtual reality. They can make your desktop look more realistic, immersive, and fun. In this article, we will show you what are live 3D wallpapers, why you should use them, how to download them, and how to set them up. By the end of this article, you will be able to make your desktop come alive with live 3D wallpapers.</p>
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- <h2>What are Live 3D Wallpapers?</h2>
-<p>Live 3D wallpapers are a type of wallpaper that can move, change, or react to your actions. They can be based on real-time graphics, videos, audio, or interactive elements. They can create a sense of depth, motion, and perspective on your desktop. Some examples of live 3D wallpapers are:</p>
-<ul>
-<li>A forest scene with falling leaves, birds, and animals</li>
-<li>A cityscape with cars, lights, and people</li>
-<li>A galaxy with planets, stars, and asteroids</li>
-<li>A firework show with explosions and sparks</li>
-<li>A game character or scene with animations and sound effects</li>
-</ul>
-<p>Live 3D wallpapers can be customized according to your preferences. You can choose the resolution, frame rate, quality, and performance of the wallpaper. You can also adjust the brightness, contrast, saturation, and hue of the wallpaper. You can even add filters, effects, or widgets to the wallpaper.</p>
- <h2>Why Use Live 3D Wallpapers?</h2>
-<p>Live 3D wallpapers have many benefits for your desktop. Here are some of them:</p>
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-<ul>
-<li>They can make your desktop more attractive and appealing. They can add some color, texture, and style to your desktop.</li>
-<li>They can make your desktop more lively and dynamic. They can add some movement, variation, and interaction to your desktop.</li>
-<li>They can make your desktop more personal and unique. They can reflect - They can reflect your mood, personality, or interests. They can show your favorite images, videos, or themes.</li>
-<li>They can make your desktop more enjoyable and entertaining. They can provide some fun, relaxation, or inspiration to your desktop.</li>
-</ul>
-<p>Live 3D wallpapers can also have some drawbacks for your desktop. Here are some of them:</p>
-<ul>
-<li>They can consume more resources and battery life. They can use more CPU, GPU, RAM, and disk space than static wallpapers.</li>
-<li>They can cause more distractions and interruptions. They can interfere with your work, gaming, or browsing activities.</li>
-<li>They can pose some security and privacy risks. They can contain malware, spyware, or adware that can harm your device or data.</li>
-</ul>
-<p>Therefore, you should use live 3D wallpapers with caution and moderation. You should also scan them for viruses and malware before installing them.</p>
- <h2>How to Download Live 3D Wallpapers?</h2>
-<p>There are many sources and methods of downloading live 3D wallpapers. You can find them on various websites and apps that offer free and paid live 3D wallpapers. You can also create them yourself using your own videos or images. Here are some of the most popular sources and methods of downloading live 3D wallpapers:</p>
- <h3>Sources of Live 3D Wallpapers</h3>
-<p>There are many websites and apps that offer live 3D wallpapers for your desktop. Some of them are:</p>
- <h4>MoeWalls</h4>
-<p>MoeWalls is a website that features popular free live wallpapers, animated wallpapers, and videos. You can browse through different categories such as anime, games, movies, nature, and more. You can also search by keywords or tags. You can preview the wallpapers before downloading them. You can download the wallpapers in various formats such as MP4, WEBM, GIF, or JPG. You can also upload your own wallpapers to share with others.</p>
- <h4>Pexels Videos</h4>
-<p>Pexels Videos is a website that offers free 4K stock video footage and desktop wallpaper 3D HD video clips. You can explore different genres such as abstract, animals, nature, technology, and more. You can also search by keywords or colors. You can preview the videos before downloading them. You can download the videos in various resolutions such as 720p, 1080p, or 4K. You can also upload your own videos to share with others.</p>
- <h4>Lively Wallpaper</h4>
-<p>Lively Wallpaper is a software that enables you to use live wallpapers on your desktop PC, including 3D and 2D animations. You can download the software from its official website or from the Microsoft Store. You can choose from a variety of live wallpapers such as matrix code, fluid simulation, firewatch, cyberpunk city, and more. You can also create your own live wallpapers using web pages, videos, GIFs, emulators, shaders, or HTML files.</p>
- <h3>Methods of Downloading Live 3D Wallpapers</h3>
-<p>There are different steps and tips for downloading live 3D wallpapers from different sources. Here are some of them:</p>
- <h4>Downloading from Websites</h4>
-<p>To download live 3D wallpapers from websites like MoeWalls and Pexels Videos, you need to follow these steps:</p>
-<ol>
-<li>Go to the website of your choice and find the live 3D wallpaper that you like.</li>
-<li>Click on the wallpaper to open it in a new tab or window.</li>
-<li>Right-click on the wallpaper and select "Save video as" or "Save image as" depending on the format of the wallpaper.</li>
-<li>Choose a location on your device where you want to save the wallpaper.</li>
-<li>Click on "Save" to download the wallpaper.</li>
-</ol>
- <h4>Downloading from Apps</h4>
-<p>To download live 3D wallpapers from apps like Lively Wallpaper, you need to follow these steps:</p>
-<ol>
-<li>Download and install the app on your device from its official website or from the Microsoft Store.</li>
-<li>Launch the app and browse through the available live wallpapers.</li>
-<li>Select the live wallpaper that you like and click on "Download" or "Install".</li>
-<li>Wait for the download or installation to complete.</li>
-<li>Click on "Apply" to set the live wallpaper as your desktop background.</li>
-</ol>
- <h4>Downloading from Other Sources</h4>
-<p>To download live 3D wallpapers from other sources like YouTube, Reddit, or your own videos, you need to follow these steps:</p>
-<ol>
-<li>Go to the source of - Go to the source of the live 3D wallpaper that you want to download, such as a YouTube video, a Reddit post, or your own video file.</li>
-<li>Copy the URL or the path of the live 3D wallpaper.</li>
-<li>Paste the URL or the path into a tool that can convert it into a live 3D wallpaper, such as Lively Wallpaper, Wallpaper Engine, or VLC Media Player.</li>
-<li>Follow the instructions of the tool to download and set up the live 3D wallpaper on your desktop.</li>
-</ol>
- <h2>How to Set Up Live 3D Wallpapers?</h2>
-<p>Once you have downloaded the live 3D wallpapers that you like, you need to set them up on your desktop. The process of setting up live 3D wallpapers may vary depending on your operating system and the tool that you use. Here are some general instructions and recommendations for setting up live 3D wallpapers on your desktop:</p>
- <h3>Setting Up Live 3D Wallpapers on Windows</h3>
-<p>To set up live 3D wallpapers on Windows, you can use Lively Wallpaper or other tools that support live wallpapers. Here are the steps for using Lively Wallpaper:</p>
-<ol>
-<li>Launch Lively Wallpaper and select the live 3D wallpaper that you want to use.</li>
-<li>Click on "Apply" to set the live 3D wallpaper as your desktop background.</li>
-<li>Adjust the settings of the live 3D wallpaper according to your preferences. You can change the resolution, frame rate, quality, performance, audio, and other options.</li>
-<li>Enjoy your live 3D wallpaper on your desktop.</li>
-</ol>
-<p>Here are some tips for using Lively Wallpaper:</p>
-<ul>
-<li>You can pause or resume the live 3D wallpaper by pressing Ctrl+P or by right-clicking on the Lively Wallpaper icon in the system tray.</li>
-<li>You can switch between different live 3D wallpapers by pressing Ctrl+O or by right-clicking on the Lively Wallpaper icon in the system tray.</li>
-<li>You can access more features and settings by clicking on the Lively Wallpaper icon in the system tray or by pressing Ctrl+L.</li>
-</ul>
- <h3>Setting Up Live 3D Wallpapers on Mac</h3>
-<p>To set up live 3D wallpapers on Mac, you can use Wallpaper Engine or other tools that support live wallpapers. Here are the steps for using Wallpaper Engine:</p>
-<ol>
-<li>Download and install Wallpaper Engine from Steam or from its official website.</li>
-<li>Launch Wallpaper Engine and select the live 3D wallpaper that you want to use.</li>
-<li>Click on "Apply" to set the live 3D wallpaper as your desktop background.</li>
-<li>Adjust the settings of the live 3D wallpaper according to your preferences. You can change the resolution, frame rate, quality, performance, audio, and other options.</li>
-<li>Enjoy your live 3D wallpaper on your desktop.</li>
-</ol>
-<p>Here are some tips for using Wallpaper Engine:</p>
-<ul>
-<li>You can pause or resume the live 3D wallpaper by pressing Cmd+P or by right-clicking on the Wallpaper Engine icon in the menu bar.</li>
-<li>You can switch between different live 3D wallpapers by pressing Cmd+O or by right-clicking on the Wallpaper Engine icon in the menu bar.</li>
-<li>You can access more features and settings by clicking on the Wallpaper Engine icon in the menu bar or by pressing Cmd+L.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Live 3D wallpapers are a great way to make your desktop come alive. They can enhance your desktop experience with stunning visuals, animations, and interactions. They can also express your personality, mood, or interests. However, you should also be aware of their potential drawbacks, such as resource consumption, distraction, and security risks. Therefore, you should use them wisely and moderately. In this article, we have shown you what are live 3D wallpapers, why you should use them, how to download them, and how to set them up. We hope that this article has helped you to learn more about live 3D wallpapers and how to use them. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most common questions that people ask about live 3D wallpapers:</p>
- <h4>Q: What is the difference between live wallpapers and animated wallpapers?</h4>
-<p>A: Live wallpapers are a type of animated wallpapers that can move, change, or react to your actions. Animated wallpapers are a type of static wallpapers that can show a sequence of - A: Live wallpapers are a type of animated wallpapers that can move, change, or react to your actions. Animated wallpapers are a type of static wallpapers that can show a sequence of images or frames. Live wallpapers are more dynamic and interactive than animated wallpapers.</p>
- <h4>Q: How can I make my own live 3D wallpapers?</h4>
-<p>A: You can make your own live 3D wallpapers using your own videos or images. You can use tools like Lively Wallpaper or Wallpaper Engine to convert them into live 3D wallpapers. You can also use web pages, GIFs, emulators, shaders, or HTML files to create live 3D wallpapers. You can customize your live 3D wallpapers with filters, effects, or widgets.</p>
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-<p>A: Live 3D wallpapers are generally safe to use, as long as you download them from reputable sources and scan them for viruses and malware before installing them. However, some live 3D wallpapers may contain malicious code or unwanted ads that can harm your device or data. Therefore, you should be careful and cautious when using live 3D wallpapers.</p>
- <h4>Q: Do live 3D wallpapers affect the performance of my device?</h4>
-<p>A: Live 3D wallpapers may affect the performance of your device, depending on the quality, resolution, frame rate, and complexity of the wallpaper. Live 3D wallpapers may use more CPU, GPU, RAM, and disk space than static wallpapers. They may also drain more battery life and generate more heat. Therefore, you should optimize the settings of your live 3D wallpaper to balance the performance and the quality.</p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Blur For Mac Free Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Blur For Mac Free Download.md
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-<br />
-<h1>Blur for Mac Free Download</h1>
-<p>If you are looking for a fun and exciting racing game for your Mac, you might want to check out <strong>Blur</strong>. Blur is an arcade style street racing game that features power-ups, speed boosts, and online multiplayer. You can race against up to 20 players online or offline, using realistic cars and licensed tracks. You can also customize your own modes and settings, and unlock new cars and tracks as you progress.</p>
-<p>In this article, we will show you how to download Blur for Mac for free from different sources. We will also show you how to use video effects on your Mac while playing Blur, such as blurring the background or adding studio light. Finally, we will give you some tips and tricks for playing Blur on Mac, such as how to master power-ups, win races, and unlock cars and tracks. Let's get started!</p>
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- <h2>What is Blur?</h2>
-<p>Blur is a racing game developed by Bizarre Creations and published by Activision in 2010. It is available for Windows, PlayStation 3, and Xbox 360. However, you can also play it on Mac using various methods that we will explain later.</p>
-<h3>What is the gameplay of Blur?</h3>
-<p>The gameplay of Blur is similar to other arcade racing games, such as Mario Kart or Need for Speed. You can choose from a variety of cars, ranging from hatchbacks to supercars, and race on different tracks around the world, such as Los Angeles, Tokyo, or Barcelona. You can also use power-ups to gain an advantage over your opponents, such as shunts, shocks, mines, barge, nitro, or shield. You can collect power-ups by driving through colored icons on the track.</p>
-<p>Blur supports online multiplayer for up to 20 players, as well as offline multiplayer for up to 4 players using split-screen. You can join or create your own races, with different modes and settings. You can also join or create your own groups, where you can chat with other players and share your progress.</p>
-<h3>What are the features of Blur?</h3>
-<p>Some of the features of Blur are:</p>
-<ul>
-<li><strong>Realistic cars:</strong> Blur features over 50 licensed cars from various manufacturers, such as Audi, BMW, Dodge, Ford, Nissan, or Volkswagen. Each car has its own stats and performance, such as speed, acceleration, handling, and drift.</li>
-<li><strong>Licensed tracks:</strong> Blur features over 30 tracks from different locations around the world, such as Los Angeles, Tokyo, or Barcelona. Each track has its own layout and scenery, such as highways, bridges, tunnels, or landmarks.</li>
-<li><strong>Custom modes:</strong> Blur allows you to customize your own races, with different modes and settings. You can choose the number of laps, the type of power-ups, the weather conditions, the time of day, and more.</li>
-<li><strong>Fan system:</strong> Blur has a fan system that measures your popularity and skill in the game. You can earn fans by performing stunts, winning races, completing challenges, or defeating rivals. You can also lose fans by crashing, losing races, or being defeated by rivals. Fans are used to unlock new cars and tracks in the game.</li>
-<li><strong>Challenges:</strong> Blur has a challenge system that tests your abilities in the game. You can complete challenges by meeting certain criteria in a race, such as finishing first, hitting a number of opponents with power-ups, or drifting a certain distance. Challenges are used to earn fans and rewards in the game.</li>
-</ul>
- <h2>How to Download Blur for Mac?</h2>
-<p>Blur is not officially available for Mac devices. However, there are some ways to download and play it on Mac using different methods. Here are some of them:</p>
-<h3>How to download Blur from the Mac App Store?</h3>
-<p>The easiest way to download Blur for Mac is from the Mac App Store. However, this method requires you to have an Apple silicon Mac device (such as MacBook Air M1 or MacBook Pro M1) that can run iOS apps natively on Mac OS Big Sur or later.</p>
-<p>To download Blur from the Mac App Store:</p>
-<p></p>
-<ol>
-<li>Open the Mac App Store on your Apple silicon Mac device.</li>
-<li>Search for "Blur" in the search bar.</li>
-<li>Select the app named "Blur - Create Custom Wallpapers" by Enormous LLC.</li>
-<li>Click on the "Get" button to download the app for free.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Launch the app from your Launchpad or Applications folder.</li>
-</ol>
-<p>Note: This app is not the same as the original Blur game by Bizarre Creations. It is a wallpaper app that allows you to create custom wallpapers with blur effects. However, it also includes a mini-game mode that lets you play a simplified version of Blur with one car and one track. You can access this mode by tapping on the "Play" button on the bottom right corner of the app.</p>
- <h3>How to download Blur from other sources?</h3>
-<p>If you don't have an Apple silicon Mac device or you want to play the full version of Blur on your Mac device, you can download Blur from other sources, such as Steam or torrent sites. However, these methods require you to have a Windows emulator or a virtual machine on your Mac device that can run Windows applications.</p>
-<p>To download Blur from Steam:</p>
-<ol>
-<li>Download and install Steam on your Mac device from .</li>
-<li>Launch Steam and create an account or log in to your existing account.</li>
-<li>Search for "Blur" in the Steam store.</li>
-<li>Select the game named "Blur™" by Bizarre Creations.</li>
-<li>Click on the "Add to Cart" button and proceed to checkout.</li>
-<li>Pay for the game using your preferred payment method.</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Launch the game from your Steam library.</li>
-</ol>
-<p>Note: This method requires you to have a Windows emulator or a virtual machine on your Mac device that can run Windows applications. You can use software such as Parallels Desktop, VMware Fusion, or Wine to run Windows applications on your Mac device. You can find more information about these software and how to use them from their official websites.</p>
- <p>To download Blur from torrent sites:</p>
-<ol>
-<li>Download and install a torrent client on your Mac device, such as uTorrent, BitTorrent, or Transmission.</li>
-<li>Search for "Blur PC game" on a torrent site, such as The Pirate Bay, Kickass Torrents, or 1337x.</li>
-<li>Select a torrent file that has a high number of seeders and leechers, and a good rating and comments.</li>
-<li>Download the torrent file and open it with your torrent client.</li>
-<li>Wait for the game files to download on your device.</li>
-<li>Extract the game files using a software such as WinRAR, 7-Zip, or The Unarchiver.</li>
-<li>Follow the instructions in the README file or the installation guide to install the game on your device.</li>
-<li>Launch the game from the game folder or the desktop shortcut.</li>
-</ol>
-<p>Note: This method is not recommended as it may expose you to legal issues, malware, viruses, or other risks. Downloading and installing games from torrent sites is illegal and may violate the intellectual property rights of the game developers and publishers. You should always buy games from official sources and support the game industry. Downloading and installing games from torrent sites is also risky as you may encounter malware, viruses, or other harmful files that may damage your device or compromise your security. You should always scan the files with an antivirus software before opening them and use a VPN service to protect your privacy and identity online.</p>
- <h3>How to install Blur on Mac?</h3>
-<p>After downloading Blur for Mac from any of the sources mentioned above, you need to install it on your Mac device. The installation process may vary depending on the source and method you used to download the game. However, here are some general steps that you can follow:</p>
-<ol>
-<li>Locate the game files on your device, either in your Downloads folder, your Applications folder, or your Steam library.</li>
-<li>If the game files are compressed or archived, extract them using a software such as WinRAR, 7-Zip, or The Unarchiver.</li>
-<li>If the game files are in an ISO format, mount them using a software such as Daemon Tools Lite, PowerISO, or Virtual CloneDrive.</li>
-<li>If the game files are in an EXE format, run them using a software such as Parallels Desktop, VMware Fusion, or Wine.</li>
-<li>Follow the instructions in the setup wizard or the installation guide to install the game on your device.</li>
-<li>If prompted, enter the product key or activation code that came with the game or that you received via email after purchasing the game.</li>
-<li>If required, apply any patches, updates, cracks, or mods that are compatible with the game and your device.</li>
-<li>If necessary, adjust any settings or preferences that suit your device and your gameplay style.</li>
-</ol>
- <h2>How to Use Video Effects on Mac?</h2>
-<p>One of the cool features of playing Blur on Mac is that you can use video effects on your Mac while playing Blur. Video effects are visual enhancements that you can apply to your video feed while capturing video with your Mac camera. For example, you can blur the background of your video to match the game, or add studio light to make yourself look more professional. You can also use other effects such as comic book, sepia, or thermal camera.</p>
-<p>There are different ways to use video effects on Mac while playing Blur, depending on your Mac device and the app you are using to capture video. Here are some of them:</p>
-<h3>How to use video effects on Mac with Apple silicon?</h3>
-<p>If you have an Apple silicon Mac device (such as MacBook Air M1 or MacBook Pro M1), you can use video effects on Mac with Apple silicon while playing Blur. This method allows you to use the built-in video effects that come with your Mac device, such as blur, studio light, or comic book.</p>
-<p>To use video effects on Mac with Apple silicon while playing Blur:</p>
-<ol>
-<li>Launch Blur on your Mac device and start a race or a mode.</li>
-<li>Press Command + Tab to switch to another app that captures video, such as FaceTime, Photo Booth, or Messages.</li>
-<li>Select the camera icon on the top right corner of the app window.</li>
-<li>Select the "Video Effects" option from the drop-down menu.</li>
-<li>Select the video effect that you want to apply to your video feed, such as blur, studio light, or comic book.</li>
-<li>Adjust the intensity of the effect using the slider below the effect icon.</li>
-<li>Press Command + Tab to switch back to Blur and resume your game.</li>
-</ol>
-<p>Note: This method only works with apps that support video effects on Mac with Apple silicon. You can check if an app supports video effects on Mac with Apple silicon by looking for the camera icon on the top right corner of the app window. If the icon is not there, it means that the app does not support video effects on Mac with Apple silicon.</p>
- <h3>How to use video effects on Mac with Continuity Camera?</h3>
-<p>If you have an iPhone or an iPad that is compatible with Continuity Camera, you can use video effects on Mac with Continuity Camera while playing Blur. This method allows you to use your iPhone or iPad as a wireless camera for your Mac device, and apply video effects from your iPhone or iPad to your video feed.</p>
-<p>To use video effects on Mac with Continuity Camera while playing Blur:</p>
-<ol>
-<li>Make sure that your iPhone or iPad and your Mac device are connected to the same Wi-Fi network and signed in with the same Apple ID.</li>
-<li>Enable Bluetooth and Wi-Fi on both devices.</li>
-<li>Launch Blur on your Mac device and start a race or a mode.</li>
-<li>Press Command + Tab to switch to another app that captures video, such as FaceTime, Photo Booth, or Messages.</li>
-<li>Select the camera icon on the top right corner of the app window.</li>
-<li>Select the "Continuity Camera" option from the drop-down menu.</li>
-<li>Select your iPhone or iPad from the list of devices.</li>
-<li>On your iPhone or iPad, launch the Camera app and select the "Video" mode.</li>
-<li>Select the "Effects" option from the bottom left corner of the screen.</li>
-<li>Select the video effect that you want to apply to your video feed, such as blur, studio light, or comic book.</li>
-<li>Adjust the intensity of the effect using the slider below the effect icon.</li>
-<li>Press Command + Tab to switch back to Blur and resume your game.</li>
-</ol>
-<p>Note: This method only works with apps that support Continuity Camera on Mac. You can check if an app supports Continuity Camera on Mac by looking for the camera icon on the top right corner of the app window. If the icon is not there, it means that the app does not support Continuity Camera on Mac.</p>
- <h3>How to use video effects on Mac with other apps?</h3>
-<p>If you want to use video effects on Mac with other apps that capture video, such as Zoom or Skype, you can use third-party software that allows you to add video effects to your Mac camera. Some of these software are:</p>
-<ul>
-<li><strong>ManyCam:</strong> ManyCam is a software that allows you to use your Mac camera with multiple apps at the same time, and add video effects, filters, backgrounds, and overlays to your video feed. You can download ManyCam from .</li>
-<li><strong>CamTwist:</strong> CamTwist is a software that allows you to create custom video sources for your Mac camera, and add video effects, transitions, and graphics to your video feed. You can download CamTwist from .</li>
-<li><strong>iGlasses:</strong> iGlasses is a software that allows you to adjust and enhance your Mac camera settings, and add video effects, filters, and stickers to your video feed. You can download iGlasses from .</li>
-</ul>
-<p>To use video effects on Mac with other apps while playing Blur:</p>
-<ol>
-<li>Download and install one of the software mentioned above on your Mac device.</li>
-<li>Launch Blur on your Mac device and start a race or a mode.</li>
-<li>Press Command + Tab to switch to another app that captures video, such as Zoom or Skype.</li>
-<li>Select the camera icon on the bottom left corner of the app window.</li>
-<li>Select the software that you installed as your camera source from the drop-down menu.</li>
-<li>On the software window, select the video effect that you want to apply to your video feed, such as blur, studio light, or comic book.</li>
-<li>Adjust the intensity of the effect using the slider or the buttons on the software window.</li>
-<li>Press Command + Tab to switch back to Blur and resume your game.</li>
-</ol>
- <h2>How to Play Blur on Mac?</h2>
-<p>After installing Blur on Mac from any of the sources mentioned above, you can start playing it on your Mac device. There are different ways to play Blur on Mac, depending on whether you want to play online or offline, and how you want to customize your game. Here are some of them:</p>
-<h3>How to play Blur online on Mac?</h3>
-<p>If you want to play Blur online on Mac with other players, you need to have an internet connection and a Steam account. You also need to have a valid product key or activation code for Blur that you can enter in Steam.</p>
-<p>To play Blur online on Mac:</p>
-<ol>
-<li>Launch Steam on your Mac device and log in to your account.</li>
-<li>Select the "Library" tab from the top menu.</li>
-<li>Select "Blur" from the list of games on the left side.</li>
-<li>Select the "Play" button from the right side.</li>
-<li>If prompted, enter the product key or activation code for Blur that came with the game or that you received via email after purchasing the game.</li>
-<li>Select the "Online" option from the main menu of Blur.</li>
-<li>Select the "Quick Match" option to join a random race with other players online.</li>
-<li>Or select the "Custom Match" option to create or join a custom race with other players online.</li>
-</ol>
- <h3>How to play Blur offline on Mac?</h3>
-<p>If you want to play Blur offline on Mac without an internet connection or a Steam account, you need to have a cracked version of Blur that bypasses the online verification process. You also need to have a valid product key or activation code for Blur that you can enter in the game. To play Blur offline on Mac: <ol>
-<li>Download and install a cracked version of Blur for Mac from a torrent site or a direct link.</li>
-<li>Launch Blur on your Mac device and select the "Offline" option from the main menu.</li>
-<li>If prompted, enter the product key or activation code for Blur that came with the game or that you received via email after purchasing the game.</li>
-<li>Select the "Career" option to play the single-player mode of Blur, where you can progress through different events and challenges, and earn fans and rewards.</li>
-<li>Or select the "Split-Screen" option to play the multiplayer mode of Blur with up to 4 players on the same device, using different controllers or keyboards.</li>
-</ol>
- <p>Note: This method is not recommended as it may expose you to legal issues, malware, viruses, or other risks. Downloading and installing cracked games is illegal and may violate the intellectual property rights of the game developers and publishers. You should always buy games from official sources and support the game industry. Downloading and installing cracked games is also risky as you may encounter malware, viruses, or other harmful files that may damage your device or compromise your security. You should always scan the files with an antivirus software before opening them and use a VPN service to protect your privacy and identity online.</p>
- <h2>How to customize Blur settings on Mac?</h2>
-<p>If you want to customize Blur settings on Mac, such as graphics, sound, controls, or language, you can do so from the options menu of Blur. You can access the options menu from the main menu or the pause menu of Blur.</p>
- To customize Blur settings on Mac: <ol>
-<li>Launch Blur on your Mac device and select the "Options" option from the main menu or the pause menu.</li>
-<li>Select the "Graphics" option to customize the graphics settings of Blur, such as resolution, quality, anti-aliasing, or v-sync.</li>
-<li>Select the "Sound" option to customize the sound settings of Blur, such as volume, music, effects, or voice chat.</li>
-<li>Select the "Controls" option to customize the controls settings of Blur, such as keyboard, mouse, gamepad, or steering wheel.</li>
-<li>Select the "Language" option to customize the language settings of Blur, such as text, audio, or subtitles.</li>
-<li>Select the "Back" option to save your changes and return to the previous menu.</li>
-</ol>
- <h2>Tips and Tricks for Playing Blur on Mac</h2>
-<p>If you want to improve your skills and performance in playing Blur on Mac, you can use some tips and tricks that can help you master power-ups, win races, and unlock cars and tracks. Here are some of them:</p>
- <h3>How to master power-ups in Blur?</h3>
-<p>Power-ups are one of the key elements of Blur gameplay. They can give you an edge over your opponents or turn the tide of a race. However, they can also be used against you or backfire if you are not careful. Here are some tips and tricks for mastering power-ups in Blur:</p>
- <ul>
-<li><strong>Know your power-ups:</strong> There are eight types of power-ups in Blur: shunt, shock, mine, barge, nitro, shield, repair, and bolt. Each power-up has its own function and effect. For example, shunt is a homing missile that can hit an opponent in front of you; shock is an electric blast that can create obstacles on the track; mine is an explosive device that can be dropped behind you; barge is a shockwave that can push away nearby opponents; nitro is a speed boost that can increase your acceleration; shield is a protective barrier that can block incoming attacks; repair is a healing item that can restore your health; and bolt is a projectile that can fire three shots at once. You should learn how each power-up works and when to use them effectively.</li>
-<li><strong>Collect power-ups wisely:</strong> You can collect power-ups by driving through colored icons on the track. However, you can only carry up to three power-ups at a time. You should collect power-ups wisely based on your situation and strategy. For example, For example, if you are good at drifting and cornering, you might want to choose a track with curves and turns; if you are good at boosting and overtaking, you might want to choose a track with straight and wide roads.</li>
-<li><strong>Know your opponents:</strong> Each opponent in Blur has its own personality and behavior, such as aggressive, defensive, or balanced. You should know your opponents well and choose the ones that match your level and challenge. For example, if you are a beginner, you might want to avoid opponents that are too aggressive or too skilled; if you are an expert, you might want to challenge opponents that are more competitive or more unpredictable.</li>
-<li><strong>Know your strategy:</strong> Each race in Blur has its own mode and objective, such as time trial, checkpoint, destruction, or fan run. You should know your strategy well and choose the one that fits your goal and situation. For example, if you want to finish first, you might want to focus on speed and power-ups; if you want to earn fans, you might want to focus on stunts and challenges; if you want to have fun, you might want to focus on custom modes and settings.</li>
-</ul>
- <h3>How to unlock cars and tracks in Blur?</h3>
-<p>Unlocking cars and tracks in Blur is not only about playing more and more races. It is also about playing better and better races. Here are some tips and tricks for unlocking cars and tracks in Blur:</p>
- <ul>
-<li><strong>Earn fans:</strong> Fans are the currency of Blur. You can earn fans by performing stunts, winning races, completing challenges, or defeating rivals. You can also lose fans by crashing, losing races, or being defeated by rivals. Fans are used to unlock new cars and tracks in the game. The more fans you have, the more cars and tracks you can unlock.</li>
-<li><strong>Complete challenges:</strong> Challenges are the missions of Blur. You can complete challenges by meeting certain criteria in a race, such as finishing first, hitting a number of opponents with power-ups, or drifting a certain distance. Challenges are used to earn fans and rewards in the game. The more challenges you complete, the more fans and rewards you can earn.</li>
-<li><strong>Defeat rivals:</strong> Rivals are the bosses of Blur. You can defeat rivals by beating them in a race or a showdown. Rivals are used to earn fans and rewards in the game. The more rivals you defeat, the more fans and rewards you can earn.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Blur is a fun and exciting racing game that features power-ups, speed boosts, and online multiplayer. You can download Blur for Mac for free from different sources, such as the Mac App Store, Steam, or torrent sites. You can also use video effects on your Mac while playing Blur, such as blurring the background or adding studio light. Finally, you can use some tips and tricks for playing Blur on Mac, such as mastering power-ups, winning races, and unlocking cars and tracks.</p>
-<p>If you are looking for a racing game that combines realism and fantasy, action and strategy, solo and multiplayer, then Blur is the game for you. Download Blur for Mac today and enjoy the thrill of racing!</p>
- <h2>FAQs about Blur for Mac Free Download</h2>
-<p>Here are some frequently asked questions about Blur for Mac Free Download:</p>
- <h4>Q: Is Blur compatible with Mac OS Catalina or later?</h4>
-<p>A: Yes, Blur is compatible with Mac OS Catalina or later if you download it from the Mac App Store or use an Apple silicon Mac device. However, if you download it from other sources, such as Steam or torrent sites, you may need to use a Windows emulator or a virtual machine to run it on Mac OS Catalina or later.</p>
- <h4>Q: How much space does Blur take on Mac?</h4>
-<p>A: Blur takes about 5.6 GB of space on Mac if you download it from the Mac App Store or use an Apple silicon Mac device. However, if you download it from other sources, such as Steam or torrent sites, it may take more or less space depending on the files and the software you use to run it.</p>
- <h4>Q: Can I play Blur with a controller on Mac?</h4>
-<p>A: Yes, you can play Blur with a controller on Mac if you have a compatible controller and a software that can map the controller buttons to the keyboard keys. Some of the compatible controllers are Xbox 360, Xbox One, PlayStation 3, PlayStation 4, and Nintendo Switch. Some of the software that can map the controller buttons are ControllerMate, Joystick Mapper, or Enjoyable.</p>
- <h4>Q: Can I play Blur with friends on Mac?</h4>
-<p>A: Yes, you can play Blur with friends on Mac if you have an internet connection and a Steam account. You can join or create online races with up to 20 players, or offline races with up to 4 players using split-screen. You can also join or create groups, where you can chat with your friends and share your progress.</p>
- <h4>Q: Is Blur still supported by the developers?</h4>
-<p>A: No, Blur is not still supported by the developers. The game was developed by Bizarre Creations and published by Activision in 2010. However, Bizarre Creations was shut down by Activision in 2011, and Activision stopped supporting the game in 2012. The game servers were also shut down in 2014, making the online multiplayer mode unavailable. However, you can still play the game offline or online using third-party servers or software.</p> b2dd77e56b<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Building Your Own Illusions The Complete Video Course By Gerry Frenette.md b/spaces/tioseFevbu/cartoon-converter/scripts/Building Your Own Illusions The Complete Video Course By Gerry Frenette.md
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-
-<h1>How to Build Your Own Illusions with Gerry Frenette's Video Course</h1>
-<p>Have you ever dreamed of performing amazing illusions on stage, but don't have the budget or the skills to buy or make them? If so, you're in luck! Gerry Frenette, a professional illusionist and illusion builder, has created a comprehensive video course that teaches you how to build your own illusions from scratch, using common materials and tools.</p>
-<p>In this course, you will learn how to:</p>
-<h2>Building Your Own Illusions, The Complete Video Course By Gerry Frenette</h2><br /><p><b><b>Download</b> &#128504;&#128504;&#128504; <a href="https://urlcod.com/2uHwBs">https://urlcod.com/2uHwBs</a></b></p><br /><br />
-<ul>
-<li>Design and plan your own illusions, based on your performance style and venue</li>
-<li>Choose the best materials and tools for your projects, and where to find them</li>
-<li>Cut, drill, glue, paint and finish your illusions like a pro</li>
-<li>Add special effects, such as lights, smoke, sound and electronics</li>
-<li>Transport, set up and perform your illusions safely and smoothly</li>
-</ul>
-<p>The course includes over 20 hours of video instruction, covering 12 different illusions, such as:</p>
-<ul>
-<li>The Zig Zag Lady</li>
-<li>The Sword Basket</li>
-<li>The Sub Trunk</li>
-<li>The Head Chopper</li>
-<li>The Modern Art</li>
-<li>The Origami Box</li>
-<li>And more!</li>
-</ul>
-<p>You will also get access to PDF plans, diagrams and templates for each illusion, as well as a bonus video on how to build your own illusion table.</p>
-<p>Whether you are a beginner or an experienced magician, this course will help you take your magic to the next level. You will save thousands of dollars by building your own illusions, and impress your audiences with your originality and creativity.</p>
-<p>Don't miss this opportunity to learn from one of the best illusion builders in the world. Order your copy of <strong>Building Your Own Illusions, The Complete Video Course By Gerry Frenette</strong> today!</p>
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-<p>What makes this course different from other illusion building courses is that Gerry Frenette shares his secrets and tips from over 40 years of experience in the field. He shows you not only how to build the illusions, but also how to perform them with style and confidence. He explains the psychology and the mechanics behind each illusion, and how to avoid common mistakes and pitfalls.</p>
-<p>Another advantage of this course is that you can learn at your own pace and convenience. You can watch the videos online or download them to your device. You can pause, rewind and replay them as many times as you need. You can also ask questions and get feedback from Gerry Frenette and other students in the online forum.</p>
-<p></p>
-<p>This course is suitable for anyone who loves magic and wants to add some illusions to their repertoire. You don't need any special skills or talents to build your own illusions. All you need is some time, some space, some money and some imagination. You will be amazed by what you can create with Gerry Frenette's guidance and support.</p>
-
-<p>If you order this course today, you will also get some exclusive bonuses that will enhance your illusion building and performing experience. These include:</p>
-<ul>
-<li>A free consultation with Gerry Frenette, where you can ask him anything about illusion building and performing</li>
-<li>A 10% discount on any illusion plans or products from Gerry Frenette's website</li>
-<li>A certificate of completion and a badge that you can display on your website or social media</li>
-</ul>
-<p>This course is a limited edition and only available for a short time. Don't miss this chance to learn from one of the masters of illusion. Order your copy of <strong>Building Your Own Illusions, The Complete Video Course By Gerry Frenette</strong> now and start building your own illusions today!</p> e93f5a0c3f<br />
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-<br />
-<h1>How to Hard Reset HP PSC 1510 All-in-One Printer</h1>
-<p>If you are experiencing problems with your HP PSC 1510 All-in-One Printer, such as paper jams, error messages, or printing issues, you may need to perform a hard reset to restore the printer to its factory settings. A hard reset will erase all the settings and data on the printer memory, but it will not delete any personal files on your computer. Here are the steps to hard reset your HP PSC 1510 All-in-One Printer:</p>
-<h2>Hp Psc 1510 Hard Reset</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash; <a href="https://urlcod.com/2uHyMY">https://urlcod.com/2uHyMY</a></b></p><br /><br />
-<ol>
-<li>Turn off the printer and disconnect the power cord from the wall outlet.</li>
-<li>Press and hold the Cancel button and the Left Arrow button on the printer control panel.</li>
-<li>While holding the buttons, reconnect the power cord to the printer.</li>
-<li>Release the buttons when the printer display shows "Reset".</li>
-<li>Wait for the printer to initialize and print a test page.</li>
-</ol>
-<p>A hard reset should resolve most of the common issues with your HP PSC 1510 All-in-One Printer. However, if you still encounter problems after a hard reset, you may need to update your printer drivers, perform a system restore on your computer, or contact HP support for further assistance. For more information on how to troubleshoot your HP PSC 1510 All-in-One Printer, you can refer to the following sources:</p>
-<ul>
-<li><a href="https://support.hp.com/us-en/document/ish_1997208-1551050-16">HP PCs - How to power reset your computer</a></li>
-<li><a href="https://support.hp.com/us-en/document/ish_3151394-2749139-16">HP PCs - How to reset your computer to factory settings (Windows 11, 10)</a></li>
-<li><a href="https://hp-psc-1510-hard-reset-32.peatix.com/">Hp Psc 1510 Hard Reset micosmyh | Peatix</a></li>
-<li><a href="https://support.hp.com/in-en/product/hp-psc-1510-all-in-one-printer-series/428822/manuals">HP PSC 1510 All-in-One Printer series Manuals</a></li>
-</ul>
-
-<p>After you have hard reset your HP PSC 1510 All-in-One Printer, you may need to reinstall the printer software and drivers on your computer. To do this, you can follow these steps:</p>
-<ol>
-<li>Go to the <a href="https://support.hp.com/in-en/drivers/selfservice/hp-psc-1510-all-in-one-printer-series/428822">HP PSC 1510 All-in-One Printer series Software and Driver Downloads</a> page.</li>
-<li>Select your operating system and version from the drop-down menus.</li>
-<li>Click Download next to the full feature software and driver package.</li>
-<li>Run the downloaded file and follow the on-screen instructions to install the printer software and drivers.</li>
-<li>Restart your computer and printer after the installation is complete.</li>
-</ol>
-<p>By reinstalling the printer software and drivers, you can ensure that your HP PSC 1510 All-in-One Printer is compatible with your computer and can perform all the functions that it supports, such as printing, scanning, copying, and faxing. You can also use the HP Printer Assistant software to manage your printer settings, check ink levels, order supplies, and access online help. For more information on how to use your HP PSC 1510 All-in-One Printer, you can refer to the <a href="https://h10032.www1.hp.com/ctg/Manual/c00366227.pdf">HP PSC 1500 series User Guide</a>.</p>
-<p></p> 7b8c122e87<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/MacToolsBreastCancerToolBoxForSale.md b/spaces/tioseFevbu/cartoon-converter/scripts/MacToolsBreastCancerToolBoxForSale.md
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-<br />
-<h1>Mac Tools Breast Cancer Tool Box for Sale: A Great Way to Support a Good Cause</h1>
-<p>If you are looking for a new tool box that is not only functional but also stylish and meaningful, you might want to check out the Mac Tools Breast Cancer Tool Box for Sale. This limited edition tool box is part of Mac Tools' annual campaign to raise awareness and funds for breast cancer research and support.</p>
-<h2>MacToolsBreastCancerToolBoxForSale</h2><br /><p><b><b>Download File</b> &rArr;&rArr;&rArr; <a href="https://urlcod.com/2uHx9C">https://urlcod.com/2uHx9C</a></b></p><br /><br />
-<p>The Mac Tools Breast Cancer Tool Box is a pink version of the popular Macsimizer™ MB1900 series tool box, which features a spacious work surface, heavy-duty drawers, and a patented lock system. The tool box also comes with a matching pink cover and a special breast cancer awareness decal. The tool box measures 87 inches wide, 25 inches deep, and 46 inches high, and has a total storage capacity of 35,258 cubic inches.</p>
-<p>By purchasing the Mac Tools Breast Cancer Tool Box, you are not only getting a high-quality product that will last for years, but you are also supporting a good cause. Mac Tools donates a portion of the proceeds from each tool box sale to the Stefanie Spielman Fund for Breast Cancer Research at The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute.</p>
-<p>The Mac Tools Breast Cancer Tool Box for Sale is available for a limited time only, so don't miss this opportunity to show your support for breast cancer awareness and research. You can order the tool box online or through your local Mac Tools distributor. The tool box costs $9,999.99, and financing options are available.</p>
-<p>Order your Mac Tools Breast Cancer Tool Box today and join the fight against breast cancer!</p>
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-<p>The Mac Tools Breast Cancer Tool Box is not only a great way to store and organize your tools, but also a great way to show your support for a good cause. The tool box is designed to be durable, secure, and easy to use. Here are some of the features that make the Mac Tools Breast Cancer Tool Box stand out:</p>
-<p></p>
-<ul>
-<li>The tool box has a large work surface that can accommodate a laptop, a tablet, or a diagnostic tool. The work surface also has a built-in power strip with four outlets and two USB ports.</li>
-<li>The tool box has 12 drawers of various sizes and depths, with a total weight capacity of 6,000 pounds. The drawers have ball-bearing slides and full-width aluminum pulls for smooth operation. The drawers also have drawer liners and foam inserts to protect your tools from damage.</li>
-<li>The tool box has a patented lock system that allows you to lock and unlock all the drawers with one key. The lock system also has a remote control that lets you lock and unlock the tool box from up to 100 feet away.</li>
-<li>The tool box has a matching pink cover that protects the tool box from dust and scratches. The cover also has a zipper opening that allows you to access the work surface without removing the cover.</li>
-<li>The tool box has a special breast cancer awareness decal that shows your support for the cause. The decal features the Mac Tools logo and the pink ribbon symbol.</li>
-</ul>
-<p>The Mac Tools Breast Cancer Tool Box is a limited edition product that is only available for a short time. If you want to get your hands on this tool box, you need to act fast. You can order the tool box online or through your local Mac Tools distributor. The tool box costs $9,999.99, and financing options are available.</p>
-<p>Don't miss this chance to get a tool box that is not only functional but also meaningful. Order your Mac Tools Breast Cancer Tool Box today and help make a difference in the fight against breast cancer!</p> 81aa517590<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/__pip-runner__.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/__pip-runner__.py
deleted file mode 100644
index 14026c0d131f3382c3731ea4cd8d680d73d57899..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/__pip-runner__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Execute exactly this copy of pip, within a different environment.
-
-This file is named as it is, to ensure that this module can't be imported via
-an import statement.
-"""
-
-import runpy
-import sys
-import types
-from importlib.machinery import ModuleSpec, PathFinder
-from os.path import dirname
-from typing import Optional, Sequence, Union
-
-PIP_SOURCES_ROOT = dirname(dirname(__file__))
-
-
-class PipImportRedirectingFinder:
-    @classmethod
-    def find_spec(
-        self,
-        fullname: str,
-        path: Optional[Sequence[Union[bytes, str]]] = None,
-        target: Optional[types.ModuleType] = None,
-    ) -> Optional[ModuleSpec]:
-        if fullname != "pip":
-            return None
-
-        spec = PathFinder.find_spec(fullname, [PIP_SOURCES_ROOT], target)
-        assert spec, (PIP_SOURCES_ROOT, fullname)
-        return spec
-
-
-sys.meta_path.insert(0, PipImportRedirectingFinder())
-
-assert __name__ == "__main__", "Cannot run __pip-runner__.py as a non-main module"
-runpy.run_module("pip", run_name="__main__", alter_sys=True)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/req/req_install.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/req/req_install.py
deleted file mode 100644
index a1e376c893a7c24e3e62c560e085a1bae651f930..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/req/req_install.py
+++ /dev/null
@@ -1,879 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import functools
-import logging
-import os
-import shutil
-import sys
-import uuid
-import zipfile
-from typing import Any, Collection, Dict, Iterable, List, Optional, Sequence, Union
-
-from pip._vendor.packaging.markers import Marker
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.specifiers import SpecifierSet
-from pip._vendor.packaging.utils import canonicalize_name
-from pip._vendor.packaging.version import Version
-from pip._vendor.packaging.version import parse as parse_version
-from pip._vendor.pep517.wrappers import Pep517HookCaller
-
-from pip._internal.build_env import BuildEnvironment, NoOpBuildEnvironment
-from pip._internal.exceptions import InstallationError, LegacyInstallFailure
-from pip._internal.locations import get_scheme
-from pip._internal.metadata import (
-    BaseDistribution,
-    get_default_environment,
-    get_directory_distribution,
-    get_wheel_distribution,
-)
-from pip._internal.metadata.base import FilesystemWheel
-from pip._internal.models.direct_url import DirectUrl
-from pip._internal.models.link import Link
-from pip._internal.operations.build.metadata import generate_metadata
-from pip._internal.operations.build.metadata_editable import generate_editable_metadata
-from pip._internal.operations.build.metadata_legacy import (
-    generate_metadata as generate_metadata_legacy,
-)
-from pip._internal.operations.install.editable_legacy import (
-    install_editable as install_editable_legacy,
-)
-from pip._internal.operations.install.legacy import install as install_legacy
-from pip._internal.operations.install.wheel import install_wheel
-from pip._internal.pyproject import load_pyproject_toml, make_pyproject_path
-from pip._internal.req.req_uninstall import UninstallPathSet
-from pip._internal.utils.deprecation import deprecated
-from pip._internal.utils.direct_url_helpers import (
-    direct_url_for_editable,
-    direct_url_from_link,
-)
-from pip._internal.utils.hashes import Hashes
-from pip._internal.utils.misc import (
-    ConfiguredPep517HookCaller,
-    ask_path_exists,
-    backup_dir,
-    display_path,
-    hide_url,
-    redact_auth_from_url,
-)
-from pip._internal.utils.packaging import safe_extra
-from pip._internal.utils.subprocess import runner_with_spinner_message
-from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds
-from pip._internal.utils.virtualenv import running_under_virtualenv
-from pip._internal.vcs import vcs
-
-logger = logging.getLogger(__name__)
-
-
-class InstallRequirement:
-    """
-    Represents something that may be installed later on, may have information
-    about where to fetch the relevant requirement and also contains logic for
-    installing the said requirement.
-    """
-
-    def __init__(
-        self,
-        req: Optional[Requirement],
-        comes_from: Optional[Union[str, "InstallRequirement"]],
-        editable: bool = False,
-        link: Optional[Link] = None,
-        markers: Optional[Marker] = None,
-        use_pep517: Optional[bool] = None,
-        isolated: bool = False,
-        install_options: Optional[List[str]] = None,
-        global_options: Optional[List[str]] = None,
-        hash_options: Optional[Dict[str, List[str]]] = None,
-        config_settings: Optional[Dict[str, str]] = None,
-        constraint: bool = False,
-        extras: Collection[str] = (),
-        user_supplied: bool = False,
-        permit_editable_wheels: bool = False,
-    ) -> None:
-        assert req is None or isinstance(req, Requirement), req
-        self.req = req
-        self.comes_from = comes_from
-        self.constraint = constraint
-        self.editable = editable
-        self.permit_editable_wheels = permit_editable_wheels
-        self.legacy_install_reason: Optional[int] = None
-
-        # source_dir is the local directory where the linked requirement is
-        # located, or unpacked. In case unpacking is needed, creating and
-        # populating source_dir is done by the RequirementPreparer. Note this
-        # is not necessarily the directory where pyproject.toml or setup.py is
-        # located - that one is obtained via unpacked_source_directory.
-        self.source_dir: Optional[str] = None
-        if self.editable:
-            assert link
-            if link.is_file:
-                self.source_dir = os.path.normpath(os.path.abspath(link.file_path))
-
-        if link is None and req and req.url:
-            # PEP 508 URL requirement
-            link = Link(req.url)
-        self.link = self.original_link = link
-        self.original_link_is_in_wheel_cache = False
-
-        # Information about the location of the artifact that was downloaded . This
-        # property is guaranteed to be set in resolver results.
-        self.download_info: Optional[DirectUrl] = None
-
-        # Path to any downloaded or already-existing package.
-        self.local_file_path: Optional[str] = None
-        if self.link and self.link.is_file:
-            self.local_file_path = self.link.file_path
-
-        if extras:
-            self.extras = extras
-        elif req:
-            self.extras = {safe_extra(extra) for extra in req.extras}
-        else:
-            self.extras = set()
-        if markers is None and req:
-            markers = req.marker
-        self.markers = markers
-
-        # This holds the Distribution object if this requirement is already installed.
-        self.satisfied_by: Optional[BaseDistribution] = None
-        # Whether the installation process should try to uninstall an existing
-        # distribution before installing this requirement.
-        self.should_reinstall = False
-        # Temporary build location
-        self._temp_build_dir: Optional[TempDirectory] = None
-        # Set to True after successful installation
-        self.install_succeeded: Optional[bool] = None
-        # Supplied options
-        self.install_options = install_options if install_options else []
-        self.global_options = global_options if global_options else []
-        self.hash_options = hash_options if hash_options else {}
-        self.config_settings = config_settings
-        # Set to True after successful preparation of this requirement
-        self.prepared = False
-        # User supplied requirement are explicitly requested for installation
-        # by the user via CLI arguments or requirements files, as opposed to,
-        # e.g. dependencies, extras or constraints.
-        self.user_supplied = user_supplied
-
-        self.isolated = isolated
-        self.build_env: BuildEnvironment = NoOpBuildEnvironment()
-
-        # For PEP 517, the directory where we request the project metadata
-        # gets stored. We need this to pass to build_wheel, so the backend
-        # can ensure that the wheel matches the metadata (see the PEP for
-        # details).
-        self.metadata_directory: Optional[str] = None
-
-        # The static build requirements (from pyproject.toml)
-        self.pyproject_requires: Optional[List[str]] = None
-
-        # Build requirements that we will check are available
-        self.requirements_to_check: List[str] = []
-
-        # The PEP 517 backend we should use to build the project
-        self.pep517_backend: Optional[Pep517HookCaller] = None
-
-        # Are we using PEP 517 for this requirement?
-        # After pyproject.toml has been loaded, the only valid values are True
-        # and False. Before loading, None is valid (meaning "use the default").
-        # Setting an explicit value before loading pyproject.toml is supported,
-        # but after loading this flag should be treated as read only.
-        self.use_pep517 = use_pep517
-
-        # This requirement needs more preparation before it can be built
-        self.needs_more_preparation = False
-
-    def __str__(self) -> str:
-        if self.req:
-            s = str(self.req)
-            if self.link:
-                s += " from {}".format(redact_auth_from_url(self.link.url))
-        elif self.link:
-            s = redact_auth_from_url(self.link.url)
-        else:
-            s = "<InstallRequirement>"
-        if self.satisfied_by is not None:
-            s += " in {}".format(display_path(self.satisfied_by.location))
-        if self.comes_from:
-            if isinstance(self.comes_from, str):
-                comes_from: Optional[str] = self.comes_from
-            else:
-                comes_from = self.comes_from.from_path()
-            if comes_from:
-                s += f" (from {comes_from})"
-        return s
-
-    def __repr__(self) -> str:
-        return "<{} object: {} editable={!r}>".format(
-            self.__class__.__name__, str(self), self.editable
-        )
-
-    def format_debug(self) -> str:
-        """An un-tested helper for getting state, for debugging."""
-        attributes = vars(self)
-        names = sorted(attributes)
-
-        state = ("{}={!r}".format(attr, attributes[attr]) for attr in sorted(names))
-        return "<{name} object: {{{state}}}>".format(
-            name=self.__class__.__name__,
-            state=", ".join(state),
-        )
-
-    # Things that are valid for all kinds of requirements?
-    @property
-    def name(self) -> Optional[str]:
-        if self.req is None:
-            return None
-        return self.req.name
-
-    @functools.lru_cache()  # use cached_property in python 3.8+
-    def supports_pyproject_editable(self) -> bool:
-        if not self.use_pep517:
-            return False
-        assert self.pep517_backend
-        with self.build_env:
-            runner = runner_with_spinner_message(
-                "Checking if build backend supports build_editable"
-            )
-            with self.pep517_backend.subprocess_runner(runner):
-                return "build_editable" in self.pep517_backend._supported_features()
-
-    @property
-    def specifier(self) -> SpecifierSet:
-        return self.req.specifier
-
-    @property
-    def is_pinned(self) -> bool:
-        """Return whether I am pinned to an exact version.
-
-        For example, some-package==1.2 is pinned; some-package>1.2 is not.
-        """
-        specifiers = self.specifier
-        return len(specifiers) == 1 and next(iter(specifiers)).operator in {"==", "==="}
-
-    def match_markers(self, extras_requested: Optional[Iterable[str]] = None) -> bool:
-        if not extras_requested:
-            # Provide an extra to safely evaluate the markers
-            # without matching any extra
-            extras_requested = ("",)
-        if self.markers is not None:
-            return any(
-                self.markers.evaluate({"extra": extra}) for extra in extras_requested
-            )
-        else:
-            return True
-
-    @property
-    def has_hash_options(self) -> bool:
-        """Return whether any known-good hashes are specified as options.
-
-        These activate --require-hashes mode; hashes specified as part of a
-        URL do not.
-
-        """
-        return bool(self.hash_options)
-
-    def hashes(self, trust_internet: bool = True) -> Hashes:
-        """Return a hash-comparer that considers my option- and URL-based
-        hashes to be known-good.
-
-        Hashes in URLs--ones embedded in the requirements file, not ones
-        downloaded from an index server--are almost peers with ones from
-        flags. They satisfy --require-hashes (whether it was implicitly or
-        explicitly activated) but do not activate it. md5 and sha224 are not
-        allowed in flags, which should nudge people toward good algos. We
-        always OR all hashes together, even ones from URLs.
-
-        :param trust_internet: Whether to trust URL-based (#md5=...) hashes
-            downloaded from the internet, as by populate_link()
-
-        """
-        good_hashes = self.hash_options.copy()
-        link = self.link if trust_internet else self.original_link
-        if link and link.hash:
-            good_hashes.setdefault(link.hash_name, []).append(link.hash)
-        return Hashes(good_hashes)
-
-    def from_path(self) -> Optional[str]:
-        """Format a nice indicator to show where this "comes from" """
-        if self.req is None:
-            return None
-        s = str(self.req)
-        if self.comes_from:
-            if isinstance(self.comes_from, str):
-                comes_from = self.comes_from
-            else:
-                comes_from = self.comes_from.from_path()
-            if comes_from:
-                s += "->" + comes_from
-        return s
-
-    def ensure_build_location(
-        self, build_dir: str, autodelete: bool, parallel_builds: bool
-    ) -> str:
-        assert build_dir is not None
-        if self._temp_build_dir is not None:
-            assert self._temp_build_dir.path
-            return self._temp_build_dir.path
-        if self.req is None:
-            # Some systems have /tmp as a symlink which confuses custom
-            # builds (such as numpy). Thus, we ensure that the real path
-            # is returned.
-            self._temp_build_dir = TempDirectory(
-                kind=tempdir_kinds.REQ_BUILD, globally_managed=True
-            )
-
-            return self._temp_build_dir.path
-
-        # This is the only remaining place where we manually determine the path
-        # for the temporary directory. It is only needed for editables where
-        # it is the value of the --src option.
-
-        # When parallel builds are enabled, add a UUID to the build directory
-        # name so multiple builds do not interfere with each other.
-        dir_name: str = canonicalize_name(self.name)
-        if parallel_builds:
-            dir_name = f"{dir_name}_{uuid.uuid4().hex}"
-
-        # FIXME: Is there a better place to create the build_dir? (hg and bzr
-        # need this)
-        if not os.path.exists(build_dir):
-            logger.debug("Creating directory %s", build_dir)
-            os.makedirs(build_dir)
-        actual_build_dir = os.path.join(build_dir, dir_name)
-        # `None` indicates that we respect the globally-configured deletion
-        # settings, which is what we actually want when auto-deleting.
-        delete_arg = None if autodelete else False
-        return TempDirectory(
-            path=actual_build_dir,
-            delete=delete_arg,
-            kind=tempdir_kinds.REQ_BUILD,
-            globally_managed=True,
-        ).path
-
-    def _set_requirement(self) -> None:
-        """Set requirement after generating metadata."""
-        assert self.req is None
-        assert self.metadata is not None
-        assert self.source_dir is not None
-
-        # Construct a Requirement object from the generated metadata
-        if isinstance(parse_version(self.metadata["Version"]), Version):
-            op = "=="
-        else:
-            op = "==="
-
-        self.req = Requirement(
-            "".join(
-                [
-                    self.metadata["Name"],
-                    op,
-                    self.metadata["Version"],
-                ]
-            )
-        )
-
-    def warn_on_mismatching_name(self) -> None:
-        metadata_name = canonicalize_name(self.metadata["Name"])
-        if canonicalize_name(self.req.name) == metadata_name:
-            # Everything is fine.
-            return
-
-        # If we're here, there's a mismatch. Log a warning about it.
-        logger.warning(
-            "Generating metadata for package %s "
-            "produced metadata for project name %s. Fix your "
-            "#egg=%s fragments.",
-            self.name,
-            metadata_name,
-            self.name,
-        )
-        self.req = Requirement(metadata_name)
-
-    def check_if_exists(self, use_user_site: bool) -> None:
-        """Find an installed distribution that satisfies or conflicts
-        with this requirement, and set self.satisfied_by or
-        self.should_reinstall appropriately.
-        """
-        if self.req is None:
-            return
-        existing_dist = get_default_environment().get_distribution(self.req.name)
-        if not existing_dist:
-            return
-
-        version_compatible = self.req.specifier.contains(
-            existing_dist.version,
-            prereleases=True,
-        )
-        if not version_compatible:
-            self.satisfied_by = None
-            if use_user_site:
-                if existing_dist.in_usersite:
-                    self.should_reinstall = True
-                elif running_under_virtualenv() and existing_dist.in_site_packages:
-                    raise InstallationError(
-                        f"Will not install to the user site because it will "
-                        f"lack sys.path precedence to {existing_dist.raw_name} "
-                        f"in {existing_dist.location}"
-                    )
-            else:
-                self.should_reinstall = True
-        else:
-            if self.editable:
-                self.should_reinstall = True
-                # when installing editables, nothing pre-existing should ever
-                # satisfy
-                self.satisfied_by = None
-            else:
-                self.satisfied_by = existing_dist
-
-    # Things valid for wheels
-    @property
-    def is_wheel(self) -> bool:
-        if not self.link:
-            return False
-        return self.link.is_wheel
-
-    # Things valid for sdists
-    @property
-    def unpacked_source_directory(self) -> str:
-        return os.path.join(
-            self.source_dir, self.link and self.link.subdirectory_fragment or ""
-        )
-
-    @property
-    def setup_py_path(self) -> str:
-        assert self.source_dir, f"No source dir for {self}"
-        setup_py = os.path.join(self.unpacked_source_directory, "setup.py")
-
-        return setup_py
-
-    @property
-    def setup_cfg_path(self) -> str:
-        assert self.source_dir, f"No source dir for {self}"
-        setup_cfg = os.path.join(self.unpacked_source_directory, "setup.cfg")
-
-        return setup_cfg
-
-    @property
-    def pyproject_toml_path(self) -> str:
-        assert self.source_dir, f"No source dir for {self}"
-        return make_pyproject_path(self.unpacked_source_directory)
-
-    def load_pyproject_toml(self) -> None:
-        """Load the pyproject.toml file.
-
-        After calling this routine, all of the attributes related to PEP 517
-        processing for this requirement have been set. In particular, the
-        use_pep517 attribute can be used to determine whether we should
-        follow the PEP 517 or legacy (setup.py) code path.
-        """
-        pyproject_toml_data = load_pyproject_toml(
-            self.use_pep517, self.pyproject_toml_path, self.setup_py_path, str(self)
-        )
-
-        if pyproject_toml_data is None:
-            self.use_pep517 = False
-            return
-
-        self.use_pep517 = True
-        requires, backend, check, backend_path = pyproject_toml_data
-        self.requirements_to_check = check
-        self.pyproject_requires = requires
-        self.pep517_backend = ConfiguredPep517HookCaller(
-            self,
-            self.unpacked_source_directory,
-            backend,
-            backend_path=backend_path,
-        )
-
-    def isolated_editable_sanity_check(self) -> None:
-        """Check that an editable requirement if valid for use with PEP 517/518.
-
-        This verifies that an editable that has a pyproject.toml either supports PEP 660
-        or as a setup.py or a setup.cfg
-        """
-        if (
-            self.editable
-            and self.use_pep517
-            and not self.supports_pyproject_editable()
-            and not os.path.isfile(self.setup_py_path)
-            and not os.path.isfile(self.setup_cfg_path)
-        ):
-            raise InstallationError(
-                f"Project {self} has a 'pyproject.toml' and its build "
-                f"backend is missing the 'build_editable' hook. Since it does not "
-                f"have a 'setup.py' nor a 'setup.cfg', "
-                f"it cannot be installed in editable mode. "
-                f"Consider using a build backend that supports PEP 660."
-            )
-
-    def prepare_metadata(self) -> None:
-        """Ensure that project metadata is available.
-
-        Under PEP 517 and PEP 660, call the backend hook to prepare the metadata.
-        Under legacy processing, call setup.py egg-info.
-        """
-        assert self.source_dir
-        details = self.name or f"from {self.link}"
-
-        if self.use_pep517:
-            assert self.pep517_backend is not None
-            if (
-                self.editable
-                and self.permit_editable_wheels
-                and self.supports_pyproject_editable()
-            ):
-                self.metadata_directory = generate_editable_metadata(
-                    build_env=self.build_env,
-                    backend=self.pep517_backend,
-                    details=details,
-                )
-            else:
-                self.metadata_directory = generate_metadata(
-                    build_env=self.build_env,
-                    backend=self.pep517_backend,
-                    details=details,
-                )
-        else:
-            self.metadata_directory = generate_metadata_legacy(
-                build_env=self.build_env,
-                setup_py_path=self.setup_py_path,
-                source_dir=self.unpacked_source_directory,
-                isolated=self.isolated,
-                details=details,
-            )
-
-        # Act on the newly generated metadata, based on the name and version.
-        if not self.name:
-            self._set_requirement()
-        else:
-            self.warn_on_mismatching_name()
-
-        self.assert_source_matches_version()
-
-    @property
-    def metadata(self) -> Any:
-        if not hasattr(self, "_metadata"):
-            self._metadata = self.get_dist().metadata
-
-        return self._metadata
-
-    def get_dist(self) -> BaseDistribution:
-        if self.metadata_directory:
-            return get_directory_distribution(self.metadata_directory)
-        elif self.local_file_path and self.is_wheel:
-            return get_wheel_distribution(
-                FilesystemWheel(self.local_file_path), canonicalize_name(self.name)
-            )
-        raise AssertionError(
-            f"InstallRequirement {self} has no metadata directory and no wheel: "
-            f"can't make a distribution."
-        )
-
-    def assert_source_matches_version(self) -> None:
-        assert self.source_dir
-        version = self.metadata["version"]
-        if self.req.specifier and version not in self.req.specifier:
-            logger.warning(
-                "Requested %s, but installing version %s",
-                self,
-                version,
-            )
-        else:
-            logger.debug(
-                "Source in %s has version %s, which satisfies requirement %s",
-                display_path(self.source_dir),
-                version,
-                self,
-            )
-
-    # For both source distributions and editables
-    def ensure_has_source_dir(
-        self,
-        parent_dir: str,
-        autodelete: bool = False,
-        parallel_builds: bool = False,
-    ) -> None:
-        """Ensure that a source_dir is set.
-
-        This will create a temporary build dir if the name of the requirement
-        isn't known yet.
-
-        :param parent_dir: The ideal pip parent_dir for the source_dir.
-            Generally src_dir for editables and build_dir for sdists.
-        :return: self.source_dir
-        """
-        if self.source_dir is None:
-            self.source_dir = self.ensure_build_location(
-                parent_dir,
-                autodelete=autodelete,
-                parallel_builds=parallel_builds,
-            )
-
-    # For editable installations
-    def update_editable(self) -> None:
-        if not self.link:
-            logger.debug(
-                "Cannot update repository at %s; repository location is unknown",
-                self.source_dir,
-            )
-            return
-        assert self.editable
-        assert self.source_dir
-        if self.link.scheme == "file":
-            # Static paths don't get updated
-            return
-        vcs_backend = vcs.get_backend_for_scheme(self.link.scheme)
-        # Editable requirements are validated in Requirement constructors.
-        # So here, if it's neither a path nor a valid VCS URL, it's a bug.
-        assert vcs_backend, f"Unsupported VCS URL {self.link.url}"
-        hidden_url = hide_url(self.link.url)
-        vcs_backend.obtain(self.source_dir, url=hidden_url, verbosity=0)
-
-    # Top-level Actions
-    def uninstall(
-        self, auto_confirm: bool = False, verbose: bool = False
-    ) -> Optional[UninstallPathSet]:
-        """
-        Uninstall the distribution currently satisfying this requirement.
-
-        Prompts before removing or modifying files unless
-        ``auto_confirm`` is True.
-
-        Refuses to delete or modify files outside of ``sys.prefix`` -
-        thus uninstallation within a virtual environment can only
-        modify that virtual environment, even if the virtualenv is
-        linked to global site-packages.
-
-        """
-        assert self.req
-        dist = get_default_environment().get_distribution(self.req.name)
-        if not dist:
-            logger.warning("Skipping %s as it is not installed.", self.name)
-            return None
-        logger.info("Found existing installation: %s", dist)
-
-        uninstalled_pathset = UninstallPathSet.from_dist(dist)
-        uninstalled_pathset.remove(auto_confirm, verbose)
-        return uninstalled_pathset
-
-    def _get_archive_name(self, path: str, parentdir: str, rootdir: str) -> str:
-        def _clean_zip_name(name: str, prefix: str) -> str:
-            assert name.startswith(
-                prefix + os.path.sep
-            ), f"name {name!r} doesn't start with prefix {prefix!r}"
-            name = name[len(prefix) + 1 :]
-            name = name.replace(os.path.sep, "/")
-            return name
-
-        path = os.path.join(parentdir, path)
-        name = _clean_zip_name(path, rootdir)
-        return self.name + "/" + name
-
-    def archive(self, build_dir: Optional[str]) -> None:
-        """Saves archive to provided build_dir.
-
-        Used for saving downloaded VCS requirements as part of `pip download`.
-        """
-        assert self.source_dir
-        if build_dir is None:
-            return
-
-        create_archive = True
-        archive_name = "{}-{}.zip".format(self.name, self.metadata["version"])
-        archive_path = os.path.join(build_dir, archive_name)
-
-        if os.path.exists(archive_path):
-            response = ask_path_exists(
-                "The file {} exists. (i)gnore, (w)ipe, "
-                "(b)ackup, (a)bort ".format(display_path(archive_path)),
-                ("i", "w", "b", "a"),
-            )
-            if response == "i":
-                create_archive = False
-            elif response == "w":
-                logger.warning("Deleting %s", display_path(archive_path))
-                os.remove(archive_path)
-            elif response == "b":
-                dest_file = backup_dir(archive_path)
-                logger.warning(
-                    "Backing up %s to %s",
-                    display_path(archive_path),
-                    display_path(dest_file),
-                )
-                shutil.move(archive_path, dest_file)
-            elif response == "a":
-                sys.exit(-1)
-
-        if not create_archive:
-            return
-
-        zip_output = zipfile.ZipFile(
-            archive_path,
-            "w",
-            zipfile.ZIP_DEFLATED,
-            allowZip64=True,
-        )
-        with zip_output:
-            dir = os.path.normcase(os.path.abspath(self.unpacked_source_directory))
-            for dirpath, dirnames, filenames in os.walk(dir):
-                for dirname in dirnames:
-                    dir_arcname = self._get_archive_name(
-                        dirname,
-                        parentdir=dirpath,
-                        rootdir=dir,
-                    )
-                    zipdir = zipfile.ZipInfo(dir_arcname + "/")
-                    zipdir.external_attr = 0x1ED << 16  # 0o755
-                    zip_output.writestr(zipdir, "")
-                for filename in filenames:
-                    file_arcname = self._get_archive_name(
-                        filename,
-                        parentdir=dirpath,
-                        rootdir=dir,
-                    )
-                    filename = os.path.join(dirpath, filename)
-                    zip_output.write(filename, file_arcname)
-
-        logger.info("Saved %s", display_path(archive_path))
-
-    def install(
-        self,
-        install_options: List[str],
-        global_options: Optional[Sequence[str]] = None,
-        root: Optional[str] = None,
-        home: Optional[str] = None,
-        prefix: Optional[str] = None,
-        warn_script_location: bool = True,
-        use_user_site: bool = False,
-        pycompile: bool = True,
-    ) -> None:
-        scheme = get_scheme(
-            self.name,
-            user=use_user_site,
-            home=home,
-            root=root,
-            isolated=self.isolated,
-            prefix=prefix,
-        )
-
-        global_options = global_options if global_options is not None else []
-        if self.editable and not self.is_wheel:
-            install_editable_legacy(
-                install_options,
-                global_options,
-                prefix=prefix,
-                home=home,
-                use_user_site=use_user_site,
-                name=self.name,
-                setup_py_path=self.setup_py_path,
-                isolated=self.isolated,
-                build_env=self.build_env,
-                unpacked_source_directory=self.unpacked_source_directory,
-            )
-            self.install_succeeded = True
-            return
-
-        if self.is_wheel:
-            assert self.local_file_path
-            direct_url = None
-            # TODO this can be refactored to direct_url = self.download_info
-            if self.editable:
-                direct_url = direct_url_for_editable(self.unpacked_source_directory)
-            elif self.original_link:
-                direct_url = direct_url_from_link(
-                    self.original_link,
-                    self.source_dir,
-                    self.original_link_is_in_wheel_cache,
-                )
-            install_wheel(
-                self.name,
-                self.local_file_path,
-                scheme=scheme,
-                req_description=str(self.req),
-                pycompile=pycompile,
-                warn_script_location=warn_script_location,
-                direct_url=direct_url,
-                requested=self.user_supplied,
-            )
-            self.install_succeeded = True
-            return
-
-        # TODO: Why don't we do this for editable installs?
-
-        # Extend the list of global and install options passed on to
-        # the setup.py call with the ones from the requirements file.
-        # Options specified in requirements file override those
-        # specified on the command line, since the last option given
-        # to setup.py is the one that is used.
-        global_options = list(global_options) + self.global_options
-        install_options = list(install_options) + self.install_options
-
-        try:
-            success = install_legacy(
-                install_options=install_options,
-                global_options=global_options,
-                root=root,
-                home=home,
-                prefix=prefix,
-                use_user_site=use_user_site,
-                pycompile=pycompile,
-                scheme=scheme,
-                setup_py_path=self.setup_py_path,
-                isolated=self.isolated,
-                req_name=self.name,
-                build_env=self.build_env,
-                unpacked_source_directory=self.unpacked_source_directory,
-                req_description=str(self.req),
-            )
-        except LegacyInstallFailure as exc:
-            self.install_succeeded = False
-            raise exc
-        except Exception:
-            self.install_succeeded = True
-            raise
-
-        self.install_succeeded = success
-
-        if success and self.legacy_install_reason == 8368:
-            deprecated(
-                reason=(
-                    "{} was installed using the legacy 'setup.py install' "
-                    "method, because a wheel could not be built for it.".format(
-                        self.name
-                    )
-                ),
-                replacement="to fix the wheel build issue reported above",
-                gone_in=None,
-                issue=8368,
-            )
-
-
-def check_invalid_constraint_type(req: InstallRequirement) -> str:
-
-    # Check for unsupported forms
-    problem = ""
-    if not req.name:
-        problem = "Unnamed requirements are not allowed as constraints"
-    elif req.editable:
-        problem = "Editable requirements are not allowed as constraints"
-    elif req.extras:
-        problem = "Constraints cannot have extras"
-
-    if problem:
-        deprecated(
-            reason=(
-                "Constraints are only allowed to take the form of a package "
-                "name and a version specifier. Other forms were originally "
-                "permitted as an accident of the implementation, but were "
-                "undocumented. The new implementation of the resolver no "
-                "longer supports these forms."
-            ),
-            replacement="replacing the constraint with a requirement",
-            # No plan yet for when the new resolver becomes default
-            gone_in=None,
-            issue=8210,
-        )
-
-    return problem
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/importlib_metadata/_compat.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/importlib_metadata/_compat.py
deleted file mode 100644
index ef3136f8d2a13c3d251e146d8d754e21c3ed1c38..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_vendor/importlib_metadata/_compat.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import sys
-import platform
-
-
-__all__ = ['install', 'NullFinder', 'Protocol']
-
-
-try:
-    from typing import Protocol
-except ImportError:  # pragma: no cover
-    from ..typing_extensions import Protocol  # type: ignore
-
-
-def install(cls):
-    """
-    Class decorator for installation on sys.meta_path.
-
-    Adds the backport DistributionFinder to sys.meta_path and
-    attempts to disable the finder functionality of the stdlib
-    DistributionFinder.
-    """
-    sys.meta_path.append(cls())
-    disable_stdlib_finder()
-    return cls
-
-
-def disable_stdlib_finder():
-    """
-    Give the backport primacy for discovering path-based distributions
-    by monkey-patching the stdlib O_O.
-
-    See #91 for more background for rationale on this sketchy
-    behavior.
-    """
-
-    def matches(finder):
-        return getattr(
-            finder, '__module__', None
-        ) == '_frozen_importlib_external' and hasattr(finder, 'find_distributions')
-
-    for finder in filter(matches, sys.meta_path):  # pragma: nocover
-        del finder.find_distributions
-
-
-class NullFinder:
-    """
-    A "Finder" (aka "MetaClassFinder") that never finds any modules,
-    but may find distributions.
-    """
-
-    @staticmethod
-    def find_spec(*args, **kwargs):
-        return None
-
-    # In Python 2, the import system requires finders
-    # to have a find_module() method, but this usage
-    # is deprecated in Python 3 in favor of find_spec().
-    # For the purposes of this finder (i.e. being present
-    # on sys.meta_path but having no other import
-    # system functionality), the two methods are identical.
-    find_module = find_spec
-
-
-def pypy_partial(val):
-    """
-    Adjust for variable stacklevel on partial under PyPy.
-
-    Workaround for #327.
-    """
-    is_pypy = platform.python_implementation() == 'PyPy'
-    return val + is_pypy
diff --git a/spaces/tomg-group-umd/pez-dispenser/open_clip/pretrained.py b/spaces/tomg-group-umd/pez-dispenser/open_clip/pretrained.py
deleted file mode 100644
index 58511e6e8a9029fecce6e8439880d710de70d39a..0000000000000000000000000000000000000000
--- a/spaces/tomg-group-umd/pez-dispenser/open_clip/pretrained.py
+++ /dev/null
@@ -1,317 +0,0 @@
-import hashlib
-import os
-import urllib
-import warnings
-from functools import partial
-from typing import Dict, Union
-
-from tqdm import tqdm
-
-from .version import __version__
-
-try:
-    from huggingface_hub import hf_hub_download
-    hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__)
-    _has_hf_hub = True
-except ImportError:
-    hf_hub_download = None
-    _has_hf_hub = False
-
-
-def _pcfg(url='', hf_hub='', mean=None, std=None):
-    return dict(
-        url=url,
-        hf_hub=hf_hub,
-        mean=mean,
-        std=std,
-    )
-
-DEFAULT_CACHE_DIR = '~/.cache/clip'
-ENV_TORCH_HOME = 'TORCH_HOME'
-CACHE_DIR = os.getenv(ENV_TORCH_HOME, DEFAULT_CACHE_DIR)
-
-_RN50 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
-    cc12m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
-)
-
-_RN50_quickgelu = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
-    cc12m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
-)
-
-_RN101 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
-)
-
-_RN101_quickgelu = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
-)
-
-_RN50x4 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"),
-)
-
-_RN50x16 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"),
-)
-
-_RN50x64 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"),
-)
-
-_VITB32 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
-    laion2b_e16=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"),
-    laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/')
-)
-
-_VITB32_quickgelu = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
-)
-
-_VITB16 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"),
-    # laion400m_32k=_pcfg(
-    #     url="",
-    #     mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
-    # laion400m_64k=_pcfg(
-    #     url="",
-    #     mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
-)
-
-_VITB16_PLUS_240 = dict(
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"),
-)
-
-_VITL14 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"),
-    laion2b_s32b_b82k=_pcfg(
-        hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/',
-        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
-)
-
-_VITL14_336 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"),
-)
-
-_VITH14 = dict(
-    laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'),
-)
-
-_VITg14 = dict(
-    laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'),
-)
-
-_robertaViTB32 = dict(
-    laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'),
-)
-
-_xlmRobertaBaseViTB32 = dict(
-    laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'),
-)
-
-_xlmRobertaLargeFrozenViTH14 = dict(
-    frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'),
-)
-
-_PRETRAINED = {
-    "RN50": _RN50,
-    "RN50-quickgelu": _RN50_quickgelu,
-    "RN101": _RN101,
-    "RN101-quickgelu": _RN101_quickgelu,
-    "RN50x4": _RN50x4,
-    "RN50x16": _RN50x16,
-    "RN50x64": _RN50x64,
-    "ViT-B-32": _VITB32,
-    "ViT-B-32-quickgelu": _VITB32_quickgelu,
-    "ViT-B-16": _VITB16,
-    "ViT-B-16-plus-240": _VITB16_PLUS_240,
-    "ViT-L-14": _VITL14,
-    "ViT-L-14-336": _VITL14_336,
-    "ViT-H-14": _VITH14,
-    "ViT-g-14": _VITg14,
-    "roberta-ViT-B-32": _robertaViTB32,
-    "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32,
-    "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14,
-}
-
-
-def list_pretrained(as_str: bool = False):
-    """ returns list of pretrained models
-    Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True
-    """
-    return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()]
-
-
-def list_pretrained_models_by_tag(tag: str):
-    """ return all models having the specified pretrain tag """
-    models = []
-    for k in _PRETRAINED.keys():
-        if tag in _PRETRAINED[k]:
-            models.append(k)
-    return models
-
-
-def list_pretrained_tags_by_model(model: str):
-    """ return all pretrain tags for the specified model architecture """
-    tags = []
-    if model in _PRETRAINED:
-        tags.extend(_PRETRAINED[model].keys())
-    return tags
-
-
-def is_pretrained_cfg(model: str, tag: str):
-    if model not in _PRETRAINED:
-        return False
-    return tag.lower() in _PRETRAINED[model]
-
-
-def get_pretrained_cfg(model: str, tag: str):
-    if model not in _PRETRAINED:
-        return {}
-    model_pretrained = _PRETRAINED[model]
-    return model_pretrained.get(tag.lower(), {})
-
-
-def get_pretrained_url(model: str, tag: str):
-    cfg = get_pretrained_cfg(model, tag)
-    return cfg.get('url', '')
-
-
-def download_pretrained_from_url(
-        url: str,
-        cache_dir: Union[str, None] = None,
-):
-    if not cache_dir:
-        cache_dir = os.path.expanduser(CACHE_DIR)
-    os.makedirs(cache_dir, exist_ok=True)
-    filename = os.path.basename(url)
-
-    if 'openaipublic' in url:
-        expected_sha256 = url.split("/")[-2]
-    elif 'mlfoundations' in url:
-        expected_sha256 = os.path.splitext(filename)[0].split("-")[-1]
-    else:
-        expected_sha256 = ''
-
-    download_target = os.path.join(cache_dir, filename)
-
-    if os.path.exists(download_target) and not os.path.isfile(download_target):
-        raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-    if os.path.isfile(download_target):
-        if expected_sha256:
-            if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
-                return download_target
-            else:
-                warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
-        else:
-            return download_target
-
-    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
-        with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop:
-            while True:
-                buffer = source.read(8192)
-                if not buffer:
-                    break
-
-                output.write(buffer)
-                loop.update(len(buffer))
-
-    if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
-        raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
-
-    return download_target
-
-
-def has_hf_hub(necessary=False):
-    if not _has_hf_hub and necessary:
-        # if no HF Hub module installed, and it is necessary to continue, raise error
-        raise RuntimeError(
-            'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
-    return _has_hf_hub
-
-
-def download_pretrained_from_hf(
-        model_id: str,
-        filename: str = 'open_clip_pytorch_model.bin',
-        revision=None,
-        cache_dir: Union[str, None] = None,
-):
-    has_hf_hub(True)
-    cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir)
-    return cached_file
-
-
-def download_pretrained(
-        cfg: Dict,
-        force_hf_hub: bool = False,
-        cache_dir: Union[str, None] = None,
-):
-    target = ''
-    if not cfg:
-        return target
-
-    download_url = cfg.get('url', '')
-    download_hf_hub = cfg.get('hf_hub', '')
-    if download_hf_hub and force_hf_hub:
-        # use HF hub even if url exists
-        download_url = ''
-
-    if download_url:
-        target = download_pretrained_from_url(download_url, cache_dir=cache_dir)
-    elif download_hf_hub:
-        has_hf_hub(True)
-        # we assume the hf_hub entries in pretrained config combine model_id + filename in
-        # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and
-        # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'.
-        model_id, filename = os.path.split(download_hf_hub)
-        if filename:
-            target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir)
-        else:
-            target = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
-
-    return target
diff --git a/spaces/tomofi/MMOCR/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py b/spaces/tomofi/MMOCR/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
deleted file mode 100644
index 0270b05930a32c12d69817847b5419f08012c4cd..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
+++ /dev/null
@@ -1,33 +0,0 @@
-_base_ = [
-    '../../_base_/schedules/schedule_sgd_1200e.py',
-    '../../_base_/default_runtime.py',
-    '../../_base_/det_models/textsnake_r50_fpn_unet.py',
-    '../../_base_/det_datasets/ctw1500.py',
-    '../../_base_/det_pipelines/textsnake_pipeline.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py
deleted file mode 100644
index 873d59844f4b487a32186b0c6fd5ffea6459b373..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py
+++ /dev/null
@@ -1,105 +0,0 @@
-_base_ = [
-    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
-]
-
-# model settings
-model = dict(
-    type='CornerNet',
-    backbone=dict(
-        type='HourglassNet',
-        downsample_times=5,
-        num_stacks=2,
-        stage_channels=[256, 256, 384, 384, 384, 512],
-        stage_blocks=[2, 2, 2, 2, 2, 4],
-        norm_cfg=dict(type='BN', requires_grad=True)),
-    neck=None,
-    bbox_head=dict(
-        type='CornerHead',
-        num_classes=80,
-        in_channels=256,
-        num_feat_levels=2,
-        corner_emb_channels=1,
-        loss_heatmap=dict(
-            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
-        loss_embedding=dict(
-            type='AssociativeEmbeddingLoss',
-            pull_weight=0.10,
-            push_weight=0.10),
-        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)),
-    # training and testing settings
-    train_cfg=None,
-    test_cfg=dict(
-        corner_topk=100,
-        local_maximum_kernel=3,
-        distance_threshold=0.5,
-        score_thr=0.05,
-        max_per_img=100,
-        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
-# data settings
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PhotoMetricDistortion',
-        brightness_delta=32,
-        contrast_range=(0.5, 1.5),
-        saturation_range=(0.5, 1.5),
-        hue_delta=18),
-    dict(
-        type='RandomCenterCropPad',
-        crop_size=(511, 511),
-        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
-        test_mode=False,
-        test_pad_mode=None,
-        **img_norm_cfg),
-    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(
-        type='MultiScaleFlipAug',
-        scale_factor=1.0,
-        flip=True,
-        transforms=[
-            dict(type='Resize'),
-            dict(
-                type='RandomCenterCropPad',
-                crop_size=None,
-                ratios=None,
-                border=None,
-                test_mode=True,
-                test_pad_mode=['logical_or', 127],
-                **img_norm_cfg),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
-                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
-        ])
-]
-data = dict(
-    samples_per_gpu=3,
-    workers_per_gpu=3,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='Adam', lr=0.0005)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[180])
-runner = dict(type='EpochBasedRunner', max_epochs=210)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
deleted file mode 100644
index 2d2816c2dee68b60376e67e78e9fba277da826c0..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
deleted file mode 100644
index f77adba2f150f62900571f5f32b2083ee53b7003..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py
deleted file mode 100644
index bb7f0a630b9f2e9263183e003c288a33eb972e71..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './rpn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/scnet/scnet_r101_fpn_20e_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/scnet/scnet_r101_fpn_20e_coco.py
deleted file mode 100644
index cef0668ad8f1b767db0dc8deeb688d67005af1e4..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/scnet/scnet_r101_fpn_20e_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './scnet_r50_fpn_20e_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/trttung1610/musicgen/tests/quantization/test_vq.py b/spaces/trttung1610/musicgen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
-    def test_rvq(self):
-        x = torch.randn(1, 16, 2048)
-        vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
-        res = vq(x, 1.)
-        assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/ucalyptus/PTI/torch_utils/ops/fma.py b/spaces/ucalyptus/PTI/torch_utils/ops/fma.py
deleted file mode 100644
index 2eeac58a626c49231e04122b93e321ada954c5d3..0000000000000000000000000000000000000000
--- a/spaces/ucalyptus/PTI/torch_utils/ops/fma.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
-
-import torch
-
-#----------------------------------------------------------------------------
-
-def fma(a, b, c): # => a * b + c
-    return _FusedMultiplyAdd.apply(a, b, c)
-
-#----------------------------------------------------------------------------
-
-class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
-    @staticmethod
-    def forward(ctx, a, b, c): # pylint: disable=arguments-differ
-        out = torch.addcmul(c, a, b)
-        ctx.save_for_backward(a, b)
-        ctx.c_shape = c.shape
-        return out
-
-    @staticmethod
-    def backward(ctx, dout): # pylint: disable=arguments-differ
-        a, b = ctx.saved_tensors
-        c_shape = ctx.c_shape
-        da = None
-        db = None
-        dc = None
-
-        if ctx.needs_input_grad[0]:
-            da = _unbroadcast(dout * b, a.shape)
-
-        if ctx.needs_input_grad[1]:
-            db = _unbroadcast(dout * a, b.shape)
-
-        if ctx.needs_input_grad[2]:
-            dc = _unbroadcast(dout, c_shape)
-
-        return da, db, dc
-
-#----------------------------------------------------------------------------
-
-def _unbroadcast(x, shape):
-    extra_dims = x.ndim - len(shape)
-    assert extra_dims >= 0
-    dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
-    if len(dim):
-        x = x.sum(dim=dim, keepdim=True)
-    if extra_dims:
-        x = x.reshape(-1, *x.shape[extra_dims+1:])
-    assert x.shape == shape
-    return x
-
-#----------------------------------------------------------------------------
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/AutoPlay Media Studio 8.5.3.0 With Crack Download [Latest] [REPACK].md b/spaces/usbethFlerru/sovits-modelsV2/example/AutoPlay Media Studio 8.5.3.0 With Crack Download [Latest] [REPACK].md
deleted file mode 100644
index 2feec88990492d22a12a0a2aed01ba220e586f77..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/AutoPlay Media Studio 8.5.3.0 With Crack Download [Latest] [REPACK].md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>autorun media studio has a very rich tool with features and attractive user interface has caused users to use software to create autorun compact discs as multimedia. this software has pre-designed designs so that users can create beautiful yet professional autographs by patterning and even using these designs. the use of multimedia cds and dvds is very common among users. companies all use multimedia cds to create a resume of their work to attract customers. of course, there are many softwares that should be used in making these compact discs. it is not only companies that design multimedia cds to introduce their services, ordinary people also design and build autoresponders for their compact discs for different purposes.</p>
-<h2>AutoPlay Media Studio 8.5.3.0 With Crack Download [Latest]</h2><br /><p><b><b>Download File</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://urlcod.com/2uyU2M">https://urlcod.com/2uyU2M</a></b></p><br /><br />
-<p>autorun media studio is a development tool that will allow you to easily integrate your existing audio, video, images, text, flash, web sites, and scripts by simply dragging and dropping the media files directly into your project. make a great first impression with a professional autorun cd using autorun media studio. this easy to use software tool allows you to quickly create your own custom autorun menus, interactive presentations, and custom applications in just minutes. use your existing content such as images, music, video, flash, text, and more, and simply drag n drop your way to amazing autorun menus.</p>
-<p>autoplay media studio is a very rich tool with features and attractive user interface has caused users to use software to create autorun compact discs as multimedia. this software has pre-designed designs so that users can create beautiful yet professional autographs by patterning and even using these designs. the use of multimedia cds and dvds is very common among users. companies all use multimedia cds to create a resume of their work to attract customers. of course, there are many softwares that should be used in making these compact discs. it is not only companies that design multimedia cds to introduce their services, ordinary people also design and build autoresponders for their compact discs for different purposes.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/scripts_postprocessing.py b/spaces/user238921933/stable-diffusion-webui/modules/scripts_postprocessing.py
deleted file mode 100644
index 68f588f664c679f2e61c10823295d38301cd9e44..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/scripts_postprocessing.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import os
-import gradio as gr
-
-from modules import errors, shared
-
-
-class PostprocessedImage:
-    def __init__(self, image):
-        self.image = image
-        self.info = {}
-
-
-class ScriptPostprocessing:
-    filename = None
-    controls = None
-    args_from = None
-    args_to = None
-
-    order = 1000
-    """scripts will be ordred by this value in postprocessing UI"""
-
-    name = None
-    """this function should return the title of the script."""
-
-    group = None
-    """A gr.Group component that has all script's UI inside it"""
-
-    def ui(self):
-        """
-        This function should create gradio UI elements. See https://gradio.app/docs/#components
-        The return value should be a dictionary that maps parameter names to components used in processing.
-        Values of those components will be passed to process() function.
-        """
-
-        pass
-
-    def process(self, pp: PostprocessedImage, **args):
-        """
-        This function is called to postprocess the image.
-        args contains a dictionary with all values returned by components from ui()
-        """
-
-        pass
-
-    def image_changed(self):
-        pass
-
-
-
-
-def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
-    try:
-        res = func(*args, **kwargs)
-        return res
-    except Exception as e:
-        errors.display(e, f"calling {filename}/{funcname}")
-
-    return default
-
-
-class ScriptPostprocessingRunner:
-    def __init__(self):
-        self.scripts = None
-        self.ui_created = False
-
-    def initialize_scripts(self, scripts_data):
-        self.scripts = []
-
-        for script_class, path, basedir, script_module in scripts_data:
-            script: ScriptPostprocessing = script_class()
-            script.filename = path
-
-            if script.name == "Simple Upscale":
-                continue
-
-            self.scripts.append(script)
-
-    def create_script_ui(self, script, inputs):
-        script.args_from = len(inputs)
-        script.args_to = len(inputs)
-
-        script.controls = wrap_call(script.ui, script.filename, "ui")
-
-        for control in script.controls.values():
-            control.custom_script_source = os.path.basename(script.filename)
-
-        inputs += list(script.controls.values())
-        script.args_to = len(inputs)
-
-    def scripts_in_preferred_order(self):
-        if self.scripts is None:
-            import modules.scripts
-            self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
-
-        scripts_order = shared.opts.postprocessing_operation_order
-
-        def script_score(name):
-            for i, possible_match in enumerate(scripts_order):
-                if possible_match == name:
-                    return i
-
-            return len(self.scripts)
-
-        script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
-
-        return sorted(self.scripts, key=lambda x: script_scores[x.name])
-
-    def setup_ui(self):
-        inputs = []
-
-        for script in self.scripts_in_preferred_order():
-            with gr.Box() as group:
-                self.create_script_ui(script, inputs)
-
-            script.group = group
-
-        self.ui_created = True
-        return inputs
-
-    def run(self, pp: PostprocessedImage, args):
-        for script in self.scripts_in_preferred_order():
-            shared.state.job = script.name
-
-            script_args = args[script.args_from:script.args_to]
-
-            process_args = {}
-            for (name, component), value in zip(script.controls.items(), script_args):
-                process_args[name] = value
-
-            script.process(pp, **process_args)
-
-    def create_args_for_run(self, scripts_args):
-        if not self.ui_created:
-            with gr.Blocks(analytics_enabled=False):
-                self.setup_ui()
-
-        scripts = self.scripts_in_preferred_order()
-        args = [None] * max([x.args_to for x in scripts])
-
-        for script in scripts:
-            script_args_dict = scripts_args.get(script.name, None)
-            if script_args_dict is not None:
-
-                for i, name in enumerate(script.controls):
-                    args[script.args_from + i] = script_args_dict.get(name, None)
-
-        return args
-
-    def image_changed(self):
-        for script in self.scripts_in_preferred_order():
-            script.image_changed()
-
diff --git a/spaces/victorisgeek/SwapFace2Pon/upscaler/RealESRGAN/model.py b/spaces/victorisgeek/SwapFace2Pon/upscaler/RealESRGAN/model.py
deleted file mode 100644
index 0efe0a734517c8b591850aadc13440f6785cd0b0..0000000000000000000000000000000000000000
--- a/spaces/victorisgeek/SwapFace2Pon/upscaler/RealESRGAN/model.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import os
-import torch
-from torch.nn import functional as F
-from PIL import Image
-import numpy as np
-import cv2
-
-from .rrdbnet_arch import RRDBNet
-from .utils import pad_reflect, split_image_into_overlapping_patches, stich_together, \
-                   unpad_image
-
-
-HF_MODELS = {
-    2: dict(
-        repo_id='sberbank-ai/Real-ESRGAN',
-        filename='RealESRGAN_x2.pth',
-    ),
-    4: dict(
-        repo_id='sberbank-ai/Real-ESRGAN',
-        filename='RealESRGAN_x4.pth',
-    ),
-    8: dict(
-        repo_id='sberbank-ai/Real-ESRGAN',
-        filename='RealESRGAN_x8.pth',
-    ),
-}
-
-
-class RealESRGAN:
-    def __init__(self, device, scale=4):
-        self.device = device
-        self.scale = scale
-        self.model = RRDBNet(
-            num_in_ch=3, num_out_ch=3, num_feat=64,
-            num_block=23, num_grow_ch=32, scale=scale
-        )
-
-    def load_weights(self, model_path, download=True):
-        if not os.path.exists(model_path) and download:
-            from huggingface_hub import hf_hub_url, cached_download
-            assert self.scale in [2,4,8], 'You can download models only with scales: 2, 4, 8'
-            config = HF_MODELS[self.scale]
-            cache_dir = os.path.dirname(model_path)
-            local_filename = os.path.basename(model_path)
-            config_file_url = hf_hub_url(repo_id=config['repo_id'], filename=config['filename'])
-            cached_download(config_file_url, cache_dir=cache_dir, force_filename=local_filename)
-            print('Weights downloaded to:', os.path.join(cache_dir, local_filename))
-
-        loadnet = torch.load(model_path)
-        if 'params' in loadnet:
-            self.model.load_state_dict(loadnet['params'], strict=True)
-        elif 'params_ema' in loadnet:
-            self.model.load_state_dict(loadnet['params_ema'], strict=True)
-        else:
-            self.model.load_state_dict(loadnet, strict=True)
-        self.model.eval()
-        self.model.to(self.device)
-
-    @torch.cuda.amp.autocast()
-    def predict(self, lr_image, batch_size=4, patches_size=192,
-                padding=24, pad_size=15):
-        scale = self.scale
-        device = self.device
-        lr_image = np.array(lr_image)
-        lr_image = pad_reflect(lr_image, pad_size)
-
-        patches, p_shape = split_image_into_overlapping_patches(
-            lr_image, patch_size=patches_size, padding_size=padding
-        )
-        img = torch.FloatTensor(patches/255).permute((0,3,1,2)).to(device).detach()
-
-        with torch.no_grad():
-            res = self.model(img[0:batch_size])
-            for i in range(batch_size, img.shape[0], batch_size):
-                res = torch.cat((res, self.model(img[i:i+batch_size])), 0)
-
-        sr_image = res.permute((0,2,3,1)).clamp_(0, 1).cpu()
-        np_sr_image = sr_image.numpy()
-
-        padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
-        scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
-        np_sr_image = stich_together(
-            np_sr_image, padded_image_shape=padded_size_scaled,
-            target_shape=scaled_image_shape, padding_size=padding * scale
-        )
-        sr_img = (np_sr_image*255).astype(np.uint8)
-        sr_img = unpad_image(sr_img, pad_size*scale)
-        #sr_img = Image.fromarray(sr_img)
-
-        return sr_img
\ No newline at end of file
diff --git a/spaces/vietvd/image-enhance/models/network_swinir.py b/spaces/vietvd/image-enhance/models/network_swinir.py
deleted file mode 100644
index 461fb354ce5a7614d9ffbfcad4d32a2811134ae4..0000000000000000000000000000000000000000
--- a/spaces/vietvd/image-enhance/models/network_swinir.py
+++ /dev/null
@@ -1,867 +0,0 @@
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class SwinIR(nn.Module):
-    r""" SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(SwinIR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            if self.upscale == 4:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-        
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            if self.upscale == 4:
-                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = SwinIR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/spaces/vishnu0001/text2mesh/app.py b/spaces/vishnu0001/text2mesh/app.py
deleted file mode 100644
index 698832d3dc41ef4d5c24d04d49ffce0ff169936b..0000000000000000000000000000000000000000
--- a/spaces/vishnu0001/text2mesh/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-import gradio as gr
-
-import os
-from shap_e.diffusion.sample import sample_latents
-from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
-from shap_e.models.download import load_model, load_config
-from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-from shap_e.util.notebooks import decode_latent_mesh
-
-
-
-
-xm = load_model('transmitter', device=device)
-model = load_model('text300M', device=device)
-diffusion = diffusion_from_config(load_config('diffusion'))
-
-
-batch_size = 4
-guidance_scale = 15.0
-
-def shape_e(text):
-  latents = sample_latents(
-    batch_size=batch_size,
-    model=model,
-    diffusion=diffusion,
-    guidance_scale=guidance_scale,
-    model_kwargs=dict(texts=[text] * batch_size),
-    progress=True,
-    clip_denoised=True,
-    use_fp16=True,
-    use_karras=True,
-    karras_steps=64,
-    sigma_min=1e-3,
-    sigma_max=160,
-    s_churn=0)
-  
-  for i, latent in enumerate(latents):
-    t = decode_latent_mesh(xm, latent).tri_mesh()
-    with open(f'output/example_mesh_{i}.obj', 'w') as f:
-        t.write_obj(f)
-
-  output_dir='zipfile'
-  folder_path='output'
-  output_path = os.path.join(output_dir, "output.zip")
-  with zipfile.ZipFile(output_path, 'w') as zipf:
-      for root, _, files in os.walk(folder_path):
-          for file_name in files:
-              file_path = os.path.join(root, file_name)
-              zipf.write(file_path, os.path.relpath(file_path, folder_path))
-
-
-  return output_path                  
-
-
-
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=shape_e,
-    inputs=gr.inputs.Textbox(label="Text prompt"),    
-   
-    outputs=gr.outputs.File(label="Generated Mesh OBJ"),
-    title="Shape_e text to 3d model",
-    description="Generate 3D meshes from text input using Shape_e",
-    server_port=7860  # Set the port number as desired
-)
-
-# Launch the Gradio interface
-iface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/volhack/vits-uma-genshin-honkai/Docker/Dockerfile b/spaces/volhack/vits-uma-genshin-honkai/Docker/Dockerfile
deleted file mode 100644
index 4d39cdf02a2ec151686cc1d61234bf723068fed8..0000000000000000000000000000000000000000
--- a/spaces/volhack/vits-uma-genshin-honkai/Docker/Dockerfile
+++ /dev/null
@@ -1,12 +0,0 @@
-FROM python:3.9-bullseye
-VOLUME ["/app"]
-WORKDIR /app
-# Set apt to Chinese mirror
-RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list
-RUN apt-get update && apt-get -y install cmake git
-RUN git clone https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai
-WORKDIR /app/vits-uma-genshin-honkai
-RUN sed -i "s/\.launch()/\.launch(server_name=\"0.0.0.0\")/" /app/vits-uma-genshin-honkai/app.py
-ADD vits.sh /app/vits.sh
-EXPOSE 7860
-ENTRYPOINT [ "/app/vits.sh" ]
\ No newline at end of file
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/fileio/handlers/base.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/fileio/handlers/base.py
deleted file mode 100644
index 288878bc57282fbb2f12b32290152ca8e9d3cab0..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/fileio/handlers/base.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import ABCMeta, abstractmethod
-
-
-class BaseFileHandler(metaclass=ABCMeta):
-    # `str_like` is a flag to indicate whether the type of file object is
-    # str-like object or bytes-like object. Pickle only processes bytes-like
-    # objects but json only processes str-like object. If it is str-like
-    # object, `StringIO` will be used to process the buffer.
-    str_like = True
-
-    @abstractmethod
-    def load_from_fileobj(self, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        pass
-
-    @abstractmethod
-    def dump_to_str(self, obj, **kwargs):
-        pass
-
-    def load_from_path(self, filepath, mode='r', **kwargs):
-        with open(filepath, mode) as f:
-            return self.load_from_fileobj(f, **kwargs)
-
-    def dump_to_path(self, obj, filepath, mode='w', **kwargs):
-        with open(filepath, mode) as f:
-            self.dump_to_fileobj(obj, f, **kwargs)
diff --git a/spaces/vumichien/canvas_controlnet/ldm/modules/image_degradation/utils_image.py b/spaces/vumichien/canvas_controlnet/ldm/modules/image_degradation/utils_image.py
deleted file mode 100644
index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/ldm/modules/image_degradation/utils_image.py
+++ /dev/null
@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/spaces/weidacn/deepdanbooru/deepdanbooru/commands/train_project.py b/spaces/weidacn/deepdanbooru/deepdanbooru/commands/train_project.py
deleted file mode 100644
index 585f0c9154b07adca740cc76f4c9f86208fea272..0000000000000000000000000000000000000000
--- a/spaces/weidacn/deepdanbooru/deepdanbooru/commands/train_project.py
+++ /dev/null
@@ -1,319 +0,0 @@
-import os
-import random
-from sqlite3.dbapi2 import NotSupportedError
-import time
-import datetime
-
-import tensorflow as tf
-
-import deepdanbooru as dd
-
-
-def export_model_as_float32(temporary_model, checkpoint_path, export_path):
-    """
-    Hotfix for exporting mixed precision model as float32.
-    """
-    checkpoint = tf.train.Checkpoint(model=temporary_model)
-
-    manager = tf.train.CheckpointManager(
-        checkpoint=checkpoint, directory=checkpoint_path, max_to_keep=3
-    )
-
-    checkpoint.restore(manager.latest_checkpoint).expect_partial()
-
-    temporary_model.save(export_path, include_optimizer=False)
-
-
-def train_project(project_path, source_model):
-    project_context_path = os.path.join(project_path, "project.json")
-    project_context = dd.io.deserialize_from_json(project_context_path)
-
-    width = project_context["image_width"]
-    height = project_context["image_height"]
-    database_path = project_context["database_path"]
-    minimum_tag_count = project_context["minimum_tag_count"]
-    model_type = project_context["model"]
-    optimizer_type = project_context["optimizer"]
-    learning_rate = (
-        project_context["learning_rate"]
-        if "learning_rate" in project_context
-        else 0.001
-    )
-    learning_rates = (
-        project_context["learning_rates"]
-        if "learning_rates" in project_context
-        else None
-    )
-    minibatch_size = project_context["minibatch_size"]
-    epoch_count = project_context["epoch_count"]
-    export_model_per_epoch = (
-        project_context["export_model_per_epoch"]
-        if "export_model_per_epoch" in project_context
-        else 10
-    )
-    checkpoint_frequency_mb = project_context["checkpoint_frequency_mb"]
-    console_logging_frequency_mb = project_context["console_logging_frequency_mb"]
-    rotation_range = project_context["rotation_range"]
-    scale_range = project_context["scale_range"]
-    shift_range = project_context["shift_range"]
-    use_mixed_precision = (
-        project_context["mixed_precision"]
-        if "mixed_precision" in project_context
-        else False
-    )
-    loss_type = (
-        project_context["loss"] if "loss" in project_context else "binary_crossentropy"
-    )
-    checkpoint_path = os.path.join(project_path, "checkpoints")
-
-    # disable PNG warning
-    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
-    # tf.logging.set_verbosity(tf.logging.ERROR)
-
-    # tf.keras.backend.set_epsilon(1e-6)
-    # tf.keras.mixed_precision.experimental.set_policy('infer_float32_vars')
-    # tf.config.gpu.set_per_process_memory_growth(True)
-
-    if optimizer_type == "adam":
-        optimizer = tf.optimizers.Adam(learning_rate)
-        print("Using Adam optimizer ... ")
-    elif optimizer_type == "sgd":
-        optimizer = tf.optimizers.SGD(learning_rate, momentum=0.9, nesterov=True)
-        print("Using SGD optimizer ... ")
-    elif optimizer_type == "rmsprop":
-        optimizer = tf.optimizers.RMSprop(learning_rate)
-        print("Using RMSprop optimizer ... ")
-    else:
-        raise Exception(f"Not supported optimizer : {optimizer_type}")
-
-    if use_mixed_precision:
-        optimizer = tf.keras.mixed_precision.LossScaleOptimizer(optimizer)
-        print("Optimizer is changed to LossScaleOptimizer.")
-
-    if model_type == "resnet_152":
-        model_delegate = dd.model.resnet.create_resnet_152
-    elif model_type == "resnet_custom_v1":
-        model_delegate = dd.model.resnet.create_resnet_custom_v1
-    elif model_type == "resnet_custom_v2":
-        model_delegate = dd.model.resnet.create_resnet_custom_v2
-    elif model_type == "resnet_custom_v3":
-        model_delegate = dd.model.resnet.create_resnet_custom_v3
-    elif model_type == "resnet_custom_v4":
-        model_delegate = dd.model.resnet.create_resnet_custom_v4
-    else:
-        raise Exception(f"Not supported model : {model_type}")
-
-    print("Loading tags ... ")
-    tags = dd.project.load_tags_from_project(project_path)
-    output_dim = len(tags)
-
-    print(f"Creating model ({model_type}) ... ")
-    # tf.keras.backend.set_learning_phase(1)
-
-    if source_model:
-        model = tf.keras.models.load_model(source_model)
-        print(
-            f"Model : {model.input_shape} -> {model.output_shape} (loaded from {source_model})"
-        )
-    else:
-        if use_mixed_precision:
-            policy = tf.keras.mixed_precision.Policy("mixed_float16")
-            tf.keras.mixed_precision.set_global_policy(policy)
-
-        inputs = tf.keras.Input(shape=(height, width, 3), dtype=tf.float32)  # HWC
-        ouputs = model_delegate(inputs, output_dim)
-        model = tf.keras.Model(inputs=inputs, outputs=ouputs, name=model_type)
-
-        if use_mixed_precision:
-            policy = tf.keras.mixed_precision.Policy("float32")
-            tf.keras.mixed_precision.set_global_policy(policy)
-
-            inputs_float32 = tf.keras.Input(
-                shape=(height, width, 3), dtype=tf.float32
-            )  # HWC
-            ouputs_float32 = model_delegate(inputs_float32, output_dim)
-            model_float32 = tf.keras.Model(
-                inputs=inputs_float32, outputs=ouputs_float32, name=model_type
-            )
-
-            print("float32 model is created.")
-
-        print(f"Model : {model.input_shape} -> {model.output_shape}")
-
-    if loss_type == "binary_crossentropy":
-        loss = loss = tf.keras.losses.BinaryCrossentropy()
-    elif loss_type == "focal_loss":
-        loss = dd.model.losses.focal_loss()
-    else:
-        raise NotSupportedError(f"Loss type '{loss_type}' is not supported.")
-    print(f"Using loss : {loss_type}")
-
-    model.compile(
-        optimizer=optimizer,
-        loss=loss,
-        metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall()],
-    )
-
-    print(f"Loading database ... ")
-    image_records = dd.data.load_image_records(database_path, minimum_tag_count)
-
-    # Checkpoint variables
-    used_epoch = tf.Variable(0, dtype=tf.int64)
-    used_minibatch = tf.Variable(0, dtype=tf.int64)
-    used_sample = tf.Variable(0, dtype=tf.int64)
-    offset = tf.Variable(0, dtype=tf.int64)
-    random_seed = tf.Variable(0, dtype=tf.int64)
-
-    checkpoint = tf.train.Checkpoint(
-        optimizer=optimizer,
-        model=model,
-        used_epoch=used_epoch,
-        used_minibatch=used_minibatch,
-        used_sample=used_sample,
-        offset=offset,
-        random_seed=random_seed,
-    )
-
-    manager = tf.train.CheckpointManager(
-        checkpoint=checkpoint, directory=checkpoint_path, max_to_keep=3
-    )
-
-    if manager.latest_checkpoint:
-        print(f"Checkpoint exists. Continuing training ... ({datetime.datetime.now()})")
-        checkpoint.restore(manager.latest_checkpoint)
-        print(
-            f"used_epoch={int(used_epoch)}, used_minibatch={int(used_minibatch)}, used_sample={int(used_sample)}, offset={int(offset)}, random_seed={int(random_seed)}"
-        )
-    else:
-        print(f"No checkpoint. Starting new training ... ({datetime.datetime.now()})")
-
-    epoch_size = len(image_records)
-    slice_size = minibatch_size * checkpoint_frequency_mb
-    loss_sum = 0.0
-    loss_count = 0
-    used_sample_sum = 0
-    last_time = time.time()
-
-    while int(used_epoch) < epoch_count:
-        print(f"Shuffling samples (epoch {int(used_epoch)}) ... ")
-        epoch_random = random.Random(int(random_seed))
-        epoch_random.shuffle(image_records)
-
-        # Udpate learning rate
-        if learning_rates:
-            for learning_rate_per_epoch in learning_rates:
-                if learning_rate_per_epoch["used_epoch"] <= int(used_epoch):
-                    learning_rate = learning_rate_per_epoch["learning_rate"]
-        print(f"Trying to change learning rate to {learning_rate} ...")
-        optimizer.learning_rate.assign(learning_rate)
-        print(f"Learning rate is changed to {optimizer.learning_rate} ...")
-
-        while int(offset) < epoch_size:
-            image_records_slice = image_records[
-                int(offset) : min(int(offset) + slice_size, epoch_size)
-            ]
-
-            image_paths = [image_record[0] for image_record in image_records_slice]
-            tag_strings = [image_record[1] for image_record in image_records_slice]
-
-            dataset_wrapper = dd.data.DatasetWrapper(
-                (image_paths, tag_strings),
-                tags,
-                width,
-                height,
-                scale_range=scale_range,
-                rotation_range=rotation_range,
-                shift_range=shift_range,
-            )
-            dataset = dataset_wrapper.get_dataset(minibatch_size)
-
-            for (x_train, y_train) in dataset:
-                sample_count = x_train.shape[0]
-
-                step_result = model.train_on_batch(
-                    x_train, y_train, reset_metrics=False
-                )
-
-                used_minibatch.assign_add(1)
-                used_sample.assign_add(sample_count)
-                used_sample_sum += sample_count
-                loss_sum += step_result[0]
-                loss_count += 1
-
-                if int(used_minibatch) % console_logging_frequency_mb == 0:
-                    # calculate logging informations
-                    current_time = time.time()
-                    delta_time = current_time - last_time
-                    step_metric_precision = step_result[1]
-                    step_metric_recall = step_result[2]
-                    if step_metric_precision + step_metric_recall > 0.0:
-                        step_metric_f1_score = (
-                            2.0
-                            * (step_metric_precision * step_metric_recall)
-                            / (step_metric_precision + step_metric_recall)
-                        )
-                    else:
-                        step_metric_f1_score = 0.0
-                    average_loss = loss_sum / float(loss_count)
-                    samples_per_seconds = float(used_sample_sum) / max(
-                        delta_time, 0.001
-                    )
-                    progress = (
-                        float(int(used_sample))
-                        / float(epoch_size * epoch_count)
-                        * 100.0
-                    )
-                    remain_seconds = float(
-                        epoch_size * epoch_count - int(used_sample)
-                    ) / max(samples_per_seconds, 0.001)
-                    eta_datetime = datetime.datetime.now() + datetime.timedelta(
-                        seconds=remain_seconds
-                    )
-                    eta_datetime_string = eta_datetime.strftime("%Y-%m-%d %H:%M:%S")
-                    print(
-                        f"Epoch[{int(used_epoch)}] Loss={average_loss:.6f}, P={step_metric_precision:.6f}, R={step_metric_recall:.6f}, F1={step_metric_f1_score:.6f}, Speed = {samples_per_seconds:.1f} samples/s, {progress:.2f} %, ETA = {eta_datetime_string}"
-                    )
-
-                    # reset for next logging
-                    model.reset_metrics()
-                    loss_sum = 0.0
-                    loss_count = 0
-                    used_sample_sum = 0
-                    last_time = current_time
-
-            offset.assign_add(slice_size)
-            print(f"Saving checkpoint ... ({datetime.datetime.now()})")
-            manager.save()
-
-        used_epoch.assign_add(1)
-        random_seed.assign_add(1)
-        offset.assign(0)
-
-        if export_model_per_epoch == 0 or int(used_epoch) % export_model_per_epoch == 0:
-            print(f"Saving model ... (per epoch {export_model_per_epoch})")
-            export_path = os.path.join(
-                project_path, f"model-{model_type}.h5.e{int(used_epoch)}"
-            )
-            model.save(export_path, include_optimizer=False, save_format="h5")
-
-            if use_mixed_precision:
-                export_model_as_float32(
-                    model_float32, checkpoint_path, export_path + ".float32.h5"
-                )
-
-    print("Saving model ...")
-    model_path = os.path.join(project_path, f"model-{model_type}.h5")
-
-    # tf.keras.experimental.export_saved_model throw exception now
-    # see https://github.com/tensorflow/tensorflow/issues/27112
-    model.save(model_path, include_optimizer=False)
-
-    if use_mixed_precision:
-        export_model_as_float32(
-            model_float32, checkpoint_path, model_path + ".float32.h5"
-        )
-
-    print("Training is complete.")
-    print(
-        f"used_epoch={int(used_epoch)}, used_minibatch={int(used_minibatch)}, used_sample={int(used_sample)}"
-    )
diff --git a/spaces/whgwd2023/bingo/src/pages/api/blob.ts b/spaces/whgwd2023/bingo/src/pages/api/blob.ts
deleted file mode 100644
index fecd48031916b2284b8958892196e0a1ad420421..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/pages/api/blob.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { Readable } from 'node:stream'
-import { fetch } from '@/lib/isomorphic'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { bcid } = req.query
-
-    const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
-      {
-        method: 'GET',
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referrer-Policy": "origin-when-cross-origin",
-        },
-      },
-    )
-
-    res.writeHead(200, {
-      'Content-Length': headers.get('content-length')!,
-      'Content-Type': headers.get('content-type')!,
-    })
-    // @ts-ignore
-    return Readable.fromWeb(body!).pipe(res)
-  } catch (e) {
-    console.log('Error', e)
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/wilbertpariguana/Demo-Bot/app.py b/spaces/wilbertpariguana/Demo-Bot/app.py
deleted file mode 100644
index 253a5bdb7a4f8baa8263d000c9a8153cf5023561..0000000000000000000000000000000000000000
--- a/spaces/wilbertpariguana/Demo-Bot/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-import openai
-import gradio
-
-openai.api_key = os.environ['Demo-Bot2']
-
-messages = [{"role": "system", "content": "Act as a friendly assistant that is empathetic and proactive. Unless instructed otherwise, provide your responses in the language of the question asked."}]
-
-def CustomChatGPT(user_input):
-    messages.append({"role": "user", "content": user_input})
-    response = openai.ChatCompletion.create(
-        model = "gpt-3.5-turbo",
-        messages = messages
-    )
-    ChatGPT_reply = response["choices"][0]["message"]["content"]
-    messages.append({"role": "assistant", "content": ChatGPT_reply})
-    return ChatGPT_reply
-
-demo = gradio.Interface(
-    fn=CustomChatGPT,
-    inputs=gradio.components.Textbox(label="Input :", placeholder="type your question here..."),
-    outputs=gradio.components.Textbox(label="output :"),
-    title="Wilbert's Bot v0.02",
-    description="just a simple bot powered by ChatGPT",
-    examples=[["How many moons exist in our galaxy?"], ["Cuantas lunas existen en nuestra galaxia?"]]
-)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/wuhuik/bingo/src/components/ui/input.tsx b/spaces/wuhuik/bingo/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/wuhuik/bingo/src/components/ui/voice/index.tsx b/spaces/wuhuik/bingo/src/components/ui/voice/index.tsx
deleted file mode 100644
index 4adcb632226bfced8b97092782811edf08b56569..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/components/ui/voice/index.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import './index.scss'
-
-export interface VoiceProps extends CSSPropertyRule {
-  num?: number;
-  duration?: number;
-}
-export default function Voice({ duration = 400, num = 7, ...others }) {
-  return (
-    <div className="voice-button" { ...others }>
-      {Array.from({ length: num }).map((_, index) => {
-        const randomDuration = Math.random() * 100 + duration
-        const initialDelay = Math.random() * 2 * duration
-        const initialScale = Math.sin((index + 1) * Math.PI / num)
-        return (
-          <div
-            className="voice-button-item"
-            key={index}
-            style={{
-              animationDelay: initialDelay + 'ms',
-              animationDuration: randomDuration + 'ms',
-              transform: `scale(${initialScale})`
-            }}
-          />
-        )
-      })}
-    </div>
-  )
-}
diff --git a/spaces/xdecoder/Demo/xdecoder/utils/__init__.py b/spaces/xdecoder/Demo/xdecoder/utils/__init__.py
deleted file mode 100644
index 4ca95fb0709a0af80e45d7fc35aa3eb31bac9f13..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Demo/xdecoder/utils/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .config import *
-from .misc import *
-from .box_ops import *
-from .it_contrastive import *
\ No newline at end of file
diff --git a/spaces/xelu3banh/dpt-depth01/README.md b/spaces/xelu3banh/dpt-depth01/README.md
deleted file mode 100644
index 9d940cd173077f7045abf0651c09fdf795c00c80..0000000000000000000000000000000000000000
--- a/spaces/xelu3banh/dpt-depth01/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Dpt Depth Estimation
-emoji: ⚡
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
-duplicated_from: nielsr/dpt-depth-estimation
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/burst.py b/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/burst.py
deleted file mode 100644
index 475c09e874f973a3cadbf6e5df0c5053cf3c48a2..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/burst.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os
-from .burst_helpers.burst_base import BURSTBase
-from .burst_helpers.format_converter import GroundTruthBURSTFormatToTAOFormatConverter, PredictionBURSTFormatToTAOFormatConverter
-from .. import utils
-
-
-class BURST(BURSTBase):
-    """Dataset class for TAO tracking"""
-
-    @staticmethod
-    def get_default_dataset_config():
-        tao_config = BURSTBase.get_default_dataset_config()
-        code_path = utils.get_code_path()
-
-        # e.g. 'data/gt/tsunami/exemplar_guided/'
-        tao_config['GT_FOLDER'] = os.path.join(
-            code_path, 'data/gt/burst/val/')  # Location of GT data
-        # e.g. 'data/trackers/tsunami/exemplar_guided/mask_guided/validation/'
-        tao_config['TRACKERS_FOLDER'] = os.path.join(
-            code_path, 'data/trackers/burst/class-guided/')  # Trackers location
-        # set to True or False
-        tao_config['EXEMPLAR_GUIDED'] = False
-        return tao_config
-
-    def _iou_type(self):
-        return 'mask'
-
-    def _box_or_mask_from_det(self, det):
-        return det['segmentation']
-
-    def _calculate_area_for_ann(self, ann):
-        import pycocotools.mask as cocomask
-        return cocomask.area(ann["segmentation"])
-
-    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
-        similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)
-        return similarity_scores
-
-    def _is_exemplar_guided(self):
-        exemplar_guided = self.config['EXEMPLAR_GUIDED']
-        return exemplar_guided
-
-    def _postproc_ground_truth_data(self, data):
-        return GroundTruthBURSTFormatToTAOFormatConverter(data).convert()
-
-    def _postproc_prediction_data(self, data):
-        return PredictionBURSTFormatToTAOFormatConverter(
-            self.gt_data, data,
-            exemplar_guided=self._is_exemplar_guided()).convert()
diff --git a/spaces/xuxw98/TAPA/scripts/prepare_shakespeare.py b/spaces/xuxw98/TAPA/scripts/prepare_shakespeare.py
deleted file mode 100644
index 01a4079e37019bed4221c1a245a764772d1bef4d..0000000000000000000000000000000000000000
--- a/spaces/xuxw98/TAPA/scripts/prepare_shakespeare.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Andrej Karpathy
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-import sys
-from pathlib import Path
-
-# support running without installing as a package
-wd = Path(__file__).parent.parent.resolve()
-sys.path.append(str(wd))
-
-import numpy as np
-import requests
-
-
-def prepare(destination_path: Path = Path("data/shakespeare")) -> None:
-    """Prepare the "Tiny Shakespeare" dataset."""
-    destination_path.mkdir(parents=True, exist_ok=True)
-
-    # download the tiny shakespeare dataset
-    input_file_path = destination_path / "input.txt"
-    if not input_file_path.exists():
-        data_url = "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
-        with open(input_file_path, "w") as f:
-            f.write(requests.get(data_url).text)
-
-    with open(input_file_path) as f:
-        data = f.read()
-    n = len(data)
-    train_data = data[: int(n * 0.9)]
-    val_data = data[int(n * 0.9) :]
-
-    from lit_llama import Tokenizer
-
-    Tokenizer.train(input=input_file_path, destination=destination_path, vocab_size=100)
-    tokenizer = Tokenizer(destination_path / "tokenizer.model")
-    train_ids = tokenizer.encode(train_data)
-    val_ids = tokenizer.encode(val_data)
-    print(f"train has {len(train_ids):,} tokens")
-    print(f"val has {len(val_ids):,} tokens")
-
-    # export to bin files
-    train_ids = np.array(train_ids, dtype=np.uint16)
-    val_ids = np.array(val_ids, dtype=np.uint16)
-    train_ids.tofile(destination_path / "train.bin")
-    val_ids.tofile(destination_path / "val.bin")
-
-
-if __name__ == "__main__":
-    from jsonargparse import CLI
-
-    CLI(prepare)
diff --git a/spaces/ygangang/VToonify/vtoonify/model/encoder/readme.md b/spaces/ygangang/VToonify/vtoonify/model/encoder/readme.md
deleted file mode 100644
index 5421bfe3e67b7b6cbd7baf96b741b539d65bb0fd..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/encoder/readme.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
-
-## Description   
-Official Implementation of pSp paper for both training and evaluation. The pSp method extends the StyleGAN model to 
-allow solving different image-to-image translation problems using its encoder.
-
-Fork from [https://github.com/eladrich/pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel).
-
-In VToonify, we modify pSp to accept z+ latent code.
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/generation_utils.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/generation_utils.py
deleted file mode 100644
index 31cff9749463d941fded3390ef48a998bcdc3158..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/generation_utils.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
-# Copyright (c) 2020, NVIDIA CORPORATION.  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 warnings
-
-from .generation import GenerationMixin
-
-
-class GenerationMixin(GenerationMixin):
-    # warning at import time
-    warnings.warn(
-        "Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will "
-        "be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.",
-        FutureWarning,
-    )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/integrations/integration_utils.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/integrations/integration_utils.py
deleted file mode 100644
index 10f86ee419803249e11d16bba7eb9f4a12d41b2c..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/integrations/integration_utils.py
+++ /dev/null
@@ -1,1629 +0,0 @@
-# Copyright 2020 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.
-"""
-Integrations with other Python libraries.
-"""
-import functools
-import importlib.metadata
-import importlib.util
-import json
-import numbers
-import os
-import pickle
-import shutil
-import sys
-import tempfile
-from dataclasses import asdict
-from pathlib import Path
-from typing import TYPE_CHECKING, Dict, Optional
-
-import numpy as np
-
-from .. import __version__ as version
-from ..utils import flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
-
-
-logger = logging.get_logger(__name__)
-
-if is_torch_available():
-    import torch
-
-# comet_ml requires to be imported before any ML frameworks
-_has_comet = importlib.util.find_spec("comet_ml") is not None and os.getenv("COMET_MODE", "").upper() != "DISABLED"
-if _has_comet:
-    try:
-        import comet_ml  # noqa: F401
-
-        if hasattr(comet_ml, "config") and comet_ml.config.get_config("comet.api_key"):
-            _has_comet = True
-        else:
-            if os.getenv("COMET_MODE", "").upper() != "DISABLED":
-                logger.warning("comet_ml is installed but `COMET_API_KEY` is not set.")
-            _has_comet = False
-    except (ImportError, ValueError):
-        _has_comet = False
-
-_has_neptune = (
-    importlib.util.find_spec("neptune") is not None or importlib.util.find_spec("neptune-client") is not None
-)
-if TYPE_CHECKING and _has_neptune:
-    try:
-        _neptune_version = importlib.metadata.version("neptune")
-        logger.info(f"Neptune version {_neptune_version} available.")
-    except importlib.metadata.PackageNotFoundError:
-        try:
-            _neptune_version = importlib.metadata.version("neptune-client")
-            logger.info(f"Neptune-client version {_neptune_version} available.")
-        except importlib.metadata.PackageNotFoundError:
-            _has_neptune = False
-
-from ..trainer_callback import ProgressCallback, TrainerCallback  # noqa: E402
-from ..trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy  # noqa: E402
-from ..training_args import ParallelMode  # noqa: E402
-from ..utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available  # noqa: E402
-
-
-# Integration functions:
-def is_wandb_available():
-    # any value of WANDB_DISABLED disables wandb
-    if os.getenv("WANDB_DISABLED", "").upper() in ENV_VARS_TRUE_VALUES:
-        logger.warning(
-            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the "
-            "--report_to flag to control the integrations used for logging result (for instance --report_to none)."
-        )
-        return False
-    return importlib.util.find_spec("wandb") is not None
-
-
-def is_clearml_available():
-    return importlib.util.find_spec("clearml") is not None
-
-
-def is_comet_available():
-    return _has_comet
-
-
-def is_tensorboard_available():
-    return importlib.util.find_spec("tensorboard") is not None or importlib.util.find_spec("tensorboardX") is not None
-
-
-def is_optuna_available():
-    return importlib.util.find_spec("optuna") is not None
-
-
-def is_ray_available():
-    return importlib.util.find_spec("ray") is not None
-
-
-def is_ray_tune_available():
-    if not is_ray_available():
-        return False
-    return importlib.util.find_spec("ray.tune") is not None
-
-
-def is_sigopt_available():
-    return importlib.util.find_spec("sigopt") is not None
-
-
-def is_azureml_available():
-    if importlib.util.find_spec("azureml") is None:
-        return False
-    if importlib.util.find_spec("azureml.core") is None:
-        return False
-    return importlib.util.find_spec("azureml.core.run") is not None
-
-
-def is_mlflow_available():
-    if os.getenv("DISABLE_MLFLOW_INTEGRATION", "FALSE").upper() == "TRUE":
-        return False
-    return importlib.util.find_spec("mlflow") is not None
-
-
-def is_dagshub_available():
-    return None not in [importlib.util.find_spec("dagshub"), importlib.util.find_spec("mlflow")]
-
-
-def is_neptune_available():
-    return _has_neptune
-
-
-def is_codecarbon_available():
-    return importlib.util.find_spec("codecarbon") is not None
-
-
-def is_flytekit_available():
-    return importlib.util.find_spec("flytekit") is not None
-
-
-def is_flyte_deck_standard_available():
-    if not is_flytekit_available():
-        return False
-    return importlib.util.find_spec("flytekitplugins.deck") is not None
-
-
-def hp_params(trial):
-    if is_optuna_available():
-        import optuna
-
-        if isinstance(trial, optuna.Trial):
-            return trial.params
-    if is_ray_tune_available():
-        if isinstance(trial, dict):
-            return trial
-
-    if is_sigopt_available():
-        if isinstance(trial, dict):
-            return trial
-
-    if is_wandb_available():
-        if isinstance(trial, dict):
-            return trial
-
-    raise RuntimeError(f"Unknown type for trial {trial.__class__}")
-
-
-def run_hp_search_optuna(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
-    import optuna
-
-    if trainer.args.process_index == 0:
-
-        def _objective(trial, checkpoint_dir=None):
-            checkpoint = None
-            if checkpoint_dir:
-                for subdir in os.listdir(checkpoint_dir):
-                    if subdir.startswith(PREFIX_CHECKPOINT_DIR):
-                        checkpoint = os.path.join(checkpoint_dir, subdir)
-            trainer.objective = None
-            if trainer.args.world_size > 1:
-                if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED:
-                    raise RuntimeError("only support DDP optuna HPO for ParallelMode.DISTRIBUTED currently.")
-                trainer._hp_search_setup(trial)
-                torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0)
-                trainer.train(resume_from_checkpoint=checkpoint)
-            else:
-                trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
-            # If there hasn't been any evaluation during the training loop.
-            if getattr(trainer, "objective", None) is None:
-                metrics = trainer.evaluate()
-                trainer.objective = trainer.compute_objective(metrics)
-            return trainer.objective
-
-        timeout = kwargs.pop("timeout", None)
-        n_jobs = kwargs.pop("n_jobs", 1)
-        directions = direction if isinstance(direction, list) else None
-        direction = None if directions is not None else direction
-        study = optuna.create_study(direction=direction, directions=directions, **kwargs)
-        study.optimize(_objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs)
-        if not study._is_multi_objective():
-            best_trial = study.best_trial
-            return BestRun(str(best_trial.number), best_trial.value, best_trial.params)
-        else:
-            best_trials = study.best_trials
-            return [BestRun(str(best.number), best.values, best.params) for best in best_trials]
-    else:
-        for i in range(n_trials):
-            trainer.objective = None
-            args_main_rank = list(pickle.dumps(trainer.args))
-            if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED:
-                raise RuntimeError("only support DDP optuna HPO for ParallelMode.DISTRIBUTED currently.")
-            torch.distributed.broadcast_object_list(args_main_rank, src=0)
-            args = pickle.loads(bytes(args_main_rank))
-            for key, value in asdict(args).items():
-                if key != "local_rank":
-                    setattr(trainer.args, key, value)
-            trainer.train(resume_from_checkpoint=None)
-            # If there hasn't been any evaluation during the training loop.
-            if getattr(trainer, "objective", None) is None:
-                metrics = trainer.evaluate()
-                trainer.objective = trainer.compute_objective(metrics)
-        return None
-
-
-def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
-    import ray
-
-    def _objective(trial, local_trainer, checkpoint_dir=None):
-        try:
-            from transformers.utils.notebook import NotebookProgressCallback
-
-            if local_trainer.pop_callback(NotebookProgressCallback):
-                local_trainer.add_callback(ProgressCallback)
-        except ModuleNotFoundError:
-            pass
-
-        checkpoint = None
-        if checkpoint_dir:
-            for subdir in os.listdir(checkpoint_dir):
-                if subdir.startswith(PREFIX_CHECKPOINT_DIR):
-                    checkpoint = os.path.join(checkpoint_dir, subdir)
-        local_trainer.objective = None
-        local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
-        # If there hasn't been any evaluation during the training loop.
-        if getattr(local_trainer, "objective", None) is None:
-            metrics = local_trainer.evaluate()
-            local_trainer.objective = local_trainer.compute_objective(metrics)
-            local_trainer._tune_save_checkpoint()
-            ray.tune.report(objective=local_trainer.objective, **metrics, done=True)
-
-    if not trainer._memory_tracker.skip_memory_metrics:
-        from ..trainer_utils import TrainerMemoryTracker
-
-        logger.warning(
-            "Memory tracking for your Trainer is currently "
-            "enabled. Automatically disabling the memory tracker "
-            "since the memory tracker is not serializable."
-        )
-        trainer._memory_tracker = TrainerMemoryTracker(skip_memory_metrics=True)
-
-    # The model and TensorBoard writer do not pickle so we have to remove them (if they exists)
-    # while doing the ray hp search.
-    _tb_writer = trainer.pop_callback(TensorBoardCallback)
-    trainer.model = None
-
-    # Setup default `resources_per_trial`.
-    if "resources_per_trial" not in kwargs:
-        # Default to 1 CPU and 1 GPU (if applicable) per trial.
-        kwargs["resources_per_trial"] = {"cpu": 1}
-        if trainer.args.n_gpu > 0:
-            kwargs["resources_per_trial"]["gpu"] = 1
-        resource_msg = "1 CPU" + (" and 1 GPU" if trainer.args.n_gpu > 0 else "")
-        logger.info(
-            "No `resources_per_trial` arg was passed into "
-            "`hyperparameter_search`. Setting it to a default value "
-            f"of {resource_msg} for each trial."
-        )
-    # Make sure each trainer only uses GPUs that were allocated per trial.
-    gpus_per_trial = kwargs["resources_per_trial"].get("gpu", 0)
-    trainer.args._n_gpu = gpus_per_trial
-
-    # Setup default `progress_reporter`.
-    if "progress_reporter" not in kwargs:
-        from ray.tune import CLIReporter
-
-        kwargs["progress_reporter"] = CLIReporter(metric_columns=["objective"])
-    if "keep_checkpoints_num" in kwargs and kwargs["keep_checkpoints_num"] > 0:
-        # `keep_checkpoints_num=0` would disabled checkpointing
-        trainer.use_tune_checkpoints = True
-        if kwargs["keep_checkpoints_num"] > 1:
-            logger.warning(
-                f"Currently keeping {kwargs['keep_checkpoints_num']} checkpoints for each trial. "
-                "Checkpoints are usually huge, "
-                "consider setting `keep_checkpoints_num=1`."
-            )
-    if "scheduler" in kwargs:
-        from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining
-
-        # Check if checkpointing is enabled for PopulationBasedTraining
-        if isinstance(kwargs["scheduler"], PopulationBasedTraining):
-            if not trainer.use_tune_checkpoints:
-                logger.warning(
-                    "You are using PopulationBasedTraining but you haven't enabled checkpointing. "
-                    "This means your trials will train from scratch everytime they are exploiting "
-                    "new configurations. Consider enabling checkpointing by passing "
-                    "`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`."
-                )
-
-        # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting.
-        if isinstance(
-            kwargs["scheduler"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining)
-        ) and (not trainer.args.do_eval or trainer.args.evaluation_strategy == IntervalStrategy.NO):
-            raise RuntimeError(
-                "You are using {cls} as a scheduler but you haven't enabled evaluation during training. "
-                "This means your trials will not report intermediate results to Ray Tune, and "
-                "can thus not be stopped early or used to exploit other trials parameters. "
-                "If this is what you want, do not use {cls}. If you would like to use {cls}, "
-                "make sure you pass `do_eval=True` and `evaluation_strategy='steps'` in the "
-                "Trainer `args`.".format(cls=type(kwargs["scheduler"]).__name__)
-            )
-
-    trainable = ray.tune.with_parameters(_objective, local_trainer=trainer)
-
-    @functools.wraps(trainable)
-    def dynamic_modules_import_trainable(*args, **kwargs):
-        """
-        Wrapper around `tune.with_parameters` to ensure datasets_modules are loaded on each Actor.
-
-        Without this, an ImportError will be thrown. See https://github.com/huggingface/transformers/issues/11565.
-
-        Assumes that `_objective`, defined above, is a function.
-        """
-        if is_datasets_available():
-            import datasets.load
-
-            dynamic_modules_path = os.path.join(datasets.load.init_dynamic_modules(), "__init__.py")
-            # load dynamic_modules from path
-            spec = importlib.util.spec_from_file_location("datasets_modules", dynamic_modules_path)
-            datasets_modules = importlib.util.module_from_spec(spec)
-            sys.modules[spec.name] = datasets_modules
-            spec.loader.exec_module(datasets_modules)
-        return trainable(*args, **kwargs)
-
-    # special attr set by tune.with_parameters
-    if hasattr(trainable, "__mixins__"):
-        dynamic_modules_import_trainable.__mixins__ = trainable.__mixins__
-
-    analysis = ray.tune.run(
-        dynamic_modules_import_trainable,
-        config=trainer.hp_space(None),
-        num_samples=n_trials,
-        **kwargs,
-    )
-    best_trial = analysis.get_best_trial(metric="objective", mode=direction[:3], scope=trainer.args.ray_scope)
-    best_run = BestRun(best_trial.trial_id, best_trial.last_result["objective"], best_trial.config, analysis)
-    if _tb_writer is not None:
-        trainer.add_callback(_tb_writer)
-    return best_run
-
-
-def run_hp_search_sigopt(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
-    import sigopt
-
-    if trainer.args.process_index == 0:
-        if importlib.metadata.version("sigopt") >= "8.0.0":
-            sigopt.set_project("huggingface")
-
-            experiment = sigopt.create_experiment(
-                name="huggingface-tune",
-                type="offline",
-                parameters=trainer.hp_space(None),
-                metrics=[{"name": "objective", "objective": direction, "strategy": "optimize"}],
-                parallel_bandwidth=1,
-                budget=n_trials,
-            )
-
-            logger.info(f"created experiment: https://app.sigopt.com/experiment/{experiment.id}")
-
-            for run in experiment.loop():
-                with run:
-                    trainer.objective = None
-                    if trainer.args.world_size > 1:
-                        if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED:
-                            raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.")
-                        trainer._hp_search_setup(run.run)
-                        torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0)
-                        trainer.train(resume_from_checkpoint=None)
-                    else:
-                        trainer.train(resume_from_checkpoint=None, trial=run.run)
-                    # If there hasn't been any evaluation during the training loop.
-                    if getattr(trainer, "objective", None) is None:
-                        metrics = trainer.evaluate()
-                        trainer.objective = trainer.compute_objective(metrics)
-                    run.log_metric("objective", trainer.objective)
-
-            best = list(experiment.get_best_runs())[0]
-            best_run = BestRun(best.id, best.values["objective"].value, best.assignments)
-        else:
-            from sigopt import Connection
-
-            conn = Connection()
-            proxies = kwargs.pop("proxies", None)
-            if proxies is not None:
-                conn.set_proxies(proxies)
-
-            experiment = conn.experiments().create(
-                name="huggingface-tune",
-                parameters=trainer.hp_space(None),
-                metrics=[{"name": "objective", "objective": direction, "strategy": "optimize"}],
-                parallel_bandwidth=1,
-                observation_budget=n_trials,
-                project="huggingface",
-            )
-            logger.info(f"created experiment: https://app.sigopt.com/experiment/{experiment.id}")
-
-            while experiment.progress.observation_count < experiment.observation_budget:
-                suggestion = conn.experiments(experiment.id).suggestions().create()
-                trainer.objective = None
-                if trainer.args.world_size > 1:
-                    if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED:
-                        raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.")
-                    trainer._hp_search_setup(suggestion)
-                    torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0)
-                    trainer.train(resume_from_checkpoint=None)
-                else:
-                    trainer.train(resume_from_checkpoint=None, trial=suggestion)
-                # If there hasn't been any evaluation during the training loop.
-                if getattr(trainer, "objective", None) is None:
-                    metrics = trainer.evaluate()
-                    trainer.objective = trainer.compute_objective(metrics)
-
-                values = [{"name": "objective", "value": trainer.objective}]
-                obs = conn.experiments(experiment.id).observations().create(suggestion=suggestion.id, values=values)
-                logger.info(f"[suggestion_id, observation_id]: [{suggestion.id}, {obs.id}]")
-                experiment = conn.experiments(experiment.id).fetch()
-
-            best = list(conn.experiments(experiment.id).best_assignments().fetch().iterate_pages())[0]
-            best_run = BestRun(best.id, best.value, best.assignments)
-        return best_run
-    else:
-        for i in range(n_trials):
-            trainer.objective = None
-            args_main_rank = list(pickle.dumps(trainer.args))
-            if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED:
-                raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.")
-            torch.distributed.broadcast_object_list(args_main_rank, src=0)
-            args = pickle.loads(bytes(args_main_rank))
-            for key, value in asdict(args).items():
-                if key != "local_rank":
-                    setattr(trainer.args, key, value)
-            trainer.train(resume_from_checkpoint=None)
-            # If there hasn't been any evaluation during the training loop.
-            if getattr(trainer, "objective", None) is None:
-                metrics = trainer.evaluate()
-                trainer.objective = trainer.compute_objective(metrics)
-        return None
-
-
-def run_hp_search_wandb(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
-    from ..integrations import is_wandb_available
-
-    if not is_wandb_available():
-        raise ImportError("This function needs wandb installed: `pip install wandb`")
-    import wandb
-
-    # add WandbCallback if not already added in trainer callbacks
-    reporting_to_wandb = False
-    for callback in trainer.callback_handler.callbacks:
-        if isinstance(callback, WandbCallback):
-            reporting_to_wandb = True
-            break
-    if not reporting_to_wandb:
-        trainer.add_callback(WandbCallback())
-    trainer.args.report_to = ["wandb"]
-    best_trial = {"run_id": None, "objective": None, "hyperparameters": None}
-    sweep_id = kwargs.pop("sweep_id", None)
-    project = kwargs.pop("project", None)
-    name = kwargs.pop("name", None)
-    entity = kwargs.pop("entity", None)
-    metric = kwargs.pop("metric", "eval/loss")
-
-    sweep_config = trainer.hp_space(None)
-    sweep_config["metric"]["goal"] = direction
-    sweep_config["metric"]["name"] = metric
-    if name:
-        sweep_config["name"] = name
-
-    def _objective():
-        run = wandb.run if wandb.run else wandb.init()
-        trainer.state.trial_name = run.name
-        run.config.update({"assignments": {}, "metric": metric})
-        config = wandb.config
-
-        trainer.objective = None
-
-        trainer.train(resume_from_checkpoint=None, trial=vars(config)["_items"])
-        # If there hasn't been any evaluation during the training loop.
-        if getattr(trainer, "objective", None) is None:
-            metrics = trainer.evaluate()
-            trainer.objective = trainer.compute_objective(metrics)
-            format_metrics = rewrite_logs(metrics)
-            if metric not in format_metrics:
-                logger.warning(
-                    f"Provided metric {metric} not found. This might result in unexpected sweeps charts. The available"
-                    f" metrics are {format_metrics.keys()}"
-                )
-        best_score = False
-        if best_trial["run_id"] is not None:
-            if direction == "minimize":
-                best_score = trainer.objective < best_trial["objective"]
-            elif direction == "maximize":
-                best_score = trainer.objective > best_trial["objective"]
-
-        if best_score or best_trial["run_id"] is None:
-            best_trial["run_id"] = run.id
-            best_trial["objective"] = trainer.objective
-            best_trial["hyperparameters"] = dict(config)
-
-        return trainer.objective
-
-    sweep_id = wandb.sweep(sweep_config, project=project, entity=entity) if not sweep_id else sweep_id
-    logger.info(f"wandb sweep id - {sweep_id}")
-    wandb.agent(sweep_id, function=_objective, count=n_trials)
-
-    return BestRun(best_trial["run_id"], best_trial["objective"], best_trial["hyperparameters"])
-
-
-def get_available_reporting_integrations():
-    integrations = []
-    if is_azureml_available() and not is_mlflow_available():
-        integrations.append("azure_ml")
-    if is_comet_available():
-        integrations.append("comet_ml")
-    if is_dagshub_available():
-        integrations.append("dagshub")
-    if is_mlflow_available():
-        integrations.append("mlflow")
-    if is_neptune_available():
-        integrations.append("neptune")
-    if is_tensorboard_available():
-        integrations.append("tensorboard")
-    if is_wandb_available():
-        integrations.append("wandb")
-    if is_codecarbon_available():
-        integrations.append("codecarbon")
-    if is_clearml_available():
-        integrations.append("clearml")
-    return integrations
-
-
-def rewrite_logs(d):
-    new_d = {}
-    eval_prefix = "eval_"
-    eval_prefix_len = len(eval_prefix)
-    test_prefix = "test_"
-    test_prefix_len = len(test_prefix)
-    for k, v in d.items():
-        if k.startswith(eval_prefix):
-            new_d["eval/" + k[eval_prefix_len:]] = v
-        elif k.startswith(test_prefix):
-            new_d["test/" + k[test_prefix_len:]] = v
-        else:
-            new_d["train/" + k] = v
-    return new_d
-
-
-class TensorBoardCallback(TrainerCallback):
-    """
-    A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard).
-
-    Args:
-        tb_writer (`SummaryWriter`, *optional*):
-            The writer to use. Will instantiate one if not set.
-    """
-
-    def __init__(self, tb_writer=None):
-        has_tensorboard = is_tensorboard_available()
-        if not has_tensorboard:
-            raise RuntimeError(
-                "TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or"
-                " install tensorboardX."
-            )
-        if has_tensorboard:
-            try:
-                from torch.utils.tensorboard import SummaryWriter  # noqa: F401
-
-                self._SummaryWriter = SummaryWriter
-            except ImportError:
-                try:
-                    from tensorboardX import SummaryWriter
-
-                    self._SummaryWriter = SummaryWriter
-                except ImportError:
-                    self._SummaryWriter = None
-        else:
-            self._SummaryWriter = None
-        self.tb_writer = tb_writer
-
-    def _init_summary_writer(self, args, log_dir=None):
-        log_dir = log_dir or args.logging_dir
-        if self._SummaryWriter is not None:
-            self.tb_writer = self._SummaryWriter(log_dir=log_dir)
-
-    def on_train_begin(self, args, state, control, **kwargs):
-        if not state.is_world_process_zero:
-            return
-
-        log_dir = None
-
-        if state.is_hyper_param_search:
-            trial_name = state.trial_name
-            if trial_name is not None:
-                log_dir = os.path.join(args.logging_dir, trial_name)
-
-        if self.tb_writer is None:
-            self._init_summary_writer(args, log_dir)
-
-        if self.tb_writer is not None:
-            self.tb_writer.add_text("args", args.to_json_string())
-            if "model" in kwargs:
-                model = kwargs["model"]
-                if hasattr(model, "config") and model.config is not None:
-                    model_config_json = model.config.to_json_string()
-                    self.tb_writer.add_text("model_config", model_config_json)
-
-    def on_log(self, args, state, control, logs=None, **kwargs):
-        if not state.is_world_process_zero:
-            return
-
-        if self.tb_writer is None:
-            self._init_summary_writer(args)
-
-        if self.tb_writer is not None:
-            logs = rewrite_logs(logs)
-            for k, v in logs.items():
-                if isinstance(v, (int, float)):
-                    self.tb_writer.add_scalar(k, v, state.global_step)
-                else:
-                    logger.warning(
-                        "Trainer is attempting to log a value of "
-                        f'"{v}" of type {type(v)} for key "{k}" as a scalar. '
-                        "This invocation of Tensorboard's writer.add_scalar() "
-                        "is incorrect so we dropped this attribute."
-                    )
-            self.tb_writer.flush()
-
-    def on_train_end(self, args, state, control, **kwargs):
-        if self.tb_writer:
-            self.tb_writer.close()
-            self.tb_writer = None
-
-
-class WandbCallback(TrainerCallback):
-    """
-    A [`TrainerCallback`] that logs metrics, media, model checkpoints to [Weight and Biases](https://www.wandb.com/).
-    """
-
-    def __init__(self):
-        has_wandb = is_wandb_available()
-        if not has_wandb:
-            raise RuntimeError("WandbCallback requires wandb to be installed. Run `pip install wandb`.")
-        if has_wandb:
-            import wandb
-
-            self._wandb = wandb
-        self._initialized = False
-        # log model
-        if os.getenv("WANDB_LOG_MODEL", "FALSE").upper() in ENV_VARS_TRUE_VALUES.union({"TRUE"}):
-            DeprecationWarning(
-                f"Setting `WANDB_LOG_MODEL` as {os.getenv('WANDB_LOG_MODEL')} is deprecated and will be removed in "
-                "version 5 of transformers. Use one of `'end'` or `'checkpoint'` instead."
-            )
-            logger.info(f"Setting `WANDB_LOG_MODEL` from {os.getenv('WANDB_LOG_MODEL')} to `end` instead")
-            self._log_model = "end"
-        else:
-            self._log_model = os.getenv("WANDB_LOG_MODEL", "false").lower()
-
-    def setup(self, args, state, model, **kwargs):
-        """
-        Setup the optional Weights & Biases (*wandb*) integration.
-
-        One can subclass and override this method to customize the setup if needed. Find more information
-        [here](https://docs.wandb.ai/guides/integrations/huggingface). You can also override the following environment
-        variables:
-
-        Environment:
-        - **WANDB_LOG_MODEL** (`str`, *optional*, defaults to `"false"`):
-            Whether to log model and checkpoints during training. Can be `"end"`, `"checkpoint"` or `"false"`. If set
-            to `"end"`, the model will be uploaded at the end of training. If set to `"checkpoint"`, the checkpoint
-            will be uploaded every `args.save_steps` . If set to `"false"`, the model will not be uploaded. Use along
-            with [`~transformers.TrainingArguments.load_best_model_at_end`] to upload best model.
-
-            <Deprecated version="5.0">
-
-            Setting `WANDB_LOG_MODEL` as `bool` will be deprecated in version 5 of 🤗 Transformers.
-
-            </Deprecated>
-        - **WANDB_WATCH** (`str`, *optional* defaults to `"false"`):
-            Can be `"gradients"`, `"all"`, `"parameters"`, or `"false"`. Set to `"all"` to log gradients and
-            parameters.
-        - **WANDB_PROJECT** (`str`, *optional*, defaults to `"huggingface"`):
-            Set this to a custom string to store results in a different project.
-        - **WANDB_DISABLED** (`bool`, *optional*, defaults to `False`):
-            Whether to disable wandb entirely. Set `WANDB_DISABLED=true` to disable.
-        """
-        if self._wandb is None:
-            return
-        self._initialized = True
-        if state.is_world_process_zero:
-            logger.info(
-                'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"'
-            )
-            combined_dict = {**args.to_dict()}
-
-            if hasattr(model, "config") and model.config is not None:
-                model_config = model.config.to_dict()
-                combined_dict = {**model_config, **combined_dict}
-            trial_name = state.trial_name
-            init_args = {}
-            if trial_name is not None:
-                init_args["name"] = trial_name
-                init_args["group"] = args.run_name
-            else:
-                if not (args.run_name is None or args.run_name == args.output_dir):
-                    init_args["name"] = args.run_name
-
-            if self._wandb.run is None:
-                self._wandb.init(
-                    project=os.getenv("WANDB_PROJECT", "huggingface"),
-                    **init_args,
-                )
-            # add config parameters (run may have been created manually)
-            self._wandb.config.update(combined_dict, allow_val_change=True)
-
-            # define default x-axis (for latest wandb versions)
-            if getattr(self._wandb, "define_metric", None):
-                self._wandb.define_metric("train/global_step")
-                self._wandb.define_metric("*", step_metric="train/global_step", step_sync=True)
-
-            # keep track of model topology and gradients, unsupported on TPU
-            _watch_model = os.getenv("WANDB_WATCH", "false")
-            if not is_torch_tpu_available() and _watch_model in ("all", "parameters", "gradients"):
-                self._wandb.watch(model, log=_watch_model, log_freq=max(100, state.logging_steps))
-            self._wandb.run._label(code="transformers_trainer")
-
-    def on_train_begin(self, args, state, control, model=None, **kwargs):
-        if self._wandb is None:
-            return
-        hp_search = state.is_hyper_param_search
-        if hp_search:
-            self._wandb.finish()
-            self._initialized = False
-            args.run_name = None
-        if not self._initialized:
-            self.setup(args, state, model, **kwargs)
-
-    def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs):
-        if self._wandb is None:
-            return
-        if self._log_model in ("end", "checkpoint") and self._initialized and state.is_world_process_zero:
-            from ..trainer import Trainer
-
-            fake_trainer = Trainer(args=args, model=model, tokenizer=tokenizer)
-            with tempfile.TemporaryDirectory() as temp_dir:
-                fake_trainer.save_model(temp_dir)
-                metadata = (
-                    {
-                        k: v
-                        for k, v in dict(self._wandb.summary).items()
-                        if isinstance(v, numbers.Number) and not k.startswith("_")
-                    }
-                    if not args.load_best_model_at_end
-                    else {
-                        f"eval/{args.metric_for_best_model}": state.best_metric,
-                        "train/total_floss": state.total_flos,
-                    }
-                )
-                logger.info("Logging model artifacts. ...")
-                model_name = (
-                    f"model-{self._wandb.run.id}"
-                    if (args.run_name is None or args.run_name == args.output_dir)
-                    else f"model-{self._wandb.run.name}"
-                )
-                artifact = self._wandb.Artifact(name=model_name, type="model", metadata=metadata)
-                for f in Path(temp_dir).glob("*"):
-                    if f.is_file():
-                        with artifact.new_file(f.name, mode="wb") as fa:
-                            fa.write(f.read_bytes())
-                self._wandb.run.log_artifact(artifact)
-
-    def on_log(self, args, state, control, model=None, logs=None, **kwargs):
-        if self._wandb is None:
-            return
-        if not self._initialized:
-            self.setup(args, state, model)
-        if state.is_world_process_zero:
-            logs = rewrite_logs(logs)
-            self._wandb.log({**logs, "train/global_step": state.global_step})
-
-    def on_save(self, args, state, control, **kwargs):
-        if self._log_model == "checkpoint" and self._initialized and state.is_world_process_zero:
-            checkpoint_metadata = {
-                k: v
-                for k, v in dict(self._wandb.summary).items()
-                if isinstance(v, numbers.Number) and not k.startswith("_")
-            }
-
-            ckpt_dir = f"checkpoint-{state.global_step}"
-            artifact_path = os.path.join(args.output_dir, ckpt_dir)
-            logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. ...")
-            checkpoint_name = (
-                f"checkpoint-{self._wandb.run.id}"
-                if (args.run_name is None or args.run_name == args.output_dir)
-                else f"checkpoint-{self._wandb.run.name}"
-            )
-            artifact = self._wandb.Artifact(name=checkpoint_name, type="model", metadata=checkpoint_metadata)
-            artifact.add_dir(artifact_path)
-            self._wandb.log_artifact(artifact, aliases=[f"checkpoint-{state.global_step}"])
-
-
-class CometCallback(TrainerCallback):
-    """
-    A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.ml/site/).
-    """
-
-    def __init__(self):
-        if not _has_comet:
-            raise RuntimeError("CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.")
-        self._initialized = False
-        self._log_assets = False
-
-    def setup(self, args, state, model):
-        """
-        Setup the optional Comet.ml integration.
-
-        Environment:
-        - **COMET_MODE** (`str`, *optional*, defaults to `ONLINE`):
-            Whether to create an online, offline experiment or disable Comet logging. Can be `OFFLINE`, `ONLINE`, or
-            `DISABLED`.
-        - **COMET_PROJECT_NAME** (`str`, *optional*):
-            Comet project name for experiments.
-        - **COMET_OFFLINE_DIRECTORY** (`str`, *optional*):
-            Folder to use for saving offline experiments when `COMET_MODE` is `OFFLINE`.
-        - **COMET_LOG_ASSETS** (`str`, *optional*, defaults to `TRUE`):
-            Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be `TRUE`, or
-            `FALSE`.
-
-        For a number of configurable items in the environment, see
-        [here](https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables).
-        """
-        self._initialized = True
-        log_assets = os.getenv("COMET_LOG_ASSETS", "FALSE").upper()
-        if log_assets in {"TRUE", "1"}:
-            self._log_assets = True
-        if state.is_world_process_zero:
-            comet_mode = os.getenv("COMET_MODE", "ONLINE").upper()
-            experiment = None
-            experiment_kwargs = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")}
-            if comet_mode == "ONLINE":
-                experiment = comet_ml.Experiment(**experiment_kwargs)
-                experiment.log_other("Created from", "transformers")
-                logger.info("Automatic Comet.ml online logging enabled")
-            elif comet_mode == "OFFLINE":
-                experiment_kwargs["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./")
-                experiment = comet_ml.OfflineExperiment(**experiment_kwargs)
-                experiment.log_other("Created from", "transformers")
-                logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished")
-            if experiment is not None:
-                experiment._set_model_graph(model, framework="transformers")
-                experiment._log_parameters(args, prefix="args/", framework="transformers")
-                if hasattr(model, "config"):
-                    experiment._log_parameters(model.config, prefix="config/", framework="transformers")
-
-    def on_train_begin(self, args, state, control, model=None, **kwargs):
-        if not self._initialized:
-            self.setup(args, state, model)
-
-    def on_log(self, args, state, control, model=None, logs=None, **kwargs):
-        if not self._initialized:
-            self.setup(args, state, model)
-        if state.is_world_process_zero:
-            experiment = comet_ml.config.get_global_experiment()
-            if experiment is not None:
-                experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework="transformers")
-
-    def on_train_end(self, args, state, control, **kwargs):
-        if self._initialized and state.is_world_process_zero:
-            experiment = comet_ml.config.get_global_experiment()
-            if experiment is not None:
-                if self._log_assets is True:
-                    logger.info("Logging checkpoints. This may take time.")
-                    experiment.log_asset_folder(
-                        args.output_dir, recursive=True, log_file_name=True, step=state.global_step
-                    )
-                experiment.end()
-
-
-class AzureMLCallback(TrainerCallback):
-    """
-    A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/).
-    """
-
-    def __init__(self, azureml_run=None):
-        if not is_azureml_available():
-            raise RuntimeError("AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`.")
-        self.azureml_run = azureml_run
-
-    def on_init_end(self, args, state, control, **kwargs):
-        from azureml.core.run import Run
-
-        if self.azureml_run is None and state.is_world_process_zero:
-            self.azureml_run = Run.get_context()
-
-    def on_log(self, args, state, control, logs=None, **kwargs):
-        if self.azureml_run and state.is_world_process_zero:
-            for k, v in logs.items():
-                if isinstance(v, (int, float)):
-                    self.azureml_run.log(k, v, description=k)
-
-
-class MLflowCallback(TrainerCallback):
-    """
-    A [`TrainerCallback`] that sends the logs to [MLflow](https://www.mlflow.org/). Can be disabled by setting
-    environment variable `DISABLE_MLFLOW_INTEGRATION = TRUE`.
-    """
-
-    def __init__(self):
-        if not is_mlflow_available():
-            raise RuntimeError("MLflowCallback requires mlflow to be installed. Run `pip install mlflow`.")
-        import mlflow
-
-        self._MAX_PARAM_VAL_LENGTH = mlflow.utils.validation.MAX_PARAM_VAL_LENGTH
-        self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH
-
-        self._initialized = False
-        self._auto_end_run = False
-        self._log_artifacts = False
-        self._ml_flow = mlflow
-
-    def setup(self, args, state, model):
-        """
-        Setup the optional MLflow integration.
-
-        Environment:
-        - **HF_MLFLOW_LOG_ARTIFACTS** (`str`, *optional*):
-            Whether to use MLflow `.log_artifact()` facility to log artifacts. This only makes sense if logging to a
-            remote server, e.g. s3 or GCS. If set to `True` or *1*, will copy each saved checkpoint on each save in
-            [`TrainingArguments`]'s `output_dir` to the local or remote artifact storage. Using it without a remote
-            storage will just copy the files to your artifact location.
-        - **MLFLOW_EXPERIMENT_NAME** (`str`, *optional*, defaults to `None`):
-            Whether to use an MLflow experiment_name under which to launch the run. Default to `None` which will point
-            to the `Default` experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be
-            activated. If an experiment with this name does not exist, a new experiment with this name is created.
-        - **MLFLOW_TAGS** (`str`, *optional*):
-            A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
-            `os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}'`.
-        - **MLFLOW_NESTED_RUN** (`str`, *optional*):
-            Whether to use MLflow nested runs. If set to `True` or *1*, will create a nested run inside the current
-            run.
-        - **MLFLOW_RUN_ID** (`str`, *optional*):
-            Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When
-            `MLFLOW_RUN_ID` environment variable is set, `start_run` attempts to resume a run with the specified run ID
-            and other parameters are ignored.
-        - **MLFLOW_FLATTEN_PARAMS** (`str`, *optional*, defaults to `False`):
-            Whether to flatten the parameters dictionary before logging.
-        """
-        self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
-        self._nested_run = os.getenv("MLFLOW_NESTED_RUN", "FALSE").upper() in ENV_VARS_TRUE_VALUES
-        self._experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None)
-        self._flatten_params = os.getenv("MLFLOW_FLATTEN_PARAMS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
-        self._run_id = os.getenv("MLFLOW_RUN_ID", None)
-        logger.debug(
-            f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run},"
-            f" tags={self._nested_run}"
-        )
-        if state.is_world_process_zero:
-            if self._ml_flow.active_run() is None or self._nested_run or self._run_id:
-                if self._experiment_name:
-                    # Use of set_experiment() ensure that Experiment is created if not exists
-                    self._ml_flow.set_experiment(self._experiment_name)
-                self._ml_flow.start_run(run_name=args.run_name, nested=self._nested_run)
-                logger.debug(f"MLflow run started with run_id={self._ml_flow.active_run().info.run_id}")
-                self._auto_end_run = True
-            combined_dict = args.to_dict()
-            if hasattr(model, "config") and model.config is not None:
-                model_config = model.config.to_dict()
-                combined_dict = {**model_config, **combined_dict}
-            combined_dict = flatten_dict(combined_dict) if self._flatten_params else combined_dict
-            # remove params that are too long for MLflow
-            for name, value in list(combined_dict.items()):
-                # internally, all values are converted to str in MLflow
-                if len(str(value)) > self._MAX_PARAM_VAL_LENGTH:
-                    logger.warning(
-                        f'Trainer is attempting to log a value of "{value}" for key "{name}" as a parameter. MLflow\'s'
-                        " log_param() only accepts values no longer than 250 characters so we dropped this attribute."
-                        " You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and"
-                        " avoid this message."
-                    )
-                    del combined_dict[name]
-            # MLflow cannot log more than 100 values in one go, so we have to split it
-            combined_dict_items = list(combined_dict.items())
-            for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH):
-                self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH]))
-            mlflow_tags = os.getenv("MLFLOW_TAGS", None)
-            if mlflow_tags:
-                mlflow_tags = json.loads(mlflow_tags)
-                self._ml_flow.set_tags(mlflow_tags)
-        self._initialized = True
-
-    def on_train_begin(self, args, state, control, model=None, **kwargs):
-        if not self._initialized:
-            self.setup(args, state, model)
-
-    def on_log(self, args, state, control, logs, model=None, **kwargs):
-        if not self._initialized:
-            self.setup(args, state, model)
-        if state.is_world_process_zero:
-            metrics = {}
-            for k, v in logs.items():
-                if isinstance(v, (int, float)):
-                    metrics[k] = v
-                else:
-                    logger.warning(
-                        f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. '
-                        "MLflow's log_metric() only accepts float and int types so we dropped this attribute."
-                    )
-            self._ml_flow.log_metrics(metrics=metrics, step=state.global_step)
-
-    def on_train_end(self, args, state, control, **kwargs):
-        if self._initialized and state.is_world_process_zero:
-            if self._auto_end_run and self._ml_flow.active_run():
-                self._ml_flow.end_run()
-
-    def on_save(self, args, state, control, **kwargs):
-        if self._initialized and state.is_world_process_zero and self._log_artifacts:
-            ckpt_dir = f"checkpoint-{state.global_step}"
-            artifact_path = os.path.join(args.output_dir, ckpt_dir)
-            logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. This may take time.")
-            self._ml_flow.pyfunc.log_model(
-                ckpt_dir,
-                artifacts={"model_path": artifact_path},
-                python_model=self._ml_flow.pyfunc.PythonModel(),
-            )
-
-    def __del__(self):
-        # if the previous run is not terminated correctly, the fluent API will
-        # not let you start a new run before the previous one is killed
-        if (
-            self._auto_end_run
-            and callable(getattr(self._ml_flow, "active_run", None))
-            and self._ml_flow.active_run() is not None
-        ):
-            self._ml_flow.end_run()
-
-
-class DagsHubCallback(MLflowCallback):
-    """
-    A [`TrainerCallback`] that logs to [DagsHub](https://dagshub.com/). Extends [`MLflowCallback`]
-    """
-
-    def __init__(self):
-        super().__init__()
-        if not is_dagshub_available():
-            raise ImportError("DagsHubCallback requires dagshub to be installed. Run `pip install dagshub`.")
-
-        from dagshub.upload import Repo
-
-        self.Repo = Repo
-
-    def setup(self, *args, **kwargs):
-        """
-        Setup the DagsHub's Logging integration.
-
-        Environment:
-        - **HF_DAGSHUB_LOG_ARTIFACTS** (`str`, *optional*):
-                Whether to save the data and model artifacts for the experiment. Default to `False`.
-        """
-
-        self.log_artifacts = os.getenv("HF_DAGSHUB_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
-        self.name = os.getenv("HF_DAGSHUB_MODEL_NAME") or "main"
-        self.remote = os.getenv("MLFLOW_TRACKING_URI")
-        self.repo = self.Repo(
-            owner=self.remote.split(os.sep)[-2],
-            name=self.remote.split(os.sep)[-1].split(".")[0],
-            branch=os.getenv("BRANCH") or "main",
-        )
-        self.path = Path("artifacts")
-
-        if self.remote is None:
-            raise RuntimeError(
-                "DagsHubCallback requires the `MLFLOW_TRACKING_URI` environment variable to be set. Did you run"
-                " `dagshub.init()`?"
-            )
-
-        super().setup(*args, **kwargs)
-
-    def on_train_end(self, args, state, control, **kwargs):
-        if self.log_artifacts:
-            if getattr(self, "train_dataloader", None):
-                torch.save(self.train_dataloader.dataset, os.path.join(args.output_dir, "dataset.pt"))
-
-            self.repo.directory(str(self.path)).add_dir(args.output_dir)
-
-
-class NeptuneMissingConfiguration(Exception):
-    def __init__(self):
-        super().__init__(
-            """
-        ------ Unsupported ---- We were not able to create new runs. You provided a custom Neptune run to
-        `NeptuneCallback` with the `run` argument. For the integration to work fully, provide your `api_token` and
-        `project` by saving them as environment variables or passing them to the callback.
-        """
-        )
-
-
-class NeptuneCallback(TrainerCallback):
-    """TrainerCallback that sends the logs to [Neptune](https://app.neptune.ai).
-
-    Args:
-        api_token (`str`, *optional*): Neptune API token obtained upon registration.
-            You can leave this argument out if you have saved your token to the `NEPTUNE_API_TOKEN` environment
-            variable (strongly recommended). See full setup instructions in the
-            [docs](https://docs.neptune.ai/setup/installation).
-        project (`str`, *optional*): Name of an existing Neptune project, in the form "workspace-name/project-name".
-            You can find and copy the name in Neptune from the project settings -> Properties. If None (default), the
-            value of the `NEPTUNE_PROJECT` environment variable is used.
-        name (`str`, *optional*): Custom name for the run.
-        base_namespace (`str`, optional, defaults to "finetuning"): In the Neptune run, the root namespace
-            that will contain all of the metadata logged by the callback.
-        log_parameters (`bool`, *optional*, defaults to `True`):
-            If True, logs all Trainer arguments and model parameters provided by the Trainer.
-        log_checkpoints (`str`, *optional*): If "same", uploads checkpoints whenever they are saved by the Trainer.
-            If "last", uploads only the most recently saved checkpoint. If "best", uploads the best checkpoint (among
-            the ones saved by the Trainer). If `None`, does not upload checkpoints.
-        run (`Run`, *optional*): Pass a Neptune run object if you want to continue logging to an existing run.
-            Read more about resuming runs in the [docs](https://docs.neptune.ai/logging/to_existing_object).
-        **neptune_run_kwargs (*optional*):
-            Additional keyword arguments to be passed directly to the
-            [`neptune.init_run()`](https://docs.neptune.ai/api/neptune#init_run) function when a new run is created.
-
-    For instructions and examples, see the [Transformers integration
-    guide](https://docs.neptune.ai/integrations/transformers) in the Neptune documentation.
-    """
-
-    integration_version_key = "source_code/integrations/transformers"
-    model_parameters_key = "model_parameters"
-    trial_name_key = "trial"
-    trial_params_key = "trial_params"
-    trainer_parameters_key = "trainer_parameters"
-    flat_metrics = {"train/epoch"}
-
-    def __init__(
-        self,
-        *,
-        api_token: Optional[str] = None,
-        project: Optional[str] = None,
-        name: Optional[str] = None,
-        base_namespace: str = "finetuning",
-        run=None,
-        log_parameters: bool = True,
-        log_checkpoints: Optional[str] = None,
-        **neptune_run_kwargs,
-    ):
-        if not is_neptune_available():
-            raise ValueError(
-                "NeptuneCallback requires the Neptune client library to be installed. "
-                "To install the library, run `pip install neptune`."
-            )
-
-        try:
-            from neptune import Run
-            from neptune.internal.utils import verify_type
-        except ImportError:
-            from neptune.new.internal.utils import verify_type
-            from neptune.new.metadata_containers.run import Run
-
-        verify_type("api_token", api_token, (str, type(None)))
-        verify_type("project", project, (str, type(None)))
-        verify_type("name", name, (str, type(None)))
-        verify_type("base_namespace", base_namespace, str)
-        verify_type("run", run, (Run, type(None)))
-        verify_type("log_parameters", log_parameters, bool)
-        verify_type("log_checkpoints", log_checkpoints, (str, type(None)))
-
-        self._base_namespace_path = base_namespace
-        self._log_parameters = log_parameters
-        self._log_checkpoints = log_checkpoints
-        self._initial_run: Optional[Run] = run
-
-        self._run = None
-        self._is_monitoring_run = False
-        self._run_id = None
-        self._force_reset_monitoring_run = False
-        self._init_run_kwargs = {"api_token": api_token, "project": project, "name": name, **neptune_run_kwargs}
-
-        self._volatile_checkpoints_dir = None
-        self._should_upload_checkpoint = self._log_checkpoints is not None
-        self._recent_checkpoint_path = None
-
-        if self._log_checkpoints in {"last", "best"}:
-            self._target_checkpoints_namespace = f"checkpoints/{self._log_checkpoints}"
-            self._should_clean_recently_uploaded_checkpoint = True
-        else:
-            self._target_checkpoints_namespace = "checkpoints"
-            self._should_clean_recently_uploaded_checkpoint = False
-
-    def _stop_run_if_exists(self):
-        if self._run:
-            self._run.stop()
-            del self._run
-            self._run = None
-
-    def _initialize_run(self, **additional_neptune_kwargs):
-        try:
-            from neptune import init_run
-            from neptune.exceptions import NeptuneMissingApiTokenException, NeptuneMissingProjectNameException
-        except ImportError:
-            from neptune.new import init_run
-            from neptune.new.exceptions import NeptuneMissingApiTokenException, NeptuneMissingProjectNameException
-
-        self._stop_run_if_exists()
-
-        try:
-            self._run = init_run(**self._init_run_kwargs, **additional_neptune_kwargs)
-            self._run_id = self._run["sys/id"].fetch()
-        except (NeptuneMissingProjectNameException, NeptuneMissingApiTokenException) as e:
-            raise NeptuneMissingConfiguration() from e
-
-    def _use_initial_run(self):
-        self._run = self._initial_run
-        self._is_monitoring_run = True
-        self._run_id = self._run["sys/id"].fetch()
-        self._initial_run = None
-
-    def _ensure_run_with_monitoring(self):
-        if self._initial_run is not None:
-            self._use_initial_run()
-        else:
-            if not self._force_reset_monitoring_run and self._is_monitoring_run:
-                return
-
-            if self._run and not self._is_monitoring_run and not self._force_reset_monitoring_run:
-                self._initialize_run(with_id=self._run_id)
-                self._is_monitoring_run = True
-            else:
-                self._initialize_run()
-                self._force_reset_monitoring_run = False
-
-    def _ensure_at_least_run_without_monitoring(self):
-        if self._initial_run is not None:
-            self._use_initial_run()
-        else:
-            if not self._run:
-                self._initialize_run(
-                    with_id=self._run_id,
-                    capture_stdout=False,
-                    capture_stderr=False,
-                    capture_hardware_metrics=False,
-                    capture_traceback=False,
-                )
-                self._is_monitoring_run = False
-
-    @property
-    def run(self):
-        if self._run is None:
-            self._ensure_at_least_run_without_monitoring()
-        return self._run
-
-    @property
-    def _metadata_namespace(self):
-        return self.run[self._base_namespace_path]
-
-    def _log_integration_version(self):
-        self.run[NeptuneCallback.integration_version_key] = version
-
-    def _log_trainer_parameters(self, args):
-        self._metadata_namespace[NeptuneCallback.trainer_parameters_key] = args.to_sanitized_dict()
-
-    def _log_model_parameters(self, model):
-        from neptune.utils import stringify_unsupported
-
-        if model and hasattr(model, "config") and model.config is not None:
-            self._metadata_namespace[NeptuneCallback.model_parameters_key] = stringify_unsupported(
-                model.config.to_dict()
-            )
-
-    def _log_hyper_param_search_parameters(self, state):
-        if state and hasattr(state, "trial_name"):
-            self._metadata_namespace[NeptuneCallback.trial_name_key] = state.trial_name
-
-        if state and hasattr(state, "trial_params") and state.trial_params is not None:
-            self._metadata_namespace[NeptuneCallback.trial_params_key] = state.trial_params
-
-    def _log_model_checkpoint(self, source_directory: str, checkpoint: str):
-        target_path = relative_path = os.path.join(source_directory, checkpoint)
-
-        if self._volatile_checkpoints_dir is not None:
-            consistent_checkpoint_path = os.path.join(self._volatile_checkpoints_dir, checkpoint)
-            try:
-                # Remove leading ../ from a relative path.
-                cpkt_path = relative_path.replace("..", "").lstrip(os.path.sep)
-                copy_path = os.path.join(consistent_checkpoint_path, cpkt_path)
-                shutil.copytree(relative_path, copy_path)
-                target_path = consistent_checkpoint_path
-            except IOError as e:
-                logger.warning(
-                    "NeptuneCallback was unable to made a copy of checkpoint due to I/O exception: '{}'."
-                    "Could fail trying to upload.".format(e)
-                )
-
-        self._metadata_namespace[self._target_checkpoints_namespace].upload_files(target_path)
-
-        if self._should_clean_recently_uploaded_checkpoint and self._recent_checkpoint_path is not None:
-            self._metadata_namespace[self._target_checkpoints_namespace].delete_files(self._recent_checkpoint_path)
-
-        self._recent_checkpoint_path = relative_path
-
-    def on_init_end(self, args, state, control, **kwargs):
-        self._volatile_checkpoints_dir = None
-        if self._log_checkpoints and (args.overwrite_output_dir or args.save_total_limit is not None):
-            self._volatile_checkpoints_dir = tempfile.TemporaryDirectory().name
-
-        if self._log_checkpoints == "best" and not args.load_best_model_at_end:
-            raise ValueError("To save the best model checkpoint, the load_best_model_at_end argument must be enabled.")
-
-    def on_train_begin(self, args, state, control, model=None, **kwargs):
-        if not state.is_world_process_zero:
-            return
-
-        self._ensure_run_with_monitoring()
-        self._force_reset_monitoring_run = True
-
-        self._log_integration_version()
-        if self._log_parameters:
-            self._log_trainer_parameters(args)
-            self._log_model_parameters(model)
-
-        if state.is_hyper_param_search:
-            self._log_hyper_param_search_parameters(state)
-
-    def on_train_end(self, args, state, control, **kwargs):
-        self._stop_run_if_exists()
-
-    def __del__(self):
-        if self._volatile_checkpoints_dir is not None:
-            shutil.rmtree(self._volatile_checkpoints_dir, ignore_errors=True)
-
-        self._stop_run_if_exists()
-
-    def on_save(self, args, state, control, **kwargs):
-        if self._should_upload_checkpoint:
-            self._log_model_checkpoint(args.output_dir, f"checkpoint-{state.global_step}")
-
-    def on_evaluate(self, args, state, control, metrics=None, **kwargs):
-        if self._log_checkpoints == "best":
-            best_metric_name = args.metric_for_best_model
-            if not best_metric_name.startswith("eval_"):
-                best_metric_name = f"eval_{best_metric_name}"
-
-            metric_value = metrics.get(best_metric_name)
-
-            operator = np.greater if args.greater_is_better else np.less
-
-            self._should_upload_checkpoint = state.best_metric is None or operator(metric_value, state.best_metric)
-
-    @classmethod
-    def get_run(cls, trainer):
-        for callback in trainer.callback_handler.callbacks:
-            if isinstance(callback, cls):
-                return callback.run
-
-        raise Exception("The trainer doesn't have a NeptuneCallback configured.")
-
-    def on_log(self, args, state, control, logs: Optional[Dict[str, float]] = None, **kwargs):
-        if not state.is_world_process_zero:
-            return
-
-        if logs is not None:
-            for name, value in rewrite_logs(logs).items():
-                if isinstance(value, (int, float)):
-                    if name in NeptuneCallback.flat_metrics:
-                        self._metadata_namespace[name] = value
-                    else:
-                        self._metadata_namespace[name].log(value, step=state.global_step)
-
-
-class CodeCarbonCallback(TrainerCallback):
-    """
-    A [`TrainerCallback`] that tracks the CO2 emission of training.
-    """
-
-    def __init__(self):
-        if not is_codecarbon_available():
-            raise RuntimeError(
-                "CodeCarbonCallback requires `codecarbon` to be installed. Run `pip install codecarbon`."
-            )
-        import codecarbon
-
-        self._codecarbon = codecarbon
-        self.tracker = None
-
-    def on_init_end(self, args, state, control, **kwargs):
-        if self.tracker is None and state.is_local_process_zero:
-            # CodeCarbon will automatically handle environment variables for configuration
-            self.tracker = self._codecarbon.EmissionsTracker(output_dir=args.output_dir)
-
-    def on_train_begin(self, args, state, control, model=None, **kwargs):
-        if self.tracker and state.is_local_process_zero:
-            self.tracker.start()
-
-    def on_train_end(self, args, state, control, **kwargs):
-        if self.tracker and state.is_local_process_zero:
-            self.tracker.stop()
-
-
-class ClearMLCallback(TrainerCallback):
-    """
-    A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/).
-
-    Environment:
-    - **CLEARML_PROJECT** (`str`, *optional*, defaults to `HuggingFace Transformers`):
-        ClearML project name.
-    - **CLEARML_TASK** (`str`, *optional*, defaults to `Trainer`):
-        ClearML task name.
-    - **CLEARML_LOG_MODEL** (`bool`, *optional*, defaults to `False`):
-        Whether to log models as artifacts during training.
-    """
-
-    def __init__(self):
-        if is_clearml_available():
-            import clearml
-
-            self._clearml = clearml
-        else:
-            raise RuntimeError("ClearMLCallback requires 'clearml' to be installed. Run `pip install clearml`.")
-
-        self._initialized = False
-        self._initialized_externally = False
-        self._clearml_task = None
-
-        self._log_model = os.getenv("CLEARML_LOG_MODEL", "FALSE").upper() in ENV_VARS_TRUE_VALUES.union({"TRUE"})
-
-    def setup(self, args, state, model, tokenizer, **kwargs):
-        if self._clearml is None:
-            return
-        if self._initialized:
-            return
-        if state.is_world_process_zero:
-            logger.info("Automatic ClearML logging enabled.")
-            if self._clearml_task is None:
-                # This might happen when running inside of a pipeline, where the task is already initialized
-                # from outside of Hugging Face
-                if self._clearml.Task.current_task():
-                    self._clearml_task = self._clearml.Task.current_task()
-                    self._initialized = True
-                    self._initialized_externally = True
-                    logger.info("External ClearML Task has been connected.")
-                else:
-                    self._clearml_task = self._clearml.Task.init(
-                        project_name=os.getenv("CLEARML_PROJECT", "HuggingFace Transformers"),
-                        task_name=os.getenv("CLEARML_TASK", "Trainer"),
-                        auto_connect_frameworks={"tensorboard": False, "pytorch": False},
-                        output_uri=True,
-                    )
-                    self._initialized = True
-                    logger.info("ClearML Task has been initialized.")
-
-            self._clearml_task.connect(args, "Args")
-            if hasattr(model, "config") and model.config is not None:
-                self._clearml_task.connect(model.config, "Model Configuration")
-
-    def on_train_begin(self, args, state, control, model=None, tokenizer=None, **kwargs):
-        if self._clearml is None:
-            return
-        if state.is_hyper_param_search:
-            self._initialized = False
-        if not self._initialized:
-            self.setup(args, state, model, tokenizer, **kwargs)
-
-    def on_train_end(self, args, state, control, model=None, tokenizer=None, metrics=None, logs=None, **kwargs):
-        if self._clearml is None:
-            return
-        if self._clearml_task and state.is_world_process_zero and not self._initialized_externally:
-            # Close ClearML Task at the end end of training
-            self._clearml_task.close()
-
-    def on_log(self, args, state, control, model=None, tokenizer=None, logs=None, **kwargs):
-        if self._clearml is None:
-            return
-        if not self._initialized:
-            self.setup(args, state, model, tokenizer, **kwargs)
-        if state.is_world_process_zero:
-            eval_prefix = "eval_"
-            eval_prefix_len = len(eval_prefix)
-            test_prefix = "test_"
-            test_prefix_len = len(test_prefix)
-            single_value_scalars = [
-                "train_runtime",
-                "train_samples_per_second",
-                "train_steps_per_second",
-                "train_loss",
-                "total_flos",
-                "epoch",
-            ]
-            for k, v in logs.items():
-                if isinstance(v, (int, float)):
-                    if k in single_value_scalars:
-                        self._clearml_task.get_logger().report_single_value(name=k, value=v)
-                    elif k.startswith(eval_prefix):
-                        self._clearml_task.get_logger().report_scalar(
-                            title=k[eval_prefix_len:], series="eval", value=v, iteration=state.global_step
-                        )
-                    elif k.startswith(test_prefix):
-                        self._clearml_task.get_logger().report_scalar(
-                            title=k[test_prefix_len:], series="test", value=v, iteration=state.global_step
-                        )
-                    else:
-                        self._clearml_task.get_logger().report_scalar(
-                            title=k, series="train", value=v, iteration=state.global_step
-                        )
-                else:
-                    logger.warning(
-                        "Trainer is attempting to log a value of "
-                        f'"{v}" of type {type(v)} for key "{k}" as a scalar. '
-                        "This invocation of ClearML logger's  report_scalar() "
-                        "is incorrect so we dropped this attribute."
-                    )
-
-    def on_save(self, args, state, control, **kwargs):
-        if self._log_model and self._clearml_task and state.is_world_process_zero:
-            ckpt_dir = f"checkpoint-{state.global_step}"
-            artifact_path = os.path.join(args.output_dir, ckpt_dir)
-            logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. This may take time.")
-            self._clearml_task.update_output_model(artifact_path, iteration=state.global_step, auto_delete_file=False)
-
-
-class FlyteCallback(TrainerCallback):
-    """A [`TrainerCallback`] that sends the logs to [Flyte](https://flyte.org/).
-    NOTE: This callback only works within a Flyte task.
-
-    Args:
-        save_log_history (`bool`, *optional*, defaults to `True`):
-            When set to True, the training logs are saved as a Flyte Deck.
-
-        sync_checkpoints (`bool`, *optional*, defaults to `True`):
-            When set to True, checkpoints are synced with Flyte and can be used to resume training in the case of an
-            interruption.
-
-    Example:
-
-    ```python
-    # Note: This example skips over some setup steps for brevity.
-    from flytekit import current_context, task
-
-
-    @task
-    def train_hf_transformer():
-        cp = current_context().checkpoint
-        trainer = Trainer(..., callbacks=[FlyteCallback()])
-        output = trainer.train(resume_from_checkpoint=cp.restore())
-    ```
-    """
-
-    def __init__(self, save_log_history: bool = True, sync_checkpoints: bool = True):
-        super().__init__()
-        if not is_flytekit_available():
-            raise ImportError("FlyteCallback requires flytekit to be installed. Run `pip install flytekit`.")
-
-        if not is_flyte_deck_standard_available() or not is_pandas_available():
-            logger.warning(
-                "Syncing log history requires both flytekitplugins-deck-standard and pandas to be installed. "
-                "Run `pip install flytekitplugins-deck-standard pandas` to enable this feature."
-            )
-            save_log_history = False
-
-        from flytekit import current_context
-
-        self.cp = current_context().checkpoint
-        self.save_log_history = save_log_history
-        self.sync_checkpoints = sync_checkpoints
-
-    def on_save(self, args, state, control, **kwargs):
-        if self.sync_checkpoints and state.is_world_process_zero:
-            ckpt_dir = f"checkpoint-{state.global_step}"
-            artifact_path = os.path.join(args.output_dir, ckpt_dir)
-
-            logger.info(f"Syncing checkpoint in {ckpt_dir} to Flyte. This may take time.")
-            self.cp.save(artifact_path)
-
-    def on_train_end(self, args, state, control, **kwargs):
-        if self.save_log_history:
-            import pandas as pd
-            from flytekit import Deck
-            from flytekitplugins.deck.renderer import TableRenderer
-
-            log_history_df = pd.DataFrame(state.log_history)
-            Deck("Log History", TableRenderer().to_html(log_history_df))
-
-
-INTEGRATION_TO_CALLBACK = {
-    "azure_ml": AzureMLCallback,
-    "comet_ml": CometCallback,
-    "mlflow": MLflowCallback,
-    "neptune": NeptuneCallback,
-    "tensorboard": TensorBoardCallback,
-    "wandb": WandbCallback,
-    "codecarbon": CodeCarbonCallback,
-    "clearml": ClearMLCallback,
-    "dagshub": DagsHubCallback,
-    "flyte": FlyteCallback,
-}
-
-
-def get_reporting_integration_callbacks(report_to):
-    for integration in report_to:
-        if integration not in INTEGRATION_TO_CALLBACK:
-            raise ValueError(
-                f"{integration} is not supported, only {', '.join(INTEGRATION_TO_CALLBACK.keys())} are supported."
-            )
-
-    return [INTEGRATION_TO_CALLBACK[integration] for integration in report_to]
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnext/modeling_convnext.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnext/modeling_convnext.py
deleted file mode 100644
index e6cf336517a5636331672f627fb923e1c55ff16b..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/convnext/modeling_convnext.py
+++ /dev/null
@@ -1,559 +0,0 @@
-# coding=utf-8
-# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. 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.
-""" PyTorch ConvNext model."""
-
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from ...activations import ACT2FN
-from ...modeling_outputs import (
-    BackboneOutput,
-    BaseModelOutputWithNoAttention,
-    BaseModelOutputWithPoolingAndNoAttention,
-    ImageClassifierOutputWithNoAttention,
-)
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from ...utils.backbone_utils import BackboneMixin
-from .configuration_convnext import ConvNextConfig
-
-
-logger = logging.get_logger(__name__)
-
-# General docstring
-_CONFIG_FOR_DOC = "ConvNextConfig"
-
-# Base docstring
-_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224"
-_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7]
-
-# Image classification docstring
-_IMAGE_CLASS_CHECKPOINT = "facebook/convnext-tiny-224"
-_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
-
-CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "facebook/convnext-tiny-224",
-    # See all ConvNext models at https://huggingface.co/models?filter=convnext
-]
-
-
-# Copied from transformers.models.beit.modeling_beit.drop_path
-def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
-    """
-    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-
-    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
-    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
-    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
-    argument.
-    """
-    if drop_prob == 0.0 or not training:
-        return input
-    keep_prob = 1 - drop_prob
-    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
-    random_tensor.floor_()  # binarize
-    output = input.div(keep_prob) * random_tensor
-    return output
-
-
-# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->ConvNext
-class ConvNextDropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
-
-    def __init__(self, drop_prob: Optional[float] = None) -> None:
-        super().__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        return drop_path(hidden_states, self.drop_prob, self.training)
-
-    def extra_repr(self) -> str:
-        return "p={}".format(self.drop_prob)
-
-
-class ConvNextLayerNorm(nn.Module):
-    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
-    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
-    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
-    """
-
-    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(normalized_shape))
-        self.bias = nn.Parameter(torch.zeros(normalized_shape))
-        self.eps = eps
-        self.data_format = data_format
-        if self.data_format not in ["channels_last", "channels_first"]:
-            raise NotImplementedError(f"Unsupported data format: {self.data_format}")
-        self.normalized_shape = (normalized_shape,)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if self.data_format == "channels_last":
-            x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        elif self.data_format == "channels_first":
-            input_dtype = x.dtype
-            x = x.float()
-            u = x.mean(1, keepdim=True)
-            s = (x - u).pow(2).mean(1, keepdim=True)
-            x = (x - u) / torch.sqrt(s + self.eps)
-            x = x.to(dtype=input_dtype)
-            x = self.weight[:, None, None] * x + self.bias[:, None, None]
-        return x
-
-
-class ConvNextEmbeddings(nn.Module):
-    """This class is comparable to (and inspired by) the SwinEmbeddings class
-    found in src/transformers/models/swin/modeling_swin.py.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.patch_embeddings = nn.Conv2d(
-            config.num_channels, config.hidden_sizes[0], kernel_size=config.patch_size, stride=config.patch_size
-        )
-        self.layernorm = ConvNextLayerNorm(config.hidden_sizes[0], eps=1e-6, data_format="channels_first")
-        self.num_channels = config.num_channels
-
-    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
-        num_channels = pixel_values.shape[1]
-        if num_channels != self.num_channels:
-            raise ValueError(
-                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
-            )
-        embeddings = self.patch_embeddings(pixel_values)
-        embeddings = self.layernorm(embeddings)
-        return embeddings
-
-
-class ConvNextLayer(nn.Module):
-    """This corresponds to the `Block` class in the original implementation.
-
-    There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
-    H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back
-
-    The authors used (2) as they find it slightly faster in PyTorch.
-
-    Args:
-        config ([`ConvNextConfig`]): Model configuration class.
-        dim (`int`): Number of input channels.
-        drop_path (`float`): Stochastic depth rate. Default: 0.0.
-    """
-
-    def __init__(self, config, dim, drop_path=0):
-        super().__init__()
-        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
-        self.layernorm = ConvNextLayerNorm(dim, eps=1e-6)
-        self.pwconv1 = nn.Linear(dim, 4 * dim)  # pointwise/1x1 convs, implemented with linear layers
-        self.act = ACT2FN[config.hidden_act]
-        self.pwconv2 = nn.Linear(4 * dim, dim)
-        self.layer_scale_parameter = (
-            nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True)
-            if config.layer_scale_init_value > 0
-            else None
-        )
-        self.drop_path = ConvNextDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
-        input = hidden_states
-        x = self.dwconv(hidden_states)
-        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
-        x = self.layernorm(x)
-        x = self.pwconv1(x)
-        x = self.act(x)
-        x = self.pwconv2(x)
-        if self.layer_scale_parameter is not None:
-            x = self.layer_scale_parameter * x
-        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
-
-        x = input + self.drop_path(x)
-        return x
-
-
-class ConvNextStage(nn.Module):
-    """ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks.
-
-    Args:
-        config ([`ConvNextConfig`]): Model configuration class.
-        in_channels (`int`): Number of input channels.
-        out_channels (`int`): Number of output channels.
-        depth (`int`): Number of residual blocks.
-        drop_path_rates(`List[float]`): Stochastic depth rates for each layer.
-    """
-
-    def __init__(self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None):
-        super().__init__()
-
-        if in_channels != out_channels or stride > 1:
-            self.downsampling_layer = nn.Sequential(
-                ConvNextLayerNorm(in_channels, eps=1e-6, data_format="channels_first"),
-                nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride),
-            )
-        else:
-            self.downsampling_layer = nn.Identity()
-        drop_path_rates = drop_path_rates or [0.0] * depth
-        self.layers = nn.Sequential(
-            *[ConvNextLayer(config, dim=out_channels, drop_path=drop_path_rates[j]) for j in range(depth)]
-        )
-
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
-        hidden_states = self.downsampling_layer(hidden_states)
-        hidden_states = self.layers(hidden_states)
-        return hidden_states
-
-
-class ConvNextEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.stages = nn.ModuleList()
-        drop_path_rates = [
-            x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths)
-        ]
-        prev_chs = config.hidden_sizes[0]
-        for i in range(config.num_stages):
-            out_chs = config.hidden_sizes[i]
-            stage = ConvNextStage(
-                config,
-                in_channels=prev_chs,
-                out_channels=out_chs,
-                stride=2 if i > 0 else 1,
-                depth=config.depths[i],
-                drop_path_rates=drop_path_rates[i],
-            )
-            self.stages.append(stage)
-            prev_chs = out_chs
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        output_hidden_states: Optional[bool] = False,
-        return_dict: Optional[bool] = True,
-    ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
-        all_hidden_states = () if output_hidden_states else None
-
-        for i, layer_module in enumerate(self.stages):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            hidden_states = layer_module(hidden_states)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
-
-        return BaseModelOutputWithNoAttention(
-            last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
-        )
-
-
-class ConvNextPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = ConvNextConfig
-    base_model_prefix = "convnext"
-    main_input_name = "pixel_values"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, ConvNextEncoder):
-            module.gradient_checkpointing = value
-
-
-CONVNEXT_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
-    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-CONVNEXT_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
-            [`ConvNextImageProcessor.__call__`] for details.
-
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare ConvNext model outputting raw features without any specific head on top.",
-    CONVNEXT_START_DOCSTRING,
-)
-class ConvNextModel(ConvNextPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = ConvNextEmbeddings(config)
-        self.encoder = ConvNextEncoder(config)
-
-        # final layernorm layer
-        self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPoolingAndNoAttention,
-        config_class=_CONFIG_FOR_DOC,
-        modality="vision",
-        expected_output=_EXPECTED_OUTPUT_SHAPE,
-    )
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if pixel_values is None:
-            raise ValueError("You have to specify pixel_values")
-
-        embedding_output = self.embeddings(pixel_values)
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
-
-        # global average pooling, (N, C, H, W) -> (N, C)
-        pooled_output = self.layernorm(last_hidden_state.mean([-2, -1]))
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndNoAttention(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-        )
-
-
-@add_start_docstrings(
-    """
-    ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
-    ImageNet.
-    """,
-    CONVNEXT_START_DOCSTRING,
-)
-class ConvNextForImageClassification(ConvNextPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.num_labels = config.num_labels
-        self.convnext = ConvNextModel(config)
-
-        # Classifier head
-        self.classifier = (
-            nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
-        )
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_IMAGE_CLASS_CHECKPOINT,
-        output_type=ImageClassifierOutputWithNoAttention,
-        config_class=_CONFIG_FOR_DOC,
-        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
-    )
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor = None,
-        labels: Optional[torch.LongTensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.convnext(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
-
-        pooled_output = outputs.pooler_output if return_dict else outputs[1]
-
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return ImageClassifierOutputWithNoAttention(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-        )
-
-
-@add_start_docstrings(
-    """
-    ConvNeXt backbone, to be used with frameworks like DETR and MaskFormer.
-    """,
-    CONVNEXT_START_DOCSTRING,
-)
-class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin):
-    def __init__(self, config):
-        super().__init__(config)
-        super()._init_backbone(config)
-
-        self.embeddings = ConvNextEmbeddings(config)
-        self.encoder = ConvNextEncoder(config)
-        self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes
-
-        # Add layer norms to hidden states of out_features
-        hidden_states_norms = {}
-        for stage, num_channels in zip(self._out_features, self.channels):
-            hidden_states_norms[stage] = ConvNextLayerNorm(num_channels, data_format="channels_first")
-        self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
-
-        # initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        pixel_values: torch.Tensor,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> BackboneOutput:
-        """
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoImageProcessor, AutoBackbone
-        >>> import torch
-        >>> from PIL import Image
-        >>> import requests
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
-        >>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224")
-
-        >>> inputs = processor(image, return_tensors="pt")
-        >>> outputs = model(**inputs)
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-
-        embedding_output = self.embeddings(pixel_values)
-
-        outputs = self.encoder(
-            embedding_output,
-            output_hidden_states=True,
-            return_dict=True,
-        )
-
-        hidden_states = outputs.hidden_states
-
-        feature_maps = ()
-        # we skip the stem
-        for idx, (stage, hidden_state) in enumerate(zip(self.stage_names[1:], hidden_states[1:])):
-            if stage in self.out_features:
-                hidden_state = self.hidden_states_norms[stage](hidden_state)
-                feature_maps += (hidden_state,)
-
-        if not return_dict:
-            output = (feature_maps,)
-            if output_hidden_states:
-                output += (outputs.hidden_states,)
-            return output
-
-        return BackboneOutput(
-            feature_maps=feature_maps,
-            hidden_states=outputs.hidden_states if output_hidden_states else None,
-            attentions=None,
-        )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deberta/modeling_deberta.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deberta/modeling_deberta.py
deleted file mode 100644
index 6f6c2af63a672e69ec47a2057eadc1d7389201ef..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deberta/modeling_deberta.py
+++ /dev/null
@@ -1,1443 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Microsoft and the Hugging Face Inc. team.
-#
-# 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.
-""" PyTorch DeBERTa model."""
-
-from collections.abc import Sequence
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from ...activations import ACT2FN
-from ...modeling_outputs import (
-    BaseModelOutput,
-    MaskedLMOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import softmax_backward_data
-from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
-from .configuration_deberta import DebertaConfig
-
-
-logger = logging.get_logger(__name__)
-_CONFIG_FOR_DOC = "DebertaConfig"
-_CHECKPOINT_FOR_DOC = "microsoft/deberta-base"
-
-# Masked LM docstring
-_CHECKPOINT_FOR_MASKED_LM = "lsanochkin/deberta-large-feedback"
-_MASKED_LM_EXPECTED_OUTPUT = "' Paris'"
-_MASKED_LM_EXPECTED_LOSS = "0.54"
-
-# QuestionAnswering docstring
-_CHECKPOINT_FOR_QA = "Palak/microsoft_deberta-large_squad"
-_QA_EXPECTED_OUTPUT = "' a nice puppet'"
-_QA_EXPECTED_LOSS = 0.14
-_QA_TARGET_START_INDEX = 12
-_QA_TARGET_END_INDEX = 14
-
-
-DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "microsoft/deberta-base",
-    "microsoft/deberta-large",
-    "microsoft/deberta-xlarge",
-    "microsoft/deberta-base-mnli",
-    "microsoft/deberta-large-mnli",
-    "microsoft/deberta-xlarge-mnli",
-]
-
-
-class ContextPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
-        self.dropout = StableDropout(config.pooler_dropout)
-        self.config = config
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-
-        context_token = hidden_states[:, 0]
-        context_token = self.dropout(context_token)
-        pooled_output = self.dense(context_token)
-        pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
-        return pooled_output
-
-    @property
-    def output_dim(self):
-        return self.config.hidden_size
-
-
-class XSoftmax(torch.autograd.Function):
-    """
-    Masked Softmax which is optimized for saving memory
-
-    Args:
-        input (`torch.tensor`): The input tensor that will apply softmax.
-        mask (`torch.IntTensor`):
-            The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
-        dim (int): The dimension that will apply softmax
-
-    Example:
-
-    ```python
-    >>> import torch
-    >>> from transformers.models.deberta.modeling_deberta import XSoftmax
-
-    >>> # Make a tensor
-    >>> x = torch.randn([4, 20, 100])
-
-    >>> # Create a mask
-    >>> mask = (x > 0).int()
-
-    >>> # Specify the dimension to apply softmax
-    >>> dim = -1
-
-    >>> y = XSoftmax.apply(x, mask, dim)
-    ```"""
-
-    @staticmethod
-    def forward(self, input, mask, dim):
-        self.dim = dim
-        rmask = ~(mask.to(torch.bool))
-
-        output = input.masked_fill(rmask, torch.tensor(torch.finfo(input.dtype).min))
-        output = torch.softmax(output, self.dim)
-        output.masked_fill_(rmask, 0)
-        self.save_for_backward(output)
-        return output
-
-    @staticmethod
-    def backward(self, grad_output):
-        (output,) = self.saved_tensors
-        inputGrad = softmax_backward_data(self, grad_output, output, self.dim, output)
-        return inputGrad, None, None
-
-    @staticmethod
-    def symbolic(g, self, mask, dim):
-        import torch.onnx.symbolic_helper as sym_help
-        from torch.onnx.symbolic_opset9 import masked_fill, softmax
-
-        mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"])
-        r_mask = g.op(
-            "Cast",
-            g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value),
-            to_i=sym_help.cast_pytorch_to_onnx["Bool"],
-        )
-        output = masked_fill(
-            g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min))
-        )
-        output = softmax(g, output, dim)
-        return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.bool)))
-
-
-class DropoutContext(object):
-    def __init__(self):
-        self.dropout = 0
-        self.mask = None
-        self.scale = 1
-        self.reuse_mask = True
-
-
-def get_mask(input, local_context):
-    if not isinstance(local_context, DropoutContext):
-        dropout = local_context
-        mask = None
-    else:
-        dropout = local_context.dropout
-        dropout *= local_context.scale
-        mask = local_context.mask if local_context.reuse_mask else None
-
-    if dropout > 0 and mask is None:
-        mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool)
-
-    if isinstance(local_context, DropoutContext):
-        if local_context.mask is None:
-            local_context.mask = mask
-
-    return mask, dropout
-
-
-class XDropout(torch.autograd.Function):
-    """Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
-
-    @staticmethod
-    def forward(ctx, input, local_ctx):
-        mask, dropout = get_mask(input, local_ctx)
-        ctx.scale = 1.0 / (1 - dropout)
-        if dropout > 0:
-            ctx.save_for_backward(mask)
-            return input.masked_fill(mask, 0) * ctx.scale
-        else:
-            return input
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        if ctx.scale > 1:
-            (mask,) = ctx.saved_tensors
-            return grad_output.masked_fill(mask, 0) * ctx.scale, None
-        else:
-            return grad_output, None
-
-    @staticmethod
-    def symbolic(g: torch._C.Graph, input: torch._C.Value, local_ctx: Union[float, DropoutContext]) -> torch._C.Value:
-        from torch.onnx import symbolic_opset12
-
-        dropout_p = local_ctx
-        if isinstance(local_ctx, DropoutContext):
-            dropout_p = local_ctx.dropout
-        # StableDropout only calls this function when training.
-        train = True
-        # TODO: We should check if the opset_version being used to export
-        # is > 12 here, but there's no good way to do that. As-is, if the
-        # opset_version < 12, export will fail with a CheckerError.
-        # Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like:
-        # if opset_version < 12:
-        #   return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train)
-        return symbolic_opset12.dropout(g, input, dropout_p, train)
-
-
-class StableDropout(nn.Module):
-    """
-    Optimized dropout module for stabilizing the training
-
-    Args:
-        drop_prob (float): the dropout probabilities
-    """
-
-    def __init__(self, drop_prob):
-        super().__init__()
-        self.drop_prob = drop_prob
-        self.count = 0
-        self.context_stack = None
-
-    def forward(self, x):
-        """
-        Call the module
-
-        Args:
-            x (`torch.tensor`): The input tensor to apply dropout
-        """
-        if self.training and self.drop_prob > 0:
-            return XDropout.apply(x, self.get_context())
-        return x
-
-    def clear_context(self):
-        self.count = 0
-        self.context_stack = None
-
-    def init_context(self, reuse_mask=True, scale=1):
-        if self.context_stack is None:
-            self.context_stack = []
-        self.count = 0
-        for c in self.context_stack:
-            c.reuse_mask = reuse_mask
-            c.scale = scale
-
-    def get_context(self):
-        if self.context_stack is not None:
-            if self.count >= len(self.context_stack):
-                self.context_stack.append(DropoutContext())
-            ctx = self.context_stack[self.count]
-            ctx.dropout = self.drop_prob
-            self.count += 1
-            return ctx
-        else:
-            return self.drop_prob
-
-
-class DebertaLayerNorm(nn.Module):
-    """LayerNorm module in the TF style (epsilon inside the square root)."""
-
-    def __init__(self, size, eps=1e-12):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(size))
-        self.bias = nn.Parameter(torch.zeros(size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_type = hidden_states.dtype
-        hidden_states = hidden_states.float()
-        mean = hidden_states.mean(-1, keepdim=True)
-        variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
-        hidden_states = (hidden_states - mean) / torch.sqrt(variance + self.variance_epsilon)
-        hidden_states = hidden_states.to(input_type)
-        y = self.weight * hidden_states + self.bias
-        return y
-
-
-class DebertaSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
-        self.dropout = StableDropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class DebertaAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.self = DisentangledSelfAttention(config)
-        self.output = DebertaSelfOutput(config)
-        self.config = config
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        output_attentions=False,
-        query_states=None,
-        relative_pos=None,
-        rel_embeddings=None,
-    ):
-        self_output = self.self(
-            hidden_states,
-            attention_mask,
-            output_attentions,
-            query_states=query_states,
-            relative_pos=relative_pos,
-            rel_embeddings=rel_embeddings,
-        )
-        if output_attentions:
-            self_output, att_matrix = self_output
-        if query_states is None:
-            query_states = hidden_states
-        attention_output = self.output(self_output, query_states)
-
-        if output_attentions:
-            return (attention_output, att_matrix)
-        else:
-            return attention_output
-
-
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Deberta
-class DebertaIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class DebertaOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
-        self.dropout = StableDropout(config.hidden_dropout_prob)
-        self.config = config
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class DebertaLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.attention = DebertaAttention(config)
-        self.intermediate = DebertaIntermediate(config)
-        self.output = DebertaOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        query_states=None,
-        relative_pos=None,
-        rel_embeddings=None,
-        output_attentions=False,
-    ):
-        attention_output = self.attention(
-            hidden_states,
-            attention_mask,
-            output_attentions=output_attentions,
-            query_states=query_states,
-            relative_pos=relative_pos,
-            rel_embeddings=rel_embeddings,
-        )
-        if output_attentions:
-            attention_output, att_matrix = attention_output
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        if output_attentions:
-            return (layer_output, att_matrix)
-        else:
-            return layer_output
-
-
-class DebertaEncoder(nn.Module):
-    """Modified BertEncoder with relative position bias support"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.layer = nn.ModuleList([DebertaLayer(config) for _ in range(config.num_hidden_layers)])
-        self.relative_attention = getattr(config, "relative_attention", False)
-        if self.relative_attention:
-            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
-            if self.max_relative_positions < 1:
-                self.max_relative_positions = config.max_position_embeddings
-            self.rel_embeddings = nn.Embedding(self.max_relative_positions * 2, config.hidden_size)
-        self.gradient_checkpointing = False
-
-    def get_rel_embedding(self):
-        rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
-        return rel_embeddings
-
-    def get_attention_mask(self, attention_mask):
-        if attention_mask.dim() <= 2:
-            extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-            attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
-        elif attention_mask.dim() == 3:
-            attention_mask = attention_mask.unsqueeze(1)
-
-        return attention_mask
-
-    def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
-        if self.relative_attention and relative_pos is None:
-            q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)
-            relative_pos = build_relative_position(q, hidden_states.size(-2), hidden_states.device)
-        return relative_pos
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        output_hidden_states=True,
-        output_attentions=False,
-        query_states=None,
-        relative_pos=None,
-        return_dict=True,
-    ):
-        attention_mask = self.get_attention_mask(attention_mask)
-        relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)
-
-        all_hidden_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        if isinstance(hidden_states, Sequence):
-            next_kv = hidden_states[0]
-        else:
-            next_kv = hidden_states
-        rel_embeddings = self.get_rel_embedding()
-        for i, layer_module in enumerate(self.layer):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    next_kv,
-                    attention_mask,
-                    query_states,
-                    relative_pos,
-                    rel_embeddings,
-                )
-            else:
-                hidden_states = layer_module(
-                    next_kv,
-                    attention_mask,
-                    query_states=query_states,
-                    relative_pos=relative_pos,
-                    rel_embeddings=rel_embeddings,
-                    output_attentions=output_attentions,
-                )
-
-            if output_attentions:
-                hidden_states, att_m = hidden_states
-
-            if query_states is not None:
-                query_states = hidden_states
-                if isinstance(hidden_states, Sequence):
-                    next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
-            else:
-                next_kv = hidden_states
-
-            if output_attentions:
-                all_attentions = all_attentions + (att_m,)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
-        )
-
-
-def build_relative_position(query_size, key_size, device):
-    """
-    Build relative position according to the query and key
-
-    We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
-    \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q -
-    P_k\\)
-
-    Args:
-        query_size (int): the length of query
-        key_size (int): the length of key
-
-    Return:
-        `torch.LongTensor`: A tensor with shape [1, query_size, key_size]
-
-    """
-
-    q_ids = torch.arange(query_size, dtype=torch.long, device=device)
-    k_ids = torch.arange(key_size, dtype=torch.long, device=device)
-    rel_pos_ids = q_ids[:, None] - k_ids.view(1, -1).repeat(query_size, 1)
-    rel_pos_ids = rel_pos_ids[:query_size, :]
-    rel_pos_ids = rel_pos_ids.unsqueeze(0)
-    return rel_pos_ids
-
-
-@torch.jit.script
-def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
-    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])
-
-
-@torch.jit.script
-def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
-    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])
-
-
-@torch.jit.script
-def pos_dynamic_expand(pos_index, p2c_att, key_layer):
-    return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))
-
-
-class DisentangledSelfAttention(nn.Module):
-    """
-    Disentangled self-attention module
-
-    Parameters:
-        config (`str`):
-            A model config class instance with the configuration to build a new model. The schema is similar to
-            *BertConfig*, for more details, please refer [`DebertaConfig`]
-
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
-                f"heads ({config.num_attention_heads})"
-            )
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-        self.in_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=False)
-        self.q_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float))
-        self.v_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float))
-        self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
-
-        self.relative_attention = getattr(config, "relative_attention", False)
-        self.talking_head = getattr(config, "talking_head", False)
-
-        if self.talking_head:
-            self.head_logits_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias=False)
-            self.head_weights_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias=False)
-
-        if self.relative_attention:
-            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
-            if self.max_relative_positions < 1:
-                self.max_relative_positions = config.max_position_embeddings
-            self.pos_dropout = StableDropout(config.hidden_dropout_prob)
-
-            if "c2p" in self.pos_att_type:
-                self.pos_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
-            if "p2c" in self.pos_att_type:
-                self.pos_q_proj = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = StableDropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, -1)
-        x = x.view(new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask,
-        output_attentions=False,
-        query_states=None,
-        relative_pos=None,
-        rel_embeddings=None,
-    ):
-        """
-        Call the module
-
-        Args:
-            hidden_states (`torch.FloatTensor`):
-                Input states to the module usually the output from previous layer, it will be the Q,K and V in
-                *Attention(Q,K,V)*
-
-            attention_mask (`torch.BoolTensor`):
-                An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum
-                sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j*
-                th token.
-
-            output_attentions (`bool`, optional):
-                Whether return the attention matrix.
-
-            query_states (`torch.FloatTensor`, optional):
-                The *Q* state in *Attention(Q,K,V)*.
-
-            relative_pos (`torch.LongTensor`):
-                The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with
-                values ranging in [*-max_relative_positions*, *max_relative_positions*].
-
-            rel_embeddings (`torch.FloatTensor`):
-                The embedding of relative distances. It's a tensor of shape [\\(2 \\times
-                \\text{max_relative_positions}\\), *hidden_size*].
-
-
-        """
-        if query_states is None:
-            qp = self.in_proj(hidden_states)  # .split(self.all_head_size, dim=-1)
-            query_layer, key_layer, value_layer = self.transpose_for_scores(qp).chunk(3, dim=-1)
-        else:
-
-            def linear(w, b, x):
-                if b is not None:
-                    return torch.matmul(x, w.t()) + b.t()
-                else:
-                    return torch.matmul(x, w.t())  # + b.t()
-
-            ws = self.in_proj.weight.chunk(self.num_attention_heads * 3, dim=0)
-            qkvw = [torch.cat([ws[i * 3 + k] for i in range(self.num_attention_heads)], dim=0) for k in range(3)]
-            qkvb = [None] * 3
-
-            q = linear(qkvw[0], qkvb[0], query_states.to(dtype=qkvw[0].dtype))
-            k, v = [linear(qkvw[i], qkvb[i], hidden_states.to(dtype=qkvw[i].dtype)) for i in range(1, 3)]
-            query_layer, key_layer, value_layer = [self.transpose_for_scores(x) for x in [q, k, v]]
-
-        query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])
-        value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :])
-
-        rel_att = None
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        scale_factor = 1 + len(self.pos_att_type)
-        scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor)
-        query_layer = query_layer / scale.to(dtype=query_layer.dtype)
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        if self.relative_attention:
-            rel_embeddings = self.pos_dropout(rel_embeddings)
-            rel_att = self.disentangled_att_bias(query_layer, key_layer, relative_pos, rel_embeddings, scale_factor)
-
-        if rel_att is not None:
-            attention_scores = attention_scores + rel_att
-
-        # bxhxlxd
-        if self.talking_head:
-            attention_scores = self.head_logits_proj(attention_scores.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-
-        attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
-        attention_probs = self.dropout(attention_probs)
-        if self.talking_head:
-            attention_probs = self.head_weights_proj(attention_probs.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (-1,)
-        context_layer = context_layer.view(new_context_layer_shape)
-        if output_attentions:
-            return (context_layer, attention_probs)
-        else:
-            return context_layer
-
-    def disentangled_att_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
-        if relative_pos is None:
-            q = query_layer.size(-2)
-            relative_pos = build_relative_position(q, key_layer.size(-2), query_layer.device)
-        if relative_pos.dim() == 2:
-            relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
-        elif relative_pos.dim() == 3:
-            relative_pos = relative_pos.unsqueeze(1)
-        # bxhxqxk
-        elif relative_pos.dim() != 4:
-            raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}")
-
-        att_span = min(max(query_layer.size(-2), key_layer.size(-2)), self.max_relative_positions)
-        relative_pos = relative_pos.long().to(query_layer.device)
-        rel_embeddings = rel_embeddings[
-            self.max_relative_positions - att_span : self.max_relative_positions + att_span, :
-        ].unsqueeze(0)
-
-        score = 0
-
-        # content->position
-        if "c2p" in self.pos_att_type:
-            pos_key_layer = self.pos_proj(rel_embeddings)
-            pos_key_layer = self.transpose_for_scores(pos_key_layer)
-            c2p_att = torch.matmul(query_layer, pos_key_layer.transpose(-1, -2))
-            c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
-            c2p_att = torch.gather(c2p_att, dim=-1, index=c2p_dynamic_expand(c2p_pos, query_layer, relative_pos))
-            score += c2p_att
-
-        # position->content
-        if "p2c" in self.pos_att_type:
-            pos_query_layer = self.pos_q_proj(rel_embeddings)
-            pos_query_layer = self.transpose_for_scores(pos_query_layer)
-            pos_query_layer /= torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor)
-            if query_layer.size(-2) != key_layer.size(-2):
-                r_pos = build_relative_position(key_layer.size(-2), key_layer.size(-2), query_layer.device)
-            else:
-                r_pos = relative_pos
-            p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
-            p2c_att = torch.matmul(key_layer, pos_query_layer.transpose(-1, -2).to(dtype=key_layer.dtype))
-            p2c_att = torch.gather(
-                p2c_att, dim=-1, index=p2c_dynamic_expand(p2c_pos, query_layer, key_layer)
-            ).transpose(-1, -2)
-
-            if query_layer.size(-2) != key_layer.size(-2):
-                pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)
-                p2c_att = torch.gather(p2c_att, dim=-2, index=pos_dynamic_expand(pos_index, p2c_att, key_layer))
-            score += p2c_att
-
-        return score
-
-
-class DebertaEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        pad_token_id = getattr(config, "pad_token_id", 0)
-        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
-        self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)
-
-        self.position_biased_input = getattr(config, "position_biased_input", True)
-        if not self.position_biased_input:
-            self.position_embeddings = None
-        else:
-            self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)
-
-        if config.type_vocab_size > 0:
-            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)
-
-        if self.embedding_size != config.hidden_size:
-            self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)
-        self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
-        self.dropout = StableDropout(config.hidden_dropout_prob)
-        self.config = config
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer(
-            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
-        )
-
-    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, :seq_length]
-
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        if self.position_embeddings is not None:
-            position_embeddings = self.position_embeddings(position_ids.long())
-        else:
-            position_embeddings = torch.zeros_like(inputs_embeds)
-
-        embeddings = inputs_embeds
-        if self.position_biased_input:
-            embeddings += position_embeddings
-        if self.config.type_vocab_size > 0:
-            token_type_embeddings = self.token_type_embeddings(token_type_ids)
-            embeddings += token_type_embeddings
-
-        if self.embedding_size != self.config.hidden_size:
-            embeddings = self.embed_proj(embeddings)
-
-        embeddings = self.LayerNorm(embeddings)
-
-        if mask is not None:
-            if mask.dim() != embeddings.dim():
-                if mask.dim() == 4:
-                    mask = mask.squeeze(1).squeeze(1)
-                mask = mask.unsqueeze(2)
-            mask = mask.to(embeddings.dtype)
-
-            embeddings = embeddings * mask
-
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class DebertaPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = DebertaConfig
-    base_model_prefix = "deberta"
-    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, nn.Linear):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, DebertaEncoder):
-            module.gradient_checkpointing = value
-
-
-DEBERTA_START_DOCSTRING = r"""
-    The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
-    Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build
-    on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
-    improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-
-    Parameters:
-        config ([`DebertaConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-DEBERTA_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
-    DEBERTA_START_DOCSTRING,
-)
-class DebertaModel(DebertaPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.embeddings = DebertaEmbeddings(config)
-        self.encoder = DebertaEncoder(config)
-        self.z_steps = 0
-        self.config = config
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, new_embeddings):
-        self.embeddings.word_embeddings = new_embeddings
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        raise NotImplementedError("The prune function is not implemented in DeBERTa model.")
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
-            input_shape = input_ids.size()
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones(input_shape, device=device)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-        )
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask,
-            output_hidden_states=True,
-            output_attentions=output_attentions,
-            return_dict=return_dict,
-        )
-        encoded_layers = encoder_outputs[1]
-
-        if self.z_steps > 1:
-            hidden_states = encoded_layers[-2]
-            layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
-            query_states = encoded_layers[-1]
-            rel_embeddings = self.encoder.get_rel_embedding()
-            attention_mask = self.encoder.get_attention_mask(attention_mask)
-            rel_pos = self.encoder.get_rel_pos(embedding_output)
-            for layer in layers[1:]:
-                query_states = layer(
-                    hidden_states,
-                    attention_mask,
-                    output_attentions=False,
-                    query_states=query_states,
-                    relative_pos=rel_pos,
-                    rel_embeddings=rel_embeddings,
-                )
-                encoded_layers.append(query_states)
-
-        sequence_output = encoded_layers[-1]
-
-        if not return_dict:
-            return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]
-
-        return BaseModelOutput(
-            last_hidden_state=sequence_output,
-            hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-@add_start_docstrings("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING)
-class DebertaForMaskedLM(DebertaPreTrainedModel):
-    _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.deberta = DebertaModel(config)
-        self.cls = DebertaOnlyMLMHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_MASKED_LM,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-        mask="[MASK]",
-        expected_output=_MASKED_LM_EXPECTED_OUTPUT,
-        expected_loss=_MASKED_LM_EXPECTED_LOSS,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, MaskedLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
-            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()  # -100 index = padding token
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-class DebertaPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
-
-        self.dense = nn.Linear(config.hidden_size, self.embedding_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(self.embedding_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class DebertaLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = DebertaPredictionHeadTransform(config)
-
-        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(self.embedding_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta
-class DebertaOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = DebertaLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-@add_start_docstrings(
-    """
-    DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
-    pooled output) e.g. for GLUE tasks.
-    """,
-    DEBERTA_START_DOCSTRING,
-)
-class DebertaForSequenceClassification(DebertaPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        num_labels = getattr(config, "num_labels", 2)
-        self.num_labels = num_labels
-
-        self.deberta = DebertaModel(config)
-        self.pooler = ContextPooler(config)
-        output_dim = self.pooler.output_dim
-
-        self.classifier = nn.Linear(output_dim, num_labels)
-        drop_out = getattr(config, "cls_dropout", None)
-        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
-        self.dropout = StableDropout(drop_out)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.deberta.get_input_embeddings()
-
-    def set_input_embeddings(self, new_embeddings):
-        self.deberta.set_input_embeddings(new_embeddings)
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            token_type_ids=token_type_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        encoder_layer = outputs[0]
-        pooled_output = self.pooler(encoder_layer)
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    # regression task
-                    loss_fn = nn.MSELoss()
-                    logits = logits.view(-1).to(labels.dtype)
-                    loss = loss_fn(logits, labels.view(-1))
-                elif labels.dim() == 1 or labels.size(-1) == 1:
-                    label_index = (labels >= 0).nonzero()
-                    labels = labels.long()
-                    if label_index.size(0) > 0:
-                        labeled_logits = torch.gather(
-                            logits, 0, label_index.expand(label_index.size(0), logits.size(1))
-                        )
-                        labels = torch.gather(labels, 0, label_index.view(-1))
-                        loss_fct = CrossEntropyLoss()
-                        loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
-                    else:
-                        loss = torch.tensor(0).to(logits)
-                else:
-                    log_softmax = nn.LogSoftmax(-1)
-                    loss = -((log_softmax(logits) * labels).sum(-1)).mean()
-            elif self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
-        )
-
-
-@add_start_docstrings(
-    """
-    DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    DEBERTA_START_DOCSTRING,
-)
-class DebertaForTokenClassification(DebertaPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.deberta = DebertaModel(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
-        )
-
-
-@add_start_docstrings(
-    """
-    DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    DEBERTA_START_DOCSTRING,
-)
-class DebertaForQuestionAnswering(DebertaPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.deberta = DebertaModel(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_QA,
-        output_type=QuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-        expected_output=_QA_EXPECTED_OUTPUT,
-        expected_loss=_QA_EXPECTED_LOSS,
-        qa_target_start_index=_QA_TARGET_START_INDEX,
-        qa_target_end_index=_QA_TARGET_END_INDEX,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        start_positions: Optional[torch.Tensor] = None,
-        end_positions: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
-        r"""
-        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.deberta(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions = start_positions.clamp(0, ignored_index)
-            end_positions = end_positions.clamp(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[1:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/modules/F0Predictor/DioF0Predictor.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index 4ab27de23cae4dbc282e30f84501afebd1a37518..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self,f0):
-        '''
-        对F0进行插值处理
-        '''
-    
-        data = np.reshape(f0, (f0.size, 1))
-    
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-    
-        ip_data = data
-    
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-    
-        return ip_data[:,0], vuv_vector[:,0]
-
-    def resize_f0(self,x, target_len):
-        source = np.array(x)
-        source[source<0.001] = np.nan
-        target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
-        res = np.nan_to_num(target)
-        return res
-        
-    def compute_f0(self,wav,p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0]//self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self,wav,p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0]//self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md
deleted file mode 100644
index 5f52b2be3c9650cfc3e16ffb8fa374d3fcbad371..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md
+++ /dev/null
@@ -1 +0,0 @@
-../../INSTALL.md
\ No newline at end of file
diff --git a/spaces/yonikremer/grouped-sampling-demo/hanlde_form_submit.py b/spaces/yonikremer/grouped-sampling-demo/hanlde_form_submit.py
deleted file mode 100644
index 72098dd99d4a230c79b943de9e2f26842200f86b..0000000000000000000000000000000000000000
--- a/spaces/yonikremer/grouped-sampling-demo/hanlde_form_submit.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-from functools import lru_cache
-from time import time
-
-import streamlit as st
-from grouped_sampling import GroupedSamplingPipeLine
-
-from download_repo import download_pytorch_model
-
-
-def is_downloaded(model_name: str) -> bool:
-    """
-    Checks if the model is downloaded.
-    :param model_name: The name of the model to check.
-    :return: True if the model is downloaded, False otherwise.
-    """
-    models_dir = os.path.join(os.path.expanduser("~"), ".cache", "huggingface", "hub")
-    model_dir = os.path.join(models_dir, f"models--{model_name.replace('/', '--')}")
-    return os.path.isdir(model_dir)
-
-
-@lru_cache(maxsize=10)
-def create_pipeline(model_name: str) -> GroupedSamplingPipeLine:
-    """
-    Creates a pipeline with the given model name and group size.
-    :param model_name: The name of the model to use.
-    :return: A pipeline with the given model name and group size.
-    """
-    if not is_downloaded(model_name):
-        download_repository_start_time = time()
-        st.write(f"Starts downloading model: {model_name} from the internet.")
-        download_pytorch_model(model_name)
-        download_repository_end_time = time()
-        download_time = download_repository_end_time - download_repository_start_time
-        st.write(f"Finished downloading model: {model_name} from the internet in {download_time:,.2f} seconds.")
-    st.write(f"Starts creating pipeline with model: {model_name}")
-    pipeline_start_time = time()
-    pipeline = GroupedSamplingPipeLine(
-        model_name=model_name,
-        group_size=512,
-        end_of_sentence_stop=False,
-        top_k=50,
-        load_in_8bit=False,
-    )
-    pipeline_end_time = time()
-    pipeline_time = pipeline_end_time - pipeline_start_time
-    st.write(f"Finished creating pipeline with model: {model_name} in {pipeline_time:,.2f} seconds.")
-    return pipeline
-
-
-def generate_text(
-        pipeline: GroupedSamplingPipeLine,
-        prompt: str,
-        output_length: int,
-) -> str:
-    """
-    Generates text using the given pipeline.
-    :param pipeline: The pipeline to use. GroupedSamplingPipeLine.
-    :param prompt: The prompt to use. str.
-    :param output_length: The size of the text to generate in tokens. int > 0.
-    :return: The generated text. str.
-    """
-    return pipeline(
-        prompt_s=prompt,
-        max_new_tokens=output_length,
-        return_text=True,
-        return_full_text=False,
-    )["generated_text"]
-
-
-def on_form_submit(
-        model_name: str,
-        output_length: int,
-        prompt: str,
-) -> str:
-    """
-    Called when the user submits the form.
-    :param model_name: The name of the model to use.
-    :param output_length: The size of the groups to use.
-    :param prompt: The prompt to use.
-    :return: The output of the model.
-    :raises ValueError: If the model name is not supported, the output length is <= 0,
-     the prompt is empty or longer than
-     16384 characters, or the output length is not an integer.
-     TypeError: If the output length is not an integer or the prompt is not a string.
-     RuntimeError: If the model is not found.
-    """
-    if len(prompt) == 0:
-        raise ValueError("The prompt must not be empty.")
-    st.write(f"Loading model: {model_name}...")
-    loading_start_time = time()
-    pipeline = create_pipeline(
-        model_name=model_name,
-    )
-    loading_end_time = time()
-    loading_time = loading_end_time - loading_start_time
-    st.write(f"Finished loading model: {model_name}  in {loading_time:,.2f} seconds.")
-    st.write("Generating text...")
-    generation_start_time = time()
-    generated_text = generate_text(
-        pipeline=pipeline,
-        prompt=prompt,
-        output_length=output_length,
-    )
-    generation_end_time = time()
-    generation_time = generation_end_time - generation_start_time
-    st.write(f"Finished generating text in {generation_time:,.2f} seconds.")
-    if not isinstance(generated_text, str):
-        raise RuntimeError(f"The model {model_name} did not generate any text.")
-    if len(generated_text) == 0:
-        raise RuntimeError(f"The model {model_name} did not generate any text.")
-    return generated_text
diff --git a/spaces/yuhanbo/chat-gpt/public/serviceWorkerRegister.js b/spaces/yuhanbo/chat-gpt/public/serviceWorkerRegister.js
deleted file mode 100644
index 8405f21aaab9ddec0cff867cfe1dfff67ea01ccd..0000000000000000000000000000000000000000
--- a/spaces/yuhanbo/chat-gpt/public/serviceWorkerRegister.js
+++ /dev/null
@@ -1,9 +0,0 @@
-if ('serviceWorker' in navigator) {
-  window.addEventListener('load', function () {
-    navigator.serviceWorker.register('/serviceWorker.js').then(function (registration) {
-      console.log('ServiceWorker registration successful with scope: ', registration.scope);
-    }, function (err) {
-      console.error('ServiceWorker registration failed: ', err);
-    });
-  });
-}
\ No newline at end of file
diff --git a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/flask_api_full_song.py b/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/flask_api_full_song.py
deleted file mode 100644
index 901cdd064acc5c18a6e353c7ce390c0d39e850ac..0000000000000000000000000000000000000000
--- a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/flask_api_full_song.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import io
-import numpy as np
-import soundfile
-from flask import Flask, request, send_file
-
-from inference import infer_tool
-from inference import slicer
-
-app = Flask(__name__)
-
-
-@app.route("/wav2wav", methods=["POST"])
-def wav2wav():
-    request_form = request.form
-    audio_path = request_form.get("audio_path", None)  # wav path
-    tran = int(float(request_form.get("tran", 0)))  # tone
-    spk = request_form.get("spk", 0)  # speaker(id or name)
-    wav_format = request_form.get("wav_format", 'wav')
-    infer_tool.format_wav(audio_path)
-    chunks = slicer.cut(audio_path, db_thresh=-40)
-    audio_data, audio_sr = slicer.chunks2audio(audio_path, chunks)
-
-    audio = []
-    for (slice_tag, data) in audio_data:
-        print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-
-        length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
-        if slice_tag:
-            print('jump empty segment')
-            _audio = np.zeros(length)
-        else:
-            # padd
-            pad_len = int(audio_sr * 0.5)
-            data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
-            raw_path = io.BytesIO()
-            soundfile.write(raw_path, data, audio_sr, format="wav")
-            raw_path.seek(0)
-            out_audio, out_sr = svc_model.infer(spk, tran, raw_path)
-            svc_model.clear_empty()
-            _audio = out_audio.cpu().numpy()
-            pad_len = int(svc_model.target_sample * 0.5)
-            _audio = _audio[pad_len:-pad_len]
-
-        audio.extend(list(infer_tool.pad_array(_audio, length)))
-    out_wav_path = io.BytesIO()
-    soundfile.write(out_wav_path, audio, svc_model.target_sample, format=wav_format)
-    out_wav_path.seek(0)
-    return send_file(out_wav_path, download_name=f"temp.{wav_format}", as_attachment=True)
-
-
-if __name__ == '__main__':
-    model_name = "logs/44k/G_60000.pth"
-    config_name = "configs/config.json"
-    svc_model = infer_tool.Svc(model_name, config_name)
-    app.run(port=1145, host="0.0.0.0", debug=False, threaded=False)
diff --git a/spaces/zej97/AI-Research-Assistant/config/singleton.py b/spaces/zej97/AI-Research-Assistant/config/singleton.py
deleted file mode 100644
index 55b2aeea120bbe51ca837265fcb7fbff467e55f2..0000000000000000000000000000000000000000
--- a/spaces/zej97/AI-Research-Assistant/config/singleton.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""The singleton metaclass for ensuring only one instance of a class."""
-import abc
-
-
-class Singleton(abc.ABCMeta, type):
-    """
-    Singleton metaclass for ensuring only one instance of a class.
-    """
-
-    _instances = {}
-
-    def __call__(cls, *args, **kwargs):
-        """Call method for the singleton metaclass."""
-        if cls not in cls._instances:
-            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
-        return cls._instances[cls]
-
-
-class AbstractSingleton(abc.ABC, metaclass=Singleton):
-    """
-    Abstract singleton class for ensuring only one instance of a class.
-    """
-
-    pass
diff --git a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare-build.js b/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare-build.js
deleted file mode 100644
index 9eb881bef0fddc7bdef1084460b5588a18790377..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/functions/compare-build.js
+++ /dev/null
@@ -1,7 +0,0 @@
-const SemVer = require('../classes/semver')
-const compareBuild = (a, b, loose) => {
-  const versionA = new SemVer(a, loose)
-  const versionB = new SemVer(b, loose)
-  return versionA.compare(versionB) || versionA.compareBuild(versionB)
-}
-module.exports = compareBuild
diff --git a/spaces/zhoujiaxin/zhoujiaxinchatgpt/README.md b/spaces/zhoujiaxin/zhoujiaxinchatgpt/README.md
deleted file mode 100644
index 218767d1d7debd26932ffddca2ec0f421c0171a9..0000000000000000000000000000000000000000
--- a/spaces/zhoujiaxin/zhoujiaxinchatgpt/README.md
+++ /dev/null
@@ -1,195 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: true
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-</div>
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[![img](./docs/images/demo.png)](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[![Deploy to HuggingFace](https://img.shields.io/badge/%E7%82%B9%E5%87%BB%E9%83%A8%E7%BD%B2-%F0%9F%A4%97-fff)](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-<details>
-<summary>
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-</summary>
-
-#### 部署到 Netlify
-[![Deploy to Netlify Button](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-</details>
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge，通过人机校验，然后
-
-![BING HEADER](./docs/images/curl.png)
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-<details>
-<summary>正常格式/网页端保存的格式(格式仅供参考)</summary>
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
-  -H 'authority: www.bing.com' \
-  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
-  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
-  -H 'cache-control: max-age=0' \
-  -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
-  -H 'dnt: 1' \
-  -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
-  -H 'sec-ch-ua-arch: "x86"' \
-  -H 'sec-ch-ua-bitness: "64"' \
-  -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
-  -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
-  -H 'sec-ch-ua-mobile: ?0' \
-  -H 'sec-ch-ua-model: ""' \
-  -H 'sec-ch-ua-platform: "Windows"' \
-  -H 'sec-ch-ua-platform-version: "15.0.0"' \
-  -H 'sec-fetch-dest: document' \
-  -H 'sec-fetch-mode: navigate' \
-  -H 'sec-fetch-site: none' \
-  -H 'sec-fetch-user: ?1' \
-  -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
-  -H 'sec-ms-gec-version: 1-116.0.1938.29' \
-  -H 'upgrade-insecure-requests: 1' \
-  -H '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 Edg/116.0.0.0' \
-  -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
-  -H 'x-edge-shopping-flag: 1' \
-  --compressed
-```
-</details>
-
-<details>
-<summary>转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)</summary>
-
-```
-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
-```
-</details>
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。 
-
-
-## 答疑及交流
-
-<image src="./docs/images/wechat.png" width=240 />
-
-## License
-
-MIT © [LICENSE](https://github.com/weaigc/bingo/blob/main/LICENSE).
-
-
diff --git a/spaces/zhuowen999/vits_chinese/README.md b/spaces/zhuowen999/vits_chinese/README.md
deleted file mode 100644
index e9f36de87f09353fe4c665ad4147ecb3b53adf77..0000000000000000000000000000000000000000
--- a/spaces/zhuowen999/vits_chinese/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Vits Chinese
-emoji: 🌍
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: maxmax20160403/vits_chinese
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/zixian/Zhenhuan-VITS/text/ngu_dialect.py b/spaces/zixian/Zhenhuan-VITS/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/zixian/Zhenhuan-VITS/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
-            'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
-            'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
-            'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
-            'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
-            'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
-    try:
-        converters[dialect] = opencc.OpenCC(dialect)
-    except:
-        pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
-    dialect = dialects[dialect]
-    text = converters[dialect].convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text